esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
It has nonzero support in the interval \(-\pi\) and \(\pi\) and a concentration parameter \(\kappa\) associated with the quality of the phase adjustment promoted by the LIS and the efficiency of the channel estimation method. The moment-generating function (MGF) of the Von Mises distribution is useful since a complex exponential represents the phase adjustments. With the MGF, it is possible to calculate the statistical moments associated with the channel coefficients. Let \(X\) be a Von Mises random variable; therefore, its MGF is given by \(\varphi_{p}=E[e^{-jpX}]=\alpha_{p}+j\beta_{p}\). Since the zero mean Von Mises distribution is symmetric about zero, then the imaginary part of the MGF \(\beta_{p}=E[\sin pX]=0\), and the real part is \(\alpha_{p}=\frac{I_{p}(\kappa)}{I_{0}(\kappa)}\), where \(I_{p}(\kappa)\) is the modified Bessel function of first kind and order \(p\). Considering that the precoder is the normalized hermitian of the overall channel, the SNR of the desired link is  
\[\gamma_{D}=(\mathbf{h}_{SL}^{H}\mathbf{\Phi}^{H}\mathbf{h}_{LD}^{H}+\mathbf{h} _{SD}^{H})\mathbf{v}=\|\mathbf{h}\|^{2}. \tag{7}\]  
Assuming, as an approximation that \(\gamma_{D}\) is Gamma distributed, then its statistical moments, \(\alpha\) and \(\beta\) can be estimated as  
\[\alpha=\frac{\mathbb{E}[\gamma_{D}]^{2}}{\text{var}(\gamma_{D})},\ \ \beta=\frac{\mathbb{E}[\gamma_{D}]}{\text{var}(\gamma_{D})}, \tag{8}\]  
where \(\alpha\) and \(\beta\) are the shape and rate parameters, while \(\mathbb{E}[\gamma_{D}]\) and \(\text{var}(\gamma_{D})\) are the expected value and variance of \(\gamma_{D}\), respectively, as shown throughout A. The assumption that the distribution of \(\gamma_{D}\) is Gamma distributed can be assessed using the Hellinger distance. According to [PERSON] [21], the Hellinger distance between two arbitrary discrete probability distributions \(p_{k}\) and \(q_{k}\) can be obtained as  
\[D_{HL}=\frac{1}{\sqrt{2}}\sqrt{\sum_{k=0}^{N_{p}-1}(\sqrt{p_{k}}-\sqrt{q_{k}} )^{2}}, \tag{9}\]  
where \(N_{p}\) is the number of samples available to calculate the distance. The Hellinger distance is limited in the interval \(0\leq D_{HL}\leq 1\) and can be considered as an absolute metric. In Figure 2, the realizations of a Monte Carlo simulation of the channels involved in the system are used to compose a histogram that approximates the PDF of the overall channel that is compared to the Gamma distribution predicted by the approximation proposed in this study. To perform the analysis, \(10^{6}\) iterations were performed with unit variance for all channels, [PERSON] concentration parameter \(\kappa=2\) and the Nakagami parameter \(m=2\). The results show that the Hellinger distance decreases when \(N\) and \(K\) increase. In the last case, for \(K=16\), the decrease is even more pronounced. Therefore, this accurate approximation motivates us to formulate the problem further.
###### Abstract  
Free-space optical communications have emerged as a powerful solution for inter-satellite links, playing a crucial role in next-generation satellite networks. This paper introduces a comprehensive model that enables the dynamic evaluation of optical power requirements for realistic low Earth orbit satellite constellations throughout the orbital period. Our approach incorporates the constellation architecture, link budget analysis, and optical transceiver design to accurately estimate the power required for sustaining connectivity for both intra- and inter-orbit links. We apply the model considering Walker delta-type constellations of varying densities. We show that in dense constellations, even at high data rates, the required transmission power can be low enough to mitigate the need for optical amplification. Dynamically estimating the power requirements is vital when evaluating energy savings in adaptive scenarios where terminals adaptively change the emitted power depending on the link status. Our model is implemented in Python and is openly available under an open-source license. It can be easily adapted to various alternative constellation configurations.  
