esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Hardened State Properties
#### 3.4.5 Shrinkage  
Figure 9 presents the shrinkage up until 91 days for all mixes. The total length of shrinkage is characterized by rapid initial evolution in all mixes, reaching about 50% of the 91-day shrinkage between 14 and 28 days, except for C20 FA10 BA, which reached this value before 14 days. Comparing all SCC with the reference SCC, there is an increase in the extent of shrinkage over time with increasing MIBA. However, the C40 FA20 BA did not follow the same trend, showing lower values of shrinkage in all ages in comparison with the reference SCC of about 50 m/m. Such reduction is most likely due to the increase of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & \multirow{2}{*}{\(\mathbf{f_{cm,28}}\)} & \multicolumn{2}{c}{\(\mathbf{E_{cm,28}}\)} & \multirow{2}{*}{\(\mathbf{f_{cm,91}}\)} & \multicolumn{2}{c}{\(\mathbf{E_{cm,91}}\)} \\ \cline{3-3} \cline{5-7}  & & **EC2** & & **Measured** & & **EC2** & **Measured** \\ \hline RC & 55.4 & 36.7 & 45.4 & 65.5 & 41.5 & 41.5 \\ C10 FA20 BA & 14.4 & 24.5 & 23.3 & 16.7 & 19.9 & 19.9 \\ C20 FA10 BA & 24.6 & 28.8 & 28.3 & 26.3 & 24.9 & 24.9 \\ C20 FA40 BA & 6.2 & 19.0 & 14.6 & 8.7 & 16.3 & 16.3 \\ C40 FA20 BA & 8.6 & 12.6 & 12.6 & 10.0 & 17.5 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Modulus of elasticity (\(\mathrm{E_{cm}}\) in GPa) versus compressive strength (\(\mathrm{f_{cm}}\) in MPa) according to EC2 at 28 and 91 days.  
Figure 8: Ultrasonic pulse velocity at 7, 28 and 91 days.  
FA by 10% relative to the reference and not to the quantity of MIBA. It can also be observed that the mix containing the highest MIBA content presented greater shrinkage, -800 um/m at 91 days, while the mixture with the highest FA content the shrinkage exhibited a value of -450 um/m at 91 days. Figure 9b presents a comparative analysis of the experimentally measured \(\varepsilon_{\text{sh}}\) and that calculated using the shrinkage prediction model of EC2 [56] and Table 7 shows the corresponding statistical indicators. Despite slightly underestimating some of the results, including those of the RC, the model showed very little deviation for mixes containing additions up to 30% (MIBA and/or FA) as a cement replacement, thus suggesting the applicability of the EC2 model to mixes containing MIBA as partial cement replacement. The greater deviation was observed for mixes with 60% additions (i.e., C40 FA20 BA and C20 FA40 BA).
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 2 System Model
### Channel Stability Index  
As mentioned earlier, separate spectrum-sensors are deployed for PU detection. Each spectrum-sensor creates a vector on each sensing interval and forwards it directly to the central entity, which characterizes the channels based on their stability (PU activity) index. A channel with the least PU activity is considered to be the most stable channel. Let \(f_{k}=\{f_{1},f_{2},\ldots,f_{T}\}\) be a history status vector (HSV) indicating the PU on-off activity over the \(k\)th channel during \(T\) time slots. For better prediction of PU activity, we vertically divide the HSV into different regions where \(z=1\) represents the most recent and \(z=Z\) indicates the oldest region of the HSV, as shown in Figure 3. Suppose that \(\pi_{k}=\{\pi_{1},\pi_{2},\ldots,\pi_{Z}\}\) represents the consecutive number of free slots in different regions of HSV and that \(\omega_{k}=\{\omega_{1},\omega_{2},\ldots,\omega_{Z}\}\) indicates the weights assigned to each region of the HSV. The weights are assigned in decreasing order from the recent to the older samples of HSV. The stability index can be given using Equation (4) as follows:  
\[\theta_{k}=\sum_{z=0}^{Z}\omega_{z}\pi_{z}\hskip 36.135 pt\forall z \tag{4}\]  
where \(\theta_{k}\) indicates the stability index for the \(k\)th channel. The larger value of \(\theta_{k}\) indicates that the channel is more stable and that it has the least PU activity.  
Figure 3: Stability-index calculation framework.
