esa-sceva/satcom-chunk-collection huggingface dataset:
# Query Processing in Blockchain Systems: Current State and Future Challenges
## 3 Application Domains Identified through Literature Review
### Financial Accounting  
Today's accounting is still based on the double-entry system that was described in a treatise written by [PERSON] over 500 years ago [52]. The double-entry system has two sides known as debit and credit. Each financial record is entered into an account on both sides where the entry on the credit side is a corresponding and opposite entry of the debit side. The books are considered trustworthy if and only if the sum of the debits equals the sum of the credits [53]. Since a company is accountable to multiple parties--e.g., owners and investors--it is necessary to publish financial statements regularly. This implies that financial data must be shared with these shareholders, but also with tax advisors and financial authorities. The exchange of data is usually carried out via the error-prone import and export functionality of accounting software. As financial records must be immutable by law--i.e., they must not be tampered with retrospectively--such a modus operandi entails a considerable threat potential [54].  
Since blockchain technology has already proven to be a backbone for cryptocurrencies, they also seem suitable for financial accounting. Accounts for any kind of assets, liabilities, equity, revenue, and expenses are established [55]. As all transactions between these accounts are transparent to all participants of the blockchain and no party has sole control over the blockchain due to its decentralized and distributed design, it can be considered a trusted single view of truth. Moreover, due to the immutability of financial records, a blockchain-based financial accounting is almost immune to tampering [56].  
There are multiple examples in literature in which blockchains are used to support accounting, e.g., [PERSON] et al. [57], [PERSON] and [PERSON] [58], [PERSON] [59], [PERSON] [60], and [PERSON] et al. [61].  
Based on this research, we can conclude that there is only one type of data in financial accounting:* The data entered by companies and tax advisors are financial records that are only valid at a specific point in time. In the context of financial accounting, queries regarding the aggregated characteristic values over a given period of time or queries that support an accounting report are particularly relevant. Typical queries therefore include, but are not limited to:
* List all financial records for a given period (e.g., usually for a day, week, month, quarter, or year).
* Generate an accounting report by aggregating the financial records grouped by accounts for a given period.
###### Abstract  
This study aimed to evaluate the efficacy of various nickel-titanium (Ni-Ti) root canal instrumentation systems in preserving root canal anatomy, focusing on their capacity to limit changes in canal angulation. One hundred canals in fifty extracted human molars were prepared with different techniques: Step-Back, Reciproc, MTwo, ProTaper Universal (PTU), and ProTaper Next (PTN). The curvature of each canal was measured before and after treatment using [PERSON]'s methodology, a widely accepted method for assessing canal curvature. Descriptive and statistical analyses, including the Kruskal-Wallis test, were employed to compare angular changes across the systems. The results indicated that all techniques effectively reduced canal curvature, with each system exhibiting a reduction in mean canal angle after instrumentation. Although the Reciproc system showed the smallest mean change in angulation, no statistically significant differences were identified between any of the systems (\(p\) = 0.182). This finding suggests that while minor differences in performance may exist, they do not translate into clinically meaningful distinctions in preserving root canal anatomy. The Reciproc system's slight advantage aligns with other studies, highlighting its conservative design and minimal dentinal stress; however, its superiority was not statistically validated in this study. The results suggest that all five systems are clinically comparable in preserving root canal anatomy, highlighting that dentists can choose from these widely available techniques without compromising anatomical preservation. While this study had limitations, including a relatively small sample size and an in vitro design, it aligns with previous findings on the mechanical behavior of Ni-Ti systems in endodontic practice.  
