esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Instance Learning with Trainable Soft Decision Tree Ensembles
## 4 A Softmax Representation of the Decision Tree Function  
In order to overcome difficulties of training the oblique decision tree, we propose another representation of it, which allows us to effectively update it. Let us consider a complete binary decision tree \(f_{\theta}\) of depth \(h\):  
* the tree has \((2^{h}-1)\) non-leaf nodes parametrized by \((\mathbf{w}_{j},b_{j})\), where
* \(\mathbf{w}_{j}\) is an _one-hot vector_ having 1 at the position corresponding to the node feature;
* \(b_{j}\) is a threshold;
* the tree also has \(2^{h}\) leaves with values \(\mathbf{v}_{l}\), where \(\mathbf{v}_{l}\) is an output vector corresponding to the \(j\)-th leaf.  
In contrast to the representation (10) of the function \(q_{l}\), we propose avoiding direct comparison with the height of a tree, because this representation requires the indicator approximation to return integer values; otherwise \(q_{l}\) is always evaluated as zero. If we use (10) instead of the softmax function, then (7) provides the sum of the leaf vectors in place of selecting one of them. We use the softmax function to guarantee a convex combination of leaf vectors. We replace the outer indicator with the _softmax_ function having the trainable temperature parameter \(\tau\):  
\[q_{l}(\mathbf{x},\mathbf{W},\mathbf{b},\tau,\omega)=\text{softmax}_{\tau} \left(\sum_{d=1}^{h}\sigma_{\omega}\Big{[}[-\mathbf{w}_{I(d,l)}^{\text{T}} \mathbf{x}+b_{I(d,k)}]\cdot\hat{s}(d,k)\Big{]}\right)_{k=1}^{2^{h}}, \tag{11}\]  
where \(\hat{s}(d,k)\in\{-1,1\}\) is the node sign; \(\sigma\) is the sigmoid with the trainable temperature or scaling parameter \(\omega\).  
The proposed representation could be interpreted as selecting the most appropriate path among all candidate paths. Neural trees, defined by using the above representation, can be optimized by means of the stochastic gradient descent algorithm with _fixed node weights_\(\mathbf{w}_{j}\); i.e., by updating only thresholds, the softmax temperature parameters \(\tau\), the sigmoid temperature parameters \(\omega\), and the leaf values.
# Multiport Single Element Mimo Antenna Systems: A Review
## 4 Multiport Single Element System
A compact dual-band 2-port antenna operating at 2.4-2.5 GHz works simultaneously with a 4-port antenna operating at 4.9-5.725 GHz, without using the MEMS switching. Isolation is better than 12 dB achieved through back-to-back slot arrangement [57]. The MIMO antennas discussed in [43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57] provides good isolation but their structure is either complex or their total size is larger. Hence, designing a compact MIMO system considering all the performance parameters is a challenging task for portable devices and mobile terminals. The approach of designing common radiator MIMO antenna with multiple feed ports providing better performance could represent a considerable advantage by reducing the size and the manufacturing cost. In the existing literature, it is observed that few studies have been conducted on MIMO multiport single element antenna systems. The following sections will present the recent literature regarding two-port or four-port antenna systems sharing a single radiating element.
## 5 Conclusions  
Our experiments show that OCT dosimetry can reliably monitor sub 20 \(\upmu\)s laser pulse-induced RPE cell damage via automated detection of fringe-washouts. Furthermore, we have demonstrated that SD-OCT, even at integration times much higher than the corresponding expansion time of microbubbles, is sensitive enough to detect the optical signatures of MBF. Indeed, damage was detectable even at low radiant exposure levels, when only a few RPE cells within the treatment spot were ruptured. Future research with this system will need to verify whether real-time OCT dosimetry in patients undergoing SRT is capable of automatically controlling the level of treatment; enough to trigger RPE regeneration but without adverse effects to neighboring tissue. Real-time control holds the promise for significantly reducing the risks of SRT, while also permitting more rapid retinal microsurgery of large areas. While the focus of the current study was specific to RPE-rejuvenation, in principle, OCT-guided control of optical treatment--based on selective absorption features of the tissue--could be implemented in other biological or technical environments to achieve minimally invasive treatment.  
