esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 2 Proactive Service: Gains, Costs, and Needs  
Without waiting for users' orders, a cache-empowered RAN provides proactive services. We first review how caching benefits RANs, what it has to pay, and what is required. From both communication and memory perspectives, we come to a conclusion that a binary decision on \"to cache or not to cache\" is insufficient [11].
## 5 Data Processing and Simulation Experiment Setup  
The experiment used the CitySim dataset from the Intelligent Transportation Safety Laboratory (UCF-SST) at the University of Central Florida, with a total duration of 1200 min, including driving trajectory data for over 10,000 vehicles [28]. After visualizing and analyzing the vehicle trajectories, it was found that the data from the Expressway A urban expressway interwoven section exhibited concentrated distributions of driving behaviors such as vehicle following and lane changing. To effectively quantify the risk of various interactive behaviors of vehicles in complex traffic environments, this section was selected to validate the accuracy of the trajectory prediction model and the effectiveness of the risk quantification method. Considering the computational capacity of the equipment, the input observation time (\(T_{h}\)) was set to 1 s, the prediction time range (\(\tau\)) was set to 6 s, and the risk field half-life (\(t_{1/2}\)) was set to 0.5 s.
still, the project configuration he or she chooses may not be the best solution among all the feasible possibilities. In fact, achieving the optimal solution requires evaluating such a huge number of design parameters and alternative configurations that it seems unrealistic to be managed without the help of an automated multi-criteria optimization tool.  
In order to achieve the best feasible building-system configuration, energy optimization tools should therefore be used from the early design phases [3,4]. Nonetheless, during the early design steps, such as in feasibility analyses, a plethora of design parameters still needs to be defined, typically pertaining to both building geometry and construction. Therefore, a lot of alternative design configurations should be compared so as to make the optimal choices. This is even more important if we consider that energy projects should also embed lighting, comfort and indoor air quality assessments in addition to the energy consumption forecasts. Moreover, these kinds of analyses should also be coupled with at least financial payback estimations or life cycle assessments in order to identify the most cost-effective configuration, since economic feasibility obviously plays a huge role in energy projects.  
In this context, the authors of the present research paper suggest developing a set of automatic energy assessment tools based on machine learning techniques. These forecasting tools are meant to automatically estimate the energy consumption for any building on the basis of a limited amount of descriptive data of the property, which is suitable for the early design stages in a project. The aim is to provide an easy-to-use and extremely fast energy forecasting tool that can easily be used during the preliminary energy assessments of a project, as well as in comparative analyses or even in tandem with optimization procedures.  
In particular, the authors have developed several artificial neural networks (ANNs) to forecast the main contributions to a building's yearly energy consumption, such as heating needs, cooling needs, electricity consumption, contribution from ventilation air flow rates, solar gains and the building envelope. The ANNs developed could be very useful in the fast assessment of the energy demand for a building or even for a considerable number of buildings together, allowing the comparison of different design options and helping with identifying the optimal energy project among multiple alternatives.  
This research is focussed on the Po Valley area in Northern Italy, which is a zone with homogeneous weather characteristics and hosts a population of about 20 million inhabitants today.
## 2 Background and Proposed Method
### Proposed Method Overview
#### 2.4.2 Canopy/Mini Batch K-Means++  
In the application of K-means [51] clustering to HuBERT/WavLM, the determination of the number of clusters and the identification of initial cluster centers can present challenges, particularly when large-scale datasets are involved. Figure 5 and Algorithm 1 illustrate the approach adopted in this study, which employs Canopy [52] and Mini Batch k-means++ [53] to address these issues. The approach is as follows: (1) Set initial distance thresholds \(T_{1}\) and \(T_{2}\) (\(T_{1}>T_{2}\)). Calculate the Euclidean distance between all data and thresholds. Determine whether new clusters are formed and find the appropriate number of clusters \(K\). (2) Assuming that n initial clustering centers have been selected (\(0<n<K\)), points that are further away from current centers will have a higher probability of being selected as the (\(n+1\))th cluster center until selection of \(n\) clustering centroids is completed. (3) A portion of the dataset is randomly selected and allocated to clusters based on proximity to their centroids. The centroids are iteratively updated until stability is achieved or the maximum number of iterations is reached.  
