esa-sceva/satcom-chunk-collection huggingface dataset:
# The Challenges of Advanced CMOS Process from 2D to 3D
## 6 Challenges in Processing III-V 3D Transistor on Si
Mg doping was found to be effective to p-type dope the InP grown on patterned substrates and consequently reduce the S/D leakage path. However, introducing Mg to the heterostructure results in a significant decrease in mobility due to increased coulombic scattering. By addressing the issue of the p-type doping degrading performance, the InP buffer was removed to create GAA devices. The GAA architecture has also the advantage of increased electrostatics over that of the FinFET. The GAA transistors showed significant improvement in peak gm (1030 IS/lm vs. 588 IS/lm) as well as in subthreshold swing when \(V_{ds}=V_{dd}\) (SSSAT:125 mV/dec vs. 178 mV/dec) compared to the FinFETs. The thermal budget for the gate stack was found to have a large impact on device performance. A comparison of high-\(k\) first to high-\(k\) last processing using the FinFET showed that the high-\(k\)/III-V interface degrades when exposed to \(>\)500 \({}^{\circ}\)C processing. This results also a significant decrease in the peak gm and increase in SSSAT. A dual nanowire structure with a diameter as small as 6 nm was successfully demonstrated in a modified version of the GAA flow. The nanowire devices show excellent SSSAT values as low as 66 mV/dec despite having the same gate stack as the larger GAA devices. While many challenges still remain for 300 mm wafer based III-V devices to achieve the potential lattice matched substrates, these results are significant step towards the ultimate realization of III-V VLSI technology [151]. The TEM cross sectional images of different fabricated device structures using the dummy fin removed process are showed in Figure 23.
## 5 Discussion
### New Language Acquisition Utilizing a Technology-Based System with Background Stimuli  
Conventional in-class learning experiences are often accompanied by distractions, particularly when the learning consists of memorization and repetition, both of which negatively impact motivation. Studies have shown that a learning environment, simulating a specific language's authentic lingual environment, enhances the acquisition of a foreign vocabulary [55; 57]. Therefore, one of the basic teaching assumptions regarding foreign language acquisition is that immersion and enhanced exposure in a natural language environment contribute to accelerated learning [51; 58]. This assumption supports learning a foreign language in a VRH environment, simulating daily life situations via technology. Clear visual presentation and an interactive learning experience isolate the learner from the 'external' world, thus reducing distractions and increasing motivation [59].  
Additionally, this technology offers the advantage of shifting from theoretical learning that breaks down the language into speaking-reading-writing to an interactive learning environment that simultaneously includes multiple communication channels including speaking, reading, writing, and even facial expressions. Studies indicate a connection between the immersive experience within a virtual reality environment and improved learning. Researchers comparing learning in a VRH environment and a 2D computerized environment found that learning in an immersive VRH environment contributed to motivation and led to better results [51; 53; 58; 60; 61].  
Unlike studies where the learning occurred in an interactive space within a virtual environment, our study tested the effect of stereoscopic three-dimensional images (S3D) on vocabulary acquisition without the option of interactive spatial manipulation. As previously mentioned, no significant learning achievements were found between the 2D and VRH environments (Hypothesis 2 is refuted).  
Similar findings were observed in previous studies. In [PERSON]'s study [62], it was found that only three-dimensional stereoscopic images without the interactive component did not result in improved achievements when compared with learning from 2D images. Furthermore, in [PERSON]'s study [61]. participants were presented with 60 new words in a 25 min learning session, some images were stereoscopic images and some were 2D images. However, unlike our study, [PERSON]'s participants chose which word to learn and were able to repeat the word as many times as they deemed necessary. In [PERSON]'s post-learning test, it was found that the virtual learning environment was more effective for weaker students who were able to repeat the words many times, while the use of 2D images was more effective for stronger students [61].
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
## 4 Results
### Integration of Signalling Systems  
The ILTIS-N control system was successfully interfaced with individual block and level-crossing systems, thereby achieving full integration within the signalling network of the model railway. Visualization of operational statuses and manual control functionalities for these systems were built into the ILTIS-N system without any apparent technical issues. This integration was critical to the control system's comprehensive functionality.