Conceptualization, C.B., A.H., D.T.-K. and R.B.; methodology, C.B., A.H., D.T.-K. and R.B.; software, L.G., P.A. and S.S.; formal analysis, C.B. and A.H.; investigation, C.B. and A.H.; resources, R.B., D.T.-K. and C.M.; data curation, C.B., A.H. and L.G.; writing--original draft preparation, C.B., A.H., R.B. and B.P.; writing--review and editing, R.B., D.T.-K., B.P., C.M., P.A. and A.E.; visualization, C.B., A.H. and S.S.; funding acquisition, B.P. and C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Swiss National Science Foundation, grant number 325230-141189 and 323523-163306 as well as the Swiss Innovation Agency, grant number KTI 25030.1 PELS-LS.  
Not applicable.  
Data Availability Statement: The data presented in this study (excluding the SD-OCT dosimetry algorithm) are available on request from the corresponding author.  
We gratefully want to acknowledge the valuable support and the inspiring discussions with our colleagues [PERSON], Medical Laser Center Lubeck; [PERSON], Institute of Applied Physics of the University of Bern; [PERSON], University of Cardiff; [PERSON], [PERSON] and [PERSON], Meridian AG; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], Heidelberg Engineering.  
The authors declare no conflict of interest.  
The exact radiant exposure values per lesion for the probe region are provide in Table 11. In addition to the OCT M-scan evaluation using the dosimetry algorithm, an investigator-based evaluation of the M-scan evaluation is provided as well. The binary evaluation and comparison with RPE cell damage thresholds are represented like Figure 6 by using a Probit-Plot.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 4 Multipath Data Scheduling
### Path Correlation Analysis
Step 4: Confirm whether the selected ID path has a statistical correlation with the paths in \(P\) in turn. If the current ID path is not correlated with the paths in \(P\), add the relay node to \(S\) and add the ID path to \(P\); otherwise, the current ID path is deleted and an equal number of relay nodes are selected from \(R\) to rebuild the new ID paths. Figure 2: Example of paths overlapping of multipath transmission. Step 5: Repeat Step 4 until \(k\) relay nodes are found or already traverse all the relay nodes in \(R\) and record the path correlation information locally to provide a basis for node selection for future connections. ```
1:Input:\(G(V,E)\), \(src\), \(dst\), \(k\), MPSID
2:Output:\(S,P\)
3:\(R\) = QueryFromNRS(MPSID)
4:defaultPath = dijkstra_path(\(src\),\(dst\))
5:\(P\).add(defaultPath)
6:if hit (src, dst) cache then
7: determine \(S\) and \(P\) based on the path correlation information
8:elseif
9:for\(i\) = 1 to \(M\)do
10:if relayPath(\(i\)) is independent of the path in \(P\)then
11:\(P\).add(relayPath(\(i\)))
12:\(S\).add(Node(\(i\)))
13:endif
14:if\(|S|=k\)then
15: break;
16:endif
17:endfor
18:endif
19:return\(S,P\)
```  
**Algorithm 1** Relay-Node-Selection Algorithm
## 4 Experiments
### Evaluation Metrics  
To demonstrate how depthwise separable convolution contributes to reducing computational costs, the evaluation of the computational efficiency of the models will use the FLOPs (Floating Point Operations) metric, number of parameters, size of the model, inference time, and max GPU memory.  
For the quantitative evaluation of the quality of the generated images, the PSNR [42] and SSIM [43] metrics are used. The PSNR (Peak Signal-to-Noise Ratio) metric is defined by Equation (12).  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{\text{MAX}^{2}}{\text{MSE}}\right) \tag{12}\]  
where MAX represents the maximum possible value of the signal; as the image is normalized, the value of this variable is set to 1. MSE is the mean squared error between the generated image and the reference image and is calculated using Equation (13).  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(I_{i}-\hat{I}_{i}\right)^{2} \tag{13}\]  
where \(N\) is the total number of pixels in the image, \(I_{i}\) represents the intensity of pixel \(i\) in the reference image, and \(\hat{I}_{i}\) is the intensity of the corresponding pixel in the reconstructed image. The difference \(I_{i}-\hat{I}_{i}\) calculates the error at each pixel, this value being squared to amplify significant deviations. The sum of these squared errors divided by \(N\) gives the mean value that quantifies the overall discrepancy between the two images.  
