esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### High-Altitude Matrix Polarization Lidar (HAMPL)  
A block diagram of the HAMPL and the issues of detecting the preferred horizontal orientation of ice particles in HLCs are described in [18]. The lidar is oriented vertically in the zenith direction (Figure 1). Measurements are performed at any time of the day provided that there is no precipitation, strong wind, or low clouds. A Nd:YAG laser Lots TII LS-2137U with an operating wavelength of 532 nm, a pulse energy of up to 400 mJ, and a pulse repetition rate of 10 Hz is used as an optical radiation source. A Cassegrain mirror lens with a primary mirror diameter of 0.5 m and a focal length of 5 m is used as a receiving antenna. A Wollaston prism, which divides received backscattered radiation into two mutually orthogonally polarized beams, is installed on the output of the receiving optical channel of the lidar. These radiation beams are recorded by two Hamamatsu H5783 photomultiplier tubes (PMTs) operating in the photon-counting mode with time strobing of the signal, which provides the lidar altitude resolution from 37.5 to 150 m.  
To suppress active backscatter noise from the near-field zone of the lidar (up to 3 km), electro-optical shutters (EOSs) are installed in front of the PMTs. The EOSs are based on a potassium dideuterium phosphate (DKDP) crystal with a maximum trigger frequency of 100 Hz, a high-voltage pulse duration of 1-1000 us, and a high-voltage pulse delay relative to the trigger pulse of 1-100 us. The use of EOS allows the characteristics to remain linear even during lidar operation in the daytime at the maximum energy of the sensing pulse. Radiation pulses with four different polarization states (three linear and one circular) are sent alternately into the atmosphere, for each of which the polarization state of backscattered radiation described by the Stokes vector parameter is determined. Thus, 16 intensity profiles are measured in each sensing cycle, from which 16 BSPM elements are calculated. BSPM is a 4 \(\times\) 4 matrix with m\({}_{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\text{\text{\text{\  
rological Organization, these stations launch radiosondes only twice a day. Thus, when processing the results of lidar measurements, it is necessary to choose the data considering the above-mentioned aerological stations and the time of sounding (morning or evening).  
In connection with these circumstances, we have previously evaluated [18; 21] the possibility of using the ERA5 reanalysis data [22] from the European Centre for Medium-Range Weather Forecasts (ECMWF) to interpret the results of lidar measurements. Their verification was performed on the basis of comparison with the data of radiosonde observation at five aerological stations within 500 km from Tomsk for each day for five consecutive years. The correctness of using the vertical profiles of some meteorological variables in the range of altitudes of HLC formation was shown. Therefore, we have recently used the ERA5 reanalysis as an alternative source of meteorological information.
# Prediction of Cotton Yarn Quality Based on Attention-GRU
## 2 Cotton Yarn Quality Prediction Model
### The GRU Neural Network  
Currently, the long short-term memory (LSTM) network, a type of recurrent neural network (RNN) designed to alleviate the vanishing gradient problem, has emerged as a predominant method in text processing and time-series forecasting [21; 22]. Nevertheless, the intricate architecture of LSTM considerably extends the training time of neural networks, thereby reducing operational efficiency. Addressing this concern, [PERSON] et al. [23] introduced the gated recurrent unit (GRU) neural network, which builds upon the foundation of LSTM and effectively enhances model-training speed while also mitigating overfitting tendencies. The internal network structure of GRU resembles that of LSTM (as depicted in Figure 3).  
