esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Mushrooms  
Fruiting bodies of 30 different wild mushroom species were collected from their natural habitats in the vicinity of Strasbourg, France, between 2014 and 2019. Three species were harvested multiple times, from different places or different years: _Neoboletus erythpropus_, _Morchella esculenta_, and _Scleroderma citrium_. Two authors, [PERSON] and [PERSON], taxonomically identified sporocarps on the basis of micro- and macroscopic characteristics, and comparison with literature [5; 6]. The identification of the specimens was confirmed by DNA analyses by [PERSON]. The edible common mushroom _Agaricus bisporus_ added to the screening was purchased from a local supermarket. Information about the species collected is provided in Table 1. For each harvest, a voucher specimen was freeze-dried prior to deposit at the Laboratory of Analytical Chemistry and Pharmacognosy, University of Strasbourg.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Order** & **Family** & **Species** & **Harvest Information *** \\ \hline \multirow{6}{*}{Agaricales} & Agaraceae & _Agaricus bisporus_ & 2016 February, from supermarket \\  & _Macrolepiota procera_ & 2015 October, Fuschloch \\  & _Amanita citrina_ & 2014 October, Haguenau \\  & _Amanita muscaria_ & 2015 October, Dabo \\  & Bolbitiaceae & _Bolbitius varicolor_ & 2016 April, Illkirch \\  & _Cortinariaceae_ & _Cortinaritas semisanguineus_ & 2015 November, Wisches \\  & _Mycenaceae_ & _Mycen rosea_ & 2017 October, Ohlungen \\  & _Omphalotaceae_ & _Gymupous luxvians_ & 2016 June, Illkirch \\  & _Armillaria cepisipes_ & 2015 October, Illkirch \\  & _Physalaciaceae_ & var. _pseudobulosa_ & 2018 October, Floessplatz \\  & _Armillaria ostoyae_ & 2018 November, Barr \\  & _Pleurotaceae_ & _Pleurotus ostreatus_ & 2018 November, Barr \\  & _Pluteaceae_ & _Volvopluteus gloicephalus_ & 2016 April, Illkirch \\  & _Psaphylaceae_ & _Parasola auricoma_ & 2016 June, Illkirch \\  & _Stropharicaceae_ & _Hypholoma fasciculare_ & 2015 October, Fuschloch \\  & _Tricholomataceae_ & _Clitocyhe nebularis_ & 2017 October, Ried \\ \hline \multirow{6}{*}{Boletales} & Boletaceae & _Neoboletus erythpropus_ \#1 & 2018 October, La Fischhutte \\  & _Neoboletus erythpropus_ \#2 & 2017 August, Saint-Jean-Saverne \\  & _Hygrophoropsis auriantaca_ & 2016 October, Hohbuhl \\  & _Paxillaceae_ & _Paxillus involutolus_ & 2015 October, Dabo \\  & _Scleroderma citrium_ \#1 & 2018 October, Balbronn \\  & _Scleroderma citrium_ \#2 & 2018 October, Floessplatz \\  & _Scleroderma citrium_ \#3 & 2018 October, Floessplatz \\  & _Scleroderma citrium_ \#4 & 2019 September, Balbronn \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the mushrooms evaluated in the present study (authors of mushroom scientific names have been omitted for the sake of conciseness).
# Supply Chain Data Analytics for Digital Twins:
## 2 Theoretical Background
### Analytics Methods  
In contexts where digital twins are most applicable, different machine learning and computational techniques have been proposed, ranging from supervised, unsupervised and reinforcement. Some of them have been more thoroughly researched and can provide a basis for a comprehensive analytics framework for Digital Twin implementations.  
Association rules (ARs) are a commonly used technique also found in industrial settings. For example, in [10] a rule-based ML process is designed, applied and evaluated in an automotive industry, where firstly the prevalent and important anomaly class is identified, association rules and network analysis are employed to understand its effects to the overall equipment effectiveness (OEE), and a Deming Cycle is used as for its application and evaluation.  
