esa-sceva/satcom-chunk-collection huggingface dataset:
# Test-Retest Reliability of a 6 DoF Marker Set for Gait Analysis in Cerebral Palsy Children
## 2 Materials and Methods
### Statistical Methods
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Time-Distance** & **ICC** & **ICC 95\% CI** & **Mean** & **Mean Diff** & **Mean Diff 95\%** & **SD\%** & **95\% LOA** & **SEM** \\
**Parameters** & **ICC** & **ICC 95\% CI** & **Mean** & **Mean Diff** & **CI** & & & \\ \hline Speed (\(m/s\)) & 0.78 & (0.08, 0.99) & 0.82 & \(-\)0.08 & (\(-\)0.21, 0.06) & 0.16 & (\(-\)0.40, 0.24) & 0.12 \\ Cycle Time (\(\approx\)) & 0.86 & (0.34, 0.97) & 0.92 & 0.04 & (\(-\)0.06, 0.13) & 0.11 & (\(-\)0.19, 0.26) & 0.08 \\ Double Limb & 0.84 & (0.01, 0.97) & 0.2 & 0.05 & (0.01, 0.09) & 0.05 & (\(-\)0.05, 0.15) & 0.03 \\ Support Time (s) & 0.94 & (0.05, 0.99) & 0.74 & \(-\)0.04 & (\(-\)0.08, 0.01) & 0.05 & (\(-\)0.14, 0.07) & 0.04 \\ Stride Length (m) & 0.94 & (0.05, 0.99) & 0.12 & 0.01 & (0.00, 0.02) & 0.02 & (\(-\)0.02, 0.04) & 0.01 \\ _Average_ & 0.87 & & & & & & 0.06 \\ \hline \multicolumn{7}{c}{Left lower Limb} \\ \multicolumn{7}{c}{} \\ Cycle Time (s) & 0.84 & (0.31, 0.97) & 0.92 & 0.06 & (\(-\)0.05, 0.16) & 0.12 & (\(-\)0.19, 0.30) & 0.09 \\ Stance Time (s) & 0.85 & (0.33, 0.97) & 0.58 & 0.05 & (\(-\)0.03, 0.13) & 0.10 & (\(-\)0.15, 0.25) & 0.07 \\ Swing Times (s) & 0.76 & (0.00, 0.95) & 0.35 & 0.01 & (\(-\)0.03, 0.04) & 0.04 & (\(-\)0.07, 0.08) & 0.03 \\ Step Time (s) & 0.79 & (0.00, 0.96) & 0.45 & 0.01 & (\(-\)0.04, 0.06) & 0.06 & (\(-\)0.11, 0.13) & 0.04 \\ Step Length (m) & 0.93 & (0.63, 0.99) & 0.38 & 0.00 & (\(-\)0.03, 0.03) & 0.04 & (\(-\)0.08, 0.08) & 0.03 \\ Spider Length (m) & 0.93 & (0.63, 0.99) & 0.75 & 0.00 & (\(-\)0.07, 0.07) & 0.08 & (\(-\)0.16, 0.16) & 0.06 \\ _Average_ & 0.85 & & & & & & 0.05 \\ \hline \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ Cycle Time (s) & 0.86 & (0.30, 0.97) & 0.93 & 0.02 & (\(-\)0.08, 0.12) & 0.12 & (\(-\)0.21, 0.25) & 0.08 \\ Stance Time (s) & 0.87 & (0.44, 0.97) & 0.57 & 0.04 & (\(-\)0.03, 0.10) & 0.08 & (\(-\)0.12, 0.19) & 0.05 \\ Swing Times(s) & 0.84 & (0.24, 0.97) & 0.36 & \(-\)0.02 & (\(-\)0.06, 0.02) & 0.05 & (\(-\)0.11, 0.08) & 0.03 \\ Step Time (s) & 0.79 & (0.00, 0.96) & 0.46 & 0.00 & (\(-\)0.07, 0.07) & 0.09 & (\(-\)0.16, 0.17) & 0.06 \\ Step Length (m) & 0.64 & (0.00, 0.93) & 0.36 & \(-\)0.05 & (\(-\)0.12, 0.02) & 0.08 & (\(-\)0.21, 0.11) & 0.06 \\ Stide Length (m) & 0.64 & (0.00, 0.93) & 0.72 & \(-\)0.11 & (\(-\)0.24, 0.03) & 0.16 & (\(-\)0.42, 0.21) & 0.11 \\ _Average_ & 0.73 & & & & & 0.07 \\ \hline \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ \multicolumn{7}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reliability values for time-distance parameters.
# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 3 Results and Discussion
### Effects of Snow-Surface Characteristics on Estimated Snow Depth
The wavelet decomposition does not change the frequency composition in the original signal, so there is no obvious difference between the two approaches at this point. Upon comparing the estimation results of the two approaches for different years, it can be found that, except for 2015, the proposed approach significantly improves the accuracy when the snow changes. A possible reason is that the snow changes in 2015 were relatively slow, while the snow depths in other years experienced relatively large fluctuations. In summary, the proposed approach can significantly improve the accuracy of snow depth estimation when the snow depth changes drastically.
## 8 Final Comments and Outcomes  
Now, after this long study with many analyses and remarks, the main questions are  
* What the main achievements are?
* How the systematic approach applying the fading memory formalism allows constructions of physically adequate fractional models?  
Since developing this study, many explanations and specific remarks were made; the answers to these questions can be briefly outlined as  
1. One of the principal achievements of this study is that it shows how different fractional operators appear in physical models, avoiding unmotivated replacement fractionalizations. The emphasis was on different versions of the Mittag-Leffler functions as memory kernels.  
2. We demonstrated only six examples, but there is no limit to developing new ones if the constitutive equations are properly formulated.  
3. We emphasize the importance of correct formulations of constitutive equations because they express physical laws relating to causes and effects in mathematical forms.  
4. Therefore, the first step is to correctly formulate a constitutive equation with a hereditary term involving a memory kernel connected to the underlying physics of the modeled process. Then, the balance equation yields the final model (to which various initial and boundary conditions can be applied). However, the final models are not direct expressions of physical laws where hereditary terms can be inserted by replacements.  
5. Some comments on existing heat conduction models confirm the standpoint of point 4. There are many articles with questionable results (see, for instance, the comments of [PERSON] in [10] on physics of fractional models) where the final model is fractionalized by replacement, and the results that cannot be interpreted because the basic physics of the modeled process is violated.  
We believe that this study will provide necessary and enough instructive information on how fractional modeling should be done. There is no need for high mathematics to formulate the constitutive equation correctly with a certain memory term and to get a correct model. High mathematics applied to incorrect models does not provide useful information and could be related to mathematical experiments, not to mathematical modeling of real-world phenomena.  
The example of heat conduction used here is merely one to illustrate the general idea behind the physical basis of the formulation of fractional operators. This technique can be applied to a wide variety of other physical processes that exhibit memories and have properly formulated constitutive equations.  
This research received no external funding.  
Not applicable.  
There are no conflicts of interests.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Analysis of Experimental Results in Two Cases  
The results of the comparison experiments conducted demonstrate that the model architecture design significantly influences diagnostic performance. The lightweight architectures of SqueezeNet and MobileNetV3 constrain their capacity to capture salient local fault features, leading to poor robustness. ResNet-18 employs a simple architecture that achieves the fastest diagnostic time, but its limited depth compromises robustness to noise. While the multi-branch architectures of RepViT and MPNet enable effective capture of fault features with enhanced robustness to noise, such architectures inevitably reduce diagnostic speed. EfficientNetV2-S possesses a large model capacity, enabling it to capture deeper fault information and exhibit strong robustness to noise. However, this complexity also makes it sensitive to the size of the training dataset and results in the slowest diagnostic performance. In contrast, although HFDF-EffNetV2 is slightly slower than ResNet-18 and has a marginally higher parameters than SqueezeNet, it utilizes a lightweight multi-branch structure combined with a fine-grained feature extraction and decision fusion approach, significantly enhancing diagnostic accuracy.
# Integrating BIM and GIS for an Existing Infrastructure
## 2 Current State of BIM and GIS
### 2.2. Gis
#### 2.2.2 GIS Formats  
When working with GIS data models, there are two types of data: the raster type data and the vector type data. Within each group there are a multitude of formats, but all of them are operable together using the main applications in the sector [3]. In the case of the raster model, the space is divided into a regular grid where each cell contains a single value, stored in a band with a specific wavelength in the electromagnetic spectrum. This leads to a wide variety of formats based on this data model, from more text delimited files such as ASCII to compressed image formats such as JPEG or ECW. However, in terms of open standards, the GeoTIFF format, which includes all the information necessary for a TIFF format image file to be positioned in a Coordinated Reference System (CRS), should be highlighted.  
