esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Adaptive Nonlinear PID Controller
### Existing PID Controllers
#### 3.1.2 Nonlinear PID Controller  
For nonlinear processes, NPID controllers offer advantages, such as smoother operation and improved resilience to unforeseen disturbances. NPID controllers are more complex to design and implement than linear PID controllers due to their increased number of tuning parameters. To address this, the author proposed a novel NPID controller, an enhancement over the conventional PID framework. This NPID controller requires only three tuning parameters [15]. To mitigate the limitations of traditional PID controllers, specifically the integral action's susceptibility to overshoot, oscillations, and integral windup during dynamic conditions, the NPID controller incorporates a nonlinear gain. This nonlinear gain dynamically adjusts the error signal fed to the integrator, resulting in improved controller performance during sudden setpoint changes and disturbances. The time domain equation of this controller is described by  
\[u(t)=K_{p}e(t)+K_{i}\!\int v(t)dt+K_{d}\frac{de(t)}{dt}, \tag{19}\]  
where the error \(e(t)\) and gains \(K_{p}\), \(K_{i}\), and \(K_{d}\) are consistent with those of the previous PID controller. Meanwhile, \(v(t)\) is the scaled error given by  
\[v(t)=g(e)e(t), \tag{20}\]  
where \(g(e)\) is a nonlinear gain function of \(e\) represented by  
\[g(e)=exp(-\frac{e^{2}}{2\Delta r^{2}}). \tag{21}\]  
\(\Delta r\), a constant, represents the upper bound of the magnitude of setpoint changes between steps. Figure 5 depicts the structure of the NPID controller.  
Figure 5: Structure of NPID controller.
## 3 Multi-Marginal Optimal Transport
: _An optimal plan of the MOT\({}_{\eta}\) problem (5) is given by_  
\[\hat{\mathbf{\Pi}}=\mathbf{\mathcal{K}}\odot\hat{\mathbf{\Phi}},\]  
_where \(\hat{\mathbf{\Phi}}=\bigotimes_{k\in[K]}\hat{\mathbf{\phi}}^{k}\) and \(\hat{\mathbf{\phi}}^{k}\), \(k\in[K]\) are the optimal solutions of dual problem (6)._  
A sequence converging to the optimal dual vectors \(\hat{\mathbf{\phi}}^{k}\), \(k\in[K]\) in (6) can be iteratively determined by the _Sinkhorn algorithm_[13; 42] presented in Algorithm 1, where we note that line 4 is obtained by deriving the Sinkhorn function \(\mathcal{S}\) with respect to \(\phi^{k}\), \(k\in[K]\). The complexity of the algorithm mainly comes from the computation of the marginal \(P_{k}(\mathbf{\mathcal{K}}\odot\mathbf{\Phi})\), where the projection \(P_{k}\) is defined in (2). In general, the number of operations depends exponentially on \(K\). ```
1:Input: Initial values \((\phi^{k})^{(0)}\in\mathbb{R}^{n_{k}}\), \(k\in[K]\), regularization parameter \(\eta>0\), threshold \(\delta>0\)
2:Set \(r\gets 0\)
3:do
4:for\(k=1,\ldots,K\)do
5: Compute dual vectors \[\left(\phi^{k}\right)^{(r+1)}_{k}\coloneqq\left(\mu^{k}\odot\left(\phi^{k} \right)^{(r)}\right)\odot P_{k}\left(\mathbf{\mathcal{K}}\odot\left(\hat{\mathbf{\Phi} }\right)^{(r+1)}_{k}\right)\]
7: Increment \(r\gets r+1\)
8:endfor
9:while\(\left|\mathcal{S}\Bigg{(}\left(\phi^{1}\right)^{(r)},\ldots,\left(\phi^{K} \right)^{(r)}\Bigg{)}-\mathcal{S}\Bigg{(}\left(\phi^{1}\right)^{(r-1)},\ldots, \left(\phi^{K}\right)^{(r-1)}\Bigg{)}\right|\geq\delta\)
10:return optimal plan \(\hat{\mathbf{\Pi}}=\mathbf{\mathcal{K}}\odot\hat{\mathbf{\Phi}}\) and \(\hat{\mathbf{\Phi}}=\bigotimes_{k\in[K]}\left(\phi^{k}\right)^{(r+1)}\)
```  
**Algorithm 1** Sinkhorn iterations for the MOT\({}_{\eta}\) problem
## 5 Obstacles and Challenges in Evolving V2X Communication
### Architecture Obstacles and Challenges
#### 5.6.3 Lack of Backward Compatibility  
As we could see, backward compatibility is not possible among Releases 14/15 and Release 16 for C-V2X. In cellular networks, this is not a huge issue since the base stations support both technologies, and the service is eventually handled on IP networks so that legacy devices can use the same services as the novel ones. Contrary to this, in the case of direct communication (for example, the CA service), an ITS station is a service provider and a client simultaneously. This means that services cannot move across the radio link technologies without a degradation in the service. In the case, for example, where we use the CA service with legacy ITS stations, the new ITS stations must use the same radio technology as the legacy nodes (or they need to use both the new and old radios), otherwise they cannot facilitate the other's service.
