esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 3 Problem Formulation
### System Model
Time interval \(T_{Heartbeat}=T_{read}+T_{send}+T_{other}\), where \(T_{Heartbeat}\) is the heartbeat waiting time to check whether the file is updated, \(T_{read}\) is the time to read the size of the key file, \(T_{send}\) is the heartbeat command sending time, and \(T_{other}\) is the heartbeat waiting timeout. In the figure, when the user's heartbeat waiting for the longest time \(T\) satisfies \(T\geq T_{Heartbeat}\), the system runs normally; otherwise, the system shows that the QKD process is abnormal. (4) Meeting minutes sharing: After the meeting, user uses the keyword generation algorithm to generate the meeting minutes' keywords. Then, they hash the keyword to obtain the keyword index. The meeting minutes are encrypted with quantum keys to the ciphertext. Together with the quantum key ID used, they are uploaded to the alliance chain as a transaction and broadcast, and other users on the alliance chain verify it. The past video conference minutes' information is stored on the alliance chain for legal users to view. The structure of the alliance chain is shown in Figure 6.  
Figure 4: Data transmission between users. Figure 5: QKD real-time monitoring. Figure 6: Alliance chain structure. (5) Meeting minutes query: In order to facilitate users to query meeting minutes multiple times, we propose an efficient query method model based on B+ tree. The model consists of three layers, the data layer, cache layer, and application layer. The data query model is shown in Figure 1:0  
Data layer: The alliance chain platform, the blockchain that stores the ciphertext information of the meeting minutes. Cache layer: The cache layer reads the ciphertext transaction information of the meeting minutes in each block and caches it in the B+ tree. The transaction information indexed by the same keyword is stored in the same adaptive space. Application layer: The application layer includes synchronization, query, and decryption operations. Users search for transaction information in the cache layer. If the corresponding data are found, the query result will be returned. Then, through the decryption operation, the plaintext of the meeting minutes is obtained. If no transaction information is found, insert the newly generated transaction information of the blockchain into the B+ tree through synchronous operation and query again.
## 2 Materials and Methods
\[Nu=a_{1}\bigg{(}\frac{le}{L}\bigg{)}^{b_{1}}Re^{c_{1}}Pr^{0.37}, \tag{3a}\] \[Sh=a_{2}\bigg{(}\frac{le}{L}\bigg{)}^{b_{2}}Re^{c_{2}}Sc^{0.37}, \tag{3b}\]  
where \(Nu\), \(Re\)\(Pr\), \(Sc\) and \(Sh\) were Nusselt, Reynolds, Prantl, Schmidt and Sherwood dimensional numbers, respectively [34]; \(l_{e}\) is the specific length, computed as the inverse of \(\zeta\), and \(L\) is the thickness of the cooling pad. The \(a\)'s, \(b\)'s and \(c\)'s parameters were fitted using the inlet and outlet air thermal conditions obtained from the experimental test in the wind tunnel for each pad. The convective heat and mass transfer coefficients were then computed using the \(Nu\) and \(Sh\) definitions as \(h_{H}=Nu\times k\times l_{e}^{-1}\) and \(h_{M}=Sh\times D_{AB}\times l_{e}^{-1}\)[34], with  
Figure 1: Photographies of the pad built with the alternative materials. (**a**) Coconut shells pad. (**b**) Burnt clay hollow brick pad. (**c**) Pumice stone pad. \(k\) as the thermal conductivity and \(D_{AB}\) as the mass diffusion coefficient. A Wilcoxon rank sum statistical test (_transsum_ function, The MathWorks Inc. (Natick, MA, USA), _MATLAB_ 2018a) [49; 50; 51] was used to validate the agreement between the experimental data and the predicted thermal conditions of the modelled air outlet. The coefficient of determination (\(R^{2}\)), the Root Mean Square Error (RMSE) and regression plots of the dry-bulb temperature and relative humidity of the outlet air were also computed. A porous medium such as the cooling pad is also characterized by its porosity and permeability. Porosity or void fraction (\(\varepsilon\)) is a measure of the empty spaces between material particles. To determine the porosity of each material, a container of known volume was filled with the material and then water was added to the top of the container. The porosity was then computed using Equation (4). For hygroscopic materials, such as the pumice stone and burnt clay, the recipient was refilled after one hour of test. A porosity of 91.2% was considered for the cellulose pad [36; 52]. \[\varepsilon=\frac{\text{Water added}}{\text{Container volume}}. \tag{4}\]  
The permeability (\(K\)) is an indication of the ability of the air to flow through a porous medium such as the pad. Several expressions had been proposed to relate it to the pressure drop (\(\Delta P\)) across it and to the air velocity (\(u\)) [36; 44]. The Darcy-Forchheimer Equation (5) shows a good description of the dynamics of the fluid across the porous medium [53], where \(\beta\) is known as the Forchheimer coefficient. \[-\frac{\Delta P}{L}=\frac{\mu}{K}u+\rho\beta u^{2}. \tag{5}\]  
To fit Equation (5) to the experimental data, the Least Absolute Residuals (\(LAR\)) method from the curve fitting toolbox of MATLAB(r) was used [54].
