esa-sceva/satcom-chunk-collection huggingface dataset:
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## Materials and Methods
### Delineating the Footprint of the Wildfires and Selecting Research Plots  
The outlines of the fire-affected areas (Figure 1) were drawn using the Landsat-derived Normalized Burn Ratio-Thermal (NBRT) [20]. The open source geospatial analysis software QGIS 3.0 was utilized to clip the burned-unburned pairs from the raw imagery.  
For each wildfire, five paired burned-unburned \(3\times 3\) km plots were selected to represent the diversity of the burned area (illustrated in Figure A1). Plot pairs were selected based on similarities in elevation, slope, soil type and land cover type (Table A1). Rainfall and temperature similarities were also considered when selecting the pairings (data not shown). Elevation and slope data (Table A1) were taken from the Shuttle Radar Topography Mission (SRTM) dataset at 30 m resolution. grasslands are the predominant land cover type, per the United States Geographic Survey National Land Cover Database [21] classification, for all selected pairs (Table A1). Soil data were acquired from the Soil Survey Geographic Database (SSURGO) through the U.S. Department of Agriculture, Natural Resources Conservation Service [22].
# A Digital Framework to Predict the Sunshine Requirements of Landscape Plants
## 2 Materials and Methodology
### Digital Framework
#### 2.1.2 Solar Radiation Simulation and Correction  
The solar radiation simulation shown in Figure 1 was derived in Solar Analyst, which is a spatial analysis module in the ArcGIS desktop software [58,59]. For the model setting, we consider the geographic latitude and altitude where the study area is located, but we also balance the computer run time and the simulation accuracy. The model parameter settings are shown in Table 1.  
In order to make the simulation results closer to the local real values, we adopted a LightScout Quantum Meter (Model: QMSS-S, Spectrum Technologies USA Inc., Fort Worth, TX, USA) to carry out the solar radiation test at the sample site, and to correct the model's output. The formula is as follows:  
\[R_{solar}=\frac{R_{test}}{R_{simulation}}*R_{model} \tag{1}\]  
In this formula, \(R_{solar}\) represents the solar radiation of the plant canopy; \(R_{test}\) is the solar radiation of the test point, as measured by the LightScout Quantum Meter; \(R_{simulation}\) is the simulated sunshine radiation of the test point, as generated by the Solar Analyst model (ArcGIS software); and \(R_{model}\) is the maximum value of the model simulation. The simulation process and results of the robust model are shown in Figure 2.  
The wavelength of solar energy which can be used by plant photosynthetic activity is in the band range of approximately 300-800 nm, which is known as \"photosynthetically active radiation\" (PAR). Another means of measuring light quantity for plant growth involves discrete units of quantum flux in the PAR region called \"photons\". Photosynthetic Photon Flux (PPF) is commonly measured in units of micromodes per square meter per second (\(\mu\)moles/m\({}^{2}\cdot\)s). In this paper, in order to maintain the accuracy of the numerical conversion between model and instrument, we adopted 4.57 as the coefficient [60], from solar radiation (W/m\({}^{2}\)) to PAR (\(\mu\)moles/m\({}^{2}\cdot\)s).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Description** & **Name** & **Description** \\ \hline Slope/aspect & Flat surface & Building height & N * 3.3 m \\ Time period & 2014 (whole & Building grid size & 0.5 * 0.5 \\ Latitude & 30.5\({}^{\circ}\) N & Transmittance & 0.5 \\ Sky size & 200 * 200 & Hour interval & 0.5 \\ Proportion of diffuse radiation & 0.3 & Calculation directions & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings for the solar simulation in ArcGIS Solar Analyst [20].
## 3 Results and Discussion
### Adsorption Studies  
#### 3.3.1 Effects of pH  
The pH of the water solutions was adjusted to facilitate the monomer-template interaction. The pH was measured in the 2.5-10 range (Figure 7). Various recoveries were observed for the target molecules, especially for emtricitabine, tenofovir disoproxil, and naproxen. EMI has very stable recoveries throughout the various pH mediums, with recoveries above 100%, which was different from TENO, which had low recoveries of 49, 49, 76, and 19% across the pH range. Both of these drugs are nucleoside reverse transcriptase inhibitors and, at a pH of 4, should be more in their neutral or protonated state, which makes the adsorption of the material more effective. However, higher recoveries were seen for its more negative state at more neutral conditions at pH 7 (76%). An electrostatic repulsion was expected in a neutral state due to the type of polymer adsorption material designed; this was not observed. A slight decrease in recoveries was observed for other compounds like NAP, which ranged above 100% under acidic conditions to 71% under neutral conditions. In a previous study [30], it was found that above a pH of 4, the maximum adsorption efficiency decreased due to hydroxide ion interferences in the MIP cavities. Overall, a pH of 7 was opted for in this study as it gave the best overall performance for the six targets.
