esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MODI7A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MODI7A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m\({}^{-2}\) d\({}^{-1}\)). The functional phenology was also captured by the MODI7A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62\({}^{\circ}\)-66\({}^{\circ}\) N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region.  
GPP; carbon cycle; arctic; phenology; photosynthesis Article
# Real-Time Detection for Cache Side Channel Attack using Performance Counter Monitor
## 2 Related Work  
There were several previous works regarding the detection of cache side channel attacks. [PERSON] et al. proposed three methods to detect the Flush+Reload attack by using the _perf_ command and machine learning techniques [9]. The first method was based on correlation and the other two use machine learning techniques. The method proposed in the article had the advantage of being able to detect and prevent attacks in a relatively short time. However, they only used a cache miss counter; hence they could not detect the Flush + Flush attacks, which did not affect the cache misses [2]. [PERSON] et al. [10] used 12 hardware events to train 12 machine learning models. They could detect the Prime+Probe attack using real-time data by selecting the best four models from 12 machine learning models. Based on the relatively large number of 12 hardware events, fast and accurate attack detection was possible with a low overhead. However, this had the same limitation as [PERSON] et al. [9] since they were unable to detect the Flush+Flush attack. [PERSON] et al. [11] proposed an approach to detect the cross-Virtual Machine (VM) cache-based side channel attacks using the hardware granular information provided by the Hardware Performance Counters (HPCs) and the Intel Cache Monitoring Technology (CMT) according to the Gaussian anomaly detection method. This study had the advantage of a high detection rate because the performance overhead was 2% for the computing platform. However, their method determined which attacks were based on cache misses that were affected by high false positive alarms. Therefore, our technique, which does not rely on cache misses, would likely be more appropriate. [PERSON] et al. [12] proposed a method for detecting multiple attacks (e.g., Flush+Reload, Prime+Probe, Rowhammer [13], Spectre, Meltdown, Zombieload) using the Intel PCM and deep learning. In this investigation, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms, which are deep leaning models, were trained with performance counters, such as L1_INST_MISS, L1_INST_HIT, and LLC_MISS, to predict the next counter value. The attack was detected through a difference between the predicted counter value and the actual counter value. This method differs from the proposed method, which is capable of detecting the attacks by predicting the counter values. Unlike previous methods, it has the advantage of detecting multiple attacks. However, the reliability of the proposed method is higher since it trains machine learning models with five performance counters in comparison to the method proposed by [PERSON] et al. [12] Furthermore, this method requires much computation and many data for training, which uses deep learning. However, the proposed machine learning technique from this study is more suitable for real-time detection because it requires less computation and training data. Table 1 summarizes the comparison to other related works.
# Intelligent Interference Management in UAV-Based HetNets +
## 6 Simulation Results
The time complexity of the exhaustive search is exponential in the number of MBSs and UABSs and is given by:  
\[\mathcal{C}_{\text{time}} =\mathcal{O}\left(\left(\frac{1}{\Delta_{\alpha}+1}\right)^{N_{ \text{mbs}}}\times(1/\Delta_{\beta}+1)^{N_{\text{mbs}}}\times\left(\frac{\rho_{ \text{high}}-\rho_{\text{low}}}{\Delta_{\rho}}\right)^{N_{\text{mbs}}}\right.\] \[\left.\times\left(\frac{\rho_{\text{high}}^{\prime}-\rho_{\text{ low}}^{\prime}}{\Delta_{\rho}^{\prime}}\right)^{N_{\text{ubs}}}\times\left(\frac{l^{2}}{ \Delta_{x}}\Delta_{y}\right)^{N_{\text{ubs}}}\right)\,. \tag{18}\]  
Unlike the brute force algorithm, the greedy algorithm has linear time complexity, expressed as:  
\[\mathcal{O}(N_{\text{mbs}})+\mathcal{O}(N_{\text{uabs}})\,. \tag{19}\]  
Figure 7: SE results for an MBS density of 8 MBS per \(\text{km}^{2}\), which corresponds to two MBSs in the simulation area. (**a**) CDF of median SE. DDQN achieves 93.46% of the optimal when \(\epsilon=0.5\) and 79.73% of the optimal when \(\epsilon=0.05\). (**b**) CDF of mean SE. DDQN achieves 95.83% of the optimal when \(\epsilon=0.5\) and 82.89% of the optimal when \(\epsilon=0.05\). The time complexity of the DDQN approach during training is a function of the number of training steps,  
\[\mathcal{O}(N_{\text{train}})\, \tag{20}\]  
where \(N_{\text{train}}\) is the number of training steps. However, after training is complete and the resultant neural network is used to optimize UAV HetNet parameters, the time complexity is the number of steps in the evaluation phase:  
\[\mathcal{O}(N_{\text{eval}}), \tag{21}\]  
where \(N_{\text{eval}}\) is the number of evaluation steps. To optimze all parameters for a realization consisting of two MBSs and one UABS, considering the range of parameters given in Table 5, the brute force algorithm searches over \(409,600\) parameter combinations, the greedy approach evaluates 464 parameter combinations, while the searches over 500 parameter combinations, as per the policy learned after training.
