esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Testing and Validation of Control Mechanisms in Real Road Environments
#### 6.2.1 Testing of Vehicle-Level Collaborative Control  
At the vehicle level, Cooperative Adaptive Cruise Control (CACC) is one of the most focused control mechanisms. [PERSON] et al. utilized field data from the Netherlands CACC to compare and evaluate two CACC algorithms specifically designed for heterogeneous traffic: Linear Feedback Control (Linear-CACCu) and Adaptive Model Predictive Control (A-MPC-CACCu) [69]. Unlike traditional research focusing on highway scenarios, this study concentrated on urban arterial environments, with results showing the following: (1) both CACCu algorithms significantly outperformed conventional ACC and human driving in terms of safety, comfort, and energy efficiency; (2) traffic signal phase changes have a significant impact on optimal algorithm selection, suggesting dynamic switching of control modes based on expected signal phases.  
Also based on CACC technology, [PERSON] et al. extended their research to cooperative heterogeneous traffic platooning [70]. Their proposed framework utilizes communication between leading CAVs and intersections to optimize trajectories, with benefits transmitted through intermediate human-driven vehicles to following CAVs, forming a chain of cooperative effects. Following CAVs further optimize their behavior by receiving information from leading vehicles. This \"lead-middle-follow\" structure fully utilizes limited communication resources, achieving system-wide optimization even when only some vehicles possess communication capabilities.  
These studies demonstrate that in real traffic environments, even when only some vehicles are equipped with connected automated driving capabilities, carefully designed collaborative control strategies can significantly enhance overall traffic performance. In particular, strategies utilizing leading CAVs to influence following vehicles show strong practicality and effectiveness in transitional heterogeneous traffic environments.
## 2 Materials and Methods
### PlanetScope Data  
PlanetScope (PS) is an optical satellite imaging mission owned and managed by the private company Planet Labs. The constellation is composed of almost 130 platforms, which acquire imagery of the Earth's entire land surface on a daily basis. The PlanetScope spectral resolution spans a range between 431 and 885 nm, comprising eight different bands (Table 1). The current sensor, PSB.SD (Super-Dove), is the same that was used to acquire the dataset used for this study [15].  
The PlanetScope imagery employed in the current study was the analytic Surface Reflectance (SR) ortho scene product, in which surface-reflectance values are scaled by 10,000 [15]. Additionally, these are orthorectified and projected into a cartographic spatial reference system (UTM). The resulting spatial resolution is three meters.  
Figure 2: Six predominant cover classes within the test area. For visualization purposes, only those classes with an extent greater than 1% of total test-area surface are represented. The location of the test area is shown as a red dot in the incpets.  
The dataset, which was released by Planet Labs for the study, consists of 59 images that were acquired for twenty-five different dates between 7 March and 7 October 2023 over the test area. The images were collected at an average interval of one every eight days, with significant variability. The minimum interval between two acquisitions was two days, while the maximum was twenty-three days. The standard deviation of the acquisition intervals was determined to be six days. It is important to note that the frequency of acquisitions was contingent upon atmospheric conditions, which served to restrict the selection to images characterized by minimal cloud coverage. Indeed, the mean cloud-coverage rate across the entire image set was estimated to be 0.1%, with a maximum observed value of 0.87%. Refer to Table 2 for a comprehensive list of all PlanetScope acquisition dates.
## 1 Introduction  
The Fourier-transform infrared (FTIR) spectroscopy method is a method of bond characterization that could be used quantitatively and qualitatively with X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy, and electron energy dispersive spectroscopy (SEM/EDS) to study the chemical composition, compound composition, microstructural morphology, and their elemental compositions in CB applications, respectively. Supplementary cementitious materials (SCMs) can be used in OPC to enhance secondary hydration. These SCMs can either be pozzolanic materials or hydraulic materials, such as ground granulated blast furnace slag (GGBFS) or pozzolanic materials, which comprise alumina and/or silica in significant quantities. Pozzolanic materials can also include fly ash (Class F), silica fume, metakaolin, palm oil fuel ash, glass waste, and rice husk ash [1; 2]. Utilizing these materials in mortar and concrete productions reduces the carbon footprint and enhances cost-efficient materials for sustainable environments. These materials can be used as precursors for the synthesis of geopolymer (GP), alkaline-activated slag, or alkaline-activated binders (AABs). These precursors can also be named alkaline-activated materials (AAMs) [3; 4]. Understanding the bond characterization of the binder through the application of the FTIR technique is quite helpful for the technical analysis of the binder with a view to controlling the strength and durability of the products [5; 6; 7]. Even though there have been a lot of reviews on the use of FTIR in different areas of applications, there has not been a full review in the literature on its application and interpretation of the bond characterization in cementitious binders--ordinary Portland cement (OPC), alkaline-activated slag or binders (AAB/AAS), and geopolymers (GPs).
