esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 2 Related Works
### Machine Learning-Based Techniques
A comprehensive comparison of three categories of supervised deep learning models, namely deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN), was presented in [35] to detect GPS spoofing attacks. Therein, mode imputation was applied to replace missing values with the most frequent value, and the Min-Max scaler was used for data normalization. The authors evaluated the models using a 10-time resampling framework and concluded that the U-Net model based on a CNN achieved the best performance. A stacking ensemble approach comprising machine learning and deep learning algorithms was proposed in [36] against GPS spoofing. In the preprocessing stage, due to the high range of the numerical values of the dataset, the authors used z-score normalization (standardization) to avoid any kind of feature favoritism. Then, they examined the combination of different ML and DL models. Therefore, the SVM-CNN model showed the best performance in terms of accuracy, precision, recall, and F1-score through K-fold cross-validation. The summary of the state-of-the-art papers is illustrated in Table 1. Driven by the shortcomings outlined above and the advantages offered by machine learning over traditional approaches, we focus on designing an effective ML-based system that operates independently of any additional sensors onboard or external communication with neighboring nodes or ground control stations. Furthermore, we address GPS spoofing detection by classifying each attack type using a large-scale dataset composed of signal samples collected from real-world experiments. \begin{table}
\begin{tabular}{l l l l} \hline
**Axis** & **Paper** & **Methodology** & **Limitation** \\ \hline \multirow{3}{*}{Onboard devices} & [21] & IMU & \multirow{3}{*}{Hardware modification} \\  & [22] & Visual odometry & \\  & [23] & Impulse radio ultra-wideband & \\ \hline \multirow{3}{*}{Signal processing} & [24] & RSSI & \multirow{3}{*}{Requires stable link with GCS and neighbor nodes, lack of trust policy among UAV nodes.} \\  & [18] & TDoA & \\  & [25] & C/N\({}_{0}\) & \\ \hline \multirow{3}{*}{Cryptography} & [26] & Blockchain & \multirow{3}{*}{Computational overhead, complex key distribution in dynamic topologies.} \\  & [27] & Semi-decentralized architecture & \\ \hline \multirow{3}{*}{Game theory} & [28] & Stackelberg game & \multirow{3}{*}{Lacks real-time experiments.} \\  & [29] & Signaling game & \\ \hline \end{tabular}
\end{table} 
Table 1: State of the art summary.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
###### Abstract  
This research presents a uni-regression deep approximate forecasting model for predicting future demand in supply chains, tackling issues like complex patterns, external factors, and nonlinear relationships. It diverges from traditional models by employing a deep learning strategy through recurrent bidirectional long short-term memory (BiLSTM) and nonlinear autoregressive with exogenous inputs (NARX), focusing on regression-based approaches. The model can capture intricate dependencies and patterns that elude conventional approaches. The integration of BiLSTM and NARX provides a robust foundation for accurate demand forecasting. The novel uni-regression technique significantly improves the model's capability to detect intricate patterns and dependencies in supply chain data, offering a new angle for demand forecasting. This approach not only broadens the scope of modeling techniques but also underlines the value of deep learning for enhanced accuracy in the fluctuating supply chain sector. The uni-regression model notably outperforms existing models in accuracy, achieving the lowest errors: mean average error (MAE) at 1.73, mean square error (MSE) at 4.14, root mean square error (RMSE) at 2.03, root mean squared scaled error (RMSSE) at 0.020, and R-squared at 0.94. This underscores its effectiveness in forecasting demand within dynamic supply chains. Practitioners and decision-makers can leverage the uni-regression model to make informed decisions, optimize inventory management, and enhance supply chain resilience. Furthermore, the findings contribute to the ongoing evolution of supply chain demand forecasting methodologies.  
