esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Power Control Strategies for Convergecast  
Many researchers have investigated the effect of power in enhancing the network capacity, because the transmission range depends on power. [PERSON] _et al._[19] considered finding the maximum lifetime for the shortest path aggregation tree. They converted the problem to the load balancing scheme at each level of the fat tree, and they proved that the problem is solved by the min-cost max-flow approach in polynomial time. They validated that their approach is optimal if the produced shortest path tree is optimal, and the performance is better than the random scheme by simulations. [PERSON] _et al._[20] compared fixed-range and variable-range transmission power control and demonstrated that variable-range transmission power control performs better than fixed-range transmission power control.  
[PERSON] _et al._[21] measured several metrics, such as throughput, delay and energy consumption, and demonstrated the effect of power control on variable parameters in contention-based wireless _ad hoc_ networks. [PERSON] _et al._[22] developed two algorithms to solve multiple access problems. One is a simple scheduling algorithm to get an admissible set of users, and the other is a power control algorithm to satisfy their transmission. They applied these two algorithms to two types of networks, TDMA and TDMA/CDMA wireless _ad hoc_ networks. [PERSON] and [PERSON] [23] studied the trade-off between the scheduling time and power consumption. They developed a heuristic algorithm and proved that their algorithm consumes a maximum of _O(nlogn)_ times the minimum power. [PERSON] and [PERSON] [24] studied the packet loss problem originating from the congestion and collision during convergecast in WSNs. They showed the performance for the new protocols in comparison with other existing convergecast protocols and the trade-off among reliability, latency, throughput and power consumption.
# Chipless Radio Frequency Identification (RFID) Sensor for Angular Rotation Monitoring
## 1 Introduction
More specifically, two circular chains of rectangular SRRs are etched in the layer applied to the rotor, whereas a CPW transmission line loaded with two pairs of SRRs is placed on the stator, (with a small airgap in between the two layers). The rotor motion modulates the amplitude of the carrier and thus the rotation speed can be recovered from the analysis of the signal, with an envelope detector. In this work, a novel chipless RFID sensor able to monitor the angular rotation by exploiting the cross polar response of the tag, will be presented. This solution offers some interesting advantages. First of all, the desired information is encoded in the cross polar response of the tag, thus resulting in a reading robust to multipath and high-reflection scenarios, which may affect designs based on the co-polar response. In fact, if the tag is placed close to metallic objects, the co-polar response of the surrounding environment can largely exceed that of the sensor, making the reading procedure impossible. On the contrary, the cross polar component of the field, scattered by a properly designed chipless RFID tag, can be detected even if it is placed on large metallic hosting platforms [22; 37; 38]. Moreover, the response is analytically predictable with a single measurement. Therefore, the calibration of the sensor is straightforward, and it must only be done once (when the sensor is put in place) if the distance between the reader and the tag does not change. The angle span of the chipless RFID sensor can be made up to the maximum of 180 degrees since the polarization information is redundant after 180 degrees. Finally, this sensor can be read from a distance of up to 40 cm even if it is realized within a small footprint [22]. Therefore, this sensor can be used to monitor rotating objects in hostile environments, where the reader cannot be placed, or in cases where the reader and the sensor are separated by a protective glass [39].
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 9 Multidimensional Evaluation
### Average Latency for Delivered Messages
#### 9.2.2 Grey-Hole Attack  
For the PRoPHET and Maxprop algorithms, the data were very spread out which suggested that there was not much of a correlation between the proportion of grey-hole nodes and the average latency in the scenarios. This is an interesting observation as black holes slightly reduced the latency and so it would be reasonable to expect that grey holes would do so at half of the rate.  
For the Binary Spray and Wait protocol, it seems that the latency was approximately flat. This again is an interesting discovery as for black holes it was increasing at an exponential rate. This showed that the protocol was far better at dealing with grey holes rather than black holes.  
The graph shows that the Spray and Wait algorithm consistently provided a lower latency even when dealing with grey hole attacks. The spray phase meant that messages got around the network very quickly rather than the more calculated algorithms that took longer to deliver messages. It was also the only protocol that outperformed the Epidemic protocol, which was often used as a benchmark.
