esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Exchange in Cluster Structure for Longevity of IoT
## 2 Related Work
### Routing Protocols
#### 2.3.2 Data Collection in Cluster Structure  
Data collection in a cluster structure is similar to that of the tree architecture. Specific nodes collect data and transfer them to the sink node. However, in the cluster structure, a network is configured with multiple clusters. A cluster is composed of a specific node and several surrounding nodes. These clusters form sub-networks among themselves. A specific node in a cluster is designated as the header node, and the surrounding nodes become member nodes in the cluster. The header node receives data from its member nodes and integrates them and transmits to the sink node. The member nodes of a cluster can communicate with only their header node and transmit their collected data to the header node [26; 27].  
The clustering algorithm is composed of two steps: Construction of the cluster and data collection. In a cluster structure, to reduce the energy consumption of the header node, a cluster is re-constructed within a certain period to elect a new header node. As the election of the header node is performed repeatedly, the fairness of energy consumption in a cluster can be sustained. Various metrics have been applied for the election of the header node, such as random, remained energy, priority, etc. [28; 29].  
For collecting data in a cluster structure, the existing researches focus on both cluster construction and header node election. After constructing the clusters, the sink node receives data from the clusters according to a method by which all the member nodes in a cluster transmit the collected data to the header and each header re-transmits them to the sink node.  
WSNs have intrinsic characteristics in the flow of collected data. Since all data is unilaterally collected at the sink node, the traffic in the network flows in a fixed direction. For this reason, the installation of a routing path to reach the sink node is an important issue in both tree structure and cluster structure. However, in the IoT and EC environment, the working environment differs from that of WSNs. In IoT and EC, various things perform the role of a sink node because each thing can attempt to collect the data saved in other things to share information. This can occur in all the things deployed in IoT and EC networks. Therefore, if the data collection methods used in WSNs are applied to the IoT environment, all the things have to store a routing table for all the other things and maintain the information of all the things to communicate with each other. These requirements induce significant overhead in the efficient management of IoT networks [30].
## 3 Results
### Execution Experiments
#### 3.1.1 Experiments on Picking from Table  
During the execution of the delivery from the table to the user, various initial positions of the container were set. The variation involved changing the initial position of the container relative to the saved POI where the robot navigated to execute the picking routine, differing along both horizontal axes. Figure 19 and Table 1 show the resulting measurements in time.  
Figure 19: Graph of time measurements for the pick-from-table routine. The X-axis represents the initial position of the container relative to the saved pick point, and the Y-axis represents the execution time. The blue diamond markers correspond to the initial positions at the optimal distance, the orange squares indicate initial positions closer to the containers, and the gray triangles represent the initial positions further away from the containers.  
The results indicate that the robot completes the routine in less time when the container is positioned at the optimal manipulation distance upon arrival at the POI. However, when the container is positioned slightly off-center from the POI, there is a slight increase in execution time. It should be noted that every execution was successfully completed.
## 4 Results and Discussion
The layout of the symmetrical line makes it possible to identify the seasonal cycle, which extends from July to June of the following year, with the lowest indoor Rn concentrations recorded precisely in the summer and the lowest indoor Rn concentrations highly likely to peak in winter, as schematically presented in Figure 5.  
