esa-sceva/satcom-chunk-collection huggingface dataset:
# FPE-Transformer: A Feature Positional Encoding-Based Transformer Model for Attack Detection
## 2 Literature Review
### Transformer Models for Attack Detection
Additionally, [PERSON] and [PERSON] [58] presented an improved Transformer-based attack detection model that addresses challenges such as long training times, inaccurate detection of overlapping classes, and low multi-class classification performance in existing models. By updating the Transformer's positional encoding module, their model achieved higher accuracy than CNN-Transformer models. [PERSON] et al. [59] proposed the IDS-INT system, a Transformer-based transfer learning model, to tackle the complexities of network traffic features and imbalanced datasets. Table 2 summarizes these Transformer models applied in attack detection. Transformer-based attack detection methods offer various advantages by combining the strengths of different models. CNN-Transformer models combine CNNs' local feature extraction capabilities with the Transformer's capacity to understand the global context. They are particularly effective on large, complex datasets. However, LSTM-Transformer models are preferred for cases where CNNs may cause information loss in time series data. While LSTMs capture serial dependencies in time series data, Transformer analyzes these relationships more deeply with the attention mechanism. CNN-LSTM-Transformer models provide a broader perspective by capturing both local and time-based features, but these models have high computational costs and training time. GAN-Transformer models stand out to address data imbalance issues. GANs increase minority class examples, while Transformers improve input-output relationships with an attention mechanism. ViT models exhibit high performance in extracting global features and processing image data in image-based analysis. However, ViT models require large datasets and have high computational costs. Attention-based Transformer models, on the other hand, fully utilize the power of the attention mechanism by better capturing complex relationships and long-term dependencies. In addition, other Transformer models, such as the more specialized Denseformer, provide more optimized results in capturing data correlations. While previous research focuses on attention mechanisms and hybrid model architectures, FPE-Transformer takes a different path from these approaches by offering an innovative feature position encoder module.
## 3 Microfluidics Methods and Devices for Evaluation of RBC Deformability  
Microfluidic devices, which manipulate tiny fluid volumes in capillary-sized channels, allow for precise control over the flow conditions and enable detailed analysis of individual red blood cells (RBCs). RBC deformability is crucial for passing through narrow capillariesand is influenced by factors like membrane stiffness, cytoskeletal structure, cytoplasmic viscosity, and diseases such as sickle cell disease, malaria, diabetes, and sepsis. Studying RBC perfusion through microcapillaries offers a direct way to assess the deformability of the RBCs. As RBCs deform significantly while passing through these narrow vessels, their deformability is influenced by factors such as the membrane-cytoskeleton structure, surface-to-volume ratio, and hemoglobin content [31]. Some of the devices and methods developed for microfluidic analyses of red blood cell deformability are shown in Table 1.  
[PERSON] et al. [32] developed a precise fluidic control system combined with micromachined flow channels and real-time imaging to measure how individual red blood cells (erythrocytes) move through microvascular-scale environments under physiological conditions. The system provides volume and velocity data for around 1000 cells across various channel sizes, offering detailed insights into erythrocyte flow and deformation. This instrument serves as a powerful tool for studying red blood cell behavior at the single-cell level in microvascular conditions. [PERSON] et al. (2014) [16] developed a microfluidic system that mimics microcirculation to study red blood cell (RBC) behavior, focusing on deformation and relaxation in capillary-like channels. Using high-resolution time-lapse imaging and automated analysis, they assessed the way RBCs deform and return to their original shapes. Their findings
## 5 Experiments
### Evaluation Metrics  
We employ the following three popular metrics to assess the performance of various methods.  
1. **Recall@K** measures the proportion of ground truth locations successfully retrieved among the top-K predictions. For a given test instance, \(i\), let \(y_{i}\) denote the ground truth location, and let \(\text{list}_{i}^{K}\) denote the list of top-K predicted locations. Recall@K is defined as in Equation (29). \[\text{Recall@K}=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}(y_{i}\in\text{list}_{i}^{K }),\] (29) where \(N\) is the total number of test instances, and \(\mathbb{I}(\cdot)\) is the indicator function that returns 1 if the condition is true and 0 otherwise. This Recall@K metric emphasizes whether the true next location is included in the top-K list, regardless of its exact position in the list.