## 4 Proposed Method
### Embedding  
This section discusses the process of watermark embedding using the cryptographic algorithm stepwise. The watermark embedding architecture is shown in Figure 4.  
**Step 1:** The watermark data consisting of name and country is created as a QR code.  
**Step 2:** Generate a public key and an encrypted message by inputting private key values into the RSA algorithm.  
**Step 3:** The QR code is scrambled using a **Chaotic Logistic Map (CLM)** to provide watermark data security.  
**Step 4:** Import the significant image where the watermark has to be concealed.  
**Step 5:** Convert the imported color image into red, green, and blue components.  
**Step 6:** Consider the blue layer and apply one-level Haar wavelet decomposition to obtain the four subbands (LL, LH, HL, and HH).  
Figure 4: The proposed scheme: embedding process.  
**Step 7:** The LL subband and scrambled QR image are selected and decomposed using SVD decomposition.  
**Step 8:** Both images' singular values are considered and combined with a key value to generate watermarked singular values.  
**Step 9:** A watermarked LL subband is created using an invertible SVD.  
**Step 10:** By fusing the watermarked LL subband and the other subbands to create the watermarked blue layer, an inverse DWT is implemented for one level.  
**Step 11:** A watermarked color image is created by combining a blue layer with the red, green, and other layers.  
**Step 12:** The watermarked image with the public key and key value are communicated to the receiver.
## 4 GDOP and WGDOP Calculation with Matrix Multiplication
### Proposed WGDOP Calculation with Matrix Multiplication
#### 4.2.1 Four Measurement Units
From Table 1, the matrix multiplication scheme needs only 134 additions, 212 multiplications, one division and one square root. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Additions** & **Multiplications** & **Division** & **Square Root** \\ \hline \(M\) & 18 & 34 & 0 & 0 \\ \(M^{2}\) & 30 & 54 & 0 & 0 \\ \(M^{3}\) & 30 & 50 & 0 & 0 \\ det(\(M\)) & 45 & 68 & 0 & 0 \\ WGDOP & 11 & 6 & 1 & 1 \\ \hline Total & 134 & 212 & 1 & 1 \\ \hline \end{tabular}
\end{table} 
Table 1: The complexity of WGDOP calculation when the four measurements with different error variances. Figure 1: Block diagram of WGDOP process.
## 3 Results and Discussion
### Core Replacement Results  
Table 5 shows the final recovery rates after using different injected fluids in different core replacement experiments, and these data are essential for assessing the effectiveness of different production enhancement strategies.  
As shown in the table, the base recovery rate for the core drive using raw formation water is 25 percent. By ordinary water injection technique, the recovery rate was increased to 37% and 35% in the first and second water injection experiments, respectively, showing some production increase. However, when surfactants were introduced, the recovery enhancement was more significant. When a cationic surfactant was used, the recovery rate increased to 42%, while when a nonionic surfactant was used, the recovery rate was 40%, both higher than the recovery rate achieved through normal water injection only.  
Most notably, the highest recovery rate of 45 percent was achieved when a cationic/nonionic surfactant blend was used. This suggests that hybrid surfactants are more effective than a single type of surfactant in reducing oil-water interfacial tension, decreasing capillary confinement, and enhancing micro-scale rejection efficiency.  
The observed recovery enhancement can be attributed to the reduction of oil-water interfacial tension by the surfactants, which in turn reduces the capillary pressure and makes it easier for oil droplets to be driven out of the rock pores. Since the capillary effect is one of the main causes of crude oil residue in reservoirs, reducing capillary confinement is a key strategy for recovery enhancement.  
Figure 4: Measurement of interfacial tension in different surfactant solutions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Fluid Injected** & **Recovery Ratio (\%)** \\ \hline Original formation water & 25 \\ First ordinary water injection & 37 \\ Second ordinary water injection & 35 \\ Cationic surfactant & 42 \\ Non-ionic surfactant & 40 \\ Mixed cationic/nonionic surfactants & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Recovery rates of different core displacement experiments.  
The experimental results clearly show that the use of cationic/nonionic hybrid surfactants has significant advantages in production enhancement in carbonate reservoirs. The efficient surface-active capacity of this hybrid surfactant helps to achieve higher crude oil recovery and is an effective method of chemical drive-off in oil fields. Through careful surfactant design, a significant increase in reservoir recovery efficiency can be achieved, which in turn optimizes the field development strategy.