## 4 Model Simulation Results
### Case Study 2: Control Parameter Selection Validation  
In this subsection, since tuning principles of \(K_{p}\), \(K_{i}\), and \(K_{d}\) are the same with the conventional PID controllers, which has been pointed out before, thus, influences on the PID-CTOPC-based closed-loop performance from the control parameters \(t_{M}\) and \(\rho\) are mainly discussed. Values of \(\rho\), \(K_{p}\), \(K_{i}\), and \(K_{d}\) are the same with the ones shown in Table 1. In addition, to clearly show the influences, disturbances are not considered in next.  
First, closed-loop performance based upon difference values of the predictive period, namely, \(t_{M}=1\), \(t_{M}=0.5\), and \(t_{M}=0.3\) is illustrated.  
Second, closed-loop performance based upon difference values of the control order, namely, \(\rho=2\), \(\rho=3\), and \(\rho=4\) is illustrated.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 5 Handling Exceptions
### Communication Abnormalities  
Communication abnormalities mainly considered three situations. First, the excessive communication delay included two cases, which were the entire UAV swarm's delay and some members' delay of the UAV swarm. Second, the communication was completely disconnected; that is, the other UAV communication links in the UAV swarm were completely disconnected and could not be restored. Third, the communication was partially interrupted; that is, the UAV communication link of other parts of the UAV swarm was temporarily disconnected, and it could be restored after some time.  
If the communication of some UAVs was completely disconnected, this could be regarded as the situation of UAV formation to reconstruct the formation. If there was a delay in communication, the offset error could be eliminated according to the online formation-switching algorithm. If the communication of some UAVs was temporarily interrupted, multiple iterative formation flights could be performed based on the distributed ad hoc network online formation-switching algorithm.
## 4 Discussion
### Limitations  
The present study carries some limitations that need to be addressed. Firstly, the sample included only healthy young individuals, thus confining the extent to which the findings can be generalized. Additionally, the study duration was lengthy, with a total duration of approximately 30 min for each participant, which may have induced fatigue and potentially impacted the results. Furthermore, the test-retest reliability, crucial for clinical measurements, was not assessed. Lastly, the sequence of testing remained the same throughout the study, and the effects of varying the test procedure sequence are unknown. Future studies should aim to diversify the participant pool, minimize testing duration, evaluate test-retest reliability, and investigate the effects of different test procedure sequences. Additionally, future studies should consider randomizing the testing sequence to minimize potential biases, such as learning effects, that may arise from a fixed order. Assessing test-retest reliability through repeated measures or intraclass correlation coefficients would also ensure consistency across sessions and strengthen the validity of the findings.
# Modeling the Power Consumption of Function-Level Code Relocation for Low-Power Embedded Systems
## 6 Experimental Results and Analysis
The results are displayed as bars in Figure 9.  
