esa-sceva/satcom-chunk-collection huggingface dataset:
# Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
## 3 Experimental Results
### Parameter Settings  
The accuracy of the RLS predictor has a strong correlation with the prediction length k of the input vector \(\mathbf{d}_{z,k}(n)\). Additionally, to preserve the causality of the pixels in the context window, the local mean predictor is used to estimate \(\hat{S}_{z}(n)\). Therefore, the radius \(r\) of the context window is another important parameter that directly affects the compression results. In order to evaluate the effects of the \(k\) and \(r\) parameters on near-lossless compression performances, average bit rates of the proposed scheme at different peak absolute errors are shared in Figure 3 for the AC datasets.  
It can be seen from Figure 3a-d that the proposed compression schemes exhibit similar radio-compressed bit-rate characteristics for different peak absolute errors. The lowest bit rates are observed in the case of low \(r\)-high \(k\) parameter pairs. If the radius is treated as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Scene**} & **Number** & \multirow{2}{*}{**Rows**} & **Columns** & **Bands** & **Formation** \\  & & **of Scene** & & & & \\ \hline AC & Yellowstone & 5 & 677 & 512 & 224 & Signed 16 bit \\ AU & Yellowstone & 3 & 680 & 512 & 224 & Unsigned 16 bit \\ AIRS & Gran & 8 & 90 & 135 & 1501 & Unsigned 16 bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: The sensor names and their main features.  
a constant, we can see that the deceleration of the compressed bit rate gradually slows down as the prediction length increases. Moreover, the RLS predictor update requires the computation of \(\mathbf{P}(n)\) as well as \(\mathbf{d}_{z,k}(n)\). The algorithm has an \(O(k^{2})\) computational complexity, meaning that the computing resource consumption increases quadratically with \(k\). Considering the dual effects of the actual compression results and the computational complexity, the initial radio \(r\) and prediction length \(k\) parameters are selected as 2 and 12, respectively.
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Optomechanical Effects for Sensing Applications
The minimal detectable wavelength shift of this sensing scheme is determined by its mechanical linewidth \(\Delta\Omega_{m}\) as,  
\[\left(\frac{\delta\lambda}{\lambda_{cav}}\right)_{min}=\frac{\Delta\Omega_{m}/ \lambda_{cav}}{-d\Omega_{m}/d\Delta_{L}} \tag{9}\]  
Obviously, it can be seen from Equation (9) that a narrow mechanical linewidth \(\Delta\Omega_{m}\) and a large frequency tuning rate \(d\Omega_{m}/d\Delta_{L}\) arising from optical spring effect could provide an excellent approach to detect the optical resonance variation. By defining the sensing resolution as \(\left(\delta\lambda/\lambda_{cav}\right)_{min}=\left(\eta_{om}Q_{m}^{eff}Q_{opt }\right)^{-1}\), where \(Q_{m}^{eff}=\Omega_{m}/\Delta\Omega_{m}\) is the effective mechanical Q-factor and \(\eta_{om}\) denotes optomechanical transduction efficiency depending on laser detuning \(\Delta_{L}\), it can be seen that the sensing resolution depends not only on \(Q_{opt}\) of optical cavity in conventional microcavity sensors, but also on the mechanical \(Q_{m}^{eff}\) of optomechanical cavity. Compared with conventional mechanical tunable sensing methods, optomechanical sensing can enhance the sensing resolution by about a mechanical quality factor of \(Q_{m}^{eff}\).