## 4 Design of the Algorithm
### The Order Quantity Fair Strategy  
For the K-means algorithm mentioned above, the consumer location was distributed into K clusters only depending on the node's distance from each other, and the number of nodes in each cluster was not considered in this process. Normally, each cluster of orders will be assigned to a rider, which can lead to workload differences for different riders. Merchants must ensure that each rider has the same workload to guarantee fairness. To realize the fairness of the workload distribution, researchers modified the K-means algorithm to distribute the consumer nodes into K clusters with the same number of nodes in each cluster. Each rider will finish the same number of orders in this delivery mode.  
The dividing strategy is illustrated in Figure 4. The K-means clustering result is redistributed based on the number of nodes each cluster owns. If a cluster has more nodes than the average, parts of its nodes that are close to the other cluster center will be assigned to the corresponding cluster according to Equations (11) and (12).  
\[x_{\text{remove}}=x_{i},\quad\text{if}\quad D_{ij}=\min_{x_{i}\in c_{\text{ max,node}}}\lVert x_{i}-\mu_{j}\rVert^{2},\quad j\
eq\max \tag{11}\]  
\[c_{j}=c_{j}\cup\{x_{\text{remove}}\} \tag{12}\]  
where \(c_{\text{max,node}}\) is the cluster that has the maximum number of nodes, \(x_{i}\) is the node in cluster \(c_{\text{max,node}}\), \(x_{\text{remove}}\) is the node in cluster \(c_{\text{max,node}}\) that is closest to the center of the other clusters, \(D_{\text{ij}}\) is the distance between node \(x_{i}\) in \(c_{\text{max,node}}\), and \(c_{j}\) is the cluster whose center is closest to the nodes of cluster \(c_{\text{max,node}}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\alpha\) & 2 \\ \(\beta\) & 4 \\ \(\rho\) & 0.3 \\ \(Q\) & 10 \\ Ants number & Nodes number \\ Initial pheromone & 1 \\ Max iteration & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ant colony algorithm parameters setting.  
The redistribution process works as follows: First, \(c_{\text{max\_node}}\) is identified. Using Equation (11), \(x_{\text{remove}}\) is selected. This ensures that the node is spatially closest to another cluster, justifying its reassignment. Then, Equation (12) reassigns \(x_{\text{remove}}\) from \(c_{\text{max\_node}}\) to \(c_{j}\), reducing \(c_{\text{max\_node}}\)'s size and balancing node counts. This iterative adjustment (repeating until clusters have nearly equal node numbers) leverages spatial proximity to achieve fair order quantity distribution, with Equations (11) and (12) formalizing the node selection and reassignment logic, respectively.
## 4 Conclusions  
The real signal tests show that the camera clearly brings some improvements in the situations where NLoS signal is present but not detected by the EKF RAIM. The contribution brought by the camera allowed to reduce the HPE distribution, while only slightly increasing the HPL (less than 15% degradation, at least up to the 95 th percentile, mainly due to the DOP degradation), and very few areas remain where the camera degrades the positioning accuracy.  
Although not illustrated here, it also appeared that the \"three-levels\" discretization of the LoS probability map is beneficial with respect to a binary discretization, as it allows considering building edges as \"doubtful\" areas. At least two reasons have been evidenced for the position degradation:  
* Effect of small obstacles such as lamp poles that cause a NLoS flag by the camera while not significantly affecting the quality of the data;
* Difficult classification of the vegetation that can be considered a wrong source of NLoS, leading to the exclusion of too many measurements.  
The problem with the small objects that appear to block the satellite data will be treated in the future by integrating context into the classification. At this point, our classifier works at pixel level. Moreover, some morphological operations could refine the response. For the second disadvantage, concerning the classification of the vegetation, we envisage the use of new, more performant classifiers such as neural networks.  
