esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Model Test Program
### Model Test Design  
The selection of model similarity ratio is very important in model test. Considering the actual conditions in the laboratory and the accuracy of the model test, the geometrical ratio of this model test chosen was \(C_{L}=10\). According to the above generalized prototype construction model and the selected geometric similarity constants, the similarity ratio of some basic physical quantities can be obtained (Table 3).  
\begin{table}
\begin{tabular}{c c} \hline
**Prototype Parameters** & **Value** \\ \hline Buried depth of freezing pipe & 8.8 m \\ Frozen curtain thickness & 1.7 m \\ Freezing pipe length & 7.3 m \\ Freezing pipe spacing & 0.9 m \\ Number of freezing pipe & 36 \\ Total flow of freezing pipe & 200 m\({}^{3}\)/h \\ Single pipe flow of freezing pipe & 5.56 m\({}^{3}\)/h \\ Outer diameter of freezing pipe & 89 mm \\ Wall thickness of freezing pipe & 8 mm \\ Average brine temperature & \(-29\)\({}^{\circ}\)C \\ Average temperature of frozen soil & \(-10\)\({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 1: Simplified site construction parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Engineering Index** & **Mucky Clay** \\ \hline Water content, W(\%) & 48.1 \\ Natural bulk density, \(\gamma\)(KN/m\({}^{3}\)) & 16.9 \\ Specific gravity, \(G_{s}\) & 2.75 \\ Void ratio, e & 1.363 \\ Saturation, Sr(\%) & 97 \\ Liquid limit, \(W_{\rm L}\)(\%) & 43.9 \\ Plastic limit, \(W_{\rm p}\)(\%) & 23.1 \\ Vertical permeability, K(cm/s) & \(2.19\times 10^{-7}\) \\ \hline \end{tabular}
\end{table}
Table 2: The basic physical and mechanical properties of mucky clay.  
Figure 1: Prototype location and working condition.  
The artificial freezing model experimental system we used consists of three parts: a refrigeration system, model test box, and acquisition system. The schematic diagram of the experimental system is shown in Figure 2. The model test box is independently developed, and it can control the velocity of water in the underlying layer of clay (sand layer) at 1.2 m/d, the widespread seepage velocity in the engineering stratum of Shanghai.
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 3 Problem Formulation
### Multi-Hop Problem Formulation
#### 3.2.3 Data Rate Constraint  
For each deployed donor, the flow-in data rate should be a constant value determined by a fixed fiber data input rate. The data rate that flows out from a donor node comprises two parts: (i) the data rate required for its own access coverage, and (ii) the backhaul data rate required by the downstream nodes if the donor node provides backhaul service to them. It is important to note that the total flow-out data rate from a donor node is subject to an overhead multiplier, which accounts for any additional data processing and transmission overheads incurred in the donor node. For regular nodes, the flow-in data rate is contributed by the upstream donor or another node that provides backhaul service to them. The flow-out data rate from a regular node is constituted by its local coverage data rate requirements and the backhaul data rate for any downstream nodes, given the condition that this node provides a backhaul service to the next-hop node. Here, expression (17) describes the data rate limitation of the donor. Similar to the donor, the total data rate flow-out from a regular node is also multiplied by an overhead factor to account for additional data transmission overheads. It is also worth noting that a node will only function properly when it can secure a sufficient data rate service, or at least meet its own access consumption. Here, expression (17) describes the data rate limitation of a donor. Expressions (18) and (19) represent the data rate limitation of the node. The fundamental constraint in this optimization problem is that the total data rate flow-in for any node, whether it is a donor node or a regular node, must always exceed its data rate flow-out. This condition ensures the robustness and sustainability of the network's data rate distribution, allowing for uninterrupted data service across the network. Failure to meet this data rate condition would render the node inactive and disrupt the network deployment to the next hop:  
\[F_{i}-R_{o}(A_{i}+(\sum_{j\in I}Y_{ij}R_{ij})) \geq 0\quad\forall i\in I \tag{17}\] \[R_{ij}Y_{ij}-R_{o}(A_{j}+\sum_{n\in I,n\
eq j}Y_{jn}^{\prime}R_{jn}) \geq 0\quad\forall i\in I,\forall j\in J\] (18) \[R_{jn}Y_{jn}^{\prime}-R_{o}(A_{n}+\sum_{m\in I,m\
eq n}Y_{nm}^{ \prime}R_{nm}) \geq 0\quad\forall j,n\in J \tag{19}\]  
From the aforementioned analysis, it is evident that both access and backhaul constraints must be satisfied during the network deployment process. And the data rate constraint will automatically limit the number of the hops. Consequently, we present the optimization problem as follows:  
\[\text{Minimize}\quad\sum_{j\in J}\alpha_{j}\] (20) \[\text{subject to:}\quad\sum_{i\in I}C_{ik}+\sum_{j\in J}C_{jk} \alpha_{j}\geq 1\quad\forall k\in K\] (21) \[A_{x}=\sum_{u\in U}R_{xu}+C\quad\forall x\in I\bigcup J\] (22) \[0\leq R_{pq}\leq SNR(d_{pq})\] (23) \[(\ref{eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq: eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq:eq
## 4 Conclusions  
As a result of this study, which was conducted to investigate the impact of fly ash (FA), blast furnace slag (BFS), and limestone powder (LP) on the mechanical and physical characteristics of geopolymer mortar, the following conclusion were reached:  
1. An increase in FA content led to an augmentation in water absorption, accompanied by a decrease in dry unit weight, compressive strength, flexural resistance, impact resistance, and initial setting-time values.
