esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Advantages of Using CR in WSNs
#### 1.4.2 Multiple Channels Utilization  
Most traditional WSNs use a single channel for communication [16]. In WSNs, upon the detection of an event, sensor nodes generate the traffic of packet bursts. At the same time, in densely deployed WSNs, a large number of wireless sensor nodes within the event area attempt to acquire the same channel at the same time. This increases the probability of collisions, and decreases the overall  
\begin{table}
\begin{tabular}{c c c} \hline
**Range** & **Center Frequency** & **Bandwidth** \\ \hline
6.765–6.795 MHz & 6.78 MHz & 30 kHz \\
13.553–13.567 MHz & 13.56 MHz & 14 kHz \\
26.957–27.283 MHz & 27.12 MHz & 326 kHz \\
40.66–40.7 MHz & 40.68 MHz & 40 kHz \\
433.05–434.79 MHz & 433.92 MHz & 1.84 MHz \\
902–928 MHz & 915 MHz & 26 MHz \\
2.4–2.5 GHz & 2.45 GHz & 100 MHz \\
5.725–5.875 GHz & 5.8 GHz & 150 MHz \\
24–24.25 GHz & 24.125 GHz & 250 MHz \\
61–61.5 GHz & 61.25 GHz & 500 MHz \\
122–123 GHz & 122.5 GHz & 1 GHz \\
244–246 GHz & 245 GHz & 2 GHz \\ \hline \end{tabular}
\end{table}
Table 2: Frequency bands available for ISM applications, as defined by ITU-R.  
communication reliability due to packet losses, leading to excessive power consumption and packet delay. CR-WSNs access multiple channels opportunistically to alleviate this potential challenge.
## 5 Case Study  
In this section, we demonstrate the SFCAD process and verify its application to structured features throughout the process. To validate the capability of structured features in establishing connections between software functional requirements and component architecture, we conducted a case study and present the resulting artifacts. The case study addresses the design of a component architecture for a vehicle-door control system. Although the requirements specification comprised various requirements, in this case the designer assumed that the functional requirements and hardware system modeling from the requirements specification had been completed and that structured feature analysis had been conducted based on this information.
### Contributions of the Work  
This work is focused on the development, testing, and evaluation of the adaptive co-pilot as the enabler of a new driving cooperative framework, pursuing the idea of being a \"Team\". The specific contributions are detailed below:  
* Development (Section 2): Three components of the adaptive co-pilot are presented in this work: (1) a novel lateral shared controller, able to assist the driver with different levels of haptic authority, (2) an arbitration module that calculates the authority to be assigned to the controller, designed under two principles (minimal intervention and safety-over-comfort), and (3) an innovative visual HMI for shared control, as the enabler of trust in decisions and actions performed by the co-pilot.
* Testing (Section 3.1): The implementation of the adaptive co-pilot is then tested in a scenario where the driver is asked to repeatedly perform a secondary task, which results in two general driver states (attentive or distracted). Under this scenario, four _driving modes_ are compared (manual as a baseline, and LK and LC as two available commercial ADAS, and the proposed adaptive co-pilot). The last three offer steering support to the driver to keep within the lane in both attentive and distracted states. Tests were carried out in a driver-in-the-loop simulator with five drivers.
* Evaluation (Sections 3.2 and 3.3): A quantitative analysis of the tests was performed, considering KPIs related to tracking performance, safety indicators, and driving efforts. To complement the study, a subjective evaluation was also performed through questionnaires applied to the participants, with KPIs related to safety, comfort, and overall perception of the system. Results show that when the driver is continuously kept in the loop under the support of the adaptive co-pilot, it provides benefits in regards to the driving task that would help to avoid \"automation irony\" (as the driver has a sense of being responsible for driving even under automation support). At the same time, drivers benefit from the automation capabilities for intervening during the distraction events and improving driving safety, while reducing driver-required efforts.
# Dynamic Routings in Satellite Networks: An Overview
## 5 Potential Technologies and Future Directions
### Future Directions
The ML algorithm model is trained without complex assumptions and network modeling; (2) Efficiency. Fast inference based on the input data can obtain the optimized routing decision in polynomial time; (3) Generality. The same ML model can be used to solve different network optimization problems depending on the training data. In the next satellite routing optimization matter, scalability is an important property to satisfy the routing algorithm. Existing ML routing algorithms handle small topological networks with up to 20 satellite nodes. A larger topology means an exponentially growing number of network states and a greater difficulty in routing decisions. Future intelligent routing algorithms must achieve good results on large topologies. Compared with traditional routing algorithms, intelligent routing requires more computational resources and higher routing performance. In addition, for smart routing, how to deploy in real scenarios is also a great challenge. Although SDN networks have enhanced the computational power of the router control layer, intelligent routing algorithms are still difficult to deploy at scale in existing satellite network architectures [135]. In the future, smart routing algorithms may focus on designing smart routing devices.
