esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed CH Selection Protocol
### CH Selection Protocol  
In this subsection, we detail the proposed SSMOECHS protocol for CH selection.  
Figure 2(a) shows the communication protocol between the BS and nodes. When a WSN is established, each sensor node sends initial data to the BS, including node \(ID\) and location. The BS selects CHs through sampling-based SMO by using the received information. The BS informs the  
CHs that they have been selected as CHs and transmits synchronization information. This process is shown in Figure 3a as a line joining the gray circle A. After CH selection, data transference proceeds similarly to LEACH-C [7] and SMOTECP [18]. As shown in Figure 3b, the CHs inform their covered nodes about the selection and wait for acknowledgment, an acknowledgement (ACK) signal. The CHs that receive the ACK signal send a schedule for time division multiple access to the covered nodes and collect data for some period, after which the CHs transmit the collected data to the next CH or BS. Here, the CHs that send data to other CHs are called outer CHs, and CHs that send data directly to the BS are called inner CHs. This distinction is determined by calculating the median of the distances (MD) between the CHs and the BS, shown in Figure 3b as the flowchart of gray circle B. Data collection using wireless sensor nodes at the BS is repeated until the energy of all nodes is exhausted.  
Figure 3: Flowchart of sampling-based spider monkey optimization and energy-efficient cluster head (SSMOECHS) protocol. (**a**) Communication between the base station (BS) and nodes and (**b**) communication between cluster heads (CHs) and nodes (SSMO: sampling-based SMO and TDMA: time division multiple access).  
Figure 4 depicts inner CHs (yellow dots) and outer CHs (red dots), where the former are those relatively close to the BS and the latter are farther from the BS. Each CH collects the data of neighboring nodes (blue dots), and it then transmits them. Hence, inner CHs send data directly to the BS, whereas outer CHs determine and transmit data to their nearest inner CHs. As shown in Equation (14) that transmission energy is influenced by distance, and this transmission method can reduce energy consumption. The dataflow over the CHs is depicted with red lines in Figure 4.
## 2 Methodology
### Data Source and Preprocessing
#### 2.2.2 Pre-Processing and Feature Extraction  
The downloaded HTML document extracts the theme and content parts. The theme part is the header information in the HTML document, which can characterize the type of website, and the content part comes from the main part of the web document. The theme information was extracted from the <head></head> tag. Besides the page title described by the <title> tag, the content attributes in <meta name = \"description\" content = \"\"> and <meta name = \"keywords\" content = \"\"> also describe important information related to the theme [33; 34]. The content information is extracted from the <body> tag.  
It is necessary to preprocess the extracted website information. First, we designed a series of regular expressions to remove the meaningless information, such as tags and symbols. After that, we applied the word segmentation tool, since the research content in Chinese does not contain blank characters. In this study, we used an open-source word segmentation tool ([[https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba)]([https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba)), last accessed: 15 September 2021) named jieba library. To get website feature information, we selected the TF-IDF (term frequency-inverse document frequency) algorithm from the jieba library to implement website keyword extraction, and the key parameter was topK; that is, the number of keywords that were extracted as features. In the study, the value of this parameter was set to 50, because the features obtained from the larger range contained too much noisy information. The principle of jieba library and the TF-IDF algorithm is as follows.  
Jieba library is a famous Chinese lexical analysis system that adopts the model based on HMM (Hidden Markov Model) and contains several algorithms, among which are the Viterbi algorithm [35] for unknown words, the textrank algorithm [36], and the TF-IDF [37] algorithm for keyword extraction.  
Term frequency-inverse document frequency (TF-IDF) is a weighting technique commonly used in the field of information retrieval to measure the importance of a word in a document or corpus. TF-IDF converts a text corpus into a numerical vector, where each number represents the importance of each word for a given document [38]. The importance of a word increases proportionally with its number of occurrences in the document but decreases inversely with its frequency in the corpus. The numerical score is calculated by multiplying the term frequency (that is, the number of times a word appears in a document) by the reciprocal of the document frequency (Equation (1)).  
\[w_{i,j}=tf_{ij}\times\log(\frac{N}{df_{i}}) \tag{1}\]  
where \(tf_{ij}\) is the frequency of \(i\) appearing in \(j\), \(df_{i}\) is the number of documents containing \(i\), and \(N\) is the total number of documents.