Multi-agent systems represent an AI technique utilizing intelligent agents that interact with each other to execute specific tasks, such as handling large amounts of factory data. Such systems are evaluated for anomaly detection [11], where they join forces with the potential of ML, enabling them to reach critical solutions in the applications of industry 4.0. A demonstrated paradigm is multi-agent learning, where multiple learning agents adapt and adjust their behavior to the environment [12].  
A review [13] of 78 papers in 2024, identified a significant number of efforts for implementing anomaly detection in various advanced industry fields, with the most prominent being smart manufacturing, energy forecasting and health monitoring. Autoencoders and convolutional neural networks, and combinations of the two were prominently represented in the research efforts. Autoencoders tend to fail when reconstructing an anomaly, so when they are given anomalous data inputs, they produce reconstruction errors. Therefore, a reconstruction error that exceeds a given threshold can be used as an indicator that given data represent an anomaly. Variational Autoencoders (VAE) have been also proposed [14], which map inputs to distributions rather than fixed points in the latent space, allowing them to model uncertainty and improve robustness in cases where the data may have overlapping distributions.  
There is a transformative potential of deep learning methodologies since features learning can happen faster and without the need for manual feature engineering. However, it should be noted that given the fast evolution of the analytics landscape, and the breakthroughs achieved in areas, such as LLMs (Large Language Models), an analytics system for digital twin integration should be open to extensions and to utilization of evolving methodologies.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
###### Abstract  
The mismatch of geometric parameters in a nanotomography system bears a significant impact on the reconstructed images. Moreover, projection image noise is increased due to limitations of the X-ray power source. The accuracy of the existing self-calibration method, which uses only the grayscale information of the projected image, is easily affected by noise and leads to reduced accuracy. This paper proposes a geometric parameter self-calibration method based on feature matching of mirror projection images. Firstly, the fast extraction and matching feature points in the mirror projection image are performed by speeded-up robust features (SURF). The feature triangle is then designed according to the stable position of the system's rotation axis to further filter the feature points. In turn, the influence of the mismatched points on the calculation accuracy is reduced. Finally, the straight line where the rotation axis is located is fitted by the midpoint coordinates of the filtered feature points, thereby realizing geometric parameter calibration of the system. Simulation and actual data from the experimental results show that the proposed method effectively realizes the calibration of geometric parameters, and the blurring and ghosting caused by geometric artifacts are corrected. Compared with existing methods, the image clarity can be improved by up to 14.4%.  
n  
captionx
###### Abstract  
The CNR (Carrier-to-Noise Ratio) of GPS (Global Positioning System) satellites is highly relevant to the multipath error. The multipath error is more serious in the flood environment since the reflection and diffraction coefficients of water are much higher compared to dry soil. Thus, the amplitude of CNR will decrease in the flood environment. In this study, the relationship between multipath error, flooding, and CNR is introduced in theory. Then, by using the characteristic of the orbital repetition period, the stability of CNR between 2 adjacent days in a static observation environment is demonstrated by 32 MGEX (Multi-GNSS Experiment) stations in different latitude and longitude regions of the world. The results show that the average RMS of different CNRs between two adjacent days is only about 0.62 dB-Hz. In addition, the correlation coefficient of CNRs between two adjacent days is analyzed. The correlation coefficient of the original signal CNR is 0.997. Moreover, after mitigating the influence of random noise and lower CNR, the correlation coefficients of the fitted CNRs larger than 04 dB-Hz can reach 0.999. Thus, based on the fluctuation in original CNR, fitted CNR, and seamless series characteristics of CNR, the whole flood process from occurrence to recession can be retrieved. A flood that occurred in Zhengzhou City, China, from DOY 200 to DOY 202, 2021 is used to demonstrate the process of retrieval. The experimental results indicate that the flood appeared at about 15:30 pm on DOY 200, reached a peak at approximately 8:30 am on DOY 202, and totally subsided at about 10:00 am on DOY 202. In conclusion, the CNR can be effectively used to retrieve the whole process of the flood, which lays a foundation for researching flood detection and warning based on GPS satellites.  