# Levy Statistics and the Glassy Behavior of Light in Random Fiber Lasers
## 3 Results
### Theoretical Framework
We initially remark that in the present case of intensity fluctuations, as well as in any case of realistic stochastic phenomena, a PDF with a diverging second moment actually represents an unphysical possibility. Nevertheless, it has been demonstrated [90] that a _truncated_ power-law PDF, with a large but finite second moment, behaves rather similarly to the Levy PDF to a considerable extent, defining the so-called Levy-type (or Levy-like) statistical behavior. In this case, the crossover to the Gaussian dynamics, predicted by the CLT, is only attained in a very long term [90; 91]. In this sense, theoretically justified truncation schemes have been suitably implemented, for example, by restricting the values of the random variable to a finite range [90; 91], with \(P(I)=0\) for \(I>I_{\text{cutoff}}\), or by tempering the power law with an exponential attenuation [81; 82; 89], \(P(I)\sim\exp(-\eta I)/I^{\mu}\), in a form similar to Equation (6). Therefore, the experimental reports of Levy PDFs of intensities with index \(0<\alpha<2\) should be properly interpreted as representative of this extensive Levy-like statistical regime of intensity measurements. Lastly, in addition to the description above of the truncated power-law with exponential attenuation, which was based on the Langevin dynamics of the amplitudes of the normal modes, we also comment that a PDF of output intensities emitted by RL systems with power-law form, \(P(I)\sim I^{-\mu}\), has been derived in [92]. In contrast with the above developments, in this case, the theoretical approach took into account the statistics of the photon trajectories subjected to multiple scatterings within the sample [92]. The power-law exponent was found to be \(\mu=1+\ell_{\text{g}}/<\!\!l\!\!>\), where \(\ell_{\text{g}}\) and \(<\!\!l\!\!>\) denote, respectively, the gain length of the active medium and the average length of the photon paths.
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 3 Research Design
### Stage 2: Named Entity Extraction Model
#### 3.3.3 Feature Fusion Layer  
The attention mechanism is a method that can dynamically select important information in the input sequence. It was originally used to solve the problem of information loss in long sequence processing. It captures long-distance dependencies by generating a specific context-related vector for each output and performing weighted summation using different parts of the input sequence. This mechanism allows for the model to focus on different  
Figure 4: Comparison of normal convolution and dilated convolution sampling.  
Figure 5: Dilated convolution with residual structure.  
fragments of the input in a targeted manner rather than relying solely on the compressed overall representation. The attention mechanism operates using query vectors (Query), key vectors (Key), and value vectors (Value): First, a set of attention weights is obtained by calculating the similarity between the query vector and the key vector, and then, these weights are used to perform weighted summation on the value vector to generate the final context vector. The implementation formula is as Formula (10):  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{10}\]  
Among them, \(Q\) is the query vector, \(K\) is the key vector, and \(V\) is the value vector; \(d_{k}\) is the dimension of the key vector; softmax is used to normalize the weight.  
The multi-head attention mechanism is an extended form of the attention mechanism. Single-head attention is calculated only in one subspace, while multi-head attention projects the input sequence into multiple subspaces, each of which performs an attention calculation independently. Each head can be regarded as an independent attention unit that can capture different features and patterns in the input data. Subsequently, the results of these independent attention heads are spliced together to generate a more complex and rich context vector representation. The advantage of multi-head attention is that it can capture different dependencies and features from multiple subspaces at the same time, which greatly enhances the expressiveness and generalization performance of the model. In order to more effectively integrate the different features extracted by BiLSTM and DGCNN, this paper uses a multi-head attention mechanism in the feature fusion layer to better capture the semantic associations between different positions and enhance the feature fusion effect of the model. The implementation formula is as follows (11-12):  
\[MultiHead(Q,K,V)=Concat(Head_{1},\dots,Head_{n})W^{\alpha} \tag{11}\]  
\[Head_{i}=Attention(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}) \tag{12}\]  
Among them, \(W_{i}^{Q}\), \(W_{i}^{K}\), \(W_{i}^{V}\) and \(W^{\alpha}\) are the query, key, value and output projection matrix, respectively.
