esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 3 MKID Applications in Observational Astronomy
### Observational Astronomy: Infrared to Microwaves
#### 3.1.2 MKID Mid-IR to Microwave Imagers
GroundBIRD [77; 78; 79] is searching for the CMB B-mode polarization with a 30 cm telescope that rotates rapidly (about 20 rounds per minute) on a horizontal platform to enhance noise suppression. It uses 161 MKID pixels in two bands centered around 145 GHz (2.1 mm, 138 pixels) and 220 GHz (1.4 mm, 23 pixels) and will start observing from Teide Observatory, Tenerife soon. A similar concept of a rapidly rotating cryogenic CMB B-mode telescope has been proposed in a feasibility study for an instrument to be located at Isi Station, Greenland [80], utilizing 2300 MKIDs in bands around 150, 210, and 267 GHz (2.0, 1.4 and 1.1 mm). ACTPol, the polarization sensitive experiment at the Atacama Cosmology Telescope is developing multi-chroic MKIDs for CMB polarization studies [81]. BICEP-Array [82,83], a planned next generation CMB experiment with an array of four telescopes at the geographic South Pole is considering the use of membrane suspended MKIDs [84] for their 220/270 GHz band (1.4/1.1 mm), as the desired 22,000 detectors would otherwise be challenging to multiplex. CMB-S4, a proposed next-generation ground-based CMB experiment which plans to combine 21 telescopes and 550,000 detectors, is also listing MKIDs as a detector candidate [85] in its current design state.
# Strength and Compressibility of Ammonia-Soda Residue from the Solvay Sodium Plant
## 3 Results and Discussion
### XRD and Microstructural Analysis  
The mineral composition of ASR was investigated by means of X-ray diffraction. The results are demonstrated in Figure 2. The phase analysis was based on the two diffractograms collected from the white cement, which is the major phase and the minor grey inclusions of 1-2 cm size. Both diffractograms show the distinct Bragg peaks, suggesting highly crystalline material. The phase analysis of the white cement revealed 57 wt% of calcite (CaCO\({}_{3}\)), 31 wt% of portlandite (Ca(OH)\({}_{2}\)), 7 wt% of brucite (Mg(OH)\({}_{2}\)), and 5 wt% of barite (BaSO\({}_{4}\)). The occurrence of barite results from Solvay production of barium carbonate, which was manufactured from natural barium sulfate. The grey inclusions were found as composed of mostly still calcite (83%) but associated with gypsum (8 wt%) and analcine (7 wt%). The formation of analcine rose from the highly alkaline environment provided by soda wastes disposal [19]. The euhedral crystals of portlandite (Ca(OH)\({}_{2}\)) and gypsum (CaSO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O) with well-outlined hexagonal and prismatic morphology, respectively, were also observed at SEM images (Figure 3). The crystals of both phases grew to several microns and were embedded in fine-crystal cement composed of mostly calcite (CaCO\({}_{3}\)) (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Confining Pressure** & **G\({}_{\mathbf{s}}\)** & **n** & **e** & **S** & **Loading Rate** \\ \hline  & [kPa] & [-] & [-] & [-] & [-] & [mm/min] \\ \hline  & 75 & 2.61 & 0.84 & 5.32 & 0.98 & 0.01 \\ \cline{2-6}  & 100 & 2.61 & 0.84 & 5.26 & 1.00 & 0.01 \\ \cline{2-6} ASR I & 200 & 2.61 & 0.83 & 5.00 & 0.98  
Figure 3: SEM images collected from the white cement (**a**) and the grey inclusions (**b**). Large crystals of portlandite (**a**) and gypsum (**b**) are surrounded by the fine grains of calcite.  
Figure 2: X-ray diffractograms and mineral compositions of two phases were found in the sample: the white cement (**a**) and the embedded grey inclusions (**b**).
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Discussion  
After running each algorithm, this subsection presents the graphical analysis with the defined hyperparameters and a sequence length of 115. The GRU, BiGRU, and LSTM algorithms generalized correctly, as the training precision values obtained in each model were close to their respective validation precision values.