## 4 Results  
### Verification of PLS\(-\)MARS Method Based on a Simulated Data Set  
An illustrative function (Equation (9)) was set to validate the performance of the proposed PLS-MARS method. The method was proposed mainly for nonlinear prediction applications with numerous explanatory variables. Hence, the size of the explanatory variables in the illustrative function in Equation (9) was set as \(N\) (\(N\) = 100; \(N\) = 500), and the corresponding relationship between explanatory variables and the predictor was nonlinear. For comparison, MLR, PLS, and SVM methods were also utilized to estimate the illustrative function. Each prediction model was calibrated by 500 samples generated by the Latin hypercube sampling method [16] and tested using 1000 randomly generated samples.  
\[f=x_{1}\times\sin(x_{1})+x_{2}\times\sin(x_{2})+\cdots+x_{100}\times sin(x_{N}) \tag{9}\]  
The performance of the models was evaluated by fitness assessment indices (e.g., \(R_{c}^{2}\), RMSE, \(R_{p}^{2}\), RMSE, and RPD) (Table 2). The results showed that the MLR model performed well when estimating Equation (9) with 100 explanatory variables, but performed poorly when estimating Equation (9) with 500 explanatory variables. These results suggest the accuracy of the constructed MLR model decreases as the complexity and size of explanatory variables increase. Compared with the MLR model, the PLS method had relatively high accuracy due to the suitability of its equations with numerous explanatory variables. However, SVM and PLS-MARS methods (i.e., as nonlinear prediction methods) had the highest estimation and prediction accuracies when they were used to estimate the relationship in Equation (9).  
In summary, the comparison experiments on the simulated data set verified that the PLS-MARS method exhibits a high performance for estimating nonlinear relationships to numerous explanatory variables.
## 4 Classification Method  
### Data Splitting  
In this study, MFCC spectrogram data were integrated into an image repository, each entry associated with its corresponding labels and paths. The MFCC spectrogram data were then divided into three subsets: the training set, validation set, and test set. The dataset allocation adhered to predefined proportions, with 60% designated for the training set, 20% for the validation set, and the remaining 20% for the test set. This distribution strategy ensures an effective framework for training, fine-tuning, and comprehensive evaluation of the model, thereby facilitating an in-depth assessment of the model's capabilities and overall performance.
## 1 Introduction
Considering the efficiency of computation and the simplicity of implementation, MOPSO can be successfully adopted in the field of VPP operation [23; 24]. The methods and contributions of this paper are summarized as follows:  
1. A bi-objective optimization model of ME-VPP has been established, which takes economic cost (EC), power quality (PQ), environmental friendliness, and various physical constraints into account. 2. LSTM, a deep learning method, has been applied to wind prediction. Its superiority to the Grey model has been verified in terms of accuracy, robustness, and computational efficiency. 3. The realistic case of Hongfeng Eco-town in Southwestern China has been numerically studied by applying MOPSO algorithm. The advantages of bi-objective modeling and \"foresighted\" strategy have been quantitatively demonstrated. The remainder of this paper is organized as follows. Section 2 analyzes two objectives of ME-VPP dispatch and establishes the bi-optimization model. Section 3 presents LSTM and MOPSO as the prediction method and the solving algorithm. Computational results of case study and further discussions are demonstrated in Section 4. Finally, Section 5 summarizes the whole work in this paper and draws the conclusions.
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 3 GARSU: Automatic RSU Deployment  
The RSU deployment methods commonly based on fixed placement, using geometrical rules, have severe limitations such as areas with no coverage or unbalanced network load (i.e., some RSUs exhibit extremely high network usage, while others do not). To surpass such limitations, we propose a genetic algorithm for roadside unit deployment (GARSUD), which is based on the evolutionary computing branch of artificial intelligence [22].  
Genetic algorithms (GAs), named after the biological evolution process and its genetic-molecular basis, conduct a population of individuals to evolve by subjecting such a population to random processes similar to those present in biological evolution--i.e., mutations and genetic re-combinations--as well as a selection according to criteria to determine which individuals are more adapted to survive. The most fit individuals are selected and advanced to subsequent generations, and those less fit individuals are discharged [23].  
Genetic algorithms require establishing a relation between a set of solutions to a problem, called phenotype, and a set of individuals in a natural population. This is achieved by encoding the information of each solution in a string, usually binary, called chromosome or genotype. The symbols that compose such a string are the genes. The populations of possible solutions encoded as genotypes evolve through iterations called generations, thereby obtaining for each genotype the corresponding phenotypes,which are evaluated by using a measure of fitness. To generate the subsequent generation, the following set of genetic operators must be used: parent selection, crossover, mutation and replacement.