The seasonal variation, as determined from the daily average of the 144 measurements taken at a 10 min sampling rate, is in reality the result of a set of cyclic sinusoidal trends, which reflect variations in the indoor Rn concentration on both a daily and seasonal scale and are influenced by both natural factors (meteorological and geogenic parameters) and other variations associated with anthropogenic factors, such as forced ventilation, artificial regulation of temperature and humidity, and occupancy of spaces. Gaining a comprehensive understanding of the indoor Rn concentration variation over time and its relationship with meteorological parameters (temperature, relative humidity, pressure) and human presence parameters in spaces is essential for enhancing the effectiveness and efficiency of mitigation actions. Figure 4: Schematic representation of the Fast Fourier Transform (FFT) application to the indoor Rn concentration measurements over the period under analysis (black line) with the superimposition of the indoor Rn concentration data (orange line). Indoor air quality and energy efficiency are two crucial factors that have a significant impact on the health and well-being of building occupants. The quality of the air inside a building can be influenced by several factors, such as ventilation systems, pollutants from outdoor sources, and building materials. In contrast, energy efficiency refers to the amount of energy consumed by a building to maintain a comfortable indoor environment. In recent years, the interconnection and complementarity of these two aspects have become more apparent. Poor indoor air quality can negatively affect energy efficiency by forcing occupants to open windows or doors to improve ventilation, resulting in increased energy consumption for heating or cooling. On the other hand, energy-efficient buildings with airtight envelopes can experience indoor air quality issues due to a lack of fresh air exchange. To better understand the relationship between indoor air quality and energy efficiency, continuously active, multi-parameter indoor air quality monitoring probes have become valuable tools for data collection. These probes measure various parameters such as temperature, humidity, carbon dioxide levels, and radon concentration, among others. The data obtained can be used to gain deeper insights into the evolution of indoor radon concentrations and other relevant parameters that require analysis. The use of these probes can assist building owners and managers in making informed decisions about improving indoor air quality while maintaining energy efficiency.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 6 Scheme Analysis
### Performance Analysis
#### 6.3.1 Feature Comparison  
As shown in Table 1, we compare our scheme with schemes [6,7,21,24] and [26] in terms of how attribute authority works, underlying blockchain architecture, policy hiding, outsourcing decryption, outsourcing verification, and smart contracts. In terms of attribute authority, schemes [21,26] have assumed trusted CAs and suffer from concentration of power. Although there is no centralization problem in scheme [7], each AA in the scheme manages the attributes in the domain independently of each other, so the failure of any AA affects the attribute management. The attribute Universe in other schemes is managed by a group of AAs through joint negotiation, which can effectively avoid a single point of failure and enhance system security. Scheme [24] and our scheme combine blockchain systems. In terms of protecting sensitive attributes, only scheme [6], scheme [7], and our scheme implement policy hiding to protect users' privacy. In terms of outsourcing, schemes [21,24,26] and our scheme implement outsourced decryption, outsourcing the complex decryption operations to cloud servers to reduce the computational overhead at the user side. However, schemes [24,26] are unable to verify the correctness of the results after outsourcing decryption. Besides, only this scheme achieves distributed management of attributes and cross-domain computation of keys by using smart contracts.
## 3 Problem Formulation
### Decision Variables  
At each stage \(h\), the following decision variables are determined:  
* Task allocation: A matrix \(W^{h}\in\mathbb{R}^{N_{U}\times N_{D_{h}}}\), where \(W^{h}_{i,j}\) represents the portion of the workload \(L^{h}_{j}\) assigned to UAV \(U_{i}\) for task \(D_{j}\) at stage \(h\); \(N_{U}\) is the number of the UAVs. The allocation must satisfy the following: \[\sum_{i=1}^{N_{U}}W^{h}_{i,j}=L^{h}_{j},\quad\forall D_{j}\in\mathcal{D}_{h},\] (8) with \(W^{h}_{i,j}\geq 0\) for all \(i,j\).
* Path planning: A set of routes \(\mathcal{R}^{h}=\{R^{h}_{1},R^{h}_{2},\ldots,R^{h}_{N_{U}}\}\), where \(R^{h}_{i}\) is an ordered sequence of nodes (tasks and stations) visited by UAV \(U_{i}\) during stage \(h\). Each route \(R^{h}_{i}\) starts at a station \(S_{\text{start}_{i}}\in\mathcal{S}\) and may include intermediate visits to charging stations for recharging.
