esa-sceva/satcom-chunk-collection huggingface dataset:
# Heavy Metal Ion Detection Platforms Based on a Glutathione Probe: A Mini Review
## 3 Heavy Metal Ion Detection Platforms Using Glutathione
### Optical Techniques
#### 3.1.1 Metallic-Nanoparticle-Based Platforms
UV-vis absorption spectra was also used to indicate the sensitivity and selectivity of this strategy. It is necessary to point out that, in this report, the detection conditions were optimized carefully. The optimized parameters included the pH value of the solution (6.2), the salt concentration (50 mM), the GSH concentration (1.13 \(\upmu\)M), and the reaction time (17 min). This detection method was demonstrated to be sensitive from 9.6 nM up to 11.67 \(\upmu\)M, and it also showed a selectivity of Cd\({}^{2+}\) against Pb\({}^{2+}\), Hg\({}^{2+}\), Ni\({}^{2+}\), and seven other kinds of HMIs, all at a concentration of 11.67 \(\upmu\)M.  
After surveying metallic-NP-based HMI detection platforms, we found that GSH-functionalized metallic NPs, especially GNPs, have been widely adopted for metal ion detection. Because of the stronger binding force between gold and the (-SH) group in GSH, and the specific binding ability between (-COOH) group and Pb\({}^{2+}\), Pb\({}^{2+}\) is the most common HMI to be specifically detected. On the other hand, there were also some reports that detected HMIs other than Pb\({}^{2+}\) under different conditions, such as high pH or using unbounded GSH. As an essential part of the HMI detection platform, a number of optical characterization methods can be used to quantify the HMI concentration, such as a UV-vis spectrophotometer, dynamic light scattering (DLS), as well as colorimetry. More HMI detection examples using NPs are listed in Section 3.3.
## 3 Proposed Optimal Orchestration Process
### Framework Overview and Technical Contributions
#### 3.1.3 Company Network as a Service (CNaaS)  
The CNaaS model introduces a paradigm shift in enterprise network service delivery. Our implementation demonstrates its feasibility within current 5G ecosystems through careful consideration of technical requirements and operational constraints. The model achieves service isolation with negligible performance overhead (less than 1% additional latency), while maintaining flexible resource allocation capabilities.  
The technical implementation relies on advanced network function virtualisation (NFV) principles, with container-based network functions achieving 99.999% availability. Resource allocation efficiency is maintained through dynamic scaling capabilities that respond to demand variations within 500 ms, ensuring consistent service quality across all enterprise applications.  
Our proposed ML-based framework aims to improve the model integration challenge by implementing a series of ML algorithms pipeline as a component in the framework; a typical enterprise network environment is shown in Figure 5. This shows varying departments within an organisation with diverse needs. For example, the organisation or company marketing department might demand an eMBB slice for remote sales, while the  
Figure 4: eSS module pipeline within the CNaaS framework.  
same company with vehicles carrying bio-reactive materials might require a low-latency network slice.  
With the proposed ML-based framework, the 5G network slice requirements of live applications over the company network will be monitored, analysed, and optimally orchestrated in real time.
## 3 Simulation Modeling of Dynamics for EMU Gearbox System
### CRH5 gearbox Rigid-Flexible Dynamics Model Contact Force Setting
#### 3.2.2 Contact Force Parameter Setting  
According to the analysis of domestic and international studies [31; 32], the parameters are set as follows:  
* Modulus of elasticity \(E\). Reflecting the stiffness of the material during elastic deformation, \(E\) = 2.07 \(\times\) 10\({}^{11}\).
* Poisson's ratio \(\mu\). Reflecting the coefficient of transverse deformation of the material in one direction, \(\mu\) = 0.3.
* Contact coefficient \(K\). Related to the shape and material properties of the contact surface, it is calculated according to Hertz contact theory, \(K\) = 1.29 \(\times\) 10\({}^{6}\).
* Contact index \(e\). Reflecting the degree of nonlinearity of the material and calculated according to the Hertz contact theory, \(e\) = 1.5.
* Damping coefficient \(C\). Reflecting the energy loss when objects collide, empirically, \(C\) = 10.
* Gear cutting depth \(d\). Empirically, \(d\) = 0.1 mm.
* The coefficient of kinetic friction \(f_{dy}\) = 0.05, the kinetic slip velocity \(v_{d}\) = 10 mm/s, the coefficient of static friction \(f_{st}\) = 0.08, and the static slip velocity \(v_{s}\) = 1 mm/s.
* According to a related study [33], load-bearing loads and line excitation curves are incorporated at the axle.
## 4 Performance Evaluation
### Illustration of the Operation  
In this subsection, the operation of the HHT and F-HHT will be illustrated. First, an example is shown in Figure 6, where the shape of the HHT can be observed. The HHT function values were calculated on a grid with resolution of 0.5 m. The insets show the functions around the global maximum, which is in close proximity to the true location, indicated by a blue cross. Figure 5(a) shows a near-range example using scenario #2, while Figure 5(b) shows a long-range example using scenario #5.  
