esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 4 Results
### Stage 3: Incorporation of the Communication System  
The mobile station is characterized by its energy autonomy thanks to a set of batteries and solar panels. In addition, it incorporates two communication systems (See Figure 7).  
* _RF system:_ it allows the transmission of simple data and the activation of discrete signals at a distance of up to 8 km, with response times in the order of milliseconds. This ensures fast actuation to prevent accidents or emergency stops.
* _High-bandwidth transmission (5 GHz):_ Facilitates real-time video transmission from the high-risk sector being monitored.  
Information processing is performed in-situ, using an algorithm trained with Deep Learning techniques for equipment recognition, which eliminates the need for additional external networks. These features allow the station to move dynamically according to the requirements of the extraction process.
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
## 6 Packet Header Format  
The header format of the RR, RR_ACK, and RR_RSP packets is shown in Figure 11. There are five header fields, S_ID, D_ID, Tx_Pwr, Seq_#, and Packet Type. The S_ID (source node identification number) and D_ID (destination node identification number) fields have 8 bits length. They are used by the nodes to identify each other. The Tx_Pwr (transmission power) field is used by the receiver node to estimate the path loss. The Seq_# field is used when a packet is transmitted multiple times, e.g., the RR packet is transmitted three times by the gateway. Finally, the receiver node identifies the type of packet received by the packet type field.
## 3 Results  
### Monotonic Tensile Specimens  
True stress-strain curves obtained from testing the samples with both extensometer and digital image correlation are shown in Figure 6. The curves are plotted to the point when necking starts which is also associated with a sudden drop in the load. The average mechanical properties obtained from the stress-strain curves are listed in Table 2. Figure 7 shows the true strain-field along the loading direction at different loading stages. Figure 7a shows that in early stage of loading the magnitude of true strain along the loading direction is fairly uniform across the gauge section. As the test progresses, Figure 7b, the strain starts to localize in small region at the gauge section. Figure 7c shows the instance when the strain and stress reach their maximum value before necking starts. The localized strain region shown in Figure 7c is the location at which the necking occurs. After that, the reduction of the cross-section propagates to the remaining part of the gauge section as explained previously.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{E}\) & \(\mathbf{\sigma_{0.2\%}}\) & \(\mathbf{\epsilon_{0.2\%}}\) & \(\mathbf{\sigma_{ne}}\) & \(\mathbf{\epsilon_{ne}}\) \\  & **(MPa)** & **(MPa)** & **(mm/mm)** & **(MPa)** & **(mm/mm)** \\ \hline Average & 2267.6 & 36.1 & 0.0177 & 66.0 & 0.055 \\ St. Dev & 47.01 & 1.96 & 0.0013 & 0.59 & 0.0029 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average true monotonic tensile properties of polycarbonate.  
Figure 6: True stress–strain curves for polycarbonate obtained using both extensometer and digital image correlation.  
Figure 7: (**a–c**) True strain fields along the loading direction obtained using digital image correlation for tensile specimens at different loading levels.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 2 Literature Review  
There are many studies on the topic of meshing in wireless networks for different wireless deployments such as Mobile Networks [6], WLANs [7], Wireless Sensor Networks (WSNs) [8], and others. However, there is still limited research regarding the specific application domain of FWA, specifically with respect to 5G NPNs. In this section, we will first describe the current state of the art in FWA deployments and 5G NPNs specifically before reviewing previous research in this area to highlight its applicability to our work.
