esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology
### Link Budget Analysis  
In the present work, the communication capability of the designed wearable textile patient tracking antenna to the external monopole antenna with a gain of 2.15 dBi for off-body communication is explored. The Friis transmission equation is used to calculate the link budget [24; 25] and Table 6 shows all the link budget parameters The antenna shows higher values of link margin at the lower frequencies (1.8 GHz and 2.45 GHz) and relatively lower values of link margin at high frequency (5.8 GHz) due to increased path losses at the higher frequency.  
However, Figure 22 shows that the proposed wearable antenna can bear additional losses of more than 62.5 dB when used for patient tracking up to 100 m at high frequency (5.8 GHz). The antenna's communication link at all three reported frequency  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Transmitter**} \\ \hline  & Frequency (GHz) & 1.8/2.45/5.8 \\ \(G_{l}\) & Antenna gain (dBi) & 3.7/5.3/9.6 \\ \(P_{t}\) & Transmitted power (dBm) & 16 \\  & EIRP (dBm) & 19.7/21.3/25.6 \\ \hline  & **Receiver** & \\ \hline  & Receiver antenna gain (dBi) & 2.15 \\ \(G_{r}\) & (external antenna) & 293 \\ \(T_{o}\) & Ambient temperature (K) & 293 \\  & Boltzmann constant & 1.38 \(\times\) 10\({}^{-23}\) \\ \(N_{o}\) & Noise power density (dB/Hz) & \(-\)203.9 \\ \hline  & **Signal quality** & \\ \hline  & Bit rate (Mbps) & 0.250, 1, 10 \\ \(E_{b}/N_{o}\) & Ideal PSK (dB) & 9.6 \\ \(G_{c}\) & Coding gain (dB) & 0 \\ \(G_{d}\) & Fixing deterioration (dB) & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Link budget parameters.  
Figure 21: Simulated average SAR distribution on the cuboid phantom: at **(a)** 1.8 GHz **(b)** 2.45 GHz (c) 5.8 GHz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} }} & \multicolumn{2}{c}{**Maximum SAR (on Phantom)**} \\ \cline{2-3}  & **1 gm** & **10 gm** \\ \hline
1.8 & 0.0796 & 0.0759 \\
2.45 & 0.0575 & 0.0552 \\
5.8 & 0.0226 & 0.0204 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum SAR of the proposed antenna (at 1 W input power).  
bands (1.8 GHz, 2.45 GHz, and 5.8 GHz) is good, and the proposed antenna can easily track up to 100 m.
# From Google Earth Studio to Hologram: A Pipeline for Architectural Visualization
###### Abstract  
High-resolution holographic visualization of built environments remains largely inaccessible due to the complexity and technical demands of traditional 3D data acquisition processes. This study proposes a workflow for producing high-quality full-color digital holographic stereograms of architectural landmarks using Google Earth Studio. By leveraging photogrammetrically reconstructed three-dimensional (3D) city models and a controlled camera path, we generated perspective image sequences of two iconic monuments, that is, the Basilica de la Sagrada Familia (Barcelona, Spain) and the Arc de Triomphe (Paris, France). A custom pipeline was implemented to compute keyframe coordinates, extract cinematic image sequences, and convert them into histogram data suitable for CHIMERA holographic printing. The holograms were recorded on Ultimate U04 silver halide plates and illuminated with RGB light-emitting diodes, yielding visually immersive reconstructions with strong parallax effects and color fidelity. This method circumvented the requirement for physical 3D scanning, thereby enabling scalable and cost-effective holography using publicly available 3D datasets. In conclusion, the findings indicate the potential of combining Earth Studio with digital holography for urban visualization, cultural heritage preservation, and educational displays.  
CHIMERA; Google Earth Studio; holographic stereogram; photogrammetry +
Footnote †: journal: Applied sciences  
Article
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Continuous Flight Auger Piles (CFA)  
CFA method allows to exclude the casing of the borehole, while the soil remains at the drill thread and supports the shaft. The method is suitable for dry and saturated cohesionless soils and for most of the cohesive soils without hard drillable insertions. Drilling is executed in the shortest possible time with minimum revolutions of the auger to minimize the effect of drilling on the surrounding soil. In the first step, the auger is drilled to the designed depth. Subsequently, concrete is injected with simultaneous withdrawal of the auger with the remains of soil. Withdrawal of the auger takes place without its rotation but some rotation to the drilling direction can be performed at certain conditions. Concrete injection is performed through the auger pipe connected to the concrete mixture pump. In the final step, the reinforcement cage is inserted into the concrete [3; 38].  