The vector format is defined by a simple object that defines the data. These have traditionally comprised points, lines, and polygons, i.e., 2D objects. The most recognised and widespread format is the Shapefile. This is a file format owned by ESRI, and is characterised as a set of files. Other relevant formats considered standards by the OGC are KML/KMZ and GeoJSON, which are closely linked to the development of web-mapping applications. However, we live in the era of data, and data-related technologies are becoming increasingly important. GIS is no exception to this trend, and spatial databases are becoming more relevant, especially when working with a high data volume. This is where the open coding standard GeoPackage emerges, to transfer geospatial information (both raster and vector) within an SQLite database. When the database is spatial, it is called Geodatabase and the GDB format is used to transfer the collection of files that make it up.  
Regarding 3D, two main formats are commonly used. On the one hand, CityGML and derivates like CityJSON represent an OGC standard for representing urban objects in 3D. On the other hand, more recently, the leading company in the sector, ESRI, has developed the SLPK format, and ESRI's position as a member of the OGC has facilitated its publication by making it an open standard. The SLPK format goes further and focuses on packaging and distributing 3D scene layer data in general [54; 55]. However, even though it is an open standard, only a few applications can operate with this format.  
Finally, the OGC has defined several standards for publishing spatial data online, such as the Web Map Service (WMS) for maps in image format, the Web Feature Service (WFS) for entities, or Indexed 3D Scene Layers (I3S) for the visualisation of complete 3D scenes, among others.
## 3 Results
### RRCF on Sliding Window Statistics  
RRCF applied to point-wise features is tailored towards finding point anomalies due to its principle of isolating single points. In this experiment, we compare the baseline RRCF we used in the former experiments (RRCF@points) to an alternative (RRCF@sequences) where we preprocess the time series by computing a vector consisting of the minimum, maximum coefficient of variation and the first four moments (mean, variance, skewness and kurtosis) of a sliding window. We choose a subsequence length of 100 and a stride of 50. We also tuned the hyper-parameters _n_trees_ and _tree_size_ as described in Section 2.3. The results are shown in Figure 9. Using subsequence-wise features for RRCF increased the AUC ROC from 0.56 to 0.7, making this the best AUC ROC result among the analyzed methods. Additionally, the UCR score increased for RRCF@sequences by a factor of 5 from 0.03 to 0.15. The F1 score did not change substantially. While RRCF applied to point-wise features was the only method detecting the \"smoothed increase\" anomaly, this anomaly is not detected any more. Instead, RRCF applied to subsequence-based features detected the'steep increase' anomalies, like the other five methods. For all other anomaly types except \"midding drop\", the UCR score increased for RRCF@sequences. The highest increase is made for the \"time warping\" anomaly from 0.0 to 0.4.
## 3 Numerical Proof of the Theory
### Data  
The study was conducted with high-quality radiometric data retrieved from BSRN-- Baseline Surface Radiation Network, station Magurele, Romania [30].  
The town Magurele (44.3439 N; 26.0123 E, 110 m asl) is located in a rural area, close to the capital Bucharest. Magurele experiences a transitional climate, with both continental and subtropical influences (Koppen climate classification Cfa [31]). Global, diffuse, and direct-normal solar irradiances recorded at 1-min resolution during 1 June to 31 December 2021, were used to build the specific database. A carefully quality check was performed, at the first step the suspicious data being removed. At the second step, only data measured at the Sun elevation angle \(h>5^{\circ}\) were selected (the pyranometers accuracy is questionable close to sunrise and sunset). Finally, only those hours containing a complete dataset (60 measurements points) were retained. This dataset, further denoted hDATA, was completed with physical quantities resulted from post-processing of measurements, e.g., relative sunshine \(\sigma\), clearness index, sunshine number SSN (as a proxy for relative sunshine), and sunshine stability number SSSN (as a quantifier for the variability in the state-of-the-sky). SSN and SSSN are defined in Appendix B. Figure 1 shows the variation of \(\sigma\) and SSSN for all 2044 valid hours comprised in hDATA. Visual inspection of Figure 1 highlights the complexity of the hDATA dataset. It contains both hours with stable clear or overcast skies, partly sunny hours with moderate variability in the-state-of-the-sky, but also hours with a high degree of variability in the-state-of-the-sky occurring either on a clear-sky or cloudy background. Similar databases were built for evaluating the boundary cases \(\Delta t=0\) (iDATA) and \(\Delta t=\) daylight_length (dDATA).
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 4 Experimental Method
### Hardware Setup  
The transmission node, as illustrated in Figure 5, consists of a LoRa Arduino shield that comprise of an RN2903 transceiver (microchip) stacked on an Arduino MEGA microprocessor. It is good to know that Arduino is an open source board and contains a very simple microcontroller which can be connected to various types of sensors and devices. Furthermore, Arduino consumes less power than other tiny controlling devices such as Raspberry Pi, which is more complicated and considered as fully functional computer (Kal  
Similarly, the receiver node (see Figure 7a) consists of a LoRa RN2903 Module controlled by an Arduino Uno microprocessor and stacked on top of it. These components are also enclosed inside a weatherproof box, as shown in Figure 7b.  