## 6 Assessment of the ELM\(-\)SVM Model
### Asymmetric Rate-Dependent Non-Congruent Hysteresis  
Magnetostrictive actuators are being used more frequently for vibration control and micro\(-\)positioning. In order to determine the magnetostrictive actuator's hysteresis responses to sinusoidal input currents of constant amplitude (0.8 A) with a bias of 0.1 A over the frequency range of 10 to 300 Hz, [PERSON] and [PERSON] [33] performed laboratory testing. The results of this experiment demonstrate that the magnetostrictive actuator's hysteresis loops between the input current and the output displacement are rate\(-\)dependent, non-congruent, and asymmetric, particularly for excitations above 10 Hz. The hysteresis characteristics of the magnetostrictive actuator at various stimulation frequencies are shown in Figure 14 as solid curves. The outputs under eight input frequencies, which vary from 10 to 300 HZ, show significant variances in the hysteresis loops. Magnetostrictive actuators have asymmetric, non-congruent, and extremely rate\(-\)dependent hysteresis loops. PNN, GPNN, and Preisach\(-\)type are hence unable to learn their behaviors. We used ELM\(-\)SVM to learn the rate-dependent behavior of magnetostrictive. The ELM\(-\)SVM model cannot only learn asymmetric and non-congruent hysteresis behavior, but it can also learn hysteresis loops with rate\(-\)dependency. Figure 14 shows dashed gray curves that are difficult to distinguish from solid gray curves to represent the hysteresis loops that the ELM\(-\)SVM model simulates (experimental data). For this case as well, it displays extremely low simulation errors. The ELM\(-\)SVM test has not been run in this instance because there is insufficient experimental data. Additionally, this example with \(n=2\) and \(\beta_{max}=2\) has been trained on the prepared training dataset.
## 8 Toward a Theoretical Framework
### Security, Privacy, and Familiarity Affecting Trust in the 6 GCloHE System
#### 8.2.2 Privacy  
Privacy has many definitions in the literature. Privacy is an important topic for information security in the healthcare system in the world. In this study, privacy is defined as freedom from unauthorized intrusion and an important challenge in the IoHE environment due to the availability of sensory devices and the speed and the volume of information flow. Any compromise of privacy leads to problems such as eavesdropping, unauthorized access to or alteration or destruction of information, hacking, identity theft, forgery, and social engineering.  
Data privacy is a fundamental issue for IoE health devices and applications because of the ubiquitous character of the IoE environment. IoE health devices connect to each other for data transmission and a strong encryption algorithm that is used for data to be encrypted over a secure network such as 6G Tactile Internet. Healthcare data are collected from the IoE devices via remote access mechanisms which have some challenges regarding privacy and security. Data collected by the sensor are transmitted to the database or the cloud over a network such as 6G Tactile Internet. Since healthcare data include essential significant information, the world's hackers want to capture health data. Therefore, the privacy of data must be protected. Some health organizations are reluctant to adopt the IoE because of fears of privacy compromises, particularly in cases that involve medical data in which maintaining privacy and anonymity of the user is of the utmost importance due to legal and statutory requirements, which in turn affects trust to adopt the IoE in the healthcare domain.  
The IoHE application must have a \"need-to-know\" principle for authorization management. The stakeholders should have enough information about procedures, guidelines, standards, and policies related to data security and privacy. All IoHE devices, IoHE applications, and network components log must be collected with central log management systems. Besides, central log management or security information and event management must have auditing to ensure security. Undesirable events must be reported to a security team quickly to interfere with unwanted events. Central logs management or security information and event management must have strong authentication and authorization to monitor the audit log. The log should be checked continuously.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 5 Food Safety and Traceability in the Wine Industry
### Enhancing Traceability from Vineyard to Consumer
#### 5.1.1 Integration of IoT Devices  
The integration of IoT devices enables comprehensive monitoring of the entire wine production process. In the vineyard, sensors monitor environmental conditions such as soil moisture, temperature, and humidity, providing valuable data that AI algorithms use to optimize grape cultivation practices. These sensors can also monitor variables like light intensity, wind speed, and nutrient levels, ensuring that vines receive optimal conditions for growth [108]. [PERSON] et al. highlighted how the integration of AI and sensor data from IoT devices can enhance efficiency and precision in agriculture and the food industry, providing a valuable model for its application in viticulture [108].  