## 4 Discussion
It is not possible to attribute this difference solely to the metallurgical properties of the instruments, as their geometries vary significantly. In the case of TRN, the taper is narrower, which could result in less contact with the canal walls, leading to a reduced stress concentration. PTU, PTG, and VB all show peak values of von Mises stress at the end of insertion, whereas TRN showed the peak stress at 0.85 s (406 MPa), but the stress values decreased as the instrument was completely inserted at 1.00 s (343 MPa). By localizing the peak stress locations for von Mises and maximum principal stresses in the instruments, there was a good correlation with the experimental fracture locations for PTU, PTG, and TRN, with the peak stress locations falling within two standard deviations of their experimental counterparts for their fragment lengths, assuming that the peak stresses are the region of the highest probability where crack propagation would occur. The less impressive results for VB may have been due to incorrect assumptions about some mechanical constants used for the blue-treated wire. The rotational step did not show any changes in von Mises or maximum stresses regardless of the number of revolutions or rotational speed. Given that there are no increases in stress after a full revolution, the resulting simulation would, in effect, allow the instrument to rotate indefinitely without cyclic failure occurring. In this regard, the FE modeling was not successful in determining the fatigue life of the instruments. Hence, a failure criterion must be implemented to mimic crack propagation. A potential way to explore crack propagation could be the extended finite element method (XFEM), where nodes that contain the crack tip are enriched with discontinuous functions provided by asymptotic nodes of displacement at the crack tip. An alternative method, as described in the study of [PERSON] et al., is to implement a fatigue criterion in Matlab (The Mathworks Inc., Natick, MA, USA), such as the Carptineri-Spagnoli, or 'C-S', criterion, where the study reports no relationship between experimental NCF and FEA NCF [11; 27]. However, the C-S criterion is applied for high-cycle multiaxial fatigue, such as those in the order of 104 cycles, and thus, may not be a suitable criterion for endodontic instruments that have low cycle numbers [27; 28]. The stress values reported in FEA could be correlated to S-N curves to predict NCF. Existing literature describes a low cyclic fatigue life for NiTi [29]. Given this, further analysis of the fatigue life of NiTi is needed, and additional factors, such as the effects of electropolishing and heat treatment, need to be accounted for.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 7 RAC1B as a Prognostic Marker in Cancer
### Pancreatic Cancer and Chronic Pancreatitis  
In pancreatic adenocarcinoma (PDAC), MMP3-mediated RAC1B expression increased cellular invasiveness and tumorigenic potential [19; 68]. Interestingly, patient outcome was correlated here with subcellular distribution of RAC1B. Specifically, patients showing apolar RAC1B staining presented with significantly shorter survival than patients showing baseline and polar RAC1B staining [19]. Our own group also identified RAC1B overexpression in the ductal epithelial cells of stage III PDAC tumors and, in addition, in chronic pancreatitis tissues, in which the levels of RAC1B protein even exceeded those in PDAC [40]. Intriguingly, and in contrast to the above study [18], RAC1B expression was higher in long-time vs. short-time survivors among PDAC patients. In the light of in vitro data revealing a role of RAC1B as a TGF-\(\beta\) signaling inhibitor [35; 40], we propose that in a protumorigenic TGF-\(\beta\)-rich environment the effects of RAC1B as a tumor suppressor predominate over those as a tumor promoter.
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.8 Path Loss Prediction Method Based on PSO--Optimized Ensemble Model  
Particle Swarm Optimization is applied to optimize the output weights of the multi-neural network ensemble model for path loss prediction. PSO is a meta-heuristic optimization algorithm that finds the best combination of output weights in a multi-dimensional space. Table 13 shows the optimal combination of parameters for the PSO-optimized output weights.
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
## 2 Broadband Communication
### Millimeter Waves and Massive MIMO
#### 2.1.1 Analog Beamforming  
The architecture of analog beamforming is shown in Figure 2. A phased array is used to accomplish analog beamforming, and the single RF chain is powered by either an analog-to-digital converter (ADC) in the receiver or a digital-to-analog converter (DAC) in the transmitter. Frequency-up converters, power amplifiers, and other components form a transmitter's RF chain; low-noise amplifiers, frequency-down converters, and other components form a receiver's RF chain [27].  