Pillars of selected bridges were supported by the CFA piles. All tested piles were manufactured using same technology in similar geological conditions and with the same pile diameter of 1.0 m. The length varied from 5.4 to 8.6 m and the pile tip was situated in the layer of fluvial sandy and gravelly sediments. Because of the lower bearing capacity of  
Figure 1: Situation of tested piles and CPTu probes.  
the Neogene deposit below fluvial sediments, the pile length was adjusted so the pile can end in sandy and gravelly soils with potentially higher bearing capacity. Embedment of the pile into this layer varied depending on the actual pile shaft depth during manufacturing.
## 1 Introduction  
The Internet of Things (IoT) has transformed modern communication systems by enabling seamless interconnectivity among billions of devices in diverse applications, including smart cities, healthcare, industrial automation, and environmental monitoring [1, 2, 3]. IoT enhances automation and decision-making by leveraging real-time data exchange, improving operational efficiency and user convenience [4, 5]. However, despite its growing adoption, several challenges hinder its large-scale deployment and practicality. Energy consumption remains a critical issue, as most IoT devices are battery-powered and require efficient power management to extend operational lifespan. Battery replacement is often impractical in remote or massive deployments, increasing maintenance costs [6, 7, 8, 9]. Moreover, IoT devices are typically low-resource systems with limited computational and memory capabilities, making it difficult to process and transmit large amounts of data efficiently [10, 11, 12]. Connectivity is another challenge, as IoT networks must support seamless communication across varying network conditions and device densities [13]. Additionally, coverage limitations in conventional wireless architectures restrict reliable data transmission, particularly in highly distributed IoT environments [14, 15, 16, 17]. Last but not least, security vulnerabilities pose significant threats, as IoT systems are prone to cyberattacks, unauthorized access, and data breaches due to their decentralized nature [18, 19, 20, 21].  
To address the aforementioned challenges, cell-free massive multiple-input multiple-output (CF m-MIMO) has been proposed as a promising solution for IoT communication networks [22, 23, 24]. CF m-MIMO leverages a distributed network of access points (APs) connected to a centralized processing unit, collaboratively serving IoT devices without traditional cell boundaries. By eliminating inter-cell interference and distributing network resources efficiently, CF m-MIMO enhances the quality of service (QoS) by improving spectral efficiency, coverage, and user fairness [25, 26, 27]. The decentralized nature of CF m-MIMO significantly enhances connectivity, ensuring robust communication even in ultra-dense IoT environments. Unlike conventional cellular architectures, CF m-MIMO dynamically adapts network resources based on device locations and communication demands, providing uniform signal quality across a wide geographical area. By leveraging coordinated beamforming and joint signal processing, CF m-MIMO optimizes energy efficiency and mitigates interference, making it an ideal candidate for scalable, energy-efficient, and secure IoT communications [28, 29, 30].
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model
### LEO Satellite Network Model
#### 2.4.2 Pointing Error  
The beam radial pointing error angle of satellite \(i\) is denoted by \(\theta_{ij}=(\theta_{x},\theta_{y})^{T}\), where \(\theta_{x}\sim N(\mu_{x},\sigma_{x}^{2})\) and \(\theta_{y}\sim N(\mu_{y},\sigma_{y}^{2})\) represent the pointing error angle of azimuth and pitch, respectively. Thus, \(\theta_{ij}=\sqrt{\theta_{x}^{2}+\theta_{y}^{2}}\) obeys the Beckman distribution [25] and approximates it to the modified Rayleigh distribution with parameter \(\sigma_{\text{mod}}\)[20]. The approximate expressions of the probability density function (PDF) and cumulative distribution function (CDF) of \(\theta_{ij}\) are obtained as:  
\[f_{\theta}(\theta_{ij})\approx\frac{\theta_{ij}}{\sigma_{\text{mod}}^{2}}\exp \left(-\frac{{\theta_{ij}}^{2}}{2\sigma_{\text{mod}}^{2}}\right),\theta_{ij}\geq 0 \tag{5}\]  
\[F_{\theta}(\theta_{ij})\approx 1-\exp\left(-\frac{{\theta_{ij}}^{2}}{2\sigma_{ \text{mod}}^{2}}\right) \tag{6}\]  
where  
\[\sigma_{\text{mod}}=\left(\frac{3\mu_{x}^{2}\sigma_{x}^{4}+3\mu_{y}^{2}\sigma_ {y}^{4}+\sigma_{x}^{6}+\sigma_{y}^{6}}{2}\right)^{1/6} \tag{7}\]
## 5 Key 6G Technologies
### Other Perspectives
#### 5.3.8 Quantum Communications  
Unsupervised reinforcement learning and quantum technologies take center stage in 6G. Quantum computing, machine learning, and their synergy with communication networks enhance security against cyberattacks [1]. Quantum communications leverage quantum principles for secure data transmission and improved throughput [226].
