esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 2 Analysis of Solenoid Valve Current Characteristics
### Comparison of Simulation Experiments  
Based on the analysis presented above, in this subsection, the research group compares the corresponding results from a synthetic and a real experimental scenario. The research group exploited the Simulink software for the synthetic one and simulated the relationship between the drive end current of the solenoid valve and the spool displacement. For the real-world scenario, the research group utilized the 4 WE10E31B/CG24N9Z5L solenoid directional valve, where the force and transient characteristics of the solenoid valve were energized and closed. The structure of the solenoid valve is presented in Figure 3. The simulation and experimental results are presented in Figure 4. The former figure illustrates the drive end current under normal conditions and the spool displacement waveform diagram when the electromagnetic directional valve is opened. The latter figure compares the drive end current simulation and experimental results.  
Figure 3: The 4 WE10E31B solenoid valve: (**a**) The 4 WE10E31B solenoid valve; Structure 1 (1: valve body; 2, 3: electromagnet; 4: spool; 5, 6: return spring; 7, 8: push rod; 9, 10: manual emergency operation button; P: oil pump; A: actuator inlet; B: actuator exit; T: fuel tank) (**b**) The 4 WE10E31B solenoid valve.  
From Figure 4, the research group observed the following:  
At the initial stage of being powered on, the current could not be immediately increased to a stable value due to the self-inductance of the coil. In this process, due to the insufficient current, the electromagnetic force generated by the change in the magnetic flux in the coil was not adequate for overcoming the static friction force of the valve core and the pre-tightening force of the spring; therefore, the valve core was still in a static state.  
As the current in the loop increased, the electromagnetic force generated by the coil gradually increased. When the current reached a specific value, the current curve produced an inflection point. At this point, the electromagnetic force overcame the static friction of the valve core and the preload of the spring, and the spool started to move.  
When the spool started to move towards the maximum displacement, the speed of the spool continued to increase. Once the spool arrived at the maximum displacement, the spool no longer moved, and the self-inductance of the coil no longer changed. The loop current increased monotonically until it reached the maximum value.  
The above analysis shows that the change in the solenoid valve drive end current can reflect the dynamic characteristics of the solenoid valve. This conclusion provides the theoretical basis for a solenoid valve fault diagnosis on the basis of drive end current detection.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 1 Introduction  
The ever-increasing development of drone technology, including courier drones, precision agro-culture \"agro-drones\", military drones, and drones used in emergency situations as well as in different industrial areas, means that engineers are constantly faced with new technical challenges [1].  
By definition, the Unmanned Aerial Vehicle (UAV) is an unmanned aircraft (UA) that can fly autonomously, as opposed to the Remotely Piloted Aircraft (RPA), which is semiautonomous. The Unmanned Aerial System (UAS) is a wider term encompassing the controller on the ground, the aircraft in the air, and the communication between them, thus the whole system. The RPA can also be part of a system called Remotely Piloted Aircraft System (RPAS), where the whole system is created from RPA (see above), plus the Ground Control Station (GCS) [2].  
There are several classifications of drones. For classic UAVs, this classification can be the following: multi-rotor, fixed wing, single-rotor, fixed-wing hybrid VTOL (Vertical Take-Off and Landing), MAV (Micro Air Vehicle: \(<\)1 g), sUAS (small UAS: \(<\)25 kg), etc. More relevant to this study is the classification of \"open (public) category\", or \"specific-category\" (drones (UAVs) over 25 kg). There is also the \"game category\" (drones under 120 g weight), which will be disregarded in this project.  
The first two categories will be considered within this study, given their flight altitude, 120 m over the nearest point of ground, for \"public category\" drones. This dimension will be important in 3D map creation of the flying environment of the drone.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
## 3 Results
### Optimization of Model 2: Classification of Clinical/Subclinical Positive Samples  
Again, the optimization of the second model starts by applying each of the nine aforementioned preprocessing combinations and using each of them as input. The models used a kNN with 3 neighbors, and they were tested at different feature selection thresholds between 0 and 100.  