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 3 Methodology
### Multiclass Classification Model  
In this paper, a multiclass classification model was proposed to address the rank and wormhole attacks in an RPL-based IoT network. The light gradient boosting machine model is leveraged to perform multiclass classification for classifying benign, rank, and wormhole target classes in the dataset. The model was developed for binary classification by Microsoft in 2016 as a lightweight variant of the gradient boosting method with underlying one-side sampling and exclusive feature bundling methods. The one-side sampling method called GOSS establishes and maintains the precise information gain by keeping the high gradient data instances a high priority and dropping the limited gradient data instances [32]. Equation (1) presents the mathematical form of the GOSS function. \(\hat{V}_{j}\) presents the approximate variance gain over \(A\cup B\) subset which is presented by \(A_{I}\), \(A_{r}\), \(B_{l}\), and \(B_{r}\) in the equation while \(1-a/b\) indicates the normalization coefficient for the gradient sum. The \(\hat{V}_{j}(d)\) is used to find the optimal split point for smart sampling of the dataset and for improving the model accuracy by focusing on the instances with large gradients. This also helps in reducing the complexity.  
\[\hat{V}_{j}(d)=\frac{1}{n}((\sum_{x_{i}\in A_{1}}g_{i}+\frac{1-a}{b}\ \sum_{x_{i}\in B_{l}}g_{i})\hat{ \gamma}\ 2\ /n_{l}^{j}(d)+(\sum_{x_{i}\in A_{r}}g_{i}+\frac{1-a}{b}\ \sum_{x_{i}\in B_{l}}g_{i})\hat{ \gamma}2/n_{r}^{j}(d) \tag{1}\]  
Secondly, the exclusive feature bundling method called EFB helps in minimizing the complexity by wrapping the exclusive features into a single feature. The histogram-based algorithms underlying the model help in improving the training time as well as they use less memory which is advantageous for LLNs such as IoT [33]. Therefore, this model is leveraged to perform multiclass classification followed by hyperparameter optimization and fine tuning.
# Buffer Allocation via Bottleneck-Based Variable Neighborhood Search
## 2 Problem Statement  
In this study, the following assumptions are considered for production lines:  
* Jobs enter a server or buffer in a production line at an external arrival rate according to a Poisson process that is widely used in the analysis of production lines [21].
* Jobs leave a server or buffer at a service rate with an exponential distribution that is widely used in the analysis of production lines [21].
* Both servers and buffers in a production line have their own service rates by which the working rate of the servers and buffers can be described.
* The capacities of the server and the buffer are set as \"1\" to simplify modelling; note that the capacity can be set as any value without affecting the effectiveness of the proposed approach. If the capacity of a server or a buffer is one, it implies that a server or buffer can deal with or contain only one job at a time.
* Although both blocking mechanisms--blocking before service and blocking after services--can be applied to the proposed approach to schedule job movement when blocking occurs in production lines [21], in the study, the former is adopted.
