esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 3 The Data Distribution Service Open Standard in UAS Environments
### Spatial/Time Decoupling of Producers and Consumers  
Decoupling in space (i.e., interacting entities are not forced to know each other and of course to be co-located) and in time (i.e., interacting entities are not forced to be simultaneously present to exchange data) is very relevant in any dynamic and distributed system. This is particularly true for mobile systems such as UAS where dynamicity of interacting entities is pushed to the extreme and static previous knowledge of the deployment environment may be unrealistic. Spatial/time decoupling can generate several important advantages, primarily in terms of simplification of application/service development and easy extensibility. First, decoupling facilitates the integration of heterogeneous (possibly legacy) sub-systems because the only constraint for interworking is the ability to exchange data via the consolidated pub/sub model and via well-known APIs. In addition, wrappers/stubs to simplify DDS integration and data format transcoders could be considered as middleware components, easy to be interconnected when adopting a pub/sub paradigm with spatial/time decoupling, to accelerate integration and facilitate interoperability. This can relevantly reduce the design/implementation efforts for UAVs and GCSs, where the integration of existing (legacy) sub-systems is quite common. Similar considerations may apply to extensibility, given the importance of being capable of updating and adapting UAV/GCS systems, possibly during provisioning (dynamic openness), e.g., due to the emergence of novel sub-system implementations with better performance.  
Secondly, the specific property of time decoupling can significantly simplify the management of intermittent data-links, which are usual in this targeted application domain, in particular for wireless connectivity between UAS and their (dynamically changing) GCSs. Reliability mechanisms for producer/consumer-with-DDS (i.e., client-to-DDS, not end-to-end) interactions and maintenance of possible persistent information over DDS named data definitely simplify the design, implementation, and validation of UAS applications. Moreover, this leaves the burden of efficient management of intermittent connectivity (and its evolution to follow connectivity technology evolution and trends) to the DDS layer, with no impact on application business logics. It is manifest that this also has positive effects on overall robustness and correctness. Let us note that decoupling, together with distributed DDS support implementation, also facilitates the support of interoperation and seamless UAV mobility between GCSs during service provisioning: for instance, the complexity of efficient state migration, when needed, is delegated to the internal DDS implementation mechanisms for coordination among DDS middleware nodes.
# Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis
## 2 Materials and Methods
\begin{tabular}{l l l l} \hline \hline \(\mathbf{w}_{hg}\): weight, & \(\mathbf{\theta}_{h}\): bias term, & \(p\): number of patterns, & \(a\): learning rate, \\ \(\eta\): momentum, & \(\mathbf{x}_{g}\): input pattern, & \(\mathbf{t}_{h}\): target value, & \(\mathbf{y}_{h}\): output \\ Step 1. & Set input \(\mathbf{x}_{g}\) (where \(g=1,\ldots,d\)) and target value \(\mathbf{t}_{\text{h}}\) (_where \(h=1,\ldots,z\)_). \\ Step 2. & Initialize \(\mathbf{w}_{hg}\) and \(\mathbf{\theta}_{h}\). \\ Step 3. & Compute FMM **NET** as Equation (4). \\  & \[\textbf{NET}=\vee_{h=1}^{z}\Big{\{}\wedge_{g=1}^{d}\Big{\{}\mathbf{x}_{g},\ \mathbf{w}_{hg}\Big{\}}\Big{\}}\] (4) \\  & where \(\vee\) is fuzzy OR operator and \(\wedge\) is fuzzy AND operator. \\ Step 4. & Compute the output \(\mathbf{y}_{j}\) as Equation (5). \\  & \[\mathbf{y}_{h}=\textbf{NET}\vee\mathbf{\theta}_{h}\] (5) \\ Step 5. & Compute error term \(TSS\) as Equation (6). \\  & \[TSS=\tfrac{1}{2}\sum\limits_{p=1}^{l}\sum\limits_{h=1}^{z}\Big{(}\mathbf{t}_{h}^{p} -\mathbf{y}_{h}^{p}\Big{)}^{2}.\] (6) \\  & where \(p\) denotes the number of patterns. \\ Step 6. & Update weight \(\mathbf{w}_{hg}\) and bias \(\mathbf{\theta}_{h}\) as Equation (7). \\  & \[\Delta w_{hg}(r+1)=\Delta w_{hg}(r)+\tfrac{\partial\mathbf{y}_{h}}{ \partial\mathbf{w}_{hg}}(\mathbf{t}_{h}-\mathbf{y}_{h})\] \\  & \[\Delta\mathbf{\theta}_{h}(r+1)=\Delta\mathbf{\theta}_{h}(r)+\tfrac{\partial\mathbf{w}_{h}}{ \partial\mathbf{\theta}_{h}}(\mathbf{t}_{h}-\mathbf{y}_{h})\] (7) \\  & \[\tfrac{\partial\mathbf{y}_{h}}{\partial\mathbf{w}_{hg}}=\begin{cases}1,\ \mathbf{y}_{h}=\mathbf{w}_{hg}\\ 0,\text{otherwise.}\end{cases}\] \\  & \[\tfrac{\partial\mathbf{y}_{h}}{\partial\mathbf{\theta}_{h}}=\begin{cases}1,\mathbf{y}_{h}= \dot{\ }_{h}\\ 0,\text{otherwise.}\end{cases}\] \\  & \[\mathbf{w}_{hg}(r+1)=\mathbf{w}_{hg}(r)+\Delta\mathbf{w}_{hg}(r+1)+\eta\Delta w_{hg}(r)\] \\  & \[\mathbf{\theta}_{h}(r+1)=\mathbf{\theta}_{h}(r)+\alpha\Delta\mathbf{\theta}_{h}(r)+\eta\mathbf{ \theta}_{h}(r)\] \\ \end{tabular}  \\ Step 7. & UStop if \(TSS\) is less than predefined threshold. \\  & Otherwise, go to Step 2. \\ \hline \end{tabular}  
Figure 2 on the next page shows a flow diagram of the proposed FCM-FMM combination process.
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 3 Biochar Adsorption of Heavy Metals
### Cadmium  
[PERSON] et al. [38] reported that the special physicochemical properties of biochar could make it an effective adsorbent in Cd removal, although the mechanism could be different depending on the feedstock or the thermochemical process used [39]. In the recent literature are reported numerous studies that examined the adsorption of Cd by distinctive biochars. [PERSON] et al. [40] found the highest Cd uptake (13.2 mg/g) by biochar produced from _Miscanthus sacchariforus_ at 500 \({}^{\circ}\)C. At a high pyrolysis temperature (700 \({}^{\circ}\)C), [PERSON] et al. [41] reported that palm biochar increases this level to 43.6 mg/g. However, [PERSON] et al. [42] determined an adsorption maximum of 92.7 mg/g using pinecone biochar prepared at low temperatures (350-400 \({}^{\circ}\)C). Based on these previous results, the effect of raw material is remarkable.  
Furthermore, in the adsorption process with the increase in pH, the efficiency of Cd removal is augmented. This fact could be explained due to a low pH solution--the large amount of H\({}^{+}\) in the system protonates the functional groups on the adsorbent surface, which results in the rejection of Cd (II) in the system [41]. However, at a pH higher than 8, the HO\({}^{-}\) present in the solution could react with Cd, forming low solubility complexes, favoring its precipitation [43].  