Advanced IoT systems can also include drone-based sensors and satellite imagery. Drones equipped with multispectral cameras can capture detailed images of the vineyard,which are then analyzed by AI to assess vine health, identify areas affected by disease or pests, and monitor canopy density [109]. [PERSON] et al. discussed the implications of artificial intelligence in the agrifood sector, emphasizing the use of drones and multispectral cameras to monitor vine health and proactively manage pest and disease issues [109].  
During the harvesting phase, RFID tags attached to grape bins track the location and condition of the harvest, ensuring traceability from the field to the winery. These tags provide data on harvest time, transport conditions, and grape variety, which is crucial for maintaining the integrity and quality of the grapes [110]. [PERSON] et al. presented RFID-based solutions for traceability in wine production, underlining the importance of these systems for maintaining product quality and integrity from harvest to winery [110].  
In the winery, IoT devices continue to play a crucial role. Sensors monitor fermentation conditions, storage environments, and bottling processes, generating continuous streams of data. This data is analyzed by AI systems to ensure that each stage of production adheres to predefined quality standards. For instance, temperature and humidity sensors in storage facilities provide data that AI uses to maintain optimal aging conditions, preventing spoilage and ensuring product consistency [111]. [PERSON] et al. discussed the integration of information technology for traceability and quality control, demonstrating how temperature and humidity sensors can be used by AI systems to maintain optimal conditions during wine aging [111].
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 3 Proposed Schemes
### Quality Forwarding for Forwarder Node Selection  
Forwarding is performed for routing data packets towards the destination. There are different kinds of forwarding, such as selective diversity forwarding (SDF), next hop forwarding, host-specific forwarding, etc. [4]. Our forwarding is similar to SDF; however, we compute multiple parameters for selecting the next forwarder node. To illustrate the changes of parameters for forwarder node selection in the defined function, a mathematical formulation is carried out via taking into account: the residual energy of the forwarder node, the number of potential neighbor nodes, the distance between the sender and forwarder node and the distance of the forwarder node from the virtual vector for handling the mobility factor due to water currents. The composite function is defined in Equation (4).  
\[f_{5}=f_{1}\times f_{2}\times f_{3}\times f_{4} \tag{4}\]where, \(f_{1},f_{2},f_{3}\), and \(f_{4}\) are computed as follows:  
\[f_{1}=E_{r},\quad E_{r}\in[E_{tx}\quad E_{o}] \tag{5}\]  
Fitness function \(f_{1}\) shows that it is directly proportional to the residual energy of a node. The initial energy provided to every node is 100 J; therefore, the residual energy of a node is always equal to or less than \(E_{o}\).  
\[f_{2}=1+sign(Nn-2),\quad N_{n}<=4 \tag{6}\]  
\(f_{2}\) is based on the threshold value to ensure that a forwarder should have potential forwarders equal to or greater than the threshold value.  
\[f_{3}=D_{ij},\quad D_{ij}\in[0\quad D_{max}] \tag{7}\]  
\(f_{3}\) depicts that the distance between the sender and receiver node is directly proportional to the energy consumption. This function provides the distance between the sender node and the forwarder, for which the larger the better. If the distance between the sender node and the forwarder is large, the packet will traverse few hops during the transmission. The objective is to minimize the number of hops for qualified forwarder selection for minimizing the energy consumption.  
\[f_{4}=1/(1+d),\quad d\in[0\quad R_{t}/2] \tag{8}\]  
\(f_{4}\) denotes the distance of the forwarder node from the virtual vector. The smaller the distance, the higher will be the chances of void occurrence. The computation of this parameter is specifically to ensure that the node must be within the sensing range if data are transmitted to it.  