# Advances in Riblets Design
## 8 Fluctuating Terms of the Flow
### Far-Field Noise  
Far-field noise generation is dependent on the hydrodynamic field under the developed turbulent boundary layer. The reviewed studies evaluate the near-wall levels of flow perturbations and their relation to the far-field noise [60]. The analytical approaches mainly based on [PERSON]'s formulation made their way to a relation between far-field scattered power spectrum and boundary layer frequency spectrum. [PERSON]'s formulation, [PERSON] et al. [133], and [PERSON] and [PERSON] [134] models relate wall pressure fluctuations to the acoustical far-field noise. Figure 14 shows the quality of these approaches when compared to experimental data for a control diffusion airfoil with an angle of attack of 8\({}^{\circ}\)  
Figure 13: Wall pressure spectrum in smooth (black lines) and riblet surface (dashed lines) in three different free stream velocities 10 m/s and three different locations (**a\({}_{1}\)–c\({}_{1}\)**): \(0.2\leq f\leq 2\) kHz, (**a\({}_{2}\)–c\({}_{2}\)**): \(2\leq f\leq 6\) kHz [51].  
According to this comparison, the low frequencies are in good agreement with the experimental data, while [PERSON]'s model shows higher values when compared to [PERSON]'s model. At higher frequencies, values from [PERSON]'s model are close to values from [PERSON]'s model, although [PERSON]'s model does not predict broadband humps while [PERSON]'s model does. By utilizing these theoretical approaches, [PERSON] et al. [51] made a comparison between smooth and riblet airfoils by use of the [PERSON] [134] formula, according to wall pressure spectra and turbulent lateral coherent length. The calculated far-field noise was reduced in the low-frequency range (150 < f < 600 Hz), while in the mid-frequency (600 < f < 2000 Hz), a similar or slightly increased trailing edge noise was observed, with a positive effect in higher frequencies (2000 < f < 6000 Hz), and appropriate range of the freestream velocity based on the designed riblet geometry.  
Although the interaction of turbulent length scales can potentially scatter into broad frequencies with powerful acoustic disturbances propagated to the far field, analyzing the effect of riblets geometries on the far-field noise is an important research area that requires more detailed evaluations, especially when the relations between near-field pressure perturbations are not directly related to the far-field propagated noise. Moreover, due to the number of experimental limitations of the theoretical correlations and models available, the predictions lose accuracy when used to evaluate surfaces that contain riblets.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 2 Preliminary
### Signal Model  
The UWB signals enable high-accuracy indoor localization. Currently, market-level UWB chips are capable of recording the waveform of the received signal, i.e., the CIR. In an indoor complex environment, the anchor receives signals from multiple agents, and these signals also received by the anchor after reflection, which forms the MPCs caused by NLOS propagation. Even so, UWB signals have the advantage of high time resolution, thus the MPCs have little effect on the first path reception. During the receiving process of the anchor, the first path can be identified by the leading edge detection algorithm embedded in the chip [46]. We consider that different antenna elements and different agents are independent of each other. Thus, we model the CIR as  
\[\text{h}(t)=\sum_{i\in\{\mathcal{N}_{\text{a}},\mathcal{K}\}}a_{i}\delta(t- \tau_{i})+\text{z}(t) \tag{2}\]  
where \(\mathcal{K}\) is the index set of the MPCs, \(a_{i}\) represents the complex amplitude of the pulse, which is transmitted from the \(i\)th agent and then recorded by the anchor. The Dirac function represents the CIR of a UWB signal. \(\tau_{i}\) denotes the propagation time of the UWB signal transmitted from the \(i\)th agent. \(\text{z}(t)\) is the additive white Gaussian noise. The CIR is recorded in the register of the UWB chip and by using the response detection method (detailed in Section 4), we can obtain the arrival time-stamp of the signal. Furthermore, these time-stamps can be used to estimate the distance between the nodes, and the details of the method will be shown in Section 3.  