2. The dry unit weight, flexural strength, compressive strength, and resistance to impacts all rose significantly as the percentage of BFS in the material increased. Both the initial setup time and the amount of water that was absorbed reduced simultaneously.
3. The properties of the material--dry unit weight, flexural strength, compressive strength, impact resistance, and water absorption--decreased as the LP content increased, while the initial setting time increased.
4. The presence and increased quantity of LP enhanced the workability of geopolymer, due to the fineness and the spherical shape of LP particles for all prepared mixtures.
5. The amount of additional water used in the production of geopolymer mortar was observed to be crucial and influential on its mechanical and physical properties.
6. Despite the fixed amount of additional water in mortar production, an increase in water quantity in supplementary studies positively impacted workability but adversely affected compressive strength and the initial setting time.
7. In the future, it is recommended that studies should concentrate on the basic science of geopolymers to discover the process of chemical reactions that occur during the procedure of setting and hardening.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### Experimental Results: O-ADDV Multipath Topology with 10 Nodes  
These experiments were conducted using the 10-node AODV partial mesh topology for further protocol testing. The findings are presented in this section.  
The single source transmits data to the sink via four hops through the partial mesh, which has numerous possible routes.  
Since there are various combinations of paths through the partial mesh, Figures 20 and 21 only present the results for the selected paths to illustrate the directed forwarding behavior of the O-ADDV and AODV protocols.  
The O-ADDV and AODV routing protocols depicted in Figures 20 and 21 resulted in lower traffic loads with the ten-node complex/partial mesh scenario than the mesh (flooding) protocol, which resulted in extremely high traffic loads due to uncontrolled traffic flow, as depicted in Figure 22. Furthermore, due to the improvements discussed in the preceding  
Figure 16: Average RREQ-RREP setup time.  
Figure 17: Average end-to-end delay: O-AODV, AODV, and mesh (flooding) comparison.  
Figure 18: Overhead: O-AODV, AODV, and mesh (flooding).  
sections, the proposed routing protocol caused less traffic with the routing mechanism, resulting in better results with O-AODV due to lesser packet retransmission overhead.  
In the partial mesh scenario with 10 nodes, O-AODV showed a lower average route request to route reply time of 5900 ms compared to 6800 ms for AODV, as illustrated in Figure 23, in comparison with its predecessor version due to the optimizations made to the mesh network and AODV protocol layers.  
Figure 21: Traffic load: AODV.  
Figure 22: Traffic load: mesh (flooding)-based protocols.  
Figure 20: Traffic load: O-AODV.  