root canal treatment; rotary instruments; ProTaper; Reciproc; MTwo; Schneider's angle 2025 15 429 1
## 5 Simulation Setting and Results
### Total Alive Nodes of the Network  
The plot of the alive nodes is indicated in Figure 14. The comparison shows that the rate of alive nodes of the EERD is higher than the all other schemes. The ODBR is the second scheme after EERD, which also keeps maximum nodes alive. The count of alive nodes of the CoEERD is higher than the CoDBR. The reason for this is the consumption of minimum energy. Secondly, the selection of the best forwarder node through the weighting function helps it to choose the node which forwards the packet with the cost of low energy consumption. The ODBR considers depth for the best relay, which also minimizes the energy consumption. The CoEERD uses single RLN with the DSN, it also consumes minimum energy as compared to the CoDBR. In CoDBR, the maximum nodes are involved in packet proceeding due to the cooperation of two RLNs with the DSN, which increases energy consumption and nodes die at the quicker rate. The total number of alive nodes, at round 400 in EERD are 200, ODBR and CoEERD have approximately 160 and 130 nodes alive at that time, respectively. During this time, CoDBR has 100 nodes alive. However, after round 400, nodes die quicker in CoEERD than the CoDBR, which is due to the lowest depth nodes of the CoEERD scheme getting burdened with data traffic.  
Figure 13: Total Packets Received at Sink Node.
## 2 Methodology
### Overview of the Albedo Inversion Process  
The aim of this research is mainly to realize the daily inversion of land surface albedo in the Hefei area, so the geostationary satellite FY-2G observation data, which observe the earth once every hour, are selected as the inversion data. Since the observation angle of the Hefei area by geostationary satellites is relatively fixed, to obtain a more representative BRDF archetype, the multiangle observation data of MODIS for many years were selected to construct the BRDF archetype database. Figure 3 shows the basic flow chart of land surface albedo retrieval in the Hefei area in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Definition** \\ \hline X & Training vector (n-dimensional one-element), \(X=[[\rho_{1}^{\prime}],[\rho_{2}^{\prime}],\ldots,[\rho_{n}^{\prime}]]\) \\ y & Target vector (one-dimensional n-elements), \(y=[\rho_{1},\rho_{2},\ldots,\rho_{n}]\) \\ H & Huber loss function \\ \(\delta\) & Epsilon \\ \(\sigma^{2}\) & Variance estimate of the noise \\ \(\omega\) & Coefficient vector \\ \(\alpha\) & complexity parameter \\ n & Number of data \\ i & Serial number \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of Huber loss fitting regression parameters.  
Before the actual land surface albedo inversion, the BRDF archetype database in the Hefei area was constructed. First, data preprocessing (effective data extraction, data matching, etc.) is performed on MODIS multiangle data over the past 5 years, and the BRDF model parameter dataset is obtained by using the BRDF model. Then, the corresponding AFX index is calculated, the data are classified according to AFX, and the classification error is calculated, to obtain the optimal classification number. Then, the average BRDF model parameters are calculated within the class, and the BRDF archetype database is constructed. In the land surface albedo inversion, the FY-2G satellite data are first preprocessed (cloud mask, interest area clipping, etc.). Then, based on the one-day observation data of FY-2G and the BRDF archetype database, the adjustment coefficient and the optimal BRDF archetype are obtained through Huber loss fitting. Finally, the real land surface albedo is obtained by the semiempirical kernel-driven BRDF model.
## 5 Conclusions  
Based on the test results, the following conclusions can be drawn:  
1. The effect of formulation factors on the structure formation and properties of non-autoclaved aerated concrete with improved characteristics was studied.
2. Non-autoclaved aerated concretes with replacement of part of the cement from 4% to 16% MS showed higher strength characteristics compared to aerated concretes where part of the cement was replaced with GBFS admixture and MS:GBFS complex admixture. The maximum value of compressive strength was recorded for aerated concrete with 16% MS addition.
3. Gains in design quality factors were observed only for formulations with MS additive, MS additive contributed to gains in CCQ in the range of 11-46% compared to the control formulation.
4. Addition of MS improved the thermal conductivity characteristics of non-autoclaved aerated concrete (up to 10%). Replacing part of the cement with GBFS and MS:GBFS additives did not significantly affect the change in thermal conductivity values. In general, when replacing part of the cement with these additives in the range of 4-20%, small increases in thermal conductivity coefficients from 1% to 5% were observed compared to the control composition.
5. Theoretical ideas about structure formation at the microlevel and the influence of microstructure on the formation of the properties of non-autoclaved aerated concrete with improved characteristics based on nanomodifiers of various origins have been developed. Improvement of the microstructure of aerated concrete with the addition of MS in comparison with samples of the control composition has been proven.  
