esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The area selected for demonstrating the results of applying this methodological framework was the central region of the Yucatan Peninsula in Mexico, where the landscape is dominated by semi-deciduous tropical forests and a shifting slash-and-burn agriculture (SABA) land-use, which has been practised for centuries in these forests. This study shows the results from an empirical evaluation of its applicability with data from this specific region of Mexico. At the core of this methodology is the use of free satellite imagery (e.g., Landsat imagery) as a primary source of information. One of the fundamental premises underpinning the selection of forest degradation indicators is the knowledge that the accumulation of biomass and carbon is related positively to the forest Net Primary Productivity (NPP) and thus, NPP is used to estimate the potential of the forest for storage of carbon under natural conditions [20]. Our main assumption relies on the principle that changes in NPP more accurately reflect forest condition, and therefore NPP is suitable to help in the definition of the reference state. This makes NPP a most suitable variable to be used as an indicator of forest degradation. Moreover, NPP together with aboveground biomass and canopy cover are variables used as main indicators of forest degradation [20]. Since canopy cover is a variable considered part of the definition of \"forest\" [21], including it as an indicator of forest degradation would allow for connecting and sharing definitions within and between countries, and in consequence, would enable the correspondence between thresholds of forest and no- forests among REDD+ participants. Forest canopy cover is recognized as a major biophysical and structural attribute of a forest because it affects terrestrial energy and water exchanges, photosynthesis and transpiration, net primary production, and carbon and nutrient fluxes [22]. Canopy cover providesan attribute that is measurable and can be used to monitor and retrieve site-specific histories of different stages within the forest landscape dynamic [23]. Canopy Cover has already been used as an indicator to monitor and map forest degradation in different contexts [24; 25]. As the avoidance of forest degradation (under the REDD+ strategy) seeks to maintain carbon in the living biomass on the ground, the most practical monitoring approach focuses on the assessment of Above-ground Biomass (AGB) as the main indicator of forest degradation. Assessment of AGB makes the monitoring of changes more targeted and efficient [14; 26]. Although the design and implementation of a methodological framework for REDD+ presents many technical and practical challenges, the progress in the application of remote sensing science for forest assessments [9], together with the relatively recent changes in policies for access to remote sensing data [27; 28] and the availability of consistent National Forest Inventory data converge in providing a valuable set of tools that yield valuable information is considered as the the basis for designing the methodology tested in our study.
# Acoustic Emission for Evaluating the Reinforcement Effectiveness in Steel Fiber Reinforced Concrete
###### Abstract  
Steel fiber reinforcement in concrete strongly enhances its ductility and toughness. This is basically due to the additional fracture mechanisms and energy used to overcome the interlocking and adhesion between the fibers and the cementitious matrix. The enhancement of the final properties is measured by mechanical tests and can be assessed only at the end of loading. These processes can be targeted and monitored by acoustic emission (AE) indices offering real-time characterization of the material's performance much earlier than the final failure or the termination of loading. In this study, steel fiber reinforced concrete (SFRC) beams were tested in bending with simultaneous AE monitoring. Tests conducted independently in different laboratories confirm that the AE behavior at low load levels is very indicative of the amount of reinforcement and consequently, of the final mechanical properties. The reason is that the reinforcement phase is activated through shear stresses in its interphase, a mechanism that is more profound in the presence of higher fiber content, and correspondingly is absent in plain unreinforced material. This finding opens the way to characterize the effectiveness of reinforcement with just a proof loading at less than 30% of the final load bearing capacity.  
AE parameters; bending; concrete; RA-value; duration; interphase; steel fibers Article
# Multi-Classifier Pipeline for Olive Groves Detection
## 2 Materials and Methods  
### Study Area  
The area of interest is located in Southeast France (Figure 1) within the Alpes-Maritime over 272.1 km\({}^{2}\). The experiment is centred in Grasse and its surroundings including Saint-Cezaire-sur-Siagne, Cabris, and Saint-Vallier-de-Thiey. Olive trees can be found around these areas either in the wild, planted in the small-scale private garden, or as a grove in olive farms.