## 4 Optimization Problem Modeling
### Optimization Constraints
#### 4.4.2 Link Availability  
IoT devices can transmit computational tasks to relay satellites only when connectivity is established, which is defined by  
\[\sum_{t^{\prime}=1}^{t-1}z^{i}_{m,t^{\prime},k,t}\cdot 1_{\{x^{i}_{n,k}=0\}}=0, \quad\forall m,k,t \tag{17}\]  
where \(1_{\{x\}}\) is an indicator function that returns \(1\) if \(x\) is true and \(0\) otherwise. Similarly, relay satellites can relay data to ground stations only with an active link:  
\[\sum_{n=1}^{N}\gamma^{g}_{n,k,t}\cdot 1_{\{x^{g}_{k,q}=0\}}=0,\quad\forall k,t \tag{18}\]  
\[\sum_{t^{\prime}=1}^{t-1}\lambda^{g}_{m,t^{\prime},k,t}\cdot 1_{\{x^{g}_{k,q}=0\}} =0,\quad\forall k,t \tag{19}\]  
For data transfer from observation satellites to relay satellites, a valid connection is necessary:  
\[\sum_{t=1}^{T}\gamma^{o}_{n,k,t}\cdot 1_{\{x^{o}_{n,k}=0\}}=0,\quad\forall n,k \tag{20}\]  
Finally, data can only be exchanged between relay satellites if they are interconnected:  
\[\sum_{t=1}^{T}\gamma^{r}_{n,k,k^{\prime},t}\cdot 1_{\{x^{\prime}_{k,t^{ \prime}}=0\}}=0,\quad\forall k,k^{\prime},k\
eq k^{\prime} \tag{21}\]  
The term '\(x\)' serves as an indicator of link availability between IoT devices and satellites. When \(x=0\), it signifies that the link is not available; thus, the transmission variables must be set to zero, reflecting the inability to transmit data. This is supported by the indicator function, which will yield a value of 1 under these circumstances. Conversely, if \(x=1\), which indicates an available link, the transmission variables can assume non-zero values, allowing for data communication. This logical structure ensures that our model accurately represents the conditions of link availability within the network.
# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 3 Discussion
### The Challenges of Detecting Solid Content in Concrete Wastewater Using Deep Learning
[37] conducted comprehensive investigations into the mechanical properties and durability aspects of concrete made with wastewater to point out the similarities in the properties from different sources of wastewater. All such research work has consolidated the significance of model generalization and its applications in industrial practices. To overcome these challenges, larger datasets are crucial for model training. Additionally, pre-trained models can be exploited via transfer learning to fine tune the parameters for a targeted image recognition task, improving the accuracy of the models. Moreover, specialized hardware and powerful computational software solutions can be further developed for improving the training efficiency. Finally, exploring new generalized imaging techniques such as hyperspectral imaging or X-ray imaging can facilitate deep learning models to comprehensively learn and recognize, leading to more accurate solid detection in concrete wastewater.
# Digital Cloud Environment: Present Challenges and Future Forecast
## 6 Implementation and Experimental Results  
The proposed monitoring and forecasting models are implemented at Genesys International Telecommunications Company [16]. Production VMs of the customers with relatively low user workload are analyzed using Zabbix monitoring and prediction system. Some experimental results are presented in Figures 7 and 8 (Supplementary Materials contain the Figures 7 and 8's statistical raw data and calculations).  