## 3 Database of Water Content and Hyperspectral Information
### Analysis of Measured Water Content  
#### 3.2.1. Oven-Drying  
The water content according to ASTM [34] can be obtained using Equation (2). Here, \(w\) is water content (%), \(M_{cms}\) is the mass of the container and moist specimen (g), \(M_{cds}\) is the mass of the container and oven-dried specimen (g), and \(M_{c}\) is the mass of the container.  
\[w=[(M_{cms}-M_{cds})/(M_{cds}-M_{c})]\times 100\% \tag{2}\]  
\(M_{cms}\) is the weight of the container and the soil collected from the field, \(M_{cds}\) is the weight of the container and completely dried soil, \(M_{c}\) is the weight of the container (paper cups were used in this study). Soil is mainly dried using the oven-drying method, which is performed in an oven at 110 \(\pm\) 5 \({}^{\circ}\)C for 12 to 16 h. The fieldwork may be delayed until experimental data are obtained because this method takes a long time. Accordingly, in order to quickly obtain the water content, a rapid water-content measurement method using the microwave oven method and calcium carbide [35; 36] has been proposed. However, these  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flight Altitude (m)** & **Flight Attitude (m)** & **Velocity (m/sec)** \\ \hline
40 & 20.39 & 3.66 \\
50 & 25.49 & 4.57 \\
60 & 30.59 & 5.49 \\
70 & 35.69 & 6.40 \\
80 & 40.79 & 7.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Settings of drone flight with shooting width.  
Figure 7: Soil sampling for measuring surface and inside water contents.  
methods are lacking compared to the oven-drying method in terms of accuracy, and in this study, the oven-drying method was applied since high-accuracy data must be secured. \(M_{cms}\), the weight of soil and paper cups, was measured immediately after sampling in the field, and \(M_{cds}\) was obtained by drying the samples in an oven at 110 \({}^{\circ}\)C in the laboratory for 24 h.
###### Abstract  
PbO-SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\) (PSAB)-based glasses were prepared in order to determine the feasibility for laser sealing in the form of fiber. To reach a high quality of laser sealing, the tuning capability of CuO and Na\({}_{2}\)CO\({}_{3}\) dopant concentration was examined on thermal, thermo-mechanical, and optical properties of glasses. The difference of thermal expansion coefficient was reduced with codoping of 1 wt% CuO-2 wt% Na\({}_{2}\)CO\({}_{3}\) into the PSAB glass system, and it amounted to \(0.34\times 10^{-6}\)/K. The codoped PSAB glass system reached 100% absorption at 810 nm, which corresponds to the wavelength of laser. The glass fiber with a diameter of 180 \(\upmu\)m was successfully pulled from the codoped PSAB glass system. The fluorine-doped tin oxide (FTO) glass substrate using the glass fiber was successfully sealed. It presented a crack-free sealed surface with a sealing strength of about 41 MPa. These results indicate that the PSAB-based glass system in the form of fiber is proved as a laser sealing material in packaging systems.  
lead silicate glass; glass transition temperature; coefficient of thermal expansion; glass fiber; laser sealing +
Footnote †: journal: Applied Physics Letters  
1 December 20192010331 December 20192  
laser sealing glass should be lower than 1.0 \(\times\) 10\({}^{-6}\)/K [11]. If the CTE is not matched, stress will be generated between the two materials, causing deformation and breakage of the material in all kinds of packaging applications [10; 11; 12; 13; 14; 15]. A few studies were carried out on the tuning of CTE between laser sealing glass and glass panels. The CTE of Bi\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-ZnO-SiO\({}_{2}\)-based glass systems was found to be in the order of 10.0 \(\times\) 10\({}^{-6}\)/K for solar cell panels.  
In this work, for the first time, to the best of our knowledge, we investigated the thermal, thermo-mechanical, and optical properties of the PbO-SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\) (PSAB) glass system doped with CuO and Na\({}_{2}\)CO\({}_{3}\) for laser sealing in the packaging systems. We studied the role of CuO and Na\({}_{2}\)CO\({}_{3}\) as dopants in this system with two functions that control the CTE between the panels and increase the absorption of the 810 nm band that corresponds to the laser wavelength. The optimized CuO and Na\({}_{2}\)CO\({}_{3}\) codoped PSAB glass system was also pulled into a glass fiber. The fluorine-doped tin oxide (FTO) glass substrates were sealed using glass fiber by controlling the laser power, laser scan velocity, and laser scan cycle. Finally, the optimized sealing condition was evaluated by photography of sealed fiber and tensile strength.
