esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 2 System Model
### Energy Efficiency Proportional Fairness Utility Function  
Considering that both information transmission and wireless power transfer contribute to the energy efficiency of networks, we are interested in the typical node since its average performance represents the average system performance. The energy efficiency of typical node associated with BSs in tier \(k\) can be defined as \(\frac{R_{k}}{P_{t,k}+P_{s,k}-P_{EH,k}}\), where \(P_{t,k}\) and \(P_{s,k}\) are the transmit power and static power of BSs in tier \(k\), respectively.  
Considering that both information transmission and wireless power transfer can improve the energy efficiency, we use the proportional fairness algorithm to schedule the users for balancing the two kinds of users in WPCN. The proportional fair utility [31; 32], captures the tradeoff between opportunism and user fairness, by encouraging low rate users to improve their rates while saturating the utility gain of high-rate users. According to the statement in [33], the proportional fairness should be defined as the sum of logarithm function. Therefore, the average energy efficiency-based proportional fairness of the typical node can be described as  
\[U_{EE,k}=\mathbb{E}\left[\log\left(\frac{R_{k}}{P_{t,k}+P_{s,k}-P_{EH,k}}\right) \right]. \tag{6}\]  
Considering the fairness of typical node among its serving BS (termed the typical BS), the average energy efficiency-based proportional fairness of the typical node in the multi-tier wireless network is  
\[U_{EE} = \sum_{k=1}^{K}A_{k}U_{EE,k}, \tag{7}\]  
where \(A_{k}\) is the probability of a node associated with BSs in \(k\)-th tier and is described in (1).  
Note that the utility of each node is based on its average energy efficiency averaged over the fading channel. The mean system utility is the average of such utilities of all users over the network topology, which is equivalent to the mean utility of the typical user according to the Palm theory.
## 2 Modeling and Experimental Validation
### Model Validation  
#### 2.2.1 Field Tests  
To obtain the single-value condition and validate the accuracy of the numerical model, full-scale field tests were conducted in May 2021 at the Tianjin Metro Line 1 subway station. The measurement points were placed in the station hall, passageways, and outside [35]. Temperature and humidity loggers were used to measure the air temperature. The wall surface temperature of the station hall was obtained using a data acquisition instrument and K-type thermocouples, the indoor air velocity was multi-point tested using hot-wire anemometers, and the outdoor wind speed and direction were measured using a portable weather station. The key test instruments are shown in Figure 5, and the important parameters are listed in Table 3. Moreover, all the instruments were calibrated prior to the field test. The temperature and humidity loggers were uniformly arranged on the columns of the station hall, the hot-wire anemometers were arranged on the exhaust vents and at three entrances and exits of the station hall, and the portable weather stations were placed near the subway station, 10 m above the ground.  
The cold smoke test, which involves lighting a smoke cake to create smoke, has been conducted extensively, but cold smoke cannot adequately reflect the thermal buoyancy of the rising smoke. Therefore, in this investigation, a hot smoke test was carried out by combining burning alcohol with smoke cake, as illustrated in Figure 5e. The main product of the burning smoke cake was CO\({}_{2}\), and all the test data were recorded while the operation of the system was steady.  
Figure 4: Mesh of the model: (**a**) average wind speeds of different grid numbers, (**b**) grid detailing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Equipment Name** & **Range** & **Measurement Accuracy** \\ \hline Air temperature & Temp/RH logger & \(-\)20–70 \({}^{\circ}\)C & \(\pm\)0.21 \({}^{\circ}\)C \\ Wall temperature & Thermocouple & \(-\)50–300 \({}^{\circ}\)C & \(\pm\)0.15 \({}^{\circ}\)C \\ Indoor wind speed & Hot-wire anemometer & 0–30 m/s & \(\pm\)0.1 m/s \\ Outdoor wind speed & Portable weather station & 0–70 m/s & \(\pm\)0.1 m/s \\ Outdoor wind direction & Portable weather station & 0–360\({}^{\circ}\) & \(\pm\)1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test instrument and related parameters.
## 4 Conclusions  
Sensor networks have been adopted in various scientific and commercial applications. Gathering data from sensors is achieved by modeling it as a distributed database where sensor readings are collected and processed using queries. Sensor nodes are generally highly constrained, in particular regarding their energy and memory resources. While simple queries such as SELECT and AGGREGATE queries in wireless sensor networks have been addressed in the literature, the processing of join queries in sensor networks remain to be investigated. Previous approaches have  
Figure 5: Cases for varied synopsis rates. **(a)** Communication cost as changing synopsis rates from 0% to 100%. **(b)** Comparison IJA with synopsis algorithm.  
either assumed that the join processing nodes have sufficient memory to buffer the subset of the join relations assigned to them, or that the amount of available memory at nodes is known in advance.  