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 2 System Components
### Electrolyzer Technologies  
An electrolyzer is a device that receives DC electricity and demineralized water and separates the hydrogen and oxygen atoms from the water molecule through a chemical reaction, generating high purity oxygen and hydrogen. While different technologies for electrolyzers operate in slightly different ways, all have an anode and cathode that are separated by an electrolyte.  
Currently, there are two technologies used in commercial applications for the production of hydrogen: Alkaline Electrolyzer (AEL) and Proton Exchange Membrane Electrolyzer (PEMEL) [11; 12]. Another technology undergoing intense research and development is Solid Oxide Electrolyzer (SOE), which promises high efficiencies and flexibility, but at the cost of both high operating temperatures (700 to 900 \({}^{\circ}\)C) and durability [11; 12].
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 4 Experimental Evaluation  
In this Section, we conduct extensive experiments to evaluate the efficiency and effectiveness of the proposed algorithm. We implement all algorithms using python 3.5.2. We select our previous work GASKY as the baseline and compare it with the proposed MRGASKY algorithm. We conduct GASKY on Linux operating system with Intel Core i7 3.40 GHz processor. It has 4 GB main memory. For the experiments of MRGASKY, there are a total of four compute nodes for the MapReduce framework. One computing node is the PC of GASKY. The remaining three PCs are conducted on Linux operating systems with Intel Core 2 3.16 GHz, 3.16 GHz, and 2.13 GHz processors. These PCs have 4 GB main memories. We implement the MRGASKY algorithm on Hadoop 2.5.2 version.  
**Synthetic datasets:**: We create a synthetic dataset to study the scalability of the algorithms, denoted by SYN, in the experiments. The objects and facilities are randomly generated in two-dimensional  
Figure 11: Example of the Step 2 Process.  
Figure 12: MapReduce Data Flow of MRGASKY Algorithm.  
space. Specifically, there are four synthetic datasets, SYN_A1, SYN_A2, SYN_B and SYN_C with different number of grids, number of facility types and number of objects respectively.
**Real datasets:**: The real dataset, called US, is employed in the experiment. US dataset comes from the U.S. Geological Survey (USGS). It consists of 406,709 locations with 40 types. The number of objects in the US dataset is 2,033,545. We create three datasets by using these real data, denoted US_A, US_B, and US_C, with the different number of grids, facility types and objects respectively.  
To compare the performance across the GASKY and the MRGASKY, we evaluate the average processing time. Since it is the same process to remove the dominated objects from the dataset for both two algorithms, and there is no difference in performance evaluation, we do not calculate the time cost of this step.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 3 Security, Privacy, and Confidentiality
### Artificial Intelligence Security  
The rapid advancement of technology has made artificial intelligence (AI) increasingly capable of identifying and classifying potential cyber threats. These threats can then be communicated to relevant personnel for prompt action. Nevertheless, due to the black box (i.e., a system to which the user interacts through its external parameters without any or limited knowledge of what happens inside it) characteristics of AI and the identification of certain cyber attacks can present significant challenges.  
Despite the efficacy and accuracy of AI in identifying threats, the lack of transparency in the decision-making process can make it difficult to comprehend the basis for certain classifications. The Generative Adversarial Networks (GANs) attack is one of the most commonly used attack models that targets black box systems [26]. The GAN model comprises a pair of networks, where one generates data and the other determines whether the output is authentic or counterfeit. The two networks continuously interact in this manner until the first network reliably produces content that can deceive the other [27].  
Artificial intelligence (AI) in MR is crucial for enhancing user interactions by dynamically adjusting digital elements based on real-world inputs. However, MR's dependency on AI-driven contextual recognition, object identification, and behavioral prediction exposes it to additional security risks. AI models in MR must process vast amounts of personal data in real time, making them vulnerable to attacks that exploit model weaknesses, such as adversarial attacks [25].
## 3 Empirical Results
### Efficiency Evaluation over the Period 2018-2022
Moreover, it is observed that, during 2018-2022, the lowest average score will be 0.38, while the highest will be 8.89, which indicated the larger distance between different DMU. This finding raises an alert to the DMUs which poorly perform in the industry as by using the same level of given inputs other companies can generate a much higher level of outputs than them or by using far fewer inputs to generate the same level of outputs.
