esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Background
#### 3.1.3 Ensemble Learning
Gradient Boosting [60] is a popular ensemble learning method based on the boosting technique. It is designed to build strong models from multiple weak learners. It starts with a decision tree making an initial prediction. Later, it calculates the difference between the actual values and predicted values (residuals). In light of that data, a new decision tree is trained to predict these residuals. This new model's predictions are added to the previous predictions to increase the accuracy, and it repeats for multiple iterations. It reduces the error by optimizing a loss function, which varies due to the nature of the application. However, Gradient Boosting can be computationally expensive and may have low performance with large datasets. To overcome such limitations, there are several implementations introducing enhancements. XGBoost [61] by [PERSON] and [PERSON] provides several improvements over traditional Gradient Boosting to improve performance and computational speed. By employing enhancements such as second-order gradient optimization and L1 (lasso) and L2 (ridge) regularization to improve generalization, XGBoost has established its position as one of the most efficient machine learning algorithms. LightGBM [62] by Microsoft is a variant of Gradient Boosting that uses a histogram-based algorithm to construct trees. This speeds up construction and reduces memory usage. Traditional boosting algorithms construct trees level by level. However, LightGBM uses a leaf-wise growth strategy, extending the leaf with the highest possible loss reduction. Also, by employing techniques such as exclusive feature bundling (EFB) and gradient-based one-sided sampling (GOSS), it can outperform other Gradient Boosting algorithms [63]. CatBoost by Yandex is designed to handle categorical features effectively. This makes it a popular and effective gradient boosting algorithm in several domains, especially with mixed data types. It employs order boosting to make sure that the model is only using past data for tree construction, thereby reducing target leakage and overfitting [64]. It uses symmetric trees as the base model, improving training and inference speed by maintaining consistent feature splits[65].
## 5 Conclusions  
In the current study, the efficiency properties of a LaBr\({}_{3}\):Ce crystal were investigated under X-ray excitation with X-ray tube-voltages (50-150 kVp) and compared with previously published data for BGO, LSO, CdWO\({}_{4}\) and LaCl\({}_{3}\):Ce crystals. The maximum AE of the examined crystal was obtained at 150 kVp (60.9 EU). The values of AE of the LaBr\({}_{3}\):Ce were found to be considerably higher than those of the BGO, LSO, CdWO\({}_{4}\) and LaCl\({}_{3}\):Ce crystals in the examined energy range. These results are also considered important from the point of view of radiation protection, as the use of the crystal can reduce the dose received by the examinee. The emission spectrum of LaBr\({}_{3}\):Ce showed an exceptional match with the spectral sensitivities of many commonly employed photocathodes and silicon photomultipliers (SiPMs). In addition, the LaBr\({}_{3}\):Ce crystal exhibited excellent performance concerning its detective quantum efficiency for zero spatial-frequency. Taking into account the high AE values, the spectral compatibility with several optical-sensors, the DQE(0) values, the short decay-time, as well as the optimum X-ray tube-voltage range, which was found to be above 120 kVp, the LaBr\({}_{3}\):Ce scintillation crystal might be considered as a radiation-detector option for hybrid medical-imaging modalities, such PET/CT and SPECT/CT scanners.  
Conceptualization, C.M. and S.T.; methodology, I.V.; software, S.T.; validation, N.K., A.B. and G.F.; formal analysis, N.K.; investigation, S.T. and K.N.; resources, G.F.; data curation, I.V.; writing--original draft preparation, S.T.; writing--review and editing, I.K., N.K. and C.M.; visualization, N.K.; supervision, N.K., C.M. and I.V.; project administration, N.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Exploiting Outage and Error Probability
## 4 Simulation Results and Discussions  
In this section, the performance of the proposed protocols ACE and E-ACE are compared with other proposed protocols referred as EEDBR [3] and Cooperative ARQ (CARQ) [11]. CARQ is is an ARQ based scheme in which cooperative nodes are used to provide alternative paths for a specific source destination link. Relay nodes are selected based on the smallest distance from the destination. When the destination node receives an erroneous packet, it asks for retransmission from a cooperative node, which is selected in a closest-one-first manner from the nodes in the cooperative region. EEDBR is another routing protocol that takes depth and residual energy information in selection of next hop. It is a non cooperative routing protocol and relies on single link for data transmission.z  
In simulations, 250 nodes are randomly deployed in an area of 500 m \(\times\) 500 m. The network is homogenous and each node has initial energy of 30 J and a fixed transmission range of 100 m. The values of energy consumption are 2 W for transmission, 0.1 W for reception and 10 mW for idle sensing. Maximum allowable BER is set to 0.49 and \(m=3\). Channel bandwidth is 30 kbps. The size of data packet is 1000 bits and that of control packet is 48 bits. We assume that the nodes are anchored with wires in seawater such that the nodes can slightly move horizontally and the horizontal movement is small enough to be neglected. Four unconstrained sinks (in terms of energy) are deployed on the surface of water at an equal distance of 100 m. Underwater nodes are equipped with acoustic modems and sinks are equipped with both acoustic and radio modems. It is worth mentioning here that only for the sake of fair comparison, the simulation parameters are chosen according the existing works used for comparison.