The SSIM (Structural Similarity Index Measure) metric is defined by Equation (14).  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{ 2}+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{14}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the means of the images \(x\) and \(y\), \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances, \(\sigma_{xy}\) is the covariance, and \(C_{1}\) and \(C_{2}\) are constants for numerical stabilization.  
The PSNR and SSIM metrics are calculated in their linear form (\(-l\)), using the image in HDR format, and in their tone-mapped form (\(-\mu\)), using the transformation defined by Equation (6). Additionally, emerging approaches are used for deeper and more detailed analysis, including PSNR and SSIM in the perceptually uniform (\(-PU\)) domain [44], HDR-VDP2.2 [45; 46], HDR-VDP3 [47], and IL-NIQE [48]. The IL-NIQE metric, which does not need reference images, aims to analyze the model's generalization capacity for high-resolution, static, and moving images.
# The Impact of Thermal Treatment on Structural Changes of Teak and Iroko Wood Lignins
###### Abstract  
Thermal modification is an environmentally friendly method to improve dimensional stability, durability, and aesthetic properties of wood. Changes in lignin as one of the main wood components markedly influence wood product properties and recycling possibilities of thermowood at the end of its life cycle. Teak and iroko wood samples were thermally treated at the temperatures of 160 \({}^{\circ}\)C, 180 \({}^{\circ}\)C and 210 \({}^{\circ}\)C following the Thermowood process. Dioxane lignin was isolated from treated and untreated wood and analysed by nitrobenzene oxidation (NBO), size exclusion chromatography (SEC) and Fourier transform infrared spectroscopy (FTIR). The yields of both acid-insoluble and dioxane lignins increased with an increasing treatment temperature. Dioxane lignins are GS-types containing more guiaacyl units compared to syringly ones with S/G ratios of 0.91 and 0.84, respectively. In the process of thermal modification, several degradation and condensation reactions were observed. The cleavage of methoxyl groups and side chains, oxidation reactions, cleavage of the \(\beta\)-O-4 ether linkage and cross-linking radicals arising at higher temperatures were all confirmed. However, during the thermal treatment, teak lignin changed in a different way than iroko lignin, e.g., the molecular weight of iroko lignin decreased at all applied temperatures while it increased at 180 \({}^{\circ}\)C and 210 \({}^{\circ}\)C in teak lignin, and the change in S/G ratio and the cleavage of alkyl-aryl bonds are different in both wood species.  
wood; teak; iroko; thermal treatment; lignin +
Footnote †: journal: Applied Sciences
## 3 Proposed Methodology
### Cluster Head Selection  
In this study, two processes of TCH and FCH selection are used to determine cluster heads. Using a tentative CH selection method based on EBT and Trust Value, the cluster head is chosen. To choose TCH, the node is given a timer, and trust values are calculated using the node's total Trust value. The TCH is determined by the node with the highest trust value and energy. Additionally, the planned head count, node degree, and competition range are used to determine the final cluster head is shown in Figure 4.  
Figure 3: Proposed system architecture.