# Effect of Ultrasonic Treatment on Taste and Flavor Quality of Japonic Rice
## 3 Results
### GC-MS Data Analysis
#### 3.5.3 Analysis of Aroma Compounds

Table 2: Aroma characteristics and thresholds of volatile aroma compounds. The ROAVs of alcohol were low, and only n-hexanol (3.50-5.08) and 1-octen-3-ol (2.97-6.16) were detected in all the samples, and their rOAVs were greater than 1 but with slight variations, presenting fruity aromas and mushroom aromas. Geranyl acetone was detected in all samples but with low rOAVs, which slightly affected the aroma of the cooked rice before and after ultrasound treatment. 2-Pentadecanone (0.44-2.89) and indole (0.38-9.48) exhibited rOAVs ranging from below 1 to above 1. Ultrasound treatment affected these aroma compounds during the formation of the aroma of the cooked rice. Esters and phenols, in addition to the volatile chemicals listed above, were attributed to the complicated aroma of the cooked rice. The low rOAVs of esters and their decreased aroma also contributed less to the aroma of the cooked rice. Phenols are redox products and are decarboxylated by phenolic acids during heating [29]. Phenols, such as 2-methoxy-4-vinylphenol (2.39-7.01), are mainly associated with smoky aromas; it is a derivative of guaiacol and has an unpleasantly nutty, pungent, and clove odor [30]. Slight changes in ester and phenolic rOAVs slightly affected the aroma of the cooked rice after ultrasound treatment. Hence, the most dominant compounds in the aroma of the cooked rice were hexanal, heptanal, 2-pentylfuran, octanal, nonanal, trans-2-octenal, decanal, undecanal, trans-2-nonanal, trans-2-dodecenal, trans-2-decenal, trans-2,4-decadienal, 2-pentadecanone, and indole before and after ultrasound treatment. The 14 compounds mentioned above were the volatile aroma compounds that significantly influenced the aroma of the cooked rice before and after ultrasound treatment. These compounds showed similar compositions to the key aroma compounds identified in cooked rice in previous studies [21].
## 2 Materials and Methods
### Fractal and Multifractal Analysis Methods
#### 2.2.2 Multifractal Analysis Using the MFDFA Method  
MFDFA [47; 48; 49] is a method that enables the analysis of the multifractal properties of time series, such as ECG signals and, specifically, the RR interval series. While the standard DFA method assesses the global scalability of fluctuations (fractality), MFDFA extends the analysis by examining how these properties vary across different scales and moments of the fluctuations, which is the main characteristic of multifractal signals. Multifractality is a property of time series that cannot be described by a single Hurst exponent or a single fractal dimension value, as is the case with monofractal signals. The MFDFA method examines several key parameters that characterize the complex dynamics of time series. These include the following:  
* The fluctuation function \(\mathrm{Fq}(\mathrm{s})\), which describes the degree of fluctuations depending on the scale \(\mathrm{s}\), providing information about the structural instability of the time series;
* The generalized Hurst exponent (\(\mathrm{Hq}\)), which is a measure of self-similarity and long-term dependence, which varies with changes in the parameter \(\mathrm{q}\) and reveals the heterogeneity of the fractal structure of the analyzed signals;
* The multifractal scaling exponent \(\tau(\mathrm{q})\), which indicates scale dependencies and reveals the multifractal nature of the studied data at different values of parameter \(\mathrm{q}\);
* The multifractal spectrum \(\mathrm{f}(\mathrm{\alpha})\), which serves as a tool for the quantitative description of signals with a high degree of heterogeneity, providing information on the degree of non-uniformity and complexity of the signal, showing the distribution of different local dimensions in it.
## 2 Materials and Methods
### Permutation-Variable Importance  
When using the small sample dataset to classify the severity of the disease, we chose to first calculate some gait characteristics, in order to find out the characteristics that dominated the difference of different categories and improve the discrimination degree of the samples. For the measurement of the importance of variables, this study used the random forest method to evaluate the importance of features. Using this method, the aim was to identify the dominant factors that influence the different manifestations of PD at different severity levels, and to exclude irrelevant characteristics. The measurement of the importance of variables can reduce the dimension of the input sample data. On one hand, it eliminates the influence of irrelevant factors, while on the other hand, it facilitates the subsequent processing of the data. The random forest method can be used to select the characteristics that have the greatest impact on the severity level, so as to reduce the number of features in the model building and make the classifier achieve good results in training. When we use the random forest method to obtain the importance of certain characteristics in disease classification, the specific steps are as follows:  
1. For each decision tree, select the corresponding out-of-bag (OOB) data to calculate the out-of-bag data error, which is denoted as \(err1\).
2. Random noise interference is added to such characteristics of all samples of out-of-bag data, and the out-of-bag data error is calculated again, denoted as \(err2\).
3. The permutation-variable importance is obtained by Equation (3): \[PVI=\frac{\sum\limits_{i=1}^{N}err2_{i}-err1_{i}}{N}\] (3) where \(N\) is the number of decision trees in the random forests, \(err1_{i}\) is the OOB error of the \(i\)-th decision tree for the feature to be evaluated, and \(err2_{i}\) is the OOB error of the \(i\)-th decision tree for an assessment feature after noise interference is added to the feature.  
