esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 4 Discussion  
### Communication within a 20 km Range  
The measurement campaign proves that LoRa modulation is an excellent candidate for long-range low-rate data communication in the Antarctic, within a radius of at least 20 km. During the measurements, directional high-gain antennas were employed at the base station to extend the range. However, the SNR measurement was saturated and no packet loss was recorded at distances smaller  
Figure 16: Possible reflection point.  
Figure 15: NLoS LoRa communication, with an indication of the potential propagation paths via reflection.  
than 20 km. Hence, omnidirectional antennas such as a vertical monopole or dipole would still have been sufficient to maintain a reliable link up to 20 km. Larger distances are clearly possible with antennas that provide higher gain. If antenna gain needs to be combined with an omnidirectional radiation pattern, vertical collinear antennas can be used. For example, the X-700 HN antenna, manufactured by the Diamond Antenna Corporation [20], provides 13 dBi omnidirectional gain at 434 MHz by means of a collinear array of 11 elements of \(5/8\lambda\) (the Yagi-Uda antenna used during this measurement campaign provides a gain of 9 dBi).  
Note that European or US spectrum regulations are not violated when deploying a high-gain antenna at the receive side only. In all the experiments described in this paper, the mobile transmitter has never used a high-gain antenna. However, in the Antarctic it would be allowed to deploy high-gain antennas at both ends of the link, since there are no specific spectrum regulations that apply to this area.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 7 Evaluation Results
### Result Representation of the Three Evaluated Classifiers Using the Confusion Matrix
#### 7.2.2 Comparative Analysis of the Two Test Scenarios for the RF Classifier  
Similar to the SVM classifier, the overall accuracy of the RF classifier degraded strikingly from 91.9 to 73.5 when the test and training environments were different (Figure 9). Again, the cause of this significant decrease in RF was evident in the predicted accuracy of the MP conditions, i.e., it reduced from 30.4% to 19.6%. Unlike the SVM classifier, the predicted accuracy of the RF classifier in both the LOS and NLOS conditions noticeably declined as well. This implied the predicted accuracy of LOS degraded from 30.9% to 26.9% while NLOS decreased from 30.6% to 27.0%. This pushed the overall accuracy of the RF classifier to be worse than the SVM in the scenario where the training and test environments were different.  
Similar to the aforementioned reason and condition in the SVM classifier, the misclassification rate of the MP condition as either the LOS or NLOS was quite high in the RF classifier as well (Figure 9). To give the exact values, the misclassification rate of the MP condition as NLOS increased from 0.9% to 6.5%. Similarly, the misclassification of the MP as LOS grew from 2.0% to 7.2%. In the RF classifier, the misclassification rate of the NLOS as an LOS was also noticeably high, i.e., it increased from 1.8% to 5.6%. Again, the predicted accuracy of the NLOS condition in both of the two test scenarios was quite satisfying; that is, 30.6% out of 100% for the three classes in the scenario when the training and test data were in the same environments. Similarly, it was 27.0% when the data for the training and test environments were different.
## 1 Introduction
Therefore, this paper attempts to reconstruct an industrial product quality traceability system with the help of blockchain technology, aiming to standardize the functions of secure storage, collaborative sharing, and the traceability of the data for critical information in the whole life cycle of product design, manufacturing, logistics, warehousing, use, and operation and maintenance, with a view to strengthening the application of the quality traceability system to the market circulation of industrial products and assisting the supervisory authorities in making intelligent decisions. The main contributions of this paper are summarized as follows: (1) Based on blockchain and industrial internet identity resolution technology, the overall technical architecture of a blockchain-enabled industrial product quality traceability system is designed, which eliminates the information barriers between different departments and enterprises for products at all stages of the whole life cycle through research on industrial product identity code design and data storage technology; (2) From the perspective of the full life cycle management of industrial products, industrial product information traceability is divided into pre-preparation monitoring, mid-term production practice, and post-life cycle management along the supply chain to track and store link information, which ensures the credibility and completeness of the link information; (3) Adopting the hybrid blockchain structure of \"alliance chain + private chain\" solves the shortcomings of the traditional alliance chain mode, such as heavy node storage, low throughput, high cost, etc., ensures the security and privacy of data, reduces the pressure of the overall data processing and storage, improves the efficiency and performance of the system, and provides a suitable opportunity for the dynamic supervision of industrial product supply chain information; (4) Using the Postman (version 8.0.6) system performance testing software, the whole life cycle information traceability system for industrial products proposed in this paper is verified, which can realize rapid tracing of problematic products and rapid positioning of responsible subjects. The rest of the article is structured as follows. Section 2 describes the design of the system, including industrial product quality traceability process analysis, system architecture design, and application module weight design. Section 3 introduces key blockchain technologies. Section 4 describes the implementation of the blockchain-based full life cycle information traceability system for industrial products. Section 5 validates the system built in this paper by writing data and querying data through the testing tool Postman (Version 8.0.6).