## 5 Results
### Verification and Analysis of X-Band Test Data
The time delay estimation is finally achieved through a systematic ambiguity resolution process, and the results are illustrated in Figure 13. The time delay estimation results of the two signal combinations, traditional combination (denoted as 'Original' in Figure 13, \(\pm\)19.2 MHz DOR, \(-\)3.8 MHz DOR, carrier) and proposed combination (denoted as DOR+PNc, \(\pm\)19.2 MHz DOR, \(\pm\)PN-Clock, carrier) are basically the same and are of close accuracy. The deviation between them is only about 5 ps. In other words, the same measurement accuracy as with the traditional signal system can be achieved by only using the DOR+PNc signal combination. Furthermore, this method transcends the traditional approach by allowing one set of DOR beacon signals to be detuned from the downlink carrier, which enhances the downlink signal power utilization efficiency or improves the deep space exploration performance. Meanwhile conversely, when the downlink signal power is tight for the X-band, the DOR\({}_{1}\) beacon can be detuned and replaced by the clock component of the PN code signal for high-precision interferometry time-delay measurement in lunar and deep space exploration. Figure 11: The cross-phases of DOR beacon signal or the clock component of PN code ranging signal.
## 6 Conclusions  
The cost of ESS is relatively high at this stage. When microgrid operation economics are optimized, the impact of ESS life loss needs to be considered. This paper establishes an operation control strategy model of a rural community PV and storage microgrid system and proposes an economic optimized operation strategy suitable for the \"self-generated self-use, surplus electricity to the utility\" mode. The strategy considers the influence of the cost per kWh of the energy storage battery and adds a long-term operating ESS pre-charge strategy. The operating strategy uses different control strategies to control the ESS at different times of the time-of-use electricity price to achieve economic optimization under real-time operation.  
This paper focuses on the setting of two important parameters, SOCpro and SOCmin, and proposes a parameter optimization method using PSO. The objective function considers two factors: the electricity cost and the ESS life loss. The calculation of electricity costs considers factors such as time-of-use electricity prices, subsidies for distributed photovoltaic power generation, load electricity consumption, and other factors. The ESS life loss is calculated using the equivalent throughput method, and the impact of low battery static electricity on life loss is added.  
Combined with the actual operation of the rural community microgrid in western China, four typical operating scenarios are summarized, namely, sunny in the heating season, cloudy in the heating season, sunny in the non-heating season, and cloudy in the non-heating season. Parameter optimization was performed for four typical scenarios to obtain the optimal parameters in different scenarios. Comparing the annual time scale operation effect of the variable parameter strategy and the constant parameter strategy, the results show that the variable parameter strategy can reduce the overall cost by 4.15%, which is worth adopting.  
Relying on the 100-kilowatt-level engineering demonstration system, a community-level microgrid energy management monitoring system is built, and the control strategy proposed in this paper and the multi-scenario parameter optimization system obtained through simulation are experimentally verified. The experimental results of four typical operational scenarios are introduced, and the operation data of the demonstration system show that the proposed method is feasible and effective.  
Conceptualization and methodology, L.G.; validation, L.G. and Z.Y.; writing, L.G.; supervision, H.X.; project administration, Z.Y.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by [the Qinghai Major Science and Technology program] grant number [2019-GX-A] and [the K.C. Wong Education Foundation] grant number [GJTD-2018-05]. The authors declare no conflict of interest.
## 2 Materials and Methods
### Characterization of La-EPRC Particles for Static Adsorption of Phosphorus Removal
#### 2.3.3 Adsorbent Dosage Experiment  
A total of 5 mL of the phosphorus standard reserve solution with a concentration of 1 gP/L was taken for nine samples. Deionized water was added to dilute the samples to 250 mL and mixed well to create a solution with a concentration of 20 mgP/L. The following amounts of La-EPRC particles were weighed: 1 g, 1.5 g, 2 g, 2.5 g, 3 g, 3.5 g, 4 g, 4.5 g, and 5 g. These were sequentially added to conical flasks No. 1-9. The flasks were placed in a shaker (25 \(\pm\) 1 \({}^{\circ}\)C, 150 \(\pm\) 5 r/min) for continuous shaking. After the adsorption reaction reached equilibrium, typically in 48 h, the reaction was stopped and supernatant samples were collected. All nine samples were filtered through a 0.45 \(\upmu\)m filter, absorbance was determined using ammonium molybdate spectrophotometry, and the remaining phosphorus concentration in the solution and the adsorbent adsorption amount were calculated.