GPS; flood retrieval; Carrier-to-Noise Ratio; fitted CNR Article
## 4 Experimental Verification  
### Experimental Setup  
The experimental system is composed of an ACT, an LDV, an ultrasonic power device, a signal amplifier, a digital phosphor oscilloscope, and a data acquisition interface. The ACT is a piezoelectric ceramic-based transducer with a 200 kHz center frequency, and a bandwidth of \(\pm\)25% of the center frequency. The incidence angle is set at 12.75\({}^{\circ}\), which is suitable for the ACT to actuate the A0 mode. The LDV is employed to measure the out-of-plane velocity on the surface of the plate. The sampling frequency is set at 500 MHz. The signal should be executed at a suitable time when the waveform displayed on the oscilloscope tends to be stable. Each measurement is repeated 10 times in order to reduce the measurement error. The received signals need be amplified by a wideband amplifier for the purpose of improving the signal-to-noise ratio. The gain of the signal amplifier is 60 dB. Moreover, it can be adjusted according the central frequency of the excitation signal. The data acquisition subsystem provides detailed information of the recording signals, such as the sample interval, trigger point, vertical scale, horizontal scale, etc. The experimental system is shown in Figure 16. The experimental conditions are in agreement with the simulation conditions.
## 12 Future Research  
There are numerous areas of future research in space mission optimization that should be considered in terms of risk, sustainability, and supply chain [109]. Long-duration space travel requires research to create effective countermeasures, such as better radiation shielding and physiological conditioning techniques to reduce health hazards for astronauts. Propulsion system advancements, including research into alternative technologies such as ion propulsion, nuclear propulsion, and solar sails, will enable faster and more efficient interplanetary travel, lowering mission risks and boosting sustainability. In-situ resource use research is critical for long-term space exploration. Exploring extraction and use methods for celestial body resources including water, ice, and minerals can lessen reliance on Earth and enable self-sufficiency during missions. Increasing robotic exploration capabilities through autonomous system research and development, increased mobility, and dexterous manipulation would improve mission efficiency, reduce human risk, and permit remote operations in hazardous settings [110].  
Future studies should concentrate on encouraging international cooperation, developing frameworks for resource sharing, and harmonizing safety and sustainability requirements. To reduce mission hazards and boost efficiency, sophisticated materials science research is critical for producing lightweight and durable spacecraft components, radiation shielding materials, and sustainable energy solutions. Continued study into space medicine and psychology is critical for better understanding and mitigating the physiological and psychological impacts of space exploration, which will lead to enhanced astronaut well-being and mission success.
# Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
## 3 Bayesian Formulation  
### Permeability Vector  
The unknown model parameters that represent the permeability are described by \(\mathbf{k}=[c_{1},c_{2},c_{3},\alpha_{1},\alpha_{2},\ldots,\alpha_{n}]\). We use \(c_{1}\), \(c_{2}\), and \(c_{3}\) to represent the log-Cholesky factorisation of the bulk permeability tensor, \(\mathbf{K_{\text{bulk}}}\)--see (20). \(\mathbf{K_{\text{bulk}}}\) represents the permeability tensor for the majority of the domain, aside from the region experiencing race tracking. Specifically, we take:  
\[\mathbf{C}^{T}\mathbf{C}=\mathbf{K_{\text{bulk}}},\quad\mathbf{C}=\begin{bmatrix}e^{c_{1}}&c_{ 2}\\ 0&e^{c_{3}}\end{bmatrix}. \tag{20}\]  
Parameterising the bulk permeability in this way ensures that the bulk permeability tensor is symmetric and positive definite, regardless of the values of \(c_{1}\), \(c_{2}\), and \(c_{3}\)[30]. The remaining parameters \(\alpha_{1},\alpha_{2},\ldots,\alpha_{n}\) represent race tracking in various areas. For given values of these parameters, the permeability tensor of each area can be constructed as follows.  
\[\mathbf{K}_{j}=\exp(\alpha_{j})\mathbf{K_{\text{bulk}}},\quad j=[1,2,\ldots,n], \tag{21}\]  
where \(\mathbf{K}_{j}\) is the permeability tensor in a specific race-tracking region. The use of the exponential function is convenient, as it allows the parameters \(\alpha_{j}\) to be normally distributed, but gives an exponential distribution in the race-tracking multipliers. This exponential distribution reflects typical race-tracking multipliers, which are often 1-5 times larger than the bulk permeability but can be 100 times higher in some instances [1; 5; 13; 20]. The benefit of using normally distributed random parameters will be discussed in the following sections.  