* Designed buffers that are allocated by the proposed approach have service rates that are equal to the highest service rate in an initial production line.  
In [22], the goal of the dual buffer allocation problem is to maximize throughput of a production line subject to total designed buffer size.  
In this study, computation time is also considered, and therefore, the efficient buffer allocation problem in this study is to find a buffer configuration vector for a production line to maximize throughput subject to available computation time and total designed buffer size decided by the available computation time. The problem can be described as  
\[\text{Find }B_{sol}=\left(b_{h,i},\cdots\right) \tag{1}\]  
\[\text{maximize }T_{sol} \tag{2}\]  
\[\text{subject to }t_{sol}\leq t_{available}, \tag{3}\]  
\[N_{sol}\leq N_{max} \tag{4}\]  
where \(B_{sol}\) represents the buffer configuration vector which is the combination of designed buffer size in the solution, \(i\) represents a node that describes a server or buffer, \(b_{h,i}\) represents the designed buffer size allocated between nodes \(h\) and its following node \(i\), \(T_{sol}\) is the obtained throughput in the solution, \(t_{sol}\) is the computation time of buffer allocation and \(t_{available}\) is the available computation time.  
Furthermore, \(N_{sol}\) is the designed buffer size and \(N_{max}\) is the maximum designed buffer size, which is decided by \(t_{available}\). The term \(t_{available}\) typically lies within 8 h for a large-scale production line and 4 h for a small-scale production line.
# Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET
## 6 Results and Discussion
### Packet Delivery Ratio (PDR)  
PDR refers to the ratio of the number of packets that were successfully received to the total number of packets sent in the network [42]. \(PDR\) was obtained by determining the ratio of total number of packets received, \(P_{r}\), to the total number of packets sent, \(P_{s}\), in the network, as shown in (1):  
\[PDR\ =\ \frac{P_{r}}{P_{s}}\ \times\ 100\%. \tag{1}\]  
Figure 12 illustrates the impacts of PDR when the attacker or malicious node was non-existent. Figure 12 shows that the proposed solution incurred higher PDR, with a difference of 8.0%, 10.0%, and 2.4% as opposed to ASC, LAKAP, and HEPPA, respectively. In terms of statistical analysis, the proposed solution resulted in an average PDR value of 0.94, standard deviation of 0.033483, and a confidence interval of 0.023952 with 100 nodes in the network. During execution, all solutions showed a slump in PDR value as the number of nodes increased. This is due to the communication channel becoming congested with the nodes trying to transmit data across the network, therefore resulting in packet drops.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Number of Nodes & 100 \\ Maximum Vehicle Speed & 33 m/s \\ Maximum Acceleration & 2.6 m/s\({}^{2}\) \\ Maximum Deceleration & 4.5 m/s\({}^{2}\) \\ Vehicle Length & 5 m \\ Vehicle Width & 3.5 m \\ Number of RSUs & 10 \\ RSU Coverage & 1 km \\ Driver Imperfection & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters set for the SUMO simulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Sim-Time-Limit & 6000 s \\ Mac.queuelength & 5 \\ Mac.maxTxAttempts & 14 \\ Mac.bitrate & 11 Mbps \\ Mac.txpower & 100 mW \\ Mac.contentionWindow & 20 \\ Mac.slotduration & 0.04 s \\ Phy. Sensitivity & \(-80\) dBm \\ UpdateInterval & 0.1 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters set for the OMNeT++ simulation.  
However, the PDR of the proposed solution was higher with a much steadier decrease as the number of nodes were increased because the proposed solution involved a lower intense computational operation in order to execute the algorithm. This caused a decrease in the delay of packet transmission, resulting in higher PDR. Similarly, it can be seen from Figure 13 that the proposed solution maintained PDR at a tolerable level with a difference of 21.5%, 17.7%, and 17.5% as opposed to ASC, LAKAP, and HEPPA, respectively, which showed rapid slump when the number of nodes was increased from 80 to 100 when a denial of service (DoS) attack was executed in the network. In terms of statistical analysis, the proposed solution resulted in an average PDR of 0.75, standard deviation of 0.027264, and a confidence interval of 0.019504 with 100 nodes in the network. Based on Figure 13, the PDRs of the benchmark protocols were much lower than the proposed solution, because the algorithms were not able to properly protect the network from the DoS attack, resulting in a higher loss of packet.