## 3 Results
### Statistical Model

Table 4: The four equivalent stress parameters estimated from the experimental data. Figure 3: Probability–probability diagram of the estimated CDF (\(F_{mod}\)) versus the experimental empirical CDF (\(F_{exp}\)) for the 24 mm wide (in blue) and 36 mm wide specimens (in orange). The coefficient of determination, \(R^{2}\), of the least squares fit of the bisector to the points is also shown. provides a CDF for each specimen width, as plotted in Figure 4. An inverse size effect is apparent from the curves. For a given probability of failure, the critical equivalent stress in the wider specimens (w = 36 mm) is lower than the one predicted for the narrower specimens (w = 24 mm). In the authors' opinion, the size effect observed could stem from a geometrical effect. The highest stresses arising from the concentration around the hole are carried by the tows in the longitudinal direction. For a scaled-up specimen, as in a 36 mm wide versus a 24 mm wide one, the relative size of the tow to the hole decreases. This leads to a lowering in the effective stress concentration factor for the tows around the hole, due to a larger relative radius of the hole compared to tow size. The resulting improved distribution of stresses in the immediate vicinity of the hole enhances the load-bearing capacity of the material. A similar effect occurs for transverse stresses. The different specimen width-to-hole diameter ratios are indicated in the plot with different markers. The observed distribution of the specimen hole diameters in each group confirms the approach taken, which considers only specimen width as the size effect scaling factor. Hole sizes are mixed up along the curve, suggesting a lesser impact on size effect of width-to-diameter in the range considered. This is especially true for the wider specimens. For example, a C33 specimen, with the largest hole diameter in the width group, has the third lowest equivalent stress, while a C31 specimen, with the smallest hole diameter, shows the third highest equivalent stress.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 5 Discussion
### Predictive Model Performance: ARIMA and MLR
#### 5.1.2 MLR Model Insights  
The MLR model captured multivariable factors influencing demand, such as product type, customer behavior, and promotional activity, making it valuable in high-variability contexts. With an MAE of 5.1% and RMSE of 7.4%, the MLR model provided nuanced insights into short-term demand drivers, particularly during promotional campaigns. These insights are critical for perishable goods in cold chains, where demand can fluctuate significantly.  
However, the absence of real-time environmental and customer demographic data limited the model's accuracy. These findings align with [PERSON] et al. [4], emphasizing that predictive models benefit from multidimensional, real-time data. Incorporating IoT-enabled metrics, such as temperature and humidity, would enhance the MLR model's precision and adaptability across diverse conditions, further improving inventory and demand forecasting for cold chain logistics.
## 2 Materials and Methods
### Analytical Procedure for PTEs Determination  
The analysis of the samples was carried out using a Perkin Elmer SCIEX Elan DRC-e ICP-MS (MDS Inc., Concord, ON, Canada) system. The instrumental parameters and isotopes used for the analysis are presented in Table 1.  
The determination of the macroelements (Al, Ca, Fe, Na, Mg, Mn, K, P, and Ti) in mg/kg and wt.% concentrations was conducted after the optimization and introduction of a dynamic bandpass tuning parameter (RPa) for the signal reduction of the isotopes used, as described in [PERSON] et al. (2020) [42]. The selected individual RPa values are presented in brackets. The concentrations of the micro- and trace elements were determined in standard conditions, except for As and Se. For the correction of the spectral interferences, correction equations were introduced and are listed in [PERSON] et al. (2013) [39]. The potential polyatomic interferences of the selenium isotopes and \({}^{75}\)As were eliminated using a dynamic bandpass tuning parameter (RPq) with oxygen as a reaction gas, as described in [PERSON] and [PERSON] (2015) [43].  
External calibration with multielement standard solutions was performed. In order to resemble the analyzed matrices as much as possible, matrix elements were added to the calibration standards. The calibration standards were in the concentration range of 0.1-10 mg/L for Na, Mg, Al, K, Ca, and Fe; in the range of 0.1-1 mg/L for P, Ti, and Mn; in the range of 0.001-100 \(\upmu\)g/L for the microelements and trace elements.  