## 5 Results and Discussion
### Performance Units
#### 5.2.6 Reliability  
It is defined as the probability of successful message transmission and reception within the cluster and its single-hop remote adjacent cluster. When we focus on safety message diffusion, reliability is one of the utmost key variables. A reliability comparison of our proposed work with other protocols is shown in Figure 11. Under a similar vehicular scenario, the DMMAC protocol only is focused on delivering safety messages. It is noticed that, under the specified simulation conditions, both MAMC-MAC and DMMAC accomplish uniformly good reliability. It is observed that the system accuracy is high in low-density area and marginally minimizes with the increase in vehicular scale.  
Figure 10: PSD comparison.  
Figure 9: Average end-to-end delay comparison.
## 6 Simulation Results and Discussion
### Performance Trade-Off  
In this subsection, the trade-off between the benchmark and the proposed protocols is discussed. In benchmark protocols, LETR performs error resilient transmission range adjustment that consumes  
Figure 20: Packets E2E delay.  
high EC by compromising E2E delay. Therefore, the protocol has compromised its E2E delay by avoiding the void hole problem to provide error resilient transmission. While in Hydrocast routing protocol [26], PDR is achieved on the cost of E2E delay. Moreover, Improved Hydrocast protocol has compromised its EC on fixed nodes deployment by paying the cost E2E delay. In addition, Co-Hydrocast routing protocol improved its PDR by paying the cost on opportunistic and cooperative routing. In the end, Co-Improved Hydrocast routing uses both features including opportunistic and cooperative routing and fixed nodes deployment to avoid the void hole. Therefore, this protocol has compromised its EC on the cost of E2E delay. While in GEDAR, void hole avoidance is tackled on the base of DA of the nodes which consumes high EC, there exists a trade-off between EC and E2E delay. In addition, LMPC uses a tree based multi path routing approach for reliable data delivery. In this protocol, EC and the active number of nodes are compromised over packet received ratio.  
In the proposed protocol EP-VIR-Three, a proactive routing approach is implemented to search the next forwarder node using 3-hop neighbor's information. Therefore, EP-VIR-Three has compromised reliable data delivery by paying affordable E2E delay, resulting in high PDR. Meanwhile, BF-SPR-Three selects the single shortest and fastest path for reliable data delivery (additionally the binary tree generation) on the cost of affordable E2E delay. Moreover, performance trade-off and achievements with compromised parameters of benchmark and the proposed protocols are shown in Table 5.
## 3 Proposed Method  
This paper proposes a fault diagnosis method for a missile air data system based on the unscented Kalman filter and Inception V3. Firstly, an unscented Kalman filter model for atmospheric parameters is established based on the missile's kinematic equations described in Section 2.1. Then, sequences such as innovations and residuals, which contain fault information, are extracted from the model as inputs for the neural network. Finally, an Inception V3 network architecture is designed, leveraging its multi-scale feature extraction and sparse structure characteristics, along with the efficient channel attention (ECA) characterized as a lightweight attention mechanism, to enhance feature extraction capabilities while reducing computational complexity. This ultimately achieves fault diagnosis for the missile air data system. The overall scheme of the method is shown in Figure 3.  
