esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Importance  
Understanding the most important features that contribute to the classification of ASD and TD individuals is crucial for advancing diagnostic and therapeutic strategies. To determine the most discriminative pairs of regions for distinguishing TD from ASD, the permutation importance [43] technique was used for each analyzed classifier, since it can be applied uniformly to all of the classifiers tested.  
Permutation importance is generally useful for understanding data and interpreting classifier: by calculating the score for each feature, one can determine which features most influenced the utilized classifier. Permutation importance is considered as one of the global Explainable Artificial Intelligence (XAI) methods. It provides insights into the overall behavior of a classifier and offers a comprehensive view of feature contributions across the entire dataset. Using a global XAI approach, the interpretability and reliability of the classifier are increased. The basic concept of permutation importance involves observing how much a particular metric decreases when a feature is not available, with the score representing the importance of each feature. A higher score indicates that the feature in question has a greater impact on the classifier. In principle, one could remove features, retrain the classifier and check the score. However, this approach can be computationally complex because it would require retraining the classifier for each feature removal. Additionally, this method demonstrates which features might be important in the dataset rather than which features are important for the classifier. If a feature is replaced with noise, derived from the same distribution, as the original feature values during each permutation, it is possible to avoid retraining the classifier. The simplest way to derive this noise is by shuffling the values for a feature by using the values of the same feature across different examples.  
In this study, we implemented the permutation importance as described above to determine whether the key features for the classification vary depending on the classifier used. We used the feature permutation importance implemented in the ELI5 python library [44]. This library offers a function that takes into account a trained classifier, a validation dataset, and a scoring metric, and it returns the importance score for each feature. The importance score reflects the decrease in the classifier's performance: the greater the drop in performance when a feature is shuffled, the more significant that feature will be considered. When a feature has a negative score, it means that the performance of the classifier has increased when this feature is replaced with noise, meaning that it is not important. We employed the AUC as a scoring metric and computed the permutation importance for each fold of the 5-fold cross-validation and repetition. The final results were obtained as the average importance score across the folds and repetitions.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 1 Introduction
### Problem Statement  
Targeting of content-aware adverts is only possible after analysing a user's behavioural conducts. This practice utilizes private data that may reveal sensitive information, such as demographics, ideological and religious inclinations, shopping and browsing practices, lifestyle preferences and pretty much any other intelligence that can be deduced depending on the platform.  
Although OBA has been employed for years, the ever-increasing usage of smartphones further escalates the existing problem. In addition to being more pervasive than computers, mobile devices incorporate a multitude of tracking capabilities that computers simply do not have. Smartphones have access to information such as personal text messages, emails, GPS coordinates, WiFi access point locations, purchasing habits through applications, such as Apple pay or loyalty point apps, and activity sensors.  
Although the general public may not be fully aware of the technical details of OBA, numerous relevant studies indicate that the majority of people disapprove of being tracked. Specifically, a survey conducted by Pew Research Center showed that 68% of Americans view targeted ads in a negative way [4]. Despite more than half of the population disapproving of ads, we still need to acknowledge that targeted ads are vital for the offering of free services. Finding an alternative method of delivering personalized ads without compromising privacy is therefore an important problem that deserves to be addressed.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 3 Results  
### Impact of Feature Reduction on the Model  
The PCA algorithm was employed to reduce the 47 extracted features to 36. However, while PCA effectively reduces the dimensionality of the independent variables, it does not clarify the significance of these variables in relation to the target variables. To assess whether the reduction in feature count positively impacts valence classification, we constructed binary and ternary classification models using both 47 and 36 signal features, respectively,with XGBoost and Random Forest as classifiers. The model performance results, before and after the reduction in valence and arousal features, are presented in Tables 2 and 3.
## 4 Conclusions  
The _Schizochytrium_ sp. biomass incubated in the culture with waste glycerol produced EPS, the highest concentration of which was determined in the stationary growth phase. Concentrations of saccharide monomers of the EPS fraction remained stable over the incubation period. The monosaccharides present in the EPS fraction included hexoses, except for xylose which is classified among pentoses. The major EPS component turned out to be glucose, with the concentration reaching 60.64 \(\pm\) 1.93% mol.  
EPS synthesized by _Schizochytrium_ sp. biomass cause a significant increase in culture viscosity, which in turn contributes to diminished oxygen availability for the cells and to reduced effectiveness of waste glycerol utilization, lipid production, and DHA accumulation in cells.  