In the near-range example, one local minimum is visible (around location (160,20)), while in the long-range case several local minima can be observed. Thus, finding the global maximum is not a trivial problem. The impact of GDOP is also apparent in the figure: although the measurement noise is the same in both examples (\(\sigma_{i,1}=1\) m), in the near-range case the peak is narrow and can be contained in a 20 m \(\times\) 20 m box, while in the long-range case the peak is spread over a much larger area of approx. 100 m \(\times\) 500 m.  
Figure 7 illustrates the operation of the F-HHT in scenario #2. The figure shows steps 1, 2, 3, and 10 of the iteration. Red dots indicate the centers of the actual promising tiles, the figure titles also show their number \(N_{tile}\). In the final step, the best grid point (_bestP_) is also shown, which is the position estimate.  
In the experiment shown in Figure 7, the total number of processed tiles was approximately 1200, resulting in approximately 1200 evaluations of (25) and the same number of (26) (which has approximately the same computational complexity as (25)). This means that there were a total of around 2400 HHT evaluations. It is worth noting that the final grid size was 0.1 m, which would have required 25\(\cdot\)10\({}^{6}\) HHT calls of (25) in the brute force solution. Thus, in this example, the F-HHT resulted in an approximately 10,000-fold decrease in computational complexity.  
Figure 8 shows the distribution of the F-HHT location estimates. Again, scenarios #2 and #5 were used; in both scenarios, 100 independent experiments were conducted; the estimates are shown by red dots. The estimates are scattered around the true source position. Despite both scenarios having the same noise level of \(\sigma_{i,1}=1\) m, the long-range scenario has significantly higher variance due to the higher GDOP.  
Figure 6: The HHT. (**a**) Near-range example, (**b**) long-range example.  
Figure 8: The near-range and the long-range experiments, conducted with a distance noise standard deviation of \(\sigma_{z1}=1\) m.  
Figure 7: Operation of the F-HHT. Sensor positions are shown by blue circles. The source position and the estimated position are shown by a blue cross and a green x, respectively. The centers of promising tiles are shown by red dots. \(n\): iteration, \(D_{n}\): grid size, \(N_{tile}\): number of promising tiles.
## 1 Introduction
In order to form a beam in the direction of a moving vehicle, beam alignment must be performed periodically. The beam alignment between the transmitting and receiving antennas requires sending and receiving pilot signals, which results in signal overhead. In particular, as the beamwidth becomes narrower, the directional gain of the antenna increases, but more frequent beam alignment is required because it is more prone to misalignment due to the vehicle's mobility, and the beam alignment takes longer because many directions have to be searched [17]. Hence, it is important to find a compromise between the antenna beamwidth and the achievable data rate [17; 18]. In this paper, we propose a joint consideration of the problems of determining the beamwidth and aperiodic beam alignment interval in order to maximize the throughput in highway vehicle-to-infrastructure (V2I) communication. The contributions of this paper are as follows. First, we propose aperiodic beam alignment for the directional beamforming in V2I communication. To the best of our knowledge, for the first time, we dynamically determine when to start the next beam alignment. That is, the proposed scheme dynamically adjusts the beam alignment interval according to the channel environment. Second, we formulate an optimization problem that aims to maximize the data rate by jointly controlling the antenna beamwidth and the timing of the next beam alignment. Third, we propose a Q-learning model to solve the optimization problem, where the reward is determined as a weighted sum of current and past data rates and expected future data rates. The rest of this paper is organized as follows. Section 2 investigates the previous studies and analyzes the limitations. Section 3 describes the system model. Section 4 presents the proposed RL-based beamforming scheme, where the state, action, and reward of the RL are described in detail. Section 5 provides the simulation results, and Section 6 concludes this paper.