## 5 Self-Sufficient Power Supply Evaluation  
When the solar energy charging system was completed, the availability of the equipment used for the solar tracking system was investigated. Figure 9 shows the external power supply used in this study. First, for the lead-acid battery (charging and equipment power supply are individual batteries), a DC-DC converter was used to increase the voltage from 12 V to 24 V for the solar tracking equipment. The equipment included an NI 9642 controller, an NI 9516 motion card, an ADP-055-18 motor driver and motor.  
Figure 8: Temperature rise curve comparison diagram.  
Figure 7: CV-Reflex charging. (**a**) Power end voltage, current and power curves; (**b**) battery end charging voltage, current and power curves; (**c**) SOC (%), actual voltage and temperature (\({}^{\circ}\)C) curves inside battery.  
Equations (4) to (8) show the current and operating voltage of equipment measured by the probe, calculated to obtain the power consumption for each piece of equipment. Equation (4) shows the total power exported from the battery to the converter and equipment:  
\[P_{\text{battery}}=1.46\text{A}\times 12\text{V}=17.52\text{W} \tag{4}\]  
Equation (5) shows the total power for the load end:  
\[P_{\text{LOAD}}=0.627\text{A}\times 24\text{V}=15.048\text{W} \tag{5}\]  
Equation (6) is the power for NI 9642:  
\[P_{\text{NI9642}}=0.325\text{A}\times 24\text{V}=7.8\text{W} \tag{6}\]  
Equation (7) is the total power for motion control card, motor driver and dual-axis motor:  
\[P_{\text{Motion\_Card,Drive,Motor}}=15.048\text{W}-7.8\text{W}=7.248\text{W} \tag{7}\]  
Equation (8) is the power for the DC-DC converter:  
\[P_{\text{Converter}}=17.52\text{W}-15.048\text{W}=2.472\text{W} \tag{8}\]  
The lead-acid batteries required for the solar power system and the solar tracking system are different, and experimental evaluation of the charging power consumption for the system shows that the power needed for charging the total system is 25 W. The total power consumption of the battery supply system is 17.52 W, which means that the solar charge is sufficient for all the equipment and a small amount of charging can be implemented by the self-sufficient power supply.
## 4 Methodology
### Feature Extractions  
This section provides a thorough explanation of the fundamental feature extraction techniques used to construct the empirical models that were adopted for our tests: _n-grams_, which is a group of n co executive text, that can be words, numbers, symbols, or punctuation, _textual features_, consisting of elements or components of the text, such as nouns, verbs, and pronouns, which are used to create meaning, and _word embeddings_, which convert word into vector representation used in text analysis.