To be more specific, in the process of data acquisition, the measurements of airspeed, angle of attack, and sideslip angle are obtained based on relevant sensors of the ADS. These values are then combined with information collected by the INS, such as acceleration, angular velocity, and attitude angles, to construct the UKF model. In the process of model establishment, the innovations and residuals of airspeed, angle of attack, sideslip angle, and three-dimensional attitude angles are extracted as inputs for the neural network model. In the process of creating the Inception V3 network, the specific structure is as follows: Initially, the features are preliminarily extracted through a convolutional layer and max pooling layer. Then, the Inception V3 module is applied to capture multi-scale features using different convolutional kernels, which are concatenated along the channel dimension. The ECA mechanism is employed, and finally the output passes through fully connected layers and the SoftMax classifier to generate diagnostic results. The detailed description of the proposed method will be demonstrated below.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 2 Extended Long Short-Term Memory Networks
### mLSTM Model
```
1:Initialize: Cell state \(\mathbf{C}_{0}=\mathbf{0}\), hidden state \(\mathbf{h}_{0}=\mathbf{0}\), normalizer state \(\mathbf{n}_{0}=\mathbf{0}\), stabilizer state \(\mathbf{m}_{0}=\mathbf{0}\)
2:for\(t=1\) to \(T\)do
3: SCADA data preprocessing
4: Compute cell input: \(z_{t}=\phi(\omega_{z}^{\top}\mathbf{x}_{t}+r_{z}h_{t-1}+b_{z})\)
5:if using sLSTM variant then
6: Cell input computation
7:\(z_{t}=\phi(\tilde{z}_{t})\)
8:\(\tilde{z}_{t}=\omega_{z}^{\top}\mathbf{x}_{t}+r_{z}h_{t-1}+b_{z}\)
9: Input gate computation
10:\(\dot{t}_{t}=\exp(\tilde{t}_{t})\)
11:\(\dot{t}_{t}=\omega_{t}^{\top}\mathbf{x}_{t}+r_{z}h_{t-1}+b_{i}\)
12: Forget gate computation
13:\(f_{t}=\gamma(\tilde{f}_{t})\)
14:\(\dot{f}_{t}=\omega_{t}^{\top}\mathbf{x}_{t}+r_{f}h_{t-1}+b_{f}\)
15: Output gate computation
16:\(o_{t}=\sigma(\tilde{o}_{t})\)
17:\(\delta_{t}=\omega_{o}^{\top}\mathbf{x}_{t}+r_{o}h_{t-1}+b_{o}\)
18: Cell state update
19:\(c_{t}=f_{t}c_{t-1}+i_{t}z_{t}\)
20: Normalizer state update
21:\(n_{t}=f_{t}n_{t-1}+i_{t}\)
22: Hidden state computation
23:\(h_{t}=o_{\frac{\tilde{z}_{t}}{\tilde{z}_{t}}}\)
24: Gate stabilization mechanism
25:\(m_{t}=\max(\log(f_{t})+m_{t-1},\log(i_{t}))\)
26:\(i_{t}^{\prime}=\exp(\log(i_{t})-m_{t})\)
27:\(f_{t}^{\prime}=\exp(\log(f_{t})+m_{t-1}-m_{t})\)
28:endif
29:if using mLSTM variant then
30: Compute query, key, and value vectors:
31:\(\mathbf{q}_{t}=\mathbf{W}_{t}^{\top}\mathbf{x}_{t}+\mathbf{b}_{\tilde{q}}\)
32:\(\mathbf{k}_{t}=\frac{1}{\sqrt{d}}(\mathbf{W}_{k}^{\top}\mathbf{x}_{t}+\mathbf{ b}_{k})\)
33:\(\mathbf{v}_{t}=\mathbf{W}_{v}^{\top}\mathbf{x}_{t}+\mathbf{b}_{v}\)
34: Compute gates:
35:\(\mathbf{i}_{t}=\exp(\mathbf{W}_{t}^{\top}\mathbf{x}_{t}+\mathbf{b}_{i})\)
36:\(\mathbf{f}_{t}=\gamma(\mathbf{W}_{t}^{\top}\mathbf{x}_{t}+\mathbf{b}_{f})\)
37:\(\mathbf{o}_{t}=\sigma(\mathbf{W}_{v}^{\top}\mathbf{x}_{t}+\mathbf{b}_{o})\)
38: Matrix memory update:
39:\(\mathbf{C}_{t}=\mathbf{f}_{t}\mathbf{C}_{t-1}+\mathbf{i}_{t}(\mathbf{v}_{t} \mathbf{k}_{t}^{\top})\)
40:\(\mathbf{n}_{t}=\mathbf{f}_{t}\mathbf{n}_{t-1}+\mathbf{i}_{t}\mathbf{k}_{t}\)
41:\(\mathbf{h}_{t}=\mathbf{o}_{t}\odot\frac{\mathbf{C}_{t}\mathbf{q}_{t}}{\max( |\mathbf{n}_{t}\ \dot{\mathbf{q}}_{t}|,1)}\)
42:endif
43:endfor
```  
**Algorithm 1** xLSTM forward pass with sLSTM and mLSTM components
## 1 Introduction
However, current studies predominantly rely on empirical data and trial-and-error experiments, resulting in inefficiency and inconsistent performance. In the production of the OPC clinker, to obtain the desired oxide proportions and subsequently achieve the targeted clinker properties, the admixture ratio of raw materials is adjusted through the use of the three chemical moduli (TCM), which encompass the silicon modulus (SM), iron modulus (IM), and lime saturation coefficient (KH) [53]. Nowadays, TCM have been extensively utilized in the quality control of the OPC clinker and also provide a design framework for the proportioning of raw materials in ISW-CMs. Studies have shown that the soft soil stabilized using ISW-CMs developed with TCM achieve comparable or even higher strength than that stabilized with OPC [53; 54]. In addition, mixture experimental design based on the simplex lattice/centroid method is another approach to determine proportion, with advantages including a reduced number of experimental points, high prediction accuracy, and the ability to swiftly identify optimal proportion. This approach involves strategically designing a few experiments with varying proportions in a simplex of two or more dimensions to obtain a regression equation