free space optics; link budget analysis; low earth orbit; optical inter-satellite links 2025
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 3 Method
### Semi-Supervised Learning Strategy  
To address the limited availability of labeled data, we employ a semi-supervised learning (SSL) approach that leverages both labeled (5%) and unlabeled (95%) samples. A forward diffusion process introduces Gaussian noise into unlabeled samples:  
\[q(x_{1:T}\mid x_{0})=\prod_{t=1}^{T}q(x_{t}\mid x_{t-1}),\quad q(x_{t}\mid x_{ t-1})=\mathcal{N}(x_{t};\sqrt{1-\beta_{t}}x_{t-1},\beta_{t}\mathbf{I}),\]  
where \(\beta_{t}\) is a noise schedule and \(x_{0}\) is the original image.  
During training, the model predicts clean data from noisy samples via a learned reverse process \(p_{\theta}(x_{0}\mid x_{t})\). In addition, pseudo-labels are generated for unlabeled samples if the model's confidence exceeds a threshold \(\tau\). These are incorporated into the loss:  
\[\mathcal{L}_{\text{total}}=\mathcal{L}_{\text{sup}}+\lambda\cdot\mathcal{L}_{ \text{pseudo}},\]  
where \(\lambda\) balances contributions from labeled and pseudo-labeled data.  
To determine an optimal confidence threshold, we performed tuning on the BreakHis dataset under 5% labeled settings. Table 1 shows that \(\tau=0.9\) provides the best trade-off between pseudo-label quantity and quality in AUC-ROC and F1 score. Hence, we fix \(\tau=0.9\) throughout all experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Magnification** & \(\tau\) & **Pseudo Labels** & **AUC-ROC** & **Macro F1** \\ \hline \multirow{4}{*}{40\(\times\)} & 0.70 & 480 & 0.8835 & 0.8840 \\  & 0.80 & 400 & 0.9021 & 0.8947 \\  & **0.90** & **320** & **0.9187** & **0.9004** \\  & 0.95 & 220 & 0.9123 & 0.8968 \\ \hline \multirow{4}{*}{100\(\times\)} & 0.70 & 450 & 0.8955 & 0.9005 \\  & 0.80 & 370 & 0.9262 & 0.9100 \\  & **0.90** & **300** & **0.9614** & **0.9217** \\  & 0.95 & 200 & 0.9551 & 0.9180 \\ \hline \multirow{4}{*}{200\(\times\)} & 0.70 & 460 & 0.9250 & 0.9100 \\  & 0.80 & 390 & 0.9405 & 0.9225 \\  & **0.90** & **310** & **0.9528** & **0.9339** \\  & 0.95 & 210 & 0.9483 & 0.9298 \\ \hline \multirow{4}{*}{400\(\times\)} & 0.70 & 440 & 0.8600 & 0.8400 \\  & 0.80 & 360 & 0.8755 & 0.8600 \\ \cline{1-1}  & **0.90** & **290** & **0.8945** & **0.8850** \\ \cline{1-1}  & 0.95 & 190 & 0.8885 & 0.8790 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation performance under different pseudo-labeling thresholds \(\tau\) (5% labeled data) on BreakHis dataset at 40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\) magnifications. The best result for each magnification is highlighted in bold.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 4 Strategies for Noise Reduction and Design Optimization
### Strategies based On Signal Processing Schemes  
#### 4.3.1 Baseline Fitting for Blended Absorption Feature  
As discussed above, using the AI or LM fitting algorithms to calculate the laser intensity baseline requires proper identification of the absorption-free flanks. However, for spectra with low SNR, distinguishing the non-absorption area using direct visual inspection (DVI) is hard. A new strategy was proposed by [PERSON] et al. using wavelet decomposition and iteration to remove the background drift [98]. The application of wavelet transform (WT) for TDLAS signal denoising is based on finding the optimal wavelet pairs to determine the baseline. Figure 11 shows effective nonlinear baseline correction and denoising using discrete wavelet transform (DWT). Compared with the commonly used DVI method, this DWT algorithm demonstrated potential for batch processing of TDLAS spectra. However, system cost and complexity increased.  