## 3 Materials
### Baseline Drift Elimination  
In the UCI dataset, [PERSON] et al. [17] performed moving average filtering on the raw PPG signals and eliminated segments with severe discontinuities or large periodic differences. However, they did not address the common issue of baseline wander present in the raw signals. Baseline wander is caused by various factors such as respiration, changes in body position, and temperature variations. It leads to an overall upward or downward trend in the PPG signal, resulting in scale transformations that make it difficult to compare and match different PPG signal cycles. Additionally, baseline wander introduces extra noise and interference, reducing the signal-to-noise ratio of the PPG signal and increasing the error and uncertainty of blood pressure estimation models. Therefore, it is necessary to perform baseline wander correction on the PPG signal to eliminate its impact on model predictions and improve the accuracy and stability of the model. In this study, the variational mode decomposition (VMD) [18] algorithm is employed to remove baseline wander from the raw signals. VMD is a signal decomposition method based on the idea of representing a signal as a series of locally frequency-modulated components, with each component corresponding to a mode. Each mode function has a specific centre frequency and bandwidth, allowing it to capture different frequency components in the signal. By summing these mode functions, the original signal can be reconstructed. The low-frequency intrinsic mode functions (IMFs) obtained through the VMD decomposition of the signal typically contain information about baseline wander. By selecting the low-frequency IMFs and subtracting them from the original signal, baseline wander can be removed. The process of using the VMD algorithm to remove baseline drift is as follows:  
Define the original signal as x(t), where t represents time. This step involves an iterative optimization process that calculates IMFs and residual terms by minimizing a set of constrained optimization problems. The optimization problem of the VMD algorithm can be expressed as:  
\[min\underset{\text{i}}{\sum}\left|\text{A}_{\text{ni}}\otimes\text{u}_{\text{ n}}-\text{x}(\text{t})\right|^{2}+\lambda\underset{\text{i}}{\sum}\int\left|\partial \text{t}\text{u}_{\text{p}}(\text{t})\right|^{2}\!\!\!\!\!\!\  
obtained. The IMFs have different frequencies and bandwidths, where the low-frequency IMF captures the slow-changing component of baseline wander, while the high-frequency IMF captures faster changes. Here, we choose the low-frequency IMF as an approximation of baseline wander and subtract it from the original signal. The resulting signal after removing baseline wander is shown in Figure 1. It can be seen that there is an obvious baseline wander in the original PPG signal, and the overall signal offset is very similar to the trend of the low-frequency IMF obtained using VMD decomposition. After decomposing the original signal using the VMD algorithm, removing the low-frequency components, and reconstructing it, the periodicity of the new signal becomes more pronounced.
## 3 Results and Discussion
### The Study of Combined Remediation Applied in Actual Field Site  
In order to further verify the remediation effectiveness of the combined remediation scheme in actual field sites, an on-site material-plant combined remediation experiment was conducted in the contaminated soil based on the selected remediation plant and material formulation. The experimental results are presented in Table 5.  
From Table 5, it can be observed that, when the remediation time was 30 days, the reduction rates of bioavailability for As and Cd in the site soil was as high as 88%, indicating that the combined remediation approach exhibited strong adaptability to the field environment. As the remediation time extended to 60 days, 90 days, and 120 days, the reduction rates of bioavailability for As and Cd in the site soil remained above 88%, demonstrating the stable and persistent remediation effect of the combined remediation approach.  
Figure 5: The relationship between the reduction rate of bioavailability for As/Cd and the variation in material dosage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Days** & \
\begin{tabular}{c} **The Reduction Rate of Bioavailability** \\ **for As (\%)** \\ \end{tabular} &
\begin{tabular}{c} **The Reduction Rate of Bioavailability** \\ **for Cd (\%)** \\ \end{tabular} \\ \hline
30 days & 89.3 & 88.5 \\
60 days & 88.7 & 89.2 \\
90 days & 89.1 & 89.4 \\
120 days & 88.9 & 89.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The reduction rates of As and Cd in soil at the remediation site over time.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 3 Contribution to Work  
Previous studies on semantic analysis, computational linguistics, natural language processing, and different statistical methods contribute to understanding various challenges of short-text semantic similarity (STSS). The contribution of our work is methodological. The following are the key contributions of our study:  
* We focused on various deep learning techniques that are compact, dense, and appropriate for analyzing the semantic and lexical meaning of short texts.
* We compiled research that mostly deals with short text, short text classification, short question-answers, short tweets, and short sentences.
* This study also included three essential methods for emphasizing the context that underlie the semantics of short text.
* This study also concentrated on performance assessments, short text datasets, and suggested improvements for future initiatives.  
The rest of the paper is organized as follows: In order to address the first RQ, Section 4 provides information on several deep learning approaches and their impacts on the semantic similarity of short text. Section 5 explains those deep learning techniques that generate high-level textual representations in order to improve sentence similarity. Furthermore, the datasets for short text similarity tasks are presented in Section 6. Moreover, Section 7 elaborates on the STSS challenges in QAS systems and presents suggested improvements. Section 8 has been written with concluding remarks.
# A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning
## 4 Experiment
### Experiment Setup
#### 4.1.2 Evaluation Strategy  
First, the malicious HTTP traffic detection is essentially a classification problem. Thus, we measured the performance based on three metrics, i.e., Precision, Recall, and F1-Measure.  
Second, this paper focuses on cross-system malicious HTTP traffic detection problem. Therefore, we treated Dataset A as the large public labeled dataset and Dataset B as the small dataset of the target system. In addition, Dataset B was further divided into two subsets, i.e., the labeled subset (called Subset BL) and the unlabeled subset (called Subset  
Figure 3: The proposed transfer semi-supervised learning framework.  