## 3 Methodology Research  
Broadly speaking, landslide susceptibility assessment methods can be classified into four main types: physics-based models, heuristic models, statistical models, and machine-learning models [40; 41; 42; 43]. These methods have been proven to have both advantages and limitations [44]. The users of physical-based models need to understand how landslide forces and resistances interact, which requires detailed information on rock types, soil properties, slope shapes, and water conditions. While they can provide the highest evaluation accuracy, these models are only suitable for individual landslides or small-scale studies and cannot be extrapolated to fit larger scales [45]. Heuristic models include geological and geomorphological analysis, factor index analysis, fuzzy comprehensive evaluation, and analytic hierarchy processes. These methods require experts to score them based on their professional knowledge, thus exhibiting a high degree of subjectivity [46; 47; 48; 49]. Statistical models assume that there is no correlation among influencing factors; hence, they use bivariate or multivariate algorithms to assess landslide susceptibility. These mainly include the information value method, evidence weight method, logistic regression method, certainty factor method, entropy index method, and multiple linear regression method. Compared to heuristic models, these methods avoid the subjectivity of experts. However, they overlook the complexity of landslide causes, and the correlation among factors is not adequately described [50; 51; 52; 53; 54]. Machine learning models can estimate the relationship between landslide distribution and influencing factors by learning from training data. They do this without needing specific functions or extensive prior knowledge, which helps them produce more accurate evaluations. The application process of machine learning in landslide susceptibility assessment is shown in Figure 2[55]. Commonly applied machine learning models in research include logistic regression, random forests, support vector machines, artificial neural networks, various ensemble models, and deep learning models [56; 57; 58; 59].  
Figure 2: Landslide susceptibility assessment flow chart [55].  
This section mainly analyzes the machine learning models. Compared to traditional statistical models, machine learning models demonstrate strong nonlinear fitting capabilities in landslide susceptibility assessment. Their predictive accuracy and precision are higher than traditional statistical models [17]. They are capable of more accurately reflecting the nonlinear relationship between various evaluation factors and landslide susceptibility, thus finding broader applications in landslide susceptibility assessment [60,61]. A brief introduction to the typical machine learning models used is provided below.
## 4 Experiments and Results
### Training Parameters of the Classifier  
To confirm the performance of the DeepCA feature extraction approach, we adopt DeepCA in combination with FCNNs. In this regard, the input layer of each FCNN is defined as \(64\times 64\times 3\) or 12,888 neurons according to the image channels. In the experiment, we combine DeepCA with three types of FCNNs, in which the network configurations are defined as (64), (64, 64), and (64, 64, 32) (see Table 2). In addition, we implement stochastic gradient descent with momentum (SGDM) [63] as an optimizer for training the FCNNs with the following parameters: the number of dataset training iterations is set to 30 epochs, the minibatch size is set to 1, the momentum is defined as 0.9, and the learning rate is set to 0.000001.
## 3 Background
### A QKD Overview  
The detailed design of the AirQKD FSO-QKD system layer is not covered in this work. The main focus of this work is the use of the QKD-generated encryption keys to secure V2I communication. Readers who want a deeper understanding of how QKD operates can refer to the wealth of existing publications. For example, see [13; 21] insightful overviews of QKD's functionality, research, and applications.  