Conceptualization, A.C. and M.M.; methodology, C.D. and G.C.; software, C.D., C.N. and G.C.; validation, C.D., C.N. and G.C.; formal analysis, V.G. and A.C.; investigation, C.D. and G.C.; resources, A.C. and M.M.; data curation, G.C.; writing--original draft preparation, C.D., C.N., G.C.; writing--review and editing, C.D. and C.N.; visualization, C.D. and G.C.; supervision, V.G., A.C. and M.M.; project administration, A.C. and M.M.; funding acquisition, A.C. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Space Agency with ESA Contract No. 4000111852 /14/NL/Cbi (Quality of Services Improvement for GNSS Localisation in Constraint Environment by Image Fusing Techniques (IMFUSING)) with Politehnica University, Timisoara, Romania.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Aim: The study aimed to compare the depth of cure, hardness, surface roughness, and wear resistance of four restorative materials used in pediatric dentistry: FUJI IX GP FAST, RivaSilver, SDR flow+, and Vertise Flow. Materials and Methods: The depth of cure was measured per ISO 4049 standards using a digital caliper, with 15 samples of each material. Hardness was evaluated using a Vickers indenter under a 10 N load for 20 s. Surface roughness was assessed before and after acid exposure using an optical profilometer according to ISO 4288. Brushing wear resistance was analyzed by subjecting samples to 20 and 60 min of brushing, followed by roughness measurements. Statistical analysis was performed using independent sample \(t\)-tests to determine the significance of differences between the materials, with \(p\)-values \(<\) 0.05 considered significant. Results: SDR flow+ exhibited the highest depth of cure with an average of 3.5 mm (\(\pm\)0.2 mm), significantly higher than Vertise Flow at 2.8 mm (\(\pm\)0.3 mm) (\(p<\) 0.001). Hardness testing revealed SDR flow+ had the highest average hardness (85 HV \(\pm\) 4 HV), while Vertise Flow had the lowest (72 HV \(\pm\) 5 HV) (\(p<\) 0.001). Surface roughness increased significantly after acid exposure for RivaSilver (from 1.2 \(\upmu\)m \(\pm\) 0.12 \(\upmu\)m to 1.6 \(\upmu\)m \(\pm\) 0.15 \(\upmu\)m, \(p\) = 0.007) and for SDR flow+ (from 0.85 \(\upmu\)m \(\pm\) 0.08 \(\upmu\)m to 1.3 \(\upmu\)m \(\pm\) 0.14 \(\upmu\)m, \(p\) = 0.001). Brushing wear resistance was highest in RivaSilver (Ra increase from 1.2 \(\upmu\)m to 1.4 \(\upmu\)m \(\pm\) 0.11 \(\upmu\)m) and lowest in FUJI IX GP FAST (Ra increase from 1.5 \(\upmu\)m to 1.9 \(\upmu\)m \(\pm\) 0.15 \(\upmu\)m, \(p\) < 0.001). Conclusions: The study demonstrates significant differences in performance among the tested materials. SDR flow+ showed a superior depth of cure and hardness, making it suitable for high-stress applications. However, all materials displayed increased surface roughness following acid exposure and brushing, with FUJI IX GP FAST showing the highest wear. These findings highlight the need to select restorative materials based on the specific clinical demands of pediatric patients, considering both their mechanical properties and resistance to wear and acid.  
dental materials; pediatric dentistry; depth of cure; hardness; roughness +
Footnote †: journal: Applied Sciences
## 5 Challenges, Open Research Problems, and Future of WBANs
### Bandwidth Problems
#### 5.1.3 Network Layer Challenges  
Energy-efficient techniques used in WBAN networking have recently acheived considerable importance and are defined as green technology. Reducing the energy consumption of computing and communication infrastructure in WBSNs environments is an area of increasing importance for researchers and presents an open research challenges for applications of wireless sensor networks for WBANs and healthcare monitoring systems. The convergent traffic inherent in wireless sensor networks may cause choke effect at the nodes closer to the base station. For this reason, load balancing routing protocols need to be developed. Moreover, when multimedia traffic is encountered with the emergence of multimodal sensor networks for healthcare monitoring applications, congestion avoidance and rate control issues become significant [91]. These techniques should also be integrated with data compression techniques for better utilization.  
There is also a problem of sensor nodes' temperature _in vivo_ sensor networks, as they perhaps harm the tissue when they are overheated. To overcome this problem, [PERSON] _et al._[92] proposed a thermal-aware routing protocol for implantable biosensor networks. These protocols should avoid the degradation of delay performance. However, for reliable data delivery, multipath routing protocols for medical applications based on sensor networks should be studied.