between the response values and raw material percentages. Consequently, the optimal proportion can be determined, and further investigation into the inherent relationship between proportions and response values can be conducted. Currently, studies employing this method to develop ISW-CMs that simultaneously meet multiple property requirements have been reported [55, 56]. Despite numerous practical applications of ISW-CMs as an alternative to OPC for soil stabilization, the current literature primarily focuses on the stabilizing of clays with certain bearing capacity in their natural state. Research on aeolian sand, which exhibits inferior engineering properties and necessitates higher solidification demands, remains limited. Furthermore, trial-and-error-based development approaches for ISW-CMs frequently exhibit inefficiency and theoretical deficiencies, often yielding materials that fail to concurrently satisfy strength and workability requirements, thereby imposing significant barriers to their widespread implementation. This study prepared a fully ISW-based cementitious material for stabilizing aeolian sand in desert regions using four complementary industrial solid wastes--blast-furnace slag (GGBFS), steel slag (SS), phosphorous slag (PS), and carbide slag (CS)--as raw materials. The TCM theory of the cement clinker and the simplex lattice design were used to systematically investigate the influence of ISW dosages on the _UCS_ of stabilized aeolian sand and determine the optimal proportion of ISWs. Phase and microstructural analyses were employed to investigate the content of hydration products and their cementitious effects on particle bonding in aeolian sand stabilized with varying proportions of the cementitious materials. The findings provide novel insights and references for the development of ISW-based cementitious material for aeolian sand stabilization.
## 3 Theoretical Background  
Support vector machines (SVMs) are being increasingly utilized in real-world applications, and have been labeled by researchers as a state-of-the-art tool among existing soft-computing techniques. SVMs ensure a precise perception of learning from samples, and can lead to the production of better outcomes for many practical real-world applications. The motivation behind the ease in deploying SVMs is that the mapping of non-linear data to high-dimensional feature space acquires a non-linear decision boundary in input space. In our study, input data representations were mapped into feature space by kernels.  
SVMs come under the category of non-parametric classifiers, and have many types of kernels. The kernel functions are introduced in SVMs to solve classification or regression difficulties in which the data are not linearly separable [17]. The various types of SVMs with kernels are shown in Table 1, where \(\gamma\), \(d\), and \(r\) are kernel parameters. Researchers recommend choosing appropriate sensible kernel functions, and tuning related significant parameters mapping the non-linearly-separable data. The mapping of non-linearly-separable data into feature space is pictorially represented in Figure 1.  
Mathematically, two critical points define a channel called a support vector, where one point exists from each class. Support vectors define the boundary between classes, and all other instances will be deleted without changing the boundary. SVMs drive between two classes, but in the case of multi-dimensional class separations, it is called a hyperplane (not a line). When classes are not linearly separable, complexity exists through complex boundaries called kernel tricks on non-linearization. Specifically, the input training dataset, \(\{x_{i}\}i=_{1}^{n}\), was transformed to feature space using a non-linear function, \(\varphi\). The later transformed data obtained in the feature space, \(\{\varphi(x_{i})\}i=_{1}^{n}\), underwent a non-linear kernel trick as depicted in Figure 1. When multiple hyperplanes existed, we selected the hyperplane with the best max margin. Researchers have proposed many kernels with relevance to their experiments. Among a few, the most commonly used ones are linear, polynomial, sigmoidal, Gaussian, and exponential RBFs [18]. The selection among the above-mentioned kernels was made based on three performance features. The features applied for the performance measurements were complexity, optimality, and accuracy. The measured data of these features are rated accordingly in Table 2. The initial choice made was the RBF, as it seemed to lead the performance ratings specified in Table 2. The RBFs have high prediction accuracy and optimality, and the complexity measure is quite high for both linear and polynomial kernels, as they involve numerical difficulties when computing the kernel inner products for the featured vectors.