Figure 4: The PNR-TDNN pipeline and the specifics of the proposed TDNN architecture are as follows: (**a**) depicts the conversion of speech to loss; (**b**) outlines the structure of the fusion Res2 Net within the TDNN module; (**c**,**c1**) detail the dimensionality of the features in the SE-attentive mean module; (**d**) illustrates the dimension transformation of features in the multi-head attentive statistics pooling module, while (**d1**) provides the computation details of the pooling factor \(\alpha\).
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 2 Health-Promoting Carotenoids from Microalgae and Their Biofunctions
### Health-Promoting Carotenoids and Their Production from Microalgae
#### 2.2.3 \(\beta\)-Carotene  
\(\beta\)-carotene (C\({}_{40}\)H\({}_{56}\)) is a pure hydrocarbon carotenoid and commonly exists in plants (pumpkins, mango, carrots, etc.), fungi (_Plaffia rhodozyma_) and algae [57]. It is lipid-soluble and serves as the precursor of Vitamin A in human body, benefiting the treatment of night blindness [2]. Currently, \(\beta\)-carotene is widely used in foods and medicines for its excellent antioxidant, anti-cardiovascular and immune enhancement properties [57]. Although chemically synthetic \(\beta\)-carotene is low in cost, natural \(\beta\)-carotene is more acceptable to consumers.  
\(\beta\)-carotene is a primary carotenoid in microalgae, responsible for transferring light energy to chlorophylls to expand the light absorbing spectrum [40]. Moreover, it is also the first ever commercialized high-value product from microalgae. \(\beta\)-carotene from microalgae has been approved by the FDA for direct human consumption [8].  
The marine microalga _D. salina_ can accumulate \(\beta\)-carotene up to 13% DW [58] when induced by different stress factors, such as high light, high temperature, nutrient deprivation and high salinity [59], which is the main source of natural \(\beta\)-carotene. As _D. salina_ is capable of growing in high salinity, cultivation of _D. salina_ in seawater with high yield of \(\beta\)-carotene is profitable, which may also avoid microorganism contamination. In addition, many species of microalgae can synthesize \(\beta\)-carotene. _Chorella zofingiensis_, _Spirulina platensis_ and _Caulerpa taxifolia_ accumulate \(\beta\)-carotene at an average yield of 0.1-2% DW [40]. Studies have shown that the \(\beta\)-carotene content of _Spirulina_ is 10 times that in carrots [57]. A mutant _C. zofingiensis bkt1_ can accumulate high amounts of three carotenoids, as lutein (13.81 mg/g DW), \(\beta\)-carotene (7.18 mg/g DW) and zeaxanthin (7.00 mg/g DW) under high light and salt stress [33]. Results of satisfactory \(\beta\)-carotene production from microalgae are summarized in Table 2 with details of contents and productivities.  
Moreover, the profiles of chemically synthesized and microalgae-derived \(\beta\)-carotene are different. The synthetic \(\beta\)-carotene only has (_all-E_)-isomer. In contrast, \(\beta\)-carotene from \(D\). _salina_ mainly contains three isomers, (_all-E_)-\(\beta\)-carotene (-42%), (9Z)-\(\beta\)-carotene (-41%), (15Z)-\(\beta\)-carotene (-10%) and others (-6%) [60]. As the (9Z)-\(\beta\)-carotene plays a key role in antioxidation, microalgae have potentials in producing low-cost and safer natural \(\beta\)-carotene for stronger biofunctions.