## 1 Introduction
### Technological and Implementation Challenges  
Despite the great benefits of interactivity, connectivity, and information transfer, some limitations must be considered when using these tools, such as the limitations of GNSS systems. One of the main challenges when implementing navigation with AR is its dependence on GPSs (MAR), which may be inaccurate or unavailable in specific environments. Solutions such as step-count-based localisation and data fusion techniques have been proposed to mitigate these issues and improve the accuracy and reliability of MAR.  
Device compatibility and accessibility: While modern smartphones have augmented reality capabilities, ensuring compatibility between devices and platforms remains challenging. In addition, the cost and availability of the proper devices may limit access for some users.  
In contrast to the positive implications, there are considerations regarding integrating AR and MPN for heritage education. While these technologies offer enhanced learning experiences, they also require significant resources to develop and implement. In addition, the effectiveness of these tools can vary depending on the topic and context in which they are used. For example, while AR is highly effective in teaching spatial and contextual issues, its benefits may be less pronounced in more abstract disciplines. In addition, reliance on technology raises concerns about digital literacy and the potential for technological distractions in learning environments. Despite these challenges, the potential benefits of augmented reality and mobile pedestrian navigation in education are substantial, offering new avenues for interactive and immersive learning experiences.
# Survey on Security Threats in Agricultural IoT and Smart Farming
## 2 State-of-the-Art ICT in Agriculture: Definitions, Technologies, Applications and Benefits
Some well-known areas where the integration and usage of IoT in agriculture has been applied, are shown in Table 1:Based on the above applications and analysis of IoT and ICT in agriculture, there are several benefits from the integration of IoT in the agricultural sector. Thus, according to [12; 54; 55; 56; 57], the benefits from IoT and ICT integration in the agri-sector can be summarized as follows:  
* Crop monitoring, decreasing the overall costs;
* Logistic and qualitative traceability of food production combining decision making processes with real-time data for reducing the waste of inputs and overall costs;
* Capitalizing on Big Data resources, at hardware and software level, establishing new agriculture communities in urban and rural areas;
* Generating novel business models in the sector, creating a new retailer-consumer relationship;
* Automatic irrigation systems development that adjust their operations based on the humidity, temperature and soil moisture values which are retrieved through the embedded sensors;
* Collection of environmental parameters, in an automatic way using IoT, which are usable for further analysis;
* Big Data analytic processes and tools for enhancing productivity using decision support systems. New standards, in combination with the rapid technological advance, lead the agricultural sector, as well as many others, in a new promising future [58]. For agriculture, the main focus of this evolution is the development and innovation of processes that use fewer resources with better production results, keeping in mind that food production is of significant importance for the entire human population. As digital transformation progresses in the agricultural industry, many businesses face new emerging challenges posed by cyber criminals, who are looking for sectors and organizations that are digitally exposed and might not have built adequate security systems and defensive mechanisms [59]. With the agricultural sector getting more and more dependent on ICT systems, new emerging vulnerabilities, threats and risks, which are described in more detail in the next section, also arise.
# Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking
## 1 Introduction
### Current Research  
The primary aim of the current study was to investigate if and how a variety of self-reported everyday attentional failures, including attentional switching and distractibility, deliberate and spontaneous mind wandering, attentional lapses, and attention-related cognitive errors, were associated with phubbing behavior. As mentioned above, individuals who phub may also struggle with directing and maintaining their attention, which would suggest positive associations between phubbing behavior and many of our self-reported cognitive measures, including mind wandering, attentional lapses, and attention-related cognitive errors. If several of these everyday attentional failures were correlated with phubbing behavior, we were particularly interested in which could best predict this behavior, both among this selection of cognitive abilities and when accounting for select psychosocial and personality constructs.  
Our secondary aim was to explore the indirect and direct associations between phubbing behavior and media multitasking. We explored the indirect relationship between these two behaviors by investigating if they shared the same directional relationship among various measures of everyday attentional failures. We investigated the direct relationship between these two behaviors by testing if they were correlated with one another. If phubbing was positively associated with media multitasking, this would suggest that individuals who frequently engage in phubbing behavior also frequently engage in media multitasking, which would fill a gap in the literature between these two technology use behaviors.  