###### Abstract
However, due to the harsh environment of an underground coal mine, the image has the characteristics of mixed noise, non-uniform illuminance, low contrast, inconspicuous gray transformation, etc. The traditional image processing method makes it difficult to accurately extract the target features. In recent years, image processing technology based on deep learning has been widely used, including recurrent neural network, convolutional neural network (CNN), and GAN [15; 16; 17; 18], among which the CNN is the most widely used in the field of image-based target recognition. Representative network models include R-CNN, Fast R-CNN, Fast R-CNN, SSD (Single Shot MultiBox Detector), and YOLO (You Only Look Once) [19; 20]. [PERSON] [21] presented a threaded hole target detection method based on Faster R-CNN by combining the dual camera vision system and Hough transform circle detection algorithm. The experimental results show that this method can effectively avoid the detection of small targets in threaded holes, and has high recognition and positioning accuracy. [PERSON] [22] compared and analyzed the network structures of R-CNN and Faster R-CNN, and proved the superiority of Faster R-CNN algorithm in the hole identification of an explosive filling robot from the aspects of accuracy and recognition speed. [PERSON] [23] proposed an extended deconvolution SSD target detection model based on multi-scale feature fusion to solve the problem that the depth learning target detection algorithm, SSD, has a poor recognition effect on small-scale targets. The test results from the public dataset show that the proposed method improves the accuracy of small target object detection. Although many scholars have conducted a significant amount of research on hole recognition in different fields and made certain achievements, there are still the following problems: (1) Deep neural networks require a large number of samples to support the practical application process, but it is difficult to obtain images of pressure relief holes due to the complexity of the underground working environment. The quality and quantity of the images obtained are not enough to support the operation and optimization of existing deep learning algorithms, and the existing images need to be expanded. (2) The research on hole identification technology in various fields mainly focuses on hole identification with continuous smooth edges. However, there are problems, such as the collapse and deformation of coal mine pressure relief holes. In addition, hole edges appear as three-dimensional complex intersecting curves, which leads to an unsatisfactory application effect of traditional hole identification methods. In order to improve the recognition effect of the pressure relief hole target of the drilling robot, this paper designs an improved SinGAN model to solve the problems of insufficient diversity of sample generation and poor performance of the model. Then, an accurate target recognition model of the relief hole based on improved Faster R-CNN is constructed, and the feasibility and effectiveness of the proposed method are verified through experimental tests.
## 2 Related Work
### Scenario  
Query request scenarios include the single query and the continuous query. In the single query, a user usually sends a single query request for a period of time. Many methods such as pseudonyms, perturbations, dummies, etc., are used to protect the identity, location, and query content of the user. In [14], authors first introduced \(k\)-anonymity to protect location privacy. Based on [14], the authors in [15; 16] pointed out the equilibrium between the personalized privacy protection and quality of service. Methods [12; 21; 28] raised the anonymous group that is composed of the user's real location and \(k-1\) dummies selected from historical queries. Inspired by [12; 15; 16; 21; 28], we consider the equilibrium between privacy protection level and system overhead and select dummy locations from historical queries.  
In the continuous query, a user continuously sends multiple query requests for a period of time. Most methods complete privacy protection by randomly adding dummies, using historical data to generate dummies, etc. In [19], the authors first proposed a technique of randomly generating dummies to complete anonymity, but the generated dummies have weak authenticity and can be easily identified. The methods in [20; 21; 22; 23; 24; 25] study how to generate more realistic dummy trajectories. In [20], the authors presented trajectory \(k\)-anonymity, using the users' historical trajectory data to generate \(k-1\) dummy trajectories. Methods in [16; 17; 29; 30; 31] consider separately a user's motion patterns, physical constraints in the real world, and spatiotemporal correlation awareness to generate dummy trajectories with higher realism.  
A basic assumption commonly used in the above two scenarios is that users send query requests in different locations. However, in reality, a user often sends a single or short-time continuous query in the same location. For example, a user at her home would continuously query nearby restaurants within one minute. In this paper, we mainly study the privacy protection of the same user submitting a single or continuous query scenario in the same location.
## 2 RISs and Their Integration with NTNs
### Application of RISs to NTN Communications
#### 2.5.3 RIS-Assisted Communication Networks  
Numerous studies on using RISs in different communication networks, including non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiplexing (OFDM) systems, have been inspired by the RIS's attractive characteristics [83; 84; 85; 86; 87; 88; 89]. The fundamental idea of RISs, where the RIS is installed on the satellite or close to the terrestrial users to improve their network throughput, has just been applied to satellite relay in some earlier research [90; 91]. These works, nevertheless, assumed the ideal channel estimation for the design and operation of detached beamforming and neglected to take into account the crucial channel approximation/beam traceability problem for RISs, which remains a difficult task in practice owing to its numerous reflecting components that do not have data communication or processing capabilities, particularly for the scenario of LEO satellites with excellent mobility [92; 93; 94]. Even though effective passive beamforming models and channel coding strategies have been developed for various RIS-supported structures, most of these are focused on terrestrial communication networks with BSs and RISs fixed in position to serve steady-state users, which might not be suitable for high-mobility LEO satellite systems [95]. For example, the fast mobility of LEO satellites might lead to highly time-varying wireless channels with ground nodes (GNs) and satellites which can complicate regular channel estimation and beam management for both active and inactive beam positioning. Such challenges could significantly degrade communication performance [95; 96]. Furthermore, the route identification and beam monitoring in RIS-supported LEO satellite communications are more difficult owing to the substantial Doppler impact generated by the LEO satellite's rapid motion [96; 97].