supply chain; temporal pattern; demand indicator; fusion forecasting; bidirectional long short-term memory +
Footnote †: journal: _sciences_
## 3 Results  
### Spatial Distribution of Snowfall in the Sanjiang Plain in the Last Two Decades  
Through the remote sensing data (MOD10A2), the snowfall accumulation of four phenological periods (2003-2005, 2012-2010, 2014-2015, and 2019-2016) in snow drought and snow-abundant years in the Sanjiang Plain is displayed separately (Figure 4). In addition, this result divided the eight years into four phenological periods and could reveal the snow drought year adjacent snow-abundant year conditions more clearly and directly. From the spatial distribution of snow cover data, 2019 was the year of least snowfall accumulation in the past 20 years, which reached just 27.2 mm, and the temperature was the highest for the whole period.  
Among the four phenological periods, there are two parts: the temperature increased with the decreased snowfall (2003-2005, 2012-2010, and 2019-2016), and the temperature decreased as the snowfall decreased (2014-2015). In the first part, the snowfall in the snow drought year was lower than a snow-abundant year at 119.5 mm, 223.0 mm, and 253.8 mm, and the decreasing rate in the snow drought year was 62.2%, 80.6%, and 90.3%, respectively. From the perspective of spatial distribution, the snow drought region spread from the local southern area to almost the whole area. These two features indicated that preseason drought conditions possess less snow accumulation, a wider decrease in snowfall and a larger gap between the snow drought and abundant year from 2000 to the 2019 year in the Sanjiang Plain. Furthermore, the 2019-2016 period had the strongest snowfall in contrast to the other three phenological periods. In the second part, the snow accumulation was decreased 197.0 mm, and the decrease ratio was 77.1%. Indeed, the phenological period 2014-2015 was the only period where its temperature (\(-16.1~{}^{\circ}\)C) was lower in the snow drought year; compared to the others, this was abnormal and would produce dissimilar vegetation growth regulation.
## 5 Performance Evaluations
### Results and Discussions
#### 5.3.3 Number of Control Packets  
Figure 7 illustrates the numbers of control packets generated by the proposed scheme as well as SEED protocol as a function of the number of rounds.It is evident from Figure 7 that control overhead in the SEED was high as compared to the proposed scheme. We analyzed the behavior of control overhead in the proposed scheme and SEED with varying packet rates e.g., 5 and 10 packets per second. In both cases, the proposed scheme outperformed the SEED. The fundamental reason is that the proposed scheme decreased the frequency of re-clustering, thereby, the number of control packets was significantly reduced in the proposed scheme. On the other hand, in the SEED protocol, since the re-clustering triggered very frequently, the number of control packets increased more than that in the proposed scheme.  
Figure 6: Frequency of re-clustering as a function of the number of rounds.
# On the Optimal Calculation of the Rice Coding Parameter
## 6 Performance on Different Datasets Using Multi-Parameter Rice Coding  
In this section, we validate the multi-parameter Rice coding methods presented in previous sections. Our aim is to understand whether there is any benefit from partitioning the dataset for data compression as mentioned earlier. The dataset previously described in Section 4.1 is used in this section.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was carried out using the SPSSv25 software (SPSS Inc., Chicago, IL, USA). Because of the small sample sizes (<50 data points), the Shapiro-Wilcoxon test was performed to observe the data distribution for each time point (day 0, day 4, day 8, day 12, day 16, day 20, and day 24). The Shapiro-Wilcoxon test showed that the data distribution is not normal. Therefore, nonparametric (or distribution-free) tests were applied. In order tomake comparisons between three or more data sets, the Kruskal-Wallis test was employed as an overall omnibus test in the case of independent samples (synthetic antioxidants and NEO). Multiple pairwise comparisons were performed via Mann-Whitney tests to detect statistically significant differences between specific samples. All the above mentioned tests were applied for each time point (level of incubation period). Regarding the evolution in time of the analyzed samples (over the incubation period levels), the Friedman test for dependent samples (repeated measurements) was used. Each statistical method was applied separately for peroxide and TBA values. The Tukey test was applied to detect significant differences (\(p\leq 0.05\)) between the mean values obtained from three replicates performed in antimicrobial analysis. All statistical tests were employed to detect statistically significant differences at the 95% significance level (\(p<0.05\)).
