esa-sceva/satcom-chunk-collection huggingface dataset:
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
## 2 System Model  
Figure 1 illustrates the system model in which the designer can choose one out of three options of essential fading environments. LoRa devices use shift chirp modulation scheme for communication. The modulation consists of a signal which chirps within a frequency range in a given period. It is also known as spread spectrum modulation, and different symbols can be composed by varying bandwidth and spread spectrum parameters. The spreading factor (SF) determinates the number of samples within a symbol duration such that \(T_{s}=\frac{2^{SF}}{B}\), where \(B\) is the signal bandwidth and \(SF\in\{6,7,\ldots,12\}\) which are the values available for typical commercial devices [18].  
The encoder maps every SF bits to a symbol \(s_{k}\), such that \(k\in\{0,1,\ldots,2^{SF}-1\}\). We then understand that \(k\) is related to a \(f_{k}=B\frac{k}{2^{SF}}\) offset frequency. Each chirp signal has a specific starting frequency, and it wraps around bandwidth \(B\) as long as frequency keeps increasing. The chirping rate is given by \(\frac{B^{2}}{2^{SF}}\) and signal decodes according to the position offset of \(f_{k}\). The transmitted waveform can be written as [11]  
\[\begin{split} s_{k}(nT)&=\sqrt{E_{s}}\omega_{k}(nT) \\ &=\sqrt{\frac{E_{s}}{2^{SF}}}e^{j2\pi\left[(k+n)\mod 2^{SF}\right] \frac{B}{2^{SF}}},\end{split} \tag{1}\]  
where \(T=\frac{1}{B}\) is the sampling period, \(n=0,1,2,\ldots,(2^{SF}-1)\) is the sample index at time \(nT\), \(E_{s}\) is the signal energy, and \(\omega_{k}(nT)\) are the \(2^{SF}\) orthonormal basis functions. The receiver demodulates the signal using the outputs of the correlator as [11]  
\[\sum_{n=0}^{2^{SF}-1}r_{k}(nT)\cdot\omega_{i}^{*}(nT)=\begin{cases}\sqrt{E_{s }}+\phi_{i},&i=k\\ \phi_{i},&i\
eq k\end{cases}, \tag{2}\]  
where \(r_{k}(\cdot)\) is the received signal, \(\phi_{i}\) depicts a complex Gaussian noise process, and \(\omega_{i}^{*}(nT)\) is the complex conjugate of the \(i\)-th basis function.  
As shown by (2), the chirp signal demodulation is based on orthogonality properties. Thus, the detection of the symbols happens with the index selection of the basis waveform that  
Figure 1: System Model.  
has the highest correlation magnitude with respect to the received signal. Therefore, for a received waveform of \(r_{k}(nT)\), the detected index symbol is computed as [11]  
\[\tilde{k}=\{i\mid\arg_{i}\!\max\left(|\delta_{k,i}\sqrt{E_{s}}+\phi_{i}|\right)\}, \tag{3}\]  
where \(|\cdot|\) is the absolute value operator, \(\delta_{i,k}=1\) for \(i=k\) and \(\delta_{i,k}=0\) otherwise. We also must define \(\gamma\) as the signal-to-noise ratio (SNR) which is expressed as [11]  
\[\gamma=\frac{E_{s}}{T_{s}}\frac{1}{N_{0}B}=\frac{E_{s}}{N_{0}\cdot 2^{SF}}. \tag{4}\]
## 1 Introduction
### Research Questions and Goals  
On the basis of the previous paragraphs, this paper aims to provide a methodology for the improvement of internal processes in regard to production planning and control by optimizing internal parameters, work-in-progress (WIP), and production lead times, as well as end-customer service level, and order-to-delivery (OTD). The final goal of this optimization would be to increase the company's competitiveness securing its long-term existence. Based on these objectives the following research questions can be declared:  
* Which are current challenges of ETO manufacturers?
* Which is the current common production planning strategy being used in ETO industries?
* How do other production planning strategies can be applied to the ETO sector?
* What is the potential benefit of this change?
* In order to achieve these goals, this paper presents the following structure:  
* Introduction: definition of challenge, objectives, and simulation.