## 2 A Microsatellite Launcher Fixed on Top of an Aircraft  
Electromagnetic launchers are still in the modification phase and the exit velocity of these launchers does not reach the escape velocity of the Earth. Therefore, a multi-stage electromagnetic launcher is not used alone to launch microsatellites and nanosatellites into low Earth orbit. For instance, in [4], Sandia researchers built a 9000-coil launcher to accelerate a 600 kg armature at 6 km/sec. But this velocity is not enough to reach to low Earth orbit, therefore they added a boost rocket to circularize the orbit of the satellite and complete the journey to low Earth orbit. To gain the advantage of the launcher's merits, in addition to achieving the goal of propelling satellites to low Earth orbit without adding a boost rocket, an aircraft is used to reduce the distance between the launchpad and the low Earth orbit. The launcher is fixed on top of an aircraft as shown in Figure 1. This solution will allow the launcher to be used with its available capabilities. Launching satellites from high-altitude pads has many advantages. Some of these advantages are that the atmosphere at high altitudes is thinner, which means less friction and energy consumption, and the airplane or the aircraft can move the launchpad anywhere. For instance, NASA used the same technique to launch Orbital ATK's Pegasus rocket (manufactured by NASA) as in [28]. The Pegasus rocket got eight microsatellites into space. The Pegasus rocket is conventional but air-launched instead of lifting off from the ground. The Pegasus rocket started its trip from a flying launchpad at about 39,000 feet.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 10 Open Challenges with Wi-Fi 6
### Wi-Fi 7
#### Mu-MIMO  
To double the nominal throughput, Wi-Fi 7 aims to use 16 spatial streams compared to the 8 spatial streams in Wi-Fi 6. Doubling the spatial streams causes overheads on channel sounding that may affect the accuracy of the CSI. Therefore, Wi-Fi 7 introduces an implicit channel-sounding procedure to have the correct CSI.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 5 Conclusions  
Knock has been a key issue in engine design since the inception of the SI engine. As engines evolved, the damage and noise associated with knock became less of an issue due to the ability to accurately assess when an engine is starting to knock and then modify the engine's operating conditions to avoid knock. Future SI engines, including downsized, turbocharged engines that may be used in hybrid configurations, will still require active monitoring of signals from the engine to mitigate knock. As such, although the automotive market is moving to electric vehicles, there is still a substantial amount of research being carried out on knock detection.  
This paper reviewed advances in different knock detection techniques. It initially focused on the primary detection techniques, including in-cylinder pressure, engine block vibrations, and audible noise. The paper also addressed a number of new knock detection approaches, including measuring the exhaust gas temperature, perturbations of the engine speed, and ion measurements across the spark plug. The study found that these detection techniques are being combined with new processing techniques, including artificial intelligence and machine learning algorithms, to more accurately determine if an engine is knocking.  
Regardless of the advances, this review indicated that in-cylinder pressure measurements with spectral analysis remain the most reliable method for assessing knocking cycles. Further, advances in vibration sensors and signal processing will result in less expensive and more accurate knock sensors being integrated into vehicles in the short term. While a number of other methods for assessing knock are under development, these technologies require further advancement before being integrated into vehicles. However, they have the benefit of being able to use components that are already in the engine.  
As engines continue to evolve, the ability to detect knock must evolve as well. Research trends show that there is continued research in this field, improving vibration-based sensors in the short term, while developing novel processes for the longer term.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 3 Results
** The \(F1\) score is a commonly used metric in machine learning, particularly in tasks that deal with binary or multi-class classification. It is a balanced measure of precision and recall, providing a single metric that encapsulates model performance in terms of both false positives and false negatives. Precision measures the correctness of the positive predictions. It calculates the ratio of correctly predicted positive observations to the total predicted positives. Recall measures the ability of a model to find all the relevant cases in a dataset. It calculates the ratio of correctly predicted positive observations to all the observations in the actual positive class. \[\text{Precision}=\frac{\text{True Positive (TP)}}{\text{True Positive (TP) + False Positive (FP)}} \tag{3}\]  
\[\text{Recall}=\frac{\text{True Positive (TP)}}{\text{True Positive (TP) + False Negative (FN)}} \tag{4}\]  