As depicted in Table 1, methods to improve biochar characteristics could include chemical treatment or the inclusion of metals. In the study by [PERSON] et al. [44], they determined that the KMnO\({}_{4}\) impregnation of rape straw biochar enhanced the adsorption capacity, achieving a maximum uptake of 81.10 mg/g. They attributed this enhancement to the increase in specific surface area, the number of oxygen-containing functional groups, and pore size achieved that favor the main mechanisms: cation exchange and cation-\(\pi\) bonding (Figure 3). [PERSON] et al. [45] reported that among the different composites of tea waste biochar impregnated with Mg, Fe, Mn or Al salts, Mg and Mn biochar composites increased the surface area slightly, being the most efficient in Cd removal. However, these levels have recently been exceeded by [PERSON] et al. [46], who evaluated the ability of ZnCl\({}_{2}\), H\({}_{3}\)PO\({}_{4}\) and KMnO\({}_{4}\) to modify the _Entempora prolifera_-biochar, showing that the biochar modified with H\({}_{3}\)PO\({}_{4}\) considerably increased the uptake (until 423 mg/g) in a short period (\(\approx\)1 h). As has been highlighted in a previous study [47], H\({}_{3}\)PO\({}_{4}\) worked as a dehydrating agent, reacting with raw biochar that reduces a substantial amount of hydrogen because of the asymmetric stretching of C-H.
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 4 Improved LDPC Decoder Hardware Design and Implementation
### Comparison before and after Improvement  
Originally, the NMS decoder required each check node's minimum and second minimum values to undergo correction using factor \(\alpha=0.8\) in the check node information processing module. This involved shifting the minimum values right by 1, 2, 5, and 6 bits, respectively, and then summing them to achieve correction, with the second minimum values processed similarly. In each iteration, for the 336 check nodes, this resulted in a total of 2688 shift operations and 2016 addition operations.  
Figure 9: Variable node processing unit.  
The improved decoder, however, eliminates the correction of the minimum and second minimum values using factor \(\alpha=0.8\) in each check node. Instead, it applies correction factor \(\beta=1.25\) in the variable node information processing by shifting each input channel information right by one bit and adding it back to the original pre-shift information before updating the variable nodes. To prevent signal data overflow due to limited data width, every first, fourth, seventh, etc., iteration--every third iteration--the channel information and check node information are both shifted right by one bit together.  
In each iteration, the improved decoder requires a total of 1120 shift operations and 672 addition operations for the correction factor \(\beta=1.25\). Compared to the original 2688 shifts and 2016 additions, this undoubtedly reduces logic resource usage and power consumption. Since both correction factor processes before and after improvement involve shift-add combination logic, they do not impact timing, and therefore, the decoding operation speed remains unaffected.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Cavity at** \\ **Category** \\ \end{tabular} & \multicolumn{6}{c}{**Survival of ART** restorations at the 24-Month Assessment**} \\ \cline{3-8}  & \begin{tabular}{c} **Baseline** \\ **(\(n\) = 180)**. \\ **n (Mean** \\ **Volume**) \\ \end{tabular} & \multicolumn{6}{c}{**Overall Survival of ART**} & \multicolumn{6}{c}{**Conventional GIC (\(n\) = 81)**} & \multicolumn{6}{c}{**CHX-Modified GIC (\(n\) = 81)**} \\ \cline{3-8}  & \
\begin{tabular}{c} **Success** \\ **(\(n\) = 135) n (\%)** \\ \end{tabular} & \multicolumn{6}{c}{**Failure (\(n\) = 27)**} & \multicolumn{6}{c}{**Success**} & \multicolumn{6}{c}{**Failure (\(n\) = 13)**} & \multicolumn{6}{c}{**Success**} & \multicolumn{6}{c}{**Failure (\(n\) = 14)**} \\ \cline{3-8}  &