Figure 8 shows the receiver node installed on the back of the four-wheel drive vehicle in order to test it at different distances.  
Figure 5: Components of the LoRa transmission node: (**1**) Arduino Mega microprocessor. (**2**) TTL Serial JPEG camera. (**3**) LoRa Shield radio adapter. (**4**) SD Card Module.  
Figure 6: Completed transmission node enclosed in a weatherproof box and fixed on top of a Skylift crane at a height of 40 m.  
Figure 7: (**a**) Receiver node: (1) Arduino Uno (2) LoRa RN2903 Module. (**b**) The receiver node enclosed inside a weatherproof box.
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
###### Abstract
These are present mainly due to fractional-order PID controllers [28; 29; 30; 31], which aim to introduce additional \"tuning knobs\" that can be used to adjust the controller to benefit the controlled processes. The higher-order controllers can also reduce the sensitivity to the modeling uncertainties of controlled systems, as shown in [32]. Starting with the dead-time compensation in ESO proposed already in [33], this paper develops new dead-time compensating ADRC structures based on higher-order stabilizing controllers not yet included it an overview of the current state of the literature [12]. The paper is organized as follows. Section 1 presents several possible interpretations of the two-parameter Integral Plus Dead Time (IPDT) plant models for designing higher-order Active Disturbance Rejection Control (ADRC) based control algorithms with dead-time compensation. Section 2 discusses the optimal tuning of the proportional (P), proportional-derivative (PD), or proportional-derivative-accelerative (PDA) controllers used to stabilize the IPDT models. Section 3 discusses the Extended State Observer (ESO), for reconstructing lumped disturbances and uncertainties affecting the considered (generally higher-order) integrative plant models. Section 4 then deals with an illustrative experiment with description and results of near minimum-time and, at the same time sufficiently smooth temperature control of a laboratory thermal plant. After discussing the obtained results in Section 5, the paper is concluded by Section 6.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 5 Conclusions and Future Work
### Outlook
#### 5.2.4 IoT Data for Urban Traffic Management  
How can we fully exploit the advantage of multimodal, multi-fidelity IoT data, including, but not limited to, individual trajectories, camera images, radar heatmaps, and lidar cloud points? The existing practice is to preprocess these multimodal, multi-fidelity measurements using computer vision and extract aggregate traffic information in terms of traffic velocity and/or density within discretized spatial cells and time intervals. Ratherthan converting these data formats to conventional ones, we should think outside the box and potentially redefine the entire TSE framework. It thus demands a shift in paradigm from what constitutes traffic states to taking individual trajectories and images as inputs. In other words, is it sufficient to simply use aggregate traffic velocity, density, and flux to describe traffic states? The aggregate traffic measures were introduced decades ago when inductive loop detectors were deployed to measure cumulative traffic counts. Traffic flow has long been regarded as analogous to fluids, and, accordingly, hydrodynamic theory is applied to model traffic flow dynamics. It is natural to adapt physical quantities defined for fluids to traffic flow. However, traffic systems are not physical but social systems, in which road users constitute a complex traffic environment while interacting continuously with the built environment. Contextual information greatly influences driving behaviors and traffic dynamics. Accordingly, the question is, when we describe the state of traffic and aim to optimize traffic management strategies, would traffic contextual information perceived by our eyes and brains (represented by DNNs) be helpful as an addition to those widely used quantitative measures, especially when this type of information becomes more widely available thanks to IoT and smart city technology?  
Furthermore, if TSE models can be enriched with multimodal data, what are the new challenges and opportunities to traffic control and optimization models that rely on traffic state inference? There are extensive studies on causal discovery and causal inference using observational data when unobserved counfounders are present [120; 121]. However, little work has been performed to leverage explicit causal relations from physical knowledge to improve PIDL. For example, counterfactual analysis of physical dynamics concerns identifying the casual effects of various interventions, including traffic control and the sequential decision making of other agents in the environment [122; 123; 124]. Without performing extra experimentation that is risky and unsafe, how can we design and validate traffic management strategies using new information provided by IoT data?