Increased effectiveness of culture medium aeration boosts technological effects. The highest culture efficiency was observed with volumetric oxygen mass transfer of k\({}_{\rm L}\)a = 600 1/h. The increase of its value to 750 1/h did not cause any significant changes in the values of the analyzed parameters. This variant was not justified by either the technological effects achieved nor by the operating costs. Glycerol consumption was increased linearly depending on the applied oxygen mass transfer coefficient, ranging from k\({}_{\rm L}\)a 150 1/h to k\({}_{\rm L}\)a 600 1/h.  
Figure 5: Correlations between DCW, DHA, and lipid concentrations and k\({}_{\rm L}\)a values.  
**Author Contributions:** Conceptualization, N.K., S.T. and M.D.; Data curation, M.D.; Formal analysis, M.D. and M.Z.; Funding acquisition, J.K. and M.Z.; Investigation, N.K., S.T. and M.D.; Methodology, N.K. and S.T.; Project administration, M.Z.; Resources, M.D. and J.K.; Software, N.K. and S.T.; Validation, M.Z.; Visualization, M.D. and J.K.; Writing--original draft, M.D. and J.K.; Writing--review and editing, M.D., J.K. and M.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The manuscript was supported by Project financially supported by Minister of Education and Science in the range of the program entitled \"Regional Initiative of Excellence\" for the years 2019-2022, project No. 010/RID/2018/19, amount funding 12.000.000 PLN and the work WZ/WB-IIS/2/2019, funded by Minister of Education and Science.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 5 Experiment Results
### BEISTA-Net
#### 5.1.2 Complexity Analysis of Reconstruction Algorithms  
To compare the computational complexity of different reconstruction algorithms, the number of floating point operations (FLOPs), running time, video memory usage, and the number of learnable parameters are evaluated for BEISTA-Net, ISTADR-Net [18], R-ADMM [13], and ISTA-Net. It is important to note that the computational complexity of the reconstruction algorithm only depends on the size of input and output. Since the size is identical in the TVWS dataset and NR dataset during reconstruction, the computational complexity of the reconstruction algorithm is the same in both datasets. Here, we use the parameters \(P=8\), \(L=16\), and \(M=128\). We implemented these algorithms on a PC equipped with an Intel Core i9-13900 KF 3 GHz CPU and two Nvidia GeForce RTX 4080 Super GPUs to evaluate their runtime. As shown in Table 2, compared to ISTADR-Net and R-ADMM, the FLOPs and the number of learnable parameters required by ISTA-Net and BEISTA-Net are significantly larger. This is because ISTA-Net and BEISTA-Net rely on a large number of convolutional kernels to extract features, while ISTADR-Net and R-ADMM rely on multiple iterations to reconstruct signals. However, the runtime required by ISTA-Net and BEISTA-Net is shorter because the neural network has a higher degree of parallelism in execution, significantly reducing the computation time. Compared to ISTA-Net, the runtime required by BEISTA-Net is approximately 1.9 times greater due to the inclusion of BE-Block. However, the FLOPs required by BEISTA-Net only increased slightly. The video memory usage of BEISTA-Net is the highest among the four algorithms, which is attributed to its large number of learnable parameters. BEISTA-Net effectively improves the reconstruction accuracy of the WSS algorithm at the cost of an acceptable increase in computational complexity. However, due to the higher video memory usage of BEISTA-Net, its deployment on embedded devices incurs additional resource consumption.  
Figure 7: NR dataset. (**a**) r-MSE versus SNR (dB) of ISTADR-Net, R-ADMM, ISTA-Net, and BEISTA-Net when \(P=8\), \(L=16\), and \(\mu_{NR}\in(0,0.5]\). (**b**) r-MSE versus sparsity level of ISTADR-Net, R-ADMM, ISTA-Net, and BEISTA-Net when \(P=8\), \(L=16\), and \(\text{SNR}\in[-10,10]\) dB.
## 3 Demand Forecasting Models and Power Theft Detection Strategy
### Proposed RNN-GBRT Hybrid Model  
An RNN's goal is to forecast the next step in a sequence of observations in relation to the previous phases in the series [31]. In order to predict future trends, RNN makes use of consecutive observations and learns from previous phases. Data must be remembered throughout the early phases while estimating the following moves. The hidden layers in RNN serve as internal storage for the information obtained during the previous phases of the sequential data processing.  