When the random noise is added, the accuracy of data outside the bag will decrease. When this feature is of high importance, the value of OOB error \(err2_{i}\) will increase significantly, and the calculated measurement value will increase, indicating that this feature has a great impact on the prediction results of disease grade identification, and thus indicates that this feature is of high importance. In this study, we measured the importance of variables in patients with PD and normal subjects. The results of our assessment of the importance of all the features are shown in Figure 4. We also ranked the evaluation results in order of importance in the two cases, calculated the average value of importance in the two cases, and selected the characteristics that rank in the top 15 for importance.
# Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
###### Abstract  
Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distribution (OOD) data and prompt human intervention, a critical competency. While several frameworks for OOD detection have been introduced and achieved remarkable results, most state-of-the-art (SOTA) models rely on supervised learning with annotated data for their training. However, acquiring labeled data can be a demanding, time-consuming or, in some cases, an infeasible task. Consequently, unsupervised learning has gained substantial traction and has made noteworthy advancements. It empowers models to undergo training solely on unlabeled data while still achieving comparable or even superior performance compared to supervised alternatives. Among the array of unsupervised methods, contrastive learning has asserted its effectiveness in feature extraction for a variety of downstream tasks. Conversely, auto-encoders are extensively employed to acquire indispensable representations that faithfully reconstruct input data. In this study, we introduce a novel approach that amalgamates contrastive learning with auto-encoders for OOD detection using unlabeled data. Contrastive learning diligently tightens the grouping of in-distribution data while meticulously segregating OOD data, and the auto-encoder augments the feature space with increased refinement. Within this framework, data undergo implicit classification into in-distribution and OOD categories with a notable degree of precision. Our experimental findings manifest that this method surpasses most of the existing detectors reliant on unlabeled data or even labeled data. By incorporating an auto-encoder into an unsupervised learning framework and training it on the CIFAR-100 dataset, our model enhances the detection rate of unsupervised learning methods by an average of 5.8%. Moreover, it outperforms the supervised-based OOD detector by an average margin of 11%.  
Keywords:contrastive learning; auto-encoder; out-of-distribution; representation learning; unsupervised learning +
Footnote †: journal: _Applied_
## 4 Experiments and Results
### Ablation Study  
To further test our module, we performed ablation studies to evaluate the LPTM and Transformer Module as shown in Table 2. We used the OpenCV linear polar transformation implementation to compare with our Polarformer. In our experiments, we took ResNet-101 [48] and EfficientNet-B4 [49] as the CNN backbone. We used three layers of the transformer. In most cases, the Dice score of OD's Dice scores only changed by \(\pm 0.005\), so here we only report the Dice scores of OC.  
Two conclusions can be made from Table 2. (1) The polar origin detector (POD) achieved higher performance than training without it. LPTM played a crucial role in performance by converting the original images through the POD and polar converter. (2) The combination of the transformer module had a different effect on the task when  
Figure 8: Visualization of OD and OC segmentation results on RIM-ONE v3 dataset. From top to bottom:(**a**) RIM-ONE v3 images. (**b**) The ground truth. (**c**) The segmentation results of Unet. (**d**) The segmentation results of Deeplabv3 plus. (**e**) The segmentation results of AttU-Net. (**f**) The segmentation results of nnU-Net. (**g**) The segmentation results of our Polarformer.  
Figure 7: Visualization of OD and OC segmentation results on the REFUGE dataset. From top to bottom: (**a**) REFUGE images. (**b**) The ground truth. (**c**) The segmentation results of U-Net. (**d**) The segmentation results of Deeplabv3 plus. (**e**) The segmentation results of AttU-Net. (**f**) The segmentation results of nnU-Net. (**g**) The segmentation results of our Polarformer.  
compared with the combination without the module. According to the results, we the modules in our Polarformer indicate the contributions of the discriminative features.
## 3 Methodology
### Data Pre-Processing  
This study aimed to design a versatile model to predict Vp based on conventional well-logging data. Accordingly, the optimal input combination is searched considering GR, RHOB, RT, PEF, and TNP among 17 logging data types addressed in Section 2. The scope of this study is synthesizing Vp from 95 to 255 m b.s.f. to include the BSR range between 100 and 250 m b.s.f. Because the logging data interval was 0.5 ft, the number of logging data points was 1050 for every wellbore except for UBGH2-11, with 878 logging data points--its well depth (228.78 m b.s.f.) being smaller than the upper limit of this analysis (255 m b.s.f.).  
A robust scaler was used to minimize the effects of outliers on the overall training performance of a neural network. For every logging data type, logging data were normalized using the robust scaler:  
\[x^{\prime}=\frac{x-Q_{2}}{Q_{3}-Q_{1}}, \tag{8}\]  
where \(x\) and \(x^{\prime}\) are the original datum and corresponding scaled datum at an arbitrary measurement point, respectively. \(Q_{1}\), \(Q_{2}\), and \(Q_{3}\) are the first, second, and third quartiles of the logging data.  
