esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Future Works  
Based on highly incorporated ecological processes and mechanisms, ecosystem models can be used to extrapolate the in-site knowledge to large temporal-spatial scales [46]. The Biome-BGC model can simulate the different responses of carbon dynamics among the different arid and semi-arid ecosystems in Central Asia. However, this study showed that using a process-based ecosystem model has limitations, such as input data limitations. In addition, management effects on plant growth, such as cropland irrigation, fertilizer application, and insect control, have not been considered in this study. Although we analyzed the direct effect of temperature on NPP and NEP, we did not comprehensively analyze the impacts of changing vapor pressure deficit (VPD) on stomata conductance under the context of temperature increasing, because there are studies indicating that VPD might have more influence on grassland carbon dynamics precipitation [46; 66]. In addition, there are also researchers who found that seasonal difference in water availability affected plant primary productivity to a different extent. Therefore, monthly precipitation was supposed to be a better indicator than annual precipitation. In summary, the input data should be refined, a refined Biome-BGC model should be developed, and the carbon-nitrogen-water process should be analyzed comprehensively.
## 4 Optimization Problem Raising and Solving
### Solution to the Optimization Problem
Since the channel fading \(L\) is nonnegative, the second derivative of \(L\) with respect to \(\theta\) is less than or equal to 0, which indicates that the objective function \(L\) is a convex function about \(\theta\). In other words, there must exist a value of \(\theta\) that maximizes \(L\) and thus maximizes the capacity lower bound \(C_{Low}\). Since (47) is convex and the constraint is also convex, the optimization issue of (46) is a convex optimization problem. With the first derivative of \(L\) with respect to \(\theta\) being equal to zero, the optimal tilting angle \(\theta_{0}\) for reaching the maximum lower capacity bound is  
\[\frac{\sqrt{\mu}}{\sqrt{\pi}erf\big{(}\sqrt{\mu}\big{)}}\cdot\frac{1}{H\sqrt{ \varsigma^{2}PH}}\cdot tan^{-1}\sqrt{\varsigma^{2}PH+}\]4  
where \(d^{\prime}\) is the distance between the receiver and the projection point of light source on the \(X\)-\(Y\) circular plane (see Figure 2). Combining Figure 2 and (48), it is easy to see that the pointing error angle \(\beta\) is equal to 0 degree while the lower capacity bound \(C_{Low}\) obtains its maximum, that is, the optimal tilting angle \(\theta_{0}\) is the case that the normal vector \(\overset{\rightarrow}{V}_{n}\) and the incident light are fully aligned.
## 4 Targeted Delivery of Antibiotics to Sites of Infection  
Compared with controlled drug delivery, the selective delivery of the antimicrobials to the infected site is an equally important function of an effective nanocar. Indeed, systemic administration of antibiotics is the preferred regimen in the treatment of most infectious diseases. However, the uniform distribution throughout the entire body results in only a small fraction of the antibiotic reaching the site of infection, especially in poorly irrigated areas. The subtherapeutic antibiotic concentration is known to exacerbate infectious complications further and promote antibiotic resistance. Indeed, resistance is mainly acquired by the stepwise accumulation of mostly low-effect mutations that occur in low-antibiotic concentrations.  
In this context, the administration of targeting drug-loaded nanocarriers is one of the promising solutions to alter the tissue distribution and plasma pharmacokinetics of the antibiotics. After administration into the systemic circulation, nanocarriers will reach the infected sites through passive and/or active targeting mechanisms (Figure 7). In passive targeting, the nanocarriers use their intrinsic properties to achieve better accumulation at the site of infection, known as the enhanced permeability and retention effect (EPR effect) [88]. Subsequently, active targeting is applied based on the affinity of the ligand on the nanocar surface toward the receptors that are highly expressed on bacterial cells or infected tissue. Broadly, by adjusting the characteristics, such as size, shape, and surface coating, targeting nanocarriers could provide a higher concentration of antibiotics at the site of infection while maintaining standard dosing frequency [89]. The high-dose exposure of antibiotics to bacterial cells could overload the defenses of drug-resistant bacteria. At the same time, the lower dose at normal tissues minimizes off-target effects such as toxicity or the emergence of reservoirs of microbial resistance genes in commensal bacteria.  