\[F1=2\cdot\frac{precision\cdot recall}{precision+recall} \tag{5}\]  
where True Positive (TP) means images correctly classified as positive, False Positive (FP) means images are incorrectly classified as positive and False Negative (FN) means positive images are incorrectly classified as negative. Tables 1 and 2 outlines the performance metrics of the various models tested against three classes of road damage, namely, Crack, Pothole, and SealedCrack, and their average performance across these classes. In terms of the average performance, our method significantly outperforms all other models with average \(F1\) scores of 0.94, an improvement over  
Figure 5: A sample of pavement distress types. the closest competitors, EfficientNet and REXNet, which both achieved an average \(F1\) score of 0.92. Our model also excelled in the identification of Crack and Pothole defects, recording top \(F1\) scores of 0.99 and 0.92, respectively. Given the inherent sparsity of cracks and potholes in pavement images, our model has showcased impressive performance gains in these categories. Cracks and potholes present unique challenges due to their infrequent occurrence and diverse characteristics, calling for a model that can effectively isolate and recognize these sparse yet critical data points. Our proposed model, designed with a focus on the unique sparsity of such features, has excelled in the accurate detection of both crack and pothole defects, recording top \(F1\) scores of 0.99 and 0.92, respectively. The effective capture and identification of these sparse and intricate patterns in road images demonstrate the adaptability and efficacy of our method. The superior performance in detecting these specific distress types underscores the model's ability to address the critical aspect of sparsity in pavement distress detection. These findings robustly affirm the efficacy of our proposed model in recognizing a wide array of road damage types. The superior performance of our model can be largely attributed to the innovative design of our sparse feature module and the domain-specific sparse constraint, which contribute to the robustness of the model in a variety of detection scenarios.
## 4 Descriptions of Different Topologies of WSNs
### Cluster-Based Topology
#### 4.2.1 Elements in a Cluster  
In general, when working with clusters it is possible to identify three main different elements in the WSN: sensor nodes (SNs), base station (BS) and cluster heads (CH) (see Figure 3). The SNs are the set of sensors present in the network, arranged to sense the environment and collect the data. The main task of a SN in a sensor field is to detect events, perform quick local data processing, and then transmit the data. The BS is the data processing point for the data received from the sensor nodes, where the data is accessed by the end-user. It is generally considered fixed and at a large distance from the sensor nodes. The CH acts as a gateway between the SNs and the BS. The function of the cluster head is to perform common functions for all the nodes in the cluster, like aggregating the data before sending it to the BS. In some way, the CH is the sink for the cluster nodes, and the BS is the sink for the cluster heads. This structure formed between the sensor nodes, the sink and the base station can be replicated as many times as it is needed, creating the different layers of the hierarchical WSN. The greatest constraint it has is the power consumption, which usually is caused when the sensor is observing it surroundings, and communicating (sending and receiving) data.  
Figure 3: Cluster-based topology architecture. Dotted line boundaries refer to a cluster.
# Standalone GPS L1 C/A Receiver for Lunar Missions
###### Abstract  
Global Navigation Satellite Systems (GNSSs) were originally introduced to provide positioning and timing services for terrestrial Earth users. However, space users increasingly rely on GNSS for spacecraft navigation and other science applications at several different altitudes from the Earth surface, in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), Geostationary Earth Orbit (GEO), and feasibility studies have proved that GNSS signals can even be tracked at Moon altitude. Despite this, space remains a challenging operational environment, particularly on the way from the Earth to the Moon, characterized by weaker signals with wider gain variability, larger dynamic ranges resulting in higher Doppler and Doppler rates and critically low satellite signal availability. Following our previous studies, this paper describes the proof of concept \"_WeakHEO_\" receiver; a GPS L1 C/A receiver we developed in our laboratory specifically for lunar missions. The paper also assesses the performance of the receiver in two representative portions of an Earth Moon Transfer Orbit (MTO). The receiver was connected to our GNSS Spirent simulator in order to collect real-time hardware-in-the-loop observations, and then processed by the navigation module. This demonstrates the feasibility, using current technology, of effectively exploiting GNSS signals for navigation in a MTO.  