The accuracy achieved by each configuration of this second model can be seen in Figure 4 (left). In this case, the maximum accuracy was reached when the spectra were processed by the combination of applying the Beer-Lambert and then the detrending technique. Finally, the feature selection threshold was between 40 and 55.  
Once the preprocessing technique was chosen, the second step sought to optimize the size of the neighborhood and feature selection threshold. Models were created with k-nearest neighbors from 1 to 10 neighbors and with different thresholds from 0 to 100.  
Figure 4 (right) shows the accuracies of different configurations. As can be seen, good results were achieved with any number of neighbors between three and six. Using the selected preprocessing technique, the model yielded accuracy values above 0.83 with any value of k. However, the best results were achieved when the model used three nearest neighbors. Focusing the attention on the feature selection threshold, any value between 85 to 87 allowed model 2 to achieve the best results with 3 nearest neighbors.  
Processed NIR spectra and relevant wavelengths (shadowed) are shown in Figure 5. Notice the difference between the spectra of mastitis in negative and positive samples in Figure 5 (left). These differences were statistically significant (\(p<0.05\)\(t\)-test), with a feature  
Figure 4: Accuracy of k-NN model 2 in nested-leave-one-cow-out cross-validation: with 3 neighbors, according to the preprocessing combinations described in Figure 2 (letters from a to i) and feature threshold (**left**), with different values of k-nearest neighbors, and feature selection thresholds (**right**).  
selection between 11 and 18. Figure 5 (right) shows the combination of the Beer-Lambert and detrending techniques; the preprocessing technique made it possible to accurately classify athe mastitis positive milk samples as weak or strong positive--the differences between weak positive and strong positive samples were statistically significant (\(p<0.05\)\(t\)-test).
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### IEEE 1609  
The protocol incorporates communication and operational requirements, as well as information security and management, to allow multichannel functioning. IEEE 1609 [52] is composed of four standards: IEEE 1609.1 for application development, IEEE 1609.2 for privacy and protection measures, IEEE 1609.3 for WAVE managerial and networking services, and IEEE 1609.4 for multichannel processes with logical connections and rules at every level.
## 4 Performance Analysis and Evaluation
### Convergence and Computational Complexity of the EESA-RLC Algorithm
Less SSE translates into minimum network energy consumption, which means more energy efficiency can be achieved with the clustering schemes that has less SSE. The result shows that SSE increases along with increase in number of member nodes. It is observed that our clustering scheme achieves much lower SSE than the GWSA. For example SSE for 200 member nodes is approximately 100 which is 50% less than that of GWSA for the same size of member nodes. This suggests that our approach is more energy efficient. Figure 12: Average SSE for CRSN size. Figure 13 further reveals the performance improvement of our approach over the GWSA in terms energy efficiency. The result indicates that average energy dissipation decreases with increase in size of clusters. It is evident from the result that our approach achieved the least average energy dissipation compared with the GWSA approach. For example, the average energy dissipation for a five-cluster size of our approach is about 60 J, which is 8.4% lower than that of GWSA approach for the same cluster size. Furthermore, the total network energy dissipation based on our clustering scheme is 417.3 J, while that of GWSA is 458.8 J. This implies that an energy savings of 9% can be achieved with our approach. This shows that the RL-based approach learns energy dissipation for each neighboring cluster through  
Figure 14: Average cooperative probability of detection for various CRSN size. Figure 8:0 Average energy consumption for clusters. exploration of the clusters and chooses the most favorable cluster that attracts low dissipation through exploitation. Figure 14 compares the average probability of detection \(\mathrm{Q_{d}}\) for the two clustering schemes with different numbers of cognitive radio sensor nodes. The result indicates that our approach performs much better than the GWSA clustering scheme in terms PU detection. This is because the GWSA approach does not incorporate cooperative spectrum sensing which is vital for improving PU detection. It is observed that the average cooperative probability of detection \(\mathrm{Q_{d}}\) of our approach rapidly reaches a relatively high value that satisfies the required detection accuracy of \(\overline{\mathbf{Q}}_{\mathbf{d}}\geq\mathbf{0}.\mathbf{9}\) at the initial stage and then increases slowly with increasing number of cognitive radio sensor nodes. This suggests that multi-user sensing diversity exploration is crucial for enhancing PU detection and discovering of more spectrum opportunity.