It is evident that distinct from LSTM's three gates--output, input, and forget--GRU incorporates only two gates: the update gate and the reset gate. The information propagation process within the GRU cell is illustrated by Equation (1):  
\[\left\{\begin{array}{l}r_{t}=\sigma(W_{rx}\cdot c+W_{rh}\cdot h_{t-1})\\ \widetilde{h}=\tanh(W_{hz}\cdot x_{t}+W_{hh}(r_{t}\cdot h_{t-1}))\\ z_{t}=\sigma(W_{zx}\cdot x_{t}+W_{zh}\cdot h_{t-1})\\ h_{t}=(1-z_{t})\cdot h_{t-1}+z_{t}\cdot\widetilde{h}\end{array}\right. \tag{1}\]  
where: \(x_{t}\) represents the input at time step \(t\); \(h_{t-1}\) denotes the hidden state at time step \(t-1\); \(\sigma\) signifies the sigmoid activation function; \(r_{t}\) stands for the reset gate; \(\widetilde{h}\) represents the candidate information at time step \(t\); \(z_{t}\) signifies the update gate; \(h_{t}\) denotes the hidden state at time step \(t\); \(W_{rx}\), \(W_{rh}\), \(W_{hx}\), \(W_{hh}\), \(W_{zx}\), \(W_{zh}\) represent the weight parameters of the unit.  
Figure 2: Spinning quality prediction model based on BP neural network.  
Therefore, within the GRU neural network, the reset gate selectively discards the previous output information based on the current input, while the update gate determines the extent to which the prior output information is integrated into the current output information. By employing this mechanism, GRU continuously forgets less significant historical data and retains crucial new information, facilitating a more effective capture of temporal dependencies within sequential data.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 3 Techniques for Explainable AI
### Model-Based Interpretation Techniques
#### 3.1.3 Decision Trees (DTs)  
The idea of DTs was introduced by [PERSON] [19]. His team designed Iterative Dichotomiser 3 (ID3) algorithm that automatically learned trees from data. The ID3 algorithm uses a top-down approach to start from a single node and split the data recursively into smaller subsets using the most informative feature. This splitting continues until all of the instances in a subset belong to the same class or when no more splits are possible. Overall, the DT technique performs well on tabular-style datasets with individually meaningful features and with no strong multi-scale temporal or spatial structures. DTs are considered to be explainable due to their ability to represent the decision-making process of a model in a clear and transparent way [28]. They are particularly useful in applications where transparency and interpretability are essential for ensuring the fairness and trustworthiness of AI-based decision-making systems.  
In a similar vein, [PERSON] et al. [29] explore the utility of decision trees in medical diagnosis, where the trees leverage easily interpretable variables to make decisions, offering straightforward explanations for their outcomes. This approach has potential applicability in other domains that prioritize transparency in decision-making.  
However, if the complexity of the relationships in a given dataset is high then the interpretability of a DT decreases. If the relationships between the input features and the output variable are highly nonlinear or complex, a DT may struggle to capture them accurately using a simple set of rules. In such cases, the decision tree may require a larger number of nodes and branches, which can make it difficult to interpret and understand. To overcome this limitation, [PERSON] et al. proposed a stochastic gradient descent approach to enable efficient optimization for large-scale datasets [30]. To further improve the model's explainability and generate additional insights, [PERSON] provides a practical guide for incorporating Python-based explainable AI libraries such as SHAP, LIME, and Skope-Rules into a DT [31].  
Another challenge faced by DT algorithms is the use of local optimization functions, meaning that inaccurate splits at the top of the tree may result in poor predictions. Therefore, various techniques have been developed to simplify and optimize DT, such as pruning, ensemble methods, and regularization. These techniques can help to reduce the complexity of the tree and improve its interpretability, while still capturing the essential relationships in the data. Gradient boosting can also be used to improve the interpretability of DT. By combining multiple decision trees into an ensemble model, gradient boosting can create more accurate and robust models that are less prone to overfitting. [PERSON] [32] provided a comprehensive review of the recent advances in the field of optimal DT and their interpretability.
## 2 Materials and Methods
### Analysis of Tyrosine Activity and Melanin Content  
The antityrosinase activity of various CF extracts was analyzed using the method reported by [PERSON] et al. (2012) [22]. These CF extracts were prepared in DMSO solution at different concentrations. Subsequently, 30 uL of the sample was added to 970 uL of 0.05 mM PBS and homogeneously mixed with 2 mL of solution containing L-tyrosine (1 mL, 100 mg/L) and mushroom tyrosinase solution (1 mL, 350 units/mL) in the dark. After a 20 min reaction period, the absorbance of the solution was measured at 490 nm by using a UV-Vis spectrophotometer. Koji acid was used as the positive control. All experiments were conducted in triplicate. The concentration of half the original tyrosinase activity inhibited was defined as the IC\({}_{50}\) value. The antityrosinase activity of the extract was calculated as follows:  
\[\text{antityrosinase activity}(\%)=\frac{[(A-B)-(C-D)]}{(A-B)}\times 100 \tag{4}\]  
where A is the absorbance without the extract (control), B is the absorbance without the extract and enzyme (blank of A), C is the absorbance with the extract and enzyme, and D is the absorbance without the enzyme (blank of C).  