## 3 Numerical Model
### Theory of OrcaFlex  
A similar approach is used to model the whole structure in OrcaFlex, a well-known commercial program. The tunnel and mooring lines are modelled by line elements, and the line-element theory is based on the lumped mass method. The line element consists of a series of nodes and segments. Force properties are lumped in the node, which includes weight, buoyancy, and drag etc. Stiffness components i.e., axial, bending, and torsional stiffness, are represented by massless springs [44]. The equation of motion is expressed in Equation (27).  
\[\mathbf{M}(\mathbf{p},\mathbf{a})+\mathbf{C}(\mathbf{p},\mathbf{v})+\mathbf{K }(\mathbf{p})=\mathbf{F}(\mathbf{p},\mathbf{v},t) \tag{27}\]  
where \(\mathbf{M}(\mathbf{p},\mathbf{a})\), \(\mathbf{C}(\mathbf{p},\mathbf{v})\), and \(\mathbf{K}(\mathbf{p})\) are mass, damping, and stiffness matrices. \(\mathbf{F}(\mathbf{p},\mathbf{v},t)\) is external force vector, which is hydrodynamic force in this case. Symbols, \(\mathbf{p}\), \(\mathbf{v}\), \(\mathbf{a}\), and \(t\) denote position, velocity, acceleration vectors, and time, respectively. Hydrodynamic force is also computed by the same Morison equation for a moving object with consideration for relative velocity and acceleration. The advantage  
Figure 2: Stiffness matrix for the simulated SFT (line #1-#16 are for tunnel and line #17-#N are for mooring lines, n(1) means number of sub-elements of line #1, k is the number of the 6 DOF rigid body).  
of the developed program compared to OrcaFlex for the present application can be summarized as follows: (i) In OrcaFlex, hydrodynamic force generated from the seaquake effects is not included. (ii) In CHARM3D, higher-order rod FE elements are used compared to lumped-mass-based OrcaFlex. (iii) The seabed movements can be directly imputed in the developed program.
## 1 Introduction  
Wireless sensor networks (WSNs) have emerged as a transformative technology with diverse applications in environmental monitoring, security/surveillance, and health care. These networks consist of a system of spatially dispersed and dedicated sensors that are aimed to monitor and cover a field. The core challenge lies in achieving optimal coverage while considering the inherent constraints of WSNs, such as limited energy resources, including processing power, energy consumption, and communication range.  
The primary objective with coverage is to be able to monitor a field for the longest amount of time while minimizing energy consumption. There are different types of coverage that can be achieved: area coverage and target coverage. Area coverage refers to the extent to which the sensing area is covered by the sensing nodes. Sensors are strategically placed to ensure that the entire area is covered and overlap exists between neighboring sensors. This overlap, or redundancy of sensors, allows us to achieve \"k-coverage\", which guarantees that every point in the field of interest is covered by at least k sensors. On the other hand, target coverage refers to the extent to which specific points of interest are covered by sensor nodes. Rather than monitoring the entire field, target coverage involves positioning the sensor nodes in a way that they can detect events occurring at specific points. Target coverage is crucial for applications such as surveillance, event detection, and localization.  
To address the problem of coverage in WSNs, various nature-inspired algorithms have been employed. For example, particle swarm optimization (PSO) emulates the behavior of flocks of birds where particles are used to explore the search space to find the most optimal solution. Each particle represents a possible solution and moves with the search space based on its own experience and the experiences of neighboring particles. Another approach is known as the genetic algorithm, which simulates the process of natural selection and evolution. It uses methods such as reproduction, crossover, and mutation to introduce new potential solutions toward an optimal or near-optimal solution.