Figure 24 shows that the O-AODV had a lower average end-to-end delay of 5300 ms compared to 6200 ms and 7200 ms for AODV and mesh (flooding), respectively, due to the controlled traffic load. Furthermore, O-AODV had lower overheads of 22% compared to 42% for the AODV and 80% for mesh (flooding) protocols due to less retransmission than AODV and mesh due to an improved routing mechanism, as shown in Figure 25. O-AODV also demonstrated its efficiency in terms of the PDR with 81% compared to AODV with 69%, but it was lower than mesh flooding, as shown in Figure 26, because mesh uses a flooding mechanism that favors the packet delivery ratio at the cost of much higher overheads.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Performance Metrics  
Performance metrics are used to evaluate NTL detection algorithms and, in some cases, compare them with each other to determine the best-performing one. Every algorithm outputs a list of customers classified as likely fraud or non-fraud, so the metrics use the following parameters to assess them [4; 16; 66]:  
* True positive (TP): when the consumer is correctly classified as fraud [4; 16; 66; 40];
* False positive (FP): when the consumer is incorrectly classified as fraud, that is, they did not commit fraud but are classified as fraud [4; 16; 66; 40];
* True negative (TN): when the consumer is correctly classified as non-fraud [4; 16; 66; 40];
* False negative (FN): when the consumer is incorrectly classified as non-fraud, that is, they committed fraud but are classified as non-fraud [4; 16; 40; 66].  
The results are generally presented in the confusion matrix shown in Table 2.  
The following metrics are calculated from the confusion matrix:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Predictions**} \\ \hline Actual values & TP & FP \\ \cline{2-3}  & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
* Accuracy: It reveals the accuracy (Acc) of the technique in classifying the samples. However, it is sensitive to imbalanced databases (a greater number of samples of one label than another) [4,16]. \[Acc=\frac{TP+TN}{TP+TN+FP+FN}\] (2)
* Detection rate (DR): It indicates the percentage of positive samples correctly classified, that is, it shows the performance of the classifier with respect to the false negatives (FNs) [4,16]. \[DR=\frac{TP}{TP+FN}\] (3)
* Precision: It shows the percentage of performance with respect to the false positives (FPs) [4,16]. \[Precision=\frac{TP}{TP+FP}\] (4)
* False-positive rate (FPR): It presents the percentage of samples incorrectly classified as negative out of the total number of true negatives (TNs) [4,16]. \[FPR=\frac{FP}{TN+FP}\] (5)
* True-negative rate (TNR): It is the opposite of the DR, so it shows the percentage of samples correctly classified as negatives out of the total number of negatives [4,16]. \[TNR=\frac{TN}{TN+FP}\] (6)
* False-negative rate (in English FNR): It shows the percentage of samples incorrectly classified as negative out of the total number of true positives (TPs) [4,16]. \[FNR=\frac{FN}{TP+FN}\] (7)
* F1 score: This metric is used to compare various techniques and is a combination of two metrics, the detection rate (DR) and precision [4,16]. \[F1_{score}=\frac{2 TP}{2 TP+FP+FN}\] (8)  
Other metrics described in the literature are the ROC and the AUC. The ROC is a graph that indicates the performance of a classifier, plotted with two indicators. On the x-axis is the FPR (false-positive rate), and on the y-axis is the DR (detection rate); points can be plotted with different thresholds to evaluate the classification. On the other hand, the AUC is the area under the ROC curve. This metric provides the performance of the classifier across all evaluated thresholds [4,16].
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Evaluations
#### 4.6.1 Optimization of Detection Interval  
First, the detection interval is optimized. Figures 4 and 5 show the detection accuracy and the number of fingerprints for each interval respectively. From 0 s to 30 s, both the detection and the number of fingerprints gradually increase. Then, both are saturated. The best detection accuracy is obtained when the interval is 40 s, where the total number of fingerprints is 109.  
Figure 4: Detection accuracy with interval variation.