Figure 7: Schematic representation of antibiotic-loaded nanocarriers that passively or actively target infection sites. Both types of nanocarriers firstly reach infection sites through the enhanced permeability retention (EPR) effect caused by infection-induced inflammation (passive targeting). Surface coating strategies have been used to camouflage NPs, preventing opsonization and sequestration by the MPS system for prolonged circulation (stealth coating). Active targeting is then mediated by selective binding between ligand-coated nanocarriers and receptors overexpressed or uniquely expressed by target infection sites.
## 2 Rock Mass Characteristics
### In Situ Stress  
The pre-mining state of stress is one of the most important factors in the design and construction of underground excavations. Results from in situ stress measurements indicate that the ratio of horizontal to vertical stress (\(k\)) is highly dependent on depth. At shallow depths, \(k\) values are often greater than unity, while at greater depths, the upper bound of the \(k\) ratio approaches unity [33].  
Therefore, for the purposes of the present study, a hydrostatic state of stress was assumed, meaning that horizontal and vertical stresses are equal (\(k\) ratio equals 1). Consequently, values of the in situ stresses were determined based on the following relation:  
\[p_{z}=p_{x}=\gamma_{o}\cdot H, \tag{12}\]  
where \(p_{z}\) and \(p_{x}\) represent the vertical and horizontal in situ stress, respectively, \(\gamma_{o}\) denotes the average bulk weight of the overburden rock, and \(H\) is the overburden thickness. Assuming an average bulk weight of the overburden rock of 0.024 MN/m\({}^{3}\), estimated  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Hoek–Brown**} & \multicolumn{2}{c}{**Coulomb–Mohr**} \\ \cline{3-6}
**Lithology** & **Depth** & \multicolumn{1}{c}{} & & \\ \cline{3-6}
**m** & **m** & **s** & **a** & **c** & **\(\varphi\)** \\  & **(m)** & **s** & **a** & **(MPa)** & **(c)** \\ \hline Medium-grained sandstone & 957.00–983.50 & 0.637 & 0.000 & 0.527 & 1.27 & 21.19 \\ Mixed-grained sandstone & 983.50–988.50 & 0.640 & 0.000 & 0.527 & 1.50 & 24.09 \\ Mixed-grained sandstone & 988.50–993.20 & 0.643 & 0.000 & 0.527 & 1.61 & 25.18 \\ Claystone & 993.20–995.70 & 0.550 & 0.000 & 0.529 & 1.14 & 19.62 \\ Mudstone & 995.70–999.20 & 0.576 & 0.000 & 0.528 & 1.61 & 25.13 \\ Fine-grained sandstone & 999.20–1012.20 & 0.650 & 0.000 & 0.528 & 1.64 & 25.36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Post-peak strength properties of the rock masses in the area of Shaft II.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lithology** & **Depth** & \(E_{i}\) (**MPa)** & \(E_{m}\) (**MPa)** \\ \hline Medium-grained sandstone & 957.00–983.50 & 7903 & 5391 \\ Mixed-grained sandstone & 983.50–988.50 & 12,254 & 7285 \\ Mixed-grained sandstone & 988.50–993.20 & 14,408 & 8788 \\ Claystone & 993.20–995.70 & 8932 & 1820 \\ Mudstone & 995.70–999.20 & 17,145 & 6136 \\ Fine-grained sandstone & 999.20–1012.20 & 14,941 & 5077 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of deformation moduli of intact rock and jointed rock mass for different lithological units in the area of Shaft II.  
values of the horizontal and vertical stress at a depth range of 957.00-1012.20 m vary between 22.97 and 24.29 MPa.
## 3 Case Study
### Missing Data Processing for Time Series  
In order to objectively describe the validity of the prediction model, two different data missing processing methods, random and non-random, are used to carry out certain data missing processes on the original time series dataset of landslides by combining the characteristics and main types of missing landslide monitoring data.  
Random missing (RM): Random missing indicates that the landslide monitoring equipment has sporadic and random data loss in the operation process. The whole time series is divided according to the proportion of random missing data, with 5%, 10%, 20% and 40% set, respectively, representing different scales and levels of data loss of monitoring equipment.  