* Literature review of: \(\bigcirc\) Engineer-to-order typology and characteristics;
* Organizational model for the order management process;
* Production planning and control: push versus pull;
* Capacity planning and maintenance;
* Theory of constraints (TOC) and drum-buffer-rope (DBR);
* Agent-based simulation.
* Typical initial situation and development of pull production control for ETO manufacturers.
* Applying the model for production control to the metallurgical industry through simulation.
* Simulation results and implementation.
* Conclusions.  
The main expected outcome of the research paper is that a pull control approach using drum-buffer-rope (DBR) approach will lead to an improvement of OTD and to a reduction of lead times and WIP stocks.
## 4 Experimental Results and Analysis
### SOH Prediction Results Using Different Battery Types  
To validate the model generalization capability, cross-type battery experiments were conducted using cycle data from two 18650-type lithium-ion cells (cell B0005 and B0006) obtained from the Prognostics Center of Excellence at the NASA Ames Research Center. The cells with a nominal capacity of 2 Ah underwent accelerated aging tests under controlled ambient temperature (24 \({}^{\circ}\)C) to simulate typical operational scenarios. A standardized charge-discharge protocol was implemented: (1) CC-CV charging at 1.5 A until reaching 4.2 V followed by voltage holding until current decayed to 20 mA; (2) CC discharging at 2 A to cutoff voltages of 2.7 V (B0005) and 2.5 V (B0006), deliberately set below manufacturer specifications to induce accelerated degradation through deep discharge cycles.  
The prediction curves are shown in Figure 13, and the prediction results are presented in Table 6. The prediction errors are all within 1%, which demonstrates that the model proposed in this paper has a certain degree of generalization ability.  
Figure 12: The optimization process of three algorithms.
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
###### Abstract  
This paper proposes a tree-based regression model for hybrid channel estimation in wireless sensor networks (WSNs) in generalized frequency division multiplexing (GFDM) over both visible light communication (VLC) and radio frequency (RF) links. The hybrid channel incorporates both additive white Gaussian noise (AWGN) and Rayleigh fading to mimic realistic environments. Traditional estimators, such as MMSE and LMMSE, often under-perform in such heterogeneous and nonlinear conditions due to their analytical rigidity. To overcome these limitations, we introduce a data-driven approach using a decision tree regressor trained on 18,000 signal samples across 36 SNR levels. Simulation results show that support vector machine (SVM) achieved 91.34% accuracy and a BER of 0.0866 at 10 dB, as well as 96.77% accuracy with a BER of 0.0323 at 30 dB. Random forest achieved 91.01% accuracy and a BER of 0.0899 at 10 dB, as well as 97.88% accuracy with a BER of 0.0212 at 30 dB. The proposed tree model attained 90.83% and 97.63% accuracy with BERs of 0.0917 and 0.0237, respectively, at the corresponding SNR values. The distinguishing advantage of the tree model lies in its inference efficiency. It completes predictions on the test dataset in just 45.53 s, making it over three times faster than random forest (140.09 s) and more than four times faster than SVM (189.35 s). This significant reduction in inference time makes the proposed tree model particularly well suited for real-time and resource-constrained WSN scenarios, where fast and efficient estimation is often more critical than marginal gains in accuracy. The results also highlight a trade-off, where the tree model provides sub-optimal predictive performance while significantly reducing computational overhead, making it an attractive choice for low-power and latency-sensitive wireless systems.  
wireless sensor networks (WSNs); generalized frequency division multiplexing (GFDM); tree-based machine learning; channel estimation; visible light communication (VLC); radio frequency (RF); hybrid channel +
Footnote †: journal:  
Article
## 5 Experimental Evaluation
### Task Prediction Experiment  
In a stable real edge computing scenario, the tasks generated by terminal devices are highly correlated with time and have a certain continuity and regularity. Generally, a T times history window is used to predict the task at T + 1 times. In the experiments, we set the history window to 50 and set different thresholds for the optimization target \(|D_{t}-D_{t}^{\sim}|\). The experimental results are shown in Figure 6. When the threshold value is set small, the LSTM prediction model describes the historical data volume with higher accuracy and can fully explore the changing pattern of the data volume, however, it will introduce a larger prediction overhead, such as will increase the training time of the LSTM model.