Before determining the input combination, we calculated the coefficient of determination (\(R^{2}\)) between each input logging variable and the output logging variable (Vp) for all wellbores (Table 1). On average, the output Vp exhibited a high correlation in the descending order of TNPH, RHOB, RT, GR, and PEF. The difference between the minimum and maximum was greater than 0.3, yielding a significant variance for every input logging variable. Because it was insufficient to match the input and output logging variables one by one, the neural network model was used to capture the nonlinear relationships between multiple inputs and output.
## 4 Experimental Analysis and Results
### Implementation of Metaheuristic GA-Based Feature Selection
#### 4.6.3 Results on the Arabic Sarcasm  
The second scenario is performing experiments on the sarcasm of users' tweets about digital transformation topics in which we consider only two cases: sarcasm if sarcasm is there and not sarcasm if not. Table 10 gives the results with two different scenarios with feature selection and without feature selection. Furthermore, we have applied the proposed models for the sarcasm class, as shown in Table 11.  
The adopted classifiers are also trained on the training set of sarcasm classes. The assessment outcome for the trained models is summarized in Table 10. The numbers in bold font represent the highest F1 score results for each data split. Using a GA with different feature vectors, we classify the sarcasm task scenario combined with feature selection. Ensemble classifiers have also been employed with the unigram feature to boost the outcome. The ensemble ETC is the most successful, according to the results. The best F1-score for the sarcasm scenario is 95.00% with feature selection and 95.38% without feature selection using ETC. The best F1-score of proposed GA-based ensemble models for augmented sarcasm data is 96.52% with feature selection for the ETC model.
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 2 Dataset
### Data Acquisition  
A single-camera is mounted on top of the conveyor belt to capture the dataset. Due to environmental and industrial constraints, it is not precisely on top of the belt instead it is mounted on a pole of the conveyer. The belt operator carries out the weekly experiments and measured flow values are noted for the complete recording. All the experiments took around 30 to 35 min to complete. The primary factor is the amount of coke and limestone carried out on the belt. Hence, we tried to estimate the flow by analyzing the experimental data provided to us. Since the belt conveyor is installed in an outdoor environment, environmental factors such as weather, time of the day, and sunlight affect the quality of the videos, as shown in Figure 2. The dataset was captured using a Samsung high-resolution weatherproof camera as AVI videos over a span of twenty-four weeks. The videos have a resolution of standard definition widescreen video, which is 720 \(\times\) 576 (16:9). Each video in the dataset is between thirty-five to forty minutes long, where the actual flow rate measurement is spanned over thirty to thirty-five minutes. From a set of twenty videos, sixteen are being used to train the machine learning model and four are being used to test the trained model's performance.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.8 Genetic Algorithm  
Genetic algorithm (GA) is an evolutionary algorithm that uses natural selection and genetic operators, such as mutation and crossover, to search for optimal solutions to a problem. GA can be used to optimise system configuration or find the best recovery strategy for fault or failure. GA creates a population of candidate solutions and evaluates their fitness based on a fitness function that measures how well the solution meets the desired criteria. The fitness solutions are then selected to produce offspring through genetic operators, and the process is repeated until a satisfactory solution is found. A genetic algorithm was utilised by [37] to train long short-term memory (LSTM) models in their study. To apply the GA, the set weight matrix that represents the LSTM model of each device was extracted and transformed into a vectorial representation by concatenating all the columns of the weight matrix together into one vector. The devices share their health scores and neural network weights using the blockchain framework so that each device can use it.  
For the fitness score, the health score provided by the health-monitoring module was used, and the selection procedure in the GA was performed by selecting just the weight vectors that belonged to the devices with the highest health scores. The combination procedure in the GA was accomplished by taking the weighted means of a small random group from the selected weight vectors. Taking the weighted mean of random groups will ensure that the population of the LSTM model's weight vectors will be diverse and not converge into a single-weight vector. The mutation procedure added random noise drawn from a normal distribution. The GA implementation utilises the same blockchain infrastructure as the CIOTA framework. The utilisation of the same blockchain infrastructure as the CIOTA framework in the GA implementation signifies that the algorithm operates decentralised, eliminating the requirement for a central server for distribution. In simpler terms, the algorithm is executed locally on IoT devices within the network, with each device generating its distinctive model. The IoT devices share their health score and neural network to make the genetic algorithm distributed.GA can be used to improve system resilience and reduce the likelihood of faults or failures, with one advantage being GA's ability to search an ample space of possible solutions and find optimal solutions quickly. GA can also adapt to changes in the system over time, allowing it to optimise system performance continuously. However, a potential limitation of GA is its reliance on a well-defined fitness function, which can be difficult to specify in some situations. GA may also require significant computational resources, especially for complex or large systems.