# Vegetation Health in China Is Severely Compromised by Drought, Wet and Heat Stress Events
## 2 Materials and Methods  
### Study Area  
China is located in East Asia and features a complex and variable geographical environment. From the coastal plains in the east to the mountains and plateaus in the west, China's land area reaches 9.6 million square kilometers. Most of China's terrain consists of mountains, plateaus, and hills with plains and basins comprising about one third of the total land area (Figure 1a). China's complex terrain, including plains, plateaus, and basins, creates varied environmental conditions for vegetation distribution and ecosystem diversity. This study delineates nine subregions based on vegetation distribution characteristics: R1-R9 represent the Northeast Plain, Yunnan-Guizhou Plateau, Northern Arid and Semi-Arid Region, South China, Sichuan Basin and surrounding areas, Middle and Lower Yangtze River Region, Qinghai-Tibet Plateau, Loess Plateau, and Huang-Huai-Hai Plain (Figure 1b). For example, the Northeast Plain Region (R1) and the Huang-Huai-Hai Plain Region (R9) represent typical plain ecosystems, which are ideal for studies on temperate grassland and farmland vegetation. The Qinghai-Tibet Plateau (R7) exemplifies alpine meadows and scrub vegetation in high-altitude environments, while the Sichuan Basin and its surrounding areas (R6) demonstrate basin ecosystems under a humid subtropical climate. Across different climatic zones, these subregions range from arid and semi-arid areas (R2) to humid regions (R4, R5, R6), reflecting a vegetation gradient from deserts to forests. Driven by climate, the interaction between various vegetation types and climate change has shaped China's vast and diverse landscape. The differences in temperature and precipitation are key factors in shaping the vegetation growth environments between the north and south. The southern regions, with abundant annual rainfall, offer favorable conditions for vegetation growth, resulting in relatively stable ecological environments. In contrast, the northern regions face harsh climatic conditions where, except for high-altitude mountainous areas, the insufficient water uptake by vegetation roots cannot support the growth of canopy branches and leaves, resulting in shorter plants predominantly consisting of meadows, grasslands, and shrubs. Using a 1:1,000,000 scale digital vegetation type map of China, this study evaluates the changes in different vegetation health under stress events (Figure 1c).
## 2 Network Compression
### The Observability of Distribution Network  
For the distribution network, when the voltage phasor of a bus can be measured directly or indirectly, the bus is identified as observable. PMU can acquire the voltage phasor at the installed bus and the current phasor of all the branches connected to the installed bus at high frequency. Based on the characteristics of PMU, the rules of observability are as follows:  
(1) If a bus is placed with a PMU, this bus and its neighbors are identified as observable buses (neighbors refer to buses which are connected to the bus).  
(2) If all neighbors of ZIB are observable buses, the voltage phasor of this ZIB can be calculated by Kirchhoff laws (KCLs), so this ZIB is also identified as an observable bus.  
ZIB has no load or generator. It acts as a transshipment bus in distribution networks. The sum of currents flowing into a ZIB is zero.  
(3) If a ZIB is an observable bus and the observability of only one of its neighbors is unknown, the voltage phasor of this neighbor can be calculated by KCLs, so this neighbor becomes an observable bus.
# Dynamic RACH Partition for Massive Access of Differentiated M2M Services
## 2 Related Work
We will show later that the partition of access preamble resources for differentiated services is the key to optimize the access performances, which have not been thoroughly studied in the literature. [PERSON] _et al._[26] proposed a mechanism for congestion control in M2M networks. In this paper, congestion concerns both the radio access network and the mobile core network. The core idea of this work is to mitigate the MME/S-GWoverload by rejecting the MTC traffic at the radio access network [26]. This work can reduce the amount of signaling for MTC devices and can satisfy the desired resource utilization ratio in the core network. [PERSON] _et al._[11] comprehensively discussed ubiquitous massive access via 3 GPP M2M communications. This work proposed an effective solution to provide QoS guarantees to facilitate M2M applications with hard timing constraints. [PERSON] _et al._[27] proposed a self-optimizing overload control (SOOC) scheme that can configure RA resources according to the load condition. A typical feature of the SOOC scheme is that it can collect useful information via overload monitoring. Then, it can dynamically adjust the RA resources. However, this work did not present simulation or experiment results for performance evaluations. Aside from the above work, congestion and overload control in M2M communications was also actively discussed in [28]. Some applications based on M2M communications were described in [29; 30]. The work in [31] proposed a prioritized random access for alleviating RAN overload, which takes advantage of the joint design combing the dynamic access barring and virtual resource allocation. [PERSON] _et al._[32] proposed a backoff adjustment scheme, which can improve the performances for low congestion levels. A code-expanded RA mechanism was developed by [33], where RA slots can be assembled in groups. This approach can increase the amount of contention resource at the cost of extra energy. As elaborated above, extensive research has been dedicated to the RAN-level contention mechanism. In summary, the essence of RAN overload control mechanisms includes: dispersing the load of random access to different time slots, barring the random access behaviors and tuning system parameters based on MTC access traffic [34]. Existing research outcomes on the ACB mechanism indicate that the ACB scheme is effective mainly for delay-tolerant devices. While for the EAB mechanism, although it takes delay-sensitivity into consideration and divides all devices into two groups, it cannot make full use of the preambles well. This is because it ignores the fact that the number of delay-sensitive services is far less than delay-tolerant ones in M2M networks. Moreover, it does not perform dynamic adjustment of preamble allocation to the two groups, and thus, it is hard to further enhance the performance. Note that in this paper, we use the term \"group\" and \"cluster\" for MTC devices exchangeably for presentation convenience.
