esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Wireless Sensor Networks (WSNs) are formed from collections of sensing nodes, placed in a sensor field, that collect environmental data and report it via a gateway or base-station to an application. Typically the nodes are energy constrained, and the major focus of research is how to balance acceptable level of performances against network lifetime. Much research has taken place in the pre-deployment/design space, both in hardware and software, with a focus on the physical layer, datalink, and routing issues. There has been comparatively little research into post-deployment issues, but these are becoming more important as real-world deployments are being realized. These issues include debugging, fault isolation, and software updating. _Over-the-air_ software updating, being a general term associated with software updates for mobile wireless devices, has been available for many years. Other terms used include OAP/OTAP (Over the Air Programming), remote updates, remote reprogramming. A lack of published results in the many reported deployments led us to speculate that there is not sufficient support for this feature, and that such updates may be regarded as too risky as, if the updated software fails an expensive network, it may become inoperative.  
In this paper we present the first broad-based survey that discusses in detail the need for autonomous update support, significantly extending the results of previous surveys [1; 2; 3; 4]. This paper extends an earlier technical report [5], and identifies key future research challenges for autonomic software and configuration updates (SCU)--the _lacunae_ in current research. In addition to the survey, the key contributions of this paper are: a model for autonomous software updating, a taxonomy for classifying software update features, a model for fault detection and recovery, and the identification of future research topics in the area of autonomous software and configuration updates.
# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 4 Experiments
### Experimental Settings
#### 4.2.3 Experiment Setup  
In this paper, two sets of experiments were conducted.  
1. For model validation and comparisons, we conducted experiments using the proposed model and comparison methods with different feature set combinations on the split training and test datasets, and then compared the resulting performances with the evaluation metrics.
2. For privacy-restrict online social network analysis, experiments were carried out with partially reduced data to further explore the proposed model's trust inference capability in a real-world scenario. Hereinafter, the reduced data means that features from a certain set for a portion of users were missing for a specific experiment. As stated earlier in Section 1, in real-world online social networks, some users may choose to opt out of part of or all of their data being used by online social services.  
As was discussed in Section 3.2.2, the proposed model will still work without auxiliary features. Nevertheless, the types of auxiliary features are a particular coexistent by-product of how the proposed model was built, and also reflect the real-world mechanism of trust relationship formation. Therefore, these features were used in all experiments for the proposed model, acting as part of the foundation of the model. Note that these features only work with the proposed model.  
In all experiments for model validation and comparison, we used different combinations of feature sets to show each method's ability in trust inference. Table 8 lists the different feature sets for experiments. (Refer to Table 2 for the types of features used in this paper.) For the comparison methods, the feature set combinations available for them are similar but without any auxiliary features from \(\mathcal{F}_{\text{TAUX}}\) or \(\mathcal{F}_{\text{TPAUX}}\).  
For model learning and predicting, the set of user trust relationships was randomly split into a training set and test set, and the ratios of sizes of the two sets were 50-50%, 0-40%, 70-30%, 80-20%, and 90-10%. (A better way to split the data is to split data according to trust relationship creation time. However, in the chosen dataset, such information of trust relationship creation time was missing, and thus we resorted to splitting the data randomly.) All the second set of experiments used the 90-10% split for training and test datasets.  
For the second set of experiments, the reduced feature data were generated by randomly removing one type of features from a certain percentage of users. The percentages for feature removal are 20%, 40%, 60% and 80%, respectively. If two types of features are used at the same time for an experiment, the random removal for each type of features is independent.  
Other experiment setup included: in all experiments for the proposed model, we set the maximum number of gradient descent epochs to 20 and the maximum number of LBP iterations to 10.