The accuracy of the ICP-MS measurements was checked by analysis of the plant, soil, and sediment reference materials: CRM DC 73348 and CRM DC 73349 (trace elements in bush branches and leaves, China National Analysis Center, Beijing, China), SRM NIST 1547 (peach leaves, National Institute of Standards and Technology, Gaithersburg, WA, USA), IAEA-Soil-5 and IAEA-Soil-7 (trace elements in soil, IAEA, Seibersdorf, Austria), and STSD-3 and STSD-4 (stream sediments, Canada Center for Mineral and Energy Technology, Geological Survey of Canada, ON, Canada).
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.13. RT3D  
RT3D (Reactive Transport in 3-Dimensions) simulates the reactive flow and transport of various mobile and/or immobile species in aquifers [664]. RT3D is based on the code MT3D, but has considerably extended the possibilities for reaction modelling. RT3D offers an operator splitting strategy [20, 712] for the contaminant transport equation. RT3D is equipped with several pre-programmed reaction modules and one user-defined reaction module. The list of ready-to-use modules includes: aerobic instantaneous degradation of BTEX, kinetically limited degradation of BTEX, a double-monod model, sequential degradation reactions, an aerobic/anaerobic model for the degradation of PCE/TCE, natural and enhanced attenuation of chloroethanes, chloroethenes, chloromethanes, and daughter products [713, 714]. Similar to MT3D, RT3D uses a separate model, usually Modflow [715], to determine the velocity and groundwater pressure distribution. As the RT3D code can be customised to simulate various processes, including microbially mediated responses, it has been used in several laboratory and pilot bioremediation experiments [716]. It can, and has, been used in many regional environmental studies on the fate of pollutants [23, 717, 718, 719, 720, 721]. Joo et al. [721] tested the RT3D model in a lumped approach to simulate the transport of 12 organic compounds in mixtures through a column filled with sands containing different proportions of organic carbon, generating new breakthrough curves and evaluating sorption capacity. RT3D is also integrated into the Visual Modflow program, the GMS package, and the Groundwater Vistas tool.
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 5 Conclusions  
The QKD network is a key infrastructure that allows end users/applications to access the key distribution service with unconditional security. To date, various countries and research institutes have invested numerous resources to execute theoretical studies and practical field experiments on QKD networks. This study surveys and summarizes the existing results of these studies and experiments and then proposes three security challenges: (1) the lack of a P2M mechanism in QKD networks, (2) many quantum node resources being consumed by the multiple-path strategy, and (3) no suitable security interface between the classical end users/applications and quantum nodes. In addition, some feasible solutions and strategies for these challenges are indicated to allow researchers to understand these challenges easily and to inspire them to propose the appropriate solutions.  
Conceptualization, C.-W.T. and Y.-C.C.; methodology, C.-W.T., C.-W.Y. and J.L.; investigation, C.-W.T. and Y.-C.C.; formal analysis, C.-W.T., writing--original draft, C.-W.T. and Y.-C.C.; writing--review & editing, J.L. and R.-S.C.; project Administration, R.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Ministry of Science and Technology, Taiwan, R.O.C. (Grant Nos. MOST 107-2218-E-143-002-MY2, MOST 106-2218-E-039-002-MY3 and MOST 107-2627-E-006 -001-), and China Medical University, Taiwan (Grant No. CMU109-S-01).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Problem Formulation
### Motion and Structural Parameters of the HAUV  
The HAUV discussed in our work is an integration of fixed-wing UAVs, rotary-wing UAVs, and UGs [8]. Details are illustrated in Figure 2. The mass of the HAUV is 15 kg, each wing is 750 mm \(*\) 300 mm, and the length of the glider is 1000 mm. The main structures of the HAUV are fixed wings, rotary wings, and a waterproof container.  
Figure 1: The path segment will be weighted according to the environment.  
Since the HAUV can move both underwater and aerially, the motion modes of the HAUV can be divided into three categories: underwater, aerial, and transitioning (between water and air). Figure 3 illustrates the motion of the HAUV.  
Consider the specific environment of the path between each two spot. The path that the HAUV visits between target spots \(p_{i}\) and \(p_{i+1}\) can be divided into four types. The four types of path in Figure 4 are as follows.  
1. Both \(p_{i}\) and \(p_{i+1}\) are underwater;
2. Both \(p_{i}\) and \(p_{i+1}\) are aerial;
3. \(p_{i}\) is underwater but \(p_{i+1}\) is aerial;
4. \(p_{i}\) is aerial but \(p_{i+1}\) is underwater.  