The methodology for code migration, which is based on the relocation of a function, previously confirmed that, in all cases, the power consumed when the function is placed in SRAM is lower than when it is located in Flash, as shown in Figure 9. The results reveal a clear difference in the extent to which the reduction in power consumption depends on the function that constitutes the application. This is the same trend as the result of the code profiling, which was performed to establish the prior relocation-scoring model. In other words, functions with comparatively low differences in their relative power consumption contain fewer instructions than other functions of the applications. Moreover, this experimental work proved once again that the number of instructions constituting a function should be considered when establishing the model; in addition, it is proven that the application of the code migration method to an actual embedded system enables the system to operate at low power. For greater clarity, it would be necessary to analyze not only the power consumed but also the energy consumed by an application. The energy consumed by the application is calculated as the product of the voltage, current, and time, with the voltage being proportional to the time and current because it is supplied in equal amounts. At this point, however, the application must compare the amount of energy consumed per unit of time rather than comparing the total amount of energy consumed. The less energy consumed per unit time, the longer the battery lifetime. The results shown in Table 5 confirm that less energy is consumed per unit time when any function is rearranged than when the application is executed without rearranging any functions. In other words, when our code  
Figure 9: Relative power consumption of benchmark functions. migration methodology based on function relocation is applied, less energy is consumed per unit time than without this methodology. This proves that applying this methodology to a small-sized embedded system that needs to be operated for a few months only, powered by a battery without an external power supply once installed, enables long-time operation of the system because the energy used per unit time is less. The validity of the code migration method was demonstrated by conducting an experiment. In addition, the performance of the proposed prior relocation-scoring model was analyzed by using the selected benchmarks for the experimental group in the instruction trace, as mentioned in Section 3. This provided the value of the variables applicable to the model, which are listed in Table 6.
# Protein Dynamics in F-like Bacterial Conjugation
## 5 The Structures of Conjugative DNA Transfer Proteins
### TraM  
The signal protein, TraM is a 127 aa cytoplasmic protein thought to provide the signal required to unwind DNA by the _oriT_ nicking machinery [64]. TraM is capable of binding to three genes within the _oriT_; _sbmA_, _sbmB_, and _sbmC_ with the interaction between TraM and _sbmC_ being essential to its interaction with the relaxosome allowing for the efficient mobilization of plasmids [123; 124]. TraM has highest affinity for the _sbmA_ gene; the structure of this interaction has been determined to high-resolution (Figure 5A) [137]. Sites _sbmA_ and _sbmB_ overlap the promoter for the _traM_ gene and _sbmC_ is found within the _oriT_ sequence [124]. The F TraM protein is tetrameric and composed of approximately 79% \(\alpha\)-helix, with the remainder exhibiting \(\beta\)-strands [137]. The CTD has been observed to tetrameric and is the region responsible for interactions with TraD, while the NTD homodimerizes to form a ribbon-helix-helix (RHH) DNA-binding motif. TraM proteins have been found to bind specific sites on the top strand of F DNA following nicking at the _oriT_[138]. A single TraM tetramer binds DNA and induces unwinding to allow recognition by the RHH domain; the DNA is then kinked by the protein leaving it in a high-energy state, at which point a second TraM tetramer binds to stabilize the complex [137]. Pairs of TraM tetramers cooperatively bind DNA without further homo-multimerizing, allowing their CTDs to interact with TraD.
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 2 Definition of a Goal
### The Goal Value, Achievability and Effectiveness
In the most general case, we can write these long-term constraints as follows:  
\[\mathcal{G}_{s}:=\lim_{T\to\infty}\frac{1}{T}\sum_{t=0}^{T-1}\mathbb{E}\{G_{ s,t}\}\leq\mathcal{G}_{s,\text{th}},\quad\forall s=1,\ldots,S. \tag{3}\]  
Building on (2) and (3), we can write the following definition for goal achievability: _(i)_ a goal is _achievable_ if there exists at least one policy that guarantees (2) to be above a predefined threshold \(\mathcal{E}_{g,\text{th}}\); _(ii)_ a goal is _strongly achievable_ if there exists at least one policy that guarantees (2) to be above a predefined threshold \(\mathcal{E}_{g,\text{th}}\), while guaranteeing all other long-term constraints (3). In typical communication system management, long-term constraints entail communication KPIs, e.g., PER below a threshold. This translates into, e.g., adaptive Modulation and Coding Scheme (MCS) selection, able to guarantee the target PER. Instead, with the GO paradigm, _communication_ KPIs are not necessarily required to be satisfied a priori, while measures belonging to the physical/human world, or, in general, to the application, are used to assess the performance of the communication system. In this case, classical communication KPIs can be _learnt from experience_, and _dynamically adapted_, to achieve the target goal effectiveness in (2) in the most efficient way, possibly subject to constraints (3).