2. **The normalized discounted cumulative gain (NDCG@K)** measures a position-aware metric that assesses the quality of the ranking by considering the position of the relevant location prediction in the predicted list. It penalizes relevant items that appear lower in the ranked list. For each test instance, \(i\), the DCG (discounted cumulative gain) at rank \(K\) is computed as in Equation (30). \[\text{DCG@K}_{i}=\sum_{j=1}^{K}\mathbb{I}(r_{ij}=y_{i})\frac{1}{\log_{2}(j+1)},\] (30) where \(r_{ij}\) denotes the location prediction ranked at position \(j\) in the top-K predicted list \(\text{list}_{i}^{K}\). The ideal DCG (IDCG) corresponds to the DCG when the ground truth location prediction appears at the top position as in Equation (31). \[\text{IDCG@K}_{i}=1/\log_{2}(2)=1,\] (31) and thus, the NDCG@K is defined as in Equation (32). \[\text{NDCG@K}=\frac{1}{N}\sum_{i=1}^{N}\frac{\text{DCG@K}_{i}}{\text{IDCG@K }_{i}},\] (32)  
Figure 5: Number distribution of locations. (**a**) NYC dataset. (**b**) TKY dataset.  
this NDCG@K metric not only considers whether the true location prediction is present in the predicted list but also rewards higher ranks more significantly.
3. **The mean reciprocal rank (MRR)** evaluates the average reciprocal rank of the ground truth in the prediction list, and its calculation is as in Equation (33). \[\text{MRR}=\frac{1}{N}\sum_{i=1}^{N}\frac{1}{\text{rank}_{i}},\] (33) where \(N\) is the number of queries, and \(\text{rank}_{i}\) represents the rank position of the ground truth for the \(i\)-th query. To ensure statistical reliability and fairness, each experiment is repeated 10 times, and we report the average values of Recall@K and NDCG@K and MRR. We evaluate the model performance at two commonly \(K\in\{5,10\}\).
## 3 Theoretical Background
### Baseline Models
#### 3.1.2 Ensemble of Regression Chains  
This method is similar to the previous one in that it builds a classifier for each time step of the output, but the difference now is that the inputs differ for each of these classifiers. Namely, this method considers as input to each regressor, in addition to the input parameters \(x\), some previous time steps of the output curve [24]. Although we could consider all previous time steps for predicting, we implement a modified scheme in which we consider a window of a limited number of previous targets instead of all the previous targets. Moreover, for each target, the best model is selected in terms of some evaluation metrics (R-squared and Mean Squared Error (MSE)) out of different combinations of the four input parameters and the previously predicted targets within the proposed window. In mathematical terms, we have that each of the \(M\) regressors solves the following problem:  
\[\hat{y}_{i}^{j}=f\Big{(}x_{i},y_{i}^{j-L},y_{i}^{j-L+1},...,y_{i}^{j-1}\Big{)},\quad j\in\{1,2,...,M\} \tag{2}\]  
where \(f\) represents the chosen regressor and \(L\) is the window size used. Again, the total output curve is estimated by a global model created by stacking each of the individual predictions: \(\hat{y}_{i}=\left[\hat{y}_{1}^{1},\hat{y}_{2}^{2},...,\hat{y}_{M}^{M}\right]\).  
As mentioned, both SST and ERC build an independent regression model for each time step in the output curve \(y\), but we know that there is a significant correlation between predicted outputs, as can be seen in Figure 2. While the SST method does not account for this correlation, the ERC does by taking as an additional input a window of previous outputs.
## 3 Energy Efficient Schemes for Green C-IoT
### Efficient Random Access and Barring Mechanisms  
In the presence of massive IoT connectivity, the issue of random access is more challenging than in traditional cellular networks. In order to deal with this problem, an efficient random access procedure is strongly demanded [16]. Note that there is a tradeoff between the access attempt and successful access probability. To be more specific, generally, if there are more access attempts for a certain node, the successful access probability of this node will increase. However, more access attempts may lead to more collisions, which will damage the performance of the successful access probability to other nodes and lead to more power consumption. Therefore, the mutually incompatible relationship among them should be considered in the C-IoT deployment.  
Additionally, when the network is loaded heavily, the sensed information for time-tolerant applications can be cached in devices [48], in which case the access attempts can be decreased and less power will be spent in the meantime. Then, in light traffic periods, the gathered data will be transmitted and processed. Moreover, if the current network is in a heavily loaded state, the extended access barring (EAB) strategy can also be employed [56]. Under this strategy, eNBs will decline some access requirements of MTC devices, which are assigned with lower priority or looser delay requirements.
## 6 System Evaluation
### Latency  
The first comparison benchmark was latency. Figure 24 illustrates the query transactions, where the blue bars represent [PERSON] et al.'s study, and the orange bars represent this study. The latency of the query transaction was less than one millisecond for all rounds. In contrast, in the other study, the first round was only 1 s less, and it increased as the transactions increased, with approximately 1 s for each round.  
On the other hand, Figure 25 illustrates the open transactions. Our study demonstrated less than 1 s in the first round, and this slightly increased by less than a second as long as the transactions increased, reaching 3.43 in the fifth round. In [PERSON] et al.'s study, the first two rounds demonstrated more than 2.50 s, and this increased until reaching 7.78 in the fifth round. In summary, our study's overall open and query latency transactions were lower than those of [PERSON] et al.'s study for all rounds.