Figure 3: The three mission modes of the HAUV. The edge weight is different per unit of distance for each situation: the fulfill per meter underwater is 5 times of that of flying. The motion constraints also vary: the maximum velocities are: 3 m/s underwater, 10 m/s aerially, and 1 m/s while transitioning (between water and air). The accelerations are: 2 m\({}^{2}\)/s underwater, 3 m\({}^{2}\)/s aerially, and 1 m\({}^{2}\)/s while transitioning.  
Figure 2: A 3D rendering of the proposed HAUV integrating the features of fixed-wing UAVs, rotary-wing UAVs, and UGs [8].  
As the HAUV has different motion capabilities in diverse mission environments, different dynamic constraints should be set. Considering the experimental data of our former work, NEXHA, in this paper, we roughly define the motion capabilities of the HAUV as follows: The aerial velocity of the HAUV can range from 0 to 10 m/s, and it can range from 0 to 3 m/s under the water. The acceleration can vary from 0 to 3 m\({}^{2}\)/s aerially, and from 0 to 2 m\({}^{2}\)/s underwater. In addition, the HUAV can leave the water with a velocity no higher than 1 m/s and acceleration no greater than 1 m\({}^{2}\)/s. Within a fixed period, the HAUV will move from \(p_{i}\) to \(p_{i+1}\). The trajectory should conform to motion and space constraints.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Model Development
#### 2.3.2 Mathematical Model RANDOM Forest (RF)  
Random Forest builds with the use of several Decision Trees, an ensemble learning approach for forecasting energy demand, from arbitrary selections of training datasets and variables. Each tree independently makes its own forecast depending on inputs such as weather, socioeconomic, and past consumption design. The mathematical representation of this averaging process, given by Equations (17)-(22), results in the actual  
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & **Equation Description** & **Equation** & **NO. Equation** \\ \hline Input Layer & Input Features & \(\mathrm{x}=[x_{1},x_{2},\ldots,x_{n}]\) & (1) \\ Hidden Lever 1 & Weighted Sum & \(z^{1}=W^{1}\mathrm{x}+\mathrm{b}^{1}\) & (2) \\ Hidden Layer 2 & Weighted Sum & \(z^{2}=W^{2}\mathrm{h}^{1}+b^{2}\) & (3) \\ Output Layer & Weighted Sum & \(z^{3}=W^{3}\mathrm{h}^{2}+b^{3}\) & (4) \\  & Final Output (Prediction) & \(\widehat{y}=gz^{3}\) & (5) \\ Loss Function & Mean Squared Error & \(L=\frac{1}{N}\sum\limits_{i=1}^{N}\ (\widehat{y}-E_{actual}\ )^{2}\) & (6) \\ Backpropagation & Gradient of Loss with & \(\frac{\partial L}{\partial y}=-\frac{2}{N}E_{\mathrm{actual}}\ -\widehat{y}\) & (7) \\  & Gradient with respect to & \(\delta^{3}=\frac{\partial L}{\partial z^{3}}=\frac{\partial L}{\partial y} \delta^{\prime}z^{3}\) & (8) \\  & Last Layer & \(\delta^{2}=\delta^{3}\cdot W^{3}\cdot f^{\prime}z^{2}\) & (9) \\  & Gradient with respect to & \(\delta^{1}=\delta^{2}\cdot W^{2}\cdot f^{\prime}_{z}\) & (10) \\ Weight Updates & Update Rule for Weights & \(W^{1}\gets W^{1}-\eta\cdot\delta^{1}\cdot x^{T}\) & (11) \\  & Update Rule for Weights & \(W^{2}\gets W^{2}-\eta\ \delta^{2}\cdot\mathrm{h}^{1T}\) & (12) \\  & Update Rule for Weights & \(W^{3}\gets W^{3}-\eta\delta^{3}\cdot\mathrm{h}^{2T}\) & (13) \\  & (Output Layer) & \(b^{1}\gets b^{1}-\eta\cdot\delta^{1}\) & (14) \\  & (Hidden Layer 1) & \(b^{2}\gets b^{2}-\eta\cdot\delta^{2}\) & (15) \\  & Update Rule for Biases & \(b^{3}\gets b^{3}-\eta\cdot\delta^{3}\) & (16) \\ \hline \end{tabular}
\end{table}
Table 2: Mathematical model for ANN.  
forecast from Random Forest, which enhances accuracy and reduces overfitting. Moreover, as seen in Table 3, Random Forest can also give some insight into feature relevance, which can help an energy provider decide on which factor is influencing demand most [46].