GNSS; GPS; orbital filter; space navigation; Kalman filter; lunar mission; signal processing Article
# ICN-Based Enhanced Content Delivery for CDN
## 5 Simulation Study
### Result Analysis
#### 5.2.2 Impact of Content Popularity  
As discussed in the above section, the Zipf distribution exerts inherent influence on our model. In this experiment, we attempt to explore this further by studying the most import factor of Zipf distribution: for the \(\alpha\) parameter, the higher the value of \(\alpha\), the more focused the users' preferences. We still use the Abilene topology for analysis and change the \(\alpha\) parameters of the Zipf distribution. Other parameters are set as the default. Additionally, we present the results with both the comparison between the two architectures and their performance gap as the \(\alpha\) parameter varies. As demonstrated in Figure 8a,b, as \(\alpha\) increases, both the total bandwidth consumption and average delay decrease as expected because with a higher \(\alpha\) value, there are more requests for popular content, which makes caching play a more important role in improving performance. However, as \(\alpha\) increases, the performance gaps between IECDN and conventional CDN also tend to narrow (as indicated by the green columns). Specifically, with the \(\alpha\) value of 0.6, the total bandwidth consumption is reduced by 19.24 Gbps, and the average delay is reduced by 77.16 ms, whereas these two gaps narrow down to 6.66 Gbps and 27.43 ms, respectively, with the \(\alpha\) value of 1.2. This is also because a higher \(\alpha\) value leads to a more focused preference for popular content. Most content with high popularity is requested and cached on CDN servers, which in turn makes more content readily available on CDN servers. Therefore, the ICN in-network caching tends to be of service for fewer content requests, resulting in smaller performance improvement.
## 4 Results and Discussion
### Simulation Data Analysis  
Figure 4 provides the calculation results compared with experiment results from MAS for the rotor with rectangular blades. The case 1 curves are named as With Bench, case 2 as Without Bench, and case 3 as Mars Air condition. It is clear that, for the lift prediction, the results of case 1 agree well with the experiment results, with an error within 2%. However, the torque prediction is not so promising, as there is an approximately 20% deviation for all cases from the experiment results for all CFD calculations. By comparing case 2 with case 1, it is discovered that there is about a 5% difference between lift data. The results of case 3 are in good agreement with case 2. According to the results, the following conclusions can be made. Firstly, the established method can predict the lift of rotors in ultra-low density Martian air conditions in an accurate way, but the torque prediction is not as promising. Secondly, the test bench in the MAS has a little influence on the test results, with only about 5% of difference between values. Thirdly, the MAS can be used to simulate the aerodynamic performance of rotors under Martian air conditions in a very accurate manner, signifying that MAS is a good tool for a Martian helicopter study.  
As for the 20% deviation of torque values between simulation results and experimental data, there may be two explanations for it. Firstly, it can be seen from Figure 1b,d that, in addition to the force sensor, the shaft is also connected to the sleeve by solid contact between the shaft and the sleeve. Therefore, the torque may be transferred to the bench through the sleeve due to the friction between the shaft and the sleeve. Therefore, the torque applied on the force sensor is lowered under this circumstance. Secondly the torque values are very small, only creating a lift value of about 1 N, approximately two eggs. Therefore, a small measurement error will amplify the deviation. Thus, the friction-induced error may be amplified in a proportion indication.  
In addition to the data analysis, further study in the flow field needs to be conducted to explore the flow mechanisms.
## 2 Analysis of SAS Image Characteristics  
During the operation of a synthetic aperture sonar (SAS), the sonar moves at a uniform speed along the azimuth and emits signals at a certain pulse interval while receiving the echoes reflected from the object [23]. The acquired images are characterized as follows:(1) Due to the influence of the imaging equipment and marine environment, there is speckle noise in sonar images, resulting in a low signal-to-noise ratio of images. The edge of the object in the image is blurred, and there is less detailed information, such as texture [24]. On the whole, the object is higher in energy and more complete in form but does not have a regular geometry.  
(2) SAS adopts the principle of side-view imaging, which may lead to a large difference in the appearance of the same object at different angles, and the morphology of different objects at a certain angle may have a large similarity. The objects in the image have the problems of high inter-class similarity and high intra-class difference.  
(3) The complexity of the underwater environment and the speed of the platform may lead to some geometric distortion in the sonar image. Under this influence, the object may also undergo certain geometric changes [25].  
The sonar images of several different classes of objects are shown in Figure 1.