Statistics in Figure 6 shows that the users (agents) are not as active as configured (20 logged on agents as maximum out of 75 configured that is less than 30%). System statistics in Figure 8 confirms less than 20% of CPU utilization even in peak time. Memory consumption is about 5.55 GB and is stable. In Table 3, all the instances of M4 type have at least 8 GB memory, which would be enough for running cloud services. Therefore, the VM instance could be scaled down without a risk of system overload.  
Similar calculations are verified for some more VMs and the results are put into pivot Table 5 (Supplementary Materials contain the statistical raw data and calculations). CPU utilization is computed given multiple cores. Price savings are converted from \"$ per hour\" to \"$ per year\", taking into account the same VM type for primary and backup servers and multiple regions for some customers, using the following formulas:  
\[\text{Price per year}=\text{Price per hour}\text{ }^{*}24\text{ }^{*}365;\]  
\[\text{Price saving per year}=\text{Price saving per hour}\text{ }^{*}24\text{ }^{*}365\text{ }^{*}2\text{ backups}\text{ }^{*}2\text{ regions}.\]  
The estimated price saving is $3504 per year for each affected VM. If evaluating the whole GCC infrastructure of a low loaded customer, the other VMs most probably could also be reduced in size and reach the total price saving up to 50%, that is about $13K per year (Figure 9).  
Figure 8: Monitoring statistics for system resources.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**AWS** & **\# of Configured** & **Max \# of Logged** & **Max/avg CPU** & **Max Memory** & **AWS Instance** & **Recommended** & **Price Savings, \$** \\
**Region/VM** & **Agents** & **on Agents** & **Usenger Core, \%** & **Usenger Core, \%** & **Usenger, \%** & **Type** & **AWS Type** & **per Year** \\ \hline euro1/vmp-400 & 75 & 20 & 87/25 & 40 & m3 large & m3 medium & $3504 \\ euro2/vmp-400 & 75 & 0 & 0/0 & 0 & m3 large & m3 medium & $3504 \\ use1/vmp-220 & 414 & 121 & 95/25 & 50 & m3 large & m3 medium & $3504 \\ euro1/vmp-080 & 1854 & 610 & 95/50 & 50 & m3 large & m4 large & $3504 \\ \hline \end{tabular}
\end{table}
Table 5: Evaluation of AWS instances downscale and price savings.  
Some instances could probably be downscaled twice depending on the actual monitoring statistics. Reducing the size of VMs is based on objective, historical statistics and safety. If the workload increased at some point, the triggers of the monitoring system will detect this and VM could be upsized back at any moment.
###### Abstract  
With the rapid growth of internet-connected devices and their resource-constrained capabilities, the current authentication mechanisms are unable to meet the complex IoT application requirements, such as in the Industrial Internet of Things (IIoT), due to the increased computation, communication, and storage overhead arising from these mechanisms. In the IIoT, machine-to-machine (M2M) communication is an underlying technology where devices (e.g., sensors, actuators, and controllers) can be enabled to exchange information autonomously; thus, the massive data generated by these devices can increase latency, network congestion, and the complexity of security management. Message queue telemetry transport (MQTT) is one of the promising M2M protocols used in the IoT that could encounter such issues because it relies on a central broker in the cloud and implements a heavyweight authentication mechanism based on TLS. Therefore, this paper proposes an MQTT architecture with multi-tier brokers based on fog computing, where each broker is deployed with an authentication manager. In addition, the paper presents a lightweight mutual authentication scheme based on hash function and XOR operation. Comparing the results given in the benchmark, the overall performance of our scheme shows that storage and communication overheads are reduced to 89% and 23%, respectively. Furthermore, our system can resist against several cyberattacks and provide scalability.  
Keywords:lightweight authentication; message queue telemetry transport (MQTT); Internet of Things (IoT); fog computing +
Footnote †: journal: Applied
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.3 Influence of Contact Time  
Contact time is an essential factor in all transfer phenomena for the adsorption process, and the equilibrium time is important when considering economical water in addition to wastewater applications. The results are presented in Figure 8, indicating that the adsorption of IV2R dye ion removal increased to reach equilibrium, with 91.67% at 120 min.  