## 2 Material and Methods
### Photosynthetic Activity of Plants  
To estimate the actual photochemical activity of PSII in situ, chlorophyll \(a\) fluorescence in plants was measured using a pulse amplitude-modulated (PAM) fluorimeter (Mini PAM, Walz, Germany) with a light emitting diode at 650 nm and a standard intensity 0.15 umol m\({}^{-2}\) s\({}^{-1}\) PAR (photosynthetically active radiation). The device's built-in sensor projects light impulses (triggered on and off at a very high frequency) onto a leaf placed in a metal clasp, and registers the fluorescence signal from excited chlorophyll \(a\) molecules. The following fluorescence parameters were measured: F-the present fluorescence yield, M-fluorescence after saturation pulse was applied, Y(II) = (M - F)/M, PAR-photosynthetically active radiation, ETR-electron transfer rate calculated as: ETR = Y(II) \(\times\) PAR \(\times\) ETR-factor; i.e., ETR = Y(II) \(\times\) PAR \(\times\) 0.5 \(\times\) 0.84. The standard factor 0.84 corresponds to the fraction of incident light absorbed by a leaf [20; 55]. The study included measurements of the greenness index (SPAD) with the use of chlorophyll meter (model SPAD-502, Konica Minolta, City, Japan) in which a leaf is placed in a clasp and 2 s later the result appears on the screen and the device is ready to scan another leaf. SPAD greenness index and photosynthesis efficiency using Mini-PAM were determined on 10 randomly selected leaves. Moreover, the measurement of the greenness and efficiency of photosynthesis takes place in four phases of plant development BBCH scale (the scale used in the UE to identify the various stages of plants) (Table 1).
# Spatial Sound Rendering Using Intensity Impulse Response and Cardioid Masking Function
## 7 Conclusions  
Techniques for spatial recording, rendering, and audio signal processing in phonography and virtual reality have been developed for many years. However, they are based on pressure signals recorded by various multi-microphone systems.  
The paper proposes a method for combining data from an omnidirectional pressure transducer and an intensity probe. Using a mechatronic probe with a single, moving microphone simplifies the signal recording process for database creation even more. Finally, the proposed method requires only a two-channel recording system: one for a high-quality omnidirectional music recording microphone and a second for a small moving microphone of the mechatronic intensity probe. The expensive sound intensity probe is unnecessary in this case. The database that must be stored for post-production and 3D rendering includes only four data records per source position (omnidirectional pressure response and three spatial components of the intensity response). However, the proposed method has the potential to create arbitrary sets of impulse responses in post-production based on a limited number of channels acquired during measurements and any arrangements of spatially oriented masks. It is, therefore, an alternative to higher-order ambisonics systems and can be used in real-time rendering applications. High-speed hardware-assisted convolution processors can provide a real-time operation for VR applications without a large memory requirement for storing large amounts of data, and corresponding sets of impulse responses can be generated on the fly.  
Conceptualization, W.M. and M.L.; methodology, W.M. and M.L.; software, W.M. and M.L.; validation, W.M. and M.L.; data curation, W.M. and M.L.; writing--original draft preparation, W.M. and M.L.; writing--review and editing, W.M. and M.L.; visualization, W.M. and M.L.; supervision, W.M. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of West Pomeranian University of Technology on 13 January 2025.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data sets for this study are available upon request from the corresponding author.  
**Acknowledgments:** The authors thank [PERSON] of the West Pomeranian University of Technology in Szczecin for providing the specialized microphone systems.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
### Objectives Development  
_Industry 4.0 as a Problem Solver in Obsolescence_  
As described in the previous section, the major mitigation strategies for functional software obsolescence are open, standardized, configurable and modular systems. Additional requirements (described in the following Section 2.1) concern especially the real-time capability of the avionic system and thus, the test bench and its I/O.  
