esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 3 Reception of the Composite Signal
### Independence of the Reception of the BOC and OFDM Signals  
The main basis of BOC signal acquisition and tracking is the correlation function between the received signal and the local BOC signal. This means that the high-precision Doppler frequency offset and code phase offset of tracking are obtained through correlation. The correlation function, ignoring noise, is shown in Equation (9):  
\[\begin{split} R(t,t+\tau)&=E\big{(}S_{OFDM\_BOC} \cdot\hat{S}_{BOC}(t+\tau)\big{)}\\ &=E\big{(}(\alpha\cdot S^{\prime}_{OFDM}(t)+\beta\cdot S^{\prime} _{BOC}(t))\cdot\hat{S}_{BOC}(t+\tau)\big{)}\\ &=E\big{(}\alpha\cdot S^{\prime}_{OFDM}(t)\cdot\hat{S}_{BOC}(t+ \tau)+\beta\cdot S^{\prime}_{BOC}(t)\cdot\hat{S}_{BOC}(t+\tau)\big{)}\\ &=\alpha\cdot R_{BOC,OFDM}(\tau)+\beta\cdot R_{BOC}(\tau)\end{split} \tag{9}\]  
where \(S_{OFDM\_BOC}\) is the received signal and \(\hat{S}_{BOC}(t)\) denotes the locally generated BOC signal. \(R_{BOC}(\tau)\) is the auto-correlation function of the BOC signal, as shown in Figure 6a. \(R_{OFDM\_BOC}(\tau)\) is the correlation function of the BOC and OFDM signals, as shown in Figure 6b.  
As shown in Figure 6, the normalized correlation function between the BOC and the OFDM signals is considerably lower than the normalized auto-correlation function of the BOC signal. According to the results, the correlation function of the BOC and OFDM signals can be neglected. Thus, Equation (9) is rewritten as Equation (10):  
\[\begin{split} R(t,t+\tau)&=\alpha\cdot R_{BOC,OFDM} (\tau)+\beta\cdot R_{BOC}(\tau)\\ &\approx\beta\cdot R_{BOC}(\tau)\end{split} \tag{10}\]  
Hence, the acquisition and tracking of the BOC signal are minimally affected by the OFDM signal. Moreover, Section 2.1 indicates that the subcarriers in which the BOC signal's main lobe is located do not contain the OFDM signal, and the power of the sidelobes of the BOC signal is 15 dB lower than the power of main lobe. This means that the BOC signal sidelobes are equivalent to noise for the OFDM signal. Therefore, demodulation of the  
Figure 5: Reception of the composite signal.  
OFDM signal is minimally affected by the BOC signal. In conclusion, the BOC and OFDM signals are independently received.
## 3 Field Trialed System  
This section describes the components and subsystems that were integrated into the field trial that was held in Saarijarvi, Finland, in June 2021. Different technologies and assets in the smart farm were interconnected with several alternative wireless private networks. The network architecture and technology configurations are described in Section 3.1. A smart unmanned tractor prototype, Valtra N175D Direct Model [40], from AGCO, was remotely controlled through the network. Section 3.2 describes our implementation and optimization to enable remote control. We also tested tractor's autonomous driving capabilities, which were in level 2 (\"hands off\"). A large number of sensors exist on smart farms. These sensors provide situational information that may be applied, for example, when planning tractors' operations and when operating tractors. Access to sensitive information, tractor subsystems, and other farm assets is restricted to identified, authorized, and trusted users and devices. Section 3.3 describes cyber and network security building blocks.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 7 Discussion of the Results
### Pros and Cons of the PMS Design  
In addition to the above, a beam steering scenario was experimentally tested with a prototype of an electrically controllable PMS studied in our previous works [28; 29; 46]. Although our beamforming methodology is applicable to a wide class of reactive MS structures that are able to control reflection phase, we decided to focus our attention on the passive varactor-loaded PMS with capacitive memory proposed in [29]. When compared to other realizations, such a PMS has certain advantages. In order to illustrate it, we have compiled a comparison table (Table 1).  
The items in this table are sorted into three groups organized by the complexity class criterion, which is defined based on the set of features that are present in a design:  
* Class I: Passive structures without dc feed or with just (pulsed) control voltage(s) and with a small number of passive components per unit cell and a relatively simple layout;
* Class II: Active structures with dc feed and control voltage, with a moderate number of components (both active and passive) and with a moderate layout complexity;
* Class III: Active structures with a dc feed, high number of components, and a complex layout.  
As can be seen from this table, our design belongs to Class I, due to the fact that it is fully passive and does not require any dc feed, has a small number of passive components per unit cell, and is controlled with pulsed voltage. The designs that belong to Class II are more complex, as they need more components per unit cell. They also incorporate active devices (e.g., flip-flops, shift registers, etc.) that constantly consume dc power. Finally, Class III is for even more elaborate designs that require continuous dc feed and a significant number of active components assembled in a circuit with complex topology.  