By means of normalization, we have computed the value for the proposed composite function within interval \([0,1]\). This is intended to avoid undue favor for any of the aforementioned parameters, while computing \(F_{p}\) for a qualified forwarder node.  
\[F_{p}=\frac{(1+Sign(x-2))\times D_{ij}\times E_{r}}{2\times(1+d)\times D_{max} \times E_{o}} \tag{9}\]  
Figure 3: Illustration for holding time.  
Once the composite function is calculated, now potential forwarder nodes are nominated. In order to find out the optimal holding time for each qualified node \(F_{p}\), the following equation is used.  
\[\mathit{Thold}_{QF}=T_{pre}\times F_{p}+\frac{R_{t}-|SF|}{V_{sound}} \tag{10}\]
## 4 Discussion  
The use of services with global coverage like GlobalStar as a link to download data from space seems to be a good alternative to other traditional forms of communication such as ground-to-satellite communication. Beyond traditional radiofrequency links, these services including GlobalStar, Starlink, and Iridium, offer unprecedented global reach, low-latency connections, and reliability that are essential for a broad spectrum of space missions. Their cost-effectiveness, scalability, and ease of deployment make them an attractive option for data transfer in remote or challenging space environments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Message Length**} & \multirow{2}{*}{**Received Packets**} & **Received** & **Received Messages** & **Decoded Messages** & \multirow{2}{*}{**Success Rate**} \\  & & **Messages (Partial)** & **(Complete)** & **(Success)** & \\ \hline
9 & 5246 & 145 & 0 & 0 & 0.00\% \\
18 & 2857 & 143 & 5 & 4 & 3.50\% \\
36 & 2166 & 142 & 32 & 16 & 22.54\% \\
72 & 1409 & 140 & 59 & 55 & 42.14\% \\
144 & 692 & 135 & 60 & 60 & 44.44\% \\ \hline Total: & 12,370 & 705 & 156 & 135 & 22.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained with different configurations of message length.  
In this work, we study the idea of enhancing ground-to-satellite communication with satellite-to-satellite links. Our results show a better performance of data covering more geographical areas using intersatellite communication than only using ground station communication.  
So far, successful mission and the current literature shows little information in a brief period in similar orbits. The AztechSat-1 project has demonstrated two kinds of satellite communication which have enhanced the received data. Data obtained from GlobalStar has increased the amount of information in comparison to only ground-satellite communication.  
In our future work, we will study the performance of a constellation of proprietary nanosatellites with a commercial constellation in an environment that involves real-world constraints. Our next step is a collaborative initiative between the Mexican Space Agency and the National Aeronautics and Space Administration which is currently underway to develop a constellation of at least four nanosatellites for animal telemetry applications. This joint project has been named the AztechSat Constellation. Its primary objective is to establish a marine fauna monitoring system. The project will involve professors and students from various Mexican universities, who will contribute their expertise to the design and integration of nanosatellites for deployment in Low Earth Orbit.
# A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones
## 3 Unmanned Aerial Vehicle Manipulation
### Manipulation Module  
The manipulation module provides the system with the equipment and functionalities to manipulate overhead power line accessories. Due to the morphological and operational differences between accessories, a robotic arm integrated with the drone, in combination with dedicated end effectors, is proposed to achieve a versatile module. The developed arm is an anthropomorphic robotic arm with six degrees of freedom (DoF). In order to perform complex manipulation operations, the robotic arm has a geared spherical wrist. The weight of the arm is approximately 3 kg, with a payload of 3.5 kg.  
Some constraints are considered for the manipulation of the charging station. To reduce disturbances to the drone's flight control system, the influence on the UAV's center of gravity must be considered. In addition, the weight exceeds the payload of the robotic arm. To overcome these challenges, a novel approach to the transport and assembly of the charging station is proposed. A support platform (Figure 9) fixes the charging station to the front of the UAV during flight, close to its center of gravity, taking advantage of the UAV's payload and the large front area designed to carry additional tools.  
This approach also reduces the stress on the robotic arm during flight, minimizing the risk of malfunction. The charging station platform is located below the perching mechanism to prevent interference during the perching maneuver. For this reason, the platform has a lift to position the charging station on the cable during installation. The lift raises the charging station by inserting the clamp into the cable and holding it in position. Finally, the robot arm actuates the worm gear with an ad hoc end effector to close the clamp and secure the charging station to the cable.