Non-random missing (NM): Non-random missing means that the landslide monitoring equipment has regular equipment failure or regular network interruption during operation.  
The data missing is also simulated according to 5%, 10%, 20% and 40% and compared with random missing.  
The original spatiotemporal landslide displacement dataset is artificially treated with missing data, and the effectiveness of different models in completing and predicting the missing data can be objectively evaluated by two different treatments, random as well as non-random. For landslide disasters with relatively complex genetic mechanisms, there must be certain trend characteristics and a correlation between different sensors on the landslide. Therefore, better data recovery and prediction for missing datasets after non-random missing processing is an important indicator of this model.  
Shuizhuyuan landslide is currently in the mean-slow deformation, and the first 1300 days are selected as the training set of the time series, and the last 300 days are selected as the test set. The MAPE =\(\sum_{i=1}^{n}\left|\frac{\hat{x}_{i}-x_{i}}{\hat{x}_{i}}\right|\times\frac{ 100\%}{n}\) and RMSE =\(\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\delta_{i}-x_{i}\right)^{2}}\) are used to test the accuracy and precision of the algorithm model in terms of prediction. Moreover, the MLATC model is compared with the high-accuracy low-rank tensor completion (HALRTC) and temporal regularized matrix factorization (TRMF) methods. The completion and prediction results with a smaller MAPE and RMSE are considered to be better.
## 3 Results
### Upper Torso Front Body Garment Ease  
A descriptive comparison was made between the upper front torso measurements when wearing the TS with and without the SCBA for each participant. Garment ease was calculated for each of the 10 measurements for all nine participants between the TS  
Figure 3: The (**a**) average torso air gap volume and (**b**) average torso air gap distance of the measured participants for the base layer (BL), turnout suit (TS), and turnout suit with SCBA (TS w/SCBA) garment configurations. Error bars represent percentage error.  
versus the TS w/SCBA to better understand the effect of the SCBA on isolated microclimate air gaps. On average, when the SCBA was donned over top of the turnout suit, most of the body measurements included in this study decreased across the front torso. A bar graph was plotted to illustrate the average change in ease measurements for all participants (Figure 4).  
After donning the SCBA, garment ease decreased for seven of the ten body measurements by an average of 2.76 cm, or 5.64%. Three of the body measurements: front shoulder width, across axilla chest front, and front left waist circumference deviated from this trend as garment ease increased, on average, by as much as 3.69 cm (5.68%) in these areas after donning the SCBA. Differences in the across axilla chest front length (\(p\) = 0.02) and front waist tape measure (\(p\) = 0.04) were found to be statistically significant when donning the SCBA. A statistically significant change in garment ease was not found for the other eight front torso body measurements.  
These findings, especially those areas which experienced an increase when the SCBA was donned, likely correlate to the presence of the SCBA straps, however, more research is needed on a larger sample size to determine the potential causation. It is also possible that personal fit preferences and synching of the SCBA straps bunched up the turnout coat material in these areas impacting garment ease and air gaps.
# Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm
## 2 DIWPSO-SVM Algorithm
### Optimization of SVM Kernel Parameters Based on DIWPSO Algorithm  
The relevant parameters of the SVM algorithm can be optimized using the DIWPSO algorithm. The objective function selected in this study is the RMSE function that responds to the performance of the SVM algorithm, of which the function expression is described in Equation (14).  
\[\text{min}F_{RMSE}(C,\sigma,\epsilon)=(\frac{1}{m}\sum_{i=1}^{m}\left(y_{i}- \varphi(x_{i},C,\sigma,\epsilon)\right)) \tag{14}\]  
The fitness function is shown in Equation (15).  
\[F_{fitness}=F_{RMSE}(C,\sigma,\epsilon) \tag{15}\]  
where the parameter (C) is the penalty coefficient; \(\sigma\) is the kernel function parameter; \(\epsilon\) is the error tolerance; and \(\varphi()\) is the prediction function under the training sample.  
The specific steps for optimizing the kernel function parameters for the SVM algorithm based on the improved DIWPSO algorithm (in Figure 2) are as follows:  
Step (1): Firstly, initialize the DIWPSO algorithm parameters (\(c_{1},c_{2},w,\alpha\)), the particle swarm size, and the maximal iteration number. Randomly initialize the velocities and positions in the particle swarm of the SVM algorithm parameters (\(C,\sigma,\epsilon\)) and define the value range of parameters.  