# Modeling the Impact of Atmospheric Warming on Staple Crop Growth in China in the 1960s and 2000s
## 3 Results
### Effects of Warming on Crop Photosynthesis, Respiration and Net Primary Productivity  
Crop responses to temperature change in different rotation systems showed great regional variations. Figure 3 showed the photosynthesis production simulation results for the three studied crops for the 1960s and 2000s where GPP denoted carbon accumulated by photosynthesis, RA denoted carbon lost by crop autotrophic respiration and Biomass denoted the net accumulation of crop above ground biomass (AGB). According to the results, effects of climate warming on crop photosynthetic production differed in different regions. Warming made few contributions to AGB increases for rice in Southern China. When increases in GPP brought by warming were less than carbon losses brought by RA, AGB did not increase and slightly decreased (e.g., late rice in Taoyuan). By contrast, rice AGB increased slightly in Sanjiang in Northeast China (from 518 gC m\({}^{-2}\) in the 1960s to 559 gC m\({}^{-2}\) in the 2000s). For winter wheat in North and East China, temperature increases resulted in different degrees of increases in carbon accumulated by photosynthesis and carbon consumed by respiration in the two regions, and the carbon increment assimilated by photosynthesis was much higher than that consumed by respiration, causing AGB to increase significantly (with Changshu in East China increasing from 425 to 621 gC m\({}^{-2}\) and Fengqiu in North China increasing from 341 to 544 gC m\({}^{-2}\) during the 1960s to 2000s). The increase in temperature led to a significant increase in GPP of spring maize in Hailun in Northeast China (from 859 to 1054 gC m\({}^{-2}\)), but RA did not increase significantly, leading to a significant increase in AGB (from 601 to 743 gC m\({}^{-2}\)). However, the AGB for summer maize in Fengqiu in North China showed no significant changes, which was consistent with the temperature change.
# Antimicrobial Effect of Submicron Complex Oxide Particles CsTeMoO\({}_{6}\) under Visible Light
## 3 Results
### Antimicrobial Effect of Synthesized Material on a Fungi  
The antifungal activity of CsTeMoO\({}_{6}\) against fungi _A. niger_ and _P. chrysogenum_ to both spores and vegetative mycelium under dark and light conditions was studied (Figures 4 and 5).  
Figure 4: Survival rate of _A. niger_ (**a**) and _P. chrysogenum_ (**b**) spores in the presence of CsTeMoO\({}_{6}\) under dark and light 50 W.  
In the case of the action of the compound on fungal spores, the antifungal activity was observed both in darkness and under light exposure and increased under light exposure, which, apparently, was associated with the presence of the photocatalytic activity of the CsTeMoO\({}_{6}\) (Figure 4).  
A decrease in fungal biomass accumulation was observed under the action of the test compound in dark and light conditions in the case of experiments with vegetative mycelium (Figure 5).  
Figure 5: Reduction in biomass of _A. niger_ (**a**) and _P. chrysogenum_ (**b**) on a medium supplemented with CsTeMoO\({}_{6}\) in darkness and under the influence of 50 W light source.  
The fungicidal effect of the compound increased under the action of light compared to the experiment in the dark like in the case of bacterial studies. It should be noted that in the case of _A. niger_, the inhibitory effect both in the dark and in the light increased with increasing exposure time. The inhibitory effect on the survival rate of vegetative mycelium decreased under the action of light with increasing exposure time in variants with _P. chrysogenum_. Thus, presumably it can be said that the effect of exposure duration on antimicrobial activity was ambiguous: when studying the effect of the compound on bacteria and vegetative mycelium of _A. niger_, there was an increase in the biocidal effect with increasing exposure time, and in the case of fungal spores and mycelial cells of _P. chrysogenum_, a decrease in or absence of statistically significant changes.
# Assessment of Solar Energy Generation Toward Net-Zero Energy Buildings
## 3 Numerical Results
### Input Data  
The one-year PV dataset is derived from the output of the building's PV system, sourced from PVWatts [23]. Observing the correlations between the variables in the dataset provides valuable insights into which parameters most significantly impact PV system output. This understanding can further assist in predictive modeling, PV system design, and the selection of installation sites. As shown in Figure 2, beam irradiance--the direct sunlight received by the panels--has the strongest correlation with PV system output (r = 0.89). This indicates that as the amount of direct sunlight increases, the energy output of the system rises.  
The second strongest correlation is between diffuse irradiance and PV system output (r = 0.77). Diffuse irradiance refers to sunlight that has been scattered by the atmosphere rather than direct sunlight. Despite this distinction, it still contributes positively to system output. Both irradiance measures demonstrate a positive correlation with output, as shown in Figure 3, indicating that PV energy production increases with total sunlight exposure, whether direct or indirect.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Components** & **Min (Wh)** & **Max (Wh)** & **Ratio** \\ \hline Heat Pump & 0 & 6000 & 2 \\ \hline Heat Boiler & 0 & 6000 & 3 \\ \hline Cooling AC & 0 & 6000 & 2 \\ \hline Cooling EC & 0 & 6000 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heating and cooling equipment parameters for optimization model.  