Our study was one of the first to measure phubbing behavior and cognitive functioning in the same participants. It was plausible that we would observe positive associations between phubbing behavior and attentional failures, but it could have also been the case that there would be no associations between these measures or that there would be negative associations. Given the lack of prior research with these specific measures, our aims were thus more exploratory rather than confirmatory. Furthermore, we leveraged the link between another potentially related technology use behavior (i.e., media multitasking) and self-reported cognitive function to better understand cognitive function and phubbing behavior. However, prior findings between media multitasking and self-reported cognitive functioning have been mixed [32; 33; 34; 35; 36; 37], making strong confirmatory predictions somewhat difficult. That said, there is a growing need to understand how technology use behaviors both can influence and are influenced by our cognition. By exploring and establishing some of the first cognitive correlates and predictors of phubbing behavior, we can further characterize the relationship between humans and technology.
# Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG
## 3 Materials and Methods
### Classification with CNN  
The CNN is a particular type of neural network that implements, in at least one of its layers, a convolutional operation [38]. In this study, the architecture of the CNN was adapted from [23; 24]. As depicted in Figure 2, it consisted of five layers.  
The first two were convolutional layers: the first one performed a temporal filtering (i.e., convolution along the time axis), while the second one a spatial filtering (i.e., convolution along the channel axis). Each convolutional layer was followed by a batch normalization and an exponential linear unit (eLu) activation function. Then, an average pooling layer, which flattened the input to a single dimension, and two fully connected  
Figure 2: Schematic representation of the proposed CNN model architecture.  
layers were stacked on the top of the convolutional ones. Finally, a softmax activation function returned the probability of each sample to belong to each class. To note, since the kernel size at the output of the second convolutional layer was equal to the number of channels, this filter reduced the channel dimension to one. The input to this CNN was given by the EEG 2D images \(\mathbf{X}^{(i)}\), for every available trial \(i\), as computed in (1), which resulted in a three-dimensional tensor. To implement such architecture, several parameters had to be decided: specifically, the kernel size and the depth of the convolutional layers, and the size of the pooling and dense layers. Given each participant, we used a grid-search procedure to optimize such parameters over a priori selected ranges. Then, the optimal combination of parameter values was given by a majority vote strategy across all participants. As a result, the kernel size of the first convolutional layer was equal to 30, while, for the second convolutional layer, it corresponded to the number of channels, i.e., 58. Moreover, for both of them, the optimal depth was found to be 40 filters. The kernel size of the average pooling layer was equal to 15, the first fully connected layer had 80 neurons, while the second fully connected layer had three neurons, corresponding to the number of classes. The same CNN architecture was used for both datasets, while one CNN model was trained for each participant.
## 4 Key Agreement, Distribution, and Security Bootstrapping
### Security Bootstrapping  
All key agreement protocols require that some credentials, either public/private key pairs, symmetric keys, certificates, or others, have been previously installed and configured on nodes, so that the key agreement procedure can occur securely. Bootstrapping refers to the processing operations required before the network can operate: this requires that proper setup, ranging from link layer to application layer information, must be done on the nodes. Bootstrapping is a very important phase in the lifecycle of smart objects and can affect the way they behave in operational conditions. Even though the bootstrapping phase is outside the scope of this paper, it is important to consider security bootstrapping mechanisms and architecture [73], so that possible threats, such as cloning or malicious substitution of objects, can be tackled properly. The author of [74] provides a sketch of a possible protocol to let constrained devices to securely bootstrap into a system that uses them.
## 2 Results
### Large-Signal Investigation for PA Design
#### 2.2.1 Investigations  
Figures 10-12 show the investigation of the linearity (IMD3) and efficiency (PAE) of the proposed configuration with respect to the variation of gate bias voltages \(V_{\text{g1}}\), \(V_{\text{g2}}\), and \(V_{\text{g3}}\). As shown in the figures, \(V_{\text{g1}}\) contributes significantly to the change in both IMD3 and PAE, while \(V_{\text{g2}}\) only affects IMD3 at low-power region but it does not affect PAE; \(V_{\text{g3}}\) moderately affects IMD3 in the medium-power region and it affects PAE remarkably in the entire power range. This implies that both IMD3 and PAE can be simultaneously improved by adjusting the gate bias voltages. This is another very promising advantage of the proposed configuration because IMD3 and PAE are hard to improve simultaneously using traditional design methods, as mentioned in the introductory section.  
Figure 10: Large-signal (LS) investigation with \(V_{\text{g1}}\) variation.