The phased array's antenna weights are required to be digitally controllable phase changes. The relative amplitudes of the signal sent into the antennas of the transmitter cannot be adjusted, and the phases of the phase shifters are normally quantized to low resolution [28]. A beam is created as a result of the send signal, which is constructive in certain directions and destructive in others. On the basis of precise beam-steering tactics, the phase shifters' phases can be dynamically changed. The receiver has a similar set of features [27].
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 2 Application Scenario and System Model
### Network Topology  
The system model of HWSNs is illustrated in Figure 2. In Figure 2, it is assumed that sensor nodes are randomly distributed within the area. The BS has unlimited energy and is located at the center of the monitoring area. Among the \(N\) heterogeneous sensor nodes, there are three types of nodes: super nodes, advanced nodes, and ordinary nodes. In HWSNs, the ordinary nodes include various sensors such as soil temperature and moisture sensors, soil PH sensors, and irrigation controllers. These nodes are responsible for data  
Figure 1: Application scenario of HWSNs in smart agriculture.  
collection. Due to the different functionalities and energy consumption characteristics of these sensors, the network topology modeling becomes complex. As shown in Figure 2, to simplify the topology of HWSNs, clustering algorithms are typically employed to group ordinary nodes into different clusters.  
In addition to ordinary nodes, HWSNs also include a certain number of super nodes. These super nodes possess advanced data processing capabilities and ample initial energy resources, which effectively support the stable operation and efficient data processing of the network. Super nodes generally serve as cluster heads or relay nodes, handling data collection, fusion, and communication tasks. Ordinary sensor nodes primarily focus on data collection and transmit the collected data to the cluster heads. The cluster head nodes aggregate and process the received data and subsequently transmit them to the users via the BS. This hierarchical design enhances network efficiency and data transmission by utilizing super nodes for data processing and network management. The robust data processing capabilities and sufficient energy resources of super nodes enable them to play a crucial role in scenarios with large data volumes and complex conditions. By judiciously distributing tasks among super nodes, the network load can be balanced and resources optimally utilized, further improving the overall network performance.
# In Vitro Synovial Membrane 3D Model Developed by Volumetric Extrusion Bioprinting
###### Abstract  
(1) Background: Synovial tissue plays a fundamental role in inflammatory processes. Therefore, understanding the mechanisms regulating healthy and diseased synovium functions, as in rheumatic diseases, is crucial to discovering more effective therapies to minimize or prevent pathological progress. The present study aimed at developing a bioartificial synovial tissue as an in vitro model for drug screening or personalized medicine applications using 3D bioprinting technology. (2) Methods: The volumetric extrusion technique has been used to fabricate cell-laden scaffolds. Gelatin Metchacrylol (GelMA), widely applied in regenerative medicine and tissue engineering, was selected as a bioink and combined with an immortalized cell line of fibroblast-like synovocytes (K4 IM). (3) Results: Three different GelMA formulations, 7.5-10-12.5% \(w/v\), were tested for the fabrication of the scaffold with the desired morphology and internal architecture. GelMA 10% \(w/v\) was chosen and combined with K4 IM cells to fabricate scaffolds that showed high cell viability and negligible cytotoxicity for up to 14 days tested by Live & Dead and lactate dehydrogenase assays. (4) Conclusions: We successfully 3D bioprinted synovocytes-laden scaffolds as a proof-of-concept (PoC) towards the fabrication of a 3D synovial membrane model suitable for in vitro studies. However, further research is needed to reproduce the complexity of the synovial microenvironment to better mimic the physiological condition.  
synovial membrane; volumetric extrusion 3D bioprinting; GelMA; human cell line; osteoarthritis; rheumatoid arthritis +
Footnote †: journal: Applied Sciences
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 5 Neural Network (NN)-Based Phase-Field Brittle Fracture
### Data Collection
#### 5.1.1 Sensitivity Analysis  
Sensitivity analysis is quite useful in specifying the effect of a particular input on an output under a set of assumptions. Different methods exist in the literature for conducting a thorough sensitivity analysis. However, for our scope of the study, a scatter-plot is sufficient. Such a representation is a qualitative method, which provides no sensitivity index or numerical value. For a full dependency analysis, plots as many as the number of inputs are required.  