# A Review: Using Multiple Templates for Molecular Imprinted Polymer: Is It Good?
## 3 A Review of MT-MIPs That Have Been Created
### Template
#### 3.1.3 Proteins  
Researchers have also used proteins as templates, namely 178- oestradiol, oestriol, and diethylstilbestrol [66]. The latter is an endocrine-disrupting compound (EDC) that can interfere with the regulatory function of wildlife and humans by mimicking or antagonising endogenous hormones [141; 142; 143]. EDCs can accumulate in the environment and food chains. This is due to pollution from cosmetic products, contraceptives, hormone replacement therapy, and rapid human population growth [144; 145; 146]. Manifestations of these effects include a decrease in the quality and quantity of human sperm, an increased risk of prostate and breast cancer, and feminisation in marine life [147; 148; 149; 150]. Another study also synthesised MT-MIP using protein templates in the form of bovine haemoglobin and bovine serum albumin [67].  
Both studies were carried out using solvothermal polymerisation with APTES and PTMOS as functional monomers and produced good Qmax and IF values (Table 6). In addition, the formed MT-MIPs had good reusability. After six absorption cycles, Qmax remained >90% of the initially formed MT-MIP value [66]. This indicates that the use of multiple templates in MT-MIP does not make the MIP formed less sensitive or selective. This also makes MT-MIPs superior to ST-MIPs because they do not require separate MIP synthesis for each template.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 5 Evaluation
### Higgs Dataset  
#### 5.1.1 Analysis with all Features  
The results for the decision tree classifier are presented in Table 1. We can observe that, as expected, the metrics were getting better, but also the resulting tree was getting bigger and more complex as it had more nodes and paths, and it took more time to be evaluated as the subset size grows. However, it is worth noting that the relation between the dataset size and computation time was not linear. For instance, for a dataset 20 times bigger, as it happens from 5K rows to 100K rows, we had to spend less than twice the time for the small dataset, whereas for a dataset twice the size of the original, the computation time was almost the same.  
In the case of random forest, we can observe from the entries of Table 2 that, like the decision tree, the results were better as the dataset size grew. Additionally, the number of the trees in the forest were approximately the same in almost every examined case. The sole exception was when the dataset comprised of 200K rows, in which case the forest had less trees and also the trees themselves had fewer nodes.  
In the case of the binomial logistic regression of Table 3, since there was no optimization done, we can observe that the computation time was high. In addition, as expected, the metrics were approximately proportional to the dataset size.  
In following, in Table 4, the results for the gradient boosted trees classifier are presented. Same as before, we observe that the bigger the dataset the better the results were, as well as the complexity of the returned tree.  
Finally, the multilayer perceptron classifier is presented in Table 5, where the bigger the dataset, the more accurate classifications were performed. In terms of complexity of the neuron network, we can observe that its complexity did not scale that much as the dataset size was getting bigger, although the smallest dataset returned the most hidden layers, without showing high values at the metrics; a percentage of 60.6% in \(F1\) metric was the second lowest, after logistic regression.  
To sum up the results, the gradient-boosted trees classifier showed the best performance, achieving a 72.5% score of the \(F1\) metric, but it also had the highest training time. The binomial logistic regressor showed the poorest results, with a highest score of 64.8% in the \(F1\) metric, but it was the fastest. It should be though noted that, in terms of computational time, it is not a fair comparison since in all the other classifiers, an optimization regarding a parameter was performed. Regarding the irregularities in the results, in the sense that smaller datasets gave better results than the ones containing more entries, this is probably due to some overfitting procedures, caused by the fact that we randomly split the dataset in a certain amount of rows.