Therefore, in this paper including these assumptions, we describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor network environments. To evaluate the experiments, we compare the IJA with the typical algorithms. including the synopsis algorithm which is a representative strategy in sensor network to process queries. We also show the result of comparisons. In case of under join selectivity 0.01, typical join algorithms, such as na we, sequence and centroid join, perform worse than synopsis and IJA algorithms.  
Despite having better synopsis performance compared to a typical join algorithms, IJA performs better than the synopsis algorithm in conditions with above 60% synopsis. As future work, we will vary and study the parameters such as network density, node memory capacity and synopsis size, including communication cost.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Baseline Determination  
Baseline attenuation, also known as zero-level attenuation, is the attenuation of the total attenuation excluding the rain-induced part. Baseline attenuation is usually equal to the attenuation of the last dry interval before rainfall occurs. The NLA method takes the median signal level of all the dry periods over the past 24 h as the baseline. [PERSON] et al. [60] compared the constant baseline model and the single parameter model based on the first-order low-pass filter, and the results show that the latter model has better performance than the former, but the uncertainty of the latter model is relatively larger during the light rain. [PERSON] et al. [61] used the minimum attenuation value of CML-RSL to determine the dynamic baseline, which assumes that the rain rate is a random process, the non-rain-induced attenuation is relatively stable in a specific time period, and the real-time dynamic baseline is derived by analyzing the statistical characteristics of the minimum attenuation samples and the rain rate without the need of wet/dry classification in advance.
## 3 Path Planning in Robotics and Additive Manufacturing
### Path Planning in Robotics  
In general, trajectory planning can be understood as the determination of motion profiles for the drive system of an automated machine and thus represents the link between space and time. Knowledge of the specific kinematics (forward and inverse kinematics) is indispensable for this purpose. In order to be able to describe a movement in three-dimensional space unambiguously, seven parameters are necessary: three for the position, three for the orientation and a seventh as a path parameter, which establishes the connection between the pure geometric path and its temporal execution [24]. However, ignoring the parameter 'time' and purely planning the path to be followed geometrically, one speaks of path planning. In order to keep the definition of a path as general as possible and initially independent of the robot and tool used, it is generally planned as a movement of the so-called tool center point (TCP), i.e., the coordinate system relevant for the execution of the specific task, in relation to the robot base [25]. According to [PERSON], the planning of movements in joint space is not critical, since singularities are simply avoided [25]. In contrast, Cartesian planning often has to deal with challenges such as the unreachability of intermediate points, high joint velocities at singularities or the reachability of consecutive poses in different configurations [25]. However, many robotic tasks are bound to fixed Cartesian paths. These tasks include painting, cleaning, inspecting, welding or gluing. The corresponding path planning tasks are closely linked to the challenges of AM. For example, during painting or inspection, a path must be planned that covers the surface of an object without gaps [26]. Consequently, requirements for a path planner can be derived from robotics that complement the process requirements of AM. Specifically, it must be ensured that a planned path can be continuously executed by the robot in velocity limits and without configuration changes. Conventional geometric path planners of AM do not take this into account. Especially for paths that have to be executed in MDAM using WAAM and eccentric wire feeding generated by standard AM path planners, there are high speeds in the joints of the robot to be expected. The planned path has a direct influence on the velocity profile of the joints. Consequently, it should be taken into account already in the path planning step.
# Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network
## 5 Experimental Verification  
### Leakage Current Prediction Experiment  
To verify the feasibility of the method, the closed experimental system depicted in Figure 6 was constructed. The experiment selects 100 m of polyvinyl chloride insulated line with an inner diameter of 1.5 mm\({}^{2}\), using 1000 nF capacitance and 100 k\(\Omega\) resistor-equivalent line-to-ground capacitance and resistance, and using a high- and low-temperature-alternating humidity and heat test chamber to change the environmental temperature and humidity. A combination of 100-2000 W incandescent lamps is used as a load to generate different load currents. The test chamber is controlled to generate a temperature of \(-\)40\(\sim\)40 \({}^{\circ}\)C and a humidity environment of 20-100%. The load is changed so that the line carries a load current of 0.5\(\sim\)10 A, and the current of the transformer, and the current of the current transformer, the temperature, and humidity of the test chamber are recorded. The data sampling interval time was 1 min and the comparison of the predicted leakage with the actual leakage is shown in Figure 7. Figure 7 demonstrates that the trajectory of the prediction is in good agreement with the normal leakage current, which can accurately show the trend of the normal leakage. The prediction error is less than 5%, which satisfies the accuracy requirements of adaptive adjustment of the action threshold.  