## 3 Results and Discussion
### Cost Analysis  
The cost analysis of the HRESs has been evaluated based on all expenses associated with installing, operating, and maintaining such systems and compared to the benefits they provide. Table 5 suggests that the proposed HRES integrated with an EV charging station is economically beneficial. The HRES appears to generate electricity at a lower cost than the current grid system and has a lower overall net present cost. Additionally, the system is expected to provide substantial electricity bill savings over time. The financial performance indicators of the proposed HRES are as follows:  
* The levelized cost of energy (LCOE) of the HRES is a crucial metric used to evaluate the long-term economic viability of such a system. It represents the average cost of electricity generated by the HRES over its operational lifespan, considering factors such as initial capital investment, operating and maintenance expenses, fuel costs, and system lifetime. The estimated LCOE of the proposed HRES was USD 0.042/kWh, which is lower than the electricity price of USD 0.07/kWh in Egypt. This provides insights into the competitiveness of HRES in the energy market.
* The net present cost (NPC) of the proposed system is estimated at USD 3.0 million. This refers to the total cost of integrating the HRES, which is lower than the overall cost of the current grid system, estimated at USD 4.4 million.
* Utility bill savings are estimated at USD 67.2 k/yr, representing the annual savings on electricity bills per year.
* The net present utility bill savings are estimated at USD 2.54 million. This value likely represents the total discounted value of electricity bill savings over the lifetime of the HRES.
* The table reveals that the projected payback period for the proposed system is approximately 11.8 years. This signifies that it will take roughly 11.8 years for the total savings or profits generated by the system to offset the initial investment required for installation and operation. Following the completion of the payback period, the system is anticipated to yield net positive returns or savings.
# A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
## 2 Preliminaries
### Similarity Measures  
The process of neighborhood-based CF is characterized by defining similar users or items as neighbors and recommending items based on the rating histories of the selected neighbors. Thus, neighbor selection depends on the definition of _similarity_. The similarity measurements widely used in various fields include Pearson's Correlation Coefficient, Cosine Similarity, and Jaccard Index. Pearson's Correlation Coefficient compares the linear correlation between two variables. Cosine Similarity is a measure of the difference in orientation between two vectors, and Jaccard Index is a measure of similarity expressed as the ratio of the intersection of two sets to the size of their union. In this paper, we use Pearson's Correlation Coefficient as the primary similarity measure, which is the most widely used measure in the field of CF [24].  
The similarity between users \(a\) and \(b\) [\(Sim_{u}(a,b)\)] and that between items \(s\) and \(t\) [\(Sim_{i}(s,t)\)] are defined by Equations (1) and (2), respectively, where \(I(a,b)\) is the intersection of the items rated by users \(a\) and \(b\), and \(U(s,t)\) is the intersection of the users that rated the items \(s\) and \(t\); \(\overline{r_{a}}\) and \(\overline{r_{b}}\) are the average ratings given by users \(a\) and \(b\), respectively, and \(\overline{r_{s}}\) and \(\overline{r_{t}}\) are the average ratings received by items \(s\) and \(t\), respectively [24; 25; 26]. The denominator and numerator indicate the extent to which the ratings of two users and items change separately and together, respectively. The extent of change in user ratings can be calculated by adding up the squared deviations obtained by comparing all ratings pertaining to \(I(a,b)\) with the average rating. Similarly, the extent of change in item ratings can be calculated by adding up the squared deviations obtained by comparing all ratings pertaining to \(U(s,t)\) with the average rating. The similarity measure is a real number in the closed interval \([-1,\ 1]\), whereby a higher value indicates a higher correlation. In general, if the Pearson's correlation coefficient is greater than or equal to \(0.1\), it indicates a positive correlation. If it is less than \(-0.1\), it indicates negative correlation, and any value in the open interval \((-0.1,\ 0.1)\) indicates no correlation.  