###### Abstract  
Every vegetation colony has its own vertical structure. Forest vertical structure is considered as an important indicator of a forest's diversity and vitality. The vertical structure of a forest has typically been investigated by field survey, which is the traditional method of forest inventory. However, this method is very time- and cost-consuming due to poor accessibility. Remote sensing data such as satellite imagery, aerial photography, and lidar data can be a viable alternative to the traditional field-based forestry survey. In this study, we classified forest vertical structures from red-green-blue (RGB) aerial orthophotos and lidar data using an artificial neural network (ANN), which is a powerful machine learning technique. The test site was Gongju province in South Korea, which contains single-, double-, and triple-layered forest structures. The performance of the proposed method was evaluated by comparing the results with field survey data. The overall accuracy achieved was about 70%. It means that the proposed approach can classify the forest vertical structures from the aerial orthophotos and lidar data.  
Keywords:forestry vertical structure; stratification; forest inventory; aerial orthophoto; lidar (light detection and ranging); ANN (Artificial Neural Network); machine learning +
Footnote †: journal: Applied sciences
# SPCL: A Smart Access Control System That Supports Blockchain
## 4 Security Analysis
### Security Properties
#### 4.2.5 Replay Attack  
In our scheme, there are time stamps (\(T_{MC}\), \(T_{SL}\)) and random numbers (\(r_{MC}\), \(r_{SL}\)). In order to implement a replay attack, \(\mathcal{M}\) must forge the timestamp to pass the verification, and still needs to obtain the long-term private keys and temporary secrets of \(MC\) and \(SL\). It is extremely difficult for \(\mathcal{M}\) to crack and obtain this information, so the probability of a successful replay attack is extremely low and can almost be ignored.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 2 Related Work
A fully actuated UAV with a manipulator has been employed in [22] to compare force feedback control with and without the force/torque sensor. Researchers in [23] used a single degree of freedom manipulator with a force sensor mounted at the end-effector to press an emergency switch. Relying on the blob-like pattern detection and the impedance control, a trajectory for achieving contact is required to steer the aerial manipulator towards the contact point. While mounting the sensor, it is essential that the approach and contact are perpendicularto the wall plane. This can be considered as a task constraint imposed on the planner which the aerial manipulator has to satisfy. Researchers in [24] propose a task constrained planner for a redundant robotic manipulator that enables them to do everyday tasks such as opening drawers or picking up objects. In [25], a task-constrained planner was developed for underactuated manipulators. Since multirotor UAVs are typically underactuated systems, it is necessary to address dynamics and kinematics while planning the end-effector trajectory. Aerial manipulator 6D end-effector trajectory tracking based on the differential flatness principle was presented in [26]. The underactuated nature of multirotor UAVs can cause unexpected deviations in end-effector configuration. Researchers in [27] address this particular problem by including the dynamic model of the system into the planning procedure. In our previous work [28], a trajectory planning method based on the full dynamic model of an aerial manipulator was developed. In this paper, we further augmented this method to plan for the desired force required by the impedance controller.
## 2 Materials and Methods
### External Load  
The training and competition demands were quantified using the WIMU PRO(tm) GPS device (RealtrackSystems S.L., Almeria, Spain). Intra- and inter-unit reliability were deemed acceptable [31], with the intraclass correlation coefficient recorded at 0.65 for the x-coordinate and 0.85 for the y-coordinate for the systems analyzed. Data collected were analyzed using SPROT(tm) software (version 958; RealtrackSystems, Almeria, Spain). The data were extracted using the same software. The following metrics were introduced for statistical analysis: total distance (TD), relative total distance (TD/min), high-speed running (calculated as the distance covered at a speed greater than 80% of each player's maximum recorded speed during competition; HSR), relative high-speed running (HSR/min), player load (PL), and relative player load (PL/min). To avoid biases between units, the same unit was used by each player throughout the analysis period. The unit was placed on the upper back of the players 30 min prior to each session (training and match) and was removed immediately following the end of the session. During training sessions, monitoring was conducted throughout the entire session; for matches, players were monitored from the start until the final whistle, including the warm-up on the field for both training sessions and matches. The mean value of each training session was expressed relative to the average external load recorded during the three matches for players who competed for more than 80 min, as follows: (mean training session external load \(\times\) 100) \(\div\) mean competitive match external load.
## 3 Results
### Outcomes and Estimation
#### 3.3.2 Activation Levels of Patients by PAM  
A total of 78 completed PAM questionnaires, incorporating all data collection points, were analyzed. As presented in Table 4, the majority of patients reported activation levels 3 or 4 at both recruitment (42.3% and 32.1%, respectively) and the final follow-up (35.9% and 35.9%, respectively), with a modest increase (3.8%) in the number of patients reporting level 4 activation at follow-up. No statistically significant differences were found in the distribution of activation levels between the recruitment and last follow-up.