\begin{tabular}{c} **(\(n\) = 145) n (\%)** \\ \end{tabular} & \multicolumn{6}{c}{**n (\%)**} & \multicolumn{6}{c}{**n (\%)**} & \multicolumn{6}{c}{**n (\%)**} & \multicolumn{6}{c}{**n (\%)**} & \multicolumn{6}{c}{**n (\%)**} \\ \hline a. 0-9.9 mm\({}^{3}\) & 23 (7.9) & 16 (11.8) & 5 (18.5) & 9 (13.2) & 3 (23.1) & 7 (10.4) & 2 (14.3) \\ b. 10-19 mm\({}^{3}\) & 59 (18.4) & 55 (40.7) & 2 (7.4) & 25 (36.8) & 0 & 30 (44.8) & 2 (14.3) \\ c. 20-29.9 mm\({}^{3}\) & 39 (26.2) & 33 (24.4) & 1 (3.7) & 19 (27.9) & 0 & 14 (20.9) & 1 (7.1) \\ d. 30-39.9 mm\({}^{3}\) & 27 (37.3) & 14 (10.4) & 8 (29.6) & 8 (11.8) & 4 (08.8) & 6 (8.9) & 4 (28.6) \\ e. 40-49.9 mm\({}^{3}\) & 21 (43.4) & 12 (8.9) & 7 (25.9) & 5 (7.3) & 3 (23.1) & 7 (10.4) & 4 (28.6) \\ f. \textgreater{}50 mm\({}^{3}\) & 11 (61.4) & 5 & 4 & 2 & 3 & 3 & 3 \\ \cline{3-8} Kruskal-Wallis H test, \(p\) value & 0.03 & 0.08 & 0.03 & 0.07 & 0.03 & 0.09 \\ Man-Whitney U test & b\textgreater{} a, c, d, e, f & NA & b\textgreater{} a, c, d, e, f & NA & b\textgreater{} a, c, d, e, f & NA & b\textgreater{} a, c, d, e, f & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Distribution of cavities on cavity volume at baseline and survival at 24 months. Figure 2: Overall survival of ART restorations according to cavity size. No significant difference was observed between the survival of conventional and CHX modified GIC ART restoration at different time intervals.
###### Abstract  
This study aims to compare the potential environmental impact of the manufacture and production of recycled and by-product aggregates based on a life cycle assessment and to evaluate the environmental impact and cost when they are used as aggregates in concrete. To this end, the six potential environmental impacts (i.e., abiotic depletion potential, global warming potential, ozone-layer depletion potential, acidification potential, photochemical ozone creation potential, and eutrophication potential) of the manufacture and production of natural sand, natural gravel, recycled aggregate, slag aggregate, bottom ash aggregate, and waste glass aggregate were compared using information from life cycle inventory databases. Additionally, the environmental impacts and cost were evaluated when these aggregates were used to replace 30% of the fine and coarse aggregates in concrete with a design strength of 24 MPa. The environmental impact of concrete that incorporated slag aggregate as the fine aggregates or bottom ash aggregate as the coarse aggregates were lower than that of concrete that incorporated natural aggregate. However, concrete that incorporated bottom ash aggregate as the fine aggregates demonstrated relatively high environmental impacts. Based on these environmental impacts, the environmental cost was found to range from 5.88 to 8.79 USD/m\({}^{3}\).  
environmental evaluation; environmental impact; environmental cost; concrete; recycled aggregate; by-product aggregate; life cycle assessment Article
## Materials and Methods
### Time-Average Antenna Array Patterns
For example, taking a BS with 64 antenna elements and 200 W nominal power [21], the highest achievable \(E_{RMS}\) (\(G_{m}^{N,K}=1\)) in the direction normal to the BS array plane (\(\theta_{max}=120^{\circ}\)) equals around 18.6 V/m, which falls below the ICNIRP reference level of 61 V/m [4]. To calculate \(G_{m}^{N,K}(T)\) for given \(m\), \(T\), \(N\), and \(K\), a numerical experiment is performed. Figure 3 shows a flowchart describing the procedure. We studied configurations with 2-by-2, 4-by-4, 6-by-6, and 8-by-8 square sub-arrays selected from the center of the simulated 10-by-10 Tx array, as well as the complete array itself. These correspond to total array counts \(N\) of 4, 16, 36, 64, and 100 elements. Scenarios with \(K=1\), 2, 5, and 10 simultaneously active UEs were studied for each \(N\). Connection duration \(T_{avg}\) equal to 60 s, 10 s, and 1 s was considered for each \(N\) and \(K\). In total, \(N_{env}=25\) environment samples were simulated. Gain values obtained from the 25 th sample were found to change the 95 th percentiles of the time-averaged gain distributions by less than 1% (see Figure 5 and Section 4.2), which was accepted as a sufficient level of accuracy. Finally, in every environment sample \(N_{s}=100\) time-averaged gain evaluations were performed, which amounts to 2500 evaluations of \(G_{m}^{N,K}(T)\) for each value of \(N,K,T\) and precoding scheme \(m\). In the following section we present and discuss the distributions of \(G_{m}^{N,K}\).