\[Sim_{u}(a,b)=\frac{\sum_{i\in I(a,b)}(r_{a,i}-\overline{r_{a}})(r_{b,i}- \overline{r_{b}})}{\sqrt{\sum_{i\in I(a,b)}(r_{a,i}-\overline{r_{a}})^{2}} \sqrt{\sum_{i\in I(a,b)}(r_{b,i}-\overline{r_{b}})^{2}}} \tag{1}\]  
\[Sim_{i}(s,t)=\frac{\sum_{u\in U(s,\ t)}(r_{u,s}-\overline{r_{s}})(r_{u,t}- \overline{r_{t}})}{\sqrt{\sum_{u\in U(s,t)}(r_{u,s}-\overline{r_{s}})^{2}} \sqrt{\sum_{u\in U(s,t)}(r_{u,t}-\overline{r_{t}})^{2}}} \tag{2}\]
# Psychoeducation on Stress and Anxiety Using Virtual Reality: A Mixed-Methods Study
### Managing Stress and Anxiety Using Virtual Reality  
In addition to pharmacological treatment [41; 42], several psychological support programs are successfully used to help manage stress and anxiety. Most of these are based on the principles of cognitive-behavioral therapy (CBT) [43], which, as stated by the American Psychological Association (APA), is a psychological treatment with solid scientific evidence that underlines the strict relationship between behavior, cognition, and emotions. CBT-based programs for stress and anxiety management generally use several techniques simultaneously [44] and focus on two areas: relaxation (e.g., progressive muscle relaxation, biofeedback) and psychoeducation [45]. Such interventions effectively reduce stress and anxiety [46; 47; 48] and have been proven remarkably flexible in terms of format [49]. Along with the traditional therapist format, evidence reported that technologies, including VR [15], are also a viable delivery form [50; 51].  
VR is beneficial for relaxation, eliciting positive emotions, and diminishing levels of stress and anxiety [26; 52]. In particular, relaxing virtual environments, especially naturalistic ones [27; 28; 29], can help people learn relaxation techniques, including progressive muscle relaxation [53], biofeedback [54; 55] and mindfulness [56; 57].  
As reported by recent studies, VR-based programs resulted helpful for reducing stress and anxiety during the COVID-19 pandemic [58; 59; 60; 61; 62; 63]. For example, COVID Feel Good, a self-administered at-home daily VR-based intervention, reduced stress and increased social connectedness during the COVID-19 lockdown in healthy individuals [59]. Immersive VR exposure therapy (VRET) showing different virtual scenarios related to COVID-19 (e.g., watching pandemic news) helped in diminishing anxiety in a 57-year-old male treated in ICU due to COVID-19 [58] and among patients with fear of COVID-19 [61]. Immersive VR-based programs reduced stress and anxiety even among healthcare personnel. In particular, 360\({}^{\circ}\) videos of calming natural environments (i.e., VRelax and Tranquil Cinematic-VR) decreased stress and enhance positive emotions among ICU nurses [64] and frontline healthcare practitioners [65].  
Although these results are promising, at the moment, studies conducted on the use of VR for stress and anxiety management have a significant limitation. Namely, they are exclusively focused on using this technology for relaxation. However, VR, in addition to being useful for reducing stress and anxiety levels through immersion in relaxing virtual environments, can also be adopted for psychoeducation [30; 31; 32].
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 5 Results and Comparison
### Implementation Details and Synthesis Platform  
We developed the two-stage pipelined and non-pipelined architectures using Verilog, which is a hardware description language (HDL). The design was implemented on different Xilinx FPGA devices, specifically Virtex 4, Virtex 5, Virtex 6, and Virtex 7. For synthesizing the design, we utilized the Xilinx ISE design suite, specifically version 14.7.
# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 4 Discussion  
In this survey, we noted that in AV test scenario generation, scenarios can be classified into non-adaptive and adaptive types. A key characteristic of a non-adaptive scenario is that the movement of dynamic objects (such as vehicles, pedestrians, animals, etc.) follows predetermined rules or scripts and does not adapt to the current environment or road conditions. While such scenarios allow for repeatable experiments and reliable data comparisons, they fall short in capturing the complexity and unpredictability of real-world driving. Non-adaptive scenarios do not fully represent how vehicles or pedestrians dynamically respond to actual traffic environments, which limits their effectiveness in assessing AVs' reactions to emergencies and complex interactions.  
In contrast, research on adaptive scenario generation aligns more closely with the needs of AV system development and testing. In adaptive scenarios, the behavior of objects adjusts and responds in real time to changes in the environment, providing a more realistic simulation of varying traffic conditions on actual roads. This approach enables the capture of complex interaction scenarios, including collaborative or adversarial behaviorsamong dynamic objects. Such flexibility makes adaptive scenarios particularly valuable for evaluating the robustness, safety, and adaptability of AV systems.  