# A Feasibility Study of Utilizing Nuclear Energy for an Existing MED-TVC Desalination Plant
## 4 Water Cost Estimation
### Nuclear Heating Reactor
With respect to the tax rate, the maximum income tax for resident companies in Saudi Arabia with full foreign ownership is 20% [20]. Such a project will certainly have joint ownership between local and foreign companies, whereas in this case, the tax rate will be lower than 20%. By conducting DEEP analysis for NHR170, which is coupled with the MED-TVC, it was found that the cost of producing fresh water is 1.38 USD/m\({}^{3}\), with a simple payback  
Figure 5: Simplified schematic diagram of suggested NHR170 coupled with the MED-TVC. \begin{table}
\begin{tabular}{c c} \hline \hline
**Nuclear Heating Reactor Economic Parameters** \\ \hline Construction Duration & 40 months \\ \hline Lifetime & 60 years \\ \hline Specific Construction Cost (EPC) & 1100 USD / kW (th) \\ \hline Specific Fuel Cost & 3 USD/MWh \\ \hline Specific O\&M Cost & 2 USD/MWh \\ \hline Discount Rate (WACC) & 5\% \\ \hline Interest During Construction (IDC) & 5\% \\ \hline Annual Fuel Escalation & 2\% \\ \hline Tax rate & 20\% \\ \hline Loan Duration & 20 years \\ \hline \hline
**Desalination Plant Economic Parameters** \\ \hline Construction Duration & 30 months \\ \hline Lifetime & 30 years \\ \hline Water Purchase Price & 1.9 USD/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Economic parameters for the nuclear reactor NHR170 and the desalination plant. period of 5.8 years. A sensitivity analysis was conducted to analyze the most important factors that strongly impact the water production cost. Notably, the specific construction cost (USD/kW (th)) and the IDC are the most sensitive factors that affect water cost. Figure 6 depicts the cost of water (USD/m\({}^{3}\)) versus the cost of specific construction (USD/kW (th)), which ranges from USD 500 to USD 1500/kW (th). Also of note, the water cost increases linearly as the construction cost increases. At the lower bound of the range, the water cost is around 1.16 USD/m\({}^{3}\) when the construction cost is USD 500/kW (th), and when the construction cost is doubled, the water cost is increased by more than 15%. Figure 7, on the other hand, depicts water cost (USD/m\({}^{3}\)) versus IDC, where it is varied between 4% and 16%, which resembles the lower and upper limits of IDC in different nuclear projects, including large reactors and SMRs [21]. The water cost is directly proportional to the IDC, where the cost of water will be 40% higher if the IDC is increased to 10%, and nearly double if the IDC is increased further to 16%. Figure 6: Sensitivity analysis of water cost (USD/m\({}^{3}\)) versus specific construction cost (USD/kW (th)) of the nuclear heating reactor. Figure 7: Sensitivity analysis of water cost (USD/m\({}^{3}\)) versus IDC (%).
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Congestion Control Algorithms  
MPTCP CCAs are responsible for equally and efficiently sharing the available network resources while ensuring better throughput, fairness, and less delay in data communication. There are three primary design goals for MPTCP CCAs as follows [32]:  
* Goal One: MPTCP CCAs should always ensure incentives over single path TCP (SPTCP), implying that the throughput of MPTCP should be at least equal to or greater than SPTCP's throughput traveling through the same path.
* Goal Two: When multiple MPTCP SFs go through the same bottleneck, they should combinedly occupy a capacity equal to the capacity an SPTCP occupies while going through the same bottleneck.
* Goal Three: After fulfilling the first two goals, MPTCP CCAs should try to move the traffic from the more congested path to a less congested path among the available SFs.  
Several MPTCP CCAs have been proposed and made available in the Linux Kernel. The key MPTCP CCAs available in Linux Kernel are discussed as follows.
# Intelligent Diagnosis of Compound Faults of Gearboxes Based on Periodical Group Sparse Model
## 2 Materials and Methods
### Theoretical Methods
#### 2.1.3 Wavelet Packet Energy Features Extraction  
The decoupled and separated fault signal are both sparse, i.e., a large number of data sampling points in the signal are zero, and there is a presence of certain interference components in the decoupled single fault signal. Therefore, the direct segmentation of the fault signal as a dictionary and for learning will inevitably lead to the degradation of the dictionary quality and consume more computation time. For this reason, wavelet packet decomposition is firstly used to improve the data quality and enhance the variability of the energy distribution of different health states, which provides prerequisites for subsequent fault classification and identification.  
Wavelet packet decomposition adds the decomposition of the high frequency part of the signal to the wavelet transform. Through the multi-scale decomposition of frequency bands, the decomposition process is neither redundant nor loose, and at the same time more detailed and comprehensive, so that it has stronger transient information extraction ability. It has a better performance of time-frequency localization analysis and thus improves the time-frequency resolution.  