The effect of the fermented extract on the antityrosinase activity and melanin content in HEMn cells was analyzed using the method reported by [PERSON] et al. (2021) with slight modifications [23]. Briefly, 5 \(\times\) 10\({}^{6}\) HEMn cells/well were incubated in Medium 254 supplemented with human melanocyte growth supplement at 37 \({}^{\circ}\)C. After a 24 h cultivation period, the cells were treated with different final concentrations of the fermented extract (5-50 mg/L) in 24-well plates for 24 h. Subsequently, the cells were collected, washed with PBS, lysed with a cell lysis solution, sonicated at 4 \({}^{\circ}\)C for 5 min by using an ultrasonic sonicator (Qsonica, Newtown, CT, USA), and then centrifuged at 6500\(\times\)\(g\) for 20 min by using a micro-ultracentrifuge (Thermo Fisher Scientific, Waltham, MA, USA). The lysates were reacted with 2.5 mM L-DOPA in 0.1 M PBS in 96-well plates for 60 min. The absorbance was measured at 475 nm by using an Epoch ELISA reader. To examine the melanin content in HEMn cells, 5 \(\times\) 10\({}^{6}\) HEMn cells/well were first incubated for 24 h and then treated with the fermented extract for 24 h. Thereafter, the cells were collected through trypsinization and centrifugation. The cell pellets were suspended in 100 uL of 1 N NaOH containing 10% DMSO and heated at 70 \({}^{\circ}\)C for 1.5 h. The absorbance was measured at 405 nm by using an Epoch ELISA reader. The melanin content in the HEMn cells was determined through comparison with a synthetic melanin standard obtained from Sigma-Aldrich (St. Louis, MO, USA).
## 5 Conclusions  
Based on an analysis of vegetation spatiotemporal dynamics as well as the correlation between GPP and its driving factors, we drew the following conclusions:  
1. In the GKM, different seasons displayed different patterns of spatial distribution because of the difference in understory vegetation, altitude and land cover. Areas with bright conifer forest always have a larger GPP because the understory vegetation contributes a great deal to vegetation total productivities. Altitude impacts GPP by changing temperature. Land cover with intense human intervention showed different seasonal changes in AGG.
2. Temporal trends of AG at the regional scale showed a dramatic increase from 1982 to 2015 in summer, autumn, and the growing season. This observation could be considered to be a result of the warming and drying trend that is seen in the GKM over the past 34 years.
3. Interannual GPP trends at the pixel scale showed that the areas with significant AGG variation (mostly significant increasing trends) were strikingly expanded after 2000 because of the natural forest protection project launched in 1998, but the AGG variation displayed a flattened trend over the last five years due to the formation of a relatively stable vegetation community.
4. Upon aggregating all of the above factors, daily sunshine duration in summer, instead of GSL, acted as the dominant factor in most of the areas, whereas daily mean temperature in summer was the dominant factor in a fraction of the GKM. FPAR also acted as the dominant factor in part of the northern GKM and was closely related to vegetation change that was caused by the fire in 1987.  
This research was supported by the Major State Basic Research Development Program of China (2013 CB733402), the National Natural Science Foundation of China (grant Nos. 41271346, 91425301). We appreciate the provision of GPP and FPAR products by GLASS Team.  
All authors contributed significantly to this manuscript. Specific contributions include data collection ([PERSON], [PERSON]), data analyses ([PERSON], [PERSON]), methodology ([PERSON], [PERSON]), and manuscript preparation ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]).  
The authors declare no conflict of interest.