Step (2): Update the current position of the particle to the individual extreme value, calculate the particle's fitness according to Equation (14), and take the particle with the smallest fitness value as the global extreme value.  
Step (3): Update the velocities and positions of all particles, make \(p_{ipre}=F_{fitness}\), and compare the individual optimal solutions. If \(p_{ipre}>P_{i}\), update \(P_{i}\) and compare the global optimal solution; if \(p_{ipre}>G\), update \(G\).  
Step (4): Calculate the particle swarm aggregation distance change rate (\(k\)) according to Equation (12), redetermine the inertia weight (\(w\)) according to Equation (13), and update and calculate the fitness value.  
Step (5): Judge whether the algorithm satisfies the convergence condition; if it does, then output the optimal SVM parameters (\(C,\sigma,\epsilon\)). Otherwise, go back to step (2).  
A Newly Proposed Midspan Deflection Prediction Based on the DIWPSO-SVM Model Combined with Wavelet Transform Decomposition
# Musical Mix Clarity Prediction Using Decomposition and Perceptual Masking Thresholds
## 2 Proposed Model  
The proposed model has some similarities to a cross-adaptive approach employed to minimise masking between composite signals of a multitrack mix in automatic mixing systems [10; 14]. The automatic mixing system employed in [10] was shown to increase the perceived clarity of the mixes where the amount of masking had been reduced. To achieve this, a cross-adaptive masking metric was used to calculate the level by which a given signal in the multitrack was masked by the sum of all other signals of the multitrack mix. The signals contributing to the given multitrack mix are then processed in such a way to minimise the amount they are masked, thus lowering the amount of masking occurring in the multitrack mix overall. Their work was congruent with [25] which proposes a cross-analysis based model. This compares the partial loudness (frequency dependent loudness) of a signal in isolation to the partial loudness of the signal when masked by the other signals present in the multitrack mix. Subjective testing showed the greater the disparity in partial loudness between the masked and unmasked forms of a signal, the lower the probability of it being successfully identified in the multitrack mix. A similar approach was also proposed as a Hierarchical Perceptual Mixing (HPM) system [26], which first determines the most important signals present in the mix as a function of time based on a user parameter, then calculates the perceptual masking threshold of these dominant signals and removes the masked energy of the non-dominant signals present in the mix. It is suggested this approach may improve the clarity of the resulting mix. However, this was not related to any subjective testing.  
In typical MIR applications, a multitrack based approach cannot be used directly as it assumes access to all of the signals present which make up the multitrack mix. Additionally, it is unable to calculate masking occurring contained within a single signal, for example, if multiple instruments were captured by a single microphone. Whilst there is an emerging field of source separation methods utilising deep learning techniques to 'unmix' mixed signals, such as Spleeter [27], these systems are unsuited in this case as they are only able to classify a limited number of instruments. This results in the cross-masking being poorly represented in cases where the given mix is split into only 1 or 2 instrument categories.  
The proposed model assumes no knowledge of the individual signals making up the multitrack mix. It utilises a novel approach which calculates the level of perceived masking between the separated transient, steady-state, and residual (TSR) components of the multitrack mix. The model outline is shown in Figure 1 and consists of the TSR separation stage, a component energy and masking calculation stage (utilising the MPEG Psychaocustic model II), and finally a statistical output calculation stage which generates the representation of the masking occurring between the separated components.
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 5 Methodology
### Network Key Extraction  
Additionally, it is necessary to perform key extraction to decrypt the network traffic. In Zigbee networks, there are two types of keys: the transport key and the network key. The transport key is publicly known and facilitates the initial communication between devices. Still, it is used to decrypt the headers of Zigbee's messages, and it cannot be used to decrypt the main data payload. The network key, on the other hand, is used to secure the majority of network traffic and is essential for decrypting the encrypted data payloads. To obtain the network key, we added a new device to the Zigbee network and captured the traffic during the pairing process. During this process, the coordinator transmits the network key to the new device in a special data frame. Using the packet sniffer, we were able to intercept this key. With the network key in hand, we could successfully decrypt the previously encrypted payload data. This allowed us to extract meaningful information  
Figure 2: Process flow diagram of the data collection.  
from the devices, such as motion detection events, ambient light levels, or specific color settings for smart bulbs, enabling us to build a comprehensive and detailed dataset.