Although the increased complexity of the designs that fall into Classes II and III typically brings new functionalities, the devices that belong to Class I are best in terms of energy efficiency. They also use a small number of passive components per unit cell, whichsimplifies fabrication and reduces cost. Unfortunately, not all of them are electronically controllable. Among the designs that belong to Class I, our design has an extra advantage of reconfigurability. Moreover, although being passive, it is able to keep its configuration state for some time between sequential reprogramming. For more details, the reader is referred to [29].
## 3 Results and Discussion
### Oenological Parameters of the Cabernet Sauvignon Wines  
The impact of LP inoculation prior to/simultaneously with SC on main oenological parameters were examined at the end of the Cabernet Sauvignon fermentations (Table 1). All LP inoculation treatments and SC groups fermented to dryness, with residual sugar ranging between 1.06 (TA-SC) and 2.26 (PreAF-LP) g/L. Further analysis on main organic acids in wines show that the largest variation was found for lactic acid, which were \(\sim\)5-fold higher in LP wines compared with the D-SC wines (0.4 g/L) (Table 1). In the latter, lactic acid concentration (0.4 g/L) was likely associated with partial degradation of malic acid by autochthonous microbes, which agrees with the stoichiometry of MLF (0.67 g lactic acid is converted from 1 g malic acid) [9], so did the TA-SC treatment (Tables 1 and S1). As a result of LP introduction, more degradation of malic acid but incomplete MLF was observed for both LP treatments (Table 1), whilst previous studies reported ultimate utilisation of malic acid by co-inoculations of LP and SC multi-cultures [23; 25]. Lactic acid bacteria utilise malic acid to support cell growth [40], and bacterial population, which in turn would exert large influences on the success of MLF. Empirically, viable bacterial numbers less than 10\({}^{6}\) CFU/mL can pose a potential risk of stuck or protracted MLF. As described above, here, reduced bacterial viability may cause MLF stalled from Day 5 onwards (Figure 1C, Table 1). Taken together, lactic acid in LP wines were from both lactic and MLF, and the former can be related to the time lapse between LP and SC inoculations [14]. Based on calculation using the stoichiometry of MLF, lactic acid produced from hexose metabolism was approximately 1.14 g/L for the S-LP treatment, and 1.31 g/L when SC was inoculated 1 day after LP (Table 1). Greater levels of lactic acid might be yielded if SC was added a few days later. However, as inoculation of LP easily enabled the onset of MLF, both MLF and lactic fermentation should be carefully taken into account when using LP to ameliorate wine acidity [13]. In this study, the acidifying lactic fermentation outcompeted the de-acidifying MLF by LP, leading to the increase in TA in LP wines. The highest TA was observed in PreAF-LP wines (8.04 g/L), followed by TA-SC (7.72 g/L) and S-LP (7.58 g/L), whilst that of the D-SC wines was the lowest (6.62 g/L) (Table 1). Correspondingly, the three latter groups also possessed a slightly lower wine pH compared to D-SC, agree with previous studies [13; 14]. Bio-acidification during winemaking had been extensively investigated using the acidifying yeast _L. thermotolerans_, whose lactic acid yield can be up to 16.6 g/L under oenological conditions [11]. Similarly to LP in this study, levels of lactic acid formed by the same _L. thermotolerans_ strain can be depended on inoculation regimes with SC.
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 5 Experimental Results and Analysis
### Comparative Analysis of Model Effects
#### 5.2.2 Selection of Basic Model
When compared with models such as SVM, KNN, and decision trees, the \(p\)-values of the LightGBM model are all below 0.05. This indicates that there is a significant performance disparity between the LightGBM model and these models, and such a difference is unlikely to be attributed to random factors. The observed difference can potentially be ascribed to the superior performance of the LightGBM model. When the \(p\)-value is less than the commonly set significance level of 0.05, it can be concluded that there are significant differences between the LightGBM model and other models in terms of both prediction accuracy and fitting capability. It is highly probable that the LightGBM model outperforms other models, which accounts for the substantial disparities observed between them. The \(p\)-value for both LightGBM and GBDT is 0.943512, which is much higher than 0.05. This shows that there is not enough evidence to show that there is a significant difference in performance between LightGBM and GBDT in the current examination. Considering that the LightGBM model is an enhancement and optimization of the GBDT algorithm, it incorporates several innovative features while inheriting some of the GBDT's merits. For instance, it employs a histogram algorithm and a leaf growth strategy with depth constraints. These features endow the LightGBM model with distinct advantages in terms of training \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **LightGBM** & **SVM** & **KNN** & **Decision Tree** & **GBDT** & **XGBoost** \\ \hline LightGBM & 0.000000 & 0.002467 & 0.000839 & 0.017154 & 0.943512 & 0.079960 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The \(p\) value comparison between LightGBM and other models. speed and memory usage. Despite this, it maintains a performance level comparable to that of the GBDT, thereby demonstrating the superiority of the LightGBM model.