# Research Advances in Large Deformation Analysis and Applications of the Material Point Method
## 2 Methodology of the MPM
### Relationship with Meshless Method  
As an emerging meshless numerical method, the MPM is typically comparable with SPH, the oldest and most widely used method in the meshless family [23, 24, 25]. In this section, taking SPH as a representative of typical meshless methods, we explore the similarities and differences between the MPM and SPH.  
The MPM and SPH both belong to the category of Lagrangian particle-based numerical methods. They characterize the computational domain using discrete particles, thereby avoiding the mesh distortion issues that can arise in traditional mesh-based methods when simulating large deformations. Both methods are suitable for the numerical simulation of complex physical processes involving large deformations, material fracture, and multiphase flows, and they hold complementary application value in the field of computational mechanics.  
Despite similarities in their particle-based descriptions, the MPM and SPH exhibit significant differences in their specific implementations and applications. The MPM employs a \"mesh-particle\" hybrid strategy, relying on a temporary background mesh for spatial discretization and gradient calculations. Its mesh projection step effectively suppresses numerical oscillations, offering higher computational accuracy and stability when dealing with complex stress states and large deformation problems. It is particularly well-suited for multi-material coupling and solid mechanics issues. In contrast, SPH is a purely meshless method that solves problems entirely based on particle neighborhood interpolation. It has advantages in fluid simulation and parallel computing, especially in handling free-surface flows and wave breaking. However, it is prone to numerical instability and reduced accuracy in regions with uneven particle distribution and high gradients.
# Preventing Black Hole Attacks in AODV Using RREQ Packets
## 3 Proposed Method
### Behavior When Processing Control Packets
#### 3.2.2 When Receiving RREP Packets  
When a node receives an RREP packet, it first checks its own blacklist. If the address of the source node exists, it deletes the entry related to the source from the route table. Subsequently, it discards the received RREP packet. Next, we consider the case in which the address of the source node is not included in the blacklist. The destination address of the RREP message is checked to verify whether it is stored in the node's dummy address list. If the destination address is included in the dummy address list, the system assumes that the sender is a black hole node and the source address is registered in the blacklist. Finally, the node checks whether a route to the source has been established and discards the route information if it has been established. Then, it issues a route error (RERR) packet and requests the network to delete the route. Finally, the RREP packet is discarded. Figure 3 illustrates the process flow when RREP packets are received.
###### Abstract  
The alterations of bone remodeling are typical of multiple myeloma (MM) patients where the uncoupled and unbalanced bone remodeling caused the onset of osteolytic lesions. Moreover, bone metastasis occurs in the majority of patients with breast and prostate cancer. Skeletal-related events negatively impact on quality of life by increasing the vulnerability to fractures. Several bone-targeting treatments have been developed to control bone pain and pathological fractures, including bisphosphonates and Denosumab. Nevertheless, these agents act by inhibiting osteoclast activity but do not improve bone formation. Proteasome inhibitors (PIs) have shown bone anabolic effects and encouraging results in stimulating osteoblast differentiation and bone healing. Among these, the first-in-class bortezomib and the second-generation PIs, carfilzomib, and izazomib regulate the bone remodeling process by controlling the degradation of several bone proteins. PIs have been recently proven to also be efficacious in blocking MM-induced osteocyte death providing new possible therapeutic use in the management of bone loss. PIs have significant side effects that limit their use as bone anabolic strategy. Multiple alternative approaches have been made. The conjugation of PIs with bisphosphonates, which can target them to bone, showed good results in terms of bone anabolic activity. However, the clinical implications of these effects require further investigations.  
bone disease; bone remodeling; osteoblasts; osteocytes; proteasome inhibitors +
Footnote †: journal: Medical  
proteins, several studies have focused their attention on the potential use of proteasome inhibitors (PIs) to improve boneabolism [6]. Early in vitro studies clearly showed that the inhibition of proteasome positively affects OB differentiation in MM bone disease and bone cancers of solid tumors. In particular, the first-in-class PI bortezomib showed encouraging results in controlling MM-induced bone disease by targeting both OBs and osteocytes [7]. Afterwards, the next-generation PIs carfilizomib and ixazomib had been developed and tested for their potential bone anabolic activity. In a clinical perspective, PIs showed a great ability to normalize the levels of bone turnover markers in MM patients and improve bone formation.  