The GBRT algorithm, which is a mix of the CART (classification and regression trees) and GB (gradient boosting) algorithms, is also considered [31]. It is noted that the CART outperforms most artificial intelligence models in terms of prediction, since it can simulate  
Figure 4: Architecture of linear regression.  
nonlinear interactions without having previous knowledge of the probability distribution of variables. Inspired by the contribution of [30; 31] we propose the hybrid model RNN-GBRT in order to exploit the advantages of the two methods and obtain better forecasting performances. In GBRT, the current iteration's model reduces the previous iteration's residuals. At each iteration, it builds a new regression tree to reduce residuals with the gradient descent of the objective function. In the proposed hybrid model, RNN-GBRT, the generated series after RNN forms the training examples for GBRT. Generally, the performance of GBRT depends on learning rate and the total number of regression trees. In this paper, the value of the learning rate is set from 0.1 to 0.3, and the total number of regression trees is set from 20 to 150.
## 3 Description of QLM-ChPs Matrix Approach  
The chief goal of the current work is to devise an efficient as well as accurate matrix algorithm based on the ChPs described above. Towards this end, we first rewrite the original nonlinear model problem (1) in the form  
\[\frac{d^{2}}{dx^{2}}M(x)+\frac{n}{x}\frac{d}{dx}M(x)-\frac{2h^{2}}{\alpha+M(x) }=0. \tag{11}\]  
As mentioned before, we are particularly interested in \(n=0\) and \(n=2\). Obviously, the model under consideration is not only strongly nonlinear but also singular at the origin. Of course, when \(n=0\), the second term is annihilated. These hinder us to solve the model problem efficiently and accurately. In particular, by applying the direct collocation matrix techniques using the (finite) expansion series (8), we arrive at an algebraic nonlinear system of equations of size \((H+1)\). To obtain high-order accuracy, one has to increase \(H\). In this case, the target nonlinear solvers (such as the Newton-Raphson algorithm) are usually unsuccessful in returning the right solutions.  
In order to have no such difficulty, one remedy is to linearize the model (11) first. The [PERSON]'s quasilinearization method (QLM) can be used for this purpose. Thus, we have to solve a sequence of linearized submodels rather than a single nonlinear model. The wide range of applicability of the QLM to diverse initial and boundary value problems has been shown by many researchers; we refer for comparison [38; 39; 40].
### Related Works
Path planning for UAVs in windy environments was proposed in [35], with simulated moving targets for UAVs to pursue. Ref. [36] took advantage of the traditional collision avoidance method and DRL method, resulting in long trajectory planning with unknown obstacles. In [37], UAVs serve in a warehouse for stock inventory, updating real-time paths with image recording. Resource allocation constitutes another critical aspect of ensuring UAV assistance. The efficiency of UAVs is often limited by their battery life, prompting research into spectrum resource allocation and efficient energy management [38], including task scheduling and dynamic recharging strategies. Given that the resource allocation problem is NP-hard, researchers strive for sub-optimal solutions using methods from convex optimization, machine learning, etc. [PERSON] et al., in [39], proposed a safe-DQN method to optimize UAV trajectories, considering constraints such as user equipment (UE) energy limits and obstacles in emergency scenarios. Furthermore, for target assignment in multi-object scenes, multi-agent reinforcement learning (MARL) has been demonstrated to be effective, as shown in [40]. In the context of an evacuation, the trajectory of moving ground users requires statistical data or simulation for accurate consideration [41]. Since the moving behavior of users varies with the environment, cellular automata can be employed to adjust their trajectories. Cellular automata model the map as a two-dimensional grid space and assign different values to different grids to represent users, obstacles, exits, etc. Therefore, the state transition of certain grids can be predicted according to the states of their neighboring grids. Ref. [42] utilized cellular automata in forest fire spread prediction, specified influencing factors to adjust cell state and cell transition rules, and gave a 3D visualization for the fire spread model. In [43], cellular automata and the ant colony algorithm were used to optimize the evacuation model, which is applicable in emergency scenarios.
## 6 Analysis and Discussion
### Limitations and Future Directions
#### 6.4.1 Improvement of Detection Accuracy  
Although pruning and quantization finally led to an inevitable decrease of detection accuracy, due to time reasons, we still did not find the optimal parameters of sparse training and pruning for our dish dataset. For future research, we plan to adjust the parameters during sparse training and improve the pruning method to alleviate the accuracy decrease.  
At the same time, the dish dataset also needs to be improved due to some insufficient consideration when we built the dataset. The improvement of the dataset can also increase the detection accuracy.