## 4 Results
### XRD Results  
Figure 2a reveals XRD peaks of the Ni-Ti shape memory alloy (0 wt.% Ag) calculated at room temperature with a diffraction angle (29) varying about 25-80\({}^{\circ}\). The strongest peaks for the Ti phase formed at (29): 36.12\({}^{\circ}\), 39.24\({}^{\circ}\), 54.35\({}^{\circ}\), 56.7\({}^{\circ}\), 62.77\({}^{\circ}\), 64.12\({}^{\circ}\), 68.9\({}^{\circ}\) and 76.4\({}^{\circ}\), respectively, for hkl (100), (101), (102), (200), (110), (211), (103) and (112). While the Ni phase was created at (26): 27.47\({}^{\circ}\), 41.38\({}^{\circ}\), 44.47\({}^{\circ}\), 46.54\({}^{\circ}\) and 51.74\({}^{\circ}\) for hkl (101), (002), (111), (011) and (200), respectively. This is owed to the formation of Ti2 Ni (Austenite phase) and M or Ti002 (martensite phase). Figure 2b reveals XRD peaks for the Ni-Ti shape memory alloy for (1.5 wt.% Ag); this created three principal phases: Ni, Ti and Ag phases, which appear at (29), varying between 30 and 80\({}^{\circ}\). The Ti phase was created at (26): 42.51\({}^{\circ}\), 61.5\({}^{\circ}\) and 71.0\({}^{\circ}\) for hkl (101), (110) and (103), respectively. In addition, the Ni phase was generated at (20) at around 41.8\({}^{\circ}\) and 77.5\({}^{\circ}\) for the planes (111) and (220), respectively. The addition of element (Ag) was generated at (29): 38.26\({}^{\circ}\), 44.45\({}^{\circ}\), and 64.58\({}^{\circ}\) for hkl (111), (220) and (311), respectively, while Figure 2c reveals the XRD peaks for the Ni-Ti shape memory alloy for (3 wt.% Ag), producing three major phases: Ni, Ti and Ag phases at (28), varying between 30 and 80\({}^{\circ}\). The Ti phase was created at (20): 29.57\({}^{\circ}\), 41.57\({}^{\circ}\), 42.32\({}^{\circ}\), 61.43\({}^{\circ}\) and 70.84\({}^{\circ}\) for hkl (100), (002), (101), (102) and (103), respectively, while the Ni phase formed at (29): 44.17\({}^{\circ}\) and 64.53\({}^{\circ}\) for hkl (011) and (220). The Ag phase formed at (29): 38.24\({}^{\circ}\), 44.35\({}^{\circ}\) and 77.4\({}^{\circ}\) for hkl (111), (200) and (311), respectively. Finally, Figure 2d reveals the XRD peaks for the Ni-Ti shape memory alloy with (4.5 wt.% Ag). Three principal phases were formed: Ni, Ti and Ag phases. Together, these phases formed at (20) in the range of 30-80\({}^{\circ}\). The Ti  
Figure 1: (A): 0 wt.% Ag; (B) 1.5 wt.% Ag; (C) 3 wt.% Ag; (D) 4.5 wt.% Ag.  
phase was created at (20): 25.29\({}^{\circ}\), 41.43\({}^{\circ}\), 42.35\({}^{\circ}\), 54.3\({}^{\circ}\), 56.53\({}^{\circ}\), 61.53\({}^{\circ}\), 64.38\({}^{\circ}\) and 70.85\({}^{\circ}\) for hkl (100), (002), (101), (102), (200), (110), (211) and (103), respectively, while the Ni phase formed at (29): 46.4\({}^{\circ}\), 46.53\({}^{\circ}\) and 51.55\({}^{\circ}\) for hkl (111), (011) and (200), respectively. The Ag phase formed at (28): 27.38\({}^{\circ}\), 44.23\({}^{\circ}\) and 77.53\({}^{\circ}\) for hkl (111), (220) and (311), respectively.
## 4 Case Study  
### Study Area  
We applied the proposed framework to the road network located in Tibet in China (Figure 7). The Tibet Autonomous Region is situated in the southwestern plateau of China, characterized by high altitudes, vast territory, and sparse population. Currently, Tibet has developed a road network primarily composed of four national highways: the Sichuan-Tibet, Qinghai-Tibet, Xinjiang-Tibet, and Yunnan-Tibet highways. This network, however, remains relatively sparse and consists mainly of national, provincial, and county roads. Due to geographical and climatic conditions, the road network in Tibet features low grades, low density, long segments, few connecting nodes, weak disaster resilience, and significantly different traffic operation characteristics compared to other regions. Geological hazards, adverse weather, sudden events, and their coupling with extreme environments severely affect traffic safety. For example, on 18 August 2023, continuous rainfall triggered a flash flood and mudslide on a section of the Sichuan-Tibet Highway in Bomi County, resulting in over 500 vehicles and 1500 people being stranded. Emergency response efforts lasted 11 h before single-lane traffic could be resumed. Therefore, we selected the road network of Tibet, specifically starting from Ganzi in Sichuan, Lijiang in Yunnan, Golmud in Qinghai, and Kashgar in Xinjiang, with Lhasa as the endpoint, as the target area for our case study.
# Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm
## 5 Results and Discussion
As the method favors the nearest possible stack, the container with priority 9 will be relocated in stack with index 1 (s\({}_{1}\)) (Figure 13b). However, this is not the best possible option because, when the container 5 would have to be retrieved, the container 9 should be relocated over the container 7 or container 6 (i.e., the nearest stack). Thus, there is an additional relocation move that slows down the process of retrieving containers. Our newly proposed method is not restricted with any rule, so the genetic algorithm can randomly determine the stack with index 3 as the position for relocating container 9 (Figure 13c). In this way, containers 4, 5, 6, and 7 can be retrieved without making additional relocations resulting in a faster retrieval process. Figure 12: Total sum of average relocations for all test cases. In addition, a comparison of the results of our previous method (GA with rules) and our new method was performed for all 40 instances of the large test set for bay size 6 \(\times\) 7 (Figure 14). It can be seen that for each instance of the test set, the new method (blue line) finds a solution with less additional relocations than our previous method (orange line). Finally, this is already clear in Table 2, which shows the overall result for the 6 \(\times\) 7 test set (new method--31.62 vs. GA with rules--33.55). Figure 15 shows the average computational results (in seconds) of our new method for solving CRP of different bay sizes (from 3 \(\times\) 3 to 6 \(\times\) 7). Our method determines the solutions fast enough. Of course, the longest time needed to determine the solution was for the 6 \(\times\) 7 dock size and was 13 s on average. From this we can conclude that the dock size does not affect the performance of our new method too much. Figure 14: A comparison of the results of method GA with rules and our new method for all 40 instances of the test set for bay size 6 \(\times\) 7. Figure 13: An example of a different solution of container relocation between our previous method and the proposed method: (**a**) Current situation within the yard bay; (**b**) the situation within the yard bay after applying one possible rule proposed in [17]; and (**c**) the situation within the yard bay after applying one relocation of our newly proposed method. Considering the results obtained with the method proposed in this paper, we proved that the quality of resolving CRP can be further improved.
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 3 Recommended Deep Learning for Inverter Sizing
### Recommended Approach  
Numerous studies have been conducted on size, enabling the selection of the best PV Panel (PVP)-battery source in accordance with the loads to be used and the methods for controlling and optimizing the entire system [103; 104; 105; 106; 107].Different optimization studies using Deep Learning algorithms for PV component sizing are found in the literature. For example, an energy storage system sizing scheme and PV-dependent navigation routing [108], a sizing algorithm method of a standalone photovoltaic system for powering a mobile network base station [109], and a method for optimal operation and design for PV hybrid plants with battery storage systems [110]. A new approach to annually optimizing energy yield for on-grid photovoltaic systems that use Deep Learning networks is provided in this study, which includes: (1) a new model to compute the yearly PV average energy yield including power conversion stages, system power losses, DC/AC ratio, and overall system costs; (2) a  
Figure 4: Maximizing NPV versus PV-inverter ratio.  
Figure 5: The significant variables that affect NPV and how they interact.  
hysteresis control strategy that guarantees a lower cost with respect to the obtained annually gained PV yields; and (3) design of the solar PV system must consider three key parameters (DC/AC ratio, cost, and PV orientation) that need to be optimized simultaneously. The aim is to maximize the yearly energy yield of the PV system while minimizing its losses and costs under PV site constraints. A diagram of the recommended approach to optimize PV array DC/AC inverter power, while maximizing yearly energy yield for on-grid photovoltaic systems that use Deep Learning networks, is shown in Figure 6.  
A historical dataset for the considered PV sites, such as components degradation, shading conditions, and weather measurements, is essential to estimate the yearly energy yield. Convolutional neural network (CNN) algorithms are appropriate in such cases where discrete design variables are used to search for optimal yearly energy yield. It performs a systematic and efficient search among the developed databases for a set of components that define the optimal PV system DC/AC ratio and system costs. The presented approach can help make the task of designing such systems easier, since the yearly yield optimization depends on site conditions and restrictions, component specifications, and the PV array orientation. This will significantly reduce the time to estimate the yearly energy yield.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 7 Simulation
### End-Effector Motion Distribution Analysis  
Given some end-effector configuration, the inverse kinematics is responsible for finding the UAV position and yaw angle, as well as the manipulator joint values that satisfy the desired configuration. The parameter \(\beta\) from Equation (3) defines a ratio of how much the manipulator joints and UAV position and orientation contribute to achieving the desired end-effector configuration, as described in Section 3.1. Recalling the values, \(\beta=1\) only moves the UAV in the direction of the desired end-effector configuration; and \(\beta=0\) uses the inverse kinematics of the manipulator to achieve the desired configuration.  