This review focuses on the main mechanisms underlying the proteasome-dependent control of bone remodeling and the anabolic effects of PIs. We summarize preclinical and clinical findings obtained with PIs in bone disease focusing on the impact on OBs and osteocytes.
## 3 Results and Discussion
### Stretch Forming
#### 3.1.2 Force and Strain Evolution of MPM  
In this part, the forming behaviour of the MPM with the non-reinforced PA6 core is described. Figure 13a shows the drawing force-depth curves for the MPM01 with different core thicknesses. It can be observed that the drawing depth decreased with increasing core thickness, which is in accordance with the results published in [22]. The strain evolution of the MPM is shown in Figure 13b, in which the strain distribution corresponded to the MPM labelled in the same colour. As can be seen, a decreasing tendency of the major-minor strain evolution with increasing core thickness can be stated, which is in correlation to its force evolution. Since less plastic deformation takes place, the strain evolution was reduced accordingly. From the DIC and specimen images illustrated in the figures, a homogenous strain evolution in the whole deforming zone (\(\O\) 100 mm in the middle area) can be stated before crack appears. When the adhesion between cover and core layers is high enough, the force transferring between the layers is so good that the three MPM layers (two cover layers and a core layer) are subjected to cracking simultaneously. To conclude, the formability of MPM with thin core layer is comparable to that of a monolithic steel sheet (e.g., the MPM01-PA0.5 with a drawing depth of approximately 36 mm in comparison to that of monolithic steel sheet TS275 with a drawing depth of approximately 38 mm). However, here, its formability was reduced when core thickness increased to about 30 mm for MPM01-PA1.5.  
Figure 12: Force evolution and distortion of RG0.5 and RGUD0.5 under stretch forming at 235 \({}^{\circ}\)C.  
Figure 13: Stretch-forming behaviour in terms of: (**a**) Force and (**b**) strain evolution of the MPM01 with different core layer thicknesses at RT.
## 4 Simulations
### Simulation Result Analysis
#### 4.2.2 Comparison Result Analysis between Algorithms
The Spray and Wait algorithm effectively limits the number of message copies, so the average end-to-end delay of the algorithm is lower than the Epidemic one. Besides, the EIMST algorithm implements community division and information management, while the ICMT algorithm adopts a cooperative mechanism to realize the reasonable utilization of cache spaces of the node. Therefore, the average end-to-end delays in the EIMST and ICMT algorithms are obviously lower than the traditional algorithms. To sum up, compared with other algorithms, the FCNS algorithm is the optimal method to improve average end-to-end delays in the network. Eventually, the comparison results between the five different algorithms in terms of overhead on average are demonstrated in Figure 11. Overall, when the cache space of the node increases from 10 to 40 Mb, nodes in the network may possess sufficient resource spaces to store statistics and to process the computing tasks, meaning the overhead on average of each algorithm is continuously and dramatically decreasing. Additionally, the overhead on average in the FCNS algorithm always keeps the lowest, because it adopts a comprehensive node similarity to implement the optimal process for data transmission in opportunistic social networks. In the FCNS algorithm, mobile and social similarities mainly reflect the special social relations between nodes, and there is a high probability of successful message transmission between two closely related nodes. Therefore, in the FCNS algorithm, the total number of hops for a complete communication between the source node and the destination is relatively less. The routing and forwarding of messages take only a small amount of time and resources  
Figure 10: Average end-to-end delay with various cache spaces. in the network, and the overhead on average has been significantly improved in the data transmission process. In the Spray and Wait algorithm and the Epidemic algorithm, redundant message group copies need to consume masses of time and resources, meaning the overhead on average is obviously higher than the other algorithms. As for the EIMST and ICMT algorithms, the efficient management for the information and cache space of nodes conduces to allocate resources properly and to control transmission time, so the overhead on average of the two algorithms is at an intermediate level all the time. In conclusion, in terms of the overhead on average, the FCNS is always the best performing one among the five algorithms.