## 2 Methods and Process Procedures
### Cloud and Cloud Shadow Identification
#### 2.3.3 Cloud Shadow Pixel Identification  
Since cloud shadows alter the pixel value of near infrared (NIR) band and shortwave infrared band 1 (SWIR1) more significantly than other bands, we applied the average of the two bands (NIR and SWIR1) in Equation (1) for cloud shadow pixel identification. Similar to the identification of cloudy pixels, the difference of the digital number of the synthetic and the observation images was used to screen cloud shadow pixels as follows:  
\[dDN_{cs}(i,j)=(\sum DN_{o}(i,j)-\sum DN_{s}(i,j))/2.0 \tag{6}\]  
where \(\sum\) is the summation on NIR and SWIR1 of a pixel \((i,j)\) of a Sentinel-2 image, and its corresponding synthetic Sentinel-2 image.  
In comparison to a cloud-free pixel, according to Equation (6), a cloud shadow pixel has a large negative \(dDN_{cs}\) as clouds block the incoming radiance on cloud shadow pixels. Hence, to identify cloud shadow pixels, we have:  
\[dDN_{cs}(i,j)=\left\{\begin{array}{ll}\leq T_{cs}&\text{\emph{a cloud shadow pixel}}\\ >T_{cs}&\text{\emph{a normal pixel}}\end{array}\right. \tag{7}\]  
where \(T_{cs}\) is the threshold to be determined for distinguishing cloud shadow pixels from cloud-free ones.  
If we plotted a distribution of sorted \(dDN_{cs}\) with its accumulated pixel number of a cloud contaminated image (Figure 3), it would be similar to the distribution of the sorted \(dDN_{c}\) in Figure 2. Hence, we determined the threshold \(T_{cs}\) by using the same approach for the threshold identification for cloudy pixels. The threshold \(T_{cs}\) can be pinpointed visually, as shown in Figure 3 or by the mathematic method described above for cloud threshold \(T_{c}\) determination. The only difference was that the comparison of the curve change rate was from the middle of the graph towards the left side of the graph and \(C=3\) was reasonable for cloud shadow identification according to our experiment.  
Figure 3: A typical sorted \(dDN_{cs}\) and its accumulative pixel number distribution of a cloud contaminated image.
## 3 Design of the Mechanical Power Circulation Test Rig
### Design of gearboxes Used in the Test Rig  
The gear ratios of auxiliary gearbox #1 (Figure 7), auxiliary gearbox #2 (Figure 8), and the torqued gearbox (Figure 9) were selected to meet the rated rotational speed required by the test WTGB. Furthermore, the optimal size of the entire gearbox was determined by considering the size of the test gearbox. When the rotation speed of the electric motor was 1000 rpm, auxiliary gearbox #1's shafts 1 and 2 rotated at 1000 rpm and shaft 3 rotated at 2000 rpm. Auxiliary gearbox #2's shaft 4 rotated at 285.714 rpm, shaft 5 rotated at 1000 rpm, and shaft 6 rotated at 2000 rpm. The torqued gearbox's shaft 7 rotated at 1000 rpm and shaft 8 rotated at 285.714 rpm. Therefore, the design was made such that the test gearbox could be connected to shafts 2 and 5 or shafts 3 and 6 of the auxiliary gearboxes according to the rotational speed of the high-speed shaft of the test gearbox.  
\begin{table}
\begin{tabular}{c c} \hline
**Classification** & **Specification of Test Gearbox** \\ \hline \
\begin{tabular}{c} gearbox size \\ Max. power \\ Max. torque at high-speed shaft \\ Max. speed at high-speed shaft \\ \end{tabular} &
\begin{tabular}{c} Diameter: 4.3 m, Length: 3.3 m \\ 5800 kW \\ 50.2 kN-m at 1100 rpm \\ 2200 rpm at 25.1 kN-m \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of gearboxes that can be tested.  
Figure 6: Scheme of the power flow in the developed test rig.  
In the auxiliary gearbox design, if the number of gear pairs is small, the center distance of one gear pair will become too large, and the gear's pitch line velocity, weight, and volume, balance, etc. may yield uncertainties. To address this, the number of gear pairs was increased. Shaft design and bearing selection were performed based on the designed gear pair. The torque application gearbox is a planetary geartrain with five planet gears. Based on the total size of the gearbox, the gear ratio was designed to be 3.5.  