## 3 Proposed Methodology of Monitoring
### Tower Active Control System Components  
As shown in Figure 3(a) above, Beidou users receive the navigation displacement monitoring signal from the Beidou satellite through the Beidou satellite GNSS antenna installed on the tower and the system autonomously and accurately calculates 3D pose parameters (X, Y and Z) using the N72 reference receiver (Figure 3(b)) installed on the tower. Then, the data is transmitted to the base station through the bridge and processed and analyzed by the relevant software in the workstation. The data are sent to the workstation through wireless bridge switches. The base station and the specific working principle are shown in Figure 5.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 6 Conclusions  
This article validates an unprecedented DPD controlled 5G-NR-based hybrid fibre-wireless system equipped with 2 \(\times\) 2 MIMO. A 10 km SMF is used to send a 5G NR signal at a distant radio head targeting an eMBB scenario with a femtocell (3 GHz) and a supercell (20 GHz) for eMBB applications. The hybrid fibre-wireless system via the proposed CNN-based DPD achieves a significant performance that meets the 3 GPP specifications. The proposed CNN DPD approach reduces EVM to 3.2% and PSD to \(-41\) dBc. The integration of fibre-wireless has great potential, and the experimental results suggest that this can be a potential solution for the transport and access network of future communication networks. Future work includes the optimization of fibre-wireless infrastructure and improvement in the linearization methodologies for better performance.  
Conceptualization, M.U.H.; A.A.C.; A.R. and S.K.O.S.; methodology, M.U.H.; software, M.U.H. and J.S.; validation, M.U.H. and S.K.O.S.; formal analysis, M.U.H.; J.S. and A.R.; investigation, M.U.H.; J.S. and S.K.O.S.; resources, M.U.H. and J.S.; data curation, writing--original draft preparation, M.U.H.; A.A.C.; A.R. and S.K.O.S.; supervision, M.U.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Not applicable for studies not involving humans.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 3 Driving Behavior Classification and Sharing System
### Prediction Result Sharing via V2X  
In order to improve the traffic safety, the driving behaviors classified through 4-layer CNN-2 stacked LSTM must be communicated to other traffic participants. As a method for sharing information between vehicles and transportation system, V2X communication is a useful means. Messages delivered via V2X include BSM, Emergency Vehicle Alert (EVA), Common Safety Request (CSR), and traveler information as stipulated by Society of Automotive Engineers (SAE). Among them, BSM is a message that provides the vehicle information for safety, and is composed of Part I and Part II. Part I contains the vehicle's GNSS position, speed, steering wheel angle, acceleration, brake system status and vehicle specifications, and is an essential component of BSM. On the other hand, Part II is divided into vehicle safety extension and vehicle status. The vehicle safety extension includes information on path history and path prediction, and vehicle status is optional. We include the predicted driving behavior via 4-layer CNN-2 stacked LSTM in vehicle status of BSM Part II, following the configuration of the BSM. The BSM with predicted driving behavior through the proposed system is broadcast to the around vehicles, which can assist nearby traffic participants in making safe driving decisions.  
Figure 12 is a prototype of the 4-layer CNN-2 stacked LSTM-V2X system for predicting and sharing driving behavior. NVIDIA Jetson TX2 is used as a small computing board to drive the proposed 4-layer CNN-2 stacked LSTM-V2X system. The V2X device for sending and receiving BSM is Etifos ETF-DV-02. In addition, the MI-670 is mounted as an IMU for measurement of longitudinal and lateral acceleration. The prototype was equipped with an MRP-2000 GPS to collect the vehicle's driving trajectory and a battery to power each device. Jetson TX2 was selected because of its 4-layer CNN-2 stacked LSTM network operation and compatibility with the used V2X device. The total size of the prototype is 180 \(\times\) 180 \(\times\) 95 mm, and consists of three layers. The first layer is the battery and V2X device, the second layer is the computing board, and the third layer is the IMU and GPS.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 4 Target Distortion Optimization
### Problem Solution
#### 4.3.3 Power and Bandwidth Trade-Off  
To achieve a desirable video quality, either the power consumption is optimized under a constrained bandwidth usage or the bandwidth usage is optimized under a limited power budget. Each point of the resultant trade-off curve between power and bandwidth represents a possible power and bandwidth usage combination that attains the same video quality. In multi-user systems, each viewer has a different power and bandwidth resource budget, where a joint optimization that relies on choosing a proper power and bandwidth usage combination can be implemented to preserve resource consumption.