## 2 Proposed Interface Layer Using a CPW
### Fabrication and Measurement  
Figure 2a,b present photographs of the fabricated interface layer. The fabricated interface layer using the CPW has dense vias around the CPW line. The SMP connector and feed-pin are combined with the CPW of the interface layer by soldering. To measure its transmission coefficients, the proposed interface layer is extended to a back-to-back configuration, which allows for investigation of radar performance degradation caused by the interface layer, as shown in Figure 2c. Figure 2d illustrates a photograph of the fabricated back-to-back configuration. In this configuration, the bottoms of the CPWs face each other, and the feed-pin penetrates the two CPW layers. To verify its feasibility, the fabricated interface layer in the back-to-back configuration is measured in a full anechoic chamber. Figure 3 represents the measured and simulated reflection (\(S_{11}\)) and transmission coefficients (\(S_{21}\)) of the proposed interface layer in the back-to-back configuration. The measured (solid line) and simulated (dashed line) reflection coefficients are below \(-22.4\) dB and \(-29.2\) dB, respectively, in the operating frequency. In addition, the transmission coefficients for the measured results exceed \(-0.82\) dB (dotted line), and for the simulated results exceed \(-0.14\) dB (dashed-dotted line). The interface layer characteristics, such as reflection coefficient, target frequency, and interface layer size, are listed in Table 2 to compare them with previous studies. The proposed interface layer has good reflection characteristics and a small size in our target frequency band from \(9\) GHz to \(10\) GHz. These results demonstrate that the performance of the proposed interface layer using the CPW is suitable for X-band radar applications.  
Figure 1: Geometry of the proposed interface layer using the CPW: (**a**) isometric view; (**b**) top view; (**c**) bottom view.  
Figure 2: Photograph of the proposed interface layer and back-to-back configuration: (**a**) top view; (**b**) bottom view; (**c**) geometry of the back-to-back configuration; (**d**) fabrication of the back-to-back configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research** & \begin{tabular}{c} **Reflection** \\ **Coefficient** \\ \end{tabular} & \
\begin{tabular}{c} **Target** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Size (mm)** \\ \end{tabular} \\ \hline
[16] & \(-\)20 dB & 8–12 GHz & \(13\times 16\times\) - \\
[19] & - & 8.5–11 GHz & \(14\times 14\times 6.1\) \\
[20] & \(-\)13.3 dB & 8.5–10.5 GHz & \(20\times 20\times 3.7\) \\
[21] & \(-\)20.8 dB & 8.8–10.4 GHz & \(20\times 20\times 2.6\) \\ Proposed interface layer & \(-\)26.9 dB & 9–10 GHz & \(17\times 17\times 0.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of the interface layer characteristics.
# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 4 Experiments
### Experimental Results and Comparison  
#### 4.2.1 Baselines and Configurations  
The LSTM, GRU, and TCN models are included for an evaluation to build a baseline test benchmark. The LSTM model is built with two hidden LSTM layers. The first LSTM layer identifies 200 hidden nodes, and the second LSTM layer identifies 150 nodes. The final layer is dense. The GRU model is built with two hidden GRU layers. The first GRU layer identifies 103 hidden nodes, and the second GRU layer identifies 103 nodes. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameters** & **Dataset 1** & **Dataset 2** & **Dataset 3** \\ \hline
1D convolutional window size & 3 & 3 & 3 \\ Number of filters in each convolution layer & 32 & 32 & 32 \\ Stride 1 & 12 & 24 & 24 \\ Stride 2 & 7 & 7 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Configuration for the heuristic-stride-TCN for three datasets.  
final layer is dense with the rectified linear unit activation function. In both LSTM and GRU models, the dropout is 25%, the optimizer is Adam, and the loss function is MSE to train the model. The TCN model was built using Bayesian optimization to determine the optimal hyperparameters in Table 1.  
Both the TCN baseline model and our proposed stride-TCN are built with BO for the optimal hyperparameters. To find an optimum robust model that is general to an arbitrary training process, we keep the hyperparameters on the training phase unchanged and only search for TCN architecture involving kernel size, dilation, and the number of layers which are the most important hyperparameters. On the other hand, we further shrink the search space of the Stride-TCN family where only stride, the number of layers, kernel size, and whether using dropout is considered. Our purpose is to drive the search process to focus more on finding the best stride hyperparameters yet not too much to suppress the contribution of other important hyperparameters. The range for each hyperparameter of the stride-TCN family is given in Table 2. Additional TCN and stride-TCN use Huber loss during training, parameter \(\delta\) is set to 1 for all cases.  
As mentioned above, configurations of the training phase are kept unchanged for all learning models. For details, we set the starting learning rate at 0.001 and reduce it by 1% when there is no improvement in validation loss. We train the model for 100 epochs with the Adam optimizer batch size of 32. In addition, we apply early stopping when there is no improvement after 20 epochs.