Figure 3: Correlation plots between beam irradiance, diffuse irradiance, and PV system output. (**a**) PV output and beam irradiance; (**b**) PV output and diffuse irradiance.  
Figure 2: Correlation matrix illustrating the relationships between beam irradiance, diffuse irradiance, ambient temperature, wind speed, and PV system output.  
In contrast, ambient temperature has a weaker correlation with PV system output, with an r value of 0.44 (Figure 2), suggesting that while temperature may have some influence, it is not a key factor in determining system performance. The weakest correlation is found between wind speed and PV system output, with an r value of 0.058 (Figure 2), reflecting the minimal impact of wind speed on the system. Figure 4 further illustrates the lack of correlation between wind speed and output, highlighting its insignificant effect.n  
The output power of PVs is obtained by performing the PV prediction model, as illustrated in Figure 5a. The electric, heat, and cooling loads are taken from a combined cooling, heat, and power system in an office building [24], as depicted in Figure 5b-d. Similarly, the data for three-day-ahead optimization are presented in Figure 6a-d.  
Figure 4: Correlation plots between ambient temperature, wind speed, and PV system output. (**a**) PV output and ambient temperature; (**b**) PV output and wind speed.  
Figure 5: Input data for one-day simulation.
## 2 Design of Base Cells
### Property Evaluation for the Cellular Structure  
To design the gradient structure made up with special porous microstructure, the numerical homogenization method [26] is applied. It is an effective method to estimate the properties of periodic microstructure imposing periodic boundary conditions. When the size of a cellular structure is small enough compared to the bulk structure, the elasticity tensor \(D^{H}\) of a periodic composite material is calculated by:  
\[D^{H}_{ijkl}=\frac{1}{|V|}\int_{V}(\varepsilon_{pq}^{0(ij)}-\varepsilon_{pq}^{ *(ij)})D_{pqrs}(\varepsilon_{rs}^{0(kl)}-\varepsilon_{rs}^{*(kl)})dV \tag{4}\]  
where \(i,j,k,l=1,2\). \(|V|\) denotes the volume of the cellular structure, and \(D_{pqrs}\) is the stiffness tensor of solid material. In the 2D case, \(\varepsilon_{pq}^{0(ij)}\) represent the macroscopic strain field, including the horizontaldirectional unit strain \(\varepsilon_{pq}^{0(11)}=(1,0,0)^{\rm T}\), the vertical directional unit strain \(\varepsilon_{pq}^{0(22)}\)\(=(0,1,0)^{\rm T}\), and the shear unit strain \(\varepsilon_{pq}^{0(12)}\)\(=(0,0,1)^{\rm T}\). \(\varepsilon_{pq}^{(ij)}\) represents the locally varying strain fields, and it can be described by:  
\[\varepsilon_{pq}^{*(ij)}=\varepsilon_{pq}^{*}(u^{(ij)})=\frac{1}{2}(u_{p,q}^{ (ij)}+u_{q,p}^{(ij)}) \tag{5}\]  
The displacement field \(u^{(ij)}\) can be calculated by the Equation (6):  
\[\int_{V}D_{pqrs}\varepsilon_{rs}^{*}(v^{(ij)})\varepsilon_{pq}^{*}(u^{(ij)})dV =\int_{V}D_{pqrs}\varepsilon_{rs}^{*}(v^{(ij)})\varepsilon_{pq}^{0(ij)}dV\ \ \forall v\in V \tag{6}\]  
where \(v\) denotes the virtual displacement field in the cellular structure, while \(V\) denotes the set of displacements over the design domain. In the 2D case, the elasticity tensor \(D^{H}\) of a periodic composite material is represented by a \(3\times 3\) matrix. While for the 3D case, it is a \(6\times 6\) matrix.  
Due to the gradient characteristics, the macrostructures in practice cannot be filled exactly with a periodic arrangement of identical microstructures in the layer-wise FGMs. However, as reported in [27; 30; 31; 36; 37], the numerical homogenization method is still applicable for approximating the effective properties of layer-wise composites with FGMs, especially when the property gradient variation between the adjacent microstructures is relatively small [27; 30; 31; 36; 37].