# Machine Learning-Based Prediction and Optimisation System for Laser Shock Peening
## 2 Laser Peening Residual Stress Data
### Analysis of Residual Stresses Using Incremental Hole Drilling  
Incremental hole drilling [32] is commonly used for measuring residual strains on the surface and near the surface. The measurement procedure is described in the standardised version of the ASTM Standard Test Method E837 [33]. First, a hole is drilled concentrically at the location of interest, and then relaxed strains are measured at each increment in three directions using a strain gauge rosette attached on the surface of the component around the hole. The magnitude and directions of the in-plane stresses are calculated from these strains as a function of drilled depth. However, the hole-drilling method is limited by the measurement depth and is affected by plasticity when the magnitude of the residual stresses is close to the yield strength of the material. Laser peening typically introduces a compressive residual stress field with a relatively steep stress gradient. Therefore, holes were drilled in small increments in order to obtain a depth profile. Hole drilling measurement was undertaken using a setup developed by Stresscraft, UK. For reliable and accurate measurement, the UK NPL Good Practice Guide No. 53 and standard ASTM 837 were followed [32; 33]. Hole-drilling rosettes contain three radial strain gages to identify the in-plane stress components \(\sigma_{x}\), \(\sigma_{y}\) and \(\tau_{xy}\). The strain gages are arranged as shown in Figure 3. A 2 mm-diameter hole was drilled in an orbital motion in a series of small increments. We drilled 4 increments each of 32 \(\upmu\)m and 64 \(\upmu\)m, followed by 8 increments of 128 \(\upmu\)m, totalling 16 increments up to a depth of 1.4 mm.  
Figure 2: Schematic of the laser scanning strategy of the multiple-shot patch data with overlap showing (**a**) dimensions of test coupons and definition of the axis system. Residual stresses denoted as \(\sigma\)1 are parallel to the laser tracking direction and perpendicular to the rolling direction. (**b**) Laser offset distance \(\Delta X\) was measured from centre-to-centre points of each laser spot.
## 3 Results and Discussion
### Overall Heat Transfer Performance for Different Flow Channels
#### 3.2.2 Performance Analysis with Different Circular Radii  
Figure 6a,b shows the variation rules of the \(j\) and \(f\) factors of three arc-shaped flow channels with different radii (\(R\)). As can be seen from the two figures, the \(j\) factor and \(f\) factor decrease gradually with the increase in the Reynolds number for any circular flow channel. At the same time, the increasing trend of the \(j\) and \(f\) factors is constant during the changing process of the circle radius from 200 mm to 250 mm and from 250 mm to 300 mm. In other words, an increase in the radius of a circle does not cause drastic changes in the \(j\) and \(f\) factors. Under the same \(Re\) condition, both the \(j\) factor and \(f\) factor increase with the increase in the circle radius, and the \(j\) factor and \(f\) factor are the largest at \(R\) = 300 mm. Therefore, the large radius circular flow channel can indeed enhance the heat transfer enhancement effect. When \(1200\leq Re\leq 5100\), the \(j\) factor of the flow channel decreases by 13.1% and 6.8% for the structure with a circle radius of 300 mm and for the structure with a circle radius of 200 mm and 250 mm, respectively. For the \(f\) factor, the decrease is 39.7% and 21.6%, respectively.  
Figure 5: Comparison of heat transfer and flow performance of different circular-arc channels. (**a**) Heat transfer performance; (**b**) flow performance.  
of 300 mm has the best overall heat transfer enhancement capability. On the other hand, the overall heat transfer performance of the circular-arc flow channel increases with the increase in the circular radius. The main reason for this phenomenon is that as the radius of the circle increases gradually, the radius of curvature of the flow channel decreases. This reduces the cross-sectional area of the flow channel, which increases the speed of air flow and thus improves the local heat transfer coefficient in the flow channel.