As mentioned in the introduction, QKD systems generate encryption keys that can be used to secure information. This secured data can either be stored in digital systems or transmitted through communication protocols. QKD uses the quantum properties of light, typically various polarisation states, to ensure a statistical security guarantee, even in the presence of eavesdroppers. QKD systems using fibre optics are well-established, with commercial offerings from companies like ID Quantique, MagiQ Technologies, Toshiba, QuintessenceLabs and SEQRE.net [21]. FSO-based systems are being researched [22] for their ability to perform QKD in applications that cannot use fibre optic cable, e.g., satellite systems, expanses of land, difficult terrain, and expensive urban cable installations. However, FSO communications and systems have issues related to line-of-sight and atmospheric conditions, see [23; 24] for summaries of FSO-based technologies and their advantages and disadvantages. However, FSO has the potential to contribute to future 6G systems, potentially bridging long distances without cables, achieving terabyte data rates, and improving communication latency.  
The lack of commercially available FSO-QKD systems prompted the initiation of the AirQKD project. A schematic representation of FSO-QKD is shown in Figure 2. It operates in the same manner as a fibre optic-based QKD system but with the fibre optic cable being replaced by a free space link using photons at frequencies above visible light. This optical channel enables the statistical guarantee of key confidentiality using various protocols [13], beginning with the seminal Bennett-Brassard 1984 (BB84) algorithm [25; 26].  
The QKD algorithms utilise the optical channel and a classical communications channel when generating and exchanging keys between two parties, [PERSON] and [PERSON]. The QKD system maintains secrecy even in the presence of the eavesdropper, [PERSON]. The FSO-QKD system will generate a continuous stream of random keys, unlike the single static certificate root of a PKI system. This enables two parties, [PERSON] and [PERSON], to communicate secretly with disposable symmetric keys, similar to using one-time pads (OTP) [27]. The use of QKD-derived symmetric keys allows data security to be intrinsically designed into systems, bypassing the reliance on single-use passwords or certificates, and protecting against future PQC issues. Thus QKD can be seen as an alternative to PKI for future digital systems, including V2I applications, as tested here.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Value of Quantified Uncertainty
#### 3.2.3 Individual Value of Data Uncertainty  
The value of quantified data uncertainty lies in the acquisition of information regarding the theoretical limits of prediction accuracy. Even if the model were perfectly constructed for its task and if the current situation perfectly matched that task, it might still be impossible to make certain predictions if the data on which they are based is flawed.  
In such cases, data uncertainty indicates that the data does not contain sufficient information to make a precise prediction. In these instances, it is possible to gather better data or otherwise make a nuanced prediction, specifying multiple possibilities that could be true. Furthermore, if data uncertainty is modeled as a probabilistic embedding, the inherent uncertainty in the data, such as missing details due to resolution or motion blur, can be transferred into the embedding space.  
In practice, the boundary between data uncertainty and distributional uncertainty can be quite fuzzy. An image of a person that has been wrongly cropped, displaying only an arm, could be perceived as exhibiting high data uncertainty due to the erroneous setting of the crop. However, it could also be regarded as exhibiting high distributional uncertainty, given that images of arms belong to a distinct domain from images of people. Similarly, the introduction of minor amounts of noise into an image would be regarded as data uncertainty. However, if the noise is so extensive that the original image is no longer recognizable, this would represent an instance of the domain of images of noise, which would be indicated by high distributional uncertainty.  
In summary, the information obtained allows us to identify the sources of uncertainty. We can distinguish whether the data are ambiguous (data uncertainty), the model is unable to cope with the current situation (distributional uncertainty), or the model is underperforming even under ideal conditions (model uncertainty).