## 3 Results
### Reflectance Spectral Characteristic
#### 3.1.1 Visible and Near-Infrared (VNIR) HSI  
The mean VNIR reflectance spectra of eight grain flours were obtained from the calibrated hyperspectral images, as shown in Figure 6a. The figure shows that the overall trends of the reflectance spectra of the selected flour samples. Among them, the chestnut starch (CS, yellow line) shows relatively broad and strong absorption curves between 400 and 700 nm and peaks at approximately 525 and 551 nm. The maximum peak was observed at approximately 550 nm, and visible bands were observed in the ranges of 500-565 nm, 565-590 nm, and 620-740 nm related with the green, yellow, and red absorption wavelengths, respectively. The absorptions at wavelengths of 617, 656, and 681 nm show the presence of anthocyanin and carotenoids pigments [22; 23]. As observed in Figure 6a, the CS flour shows more dark than other items, and two peaks were measured around 500-600 nm. The bands between 500 and 600 nm indicate different peaks for different grain types, i.e.; peaks can be observed for strong flour (SF) at 496 nm, and for tapioca starch (TS) and sweat-potato starch (SS) at 520 nm. Absorptions were also measured around 760 nm and between 900 and 1000 nm. In the range of VNIR bands (400-1000 nm), we observed the spectra of the chemical information of rice ingredients, such as  
Figure 5: Three mean spectra of hyperspectral imaging modalities and its peak wavelength images. Color image of one of samples **(a)**, SWIR is at 1061 nm (**b**), VNIR at 959 nm (**c**) and fluorescence at 518 nm (**d**).  
carbohydrate, protein, amylose, and moisture, which are related to the third overtone absorption of C-H (880 nm) and second overtone absorption of O-H (750-900 nm) and N-H (962-1000 nm) [24; 25].  
Figure 6: Mean spectra of VNIR (**a**), fluorescence (**b**) and SWIR range (**c**) of eight grain flours’ data.
## 3 Materials and Methods
### Technological and Research Areas
#### 3.2.2 Cameras  
Almost any camera may be used to produce photogrammetric 3D models. However, depending on the desired accuracy of the result, the appropriate combinations of sensors, resolutions and lenses are used. One of the main advantages of the photogrammetric surveying method is that theoretically the accuracy can be increased as much as the user wishes, either by changing one of the above parameters (lens, resolution, and sensor) or by changing the reception distance of the objects.  
The following are some of the representative cameras for the capturing needs of \"Mergin' Mode\" demonstration:  
* GoPRO Hero 3 Black (12 Mpixel, wide angle lens, CMOS sensor).
* Panasonic Lumix GX80 (16 mpixel, 28-70 mm lens, M4/3 sensor).
* DJI OSMO X3 camera (12 Mpixel, 32 mm lens, 2/3\" sensor).
* NCTECH iSTAR 360 (14 Mpixel 360 camera).
* RICHO Theta S 360 (50 Mpixel 360 camera).
## 3 Analysis of Failure Characteristics of a Buried Waterstop
### Finite Element Analysis of Force and Deformation of Waterstop
#### 3.2.4 Deformation Stress on the Waterstop  
Taking working condition 1 as an example, the development of the maximum von Mises stress on the waterstop with the increase in the deformation is plotted in Figure 13. It can be seen that the overall von Mises stress value on the waterstop increases with the increase in deformation, and is approximately linearly related to the amount of deformation. With different types of deformation, different parts of the waterstop belt become dangerous. Under tensile deformation, the joint between the first rib and the side of the waterstop belt is the most stressed part. Under compression deformation, the upper and lower positions of the inner holes of the waterstop belt are the most stressed parts. Under settlement deformation, the joint between the side wing of the waterstop and the middle hole, and the position not in contact with the lining is the most stressed part.  
As can be seen from the figure, under compression deformation, the stress on the waterstop grows the fastest, followed by tensile deformation and settlement deformation. However, due to the difference in deformation, the waterstop is under the maximum stress when the settlement deformation reaches the maximum value. During tensile and compressive deformation, the von Mises stress value of the waterstop is always within the level of 2 MPa. When settlement deformation occurs, once the deformation becomes greater than 29.5 mm, the von Mises stress exceeds the dangerous level, and it is difficult to meet the service life requirements.  