## 3 Results and Discussion
### Effect of PEF Processing Intensities on the Volatile Concentrations
There was no significant difference in the concentration of PPrDS and MPrDS measured between the two time points. This implies that the enzymatic reaction had almost reached completion at T0 and no further increase in the concentration of PPrDS and MPrDS occurred during the incubation period. The application of high electric field strength resulted in the highest concentration of DPDS and MPDS in the spring onions, whereas the highest concentration of DPDS and MPDS in the Yellow Sweet Spanish bulbs was achieved after 24 h with a medium-PEF intensity treatment. The maximum concentrations of disulfide compounds could also be attributed to the relative concentration of their substrates, which differs between onion cultivars [32; 33]. Overall, it could be inferred that the impact of PEF treatment had a significant effect on the concentrations of disulfides and was based on the applied electric field strength dependent on the onion varieties tested. The application of PEF treatment was found to be insignificant on the three trisulfide compounds immediately after PEF treatment (T0, Figure 5). However, after 24 h incubation following PEF treatment, the concentrations of trisulfides were dependent on the electric field strength applied. The trisulfide compounds are formed upon the rearrangement of the respective alkyl disulfide compounds, along with a monosulfide substituent. Since the trisulfides are formed later in the reaction cascade (Figure 2), more time was required for the enzyme initiated reaction cascade to reach completion [34] and thus a difference to be measured. It was also observed that the final concentrations of trisulfide compounds were a consistent percentage of their respective disulfide concentrations, measured at a particular time point. For instance, in the Red Bunching onions, the concentration of DPDS was 10-fold higher compared to the concentration of DPTS upon the application of high-PEF treatment measured at T24. Similar to the other volatile compounds detected in the Yellow Sweet Spanish bulb onions, the highest concentrations of trisulfides were obtained upon the application of medium-PEF treatment, with a decrease at high-PEF intensities. PEF treatment had a limited effect on the concentrations of alkanes (nonane, decane, and undecane) detected in the onion headspace (Figure 6). It is suspected that the formation of alkanes are due to the fragmentation of substrate molecules (non-volatile precursors) or thiosulfinate molecules upon PEF treatment intensities [28; 35]. Moreover, the biosynthetic pathways of alkanes and their role in flavour production remains largely unknown.
## 3 Results and Discussion
### Influence of Processing  
In order to obtain emulsions with better properties, both in terms of droplet size and physical stability, a study of the influence of sample processing was carried out. The systems with a fixed concentration of 2 wt.% cricket flour and whose primary homogenization was carried out with the rotor-stator system were introduced into an ultrasonic system at three different amplitudes (25%, 50% and 75%).  
Figure 3: Backscattering versus container height as a function of time for the emulsion formulated with 2 wt.% of cricket flour and developed using only primary homogenization.  
Figure 2: Influence of cricket flour concentration on (**A**) volumetric mean diameter (D4,3) and (**B**) span parameter.  
Figure 4 shows a clear improvement in results when secondary homogenization is carried out. As the amplitude (the applied energy) is increased, the droplet size distributions shifts towards smaller sizes. This shift in the droplet size distributions towards smaller sizes is reflected in significantly smaller values of the volumetric mean diameter as the energy input increases (see Table 1). In addition, the polydispersity of the samples is lower, although there is no clear trend. It is shown that no recoalescence phenomena are observed, so perhaps increasing the energy input (e.g., by increasing homogenization time) could lead to better results.  
Figure 5 shows the results of the physical stability of the sample prepared using ultrasound at 75% amplitude as an example of the emulsions prepared with this emulsification method. A clear improvement in the stability results is shown for this emulsion for 98 h of aging time compared to the emulsion processed using only the rotor-stator device for 24 h (see Figure 3). This improvement must be attributed to the decrease in droplet diameters (see Table 1). It should be noted that in none of the samples was there any variation in backscattering in the intermediate zone of the vial, which could rule out changes in the droplet size or aggregation of the droplets. In any case, a physical stabilizer was incorporated into the formulation to inhibit this creaming destabilization mechanism.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **D4,3 (\(\mu\)m)** & **Span** \\ \hline \hline Rotor–stator (2 wt.\%) & 4.083 \(\pm\) 0.258 & 3.328 \(\pm\) 0.311 \\
25\% & 2.215 \(\pm\) 0.112 & 1.759 \(\pm\) 0.097 \\
50\% & 0.986 \(\pm\) 0.075 & 2.018 \(\pm\) 0.153 \\
75\% & 0.688 \(\pm\) 0.040 & 1.941 \(\pm\) 0.121 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Volumetric mean diameter (D4,3) and span as a function of processing for the emulsions studied.  
Figure 4: Droplet size distribution of all studied emulsions containing 2 wt.% of cricket protein as a function of sample processing. The system indicated as rotor–stator involved only primary homogenization. The values of 25%, 50% and 75% correspond to the amplitude used in an ultrasonic system in secondary homogenization.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 3 Research Gaps Addressed by This Study
### Integration of QE Metrics with Biomechanical Data  
Previous QE research has focused primarily on gaze behavior, with limited work integrating QE data with biomechanical features like body posture and shuttlecock tra jectory [19]. Our study builds on this approach by incorporating both QE duration and fixation points with detailed biomechanical data to predict shot accuracy, thus offering a richer model for performance analysis.
# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 3 Research Methods
### Mobility OLAP Operator  
Mobility analysis needs OLAP operators for the expression of queries over data cubes. Mobility OLAP enhances the analytical capabilities of OLAP by taking into account the cube's mobility information. These OLAP operators are roll-ups that aggregate measures along a hierarchical structure to obtain data with a coarser granularity. These operators are enriched with the aggregation function and operators, such as mobility aggregation, mobility spatial, and the aggregation lifting function. By selecting an individual instance at a dimension level, the slice operator removes a dimension from a cube. The dice operator reveals which cells in a cube satisfy a specified condition. The Spatial Slice and Spatial Diceenrich the OLAP operators proposed by the QB4 SOLAP vocabulary [54]. Some functions enhance the spatial mobility operators: InsideGeometry, AtPeriod, AtTimeStamp, At, etc. Finally, the drill-down operator disaggregates measures along a hierarchical structure to obtain more detailed data. It is the inverse of the roll-up operation. Table 2 shows the mobility functions and operators of mobility data types.
# Adaptive Neural-Network-Based Lossless Image Coder with Preprocessed Input Data
## 5 Preprocessing Data for AdNN
### Concept 1  
Images are locally smooth; hence, differences between neighboring image pixels used to be small. It is proposed to replace the pixel values in Formulas (4) and (9) by pixel differences; Table 1 provides their definitions. Apart from \(D(j)\) values, the AdNN input for \(j=19\) is \(P(1)\), and for \(j=20,21\), outputs from \"classical\" predictors GBSW\({}_{+}\) and GAP\({}_{+}\); their definitions can be found in [47]. Inputs are normalized; normalization consists of transforming data value range into AdNN signal range \([-0.5,0.5]\). The last AdNN input is equal to \(-1\).  
In this way, the number of AdNN inputs, \(r\), is reduced from 24 in AdNN\({}_{+}\)[16] to 22; the number of hidden layer neurons is \(n_{h}=12\). However, a much more important improvement is the reduction of epoch numbers necessary for AdNN convergence from 25 to only 11. For the four epochs, \(\eta=1\) and \(\alpha=0\), then \(\eta\) diminishes: \(\eta=0.07\cdot(12-\text{epoch number})\), \(\alpha=0.75\). Additionally, in the last three epochs window \(Q\) size diminishes from \(W=7\) to 5, 4, and 3; see Figure 3. The coding and decoding times are now 259 s for an \(512\times 512\)-pixel image, much less than for AdNN\({}_{+}\)[16]. The computational burden for an epoch is 1020 weight adaptation for each pixel consisting of \(n_{h}\cdot(2r+5)\) additions, \(3n_{h}\cdot(r+2)\) multiplications, and \((n_{h}+1)\) divisions, and applications of \(\exp()\) function. The actual numbers are \(1020\times 12\times(2\times 22+5)\) = 599,760 additions, \(1020\times 3\times 12\times(22+2)\) = 881,280 multiplications, and \(1020\times(12+1)\) = 13,260 other operations. It should be noted that the time cost of computations not linked with AdNN learning is negligible.
## 3 Results and Discussion
### Structural Characterization of the Membranes by FT-IR Analysis  
The FT-IR spectra of the prepared membranes are presented in Figure 6, where the key peaks are analyzed and their corresponding functional groups identified. The LEP-H1 confirms the presence of hydroxyl (-OH), ester (O-C), and carboxyl (O=C) functional groups, indicating the successful introduction of these groups onto the membrane surface using plasma treatment for hydrogel grafting. As shown in Figure 6, the peaks at 1232 and 1712 cm\({}^{-1}\) are representative of C-O and C=O, respectively.  
In comparison with H-1 and LEP-H1, the spectrum of H-6 exhibits a broad peak at 3396 cm\({}^{-1}\), attributed to the overlapping stretching vibrations of hydroxyl (O-H) and amide (N-H) groups. Additionally, an absorption peak at 1550 cm\({}^{-1}\) was observed, which corresponds to the stretching vibration of the sodium acrylate (-COONa) group [48; 64].  
Moreover, the preserved PVDF peaks at 1403 cm\({}^{-1}\) (-CH\({}_{2}\)), 1173 cm\({}^{-1}\) (-CF\({}_{2}\)), and 876 cm\({}^{-1}\) (C-C) indicate that the modification process retained the PVDF base structural integrity while introducing functional groups. These observations confirm the copolymer hydrogel attachment on the PVDF membrane surface.