# Automatic Era Identification in Classical Arabic Poetry
## 2 Background and Related Work
### Advancements of Deep Learning  
Notably, over the last several years, it has become common practice to employ large neural text encoders like BERT [42] to perform text classification [43]. Large language models (LLMs) are pre-trained using mass amounts of free text, encoding text into low-dimension vector embeddings that are intended to convey its contextualized meaning. Given labeled examples, such models can be specialized to perform tasks of text classification by extending and 'finetuning' the model parameters so as to map the generatedtext encodings into the specified categories [42]. Crucially, the models' 'understanding' of text depends on the compatibility of the text that it has been pre-trained on and the target domain. Researchers and practitioners have therefore constructed multiple variants of LLMs that are adapted to specific languages and genres by pre-training dedicated models using large amounts of relevant texts. To date, there exist LLMs in the Arabic language Arabic texts, for example AraBERT [44]. However, classical Arabic poetry differs from contemporary Arabic, both with respect to language and genre. [PERSON] et al. [45] finetuned Arabic BERT models to identify the meters of Arabic poems. A very recent manuscript presented a pretrained LLM for Arabic poetry analysis, named AraPoetryBERT [46]. In this work, they showed high performance in predicting the gender of a poet of a given poem as well as successfully classifying poems into meters. They obtained lower results, however, on the task of analyzing the sentiment conveyed in the poems, which involves deeper semantics (weighted F1 scored of 0.76). None of these works considered AA or era identification. We believe that finetuning a language model that has been pre-trained on Arabic poetry to the task of era prediction using our dataset is an interesting study. Nevertheless, success is not guaranteed as LLMs may emphasize content over style, for example. A main drawback of LLM for our purpose is that it serves as a 'black box', receiving plain text as input and delivering the predicted labels as output. In contrast, more traditional machine learning allows one to define a variety of non-textual features such as the stylistic rhetorical elements which we carefully model in our work. In addition, 'black box' approaches lack explainability and transparency. In future research, we may integrate the text encodings that are generated using LLMs with additional features using a dedicated classifier. Such an approach is non-trivial, however, and we leave this effort to future research.
## 4 Resulting Use Cases
### Using the ITS in the Classroom  
The use of ITS in the classroom was related to the use and integration in the COLLECE-2.0 environment to allow the teachers and the students to build programs collaboratively and to visualize them dynamically. This collaborative feature was achieved through COLLECE-2.0 sessions, whose actions were replicated on all visualization devices through network sockets. For this scenario, we considered that all the people involved in the learning process had this device to visualize and interact with the representation located on a real-world surface.  
Mainly, the teacher was the one who decided the program to be displayed, positioning the representation on a suitable place in the classroom. In this way, the teacher shared the visualization so that the students could both interact with the representation and move freely through it.  
Thus, in this use case, the ITS played a secondary role, and it was the teacher who guided the students during the visualization, stopping its execution to ask questions of the students about what was happening in the program. The students could also interact with the visualization in order to solve doubts that may arise during the lesson.  
As an example and to place the description, we considered the \"Bubble sort\" algorithm as shown in Figure 4. In this visualization, a billboard that printed the program output was placed, a break point on an expression evaluation, and a vehicle that went through the graphic representations executing sentences.  
In this context, the teacher could start an explanation by running the algorithm step-by-step during its first iterations, emphasizing the most relevant points of the execution.  
Thus, the students could understand the behavior of the algorithm, using a lower level of abstraction and handling representations that were already known. Afterwards, several questions  
Figure 7: A snapshot of the RoboTIC game running the level used during the evaluation. A video playing this full level can be found at [[https://youtu.be/T1I2](https://youtu.be/T1I2) mrx7 BUw]([https://youtu.be/T1I2](https://youtu.be/T1I2) mrx7 BUw).  
could be asked to check the degree of understanding obtained after the initial iterations; for example, what the state of the list at a given time was, what the value of a variable in any given iteration was, or which branch of the inner conditional sentence the vehicle would pass through and why. Likewise, these questions could also be asked directly using the elements of the graphic representation.  