Figure 10: Factors affecting the properties of aerated concrete: (1–3)—the main groups of factors.  
The practical significance of the study was to improve the building enclosing structures made of cellular concrete with improved characteristics, which in turn will lead to an increase in the energy efficiency of buildings and structures for various purposes.  
Conceptualization, S.A.S., E.M.S., A.N.B. and N.D.; methodology, S.A.S., E.M.S. and M.K.; software, S.A.S., E.M.S., A.N.B. and N.B.; validation, N.D., S.A.S., E.M.S. and A.N.B.; formal analysis, N.D., S.A.S. and E.M.S.; investigation, N.B., L.R.M., S.A.S., E.M.S., A.N.B. and B.M.; resources, B.M.; data curation, S.A.S., E.M.S. and M.K.; writing--original draft preparation, S.A.S., E.M.S. and A.N.B.; writing--review and editing, S.A.S., E.M.S. and A.N.B.; visualization, S.A.S., E.M.S., A.N.B. and N.B.; supervision, L.R.M. and B.M.; project administration, L.R.M. and B.M.; funding acquisition, A.N.B. and B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors would like to acknowledge the administration of Don State Technical University for their resources and financial support.  
The authors declare no conflict of interest.
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 3 Methodology
### BRDF Model
#### 3.1.1 Construction Method of BRDF Model
As to the two test sites used in this paper, under the circumstance of the solar zenith angle of MODIS imagery is greater than 55\({}^{\circ}\), which indicates that the imaging date is in winter, the ground may snow, and the directional reflection will be abnormal. 6. The images which satisfy the difference between EBT and BT less than 10 K, solar zenith angle smaller than 55\({}^{\circ}\), and VC less than 4% are used as the valid images on a sunny day of the calibration site. Then, the TOA reflectance and imaging angles of the valid calibration site images are calculated. 7. The TOA reflectance BRDF correction coefficients based on the valid calibration site TOA reflectance image and imaging angles are calculated by using the kernel-derived model [32]. The kernel-derived model is represented as follows:  
\[R(\theta_{s},\theta_{v},\varphi)=f_{iso}+f_{geo}k_{geo}(\theta_{s},\theta_{v},\varphi)+f_{vol}k_{vol}(\theta_{s},\theta_{v},\varphi) \tag{6}\]  
where \(R(\theta_{s},\theta_{v},\varphi)\) is bidirectional TOA reflectance; \(k_{geo}\) is the LiSparse-Reciprocal geometric scattering kernel; \(k_{vol}\) is the RossThick volume scattering kernel; \(\theta_{s}\) is the solar zenith, \(\theta_{v}\) is the sensor zenith, and \(\varphi\) is the relative azimuth. \(f_{iso},f_{geo},f_{vol}\) are the kernel-derived model coefficients, which represents the proportions of Lambertian scattering, LiSparse-Reciprocal geometric scattering, and the RossThick volume scattering, respectively. Figure 4: Flow chart of bidirectional reflectance distribution function (BRDF) model construction.
## 3 Proposed Method  
### Notations on MDP  
To represent inverse reinforcement learning (IRL) as a Markov decision process (MDP), we consider a tuple \(\mathcal{M}=(\mathcal{X},\mathcal{A},T,\mathcal{R},\rho_{0},\gamma)\), where \(\mathcal{X}\) is state space, and \(\mathcal{A}\) is the action  
Figure 2: Schematic depiction of the variational reward estimator.  
space. The transition probability \(T(x_{t+1}|x_{t},a_{t})\) defines the distribution of the next state \(x_{t+1}\) given state \(x_{t}\), and \(a_{t}\) at time-step t. \(\mathcal{R}(x_{t},a_{t})\) is the reward function of the state-action pair, \(\rho_{0}\) is the distribution of the initial state \(x_{0}\), and \(\gamma\) is the discount factor. The stochastic policy \(\pi(a_{t}|x_{t})\) maps a state to a distribution over actions. Supposing we are given an optimal policy \(\pi^{*}\), the goal of IRL is to estimate the reward function \(\mathcal{R}\) from the trajectory \(\tau=\{x_{0},a_{0},x_{1},a_{1},\ldots,x_{T},a_{T}\}\sim\pi^{*}\). However, building an effective reward function is challenging, especially in a multidomain task-oriented dialogue system.