## 3 Results
### Non-Optimized vs. Optimized Conditions  
In order to validate the optimization carried out in the previous trials, MB10 medium with ammonium sulphate and glutamate and glucose was compared to: (i) an inorganic optimized medium (IOM), MT1 containing only glutamate and glucose; and (ii) an organic and waste-based optimized medium (WOM), with MT1 containing corn steep powder and glycerol (fed-batch; Figure 4).  
The results obtained revealed that the starting medium, MB10, led to the lowest maximum concentration (14.78 g/L), biomass productivity (3.54 g/L/day) and growth rate (0.60 day\({}^{-1}\)) of _Aurantiochytrium_ sp. (\(p<0.05\)). The optimized culture media, IOM and WOM, improved respectively 1.5 and 2-fold the growth performance of _Aurantiochytrium_ sp. in terms of maximum concentration when compared to the starting medium. In addition, the biomass productivity increased 0.7 and 1.6-fold and the growth rate 1.2 and 1.7-fold, respectively. Interestingly, the cheapest and most sustainable medium (WOM) reached a maximum concentration of 29.78 g/L of DW.
## 3 Results
### Phylogenetic Tree of L-Fucls  
To date, no phylogenetic tree has been constructed for L-FucI. The phylogenetic tree built in our study showed that many separate clusters of L-FucI sequences were widely distributed among bacteria (Figure 5). Among the characterized L-FucIs, L-FucI from _E. coli_ (_Ec_FucI), L-FucI from _Klebsiella pneumoniae_ (_Kp_FucI), and _RdF_ucI belonging to the _Enterobacteriaceae_ family clustered together. The known thermophilic L-FucIs, such as _Df_FucI, CsFucI, and _Tt_FucI, were also clustered together as disparate groups from _Ec_FucI, _Kp_FucI, and _RdF_ucI, displaying high phylogenetic relatedness, which indicates their potential functional similarities. Notably, _Ho_FucI was positioned in the same cluster as thermophilic L-FucIs, implying that its molecular properties might be similar to those of thermophilic L-FucIs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Amino Acid** & **HoFucI (\%)** & **_RdF_ucI (\%)** & **_TtFucI (\%)_ & **Halophiles (\%)** & **Thermophiles (\%)** & **Significance** \\ \hline Alanine & 8.5  & 11.0 & 9.7 & 10.0 & 8.0 & High frequency in halophiles \\ Aspartic acid & 5.3 & 5.4 & 5.0 & 6.0 & 4.5 & Important for halostability \\ Histidine & 1.8 & 2.7 & 1.7 & 2.0 & 1.8 & Low frequency in thermophiles \\ Lysine & 5.3 & 4.2 & 4.3 & 3.0 & 6.5 & Low frequency in halophiles \\ Glutamine & 3.0 & 4.1 & 2.8 & 5.0 & 3.0 & Important for halostability, but \\ Glutamate & 7.1 & 6.9 & 7.0 & 6.8 & 7.2 & Important for halostability \\ Threonine & 5.5 & 5.4 & 5.7 & 6.2 & 5.0 & Important for halostability, but \\ \hline \end{tabular}
\end{table}
Table 2: The content of several amino acid residues related to halophilic properties in HoFucl, L-FucI from _Raoullella_ sp. (_RdF_ucI), L-FucI from _Thermanaeromonas toyohensis_ (_Tt_FucI), and proteomes from general salt-in halophiles and thermophiles [1].
## 4 Threat Model for O-RAN Optimization  
Open Radio Access Networks (O-RANs) introduce new architectural and operational paradigms by combining disaggregated network functions with embedded ML capabilities. While these innovations enhance flexibility, intelligence, and vendor interoperability, they also significantly expand the potential attack surface. In this section, we examine the key background information necessary to understand the unique security challenges and adversarial ML threats targeting O-RAN optimization workflows, particularly within the RIC and its associated xApps and rApps.
