esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 3 A Framework of 5G Transportation  
This section presents the appropriate framework of 5G transportation, which provides transport systems with high-quality services, maximum security, and low cost. Users expect more services and applications with the best and low-cost (energy-efficient) frameworks despite the many frameworks. Additionally, 5G enable more services and applications with the better complexity limitation. However, the limitations of the potential frameworks used in the 5G environments are still slowing down the flow of transportation services. Further, a large volume of unexpectedly heavy traffic prevents regular online services and applications from the providers. Accessing online requests and internet services according to users' preferences and priorities is possible through the frameworks, which manage the network traffic and improve traffic performance.  
As shown in Figure 2, the proposed framework provides the necessary facilities for improving the different types of services considered in the transportation system. Here, solutions to the future challenges and potential problems of the transportation system are the main features considered as facilities. Although many existing traffic models, such as the data traffic aggregation and the data traffic slicing models, are available, this section focuses on traffic approaches using SDN. When the application layer of SDN holds the transportation services, the management issues of the data traffic used in the transportation system improves. Controllers are the brain of the control layer of the SDN, which organizes the data traffic according to the technical properties.  
The basic properties of SDN support us to improve 5G-based transportation services in which the traffic measurement related to transportation services and transport management are considered. The proposed framework's novelty of the 5G-based designs with emerging technology such as SDN improves transportation services. Considering its novelty, the proposed framework is adaptable with many factors which evolve with technology requirements and users' priorities. They are intelligence within the transportation services, ITS of autonomous system and self-driving in connected vehicles, proactive approach of  
Figure 2: A framework of 5G transportation service.  
security solutions, etc. Adapting these factors' influence to the novel approach is possible through the layers shown in the flexible framework of the transportation service (Figure 2).
# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 4 Experiments and Results
### Parameter Settings  
The study presented in this paper was carried out using the same computer configuration. CPU: Intel Core i5-9500 @ 3.00 GHz, RAM: 24.0 GB, Graphics: NVIDIA GeForce RTX 2060 (Santa Clara, CA, USA), and AUV trajectory prediction experiments in Python 3.6.  
In this study, we configured some hyperparameters. The Adam optimizer is used because it performs well in sparse gradient environments, can effectively mitigate the gradient oscillation problem, and can achieve stable and efficient training convergence. Learning_rate is set to 0.001 to maintain stability during the training process and avoid instability due to excessive parameter updating. Batch_size is set to 64, and the stochastic gradient descent method is used for training to balance computational efficiency and accuracy of gradient estimation. Hidden_size is set to 32 to reduce the risk of overfitting and speed up the iteration speed. Epochs are set to 200 to ensure that the model can adequately iterate and learn from the dataset, comprehensively capture the features of the data, and progressively optimize the prediction performance. K is set to 3 to allow the model to focus on different parts of the input sequence simultaneously to improve the computational efficiency of different parts of the input sequence. In this study, we use data from seven consecutive time points as inputs to predict the latitude and longitude values of the AUV trajectory at the next instant, which fully takes into account the continuity and correlation of the time-series data and helps the model to capture the dynamic features of trajectory changes more accurately.
## 4 Simulation and Results Analysis
### Dynamic Traffic Grooming Algorithm Evaluation  
We select four comparative algorithms: (1) the KSP-FF algorithm; (2) the SP-FF algorithm; (3) the resource allocation algorithm based on resource evaluation collection (RAREC) [35], which selects the resource with the best evaluation result for the request; (4) the traditional dynamic traffic grooming (DTG-T) algorithm [36]; the traditional traffic grooming algorithm is divided into two parts. In the path-finding stage, the traditional two-layer elastic network virtualization algorithm is used to redirect requests into the \(G^{V}\), and in the resource allocation stage, the degree of fragmentation increasing (DFI) method is used to allocate resources.  