BU). Subset BL contained 200 samples (i.e., 50 samples for each type) and Subset BU contained 600 samples (i.e., 150 samples for each type). To cope with different evaluation scenarios, we designed the following two evaluation strategies.  
(1) Strategy A: It corresponded to the standard training and testing strategy. Specifically, it trained the target model on Subset BL and tested it on Subset BU.  
(2) Strategy B: It corresponded to the training and testing strategy for the small data condition. Specifically, it trained the target model on Subset BL \(\cup\)Subset BU (corresponding to the semi-supervised learning scenario), Subset BL \(\cup\)Dataset A (corresponding to the transfer learning scenario), or Subset BL \(\cup\)Subset BU \(\cup\)Dataset A (corresponding to the transfer semi-supervised learning scenario). Then, it tested the target model on Subset BU.  
For both strategy A and strategy B, we adopted a four-fold-cross-validation procedure, i.e., we randomly generated Subset BL and Subset BU four times and reported the average performance.
## 3 Experimental Results and Analysis
### Experimental Analysis
#### 3.3.6 State-of-the-Art Comparison  
We compared our method with the top 10 teams in the SPEC2019 pose estimation challenge, the results of which are shown in Table 9. The competition is used \(ESA_{score}\) as an evaluation metric, where PnP indicates whether the PnP algorithm is used or not, i.e., whether a CAD model of the target is required or not. It can be seen that our proposed method outperforms all non-PnP methods except the team of [PERSON] [10] in terms of accuracy. It also outperforms some methods that use PnP. Moreover, the model size of the method used by [PERSON] is 500 M, while our method is 186 M.
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 4 Experiment and Results
### Experimental Results
#### 4.4.3 Comparative Evaluation Using Various Network Architectures
By replacing the two LSTM layers with a BiLSTM, we enhanced the feature extraction and contextual understanding capabilities, allowing for better handling of complex time series data. As a result, our model achieved an F\({}_{1}\)-Score of 93.17% on the test set with only 34,631 parameters, highlighting its efficiency and effectiveness compared to other models. In summary, the performance differences between the models mainly stem from their ability to handle time series data, feature extraction mechanisms, and the complexity of the model structures. By replacing fully connected layers with BN and GAP layers and incorporating advanced methods such as LSTM, TCN, attention mechanisms, and bidirectional LSTM, our model makes significant progress in capturing complex temporal dependencies and improving recognition accuracy. At the same time, it maintains a low number of parameters and efficient computational capability.
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed  
Atomic spectroscopy comprises several analytical techniques allowing elemental quantitative analysis of a target sample. Since the determination of levels of various metals is essential in medicinal, food, environmental, and a wide range of manufacturing sectors, this method is widely applied for metal analysis. Depending on the measured parameter, atomic spectroscopy techniques can be divided into three basic types based on light absorption, emission, or fluorescence. Although atomic fluorescence can provide lower detection limits for some metal ions, the method is less commonly used in the food industry. Therefore, our discussion in this review emphasizes the application of atomic absorption and emission technologies in seaweed metal analysis.  
Atomic spectroscopy is based on the quantized changes in atomic energy upon the absorption or emission of light. In absorption, the energy of an atom increases when an electron transitions from an orbital of lower energy in the ground state to one of higher energy in the excited state. Atomic emission is the reverse of the absorption process, where atoms in the excited state emit light upon the transition of an electron back to the lower energy ground state. Since the atomic orbitals are discrete and few in number, atomic spectra contain very narrow transition bands at single wavelengths corresponding to the various transitional energy values between the atomic orbitals specific to each metal.  
Analytical techniques are often categorized according to the atomization/ionization source (flame, furnace, or plasma) or the type of spectroscopy used (absorption, emission, fluorescence, or mass). Combinations of these give rise to numerous analytical techniques with different performance characteristics [44].  
Heavy metals can be enriched in seaweed species and seriously endanger human health due to the growing interest of people around the globe in the consumption of seaweed-based food. The wide range of metal concentrations present in seaweed, ranging from ng/g levels to percent levels, combined with the analyte and matrix specificity of atomic spectroscopy methods introduces challenges in the selection of a specific technique for metal analysis. In the food industry, the most common electromagnetic techniques are based on atomic absorption and atomic emission spectrometry. This review summarizes atomic spectroscopy-based technologies that have been used for heavy metal determination in various seaweed species at different geographical locations. The applicability of each technique is discussed by providing study cases reported by the scientists in the field.