Figure 6: Influence of the pH value on the sorption of IV2R dye.  
Figure 7: Influence of the MWCNT dose on the removal method.  
The adsorption was rapid at the first time of contact, which occurred in the early stage of adsorption after a few minutes; this was due to the fact that most of the binding sites were free and the adsorbent sites were empty [52], which allowed the quick binding of dyes ions on the adsorbent. Adsorption happens rapidly and is usually controlled via the diffusion method from the bulk of the solution on the surface [53]. The increasing contact time increases the adsorption, which is possible because of a greater surface area of the MWCNTs being available at the beginning and exhaustion of the conversion of external adsorption sites, at which the adsorbate (dye particles) is transported from the external to the internal sites of the MWCNTs adsorbent molecules [54]. [PERSON] [55] recorded that adsorption occurs in the first 40 min of the sorption method, which shows the high diffusion of the dye particles into the outer surface of the adsorbent. In later stages, fewer vacant adsorption sites are converted, which reduces the rate of adsorption, in addition to reaching an equilibrium state.
# Pathway to Carbon Neutrality in the Cement Industry: CO2
## 5 Acid Attack of RAs during Forced Carbonation  
Forced carbonation exposes recycled aggregates (RAs) to high concentrations of CO\({}_{2}\), leading to the formation of carbonic acid within the pore sample. This carbonic acid can initiate an acid attack on the RA components, modifying the sample composition [77,78]. Calcium carbonate in natural environmental conditions (at constant pressure and temperature) is controlled primarily by the equilibrium reactions in Equations (2) and (14).  
\[CaCO_{3}+H_{2}CO_{3}\rightleftharpoons Ca^{2+}+2 HCO_{3}-(\textbf{Dissolution of calcium carbonate}) \tag{14}\]  
Considering Equation (14), the equilibrium is shifted to the left with the increase in CO\({}_{2}\) concentration (due to the continuous flow inside the chamber), favouring the formation of carbonic acid (H\({}_{2}\)CO\({}_{3}\)). The increase in H\({}_{2}\)CO\({}_{3}\) will then cause a shift in the equilibrium of Equation (14) to the right, causing existent CaCO\({}_{3}\) in the samples to dissolve. If the amount of CO\({}_{2}\) is reduced in the system, the equilibrium in Equation (14) is then shifted to the left, promoting the precipitation of calcium carbonate. This proposed mechanism can justify the reduction of the calcium carbonate content after long periods of carbonation, as found in MRAs and RCAs.  
This raised the question of whether an acid attack had occurred, promoting the partial dissolution of the calcium carbonate formed in the carbonation reaction and the consequent increase in pH of the RA interstitial solution. To validate this hypothesis, the pH was measured in three samples. The selected samples were MRA-RP1, which showed a reduction in CO\({}_{2}\) content after 5 h of carbonation and RCA-IW1 and RCA-RP1, which showed the same behaviour, but after 12 h.  
For MRA-RP1 the initial pH value (before carbonation, 0 h) was 9.5, decreasing to 9.1 after 5 h of carbonation (maximum capture of CO\({}_{2}\)). The decrease in the pH value is coherent with the increase in calcium carbonate content, meaning that more CO\({}_{2}\) was captured by the RAs. After 12 h of carbonation, the pH value increased slightly from 9.1 to 9.2, which is consistent with an acid attack of CaCO\({}_{3}\) and the consequent release of CO\({}_{2}\).  
The same behaviour was found in RCA-IW1 and RCA-RP1 at different carbonation ages: noncarbonated (0 h), at the age of highest CO\({}_{2}\) capture (12 h) and after 24 h. Up to 12 h, the pH decreased, 12.6 to 12.1 and 11.5 to 10.5 (for RCA-IW1 and RCA-RP1, respectively), with the consequent increase in CO\({}_{2}\) content from the formation of calcium carbonate. Then, the pH increased after 12 h as found in MRA-RP1, RCA-IW1 increased to 12.2 and RCA-RP1 to 10.8. This confirms the hypothesis of an acid attack, promoting the dissolution of part of the calcium carbonate with CO\({}_{2}\) liberation.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Iceberg
#### 2.1.1 Optical
To this end, multi-temporal Landsat images were combined, and the obtained results showed an acceptable performance compared to the NCC-based approaches.