Figure 12: Damage cloud diagram of bonding stiffness in working condition 1. (a) 1-1 bonding stiffness damage. (b) 1-2 bonding stiffness damage. (c) 1-3 bonding stiffness damage.
# Spectral Raman Database of Modern Acrylic Paints at 532 nm and 633 nm Laser Excitation Wavelengths
## 3 Results and Discussion

Table 3: _Cont._Figure 3 displays a photograph of the Phthalo Blue (PB) pigment (Figure 3a), optical microscope images of the pigment on wood (Figure 3b) and glass (Figure 3c) and Raman spectra acquired at 532 nm (blue line) and 633 nm (red open circle-line) for PB on wood (Figure 3d) and glass (Figure 3e). Figure 4 shows the molecular structure of phthalocyanine. The PB pigment exhibits a dark blue hue (Figure 3a), and its morphology appears unaffected by the substrate (Figure 3b,c). The Raman spectra collected on the two different substrates with either 633 nm or 532 nm excitation wavelength show no significant differences. Nevertheless, spectra acquired with different lasers show different spectral patterns. Such differences are ascribed to a Raman Resonance effect that often occurs during the Raman investigations on phthalocyanine systems. This effect explains why certain Raman bands appear prominently with higher-energy laser excitation (532 nm)
## 3 Results
### Tensile Fracture Mechanism Under High-Current Electrical Fatigue  
Post-electrical fatigue tensile testing (Figure 19) reveals that 0.39 A cycling enhances total elongation and toughness [31] despite minor reductions (\(\sim\)10%) in YS and UTS relative to 0.37 A. This response is attributed to the temporary presence of carbon-stabilized \(\gamma\)-austenite (Figure 16), which, together with moderate recovery, enhances the ductility [32].  
In contrast, 0.41 A specimens show concurrent declines in strength, elongation, and toughness, indicative of early embrittlement. SEM fracture analysis (Figure 20) reveals ductile failure in the 0.39-C20 specimen.  
Figure 19: Tensile properties of 631 SS wire: (**a**) Strength-elongation curve; (**b**) YS and UTS; (**c**) TE; and (**d**) tensile toughness after low-cycle 0.37 A, 0.39 A, and 0.41 A electrical fatigue.  
Figure 18: EPMA elemental mapping of the 0.37-C40 sample after cyclic electrothermal loading: Cr\({}_{2}\)O\({}_{3}\)-rich oxide, with Al-Si enrichment and subsurface carbon segregation.  
Figure 19 summarizes the tensile properties and toughness obtained from the integrated area under the engineering stress-strain curve of 631 stainless-steel wire after high-current electrical fatigue. Relative to 0.37 A, cycling at 0.39 A raises the total elongation (TE) and increases tensile toughness, albeit at the expense of a \(\approx\)10% drop in yield strength (YS) and ultimate tensile strength (UTS). In sharp contrast, the 0.41 A condition exhibits concurrent reductions in strength and ductility, resulting in a lower tensile toughness compared to the 0.37 A group. XRD analysis (Figure 16) reveals a transient, carbon-stabilized austenitic (\(\gamma\)) peak after 0.39 A cycling but not after 0.41 A. The \(\gamma\) phase, in conjunction with moderate electrothermal recovery, may delay strain localization and thereby enhance tensile toughness [33].
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 4 Experimental Results and Discussion
### Effect of the Number of Hidden Layers on the Model  
In this section, to better optimize and evaluate the geolocation performance of SubvectorS_Geo, we introduced a new evaluation index, the mean squared error (MSE) [40], which  
Figure 6: Ablation study in the three experimental regions.  
mainly analyzes the influence of the parameters on the mean error distance and reduces the concern about the maximum error distance and median error distance. As shown in Figure 7, on the experimental data of the three real regions, the median error distance and the maximum error distance with the most advantage of the average error distance were always smaller than our selected baseline, which verifies the validity of SubvectorS_Geo. In addition, the best-performing hidden layers in the three experimental regions (Shanghai, New York State, and Tokyo) were 2, 3, and 2, and the worst-performing hidden layers were 5, 1, and 1, respectively. Compared with the worst layers, the improvement of the average error distance was 25.8% on the Shanghai dataset, 23.7% on the New York State dataset, and 22.1% on the Tokyo dataset.