# Multi-Stage Quantum Secure Direct Communication Using Secure Shared Authentication Key
## 4 Secure Shared Authentication Key (SSAK) Protocol  
This section presents the proposed SSAK protocol. The SSAK protocol focuses on the idea of sharing a secret angle and an authentication key between [PERSON] and [PERSON] over a multi-stage quantum channel. In the proposed protocol, an Information Travel Time (ITT) or Time Stamp technique associated with the data [26] is adopted to detect the presence of eavesdroppers in the initial authentication procedure. In the condition, [PERSON] is located midway between [PERSON] and [PERSON], the ITT is obviously different due to the fact that the distances between [PERSON] and [PERSON] are greater than the distance between [PERSON] and [PERSON] as well as [PERSON] and [PERSON]. However, in the case of [PERSON] located far away from [PERSON] and [PERSON], the ITT would be doubled and shows a sign of eavesdroppers' attack. Thus, when quantum particles pass through optical polarization devices on the sender's site, and an eavesdropper is present in the communication to read the photon, the response by the receiver might induce abnormal timing information. Moreover, this work utilizes the Pauli-X quantum gate to increase the difficulty for [PERSON] to steal the information. Theoretically, a wire of transmission carries a qubit of information, and the quantum gate will take that qubit as an input. Here, the function of the quantum gate is to perform the unitary transformation on this qubit and produce a new state of qubit as an output. Pauli-X gate is also known as a bit flip operator that is responsible for altering the qubit state by flipping bit 0 to 1 and vice versa. Based on previous research, the quantum gate provides many benefits such as being easy-to-use as the information related to the operators is not necessarily shared between the parties, has less precision requirement compared to in arbitrary rotation such as half-wave plate (HWP), and provides accurate measurement [27].  
The details of the proposed protocol can be described in three phases: Initial authentication, secure message sharing, and security checking. Figure 2 shows the whole process of the proposed SSAK protocol.
## 2 The Learning Algorithm
### Integer Linear Programming Formulation  
We show that the training algorithm is equivalent to solving an integer linear programming problem. We use the following definitions: \(B_{\epsilon}(\vec{x}_{i})\) is the hypersphere of radius \(\epsilon\) centered in \(\vec{x}_{i}\); \(Y_{ij}=\frac{1}{2}(1+y_{i}y_{j})\) is the indicator \(y\)-correlation matrix of training labels; \(K_{ij}^{\epsilon}=\kappa_{\epsilon}(\vec{x}_{i},\vec{x}_{j})=\lim\limits_{\tau \to\infty}\frac{1}{2}[1+\tanh(\gamma(\epsilon-||\vec{x}_{i}-\vec{x}_{j}||))]=H( \epsilon-||\vec{x}_{i}-\vec{x}_{j}||)\) is the indicator kernel matrix, where \(H(x)\) is the Heaviside step function. The indicator kernel matrix satisfies  
\[K_{ij}^{\epsilon}=\begin{cases}1,&\text{if }\vec{x}_{j}\in B_{\epsilon}(\vec{x}_{ i})\\ 0,&\text{otherwise}\end{cases}. \tag{5}\]  
The indicator \(y\)-correlation matrix satisfies  
\[Y_{ij}=\begin{cases}1,&\text{if }y_{i}=y_{j}\\ 0,&\text{if }y_{i}=-y_{j}\end{cases}. \tag{6}\]  
From the definitions, we have  
\[\prod\limits_{p=1}^{l}Y_{jp}^{K_{jp}}=\begin{cases}1,&\text{if }\{y_{k}|\vec{x}_{k} \in B_{\epsilon}(\vec{x}_{j})\}\text{ is constant}\\ 0,&\text{if }\{y_{k}|\vec{x}_{k}\in B_{\epsilon}(\vec{x}_{j})\}\text{ is mixed}\end{cases}. \tag{7}\]  
Then, the counting number returned by the DBSCAN-DJ training procedure is \(c_{i}=\sum_{j=1}^{l}K_{ij}(1-\prod_{p=1}^{l}Y_{jp}^{K_{jp}})\). Since all \(c_{i}>0\), the ranking algorithm selects support vectors that satisfy the solution for the constrained integer linear programming problem:  
\[\min\limits_{a_{i}}\sum_{i}^{l}c_{i}\alpha_{i} \tag{8}\] \[\alpha_{i}\in\{0,1\}\] (9) \[\sum_{i}^{l}\alpha_{i}=\lfloor\frac{1}{\alpha}\rfloor, \tag{10}\]  
where \(\alpha_{i}=1\) if and only if \(\vec{x}_{i}\) is selected as a support vector. Notice that, if the number of training data is \(l\), the sorting only takes \(O(l\log l)\) time, while general integer linear programming is NP-complete.