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 4 Error Synthesis and Distribution
### Assigning Errors Using Improved Dung Beetle Optimization Algorithm  
#### 4.2.1 Target Allocation Model  
In this study, the difference in latitude and longitude between the target and calculation points is used as the index to evaluate the positioning accuracy of the UAV. The smaller the difference, the closer the straight-line distance between the target and calculation points. At the same time, error allocation should be as close as possible to the total to ensure full utilization. The above-mentioned objective function and constraints are shown in Equation (18).  
\[\begin{split} P=\min\{&\sum_{i=1}^{n}\left(|B_{true} -B_{i}|+|L_{true}-L_{i}|\right)\}\\ s.t.&\left\{\sigma_{total}^{2}-\sigma_{xe}^{2}- \sigma_{ye}^{2}-\sigma_{ze}^{2}>0\right.\end{split} \tag{18}\]  
where \(B_{true}\) and \(L_{true}\) denote the true latitude and longitude of the target point, \(B_{i}\) and \(L_{i}\) denote the latitude and longitude of the computation point, \(\sigma_{total}\) represents the total error to be assigned, and \(\sigma_{xe},\sigma_{ye},\sigma_{ze}\) stands for the error assigned to the \(x,y,z\) axis. The constraints limit the error to be assigned by the total.
# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 3 Coupling Model Based on Multidimensional Data Source and Asphalt Pavement Construction Quality
### Selection of the Evaluation Method for Asphalt Pavement Construction Quality  
#### 3.2.1 Selection of Quality Measurement Methods  
At present, the pass rate method is usually adopted as the measurement method for pavement quality but this method cannot fully consider the data distribution and overall trend. Compared with the pass rate method, the percentage within the allowable limit (PWL) method comprehensively considers the mean and standard deviation of the sample data [43; 44], which can effectively reflect the fluctuation of the monitoring data.  
The calculation result of PWL reflects the percentage of the sample meeting the limit requirements, which is used as the quality score of each sub-index of the pavement. Therefore, this study uses the PWL method to quantify pavement quality to ensure the rationality of evaluation results [45]. The final score of the PWL is calculated according to Formulas (2)-(4).  
\[Q_{M}=\frac{T_{M}-\overline{x}}{S} \tag{2}\]  
\[Q_{L}=\frac{\overline{x}-T_{L}}{S} \tag{3}\]  
where \(Q_{M}\) and \(Q_{L}\) are the quality index of upper limit and lower limit of the allowable limit, respectively; \(T_{M}\) and \(T_{L}\) are the allowable upper and lower limit of corresponding parameters, respectively; \(\overline{x}\) and \(S\) are the mean value and standard deviation of parameter samples of each road section.  
The _PWL_ table can be queried according to the sample number \(n\) to obtain \(PWL_{w}\) and \(PWL_{L}\) values, and according to the Formula (4) to calculate the final _PWL_ value.  
\[PWL=PWL_{W}+PWL_{L}-100 \tag{4}\]  
where \(PWL_{w}\) and \(PWL_{L}\) are the percentage within the allowable limit corresponding to \(Q_{M}\) and \(Q_{L}\), respectively.