Since the anchor has multiple antenna elements for reception which are synchronized, the phase of the signal from the \(i\)th agent extracted by the \(j\)th array element is expressed as  
\[\psi_{ji}=\phi_{r}-\frac{2\pi f_{c}d_{r}}{c}\cos(\theta_{i}-\theta_{j})+\text{ n}_{ji} \tag{3}\]  
where \(\phi_{r}\) is an unknown parameter representing the reference phase. We choose the center of the circular array as a reference point. The index set of the array element is denoted by \(\  
of the array element relative to the 0 degree in the coordinate system. \(n_{ji}\sim N(0,\sigma_{p}^{2})\) is the observed Gaussian white noise, where \(\sigma_{p}^{2}\) is an unknown parameter. Then, the phase vector can be expressed as  
\[\mathbf{\Phi}=[\mathbf{\varphi}_{1}^{\mathrm{T}},\mathbf{\varphi}_{2}^{\mathrm{T}},\cdots, \mathbf{\varphi}_{N_{\mathrm{a}}}^{\mathrm{T}}]^{\mathrm{T}} \tag{4}\]  
where  
\[\mathbf{\varphi}_{i}=[\varphi_{1i},\varphi_{2i},\ldots,\varphi_{Mi}]^{\mathrm{T}} \tag{5}\]  
in which \(i\in\mathcal{N}_{\mathrm{a}}\). With the observed phase vector \(\mathbf{\Phi}\) in (4), the AOAs of all agents can be calculated based on the phase difference of arrival (PDOA). We will introduce an algorithm based on the maximum likelihood (ML) criterion in Section 4.  
Finally, the position of all agents can be calculated using the corresponding distance and AOA. Note that our system model is suitable to any UWB signal and is not limited to the type of UWB chip or device.
## 3 Results and Discussion  
### Effects of Initial Dye Concentration and Phase Contact Time  
Kinetic sorption studies of DY50 on A21, A23 and A24 anion exchangers of different matrices were estimated using varying phase contact times (1-240 min) for three different initial dye concentrations (100 mg/L, 300 mg/L, 500 mg/L) at room temperature with a constant mass of anion exchanger 0.5 g and a volume of dye solution (50 mL). An increase in the initial dye concentration from 100 to 500 mg/L causes an increase in the amount of dye adsorbed by the anion exchanger (\(q_{t}\)) as presented in Figure 3.  
Figure 3: _Cont._  
In the case of the dye solution of 100 mg/L concentration, the time needed for its removal is quite short, being 10 min, 30 min and 180 min for A23, A24 and A21, respectively. At the initial stage, adsorption is rapid, which indicates the presence of many readily accessible ion exchange sites on the surface of the anion exchanger. When the adsorption of the outer surface reaches saturation, the DY50 anions enter the pores of the anion exchanger and are adsorbed in the inner surface of the particle. After this time, the equilibrium is reached without a significant change of \(q_{t}\) values. With an increase in the amount of dye in the aqueous phase, the time needed to reach equilibrium increases; at the dye concentration of 500 mg/L it is equal to 70 min, 190 min and 220 min for A24, A23 and A21, respectively. DY50 sorption on A23 was slow compared to that on A21 and A24. For the initial concentrations of DY50 at the level of 100, 300 and 500 mg/L, \(q_{t}\) was 9.9 mg/g, 29.3 mg/g and 43.2 mg/g, respectively. A similar phenomenon was observed by [PERSON] [37] in the process of C.I. Direct Red 75 sorption on the weakly basic (Amberlite IRA67) and strongly basic (Amberlite IRA458) anion exchangers of polyacrylic matrix from an aqueous solution. The time needed to reach equilibrium lengthened with the increasing concentration of dye in the water phase and for the initial concentrations of 100 mg/L, 500 mg/L and 1000 mg/L it was 30 min, 60 min and 120 min using Amberlite IRA67 and 40 min, 60 min and 120 min using Amberlite IRA458.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods
### Verification of Data Integrity
#### 2.3.1 Angle Measurement Reliability  
Two sensors were attached securely to the pendulum and a protractor that could be read to a precision of \(\pm 1^{\circ}\) was used to determine the displaced position. The sensor readings at the initial rest position (180\({}^{\circ}\)) were recorded, and the pendulum was rotated by 90\({}^{\circ}\) to 270\({}^{\circ}\), and the sensor readings were recorded again. Then the pendulum was released and allowed to oscillate until it came to rest. This was repeated for an hour. The final rest and displaced position was then recorded for both sensors. This one-hour measurement was repeated three times and the results were then averaged and used to calculate the data presented in Section 3.2