Figure 10: Inverse WMS-\(2f\) signal strength (F, blue line), NH\({}_{3}\) interference sensitivity (\(\sigma\), red line), and cost function (C, red line) vs modulation depth for a 3 ppm NH\({}_{3}\) mixture in CH\({}_{4}\) – air \(\phi=0.6\) combustion exhaust at T = 600 K, P = 1 atm. The optimal modulation depth \(a_{m,\ opt}\) is shown in the black/dashed line. Figure adapted from [96].  
[PERSON] et al. have developed a blended-feature baseline fitting method (BFBL) using a more efficient iterative lookup table approach [99]. The baseline is estimated by coupling measured data with simulated fractional transmission at the peaks between absorption features known as baseline anchor points. Figure 12 illustrates the fitting procedure, and this technique was validated against measurements in a static heated cell and a wood-fired two-stage hydronic heater. The results indicated that it can be very useful with multiple overlapping absorption features.  
Figure 12: Flow chart for DAS baseline fitting data analysis. Figure adapted from [99].
## 4 Multihoming and Interworking
### GLI-Split with Classic IPv6 Nodes
#### 4.3.1 Modified Behavior of Local DNS Servers  
The DNS is configured to return a global GLI-address with an activated GAP-bit for GLI-nodes in a multihomed domain. When an upgraded GLI-node wants to contact another node, it receives its global address from the DNS, but uses only the integrated ID to query the local mapping system for the local or global GLI-addresses. Subsequently, an upgraded GLI-node finds out whether the communication peer resides in the GLI-domain in order to use a local GLI-address of the corresponding node for communication. Classic IPv6 nodes cannot query the mapping system and rely on the result from the DNS server. Therefore, the local DNS server should return local GLI-addresses for nodes inside its GLI-domain. However, local GLI-addresses should never leave a GLI-domain as they are not routable from the outside. Such a modified DNS server must therefore be contacted only from within the GLI-domain. This may, for instance, be achieved by checking whether the source address is a local address.
## 1 Introduction
Because an extended state observer is utilized to estimate and compensate for the modeling error in the robotic system, the accuracy of the tracking goal is enhanced significantly. Additionally, a Nussbaum gain function is integrated into the control algorithm to deal with the issue of unknown control direction. In order to demonstrate the stability and robustness of the controlled system, a Lyapunov function is utilized. Some simulations are implemented in MATLAB Simulink with a dual 3-DOF manipulator system. Finally, to ensure the proposed control's effectiveness, a comparison was conducted between its results and those of other controllers. The main merits of this paper are presented as follows:  
1. Compared with some existing works for a dual robotic arm [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], an unknown control direction is taken care of besides the other uncertainties. Both theory and simulation are used to analyze the effectiveness of the proposed method. 2. Elaborate analysis and simulations are conducted by the Lyapunov approach and MATLAB Simulink with different working conditions. The results show that the proposed control can suitably handle with the modeling error, external disturbances, and wrong control directions in the robotic system to obtain both tracking and synchronous objectives. The structure of this study is as follows. In Section 2, the kinematic and dynamic formulas are presented. The challenges in the study are also introduced. In Section 3, the proposed control based on synchronous sliding mode control, extended state observer, and Nussbaum function is described, and its stability and robustness are also analyzed by using the Lyapunov approach. In Section 4, some numerical simulations are conducted to verify the effectiveness, performance, and feasibility of the proposed control. Finally, in Section 5, some conclusions are presented.
# Decision Framework for Predictive Maintenance Method Selection
## 2 Problem Exploration
### Problem 1--Difficulties in Identifying and Selecting Methods for Predictive Maintenance  
Although many methods for maintenance policy selection are available in the literature, such as multi-criteria decision making (MCDM) [17], the analytical hierarchy process (AHP) [18], or reliability-centered maintenance (RCM) [19], methods to select the required MMs are non-existent. Moreover, the separation between policy selection and MM selection is not always completely clear. This is shown in Figure 1, providing an overview of the most common maintenance policies. At the highest level (A), a division is made between reactive, proactive and aggressive policies. The preventive maintenance branch is then subdivided into various policies at levels B and C, where they start to interact with the maintenance methods (e.g., condition monitoring and prognostic methods). The commonly used policy selection methods, such as RCM, support the selection at level A and, in most cases, also at level B. For the selection at level B, various methods are available in the literature that, for example, compare condition-based to time-based maintenance [20] or compare the performance of these methods in terms of life cycle costs [21]. However, the detailed selection at level C, where a specific MM, i.e., a specific monitoring technique or an algorithm to determine the expected failure time [22; 23], must be selected, is not supported by these methods. Moreover, the evaluation system for setting up predictive maintenance programmes proposed by [PERSON] [24] and the process reference model for predictive maintenance proposed by [PERSON] and [PERSON] [25] do mention the aspects that need to be considered but lack specific guidance in making the decisions.  