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 5 Result  
### Environment Modeling  
The actual measurement and simulation experiment area is the arrival hall of an airport. The scene description is shown in Table 1. Six pseudolites are arranged on the roof in an equally divided circle. There are 6 load-bearing columns on the central axis of the hall, and their cross-section is a square with a side length of 0.5 m, the material of the wall load-bearing column is concrete, the ground material is ceramic tile, the pseudolite transceiver antenna is an an omnidirectional antenna, and the store compartment has been simplified. There are two experimental areas. Text area 1 is an area where the fingerprints have been collected. In this area, manual CSI fingerprints collection is performed first, and then deterministic modeling and simulation are carried out to expand the fingerprint dataset, there are 400 sampling points with an interval of 0.25 m; Text area 2 is an area without fingerprints collection, and only the simulation fingerprint data is used to train and predict the position, there are 100 simulated sampling points with an interval of 0.5 m. The experimental environment and method are shown in Figure 7.
# Effect of Pouring Time on the Integrity of Pulverized Fuel Ash Concrete
###### Abstract  
The effects of pouring time intervals on the integrity of pulverized fuel ash (PFA) concrete were evaluated by measuring ultrasonic pulse velocity and compressive strength. Concrete was poured progressively at different time intervals (30 min, 45 min, and 60 min) under similar conditions after the first batch of concrete was vibrated. The effects of the water-cement ratio (w/c) on ultrasonic pulse velocity were also compared and analyzed. Both compressive strength and ultrasonic pulse velocity decreased as the pouring time interval increased. When the water-cement ratio was 0.55, the pouring time interval had little effect on compressive strength, but when the w/c was reduced to 0.45, improvements in the compressive strength of concrete with different pouring time intervals were as high as 10 MPa. Under the condition of the same w/c and the same pouring time interval, improvements in the compressive strength of the interface were as high as 15 MPa, and the prolongation of the aging period could reduce the difference in strength to 8 MPa. The formula \(F_{c}=0.63\delta^{0.95 Vc}\) was used to infer the compressive strength of fly ash concrete at different ages by ultrasonic pulse velocity.  
pouring time; integrity; pulverized fuel ash (PFA) concrete; ultrasonic pulse velocity; compressive strength +
Footnote †: journal: Applied Sciences
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 5 Case Study
### Lessons Learned  
On the one hand, dense point clouds, which have been used for the geometry generation, include detailed information. On the other Hand, dense point clouds require huge amount of memory and increase the processing time. The density of the point cloud has to be defined based on the requirements of subsequent processing steps to have a suitable trade-off between memory requirement, processing time, and details.  
To provide comprehensive damage models, the BIM model, which is the basis of the DIM, has to be complete. This requires incorporating small or invisible objects, such as the reinforcement, drainage and bearings. The model used for the case study, misses such objects, like drainage.  
Modeling and visualizing missing components in the as-damaged model needs further investigation. A missing component could be modeled by just removing the component from the model; however, this would conclude that an expected model is necessary for comparison. Another approach could be a flag at the component indicating that the component is missing. Using property sets in the IFC file may be a possible implementation for that. However, such a flag has to be visualized properly, for example, with a color.  
The inspection report contains two defects that are related to joints, for example, the joint tape is cracked. Modeling joints has gotten less attention until now. Not only joints are parts of bridges and other buildings, but also they are non-material objects and occur in huge amounts, which led to the practice to omit modeling joints. Future research has to clarify how damaged joints may be included in DIMs.  
Some defects affect the location of the structure in relation to the environment or affect the surrounding itself, for instance settling, tilting, or subsidence of the slope. A proper visualization of these defects require a model of the surrounding ground. Different from that is the distortion of the superstructure of a bridge. This changes the geometry of numerous elements. This type of defect could also not be handled.