From Figure 9(a), as the traffic load increases, the blocking probability of all five algorithms increases. The KSP-FF algorithm and SP-FF algorithm both have high blocking probability, while the DTG-VPO algorithm has the best blocking probability performance. The blocking probabilities of the RAAREC, DTG-T, and DTG-VPO algorithms are 0.018, 0.012, and 0.003, respectively. In contrast, the DTG-VPO algorithm reduces them by 83.3% and 75%, respectively, as the traffic load is 200 Erl. The reason is that, during the resource allocation stage, the DTG-VPO algorithm leverages the time elasticity of requests to allocate resources more judiciously. As a result, subsequent requests benefit from a greater availability of resources, ensuring their successful transmission from the source node to the destination node. Additionally, in constructing candidate paths, the algorithm incorporates node busyness as a factor to establish a load-balancing mechanism. This approach effectively reduces blocking probabilities, enhancing the overall efficiency and reliability of the network. From Figure 9(b), it can be seen that the KSP-FF algorithm and SP-FF algorithm have the highest wavelength utilization. Still, these two algorithms simultaneously have the highest blocking probability, so their performance is the worst. The wavelength utilization of the other three algorithms is ranked from high to low as the DTG-VPO algorithm, DTG-T algorithm, and RAAREC algorithm. When the traffic load is 200 Erl, the wavelength utilization of the DTG-VPO algorithm, DTG-T algorithm, and RAAREC algorithm are 0.306, 0.283, and 0.267, respectively. The wavelength utilization of the DTG-VPO algorithm increased by 8.1% and 14.6%, respectively, compared with the other two algorithms. While constructing the candidate path set, the DTG-VPO algorithm incorporates the established optical path into the virtual layer plane, which ensures that the virtual layer's candidate paths are given preferential selection within the path set. Consequently, resources are concentrated and utilized more efficiently, improving wavelength utilization throughout the network. The DTG-VPO algorithm can effectively improve the wavelength utilization while maintaining low blocking probability.  
Figure 9: DTG-LFA algorithm performance vs. traffic load. (**a**) Blocking probability vs. traffic load. (**b**) Wavelength utilization vs. traffic load.
# Resonance-Driven Passive Folding/Unfolding Flapping Wing Actuator
## 5 Conclusions  
The proposed resonant system was observed to show hysteresis and bistability, caused by the resonant frequency shift due to the folding/unfolding motion. This behavior was successfully explained using a simplified analytical model. This work provides a basic understanding of the resonant-driven passive folding/unfolding mechanism and could contribute to the future development of flapping-wing MAVs that are smaller than a hummingbird. However, this study has certain limitations, summarized as follows:  
* Because we only considered quasi-static characteristics of the folding/unfolding motion, the dynamic behaviors of the system, such as the response against impulsive external forces, are still unclear.
* The contributions of the neglected nonlinearities are not quantified. They possibly become indispensable in other cases.
* In our experiments, the actuator was rigidly fixed. Thus, any unexpected phenomenon occurring during flight due to the inertial force has not been considered.  
We also provided a control method to widen the stable region by simultaneously modulating \(V\) and \(\omega\). We only carried out experimental demonstrations, but not any theoretical consideration, generalization, or optimization. Thus, further examination is needed in the future.  
As a future work, we will develop an MAV equipped with an actuator with folding/unfolding capability and try to demonstrate flight takeoff.  
Conceptualization and evaluation: T.O. and N.O.; formal analysis, actuator design, and writing: T.O.; supervising: K.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 2 Hardware Description
### Input Preconditioning: Attenuation, Input Protection, and Isolation  
To prevent the isolation amplifier from breaking due to overvoltage, probe damage due to component deterioration, misuse, or improper probe connection, the proposed design includes a 2 A Fuse and an ISOMOV 5 KA 510 VRMS Variator (Figure 4). In addition, the input voltage is attenuated by a fixed factor 10 (attenuation resistor network, Figure 4), so that the maximum voltage on the IA input is limited to 60 V, for a maximum measurement voltage of 600 V of peak amplitude.  
Figure 3: Power supply block diagram.  
Finally, to ensure that the input voltage probe remains into the IA working range, a second selectable voltage divider is included in the attenuation resistor network shown in Figure 4. These characteristics allows the user to manually select between the measurement ranges shown in Table 1. Table 1 presents the relationship between the input of the differential remote probe, the attenuation factor, the isolation amplifier input, and the isolation amplifier output.  