# Using Fast Frequency Hopping Technique to Improve Reliability of Underwater Communication System
## 2 Features of a Reliable Underwater Communication System
### Fast Frequency Hopping Technique
With regard to the stream of transmitted symbols, the diversification used in the FFH technique can be interpreted in a simplified way as the use of _repetition coding_[24; 25]. Using hard-decision decoding in the receiver, a decision is made about which symbol was received for each hop with duration \(T_{H}\). If we assume that the example probability of receiving an erroneous symbol is \(P_{s}=0.3\) for a single \(T_{H}\) hopping time, which is equivalent to a single symbol transmission (\(L=1\)), then the probability of receiving an erroneous symbol for the odd values of symbol repetition (=values of the order of diversity) can be determined using Formula (4) [25]. The probability is presented in Figure 3 for several dozen consecutive values of \(L\) repetitions and it follows that as the repetitions increase, the \(P_{b}\) value decreases. At this stage of considerations, regarding the quality of data transmission, it is reasonable to suppose that the FFH technique will provide the desired \(P_{b}\) value with the optimal number of \(L\) replicates. \[P_{S}(L)=\sum_{i=\frac{L+1}{2}}^{L}\left( \begin{array}{c}L\\ i\end{array} \right)P_{S}{}^{i}\left(1-P_{S}\right)^{L-i} \tag{4}\]  
Figure 3: The value of the probability error of the received symbol for different repetition values (=order of diversity).
## 3 Results and Discussion
### Docking Study  
#### 3.4.1 Selected Bioconjugates and Protein Domains  
As cholesterol antagonists, three caffeine derivatives (**3**, **4**, and **8**) were selected for molecular docking. Additionally, derivative **1**, a gramine compound with a high probability of being an effective cholesterol antagonist (Pa = 82%) was also used for comparison purposes. As potential respiratory anaeptic compounds, derivatives **4** and **8** were selected for molecular docking. In addition, caffeine was molecularly docked as a representative anaeptic drug (CFN).  
The selection of protein domains was guided by their specific biological functions within the physiological system:  
* HMG-CoA reductase activity (PDB ID: 1 HWK): This enzyme plays a pivotal role in the mevalonate pathway, catalyzing HMG-CoA conversion to mevalonate, a key precursor in cholesterol biosynthesis. The structure of HMG-CoA reductase in complex with statins reveals their mechanism of inhibition and provides valuable insights into the design of drugs for treating hypercholesterolemia and cardiovascular disease. Understanding the binding interactions at the active site can lead to developing more effective and selective inhibitors that minimize the side effects associated with cholesterol-lowering therapies [44,45].
* Cysteinyl leukotriene receptor 1 (CysLT1R) activity (PDB ID: 6 RZ4): This G protein-coupled receptor is crucial for mediating inflammatory responses, particularly in asthma and allergic reactions. The crystal structure of CysLT1R complexed with the antagonists zafirlukast and pranlukast elucidates the binding mechanisms and conformational changes associated with receptor inhibition. These insights are essential for the design of new therapeutic agents targeting CysLT1R, potentially leading to more effective treatments for asthma and other inflammatory diseases [46,47].  
These proteins are integral to various physiological processes, and their modulation can have significant implications for treating various diseases. Both HMG-CoA reductase and CysLT1R are critical targets in pharmacology, with their structural insights providing a foundation for designing specific inhibitors that could enhance therapeutic efficacy while reducing adverse effects associated with existing treatments.