Consequently, the current trend in test scenario generation research is a gradual shift from non-adaptive to adaptive scenarios. While non-adaptive scenarios serve as a foundational testing tool, their limitations have led researchers to increasingly focus on adaptive scenario generation, which allows for a more thorough evaluation of AV systems' performance in complex, changing traffic environments. In this survey, we discussed several adaptive scenario generation methods. However, these methods still present challenges, such as the limited diversity in the generated BV driving strategies, low interpretability of models due to reinforcement learning, limited adaptability of BV models, and significant resource and time requirements for model training. This suggests that future research should emphasize not only the efficient generation of adaptive scenarios but also the establishment of effective evaluation metrics to assess generation methods from multiple perspectives. Such advancements are essential for more realistically replicating real-world driving conditions during testing and ensuring that autonomous driving technology can handle the unpredictability and diversity of practical applications.
# HelpResponder--System for the Security of First Responder Interventions
## 2 State of the Art
### Image Processing for Flame/Fire Focus Detection  
It is necessary to predict the origin of a flame before it spreads, creating a dangerous situation. Thanks to the increasing advances in neural networks applied to image processing, it is possible to train a deep neural network capable of detecting fire [15] in thermal images. However, it is necessary to improve this technique in those works to shorten the processing time and reduce false positives and false negatives.  
Most solutions usually use an RGB camera. However, it is necessary to validate artificial vision algorithms in indoor spaces without visibility. For instance, a computer vision-based fire detection method is presented in [16], where a robust color model was developed to detect all candidate fire regions reliably. However, that work is based on RGB images and is not oriented or validated to operate in indoor spaces (without visibility because of the high level of smoke and humidity) in emergencies. Finally, it is not integrated with location and guiding systems for firefighters in emergency locations. In fact, we want to investigate solutions that can be adapted to the limitations and parameters associated with indoor. One of our research's main objectives is to offer indoor solutions that offer added value to the limitations of outdoor solutions.  
Another work-related with the use of thermal images is [17]. It obtains the characteristics to classify fire. However, this work is evaluated using a database with videos and images, not in a real scenario. The researchers in [18] proposed a new flame detection algorithm based on a saliency detection technique and the Uniform Local Binary Pattern (ULBP). Moreover, they used an exponential function with two parameters to model the flame area's texture to reduce the number of false alarms. The proposed algorithm is based on images (photography) before applying the algorithm. Therefore, the experimental results are not applied in real time. Therefore, it is useful for off-line post-processing in situations where real time operation is not necessary. In [19], local binary patterns are used for solving flames detecting problems. Modifications were proposed to improve the flame detection quality using a Support Vector Machines (SVM) classifier with a kernel based on Gaussian Radial Basis Functions. It showed improvements in the time required to process the image and the detection parameters' average accuracy. However, the processing time was too long to use it in real time.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.1 Handling Missing Values  
We employed k-nearest neighbors (k-NN) imputation to address missing values in our dataset. This method was chosen for its ability to leverage the relationships between features, providing more accurate imputations than more straightforward methods like mean or median imputation. In k-NN imputation, the missing value of a song is estimated by considering the values of its \"k\" nearest neighbors. These neighbors are identified by calculating the distance between songs based on their available features. We used k = 5 for our analysis, meaning we considered the five nearest neighbors to impute the missing value. We used Euclidean distance, a standard metric for calculating the distance between data points in a multi-dimensional space, to measure similarity and determine the closest neighbors. This approach allowed us to fill in the missing values while preserving the relationships between features in our dataset.
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 3 Framework for System Design and Experimentation
### Feature Extraction from Gas Sensor Measurements Using LSTM  
Sensor measurements are sequential and hence sequence model namely LSTM Network is used for extracting the features from these measurements. The architecture of the LSTM model consists of the input layer followed by a single LSTM layer with 5 cells. LSTM layer is regularized with L2 regularization. The LSTM layers are followed by the classifier layer with the Softmax activation function.  
This LSTM network was trained on different optimizers with a fixed learning rate of 0.001 to find the best optimizer. Through the trial and error, it was observed that Adam optimizer was fitting to the model the best and also converging quickly. Hence Adam optimizer is selected for analysis and experimentation work. It can be observed that Adam optimizer fits and converges quickly. The model is trained for 300 epochs, and we obtained the testing accuracy of 83%.