## 2 Methodology
### Process Description
#### 2.1.1 Pre-Processing  
* _Load the dataset_: The images are loaded recursively based on their pathnames. Depending on the prefix of their filename, a label value of '0' or '1' is appended to each loaded image, explained in detail in Section 2.3.
* _Color conversion_: Two functions for color conversion were implemented in this study: a conversion from the BGR to the RGB color-space, where the order of the image's color channels changes to the most common (RGB) format, and a conversion from BGR to the grayscale color-space, where the images lose all the color information and maintain only the brightness and saturation for each pixel.
* _Feature extraction (only for grayscale)_: For the grayscale images, there is additionally applied a feature extraction method, the Fast Fourier Transform (FFT). This method helps bring out the edge features of the leaves, thus potentially improving the performance of the model compared to a straightforward approach of grayscale images.
* _Background segmentation (optional)_: This method removes the background from images with the use of thresholding, dilation and erosion, as commonly used methods in computer vision.
* _Data normalization_: The dataset values are normalized within the (0, 1) range in order to ensure that the loss function, which is usually not convex, finds the global minimum as easy as possible. Reducing the range of input values also assists the convergence of the backpropagation algorithm.
* _Dataset shuffling_: The dataset is being shuffled by Python's \"random.shuffle\" method, which is an order-shuffling algorithm based on a random number generator. After the application, the order of the images, which originally was alphabetical, is now mixed throughout the dataset.  
Figure 2: Process flowchart of our approach.  
* _Dataset splitting_: The dataset is being split in the following fashion: First, 15% of the total dataset is held out and set as the testing set, then the remaining dataset is split again in the 80/20 fashion, where the small portion is used as the validation set. The rest is used for training the algorithm. The training and validation sets are used for the training of the algorithm, while the testing dataset is used only after the classifier is trained, in order to make predictions.
# Conceptual Design and Preliminary Verification of Distributed Wireless System of Weigh-in-Motion
## 3 Design of Distributed WIM System
### Hardware Circuit Design  
The system's OPA128 charge conversion amplifier needs \(\pm\)5 V power supply. In order to convert the operating voltage of STM32 MCU 3.3 V to \(\pm\)5 V power supply for the OPA128 charge conversion amplifier, a voltage conversion module needs to be added. Figure 10 is the designed circuit schematic diagram. As can be seen from the schematic diagram, this paper adopts an XL6008 power supply booster chip, combined with 78M05 and 78M06 three-terminal voltage regulator chip, which uses 78M05 to stabilize +5 V voltage and 78M06 to stabilize \(-\)5 V voltage, and finally converts the input DC voltage to \(\pm\)5 V voltage. The XL6008 power boost chip was chosen because it has a wide voltage input range, accepting 3.6 V-32 V input voltages. The 78 M series can operate at a voltage input of 35 V. The minimum input and output voltage difference, which is the instability voltage value, is 2 V. As shown in Figure 10, in order to suppress self-excited oscillation, an output filter capacitor is designed. The positive terminal of the capacitor is connected to a 78M05/78M06 output. The negative terminal is connected to the power source. Meanwhile, in order to prevent the situation of the input voltage from being less than the output voltage when the whole circuit stops supplying power, a diode is designed to ensure the safety of the circuit. The physical circuit is shown in Figure 11 of 5 V to \(\pm\)5 V.  
Figure 8: Circuit diagram of NRF24L01.  
Figure 9: A prototype of wireless transmission module.
# Evaluating the Drainage Density Characteristics on Climate and Drainage Area Using LiDAR Data
## 3 Results and Discussion
### Impact of Drainage Area on Drainage Density  
In addition to climate, a relationship between drainage area (\(A\)) and drainage density (\(D_{d}\)) have been investigated in the literature. [PERSON] and [PERSON] [43] showed an inverse relationship between \(D_{d}\) and \(A\). [PERSON] [46] presented a relationship of \(D_{d}=6.6A^{-0.337}\) based on 228 watersheds over different climates even though the general consensus is that \(D_{d}\) is independent of \(A\).  