## 3 Results and Discussion  
### Texture Profile Analysis  
Texture profile analysis was conducted on patty samples with and without the addition of _K. alvarezii_, as shown in Table 2. Adding seaweed into the chicken patty significantly increased the hardness (\(p<0.05\)). Hardness is the maximum force during the first compression of the food sample on the texture analyser [29; 30]. The greatest hardness value (2.86 N, Table 2) was obtained in F4 containing 4% _Kappaphyccus alvarezii_ and a similar salt content to the control formulation. The protein from the seaweed created a more rigid protein network, creating a harder and coarse texture patty. The low-salt meat was faced with a lack of protein aggregation to form a strong protein network. However, the increased hardness indicated that _Kappaphycus alvarezii_ can overcome texture issues in low salt patties.  
In addition, F4 (2.20 N) showed a significantly higher (\(p<0.05\)) chewiness value than the control sample (1.06 N). The effort to chew the food product is positively associated with the food's hardness value, increasing the chewiness value [31]. A similar finding was also reported by [32], in which the sausage with a higher salt content tended to be harder to chew. The cohesiveness and elasticity values also rose as more seaweed was added to the patty (Table 2). However, there was no significant difference (\(p>0.05\)) in the cohesiveness of F1 (0.78) and F2 (0.79) from the control (0.76). The result demonstrates the ability of _Kappaphycus alvarezii_ as a meat extender at reduced-salt content.
# Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series
## 3 Advanced Deep Learning Techniques
### Convolutional Neural Networks  
Convolutional Neural Network (CNN) models [26; 27] were originally proposed for image recognition problems, achieving human level performance in many cases. CNNs have great potential to identify the complex patterns hidden in time series data. The advantage of the utilization of CNNs for time series is that they can efficiently extract knowledge and learn an internal representation from the raw time series data directly and they do not require special knowledge from the application domain to filter input features [10].  
A typical CNN consists of two main components: In the first component, mathematical operations, called convolution and pooling, are utilized to develop features of the input data while in the second component, the generated features are used as input to a usually fully-connected neural network.  
The convolutional layer constitutes the core of a CNN which systematically applies trained filters to input data for generating feature maps. Convolution can be considered as applying and sliding a one dimension (time) filter over the time series [28]. Moreover, since the output of a convolution is a new filtered time series, the application of several convolutions implies the generation of a multivariate times series whose dimension equals with the number of utilized filters in the layer. The rationale behind this strategy is that the application of several convolution leads to the generation of multiple discriminative features which usually improve the model's performance. In practice, this kind of layer is proven to be very efficient and stacking different convolutional layers allows deeper layers to learn high-order or more abstract features and layers close to the input to learn low-level features.  
Pooling layers were proposed to address the limitation that feature maps generated by the convolutional layers, record the precise position of features in the input. These layers aggregate over a sliding window over these feature maps, reducing their length, in order to attain some translation invariance of the trained features. More analytically, the feature maps obtained from the previous convolutional layer are pooled over temporal neighborhood separately by sum pooling function or by max pooling function in order to developed a new set of pooled feature maps. Notice that the output pooled feature maps constitute a filtered version of the features maps which are imported as inputs in the pooling layer [28]. This implies that small translations of the inputs of the CNN, which are usually detected by the convolutional layers, will become approximately invariant.  
Finally, in addition to convolutional and pooling layers, some include batch normalization layers [29] and dropout layers [30] in order to accelerate the training process and reduce overfitting, respectively.
## 4 Inner Loop Controller Design  
The inner loop control is a combination of linear PD control and nonlinear SMC with adaptive control. The PD control part is used to stabilize the nominal model, the SMC part is used to compensate the external disturbances and system uncertainties, and the adaptive control part is utilized to estimate the unknown system parameters.
## 2 Results and Discussions
### Maturation State Indices
#### 2.3.1 Characterization of Copoazu Maturation States by ATR-FTIR  
The overlaid FTIR spectra of the three maturation stages (Figure S7) revealed similar overall profiles, with variations in the intensity and presence of certain absorption bands depending on the ripening stage. The absorption band at 1589.34 cm\({}^{-1}\) increased as the maturation state progressed. This is attributed to carotenoid compounds owing to C-C stretching in highly conjugated double-bond systems [34]. The increased intensity of the band coincided with an increase in carotenoid content, which is responsible for the pale-yellow color of the pulp of the ripe copoazu (Figure 2). Conversely, the bands at 1226.73 and 1712.79 cm\({}^{-1}\) decrease with copoazu maturation. Bands corresponding to the stretching vibrations of the C-C(O)-C and (C=O\(\cdots\) H) bonds are related to compounds of the cell wall formed by polysaccharides, such as cutin, phenolic compounds, and pectin [35; 36]. During maturation, polysaccharides degrade, intensifying the organoleptic properties of fruits [37].