The ISO standard was used to assess the gear strength [9] and bearing life [10]. Detailed results are presented in Appendix B. In the results, gear pairs with the lowest safety factor were GP05 and GP09 of the auxiliary gearboxes (1.3212). The bearing with the shortest life was BRG32 of the torque application gearbox (30,683 h). The designed gear pairs and selected bearings satisfied the design criteria for a safety factor of at least 1.2 and a bearing life of 30,000 h, respectively. Therefore, the three gearboxes designed for the test rig were deemed suitable for testing WTGBs of 5.8 MW or less.
## 4 Creep Deformation Calculations and Predictions for Jinping Diversion Tunnels at Different Depths
### The Numerical Calculations and Results
Figure 8: Principal stress contours around the tunnel after excavation at different depths (unit: MPa). Figure 9: Strain contours in the engineering disturbance zone after excavation at different depths. Further calculations of the time-dependent deformation characteristics in the disturbance zone over 20 years post-excavation are presented in Figure 10. The results show that the strain of the surrounding rock increases continuously during the initial period after excavation. For the 1400 m section, rapid deformation growth occurs within the first year, followed by gradual stabilization. In contrast, the 2400 m section maintains faster deformation growth for two years post-excavation, with slower convergence rates and significantly higher final deformation magnitudes compared to those in the 1400 m case. Both sections stabilize three years after excavation. By analyzing the horizontal strain-time relationships of selected rock mass units and comparing them with nine years of field monitoring data (Figure 11), the numerical simulations align closely with the empirical measurements, validating the accuracy and engineering applicability of the customized model developed in this study. Additionally, the 20-year creep deformation predictions (Figure 10) reveal that creep-induced strains exceed instantaneous-excavation-induced deformations, though the tunnel remains globally stable after two decades. However, localized strength degradation during long-term operation may trigger minor creep failures, necessitating ongoing monitoring. The mechanical responses observed in the Jinping tunnels exhibit fundamental consistency  
Figure 10: The strain contours in the engineering disturbance zones of diversion tunnel sections at different depths. with those in established deep underground facilities, such as Gotthard Base Tunnel [35; 36] (at a 2500 m depth) and Sanshandao gold mine [37] (at a 1900 m depth).
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 6 Conclusions  
We addressed the problem of handover prediction in UWSNs. Due to the highly dynamic nature of UWSNs, the sensor node mobility model is different from the ground-based mobility model. In addition, the efforts to predict handover events in these networks are very limited. Thus, we proposed the use of marine data to simulate the sensor nodes in the UWSN based on data collected by the Korea Hydrographic and Oceanographic Agency. We collected 216,578 handover events. Finally, we utilized four machine learning models for prediction purposes: gradient boosting, decision tree, Gaussian naive Bayes, and KNN. While the datasets of the state-of-the-art methods [1, 5] and the utilized dataset in this work are not identical, both datasets are based on the same marine data (i.e., Korea Hydrographic and Oceanographic Agency data). The obtained prediction accuracy rates were above 95% for the proposed models, which is the highest reported accuracy for the problem of handover prediction in UWSNs. Future research directions include utilizing deep learning methods in the UWSN handover prediction task. This is because the performance of the deep-learning-based models outperform the classic machine-learning-based models, but this comes at the cost of higher training time and larger model size.  
E.E.: Conceptualization, Methodology, Software, Data curation, Writing the original draft. M.B.: Conceptualization, Writing the original draft. A.F.: Conceptualization, Methodology, Visualization, Software, Validation, Supervision, Writing the original draft. A.S.: Supervision, Validation, Writing--review & editing, Methodology. A.A.: Resources, Investigation, Writing the original draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 11: AUC-ROC curves for Exp-II.  
**Acknowledgments:** We would like to acknowledge with much appreciation Prince Sattam Bin Abdulaziz University for its ongoing support of our research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Data  
### Climate Data  
Climate data is required as input to the MOD17 algorithms that derive MODIS NPP and for estimating the carbon content in the litterfall with Equations (9) and (10). Daily records of minimum and maximum temperature, precipitation, vapor pressure deficit and incident short wave radiation are interpolated from weather station data using DAYMET, a climate interpolation model [10] adapted and validated for Austrian conditions [32, 33]. DAYMET interpolates daily precipitation and minimum and maximum temperatures from surrounding permanent climate stations. Based on the resulting climate data, daily solar radiation and vapor pressure data are calculated [34]. The current version of DAYMET [35] requires longitude, latitude, elevation, slope, aspect and the horizon angle facing east and west for each given plot. The meteorological data for running DAYMET were provided by the Austrian National Weather Center (ZAMG) in Vienna and include daily weather records from 250 stations across Austria since 1961. For our analysis, we generate daily weather data at a 1 \(\times\) 1 km grid across the country.  
For the MODIS NPP calculations we also use two additional climate data sets called \"GMAO\" and \"NCEP2\", which are described in the next chapter.