# Real-Time Psychological Stress Detection According to ECG Using Deep Learning
## 1 Introduction
To show the proposed network's performance, we also conducted the conventional feature extracting method applying nine machine learning methods (SVM, decision tree, random forest and so on). In the current study, we divided mental stress into three levels, i.e., low, medium and high. To the best of our knowledge, it is the first time that deep learning technology has been applied to detect three levels of psychological stress by only using a 10 s ECG signal from a data set that is collected based on the Montreal model. The proposed framework with deep learning is more suitable for real-time applications.
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 2 Methodologies
### Data Extraction and Feature Engineering
#### 2.1.4 Time Series Input and Output  
To reinforce the importance of the time series data, we reiterate the need to consider past data when making future predictions. While this has similarities with the previous section, it is paramount to understand the significance of including past movements for an accurate model.  
Vehicular movements over time form a sequential, interconnected pattern, much like any time series data. Capturing this continuous flow and understanding how past states affect future outcomes is pivotal for our analysis.  
Vehicle motion is inherently continuous, implying that past movements can influence future ones. Predicting maneuvers and trajectories thus becomes a time series forecasting challenge. For each time step, it is important to incorporate data from preceding time steps into the input features. Moreover, to enhance the model's efficacy, considering maneuvers from a prior duration as a distinct feature could be beneficial.  
Suppose that the current time step is \(T\), and the number of the previous time step is \(T_{p}\). To generate a continuous sequence of previous states, the reframed input \(X_{T}\) and output \(Y_{T}\) at time step \(T\) can be expressed as in Equation (5):  
\[X_{T}=[x_{T-1},x_{T-2},\dots,x_{T-T_{p}}],Y_{T}=[y_{T}] \tag{5}\]  
For the classification models, the state at the time step \(k\) can be represented as in Equation (6):  
\[x_{k}=[t_{k},\sigma,\delta_{k},v_{k},(v_{x})_{k},\theta_{k},a_{k},y_{k},x_{k}, m_{k}],k\in(T-T_{p},T) \tag{6}\]  
The matrix \(X_{T}\) is flattened to a \(1\times[n_{features}\times n_{T_{p}}]\) vector. The dimension of the input data \(X\) is \(n_{rows}\times[n_{features}\times n_{T_{p}}]\). The output can be expressed as \(Y_{T}=[m_{T}]\). The dimension of the output data \(Y\) is \((n_{rows}\times 1)\).  
Similarly, for the trajectory prediction model, the state at the time step \(k\) can be represented as in Equation (7):  
\[x_{k}=[t_{k},\sigma_{k},\delta_{k},v_{k},(v_{x})_{k},\theta_{k},a_{k},x_{k},y_ {k}],k\in(T-T_{p},T) \tag{7}\]  
The matrix \(X_{T}\) is directly used as the input for each time step. The input data \(X\) are a tensor with the dimension of \(n_{rows}\times n_{features}\times n_{T_{p}}\). The output can be expressed as \(Y_{T}=[x_{T},y_{T}]\). The dimension of the output data \(Y\) is \((n_{rows}\times 2)\).  
When the number of previous time steps \(T_{p}\) is large, the dimensionality of \(X_{T}\) significantly increases. This can lead to a slower training process and might adversely impact the model's performance.  
In conclusion, for the maneuver classification problem, considering both the correlation matrix and score of the features, the selected features are \(v\), \(v_{x}\), \(theta\), and \(x\). The input is expressed as \(X_{T}=[x_{T-1},x_{T-2},\dots,x_{T-T_{p}}]\). For a specific time step \(k\), \(x_{k}=[v_{k},(v_{x})_{k},\theta_{k},x_{k},m_{k}]\). The output is \(Y_{T}=[m_{T}]\).
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 3 Threat Model and Assumptions  
In this section, we introduce the main annotation and assumptions of our work, which are also illustrated in Figure 2. First, consider a simple setup where there are a legitimate transmitting node (e.g., a temperature sensor) denoted by \(s\) and a legitimate receiving node (e.g., an IoT hub) denoted by \(r\) communicating over a wireless channel. Also, we assume that \(r\) utilizes an arbitrary approach (including what we propose in this work) to profile s based on RSSI samples, it has received in a period absent of any adversary, and then uses this profile at runtime to differentiate between received _data frames_ that carry s' MAC address (legitimate vs. spoofed ones). Finally, let \(\alpha\) denote the adversary with the following characteristics:  
* The adversary is situated at a location from which it can observe/receive signals transmitted by all legitimate senders (when sending data frames) and receivers (when sending acknowledgment frames back) in the given network.