Considering that the gain of the isolation amplifier is 1, the IA voltage to it is 6 V using the attenuation factor proposed. For example, with a 600 V peak signal, the input to the isolation amplifier using \(\times\)100 attenuation will be 6 V. This allows a direct relationship with the reading voltage of an oscilloscope and, in addition, allows the use of the options of configuration of \(\times\)10 and \(\times\)100 attenuator probes at their inputs, so that these instruments display the real values of the signals on their screens.  
The circuit scheme described in the block diagram of Figure 4 is shown in Figure 5.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 5 Proposed Approach: LoRaMesh
### Power Consumption  
In a Class A LoRaWAN, the power consumption of a node can easily be calculated by \(E_{Tot}=E_{stby}+E_{Tx}+E_{Rx}\), where \(E_{Tx}\) is the energy required for the transmission of a packet, \(E_{Rx}\) is the energy required to open two receive windows, and \(E_{stby}\) is the energy consumed during standby. In a multi-hop mesh network, it is important to determine the additional power consumption required by mesh nodes. The total energy (\(E_{Tot}\)) in Joules per up-link transmission cycle is calculated by Equation (5). Where:  
* \(E_{stby}\): energy consumed during standby;
* \(E_{beacon}\): energy required for the reception and possible re-transmission of the beacon frame;
* \(E_{frad}\): energy required for the possible reception and forwarding of child node packets;
* \(E_{Tx}\): energy required for the transmission of a node's own packet.  
\[E_{Tot}=E_{stby}+E_{beacon}+E_{frad}+E_{Tx} \tag{5}\]  
Based on the research done in [31] and the datasheet of the Semtech SX1272 [7], Table 1 lists the power consumption values which are used within the ns-3 simulation model. We assume that the PA0 output is on the RFO pin, since no boost power amplifiers will be needed as we are limited to 14 dBm in the 868.1 MHz ISM band.  
Given the information in Table 1, we can calculate the theoretical energy consumption of a Class A LoRaWAN device which is transmitting a 10-byte data packet at a transmission power of 14 dBm once every 600 s at a DR 1 (selected at random). Only the energy consumption of the LoRaWAN radio is taken into account.  
The ToA for the transmission of the 10-byte LoRaWAN packet at the given datarate is 741.4 ms. The transceiver operates at 3.3 V. Hence, the \(E_{Tx}\) is calculated at 0.09297 J per transmission. The Rx duration is calculated, based on Equation (1) at the given datarate, as 16.64 ms. Since two reception windows are required in Class A LoRaWAN devices, the Rx duration will be 33.28 ms per up-link, resulting in \(E_{Rx}=0.0012\) J. Given that the transceiver is in sleep mode and the remaining time of the 600 s up-link cycle, the standby energy power consumption is \(E_{stby}=0.002966\) J. The total power consumption then results in \(E_{Tot}=0.097136\) J per up-link cycle.  
Calculating a theoretical power consumption value for an LoRaMesh device will not reflect the actual power consumption, as the reception time of waiting for beacon frames or the SF of received packets to the relay varies. To approximate the power consumption, Section 7.2 simulates the energy consumption of such relay nodes to model the effect of beacon frames and relayed packets on relay nodes.
## 2 Results and Discussion
Of the 28 identified, the main compounds were \(\alpha\)- and \(\beta\)-pinene (9 and 2.5%), \(\beta\)-myrcene (44.3%), limonene (10.6%), linalool (3.5%), D-\(\alpha\)-Fenchyl alcohol (0.9%), \(\alpha\)-terpineol (1.4%), _trans_-\(\beta\)-caryophyllene (12.8%), \(\alpha\)-humulene (3.3%), \(\beta\)-bisabolene (0.9%), \(\gamma\)-seline (1.9%), selina-3,7(11)diene (2.6%) and \(\gamma\)-eudesmol (1%), as indicated in Table 1 and shown in Figure 2.  