Figure 1: Overview of maintenance policies. At level A the main policy category is selected, level B (scheduled or dynamic) and level C (measured or calculated) distinguish the various preventive policies.  
Therefore, available methods properly advise _what to do_ but do not explain _how to do_ it. This is also reflected in the survey conducted by [PERSON] and [PERSON] [8], demonstrating about a decade ago that only 11-13 percent of UK-based manufacturers have adopted prognostics and decision-support techniques. The authors also more recently found that practitioners still often follow a costly trial-and-error process in developing more advanced approaches caused by a lack of guidance in the use of available MMs [10]. It is important that the selected combination of policy and MM suits the company's expertise, current situation and maintenance organisation. However, as research on this topic in the last decade yielded a vast amount of methods, companies typically find it difficult to identify the relevant methods and recognise the criteria to select one.
## 3 Cryptography and the Erosion of the Security Perimeter  
The major disruptive event on the security models of the 1970s was the dissolution of the security perimeter, where \"personal\" computers moved away from the centralized and protected infrastructures, while the larger systems became permanently connected to each other over communication lines, and ultimately, the Internet. This movement intensified the problems with access control, the movement of personal data to other computers, and worsened the ability to follow policies for data handling. The cryptographic techniques used to encrypt data bases and data links were based on symmetric cryptography, where all parties need to share a common key. Symmetric cryptography has, as a consequence, a high complexity in key handling and secure key distribution. It is, in addition, difficult to exclude parties who violate policies, as they can only be excluded with re-encryption of the data with a new key distributed to all other parties. The invention of public-key cryptography enabled much better handling of distributed multi-user and multi-computer scenarios, including the hierarchical certification of users and roles. In addition, researchers extended the uses of public key cryptography into a large number of higher cryptographic protocols that provide interesting properties such as multi-party secret sharing, anonymous authentication, blind attestation and secure group agreement.
## 4 Simulation Results
### Source Specification/Requirement Analysis
#### 4.1.3 Emission Rate Analysis--GEO  
Finally, GEO orbit requirements in terms of emission rates were discussed. GEO satellites provide a continuous time window and a fixed elevation angle, but the elevation angle is based on the OGS position, as in the case of MEO satellites. For this study, an elevation angle of 45 degrees was considered, which is a typical elevation angle for the corresponding GEO satellite providers of Greece. The communication time window was limited to 12 h since only night-time communication was considered. As before, Figure 5 provides, again, the end-to-end link loss over time, as well as the expected normalized SKR over time, where different transmitted block sizes values for the following three different scenarios were assumed: (a) a receiver aperture of 1.5 m and SNSPD detection with telescope-to-SMF coupling, (b) a receiver aperture of 2.3 m and SNSPD detection with telescope-to-SMF coupling, and (c) a receiver aperture of 2.3 m and free-space SNSPD detection. Free-space SNSPD detection has been presented within the literature [78; 79], but often with limited detection efficiency. In this case, we assumed that the detectors were capable of detecting photons via free space without limiting their efficiency. Considering the link budget for the scenarios above, as well as the time communication windows for GEO satellites, an emission rate of \(\sim\)200 MHz was required for the space source to provide a transmitted block size of \(N_{trans}=10^{13}\) bits over 12 h.  