## 4 Cryptographic Analysis
### Speed Performance  
To measure the time required to encrypt, we used audio files of different sizes with hardware configuration--2.00 GHz, Intel(R) Core(TM) i3-6006 CPU, Fujitsu, 4 GB RAM, Windows 10. In Table 4, we compare the speed of our method with [5; 8; 31]. The data show that the proposed audio encryption scheme has a satisfactory speed. The AES cipher takes less time than all other algorithms to encrypt audio files.
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 2 Materials and Methods
### Residual Stress Induced During Die-to-Wafer Bonding  
The utility of the measurement can be used for different purposes; one of these purposes is the in-plane distortion, which is calculated using the stress induced by the different stages that silicon wafers go through during the manufacturing process of microchips and chips in electronic devices. In this work, a method for calculating the stress induced by die-to-wafer bonding is presented. There are different methods for calculating the stress, including analytical models, finite element simulations, and calculation from wafer shape measurements [37]. This work focuses on the latter.  
The stress calculations are performed using the Stoney model, which is used to extract residual stress distributions from the wafer geometry measurements (wafer shape and thickness). In this model, it is assumed that the deformation is much thinner than the substrate, that the residual stress and thickness of the film do not vary with spatial position, and that the deformation is elastic [37; 38].  
\[\sigma_{f}=\frac{1}{6}\frac{\bar{E}_{s}h_{s}^{2}}{h_{f}}\kappa \tag{8}\]  
This equation relates the residual stress in the film \(\sigma_{f}\) to the film thickness \(h_{f}\), substrate thickness \(h_{s}\), biaxial modulus of the substrate \(\bar{E}_{s}\), \((\bar{E}_{s}=E_{s}/(1-\
u_{s}))\), where \(E_{s}\) and \(\
u_{s}\) are the Young's modulus and Poisson's ratio of the substrate, respectively, and curvature \(\kappa\).  
To calculate the stress in the film from the shape measurement, the curvature must first be estimated. This can be accomplished by direct numerical differentiation of the shapedata. To achieve accurate curvature values through differentiation, shape data must have a sufficiently high spatial resolution. In this case, the shape data have a resolution of 7.56 \(\upmu\)m.
## 5 Experiment and Results  
### Data Collecting and Experiment Setup  
We adopt IWR1843, which is produced by Texas Instruments (TI), for data collecting. The IWR1843 device is an integrated single-chip millimeter-wave sensor based on FMCW radar technology, which can operate in the 76 GHz to 81 GHz band. IWR1843 includes three transmitting antennas and four receiving antennas, ADC converters, and a DSP subsystem [23].  
Data sent by the radar to the serial port is the hexadecimal data that has been processed by the above modules. The frame structure of the radar output is shown in Figure 9. Each packet consists of the header, the TLV (Type, Length, Value) items, and the padding. Users can set different Type values to different output types of data. We achieve the data by setting the Type values corresponding to the 3D point cloud data and the range-Doppler  
Figure 8: The structure of the fusion network.  
data. Before collecting, we have configured the radar parameters, and some important parameters are shown in Table 1.  
After configuring, we connect the IWR1843 device to the computer and place it on a platform with a height of 1.2 m. The side where the antenna array is located faces the person doing the activity. In the process of data collection, the fixation of the millimeter-wave radar must be guaranteed. We design six activities, which are boxing in place, jumping in place, squatting, walking in place, circling in place, and high-knee lifting. Each activity is completed by 17 collectors and repeated for 20 s per person. During the data collection process, we decode the hexadecimal 3D point cloud data and range-Doppler data contained in the signal received by the serial port in real-time. After using one-hot encoding to label the data, we set the ratio of the amount of training data to the amount of validation data to be 4:1; that is, the training set accounts for 80% of the total data, while the verification set accounts for 20%. In order to control the variables to effectively compare the performance of the networks, we use the same set of data for training. In our experiments, we use a GPU server with a GTX3080 to train five kinds of networks.