## 4 Related Works and Contemporary VSN Projects
### Sensor Network Virtualization Projects
#### 4.1.6 Presto  
PRESTO is a Predictive Storage Architecture for Sensor Networks [48, 49]. Nowadays with the advent of sensor-cloud and sensor-grid architecture, we have seen a tremendous growth in extending the reaches of the Internet to numerous sensor data sources including RFIDs, weather and habitat monitors, building monitors, remote sensing data such as radar and others. These sensor data sources span a spectrum of power, data rate and platform requirements, from passive RFIDs and battery-powered wireless sensors to high bandwidth radar nodes. A unifying constraint across the spectrum of sensor data management applications is that the remote sensor nodes at the network edge are usually constrained in power, functionality and/or bandwidth, and communicate using multi-hop routing to a resource-rich proxy that connects the sensors to the Internet. PRESTO takes a fresh look at the design of tiered large-scale sensor networks that comprise tethered and untethered elements. It focuses three major questionnaires: (i) where should sensor data be archived? (ii) How can low-latency, interactive query processing be supported in-spite of the power and bandwidth constraints of sensors, frequent sensor failures and vagaries of wireless links? (iii) How can a user access data generated at numerous sensors and across a large geographic domain in an efficient manner? There are five main characteristics of PRESTO. It is discussed by the following features with the help of Figure 2.  
* Tiered Design: PRESTO focus the idea that scalable sensor networks of the future will have multiple tiers, with several tens of untethered sensors per tethered sensor proxy and several tens of sensor proxies per application.
* Predictive Storage: It makes broad use of predictive techniques that easily fit to the correlated behavior of the physical world. It exploits technology trends in storage to build an architecture that emphasizes archival at remote sensors. It also uses extensive modeling and predictive caching at proxies.  
* Interactive Usage: PRESTO data management architecture is designed for fast responses for _ad-hoc_ queries on distributed sensor data without acquiring the energy costs of the data streaming approach or the losses, latency and reliability concerns to the remote sensors.
* Archival Queries: PRESTO supports archival queries on data resources which is very interesting. The ability for historical data query is important in many sensor applications to conduct unexpected and unusual events to better understand them for the future.
* Single Logical View of Data: PRESTO architecture provides a single logical view of data which is distributed across many sensor proxies and numerous remote sensors. This type of view can abstract the user from the variabilities at many levels lossy and unreliable remote sensor network. It also concerns spatial and temporal consistency issues in the sensor data as well as bandwidth and connectivity issues in the case of wireless proxies.
## 1 Introduction
### Background and Related Works
#### 1.1.5. IoT, Fog and Cloud Computing  
According to Reference [71], Internet of Things (IoT) is an infrastructure enabling advanced service by interconnecting physical and virtual things based on information and communication technologies. Internet of Things devices are one of the key elements to give an environment computation ability such as context awareness. A smart classroom is a classroom with many IoT devices. With IoT device, actions such as sensing, actuation, and data capture can be performed in smart places. Internet of Things devices are often designed to cooperate with cloud services. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources [72]. With cloud services, smart places can perform complex computation, such as facial recognition applications, without installing high-performance computing resources in every smart place. In Reference [73], definitions and trends of IoT are clearly stated, and IoT elements such as Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), addressing schemes, data storage, data analytics, and visualization are reviewed. IoT applications including personal, home, enterprise, utilities, and mobile are also discussed. A cloud platform named Aneka is used to demonstrated cloud resource provisioning and data analytics for IoT applications. Finally, challenges such as architecture, energy efficient sensing, secure reprogrammable networks and privacy, quality of service, new protocols, participatory sensing, data mining, GIS-based visualization, cloud computing, and international activities are discussed in Reference [73]. Cloud-based IoT systems often encounter issues such as scale, heterogeneity, latency, and security. These issues can be improved or addressed by adding a layer between the IoT network and cloud services. Fog computing is a computing model to address cloud-IoT issues [74]. In References [75], fog computing and IoT scenarios, such as connected vehicle, smart grid, sensors, and actuators, are discussed. In Reference [76], the author reported that fog computing is needed for companies in different industries to handle the computation issues between IoT network and cloud network. In Reference [77], a smart e-Health Gateway is proposed to bring intelligence to IoT-based healthcare systems. In Reference [78], a fog computing-based solution is proposed based on Reference [77] by enhancing IoT-based healthcare applications in terms of energy efficiency, performance, reliability, and interoperability. In our proposed architecture, IoT devices are important building blocks of the proposed context-aware smart classrooms prototype, and the proposed technology integration model is similar to the Fog computing model. With our proposed model, different kinds of IoT devices and cloud computing resource and be easily linked to form new smart classroom applications.