The Industry 4.0 paradigm addresses these features. The term was initially developed as part of the public private partnership of the German government. It describes the fusion of enabling information and communication technologies described, such as the Internet of Things (IoT) or cyber-physical systems (CPSs). They form a smart factory [16] (pp. 225-226) through digitalization, automation and interconnection of the relevant stakeholders around producing companies [17] (p. 7).  
In the field of the functional testing in aviation, several concepts around the Industry 4.0 paradigm were implemented. An interconnection of the test benches with different purposes were shown by [PERSON] [18], as a configurable machine-to-machine communication. The authors see the usage of the open group standard data distribution service (DDS) [19] as a solution for the obsolescence. A different suitable standard for this purpose is OPC-UA. [PERSON] applied the SCXML standard for the creation of consistency, in exchange and execution of real time test cases, throughout the supply chain [20]. Both approaches were already applied and evaluated using the HighLift test benches.  
For the connection of sensors, actuators and control units at the field level configurable communication technologies, such as Profinet, Sercos III and EtherCAT, were standardized. In the classical Industry 4.0 hierarchy, those standards were developed for typical automation tasks. Today, only these standards are suitable for the control of electric motors [16] (pp. 147-154). The modular concept of these technologies, along with its open standards, satisfies the requirements to minimize the risk of obsolescence. Additionally, the currently available industrial assets in the smart factory environment at the field level are considered to be mature. The standardized I/O and programmable logic controller have an expected lifetime of 20 years [21].  
**In summary, the objective of the research described in this article is to show a proof of concept for the applicability of industrial standards from the aforementioned domain, at the field level, as an interface technology for test benches in avionics.**
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 4 Performance Evaluation
### Real-World Test Results of Driving Behavior Prediction  
The 4-layer CNN-2 stacked LSTM-V2X system was installed in two vehicles and verified in a real road environment. In a real-world test, human drivers select a label for their driving behavior as one of defensive, normal, and aggressive. Driving behavior is self-defined, and the guidelines of the AAA Foundation and DMV were followed in the same way as the dataset generation. The behavior classification accuracy was measured by comparing the driving behavior received through V2X with the driving behavior labeled by the driver. Figure 13(c) shows the path of the vehicle driven for real world test. The red line indicates the driving path of the vehicle and the arrow indicates the driving direction. As GPS information is not received in the tunnel section, it is not displayed on the path. Figure 13(a)-c shows the 3-axis acceleration information about driving behaviors labeled by the driver in the behavioral comparison section marked in yellow in Figure 13(c). The more aggressive the driver's driving behavior, the greater the change in 3-axis acceleration. In addition, the amount of change in the z-axis is large because the aggressive driving behavior does not reduce the speed sufficiently on the speed bump.  
Table 9 shows the classification accuracy of each network based on real world driving data. Similar to simulation, the proposed 4-layer CNN-2 stacked LSTM has the best results with over 98% performance. In addition, although ~85% of the dataset used for network training consists of simulation data, it showed high classification accuracy in the real environment. The reason that the data set contains about 15% of real-world data is partly because the simulated road and the real-world environment does not match. For example, speed bumps are not configured on the simulated road to generate the data set. In addition,  
errors may occur depending on the condition of the road surface, tire and sensor noise in real world driving environment. From the experimental results, we demonstrate the possibility of high-performance classification of driving behaviors in various real world environments from a large number of simulated driving data for general road environments and some real world data.  
Figure 14: Experimental results of safety improvement of the proposed system in the ACC scenario: (**a**) ACC simulation environment. (**b**) Three-axis acceleration of the target vehicle. (**c**) Behavior classification results. (**d**) Driving velocity. (**e**) Desired distance. (**f**) Time to collision.  
Figure 16: Comparison of 3-axis accelerations according to driving behavior: (**a**) Defensive driving. (**b**) Normal driving. (**c**) Aggressive driving.  
Figure 15: Driving path of a vehicle in real-world test.