Wavelet packet decomposition obtains the signal components of the corresponding sub-bands using the multi-level division and multi-scale decomposition of the signal frequency bands. The characteristic information contained in the original vibration data is dispersed into the sub-band signals, and according to the different energy distributions of the signals in different frequency intervals that will be healthy, the difference in the energy characteristic distribution can be used as an important criterion for fault classification and identification. The steps for wavelet packet energy feature extraction are as follows:  
1. The fault signal is decomposed into \(2^{n}\) frequency bands, \(S_{i,j}\) denotes the decomposed signal of the \(j\)th node in the \(i\)-th layer. The wavelet packet coefficients of the node are \(x_{i,j}\).
2. The corresponding low-frequency and high-frequency coefficients are obtained after multiple layer decomposition, and the signal is reconstructed, and the expression of the reconstructed signal is as follows: \[S=S_{(i,0)}+S_{(i,1)}+\ldots+S_{(i,2^{n}-1)}\] (12)Calculate the energy of each sub-band signal. \[E_{ij}=\int|S_{ij}(t)|^{2}dt=\sum\limits_{k=1}^{n}x_{ij}(k)\] (13) (4) Calculate the total energy and obtain the fault feature vectors with different energy percentages after normalization. \[E=\sum\limits_{j=0}^{2^{i}-1}E_{i,j}\] (14) \[T=\left[\frac{E_{i,0}}{E},\frac{E_{i,1}}{E},\ldots,\frac{E_{i,2^{n}-1}}{E}\right]\] (15)
# Metallic Nanoparticles Obtained via \"Green\" Synthesis as a Platform for Biosensor Construction
## Abbreviations  
NPs Nanoparticles  
gnNPs Nanoparticles obtained via green synthesis  
gPdNPs Pd-based gNPs  
gCr\({}_{2}\)O\({}_{3}\)-based gNPs  
AO Alcohol oxidase  
HRP Horseradish peroxidase  
ABS Amperometric biosensor  
GE Graphite electrode  
ABTS 2,2\({}^{\prime}\)-azinobis (3-ethylbenzothazoline-6-sulfonate) diammonium salt  
PB Phosphate buffer  
SEM Scanning electron microscope  
SEM-XRM Scanning electron microscopy coupled with X-ray microanalysis  
FM Fluorescence microscope  
RM Reaction mixture  
FTIR Fourier transform infrared spectroscopy  
BP Bisphenol A  
\(K_{M}\)\({}^{app}\) Michaelis-Menten constant  
\(I_{max}\) Maximal current response on tested analyte at substrate saturation
## 2 Methodology
### Machine Learning Pipeline
#### 2.2.2 Regressors  
Support Vector Regression  
One of the most popular machine learning tool for classification is the Support Vector Machines (SVM), first introduced in [57]. Many variations have been introduced since then, and we will focus on the Epsilon Support Vector Regression, whereby the goal is to find a function \(F\) that does not deviate more than \(\epsilon\) from \(y_{n}\) for each training point \(x_{n}\), and is as flat as possible. The resulting regressor takes the following form:  
\[F(\phi(x);\theta)=\sum_{n=1}^{N}(\theta_{n}-\hat{\theta}_{n})k(\phi(x_{n}), \phi(x))+\theta_{0}, \tag{3}\]  
where \(k(\phi(x_{n}),\phi(x))\) is a kernel function, and \(\theta_{n}\) and \(\hat{\theta}_{n}\) follow from the solution of the dual formulation [57].  
Gradient boosting is a common machine learning method where multiple weak prediction models (usually CART tree learners) are combined into one predictive model.  
The regression gradient boosting algorithm was first introduced in [56] and it modifies this algorithm such that it chooses a separate optimal value \(\gamma_{jm}\) for each of the tree's regions \(R_{jm}\), instead of a single \(\gamma_{m}\) for the whole tree. Then, the update rule for the model becomes:  
\[F_{m}(x)=F_{m-1}(x)+\sum_{j=1}^{J_{m}}\gamma_{jm}\mathbf{1}_{R_{jm}}(x), \tag{4}\]  
\[\gamma_{jm}=\operatorname*{arg\,min}_{\gamma}\sum_{x_{i}\in R_{jm}}L(y_{i},F_ {m-1}(x_{i})+\gamma). \tag{5}\]  
XGBoost  
XGBoost is an improved version of previously developed gradient boosted models (GBM) [54]. However, GBM builds trees sequentially, XGBoost is parallelized, therefore making the algorithm much faster. In addition, XGBoost also includes regularization in the objective function, which punishes more complex models and prevents overfitting.  