As an example, Figure 14 illustrates the relationship of total stress due to the degradation of stored elastic energy and total strain. Hereby, the stresses decrease after a certain strain value related to the critical fracture energy \(\psi_{c}\) introduced in (19).
# Evaluating the Utility of UV Lamps to Mitigate the Spread of Pathogens in the ICU
###### Abstract  
Contaminated surfaces in a hospital serve as reservoirs for pathogen spread. The aim of this study was to evaluate UV lights in preventing the spread of a DNA tracer in an intensive care unit (ICU) through sterilization of highly touched surfaces. In a prospective trial, a non-pathogenic DNA virus was inoculated onto surfaces in an ICU patient room. Investigators swabed frequently touched surfaces in non-inoculated ICU rooms at 24, 48, and 96 h post inoculation. Culture specimens were analyzed for the presence of viral DNA via PCR. After baseline data were obtained, UV lights were deployed in a standardized fashion onto vitals monitors, ventilators, keyboards, and intravenous (IV) pumps. Inoculation and culturing were then repeated. Prior to UV implementation, the DNA tracer disseminated to 10.10% of tested surfaces in non-inoculated rooms at 48 h. Post UV light deployment, only 1.20% of surfaces tested positive for the DNA tracer after 48 h. UV decontamination significantly retarded the spread of the virus DNA, with a relative reduction of 90% at 48 h from 10.10% of surfaces pre UV to 1.20% of surfaces post UV (\(p<0.0001\)). UV decontamination holds the potential to confer protection to patients by reducing the number of surfaces that can serve as a nidus for transfer.  
UV light; tracer virus; UV-C; hospital acquired infection (HAI); UV sterilization +
Footnote †: journal: Applied Sciences
# W-Band 4 th Order Waveguide Filter Based on Double Layer SU8 Microfabrication
## 3 Experimental Results
### Difficulties and Solutions in the Fabricating
#### 3.2.1 SU8 Micromachining Tolerance in the Thickness Direction  
The precision in the thickness direction of the fabricated chip is an important factor affecting the device performance. To the best of our knowledge, the best precision can be controlled within 5 \(\upmu\)m [9]. However, in this paper, the processing tolerance of the designed filter can be controlled within 4 \(\upmu\)m in the thickness direction, which is measured with a screw micrometer and confocal lens, as shown in Figures 5 and 6. The measurement accuracy of the screw micrometer is 1 \(\upmu\)m. To reduce the spin-coating precision control, the designed filter is fabricated using the same spin coating thickness or an integer multiple. The problem of controlling the SU-8 micromachining tolerance in the thickness direction can be solved by the method of thin resist compensation.  
Figure 4: Schematic diagram of SU-8 micromachining process.  
Figure 5: Spiral micrometer measuring SU8 chip’s thickness (**a**) the thickness of one layer and (**b**) the thickness of the SU-8 chip1 or 3.