## 2 Materials and Methods
### Training Process  
Before commencing model training, meticulous data preparation was carried out. The dataset, which included annotated images with labelled objects, was divided into three fundamental subsets: the training set, validation set, and test set, as described earlier. Each batch size variant underwent this same data partitioning process to ensure equitable training conditions. The YOLOv8 model's configuration files were tailored to accommodate the distinct requirements of each batch size. Each variant was configured to reference the appropriate dataset directories and batch size. The training process began with the initialization of the YOLOv8 model within the Google Colab environment. Importing the model and its dependencies ensured the availability of essential libraries and configurations. For the learning and identification tasks, a T4 GPU with 12 GB of RAM was used (NVIDIA GeForce GTX TITAN X). The study was conducted on a workstation operating on Ubuntu 18.04 with GPU acceleration using a virtual machine setup, while Python (Python 3.8) programming was employed for coding. The dataset consisted of images annotated with labelled bounding boxes to identify the crops and weeds. As the primary objective of this study was to examine the impact of batch size on the model's performance, a dedicated training script was executed for each batch size variant, referencing its specific dataset directories and configuration files. The training process entailed a set number of epochs,during which the model adapted and learned from the data. The batch sizes used were 10, 20, 30, 40, 50, 60, 70, 80, and 90, allowing for comparative analysis. Comprehensive monitoring and metric analysis were conducted to assess the effect of batch size on the training process. Loss, an essential metric, was monitored continuously. The TensorBoard ([[https://www.tensorflow.org/tensorboard](https://www.tensorflow.org/tensorboard)]([https://www.tensorflow.org/tensorboard](https://www.tensorflow.org/tensorboard))) tool facilitated real-time visualization of training metrics. The trained models for each batch size variant were evaluated using dedicated validation sets, which assessed their ability to generalize and detect objects accurately. Subsequently, the test set was employed to further validate the models' performance.
## 4 Simulation and Experimental Results
### Experiment
#### 4.2.2 Experimental Results and Discussion
Figure 15: Experimental results of the NELP DLL code-tracking error. (**a**) \(T_{\text{coh}}\) = 1 ms and (**b**) \(T_{\text{coh}}\) = 10 ms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Doppler Frequency** & \multirow{2}{*}{**Pulse-OTFS(10,1023)**} & **Pulse-** & \multirow{2}{*}{**OTFS(10,1023)**} & **BPSK(5)** \\
**Offset (kHz)** & & & & \\ \hline
0 & 21.30 & 21.94 & 21.30 & 20.98 \\ +0.5 & 21.14 & 21.71 & 14.39 & 13.48 \\ \(-0.5\) & 21.28 & 21.92 & 12.99 & 13.56 \\ +5 & 12.39 & 12.88 & 1.27 & 1.79 \\ \(-5\) & 13.46 & 14.41 & 0.83 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Magnitude values of correlator peaks for different signals. making a decision. Therefore, the running time is practically independent of the Doppler frequency offset. We calculate that the running time of the acquisition program decreased by about 89.3% and 88.5% for Pulse-OTFS and Pulse-BPSK, respectively. This result is in agreement with the 89.4% reduction in theoretical acquisition complexity in Section 3.2.2. The above experiments verified that the proposed signal has both the high accuracy measurement of the OTFS signal and the low acquisition complexity of the pulsed signal. Furthermore, the proposed navigation signal occupies at most 10% of the signal period in the time domain, the transmission of communication signals for the rest of the time does not cause interference between the signals, and since the OTFS modulation itself is designed for broadband communication, the navigation and communication signals can share a common modulator, thus simplifying the design of the satellite payload. It can be concluded that the proposed signal would be an advantageous choice for use as a future broadband LEO satellite navigation signal.
## 4 Discussion
### Potential Applications and Limitations  
This study analyzed the dominant factors of WUE changes in China from 2001 to 2020 by considering various climate and ecological environmental factors and using multiple trend and factor exploration methods. However, the change process of WUE is complex and involves many factors. Previous studies have shown that carbon dioxide (CO\({}_{2}\)), as an important environmental factor, has driven the increase in WUE in the past three decades [84; 85]. This is mainly because CO\({}_{2}\) can directly affect WUE through fertilization and stomatal inhibition effects, and indirectly affect the production status of plants, thereby affecting WUE [86]. Additionally, as the concentration of carbon dioxide rises, the stomatal conductance of plants undergoes corresponding alterations. In arid regions, the reduction in stomatal conductance results in a more pronounced decrease in vegetation carbon sequestration efficiency compared to that of the transpiration rate. Conversely, in humid areas, the situation is reversed [87]. Therefore, in future research, we will attempt to analyze the response mechanism of vegetation water-use efficiency to CO\({}_{2}\). Furthermore, it must also be noted that the GPP and ET data used in this study were obtained from GLASS. The GLASS GPP and ET products have been proven by many scholars to have good consistency with FLUX towers [88; 89]. In future research, it is necessary to further validate the applicability of these data in the study area in order to more accurately reflect the variation characteristics of the region's GPP and ET.