The oil fractions in the bubblers (F1, F2, F3, see Table 2 and Figures 3 and 4) were analyzed by SPME/GC-MS after the decarboxylation of _Camabis_ infloresences. F1 showed the highest volatile content (37,390.38 \(\pm\) 723.16 mg/kg oil), with \(\alpha\)-pinene, \(\beta\)-pinene, \(\beta\)-myrcene, limonene and _trans_-\(\beta\)-caryophyllene being the most important of the 21 components identified. F2 and F3 showed terpene contents of 10,819.59 \(\pm\) 187.83 and 1208.78 \(\pm\) 31.12 mg/kg oil, respectively, with only eight compounds detectable. In the hundred-percent stacked column representation of the average ppm data from Table 2 (Figure 3), it can be observed that over 92% of the single terpen (with fenchyl alcohol and _trans_-\(\beta\)-farnesene being excluded) were trapped in the first two oil fractions; this increased to 98% when the total amount was considered. Figure 4, which is a stacked column representation of the terpene composition of the three oil fractions (F1, F2 and F3), highlights the main compounds present and their different abundances in each oil fraction, confirming that the first two are the richest in volatiles. Figure 2: Chromatogram obtained from the SPME/GC-MS of _Camabis_ infloresences with 2-undecanol as IS (14.93 min).
## 3 Results and Discussion
### Simulation of Brush Seal with Clearance
A comparison between the turbulent kinetic energies in Figures 7 and 10, shows that, in the case without clearance, the turbulent kinetic energy was more evenly distributed within the bristle pack, with a lower maximum value and a broader distribution. However, in the case with clearances, a higher peak value was observed in the high-velocity region, and this peak occurred near the clearance. Figure 11 compares the velocity vectors between the contact brush seal [25] and the brush seal with the 0.27 mm clearance [27], revealing distinct differences in flow behavior. For the contact seal, the flow predominantly diffused into the bristle pack, creating strong inward radial flow near the backing plate, which resulted in lower leakage rates. Conversely, for the seal with clearance, a significant portion of the flow bypassed the bristle pack through the clearance, leading to substantially higher leakage. Figure 9: 2D CFD prediction of leakage flow rate in a brush seal considering blow-down clearance: (a) clearance = 0.27 mm; (b) clearance = 0.75 mm. Figure 10: 2D CFD predictions of flow fields around brush seals with clearance under a pressure ratio of 2.0: (**a**) pressure; (**b**) axial velocity; (**c**) radial velocity; (**d**) turbulent kinetic energy.
## 4 Conclusions  
1. This study demonstrates that full-range guide vane adjustability in longitudinal axial threshing systems offers advantages over conventional configurations in winter wheat harvesting. The modified combine harvester (Combine A), equipped with eight mechanically linked adjustable vanes, achieved: * Lower separation and sieve losses across all tested vane angles; * Reduced broken grain percentages, particularly at higher vane inclinations; * Higher feed flow rates and overall productivity; * Improved fuel efficiency and reduced engine load.
2. The most favorable balance between separation performance, grain quality, and fuel use was observed at a guide vane angle of 25\({}^{\circ}\), where Combine A reached a 20.6 kg s\({}^{-1}\) feed rate with only 0.96% separation loss and 0.86 L t\({}^{-1}\) fuel consumption.
3. These findings confirm that optimizing material flow control--especially in the front threshing zone--is essential for improving throughput, fuel efficiency, and grain preservation. The fully linked guide vane system can be implemented with moderate mechanical modification and is compatible with existing combine platforms. Its integration offers a promising direction for enhancing combine harvester performance, particularly under variable crop conditions common in high-yield environments. However, the experiment was limited to two combines operating in a single winter wheat field under controlled conditions. This presents a replicability constraint, as the findings may not yet be generalizable to other crop types, regions, or machine models. Future research will focus on broader multi-field trials to validate the system's performance across diverse harvesting scenarios.
4. Future work may explore coupling this system with sensor-based automation for real-time adaptive control, extending its application across different crops and harvesting conditions.  
Conceptualization, M.M.; Methodology, M.M. and D.S.; Software, M.M.; Validation, G.M. and M.P.; Formal Analysis, M.M.; Investigation, M.M. and N.P.; Resources, N.P.; Data curation, M.M.; Writing--Original Draft Preparation, M.M.; Writing--Review and Editing, G.M. and D.S.; Visualization, M.M.; Supervision, D.S.; Project Administration, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to institutional data handling policies.  