Figure 6: Schematic diagram of leakage prediction experiment.
###### Abstract  
In solar-terrestrial physics, there is an open question: does a geomagnetic storm affect earthquakes? We expand research in this direction, analyzing the seismic situation after geomagnetic storms (GMs) accompanied by the precipitation of relativistic electrons from the outer radiation belt to form an additional radiation belt (RB) around lower geomagnetic lines. We consider four widely discussed cases in the literature for long-lived (weeks, months) RBs due to GMs and revealed that the 1/GMs 24 March 1991 with a new RB at L\(\sim\)2.6 was followed by an M7.0 earthquake in Alaska, 30 May 1991, near footprint L = 2.69; the 2/GMs 29 October 2003 (Ap = 204) with new RB first in the slot region at \(L\) = 2-2.5 cases followed by an M7.8 earthquake on 17 November 2003 at the Aleutian Islands near footprint L = 2.1, and after forming an RB at L\(\sim\)1.5 which lasted for \(\sim\)26 months, two mega quakes, M9.1 in 2004 and M8.6 in 2005, occurred at the globe; the 3/GMs 3 September 2012 with a new RB at L = 3.0-3.5 was followed by an M7.8 earthquake in Canada near footprint L = 3.2, and the 4/GMs 21 June 2015 with a new RB at L = 1.5-1.8 was followed by an M6.3 earthquake on 7 September 2015 in New Zealand, near footprint L = 1.58. The obtained results suggest that (1) major earthquakes occur near the footprints of geomagnetic lines filled with relativistic electrons precipitating from the outer radiation belt due to geomagnetic storms, and (2) the time delay between geomagnetic storm onset and earthquake occurrence may vary from several weeks to several months. The results may expand the framework for developing mathematical magnetosphere-ionosphere coupling models.  
e 2023 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Future Perspectives  
As prevention is easier and has numerous advantages in comparison to treatment, our recommendations to the patients should be focused on prevention. In most instances, the location of nail breakage appeared to be connected to implant wear and tear caused by slow bone healing, but in rare cases, it was also caused by insufficient trauma [41]. To avoid tibial nail breakage after tibial fracture nailing, patients should follow their doctor's instructions, avoid high-impact activities, wear proper shoes, use assistive devices as directed, be careful when getting in and out of bed, avoid heavy lifting, and report any symptoms.  
To prevent nail breakage, it is important to closely monitor patients and identify those with delayed healing or where nonunion is likely. Planning revision surgery (involving replacing the nail) at an earlier stage can help avoid nail breakage. Additionally, extracting an intact implant is much easier, more cost-effective, and carries less risk when performed sooner [42].  
To avoid implant failure, further improvement of the implant is required. By using more advanced materials for implants, it could be possible to improve their strength and durability, thus reducing the risk of failure. By improving the design of the implant and by optimizing the implant size and shape, it could be possible to improve its biomechanical properties, making it more resistant to failure and to better match the implant to the patient's anatomy, and reduce the risk of failure. Moreover, by implementing more rigorous testing protocols for implants, it could be possible to better identify potential issues before they lead to implant failure. Orthbiologic products such as platelet-rich plasma (PRP), stem cells, or growth-factor therapy to promote healing could reduce the risk of complications and improve outcomes.  
New techniques for broken tibial nail removal could be considered in the future, as the technology is constantly progressing. Developing new techniques that involve smaller incisions and less tissue disruption reduces the risk of complications and speeds up recovery. Using intraoperative computer-aided navigation systems during the surgery could help the surgeon to accurately place the instruments and tools to improve the accuracy of the procedure, remove the broken implant, and reduce the risk of complications. Robotic-assisted surgery could also improve the precision of the surgery and reduce associated comorbidity. Biodegradable implants that dissolve over time could eliminate the need for a second surgery to remove the implant. Three-dimensional printing technology could be used in the future to create custom-made instruments that could improve the extraction of the implant and reduce the risk of complications. While these future ideas may seem costly and futuristic now, experience has shown that anything is achievable with time and effort.
## 4 Software Architecture
### Core Functional Module Design
#### 4.2.4 Application Testing Module  
The application testing and communication testing processes are similar; the difference is that the application scenario injection is more complex and runs through the execution of the task. This section only describes the scenario injection process of the application testing module.  