## 2 Background and Related Work
When surrounded by hazards, sensors will try to guide people as far away from emergency locations as possible. Based on this, [PERSON] _et al._[11] proposed a novel 3D emergency service that aims to guide people to safe places when emergencies happen. In their work, when emergency events are detected, the network can adaptively modify its topology to ensure transportation reliability; quickly identify hazardous regions that should be avoided and find safe navigation paths that lead people to exits. [PERSON] _et al._[12] presented a novel approach for safely evacuating persons from buildings under hazardous conditions. A distributed algorithm is designed to direct evacuees to an exit through arbitrarily complex building layouts in emergency situations. They find the safest paths for evacuees taking into account predictions of the relative movements of hazards, _i.e.,_ fires and evacuees. [PERSON] _et al._[13] solve a similar problem, but under conditions where hazards can change dynamically over time. When fire expands in an inner building, there may cause a lot of segmentation in the network. In this case, a lot of routing holes occur that lead to data routing failure. The \"Routing Hole Problem\" is a very important and well-studied problem, where messages get trapped in a \"local minimum\". Some existing \"face routing\" algorithms have been developed to bypass routing holes using geo-routing algorithms. GPSR [14] recovers holes by using the \"right-hand rule\" to route data packets along the boundary of the hole, combining greedy forwarding and perimeter routing on a planar graph. The authors of [15] proposed the first practical planarization algorithm with a reasonable message overhead, lazy cross-link removal (LCR). [PERSON] _et al._[16] presented an interesting approach, the BOUNDHOLE algorithm, which discovers the local minimum nodes and then bounds\" the contour of the routing holes. In the building fire situation, holes feature prominently and can be expected to grow in size rapidly as a fire spreads, thus demanding solutions that are robust and low complexity for quick reactions.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 5 Simulation Results
### Costs on Data Gathering
It may move to a hotspot area that could increase the energy consumption on nodes in this region. However, in LDGM, each active node only needs to upload data to the leader in the same DGA. So, total length of the transmission paths is not too long. In addition, the leader selection and the sleep scheduling strategies further balance energy consumption in our algorithm. Thus, average residual energy of nodes in LDGM is higher than MST and MWST. Figure 31: Average energy consumption of nodes (100 m \(\times\) 100 m, 200 nodes).
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Real-Time Single Image Super-Resolution Methods
#### 3.3.1 Distillation-Based Real-Time Super-Resolution Methods
In addition, [PERSON] et al. [66] proposed a information distillation network based on feature similarity ranking for lightweight image SR. They presented a feature similarity ranking system that organizes extracted feature channels according to their degree of redundancy. The ranked information distillation block (RIDB) is then utilized to extract features with a high redundancy level. Furthermore, since the feature channels have already been sorted in the channel dimension, a gated non-local attention module (GNLM) is used to capture long-distance feature correlations in the spatial dimension. The GNLM considerably decreases computing costs by employing a partitioned nonlocal module and a gate unit to assess the significance of each module, making it more appropriate for lightweight real-time SR. Apart from this, [PERSON] et al. [67] introduced a framework utilizing the teacher assistant knowledge distillation (KD) approach for SR. When the HR image is put into the teacher model to outperform the student, a considerable performance gap opens up due to the teacher's better skills. As a result, the teacher's information becomes extremely complicated and difficult to impart, thereby reducing the efficacy of the KD. To address this, they used a teacher-student (TS) network to increase knowledge transfer between the teacher and the students. Furthermore, the distribution of compact features (CFs), which are the inputs to the teacher's decoder, was discretized to match the student's input distribution, thereby improving the KD process. By deploying this approach, they significantly reduced the complexity and made it suitable for deployment in real time.