Figure 9 shows the relationship between \(D_{d}\) and \(A\) for: (a) 124 watersheds in the arid climate from Melton [33] and (b) 121 watersheds from both humid and arid climates in this study. There is a strong negative correlation between drainage density and drainage area for both datasets. The data points in Figure 9b are more scattered due to the broader range of climate aridity index.  
To investigate the climate effect on the relationship between \(D_{d}\) and \(A\), the study watersheds were categorized into two groups: 1) humid (\(E_{P}/P<1\)) and 2) arid (\(E_{P}/P\geq 1\)). Figure 10 shows the relationship between \(D_{d}\) and \(A\) for humid (Figure 10a) and arid watersheds (Figure 10b). Although the relationship in the humid region is not as prominent, a clear negative correlation was observed in the arid region.  
Figure 9: The relationship between drainage density (\(D_{d}\)) and drainage area: (**a**) 124 watersheds in arid regions from Melton [33] and (**b**) 121 watersheds in humid to arid regions from this study.  
The results indicate that the conventional U-shaped relationship between drainage density and climate aridity index could be reevaluated, especially in the humid region (low climate aridity index). The negative correlation found in the data from Madduma Bandara [39], which were from agricultural watersheds, was not prominent in the study sites, which were natural watersheds. The negative correlation between drainage density and drainage area was still valid in the data from this study. However, high-resolution DEM tended to produce greater drainage density while arid area displayed a strong negative correlation than the humid area did. The results of this study added a new perspective to understanding drainage density data, which are affected by land use practices, climate, and input topography data resolution.
## 3 Methodology
### Contextual Homogenization Module (CHM)
#### 3.2.2 Feature Integration Across Hyperedges  
The CHM integrates features from three primary types of hyperedges: semantic, narrative, and discourse. The integration process is detailed below:  
* **Semantic feature integration**: For semantic hyperedges, the embeddings of connected nodes are pooled together by using an MFB-based approach, which enhances the feature representation by emphasizing shared semantic characteristics. \[f_{\text{sem}}=\text{MFB}(\{v_{i}\}_{i\in\epsilon_{k}}),\] (6) where \(\{v_{i}\}_{i\in\epsilon_{k}}\) represents the set of node vectors in semantic hyperedge \(e_{k}\).
* **Narrative feature integration**: Narrative hyperedges connect consecutive sentences, capturing the flow of the narrative. The integration of these features aims to preserve the sequential integrity of the text. \[f_{\text{nar}}=\text{MFB}(\{v_{i}\}_{i\in\epsilon_{l}}),\] (7) where \(\{v_{i}\}_{i\in\epsilon_{l}}\) includes the node vectors linked by narrative hyperedges.  
* **Discourse feature integration**: Discourse hyperedges group sentences by their roles within the document's structure. Integrating these features helps emphasize the rhetorical significance of each sentence grouping. \[f_{\text{disc}}=\text{MFB}(\{v_{i}\}_{i\in c_{m}}),\] (8) where \(\{v_{i}\}_{i\in c_{m}}\) corresponds to nodes associated with a particular discourse role.  
The outputs from the CHM are unified feature vectors for each type of hyperedge and are then forwarded to the Hypergraph Contextual Attention Module (HCA) for further processing. This unified feature space facilitates the selective attention mechanism in HCA, allowing it to more effectively identify and emphasize the most informative elements of the document for summarization.  
This comprehensive homogenization strategy ensures that all types of contextual information embedded within the hypergraph are optimally utilized, improving the summarizer's ability to generate concise and contextually rich summaries.
## 5 Towards a Standardized Framework for Digital TPM Implementation
### Methodological Deployment Stages  
Another critical factor for the successful implementation of TPM 4.0 is the adoption of a structured and phased deployment strategy. Drawing on empirical findings and industrial case studies, the proposed framework outlines five key stages to guide this transformation:  
The first stage involves a Diagnosis and Readiness Assessment, during which organizations identify existing bottlenecks, evaluate the digital maturity of their systems, and assess the digital competencies of their workforce. This foundational step ensures that subsequent actions are tailored to the current context.  
Following this, the Pilot Implementation phase focuses on the initial deployment of IoT technologies and monitoring tools, typically applied to selected critical equipment or production lines. This controlled setup allows for manageable experimentation and early validation.  