It was calculated that, for the first two assumptions (as shown in Figure 5a,b), SKRs of less than 10 bps were calculated, whereas (as shown in Figure 5c), SKRs of up to 320 bps for the third case may be achieved. Despite this, it is yet unclear to which extent free-space SNSPDs can function without severely limiting the system efficiency and not drastically increasing the ambient background noise. Therefore, considering the very low SKR, which was provided via fiber-coupled SNSPDs, it seems that large astronomical telescopes must be used over long periods to provide the necessary key volumes to be able to distill secure keys after hours of communication. If including the possibility  
Figure 4: End-to-end link loss and normalized SKR over time for different transmitted block size values of **(a)**\(D_{rx}=1\) m and SNSPD with SMF coupling, **(b)**\(D_{rx}=1.5\) m and SNSPD with SMF coupling and **(c)**\(D_{rx}=2.3\) m and SPAD without SMF coupling.  
the night, as well as an increase in the background noise (e.g., due to the moon's presence), then the GEO-to-ground QKD will be interrupted. Therefore, we conclude that, under these assumptions, GEO-to-ground QKD communication does not seem like a viable alternative compared to LEO and MEO orbits. As such, under the selfsame aforementioned assumptions, they will thus be excluded from now on in our results. Further insights on how GEO orbit may benefit QKD communication will be discussed in Section 5.
## 4 Mathematical Programming Problem Formulation
### Minimization of Total Energy Consumption  
Constraints (7)-(13) define operating principles of the network. It is necessary to describe the goal to be attained first to find the optimal slot allocation. One may consider minimization of the overall power consumption of the network. Such a goal will promote conserving the power consumption of the network as a whole, regardless of the power consumption of a particular node. In dense topologies where some nodes may replace exhausted ones, it may lead to longer network operation. Formally it can be done by defining the performance index (see Equation (14)) in the form of the sum of energy consumed by all nodes in subsequent time slots of the frame:  
\[J_{total}=\sum_{t=1}^{T}\sum_{i=1}^{N}\sum_{k=1}^{K}\phi_{k}^{i}\epsilon_{kt}^ {i}. \tag{14}\]  
The resulting optimization problem may be defined by formula (15):  
\[\min_{\begin{subarray}{c}q_{t}^{i},q_{kt}^{i}\end{subarray}}J_{total}, \tag{15}\]  
subject to constraints (7)-(13).
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 3 Hybrid Acoustic-RF Approaches for Positioning Energy-Neutral Devices
### Hardware Implementation, Power Consumption and Backscatter Demodulation  
Here, we put the theory into practice and present the implementation of a frequency shifted, load switching backscatter mechanism in hardware. In addition, we evaluate the power consumption of the contributing components and introduce the signal demodulation.
# Linear Time Non-Local Cost Aggregation on Complementary Spatial Tree Structures
## 2 Methods  
The procedure of building initial cost volume for a rectified stereo pair can be formulated as \(f:R^{W\times H\times 3}\times K^{W\times H\times 3}\mapsto R^{W\times H \times L}\), where \(H\) and \(W\) are the height and width of input images, 3 is the channels of a color image, and it is 1 for a gray input image. \(L\) is the disparity space, and we use \(C_{i,j}(l)\) to denote the matching cost of pixel \((i,j)\) at disparity \(l\). Kinds of edge-aware filters have been designed to denoise the initial cost volume, aiming  
Figure 1: Illustration of the filtering procedures along different tree structures. The black and red arrows indicate the directions of information propagated in the first and second steps, respectively. (**a,b**) show how the messages are propagated along our complementary spatial trees (_Tree 1_ and _Tree 2_) when calculating the output of the root node (the red node). The dot lines between nodes \(p\) and \(q\) indicate the different paths used to calculate the geodesic distances between pixels. (**c,d**) show the procedures to calculate the output of the root node on _Tree 1_. The filtering procedure is performed first along horizontal and vertical directions and then along diagonal directions. (**e,f**) show the procedure to calculate the output of the root node on _Tree 2_. The filtering procedure is performed first along diagonal, and then horizontal and vertical directions. (**g**) the filtering procedure of recursive filter [19]. The filtering procedure is performed in horizontal and vertical directions. (**h**) The way information propagated along the MST. See the text for details.  
to enhance the quality of disparity images. Here, we elaborate on our non-local cost aggregation method which performs filtering along two complementary spatial tree structures with linear time complexity. The output of a pixel on each tree can be recursively computed from several sub-trees.