* The adversary is aware of the transmission power setting (\(P_{Tx}\)) of the legitimate sender(s), which is not a substantial assumption as system information about most IoT/WSN devices is publicly accessible on the Internet.  
The adversary has no prior knowledge of the actual physical/geographic locations of other (legitimate) nodes in the network.
* Network participants, including the adversary, are equipped with regular/common omnidirectional antennas, and are not capable of detecting the positional angle of the transmitting nodes. However, the adversary can move about in order to triangulate other nodes' locations based on the strength of the signal received from those nodes [10].
* The adversary itself is an _active_ node capable of adjusting its transmission power.
* The adversary is also capable of altering (i.e., spoofing) its MAC address value--i.e., it can generate data frames that carry MAC addresses of other legitimate nodes from this particular network.  
The ultimate goal of adversary \(\alpha\) is to impersonate a particular \(s\) by transmitting frames with \(s^{\prime}\) spoofed MAC address. The spoofed frames are specifically intended for a particular \(r\). Since, according to the assumptions of our work, the transmitter's RSSI values are registered and used by r for the purposes of MAC-spoofing detection, the adversary first needs to discover/adjust its transmission power (\(P_{Tx}\)) such that its spoofed frames (when received by \(r\)) get accepted as genuine with a high probability--i.e., some desired probability of evasion is achieved by the adversary. This particular problem--of how to discover/adjust the transmission power so as to achieve a certain evasion probability--is closely related to the optimal adversarial evasion problem introduced by [PERSON] et al. [15] and further extended by [PERSON] and [PERSON] [10] to the IoT realm.
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 2 Materials and Methods
### Machine Learning Models  
Four machine learning models, including DT, XGBoost, RF, and KNN, were employed to predict the minimum methanol quantity required for hydrate inhibition using the 74,000 point Aspen HYSYS dataset. Implemented in Python (Version 3.12) via scikit-learn (for DT, RF, and KNN) and the XGBoost package within PyCharm IDE, these models were selected for their effectiveness in regression tasks and handling nonlinear relationships [28].  
One of the well-known methods to tackle regression challenges is using a DT [29]. Inner nodes (database properties), branches (conclusion rules), and leaves (outcomes) describe decision nodes (determination branches) and leaf nodes (outputs). The DT divides the data into several partitions based on the differences between the expected values and the predicted values (the splits are based on the least fitness function value), and then repeats this process. Its simplicity helps with interpretability, although overfitting requires the use of ensemble methods.  
One of the most powerful algorithms that has shown high performance in predictive modeling is XGBoost [30], a supervised machine learning algorithm that is known to be efficient and effective. It is an ensemble method that turns weak learners (usually decision trees) into a strong predictor by training trees sequentially on residuals of previous iterations. It is trained using gradient boosting, along with parallelization, regularization, and pruning. It is appropriate for complex methanol predictions as it is scalable [31].  
RF aggregates multiple DTs to minimize overfitting, improving prediction accuracy through averaging via bootstrap aggregation, and it performs well for large datasets with many features [32].  
The k-nearest neighbors (KNN) model makes predictions by computing the mean of the target values of k-nearest neighbors based on the closest values of the Euclidean distance. For accuracy, it utilizes normalized features [33].  
Hyperparameters (e.g., tree depth, number of trees, and k-value) were tuned using a grid search with tenfold cross-validation [34] to optimize the methanol quantity prediction. For the DT, the maximum depth was limited to balance accuracy and complexity; XGBoost's number of estimators and learning rate were adjusted to maximize precision; RF's tree count and feature sampling enhanced robustness; and KNN's neighbor count was optimized to minimize errors in normalized data. This process ensured that the models delivered accurate, reliable forecasts for dynamic pipeline conditions.