## Materials and Methods
### Aromatic Compounds Analysis byHS-SPME-GC-MS  
Analysis of the volatile compounds was performed based on Head-Space-Solid-Phase Microextraction (HS-SPME) followed by Gas-Chromatography-Mass-Spectrophotometry (GC-MS) analysis. The sample was extracted using a 50/30 \(\upmu\)m Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Inc., Bellefonte, PA, USA). Each liquid sample (5 mL) was placed in a 20 mL SPME glass vial together with 30% _w/v_ of sodium chloride and 1 \(\upmu\)L of the internal standard 2-octanol (0.4095 mg/L in absolute methanol). The vial was tightly capped, shaken, and left to equilibrate for 30 min at 60 \({}^{\circ}\)C, and then fiber was exposed to the headspace for 30 min. The fiber was introduced into the injection port of the GC-MS system (at 250 \({}^{\circ}\)C for 10 min) and the analysts extracted from the fiber were thermally desorbed. The analysis was done in the splitless mode using helium at a total flow rate of 50 mL/min. The identification of the extracted compounds was performed in a Shimadzu GC-2010 plus with a Rtx-5 MS capillary column. The column carrier gas was helium at a flow rate of 1.5 mL/min. The mass detector operated in the electron impact mode was relative to the tuning result in a range from 35 to 550 m/z, and the ion source temperature was set at 230 \({}^{\circ}\)C. The oven temperature was held at 35 \({}^{\circ}\)C for 1 min, raised at 6 \({}^{\circ}\)C /min to 155 \({}^{\circ}\)C, then raised to 250 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C / min, and held at 250 \({}^{\circ}\)C for 20 min. The aromatic components were identified by comparison of their Retention Indices with data reported in the literature and their mass spectra the National Institute of Standard and Technology (NIST) 11 data base (matching quality higher than 90%). The Retention Indices (RI) of unknown compound were calculated by the retention time of a series of alkanes (C5-C35). Semi-quantitative analysis of the volatile compounds was performed using octan-2-ol as the internal standard. The results were reported on the basis of a mean value from two biological and analytical replicates.
## 2 Stability of Columns, Mathematical Formulation for Determining the Critical Loads  
### Euler Load  
Columns submitted to an axial load have a limit on their resistance, identical to the material's yield limit \(f_{y}\) multiplied by the cross-section area \(A\)[19, 20], expressed by plastic load \(N_{pl}\), in Equation (1).  
\[N_{pl}=A\;\;f_{y} \tag{1}\]  
Short columns fail with a critical buckling stress that can be larger than the material yield. In this zone, the material no longer performs by elasticity [20, 21] and the buckling of this region is called inelastic [20, 21].  
The long columns will tend to fail with the application of lower loads by elastic buckling, reaching the critical buckling stress [20, 21], and it is called elastic buckling [21, 22] or Euler critical load \(N_{cr}\). This critical load gives the value of the slenderness of a member in compression [22] (Equation (2)).  
For the applied boundary conditions, an effective length \(L_{e}=k\;L\) is needed to include a function of the column length \(L\). For fixed-fixed end column \(k=0.5\), in fixed-pinned end column \(k=0.7\), pinned-pinned \(k=1\), and in fixed-free ends \(k=2\). The effective length is explained as the column length involving two zero-moment locations.  
\[N_{cr}=\frac{\pi^{2}}{L_{e}^{2}}E\;I \tag{2}\]  
In Equation (2), \(E\) is the modulus of elasticity, and \(I\) the minimum moment of inertia. Figure 1 shows all the boundary conditions applied to the columns and the function of the effective length.  
The Euler load \(N_{cr}\) is the lowest or ideal quantity obtained when an ideal column is overloaded. For the elastic buckling problem, the material is linearly elastic and homogeneous, the columns are initially perfectly straight without imperfections or residual stresses, the applied load is centrally in the cross-section, and its restraints are such that only plane buckling in one direction is relevant.  
In review, buckling loads are sensible to material properties, column dimension, cross-section, boundary conditions, and primary eccentricity.  
The properties of steel S235 assumed in this work are the following [23]: Young Modulus is 210 GPa, yield strength equal to 235 MPa, and the ultimate tensile stress is 360 MPa. Also, in the finite element analysis, it is important to give appropriate material properties. It was considered that the stress-strain is elastoplastic, and it follows the parameter of the isotropic hardening plasticity.