It should be noted that we have limited the stochastic variables in our model to only those parameterised by \(k\). This includes the bulk permeability and the race-tracking strength of each region. We fix the porosity and race-tracking width parameters. The reasons for excluding each are different. Stochastic porosity has been considered in a previous study [19] but will be neglected here due to its typical low variation and because the focus of this research is specifically on race tracking. We neglect the race-tracking width, although stochastic in actuality, because the dual effect of the race-tracking width and strength can be replicated with just strength. As the interest in race tracking is in its effect rather than describing the exact source, this is an appropriate assumption.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 2 Optical CDMA with MUI Cancellation
### Conventional EWO Scheme
In Figure 3, (a) shows the two sequences encoded by the codewords \(\mathbf{w}_{2,0}\) and \(\mathbf{w}_{2,1}\), which are assigned to the second user; (b) shows the sequences transmitted simultaneously by four active users, who are the first, second, fifth, and seventh users transmitting data 1, 0, 0, and 1, respectively; and (c) shows the multiplexed sequence of the eight sequences including all-zero sequences transmitted by four non-active users. The spatially multiplexed optical signals are detected at the receiver and converted into the electric signal sequence \(\mathbf{r}\). \(\mathbf{r}\) is decoded at each user's decoder, which consists of two correlators, a difference calculator, and a decision circuit. Figure 2 illustrates a block diagram of the \(k\)th user's decoder. For the EWO scheme, the \(k\)th user's decoder computes \(\Gamma_{1}\) and \(\Gamma_{0}\), which are correlations between the received sequence \(\mathbf{r}\) and the codewords \(\mathbf{w}_{k,1}\) and \(\mathbf{w}_{k,0}\), respectively. \(\Gamma_{1}\) and \(\Gamma_{0}\) are represented as follows:  
\[\Gamma_{1}=\Gamma(\mathbf{r},\mathbf{w}_{k,1}), \tag{4}\]  
\[\Gamma_{0}=\Gamma(\mathbf{r},\mathbf{w}_{k,0}), \tag{5}\]  
where the function \(\Gamma(\mathbf{a},\mathbf{b})\) is the inner product of the vectors \(\mathbf{a}\) and \(\mathbf{b}\). Then, the decoder calculates the difference \(\Gamma_{1}-\Gamma_{0}\). If the difference is greater than or equal to the threshold _zero_, the decoder outputs \(\hat{I}=1\) as the decoded datum. Otherwise, the decoder outputs \(\hat{I}=0\). Because the threshold in the EWO decoder is always zero, this scheme cancels MUI without needing to estimate the light intensity. The fact that the optimum threshold is zero in the EWO scheme is advantageous when implementing a decoder. Suppose that the channel is an ideal link where the noise can be considered negligible, and that the light intensity of each user's positive chip is 1. Let \(\Delta(I)\) be the value \(\Gamma_{1}-\Gamma_{0}\) when the user transmits data \(I\). At the EWO decoder, \(\Delta(1)\) and \(\Delta(0)\) are certain to be \(q\) and \(-q\), respectively; for any number of active users, \(N\) less than or equal to \(N_{\text{max}}\), \(|\Delta(1)-\Delta(0)|\) is defined as the _decision distance_[24], which is a useful index for evaluating the error resistance property of MUI cancellation schemes. The decision distance of the EWO scheme is \(2q\). Figure 3: Examples of signal sequences for the EWO scheme.
# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 4 Experiments with Data from the SNS Pesedia  
We conducted experiments with our SNS Pesedia during a period of one month. During that time, we gathered the text and keystroke dynamics data of the user when he or she made a post on Pesedia, so we could analyze these two sources of information in later experiments. As stated previously, Pesedia was used by children with ages ranging from twelve to fifteen years old. In Pesedia, the users can perform several actions related to social interaction in SNSs. These actions include: posting messages on their walls or other peoples' walls; sending private messages; creating groups and inviting people; making friends; sharing content with certain people; making lists of people. In our experiments, we make use of the messages posted on walls and groups and their replies. We also use the concept of propagation. By propagation, we refer to the fact that a detected state by an analyzer on a message is also found in the majority of the replies to the message in which the state is detected. Only direct replies are considered, not replies to replies. This design decision was taken to be able to study the effect of the emotional state detected in a message on its replies; in future works the effect in multiple levels of replies or other messages might be tested.  
Our aims in the experimentation are two-fold: to discover whether the new analyzers integrated into the system predict a state that propagates more in the network, and to know which analyzer achieves the best propagation in general. In this way, we would be able to know which analyses can be more helpful for the feedback generation to the user, aiming to prevent a negative repercussion on the social network. On the other hand, to be able to know whether the combined analysis is able to detect a state of the user that propagates more in the network than the analyses using only text or only keystroke dynamics data.
# Immunohistochemical Expression of ABCB5 as a Potential Prognostic Factor in Uveal Melanoma
###### Abstract  
Uveal melanoma represents the most common primary intraocular malignancy in adults; it may arise in any part of the uveal tract, with choroid and ciliary bodies being the most frequent sites of disease. In the present paper we studied ABCB5 expression levels in patients affected by uveal melanoma, both with and without metastasis, in order to evaluate if ABCB5 is associated with a higher risk of metastatic disease and can be used as a poor prognostic factor in uveal melanoma. The target population consisted of 23 patients affected by uveal melanoma with metastasis and 32 without metastatic disease. A high expression of ABCB5 was seen in patients with metastasis (14/23, 60.9%), compared to that observed in patients without metastasis (13/32, 40.6%). In conclusion, we found that ABCB5 expression levels were correlated with faster metastatic progression and poorer prognosis, indicating their role as a prognostic factor in uveal melanoma.  
Keywords:ABCB5; uveal melanoma; prognosis; metastasis; immunohistochemistry +
Footnote †: journal: MedicalIt has been demonstrated on the basis of cytogenetic studies that monosomy 3 is the most common chromosomal aberration in UMs and correlates with lower survival rates; other cytogenetic alterations including loss of 1p, 6q and gain of 6p and 8q have been also associated with UM [7].  
The biological history of the neoplasm has been characterized in almost 50% of cases by hematogenous dissemination, with the onset of secondary disease localizations, especially at the liver. Despite the improvements in therapeutic strategies, no significant increase in survival has been obtained and liver metastases are expected within 10-15 years after the diagnosis in about half of patients [8].  
ATP-binding cassette sub-family B member 5 (ABCB5) is a human transmembrane P-glycoprotein that plays an active role in transmembrane transport of several substances including chemotherapeutic drugs; therefore, it is physiologically involved in the development of chemoresistance of cancer cells [9; 10; 11]. An overexpression of ABCB5 has been found in tumor stem cells, of which it is therefore a full-fledged marker of several malignancies such as hepatocellular carcinoma, breast cancer, cutaneous melanoma and Merkel cell carcinoma [12; 13; 14]. Furthermore, ABCB5 has been shown to be correlated with tumor growth and invasion [15; 16].  
Regarding cutaneous melanoma, ABCB5-positive malignant melanoma-initiating cells (MMICs) are believed to be involved both in the onset and in the progression of disease and ABCB5 has also been found to play a crucial role in promoting distant metastasis through the activation of the NF-kB signaling pathway [17].  
In the present study, we retrospectively investigated ABCB5 expression in primary uveal melanoma in patients both with non-metastatic and metastatic disease and we evaluated its potential role as a prognostic marker and predictive factor of metastatic potential of the neoplastic cells.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 2 Related Work  
Advancements in satellite network modeling and maximum flow algorithms have laid the groundwork for addressing the challenges of dynamic and capacity-constrained LEO satellite networks. This section reviews the main development and application of TEG and e-TEG models in satellite networks, important advancements in maximum flow algorithms, including predictive and learning-augmented approaches, and the integration of the software-defined networking (SDN) framework into the satellite constellation.