## 2 Conceptual Model and Research Hypotheses
### Social Influence and Expected Performance of EESS  
Given that EESS represent new technologies in continuous evolution, another relevant dimension of the analysis concerns the correlation between social influence and expected performance. Performance Expectancy (PE) is one of the main constructs of the UTAUT and UTAUT2 models, being also used in TAM-based research in the field of EESS-related assimilated technologies such as smart meters [5] or smart energy technologies [17]. However,other studies [18] did not indicate a significant influence of expected performance (PE) on acceptance in the target population, although the same study identified a strong correlation between SI and behavior intention to use (BIOU). This correlation is also demonstrated in the research of [PERSON] and [PERSON] [6], carried out to analyze the influencing factors that influence the adoption of smart energy technologies in Denmark. Therefore, based on the analysis of the specialized literature, we formulate the following hypotheses:  
**Hypothesis 7 (H7).**_Social influence (SI), specifically (a), the adoption of EESS at the level of the network of friends, colleagues, or family (SI_FF), and (b), the promotion of the use of EESS in the public space (SI_PS), will have positive effects on their expected performance (PE)._  
**Hypothesis 8 (H8).**_PE will mediate the relationship between (a) SI_FF and (b) SI_PS, and the behavioral intention to use EESS (BIOU)._
## 4 Doids
### Monitoring and Analysis
#### 4.1.3 Link Information  
Compared with TWSNs, underwater acoustic channels have a serious multipath and Doppler effect, high transmission loss, and environmental noise, resulting in unstable underwater acoustic links. In addition, the location of underwater sensor nodes is easily affected by wind, waves, ocean currents, and other factors, leading to the interruption of communication links. Traditional IDS for UWSNs often ignores the influence of the underwater environment on node performance. For example, after the \(n_{i}\) broadcasts a data packet, if the channel quality between the \(n_{i}\) and \(c_{j}\) is poor, \(c_{j}\) cannot receive the data packet and therefore cannot become a relay node. As a result, in a period of time, the forwarding times \(t_{j}\) will be lower than other nodes in \(C_{i}\), and the energy consumption will also be lower.  
In order to prevent the normal node \(c_{j}\) from being wrongly detected as a malicious node due to an abnormal channel, the link quality between the node \(n_{i}\) and each node in \(C_{i}\) is calculated. The link information collected during monitoring includes packet reception rate (PRR), signal-to-noise ratio (SNR), and link quality indicators (LQI). By establishing geometric triangular models of PRR, LQI, and SNR, the link quality corresponds to the distance between the coordinate points composed of SNR and LQI and the origin. Let us suppose that \(p\) is the number of packets sent from node \(n_{i}\) to \(c_{j}\), and \(q\) is the number of packets successfully received by node \(c_{j}\). First, the node calculates the LQI and SNR of the packet, which are represented as \(LQI_{l}\) and \(SNR_{l}(l=1,2\cdots q)\), respectively. Then, according to \(LQI_{l}\) and \(SNR_{l}\), PRR metrics are used to calculate the window average LQI and SNR, which are expressed as:  
\[\left\{\begin{array}{l}\overline{SNR_{W}}=\frac{\sum_{l=1}^{d}{\frac{1}{ \mathit{lqi}}}}{p}\\ \overline{LQI_{W}}=\frac{\sum_{l=1}^{d}{\frac{1}{\mathit{lqi}}}}{p}\end{array}\right\} \tag{9}\]  
As shown in Figure 3, the link quality eigenvalue \(Feature_{j}^{link}\) of \(c_{j}\) within a time slot can be obtained by calculating the following formula based on \(\overline{SNR_{w}}\) and \(\overline{LQI_{W}}\).  
\[Feature_{j}^{link}=\sqrt{\overline{SNR_{w}}^{2}+\overline{LQI_{W}}^{2}} \tag{10}\]