Next, the Evaluation and Learning phase centers on measuring key performance indicators, such as OEE and MITTR, alongside collecting user feedback and refining digital models based on operational results.  
Once early successes have been validated, organizations move to Progressive Scaling, which involves expanding the solution to additional assets, integrating it with existing MES or ERP systems, and incorporating more advanced analytics capabilities.  
The final stage, Continuous Improvement, institutionalizes the use of PDCA cycles, enabling organizations to adapt to shifting conditions and embed feedback loops for ongoing optimization.
## 2 System Model  
Similar to magnitude-domain spectra, which are widely used in mono-aural speech enhancement, both real and imaginary spectrograms exhibit a spectro-temporal structure, which is useful for training the deep neural algorithm. While the complex spectrograms provide spectral cues, the spatial cues are obtained from the directional feature as well as inter-microphone phase difference (IPD). The combination of spectral and spatial cues can be referred to as spectro-spatial features, and it enables the neural beamformer to learn to suppress the interfering signals arriving from directions other than the target speech signal. In this section, the overall procedure of MS-GLMB filtering-assisted spectro-spatial feature construction is described, followed by the minimum variance distortionless response (MVDR) beamforming. While Sections 2.1-2.3 highlight the operations of SRP-PHAT for the localization of sources, MS-GLMB filtering for tracking of sources and exploitation of spectro-spatial information for guiding the T-F masking-based speech enhancement, the operation of the beamformer is described in Section 2.4. The beamformer selected for this work is GSC-MVDR of a time-varying nature, which exploits the spatial information of the sources and dynamically enhances the speech signal corresponding to the target source.
# Machine Learning Applications for Short Reach Optical Communication
## 3 Machine Learning for Short-Reach Optical Communication System Signal Processing
### Other Machine-Learning Based Signal Processing Applications for Short-Reach Communication Systems  
In addition to serving as the signal equalizer, the ML-based technique has also been extended to other types of signal processing applications, such as auto-encoder [67; 109; 119; 120], digital predistortion [108; 121] and soft-demapping [110; 122]. In this section, we review recent studies of these ML signal processing applications in short-reach optical communications.
# A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
## 2 The Description of the Problem  
In this section, the problems to be solved by the proposed method will be further elaborated, and the definition of the mission profile should be given before the explanation. The definition of the mission profile is given as follows.  
Mission profile [33]: A time-phased description of the events and environments experienced by an item during a given mission. The profile depicts the time spans of the events and operational conditions to be anticipated. In order to illustrate more clearly, in the following sections, the mission profile is simply defined as a time-phased description of the operating condition by an item during a given mission.  
As shown in Figure 1, the mission profile is constituted by different operating conditions in a certain order. Meanwhile, the life trend and lifecycle of the mechanical system are affected by the mission profile. In the actual production process, in order to effectively complete different production tasks, the mission profile will be switched accordingly. Therefore, the life trend of the mechanical system will also change accordingly. In addition, each mission profile also consists of more than one operating condition.  
Figure 1: The relationship among operating condition, mission profile, and lifecycle. (**a**) The mission profile experienced by the mechanical system. (**b**) The life trend of the mechanical system under different mission profiles. (**c**) The composition of the mission profile. The mission profile is constituted by operating condition in a certain order as shown in (**c–a**). The life trend and lifecycle are affected by the mission profile as shown in (**a**,**b**). Tk is the switch point of mission profile. T1, T2, and T3 is the lifecycle under the mission profiles 1, 2, and 3, respectively. t1, t2, t3, t4, and t5 is the switch point of different operating condition.  
However, most of the existing prediction methods are based on the premise that the mission profile before and after prediction are identical. Only one constant mission profile cannot solve the actual prediction of the mechanical system, as the actual mechanical system cannot always work under a constant mission profile without switching. Although some prediction methods considered the switching of operating conditions, each switching is only a constant operating condition rather than the mission profile switching under different operating conditions. Therefore, the existing prediction methods cannot effectively solve the life prediction of the actual mechanical system under the mission profile, which consists of different operating condition switching.  
Therefore, in this paper, a method to solve the life prediction of the actual mechanical system under mission profile switching is proposed. That is to say, the mission profile at the time of prediction does not necessarily coincide with the known mission profile. Meanwhile, each mission profile consists of different operating conditions. The proposed method is described below.