## 3 Time-Efficient Method to Determine an Optimal Scaling Factor and the Encoder Resolution  
The most critical parameters of the existing FXECAP-L algorithm are the scaling factor and the encoder resolution since the convergence can be affected by the designer choosing a wrong value. To date, there is no established method to determine either the number of bits that are used to encode the error or scaling factor in ANC applications. The following section presents a new time-efficient method to dynamically adjust such parameters in order to optimize the designing time.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 4 Signal Identification
### Classification Metrics  
To assess the performance of the proposed technique, we need to define the three metrics mainly used for classification problems, which are precision (\(\Pi\)), recall (\(\Psi\)) and \(F_{1}\)-score. The precision gives an idea of how many of the results determined as positive are actually positive. The recall is a measure denoting how many true positives are correctly identified. The \(F_{1}\)-score is an overall measure of the accuracy of the classifier and represents the harmonic average of precision and recall. These metrics are given by [21]:  
\[\Pi=\frac{\xi}{\xi+v},\quad\Psi=\frac{\xi}{\xi+\mu},\quad F_{1}\text{-score}= 2\times\frac{\Pi\times\Psi}{\Pi+\Psi}, \tag{18}\]  
where \(\xi\), \(v\) and \(\mu\) denote the numbers of true positives, false positives and false negatives, respectively.  
In addition, we define the accuracy \(P\) as the measure of how well accurate recognition can be performed by the classifier. It is given by:  
\[P=P(\hat{\chi}_{l}=\chi_{l}),\quad l=0,1,2, \tag{19}\]where \(\chi_{l}\) and \(\hat{\chi}_{l}\) denote the label arrays of the received and the predicted signals, respectively. While the index \(l=0,1,2\) represents the label of the classes ITS-G5, LTE-V2X and NR-V2X, respectively.
## 4 Teaching Experiment
### Statistical Methodologies  
This study used four main statistical methods to investigate the learning effectiveness, learning motivation, cognitive load, and system satisfaction:  
* Analysis of Covariance (ANCOVA): This method adjusts for potential confounding variables when comparing group outcomes. In this study, it accounts for initial performance differences, ensuring a fair assessment of VR360's impact on learning.
* Paired Sample \(t\)-Test: this method compares two related measurements, like pre- and post-test scores in the same group, to assess significant changes over time.
* Independent Sample \(t\)-Test: This test evaluates the difference between the averages of two independent groups, such as experimental and control groups, to see if one method significantly outperformed the other. It produces a t-value and \(p\)-value to show if observed differences are statistically meaningful.  
Figure 12: The experimental group (**Right**: using the VR360 ecological system) and the control group (**Left**: using PowerPoint and instructional videos).  
* Descriptive Statistics: This approach summarizes central data points like means and standard deviations, providing an overview of score patterns and variability across groups. This combination clarifies overall trends and validates claims about VR360's effectiveness in enhancing learning outcomes.  
A brief overview of the statistical terms is given below. These terms help determine if findings represent genuine effects rather than random variation.  
* t-value: In a \(t\)-test, the t-value measures the difference between two group means, adjusting for data variability. A higher t suggests a stronger difference.
* \(p\)-value: The \(p\)-value shows the likelihood of observed results occurring by chance. If \(p<0.05\), the results are statistically significant, meaning they likely represent a real effect.
* Significance: when results are \"statistically significant\" (\(p<0.05\)), they are likely not due to random chance.
* Standard Deviation (SD): SD measures data spread around the mean. A smaller SD indicates less variability in the data set.
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 5 Experimental Results  
In this section, we present the experimental results evaluating the effectiveness of our pedestrian-crossing detection system under diverse environmental conditions. We first assess the baseline performance using real images, identifying significant performance variations across different scenarios. Subsequently, we evaluate the realism of the CycleGAN-generated images via the Frechet Inception Distance (FID) metric, confirming their suitability for dataset augmentation. Lastly, we demonstrate the improvement in detection accuracy achieved by augmenting the training dataset with CycleGAN-generated images, highlighting the model's enhanced generalization and robustness.