## 2 Materials and Methods
### WD-XRF Experiment  
The major elements in the samples were also determined by the complementary WD-XRF method using a Bruker S8 Tiger WD-XRF spectrometer. The spectrometer consists of an X-ray tube operating at \(<\)60 kV and \(<\)170 mA, three analyzing crystals (LiF200, PET, and XS-55), and two detectors (scintillation and gas flow proportional counters) [44]. The fusion bead sample preparation method was used, which is known to provide more accurate results than the pressed pellet/powder method by minimizing the matrix and mineral effects [45]. The fusion bead was made by mixing a sample and a lithium tetraborate flux at a 1:10 ratio with 0.5% LiBr as a wetting agent. The sample was kept on top of the flux and heated to 800 \({}^{\circ}\)C to remove the carbon content before the fusion mixture process. The fusion mixture went through a full fusion cycle to form a 40 mm diameter glass bead in a platinum (Pt-5%Au) crucible. Each sample was prepared in duplicate and measured three times. The spectrometer was calibrated using Geo-Quant CRMs [46]. To validate the methodology, the NIST SRM 1648a was prepared in the same way as the dust and ash samples. In total, 20 elements (Al, As, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Si, Sr, Ti, V, Zn, and Zr) were determined. S and Cl were analyzed but not considered in the present study, due to potential loss of content during the adopted fusion cycle process.
Therefore, the main contributions of this study are as follows:  
(1) We validate the effectiveness of using raw multi-temporal spectral reflectance directly for CLQ assessment. This approach simplifies the data preprocessing pipeline and achieves superior accuracy compared to conventional methods that rely on derived vegetation indices. (2) We develop and optimize a tailored BO-Stacking-TabNet framework for farm-scale CLQ assessment. We systematically demonstrate its high performance on this tabular classification task and comprehensively benchmark it against various baseline models. (3) We propose and apply a data-driven methodology for optimal imagery selection. By leveraging a Random Forest model on the GEE platform, we objectively identify the most suitable periods for assessment, providing a quantifiable basis for temporal data selection.
## 3 System Model and Data Preprocessing
### Problem Description
#### 3.3.1 AMC Problem  
The automatic modulation classification task is an intermediate step between signal reception and demodulation, where the received signal is transformed into a baseband complex-valued signal sequence \(X=\{x(0),x(1),\ldots,x(k-1)\}\) by the receiver. \(K\) is the number of sampling points. The received modulation signals are I/Q signals, which include a real part I and an imaginary part Q, which can be expressed as:  
\[I=[real(x(0)),\ldots,real(x(K-1))] \tag{2}\]  
\[Q=[imag(x(0)),\ldots,imag(x(K-1))] \tag{3}\]  
After data preprocessing, the time-frequency image dataset \(D_{t}=\{(x_{i},y_{i})\}_{i=1}^{N}\). The aim of AMC is to create a mapping function \(f\in F\) and to minimize its expected error.  
\[\min_{f\in F}\epsilon_{ex}=\min_{f\in F}E_{(x,y)\sim P_{X\times Y}}L(f(x),y) \tag{4}\]  
where \(L(f(x),y)\) represents the error generated by comparing the predicted value with the true label. However, the expected error cannot be calculated directly because we only have  
Figure 3: Data preprocessing.  
a few signal samples. In this case, we use training data to represent the entire distribution and thus calculate an approximation of the expected error.  
\[\min_{f\in F}\epsilon_{em}=\min_{f\in F}E_{(x,y)\sim D_{l}}L(f(x),y) \tag{5}\]  
However, this empirical error may be affected by factors such as randomness of data sampling and noise. It cannot fully and accurately reflect the generalization ability of the model. Thus, we must take into account the difference between the expected error and the empirical error, i.e., the generalization error \(\epsilon=|\epsilon_{em}-\epsilon_{ex}|\). If the two errors are not too different, this means that the model can generalize better on both training data and unknown data. The above equation can be rewritten as:  
\[\min_{f\in F}\epsilon_{em},s.t.f(x_{i})=y_{i},\forall(x_{i},y_{i})\in D_{t} \tag{6}\]