## 2 Air-to-Space Channel Models
### Theoretical Studies and Simulations for Channel Modeling
However, it is worth noting that these simulation tests did not take into account the excess loss due to rain or clouds. These observed path-loss values indicate the necessity of using large-aperture antennas, as in those previously mentioned, at both terminals, in order to compensate for the considerable loss and establish successful links. Another observation was the path-loss variation as a function of the airplane altitude. The simulation results revealed that at high-altitude links, the free-space loss dominated the signal propagation. However, the excess loss from the molecular absorption increased with frequency, even at lower altitudes, implying that A2S links demonstrate better performance at higher airplane heights. Based on the results, the THz band is promising for A2S and S2A links, although these should be limited at frequencies below 380 GHz in order to effectively compensate path loss, provided that antennas with sufficient apertures are employed. Finally, it should be mentioned that additional losses may be encountered due to weather phenomena such as rain, clouds, and fog, or scattering loss from the ground. In the frame of a SAGIN communication link, the A2S narrow-band channel was theoretically investigated in [20], exploiting UAV as a relay with a LEO satellite. The stochastic model was based on regular geometry, in which the scatterers were distributed on a hemisphere and the VFM distribution was employed to jointly consider the azimuth and elevation angles, with the aim of resolving the statistical properties of the channel. These involved the space-time correlation function (STCF), the Doppler spectrum, the average fade duration (AFD) and the level crossing rate (LCR) of the channel. The results revealed that the moving direction of the UAV, the antenna direction, and the angle all significantly affect the statistical properties of the channel.
# Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage
## 2 Material and Methods
### System Description  
This study examines a renewable-driven system for the building sector that incorporates photovoltaics and hydrogen storage. This system covers all the needs of the building and it can lead to a zero-energy building (see Figure 2). The building uses a reversible heat pump for covering the heating and cooling loads, while the DHW needs are covered with an electrical heater. The heat pump was selected to have a relatively satisfying performance for examining a well-designed building. So, the seasonal coefficient of performance (SCOP) was selected at 4 and the seasonal energy efficiency ratio (SEER) at 4. The electrical heater for the DHW was selected to have an efficiency of 95% [24]. The DHW was provided at 45 \({}^{\circ}\)C [27], while the grid water temperature has been taken from Ref. [28]. It is useful to state that the average grid water temperature is close to 18 \({}^{\circ}\)C and every person needs 50 L per day hot water [24]. The daily variation demand profile of DHW has been taken from the literature [29] and the respective electrical demand is depicted in Figure 1. It is obvious that the electrical demand in the winter is higher in comparison to the summer period due to the lower grid water temperature in the winter.  
Figure 1: Description of the electrical profile demands for DHW, and appliances—lighting.  
The electrical needs of the system are covered by photovoltaic panels of high efficiency (X63 Premium PV) which has a nominal efficiency of 19.7% (X63L345 panel) [30]. The slope of the photovoltaics was selected at 30\({}^{\circ}\) which is an optimal choice according to the PVGIS tool [31]. In the present work, meteorological data were used by the libraries of the TRNSYS tool [22] and for the slope of 30\({}^{\circ}\) in the south direction, the yearly solar potential was found at 1739 kWh/m\({}^{2}\).  
The electricity, which is not directly consumed by the building needs, feeds an electrolyzer for hydrogen production. More details regarding the electrolyzer can be found in Refs. [32, 33, 34]. More specifically, the electrolyzer is a proton exchange membrane water with a mean conversion efficiency of 60% according to the aforementioned studies. More specifically, the electrolyzer operates at 1 bar, with water at 80 \({}^{\circ}\)C and a membrane with 100 \(\upmu\)m thickness. The hydrogen is stored under high pressure of 100 bar where its density is around 7.7 kg/m\({}^{3}\), while its lower heating value (LHV) was selected at 120 MJ/kg [35]. When there is a demand for electricity and the photovoltaics cannot produce it, then the stored hydrogen feeds a fuel cell for electricity production. More specifically, the present system includes a proton exchange membrane fuel cell with a mean conversion efficiency of 40% [36]. Table 2 summarizes the main data of the present system.  
Figure 2: The suggested system for covering all the energy needs of the building.