The ridge regression is a popular variation to linear regression that adds L2 regularization to the loss function, hereby preventing overfitting to the training data. Let \(\lambda>0\) be a regularization factor. Then the penalized loss function in the ridge regression can be formulated as follows:  
\[\mathcal{L}(\theta;\mathcal{D})=\frac{1}{N}\sum_{n=1}^{N}(y_{n}-\sum_{j=1}^{p }x_{nj}\theta_{j})^{2}+\lambda\sum_{j=1}^{p}\theta_{j}^{2}. \tag{6}\]Nearest-Neighbor Regression  
The Nearest-Neighbor algorithm is a non-parametric regression introduced in [58]. It works by predicting target values through interpolation of the targets of \(k\) nearest neighbors in the training set, scaled by a weighting factor. Most commonly they are weighted by the inverse of their distance.
## 2 Related Work
To reduce the communication overhead for energy saving, graph partitioning and rejoining is utilized in some of the proposed schemes, whereas to predict throughput and minimizing power consumption, LSTM has been used [56]. Recent works have shown that UAV trajectory optimization requires free space pathloss channel model when the UAV is flying at a sufficiently high altitude where the LoS is dominant [57]. However, in urban scenarios, buildings and small-scale fading cannot be ignored, and the channel is characterized by the Rician fading model. To address random channel capacity in Rician fading, some works have proposed to approximate the maximum allowed transmission rate, based on which a trajectory design method is presented to minimize the task completion time in Rician shadowed fading channel models [58]. In some approaches, the ratio of LoS-to-NLoS components and the degree of LoS shadowing is determined. Some approaches involved time slots where the UAV position is assumed to be invariant in each slot, optimizing them to approximate the optimal trajectory [59]. The path loss between a UAV and a vehicle depends on their respective positions as well as the propagation environment, which varies when a UAV is in a rural, suburban, urban, or high-rise environment. The movement of UAVs affects the amplitude, phase, and delay of the received signal [60]. Some advantages of FR in latency minimization of UAV-assisted C-V2X networks are summarized in Table 2.
## 2 Literature Review  
### Studies Focusing on BAP or QCAP  
Some studies have been dedicated to dealing with the BAP. [PERSON] and [PERSON] [PERSON] and [PERSON] (2009) used a queuing network model to simulate the arrivals, berthing, and departures of vessels at a CT. Using the simulation tool, SLAM, they continued to find better solutions through \"what-if\" analyses. To deal with the BAP, [PERSON] and [PERSON] [PERSON] and [PERSON] (2009) formulated a MIP for the BAP and then proposed a simulated annealing algorithm to solve it. The derived solutions were found to be similar to those obtained from the MIP model. Following a multiple-stage decision procedure, [PERSON] and [PERSON] [PERSON] and [PERSON] (2009) solved the BAP using a stochastic beam search algorithm (SBSA), and the experimental results showed good performance of their algorithm. This algorithm was found to be more accurate and efficient than the state-of-the-art meta-heuristics and traditional deterministic beam search. [PERSON] and [PERSON] (2009) [PERSON] and [PERSON] (2009) presented a neighborhood search-based heuristic to determine the berthing time and space for each ship. In that study the quay was used as a continuous space with factors such as first-come-first-serve, clearance distance between ships, and the possibility of ship shifting taken into consideration. [PERSON] et al. [PERSON] et al. (2010) dealt with BAP through a two-stage decision procedure in which the arrival times and handling times of vessels were treated as uncertainties. In addition, they provided a meta-heuristic to deal with problems of practical size. [PERSON] et al. [PERSON] et al. (2010) also dealt with the BAP with discrete berths. Having investigated three relevant models for solving the BAP, they improved one of the three models. Regarding a parallel-machine scheduling problem, [PERSON] et al. (2010) dealt with the BAP with constraints that included taking into account the water depth and tidal condition. That study considered both static and dynamic versions of BAP and a heuristic was proposed to deal with them.  
The above-mentioned studies only focused on the BAP, and our literature review showed only [PERSON] and [PERSON] [PERSON] and [PERSON] (2009) focused on the QCAP alone. Most studies have combined the QCAP with BAP or QCSP.