Author [PERSON] was employed by the company CNH Industrial Danmark A/S. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Drone Registration Phase  
The drone selects an identity (\(ID_{D}\)), random number (\(r_{D}\)), and challenge (\(C_{j}\)) and computes \(PID_{D}=h(ID_{D}\parallel r_{D})\) and \(R_{j}=PUF(C_{j})\). Afterward, the drone sends \(\{PID_{D},C_{j}\}\) to the GCS.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline \(ID_{D}\) & Drone identity \\ \(ID_{M}\) & Mobile device identity \\ \(C_{j},R_{j}\) & Challenge–response pair \\ \(k\) & Secret key of the ground control station \\ \(K_{D}\) & Secret key of the drone \\ \(K_{M}\) & Secret key of the mobile device \\ \(r_{i}\) & Set of random numbers \\ \(PID_{D}\) & Pseudo-identity of the drone \\ \(HD_{M}\) & Pseudo-identity of the mobile device \\ \(F_{M},G_{M}\) & Authentication parameters for mobile user \\ \(V_{M},V_{C}\) & Verification parameter \\ \(K_{1},R_{2},R_{3},R_{4},R_{5},R_{6}\) & Messages exchanged during the key agreement phase \\ \(Auth_{i},Auth_{j}\) & Secret authentication value \\ \(SK_{D},SK_{M}\) & Session keys of the drone and mobile device \\ \(T_{i}\) & Set of timestamps \\ \(h(\cdot)\) & Cryptographic hash function \\ \(\oplus\) & Exclusive OR (XOR) operation \\ \(\parallel\) & Concatenation operation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation for the proposed protocol.  
The GCS checks \(PID_{D}\) in the record and picks a random number (\(r_{1}\)). Afterward, the GCS computes \(K_{D}=h(PID_{D}\parallel k\parallel r_{1})\) and stores \(\{PID_{D},r_{1},C_{j}\}\) and sends \(\{h(\cdot),K_{D}\}\) to the drone.  
The drone computes \(K_{D}^{*}=K_{D}\oplus R_{j}\) and stores \(\{K_{D}^{*},r_{D},h(\cdot)\}\). The drone registration phase is presented in Figure 2.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
###### Abstract  
Extending the internet of things (IoT) networks to remote areas under extreme conditions or for serving sometimes unpredictable mobile applications has increased the need for satellite technology to provide effective connectivity. However, existent medium access control (MAC) protocols deployed in commercial satellite networks were not designed to offer scalable solutions for the increasing number of devices predicted for IoT in the near future, nor do they consider other specific IoT characteristics. In particular, CubeSats--a low-cost solution for space technology--have the potential to become a wireless access network for the IoT, if additional requirements, including simplicity and low demands in processing, storage, and energy consumption are incorporated into MAC protocol design for satellite IoT systems. Here we review MAC protocols employed or proposed for satellite systems and evaluate their performance considering the IoT scenario along with the trend of using CubeSats for IoT connectivity. Criteria include channel load, throughput, energy efficiency, and complexity. We have found that Aloha-based protocols and interference cancellation-based protocols stand out on some of the performance metrics. However, the tradeoffs among communications performance, energy consumption, and complexity require improvements in future designs, for which we identify specific challenges and open research areas for MAC protocols deployed with next low-cost nanosatellite IoT systems.  
CubeSats; internet of things; medium access control; nanosatellites; sensor networks; wireless access networks +
Footnote †: journal: Sensors
## 1 Introduction
Constraining the atmospheric, reference [23] had studied the positioning accuracy and convergence time in dual- and triple-frequency PPP. It was verified that the accuracy of triple-frequency positioning was improved by 60% more than dual-frequency under atmospheric-constrained conditions. In this paper, the influence of SF PPP on positioning accuracy and convergence time under ionospheric-constrained (TIC1) and troposphere- and ionospheric-constrained (TIC2) conditions are studied. The difference between positioning accuracy and convergence time of the station in the period of magnetic storms (MS) and non-magnetic storms (NMS) and geography at different latitudes are analyzed. The organization of the paper is as follows: the introduction is in Section 1, mathematical models are in Section 2, data sets and processing strategy are in Section 3, results and discussion are in Section 4, and conclusions are in Section 5.