Similarly, the students could also pose questions to the teacher about aspects that were not understood. In this case, the student could stop the execution and ask why a variable was updated to a certain value or add a ghost vehicle that served to reproduce the behavior that the main truck performed.
## 1 Introduction
Approaches based on this type of analysis are suitable for the treatment of time series at the time-frequency scale [38]. Knowing that rainfall time series data are not stationary and show (quasi-) periodicities, albeit with changing periods and strengths, several methods should be used to study these series [39]. The most frequently applied of these methods is wavelet analysis (wavelet transform and wavelet spectrum), which gives a two-dimensional description of the time series (both for time and frequency) [34; 40; 41]. Recently, wavelet analysis was successfully applied in scientific work related to rainfall [42; 43] because it provided a comprehensive representation of localized and transient phenomena occurring at different time scales [35; 44; 45]. Thus, particular features of the physical process, such as intermittent oscillation patterns and changes in periodicity, could be identified [46]. Wavelet frequency analysis is also used in many fields where time series data are a central subject of study, including fields ranging from climatology to hydrology, economics, astronomy, physiology, physics, geology, and others [46; 47]. Reviews of the hydrological applications of wavelets exist [48]. Given this context, the first part of the study aimed to extract the temporal variability modes of precipitation in the Moulouya basin in northeast Morocco using wavelet analysis. The main objective of this step was to identify periodic or quasi-periodic patterns in the rainfall series and to evaluate how these oscillation modes varied in the study area. The wavelet spectra may reveal time-varying patterns in the main oscillation modes of the time series, which could provide evidence of intensification or attenuation of drought events in the study sites. In addition, the results extracted from the wavelet analysis could indicate (relatively) homogeneous regions subject to similar periodic patterns.
## 3 Method
### Computer Vision Module
#### 3.3.2 Intent Prediction  
Once any objects are detected, the system must predict the intent of the user. The intent prediction is based on the direction of the user's palm, the intuition being that people generally have their palm pointed towards an object when grasping it. Using this assumption also had the benefit of making it easy to explain how the system works to the users of the exoskeleton.  
The intent prediction works by projecting a ray from the palm of the user, as depicted in Figure 4. The orthogonal Euclidean distance between this ray \(\vec{R}\) and any object in the scene is then calculated and the object resulting in the shortest distance is then considered the predicted intention of the user, i.e., object \(A\). Additionally, only objects facing the palm of the user's hand are considered during the intent prediction and any objects facing the back of the hand are ignored, such as object \(C\).  
Figure 3: Overview of the pipeline for the computer vision module. An RGB-D camera (Intel RealSense D415) is mounted at the shoulder joint of the exoskeleton and captures both RGB and depth information from the area in front of the user. The object detection relies on the RGB data where objects are detected using color thresholding. The depth information is masked based on the detected objects and then converted to a point cloud. Cylinder-like shapes are then detected in the resulting masked point cloud using an RANSAC-based algorithm. Finally, the detected cylinders are converted to grasp poses for the exoskeleton using a rule-based approach.  
An alternative to this ray-based method could have been an approach based purely on distance [26], i.e., finding the nearest object. The obvious drawback of such an approach is that it would only consider the nearest object, i.e., \(B\), and disregard all other objects in the scene. Furthermore, using solely the shortest distance may also cause the intent prediction to gravitate towards the nearest object to the point where it might become difficult for the user to break away from that object. This effect is less pronounced with the ray-based method as only slight adjustments of the exoskeleton are required to point it towards the intended target. Finally, the ray-based approach for intent prediction was found to be quite stable when moving towards an object. This behavior avoids problematic scenarios where the system might suddenly change the predicted intention while moving towards an object; something that could easily occur if using a nearest object approach for the intention prediction.