###### Abstract  
Rare-earth nanomaterials are being widely applied in medicine as cytotoxicity agents, in radiation and photodynamic therapy, as drug carriers, and in biosensing and bioimaging technology. Terbium (Tb), a rare-earth element belonging to the lanthanides, has a long luminescent lifetime, large stock displacement, narrow spectral width, and biofriendly probes. In cancer therapy, cancer stem cell (CSC)-targeted treatment is receiving considerable attention due to these cells' harmful characteristics. However, CSCs remain barely understood. Therefore, to effectively label and inhibit the growth of CSCs, we produced a nanocomplex in which TbPO4-H2O nanorods were double conjugated with CD133 and PD-L1 monoclonal antibodies. The Tb3+ nanomaterials were created in the presence of a soft template (polyethylene glycol 2000). The obtained nanomaterial TbPO4-H2O was hexagonal crystal and nanorod in shape, 40-80 nm in diameter, and 300-800 nm in length. The nanorods were further surfaced through tetraethyl orthosilicate hydrolysis and functionalized with amino silane. Finally, the glutaraldehyde-activated Tb3+ nanorods were conjugated with CD133 monoclonal antibody and PD-L1 monoclonal antibody on the surface to obtain the nanocomplex TbPO4-H2O@silica-NH2+mAb^CD133+mAb^PD-L1 (**TMC**). The formed nanocomplex was able to efficiently and specifically label NTERA-2 cells, a highly expressed CD133 and PD-L1 CSC cell line. The conjugate also demonstrated promising anti-CSC activity by significant inhibition (58.50%) of the growth of 3D tumor spheres of NTERA-2 cells (\(p<0.05\)).  
cancer stem cells; CD133 mAb; ion Tb3+; nanorod; NTERA-2; PD-L1; TMC
## 4 Actual Project Study
### Comparative Analysis of Simulation Results with Different Parameters
#### 4.6.3 Influence of Spacing Between the Twin Tunnels  
The new tunnel is a two-lane separated type, and the mechanical response of the twin tunnels' horizontal spacing L change on the existing structure is highlighted in Figure 14. Here, the spacing \(L\) is taken as 3.0 m\(\sim\)12 m. As shown in Figure 15a, as the distance \(L\) between the two tunnel lines increases, the settlement curve of the existing structure's floor shifts outward, transitioning from a \"V\" to a \"W\" shape. The settlement trough becomes shallower and wider. For tunnel spacings \(L<6.0\) m (roughly equal to the tunnel span), the bottom plate deformation curve is significantly influenced by \(L\). When \(L\) exceeds 9.0 m, the deformation curve starts to change from \"V\" shaped to \"W\" shaped. This is attributed to the close proximity and strong mutual disturbance between the successively excavated tunnels, intensifying soil deformation in the commonly disturbed area and exhibiting a \"V\"-shaped settlement trough.  
From Figure 15c, the maximum bending moment \(M_{\text{max}}\) of the existing structure negatively correlates with the spacing L between the tunnels. However, as shown in Figure 14b, when L equals 9 m, the maximum positive bending moment + \(M_{\text{max}}\) reaches 51.91 kN\(\cdot\)m. In contrast, at \(L=12\) m, +\(M_{\text{max}}\) is 40.51 kN\(\cdot\)m, and the maximum negative bending moment \(-M_{\text{max}}\) is \(-39.71\) kN\(\cdot\)m, resulting in a more complex structural response. Therefore, the tunnel is excavated underneath the existing structure, not only to increase the tunnel spacing to reduce the impact on the existing structure but also to identify a reasonable spatial layout relationship.  
Figure 14: The clear distance between the newly built tunnel and the floor: (**a**) floor settlement curve, (**b**) variation curve of additional bending moment of floor, and (**c**) influence of tunnel spacing \(L\) on \(S_{\text{max}}\) and \(M_{\text{max}}\).