# Low Power EEG Data Encoding for Brain Neurostimulation Implants +
## 2 Materials and Methods
### Design Methodology
#### 2.2.2 System Memory  
After the encoding process, the data must be stored locally in the system and retrieved before being transmitted to a receiver module outside the implanted device. The original RNS system wand does not permanently store the data but instead mediates its transfer to the central computer system [9]. As a result, to implement a single storage unit that will allow data retrieval from previously stored data, buffers were used to hold the signals until they are transmitted to the BLE unit, from where they will be wirelessly transmitted. The buffers were implemented at the modelling and integrated circuit programming levels. To model in MATLAB how data is stored and read from memory, a First-in-First-Out (FIFO) buffer, which is a buffer based on the principle that whatever data is placed first is the first to leave, was simulated. A FIFO buffer is described by two pointers set at the beginning of the memory and indicates where the data is placed or removed. When pointers reach the end of the structure, they move to the beginning of the buffer to continue writing or reading, avoiding the overwriting of data that have not been transmitted and read yet.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) play a significant role in a large number of applications, e.g., healthcare and industry. A WSN typically consists of a large number of sensor nodes which rely on limited power sources in many applications. Therefore, improving the energy efficiency of WSNs becomes a crucial topic in the research community. As a fundamental service in WSNs, network flooding offers the advantages that information can be distributed fast and reliably throughout an entire network. However, network flooding suffers from low energy efficiency due to the large number of redundant transmissions in the network. In this work, we exploit connected dominating sets (CDS) to enhance the energy efficiency of network flooding by reducing the number of transmissions. For this purpose, we propose a connected dominating set-based flooding protocol (CONE). CONE inhibits nodes that are not in the CDS from rebroadcasting packets during the flooding process. Furthermore, we evaluate the performance of CONE in both simulations and a real-world testbed, and then we compare CONE to a baseline protocol. Experimental results show that CONE improves the end-to-end reliability and reduces the duty cycle of network flooding in the simulations. Additionally, CONE reduces the average energy consumption in the FlockLab testbed by 15%.  
wireless sensor networks; energy efficiency; flooding; broadcast storm; connected dominating set +
Footnote †: journal: Sensors
## 5 Experimental Part
### Testing of the Channel Layer Protocols
#### 5.2.2 Experimental Results for Connectivity Using Channel Protocols  
Metrics obtained during data transmission using DACAP and T-Lohi protocols are shown in Figures 17 and 18.  
From Figures 17 and 18, it is apparent that the data transmission success rate is 100%, with an average transmission time of 0.21 s for the DACAP protocol and 0.37 s for the T-Lohi protocol. The average transmission rate is 45 bytes per second for DACAP and 20 bytes per second for T-Lohi.  
Thus, the utilization of the DACAP protocol, incorporating acknowledgments and error control, has allowed for enhanced transmission stability (success rate). However, the use of the protocol has increased the load on the bandwidth during data transmission, resulting in a stabilized average transmission rate, including packet control (retransmission) and handshaking, at a lower level (50 bytes per second). A similar situation is observed with the T-Lohi protocol, utilizing competitive cycles, albeit with a decreased transmission rate of 30 bytes per second.  
Based on the presented graphs, it can be inferred that link layer protocols, operating based on delay times, facilitate more stable transmission (with a success rate close to 100%) of data in conditions of simultaneous transmission.
# Design and Evaluation of Vacuum Central Drum Seed Metering Device
## 3 Key Component Parameter Design and Analysis
### Ventilation Housing  
The vacuum central drum-metering device adopts the negative pressure for sucking the seeds, positive pressure for seed cleaning and seed delivery method. The ventilation housing and the drum coaxial installation also rely on the combination of mutual pressure and the dynamic friction; the ventilation housing as shown in Figure 4.  
Figure 4: The main structure diagram of the ventilation housing. (**a**) Front view and (**b**) side view.  
The seeding drum rotates clockwise and the corresponding air chamber channel and suction hole on the seeding drum rotate to 33\({}^{\circ}\) below the centre line of the air exchange shell, where the suction hole enters the vacuum suction seed area. The corresponding suction hole on the seeding drum completes the suction of the rice seed in this arc section where the sucked rice seed turns over the seed carrying area under the protective effect of negative pressure and continues to rotate to the seed dropped zone. The seed then enters the seed-carrying arc section where the negative pressure disappears and the rice seed, under the action of gravity and positive pressure, enters the seed discharge tube and is blown to the sowing furrow. After the seed is dropped, the suction hole enters the emptying zone ready for the next cycle; the functional division is shown in Figure 5.