## 1 Introduction
However, while all of these ideas are promising, they are all in the early research phase and far from real application. The objective of this work is to develop an energy management system that makes decisions on how to distribute the electrical demand within the house among the possible sources that supply it, such as the electrical grid, self-consumption (for example, with solar panels), and energy storage with electrical batteries with the ultimate goal of reducing the cost of electrical energy. To this end, this energy management system considers a residential dwelling as TES in which, taking advantage of the thermal and insulation characteristics of its structure and envelopes, excess heat can be stored in the structure and materials of the dwelling and released, when necessary, while maintaining comfortable interior temperatures. The proposed use of the building envelope as TES can be considered as a passive solution for sensible heat storage system for moderate temperature storage applications in buildings, increasing or decreasing the temperature of a given material with high heat capacity. In this case, the envelope includes solid materials, such as concrete and bricks as main components, which are materials that can be used in buildings to improve the TES capacity. Their properties have been exploited to use the thermal inertia of the building structure to attenuate the indoor temperature oscillation. Therefore, no new envelope materials will be developed, but rather, using the existing ones, an advanced energy management system will be designed with a strategy that effectively regulates the energy management of the building with an efficient use of the HVAC system. In this way, the target temperature inside the house will be slightly modified, always within the thermal comfort margins of the occupants. This is intended to supplement the energy storage capacity of the house as a whole, optimizing the use of batteries, since part of the energy that could provide the electric battery, will provide the thermal energy stored in the structure of the house. This will improve the efficiency of the system, reducing the energy consumption of heating or cooling and, consequently, reducing the cost of energy. This strategy is especially valuable in areas where renewable energy sources, such as solar and wind, produce intermittent power, as it enables energy use to be adjusted based on availability and price fluctuations. The remainder of the paper is organized as follows. Section 2 contains a comprehensive literature review and the main objectives of this paper. Section 3 describes the methods used and a general overview of the proposed solution. Section 4 includes the models developed for the thermal behavior and the HVAC control. Section 5 describes the energy consumption scheduler. Section 6 explains the implementation of the proposed solution. Section 7 presents and discusses the results obtained from the simulations performed for the different use cases indicated in a single-family house. Section 8 includes the validation in a test environment. Finally, Section 9 presents the main conclusions of this paper.
## 3 Results and Discussion
### Competitive Adsorption of Four Metal Ions
In contrast, the partition coefficients of Cu, Pb, and Cd ions in the other three adsorption principles were Pb \(\geq\) Cu \(>\) Cd, while Zn was significantly less competitive than the other three metal ions, and the partition coefficients of competitive adsorption in the eight different competitive adsorption principles were smaller than the other three metal ions. The size of the affinity between the hydrogel and the metal ion is related to three aspects: (1) the smaller the water and radius of the metal ion, the stronger the affinity between the metal ion and the hydrogel; (2) the more electronegative the metal ion is, the stronger the ability to bind to the hydrogel and the stronger the affinity will be; and (3) the stronger the affinity of the metal ions that form covalent bonds that are more likely to bind to active adsorption sites on the surface of the hydrogel, the stronger the affinity for the hydrogel. The sizes of the hydration radii of four of the Cu(II), Pb(II), Zn(II), and Cd(II) metal ions are Pb \(<\) Cd \(<\) Zn \(<\) Cu [35]. However, the electronegativities of these Cu(II), Pb(II), Zn(II), and Cd(II) metal ions are 1.90, 2.33, 1.65, and 1.69, respectively [36]. The electronegativity of Cu is greater than that of Cd and Zn, and the electronegativity of the four metal ions is ranked as Pb \(>\) Cu \(>\) Cd \(>\) Zn, which is consistent with the fact that the partition coefficients of the four metal ions are ranked as Pb \(\geq\) Cu \(>\) Cd \(>\) Zn in practice,indicating that the competitive ability of the GO/P(AA-co-AM) hydrogels when adsorbing metal ions is more related to the electronegativity of the metal ions.