To determine the influence of \(\beta\) on the overall system, an analysis was conducted with different \(\beta\) values. The desired end-effector configuration was chosen to be in contact with a plane perpendicular to the bridge wall which required the force reference along the \(x\) axis. The waypoints for the trajectory planner were kept the same across all trials, and only \(\beta\) was changed. The results of this analysis are depicted in Figure 9. As can be observed, all trials produced very similar results with oscillating force upon contact and eventually reaching the desired reference, providing us with no obvious conclusion regarding how to select the optimal \(\beta\). However, following the dexterity analysis from Section 3.1, and only relying on the manipulator motion might drive the system close to its limits due to the UAV body movement. On the other hand, the motion of the UAV induces disturbances in the end-effector pose control. The manipulator is therefore responsible for compensating errors introduced by the motion of the UAV body. Taking all of the aforementioned into account, the value is chosen as \(\beta=0.5\) so that both the manipulator and the UAV are simultaneously used to maintain a steady contact force.  
Figure 9: Force response comparison for different values of the parameter \(\beta\). The analysis was conducted on a plane perpendicular to the ground where the force reference along the \(x\) axis is required.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 3 Simulation Results
### Position
#### 3.1.3 Diagonal Axis  
In Figure 7, the ABH was shifted from the center of the panel towards the edge in a diagonal direction (P11, P22, P33).  
The closer the ABH is positioned to the center, the less the shifting of the fundamental resonance frequency \(f_{1,1}\approx 85\) Hz, affirming the same principle as before. The ABH mode also creates a resonant peak in the response at the same frequency \(f_{\rm ABH}\approx 120\) Hz as already seen in Figures 5 and 6. At position P22, the frequency response exhibits noticeable peaks at both even modal frequencies, \(f_{2,1}\approx 150\) Hz and \(f_{1,2}\approx 190\) Hz, as observed with the reference panel modes. This results in a significant improvement in the normalized mean surface velocity \(\overline{v}_{x,y}\) between \(125\) Hz \(<f<250\) Hz compared to the response at position P11 and P33. On the other hand, placing the ABH at position P33 leads to the suppression of the even modes \(f_{2,1}\approx 150\) Hz and \(f_{1,2}\approx 190\) Hz. However, it also creates high and narrow peaks above frequencies greater than \(250\) Hz. It is important to note that these peaks do not align with the objective of this study, which aims to achieve a more evenly distributed arrangement of resonant peaks on the logarithmic frequency axis.  
Referring to the descriptions from Figure 7, the best choice is position P22. It enables both even modes \(f_{2,1}\approx 150\) Hz and \(f_{1,2}\approx 190\) Hz significantly better than the other two positions and in an even frequency distance. The shifting of the fundamental mode \(f_{1,1}\approx 85\) Hz is moderate. In addition, P22 incorporates modal peaks for high frequencies \(f>250\) Hz.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 4 Simulations and Discussions
Figure 8 describes the total BER of static and mobile UAVs under a suburban environment model. Through the proposed joint optimization algorithm, the total BER decreases by about 5% with time, and the minimum BER is obtained, which tends to be stable at the minimum level. The results show that the total BER increases monotonously with the increasing distance between GUs, and the total BER of a mobile UAV is slightly lower than that of a static UAV, while the difference between the minimum BER of a static UAV and that of a mobile UAV is about 8%. The results also show that using the mobility of the UAV can improve communication performance, and the performance of a mobile UAV is better than that of a static UAV. In addition, it can be seen from Figures 4, 7 and 8 that the optimal height of different reliability measures is different; namely, the optimal height to minimize  
Figure 6: The BER for the AtA communication model. Figure 7: The downlink BER of static and mobile UAVs for a suburban environment. the total power loss is very different from the optimal height to minimize the total BER, and the optimal height to minimize the downlink BER and the total BER are also different.