## 3 Results and Discussion
### Analysis for COF and Wear Rate

Table 7: ANOVA analysis for coefficient of friction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Degrees of** & **Sequential Sums** & **Adjusted Sums** & **Adjusted Mean of** & \multirow{2}{*}{_F-Value_} & \% \\  & **Freedom** & **of Squares** & **Squares** & **Squares** & & **Contribution** \\ \hline Model & 8 & 77.4697 & 77.4697 & 9.6837 & 43.85 & \(<\) 0.0001 & 95.64 \\ A & 1 & 22.2623 & 22.2623 & 22.2623 & 100.08 & \(<\) 0.0001 & 27.48 \\ B & 1 & 7.9076 & 5.6724 & 5.6724 & 25.68 & \(<\) 0.0001 & 9.76 \\ C & 1 & 44.0833 & 33.2187 & 33.2187 & 150.41 & \(<\) 0.0001 & 54.42 \\ A\({}^{2}\) & 1 & 0.6542 & 0.6542 & 0.6542 & 2.96 & 0.105 & 0.81 For the coefficient of friction,  
\[ \begin{array}{l}\mu=6.14242\times 10^{-2}-3.34854\times 10^{-2}\ A-3.67034\times 10^{-3 }\ B+\ 4.34705\times 10^{-4}\ C+\\ 2.21161\times 10^{-2}\ A^{2}+1.72662\times 10^{-4}\ B^{2}+7.98774\times 10^{-7}\ C^{2}+ 2.05759\times\\ 10^{-3}\ A\ B-1.17572\times 10^{-5}\ A\ C\end{array}  \tag{8}\]  
For the wear rate,  
\[ \begin{array}{l}w=11.3578-4.25313\ A+3.25112\times 10^{-1}\ B+2.09611\times 10^{-3 }\ C+1.54672\ A^{2}-\\ 4.09254\times 10^{-1}\ B^{2}+1.32396\times 10^{-3}\ C^{2}-5.12327\times 10^{-1}\ A\ B+1.8322 \times 10^{-2}\ A\ C,\end{array}  \tag{9}\]  
where \(A=\) concentration of aluminum nanoparticles (wt %), \(B=\) concentration of SDS (wt %), and \(C=\) water content in glycerol (wt %). A comparison of the predicted and experimental value for both COF and wear rate is shown in Figure 4. It exhibited the proper correlation between the predicted and experimental values, which confirmed the suitability of the predictive model with the confidence interval of 95%. The effects of the combined parameters on COF and wear rate are shown in contour plots of Figures 5 and 6, respectively. It shows that the COF decreases and reaches a value of less than 0.05 at a water of 10 wt % with nanoparticles of 0.55 wt % to 0.82 wt % and SDS of 1.6 wt % to 2 wt %. The wear rate is in the range from \(7\times 10^{-10}\) (cm\({}^{3}\)/N\(\cdot\)m) to \(8\times 10^{-10}\) (cm\({}^{3}\)/N\(\cdot\)m) with the water of 10 wt % in glycerol, 2 wt % of SDS, and the nanoparticle concentration from 0.61 wt % to 1.0 wt %. Figure 4: Comparison data between experiment and expectation: (**a**) for COF; and (**b**) for wear rate. Figure 3: Normal probability plot of: (**a**) coefficient of friction; (**b**) wear rate.
# Smart Healthcare Network Management: A Comprehensive Review
## 2 Literature Review  
The vast deployment of the Internet of Things, Wireless Body Area Networks (WBANs), and the associated standards (e.g., IEEE 802.15.4 [11] and IEEE 802.15.6 [12]) have boosted the emergence of smart healthcare systems. Despite their acceptance in the healthcare sector, smart healthcare systems still face challenges that hinder their large-scaledeployment. This section surveys scholarly research studies that tackle the large-scale deployment of smart healthcare systems. This section starts with a literature review of IoT and its architectures, applications, and protocols. Then, it reviews topics related to Wireless Body Area Networks, as these networks play an essential role in the context of smart healthcare systems. Finally, a discussion of the network management of IoT systems is presented.
## 2 ADCS System Model  
In this section, the utilized mathematical methods for both attitude estimation and control are described. The model lies in the requirement of computing the imaginary part of the quaternion associated with \([BR]\) in addition to \({}^{B}\omega_{B/\mathcal{R}}\), both required for the nadir controller, and \({}^{B}\hat{\mathbf{B}}_{\text{Earth}}\) for the detumbling controller.