Figure 5 depicts the application's scenario injection process, which most C-V2X security applications are capable of. The application injection process commences after the device control is completed and the DUT is in place. When the position of the DUT is synchronized with that in the simulator, the vehicle movement scenario simulation is initiated. The BSM content of the testbed nodes is controlled, and the status of the DUT is monitored.  
The functions of the involved modules are described below.  
* Scenario Simulator The simulator models actual physical scenarios and defines vehicle movement models and safety application scenarios to simulate the C-V2X movement scenarios (e.g., FCW). During testing, the simulator specifies the BSM content broadcast by the testbed nodes through the logical controller to inject the application scenarios. Additionally, the simulator subscribes to the status information of the DUT, enabling users to monitor the execution status of the application testing and perform corrections, pauses, or other operations.  
Figure 5: C-V2X application scenario injection process.  
* Logical Controller This provides the scenario simulator with interfaces for task information acquisition, DUT status subscription, and BSM injection. It collects data from these three interfaces and prepares to analyze the test data.
* Testbed Nodes These nodes broadcast the BSM information injected by the scenario simulator and collect the BSMs broadcast by the DUT to allow for the simulator to obtain the DUT's status information.
* DUT This represents the RSU or vehicle provided by the vehicle manufacturer, equipped with C-V2X security applications and operated by the testers. In the test environment, after the application under test is activated, DUTs periodically broadcast BSMs and upload their test data through designated application testing interfaces.
## 4 Performance Evaluation
Moreover, using secret sharing with mobile edges, the proposed model prevents the malicious nodes from being part of the MIoT network, and avoids frequent transmission of false or bogus data packets. The proposed model efficiently utilizes the transmission power of mobile edge computing nodes and improves routing management. Using a multi-hop communication system, the proposed model decreases the chance for the selection of the longer route. Moreover, based on the packet variability factor among MIoT neighbors, the proposed model forwards the observing data timely, and with more consistency in the chosen route. Therefore, it prolongs the lifetime of the active routes and, accordingly, improves the performance of end-to-end delay remarkably. In Figure 6, the experimental results illustrate the performance of the proposed model, in terms of packet drop ratio, compared to the existing solution. It is observed that the proposed model decreases the packet drop ratio by 37% and 41%, respectively; this is due to the determination of packet variability by utilizing the distance and \(RSSI\) factor. It optimizes the MIoT route among constraint resources and provides strengthened peer nodes for accomplishment to data storage on the public cloud. Moreover, the proposed model offers a more secure and authenticated routing mechanism, using a secured approach that increases the confidence ratio among nodes, and incurs minor data lost in the occurrence of malicious entities. In Figure 7, the proposed model evaluates the reliability of the proposed model in the comparison with an existing solution. It is seen from the simulation-based results under a varying number of nodes that the proposed model improves the packet drop ratio against other work by 29% and 39%. It is due to the incorporation of the reliable routing and secure cryptosystem for the MIoT network, improving productivity for the industry. The use of mobile edge nodes also decreases the energy consumption among MIoT nodes and, ultimately, the lifetime of the network increases with the efficient practice of data aggregation/fusion. Moreover, the MIoT data are securely transmitted to the cloud for further processing and storage based on the lightweight cryptography asymmetric algorithm, which increases the reliability among nodes against network threats. Figure 4: Network overhead with the number of nodes. Figure 3: Delivery rate with the number of nodes. Figure 5: Processing delay with the number of nodes. Figure 6: Packet drop ratio with the number of nodes.
# Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
## Materials and Methods
### Sensor Structure Design  
Figure 1a shows the metasurface sensor with double open metal rings, which is composed of three layers. The bottom layer is high-resistance silicon with a dielectric constant of \(\varepsilon\) = 11.9, and the thickness of this layer is 500 \(\upmu\)m. The middle layer is silica with a dielectric constant of \(\varepsilon\) = 3.75, and the thickness of the middle layer is 500 nm. The topper layer is gold with a conductivity of \(\sigma\) = 4.561 \(\times\) 10\({}^{7}\) S/m, and the thickness of this layer is 200 nm. In addition, the metal of topper layer is etched into the shape of two open rings. The period of the structure is \(P\) = 80 \(\upmu\)m, the metal ring width is \(w\) = 7 \(\upmu\)m, the gap width is \(g\) = 8 \(\upmu\)m, the length of the outer metal ring is \(L_{1}\) = 72 \(\upmu\)m, and the length of the inner metal ring is \(L_{2}\) = 32 \(\upmu\)m. Figure 1b shows the simulation results of this sensor, which it can be seen that the sensor produces resonance frequency in 0.2923 THz.  
Figure 1: (**a**)\(\mathrm{THz}\) metasurface sensor, (**b**) the spectrum of simulation results.