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
## 4 Results and Discussion
### L/D = 4.0 and 4.5
#### 4.1.1 Rotation Response
Further comparing the rotational oscillation responses of \(L/D=4.0\) and 4.5, it could be found that in the upper and lower branches of VIV, the \(A^{\star}\) of \(L/D=4.5\) was significantly higher than that of \(L/D=4.0\), indicating that the spacing of the two tandem-coupled cylinders had a significant effect on the arc length responses. Even if the spacing changed slightly, the rotational vibration response could vary considerably. (3) Further analysis of the arc length response of \(L/D=4.0\) and 4.5 showed that with continuous increase of the test flow rate, the curve of \(A^{\star}\)-\(U_{r}\) had a sudden rise or fall, which meant that each branch of VIV had a clear dividing line. The reason might have been  
Figure 11: Complete rotation response of FIR system at \(L/D=4.0\) and 4.5: (**a**) Arc length response; (**b**) Frequency response. that the damping ratio of the FIR system at \(L/D=4.0\) and \(4.5\) belonged to the category of low damping ratio. The study by [PERSON] and [PERSON] [52] pointed out that when the damping ratio is small, the VIV response of the cylinder presents three obvious branches. In summary, when the spacing ratio of the FIR system was \(4.0\) and \(4.5\), the flow-induced rotation response was similar to that of VIV response. Compared with the typical VIV rule, the difference was that, after the rotational vibration entered the lower branch of VIV, there was a re-growth region in the arc length response, and this region was no longer significant as the spacing ratio increased. Note that the vibration was also suppressed in this region.
## 4 Methodology
### Case Study
#### 5.1.2 Classification an Input Using the Proposed Algorithm  
Here, we processed to classify the incomplete pattern \(inp=1?0?\) using the proposed algorithm in Section 5 according to the following steps  
1. Initialization Step: \(|\psi_{0}\rangle=|inp,qn,uv\rangle\). Here, the input register is \(|inp\rangle=|1?0?\rangle\), \(|qn\rangle\) is the memory register that holds the prototypes patterns and its state is given by Equation (11), and \(|uv\rangle\) is initialized by the state \(|00\rangle\). Due to the input, test, pattern \(|inp\rangle=|1?0?\rangle\) has two well known values in the first and third qubits, so \(h=\{1,3\}\). Therefore, the state of the system is described as follows: \(|\psi_{0}\rangle=\frac{1}{\sqrt{6}}(|1?0?,11101,00\rangle+|1?0?,11001,00\rangle +|1?0?,10111,00\rangle+|1?0?,01000,00\rangle+|1?0?,00100,00\rangle)\).
2. Apply the competitive detection operator between the input register \(|inp\rangle\) and the prototype register \(|qn\rangle\) as \(|\psi_{1}\rangle=\prod_{i\in h=\{1,3\}}X_{qn_{i}}CNOT_{inp,qn_{i}}|\psi_{0}\rangle\). \(|\psi_{1}\rangle=\frac{1}{\sqrt{6}}(|1?0?,11001,00\rangle+|1?0?,11101,00\rangle +|1?0?,10011,00\rangle+|1?0?,01100,00\rangle+|1?0?,00000,00\rangle+|1?0?,00110,00 \rangle)\).
3. Apply the Toffoli-gate between \(j+1\) qubits of the register \(|qn\rangle\) and the qubit \(|u\rangle\) as control qubits and target qubit, respectively. \(|\psi_{2}\rangle=T_{(\prod_{i\in h=\{1,3\}}qn_{i}qn_{n+1}u)}^{j+1}|\psi_{1}\rangle= T_{(qn_{1}qn_{3}qn_{5}u)}^{3}|\psi_{1}\rangle\). \(|\psi_{2}\rangle=\frac{1}{\sqrt{6}}(|1?0?,11001,00\rangle+|1?0?,11101,10 \rangle+|1?0?,10011,00\rangle+|1?0?,01100,00\rangle+|1?0?,00000,00\rangle+|1?0?,00110,00\rangle)\). Hence, the state of the two-qubit system \(|uv\rangle\) is \[|uv\rangle=\sqrt{\frac{5}{6}}|00\rangle+\frac{1}{\sqrt{6}}|10\rangle.\]
4. Repeat the steps 1, 2 and 3 to get another decoupled copy of the state \(|uv\rangle\).
5. Apply the operator \(M_{z}\) on the state \(|uv\rangle\otimes|uv\rangle\) yields the state: \[\frac{1}{\sqrt{2}}\{-\frac{\sqrt{5}}{6}|0000\rangle+\frac{\sqrt{5}}{6}|0100 \rangle-\frac{5}{6}|0011\rangle-\frac{\sqrt{5}}{6}|1010\rangle+\frac{5}{6}|01 11\rangle-\frac{1}{6}|1001\rangle-\frac{\sqrt{5}}{6}|1110\rangle\}.\] Here, it is obvious that the probability of the state \(|0000\rangle\), \(|0100\rangle\),\(|1010\rangle\) or \(|1110\rangle\) is non-zero, so according to Equation (7) the concurrence value \(C>0\). Then, the test pattern \(inp=1?0?\) belongs to the class label \"1\".