## 3 Passive Systems
### Field-Passive Fluids
#### 3.1.2 Viscoelastic Fluids  
A viscoelastic fluid (VEF) is a fluid in between the limits of a Newtonian fluid and a Hookean solid, having simultaneously viscous and elastic properties [150]. In a Newtonian fluid, the stress goes to zero instantaneously once the shearing is stopped; however, in a viscoelastic fluid there is a delay, named relaxation time, which may vary widely depending on the internal molecular or microstructural configuration. That is the characteristic memory effect of a viscoelastic fluid and it is due to elasticity [151]. The rheological behavior of viscoelastic fluids has been well studied and documented since the origin of Rheology, back in 1920s. Typically, polymeric liquids exhibit viscoelasticity. In opposition to STFs, VEFs enjoy reasonably accurate constitutive models that allow for meaningful numerical simulations in complex flow conditions [152; 153; 154; 155]. Traditionally, the viscoelastic behavior has been characterized rheologically in a rotational rheometer by means of the measurement of the viscoelastic moduli (\(G^{\prime}\) and \(G^{\prime\prime}\)) within the linear viscoelastic regime (SAOS), based on a solid theoretical background. Nevertheless, as in most processing operations flow conditions are out of that linear regime, LAOS tests are fundamental to have a more relevant sample characterization of VEFs in particular, but for any complex fluid in general, allowing an understanding of their mechanical behavior under nonlinear situations. A seminal review on nonlinear oscillatory shear tests can be found in the work of [PERSON] et al. [156]. Additionally, according to [PERSON] and [PERSON] [157; 158], Medium Amplitude (or asymptotically-nonlinear) Oscillatory Shear (MAOS) provides additional nonlinear rheological information with low dimensional, well-defined material functions. Oscillatory characterization of complex fluids in general, and viscoelastic fluids in particular, provides information about the performance of the fluid at different frequencies and amplitudes, which are also parameters relevant in vibrations. Even though in all oscillatory shear tests (SAOS, MAOS and LAOS) the viscoelastic properties are traditionally characterized by means of the frequency-dependent shear viscoelastic moduli (\(G^{\prime}\) and \(G^{\prime\prime}\)), [PERSON] et al. [159] demonstrated in their pioneering work that these are not appropriate design parameters in general, as they cannot be treated as two independent design variables. According to this, the key to optimizing the design of passive energy dissipating systems based on viscoelastic fluids is to choose design variables encompassing the most general and measurable material behavior without violating fundamental restrictions (e.g., Kramers-Kronig), such as the relaxation modulus \(G(t)\), which is a single viscoelastic function meeting all these criteria. Successful identification of targeted properties will lead to the right material selection or material synthesis and, consequently, to the optimal performance [160].
## 7 Current Challenges and Future Research Directions
### Specific Datasets  
There are a few publicly available datasets for traditional DDoS attacks that are not designed for SDN architecture, such as the NSL-KDD, DARPA98, CAIDA, and CIC DoS 2019 datasets. Few of the datasets that capture LDDoS attacks are available, such as CIC DoS, which is also not intended for SDN architecture. There is an urgent need to find a dataset for LDDoS attacks in the SDN architecture to adapt to the programmability of SDN controllers and the flow tables in OpenFlow switches.
# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
## 6 Related Works
It treats the suitable features selection as an integral part of learning a predictor in a regularized learning framework, and gradually building up a feature set while training a predictor model using gradient descent in an incremental iterative fashion. [PERSON] et al. [21] presented Alpha-investing which sequentially considers new features as additions to a predictive model by modeling the candidate feature set as a dynamically generated stream. However, Alpha-investing requires the prior information of the original feature set and never evaluates the redundancy among the selected features as time goes. [PERSON] et al. [22] presented Online Streaming Feature Selection (OSFS) algorithm with a faster version, the Fast OSFS algorithm. The computational costs of above methods are very expensive or prohibitive when the dimensionality is extremely high in the scale of millions or more. [PERSON] et al. [23] presented Scalable and Accurate Online Approach (SAOLA) algorithm, a scalable and accurate online approach, to tackle the feature selection with extremely high dimensionality. It conducted a theoretical analysis and derived a lower bound of correlations between features for pairwise comparisons, and proposed a set of online pairwise comparisons to maintain a parsimonious model over time. To sum up, the existing researches of online feature selection seldom focus on the correlation changes, thus fails to capture the characteristics of correlation changes. This leaves a large space to be improved upon in terms of prediction accuracy.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 4 Simulation and Analysis
### PCL-Indexability Conditions and MP Index Evaluation for Scalar-States  
Consider the PCL-indexability conditions stated in Section 3.5 as they apply to scalar-state targets. A sample of numerical evidence is presented below to support the conjecture that the model satisfies such conditions.  
To numerically test the conditions, consider the targets have \(M=2\) dynamic models, which are referred to as CV (\(m=1\)) and CT (\(m=2\)). Set up the parameter values as follows. \(F^{1}=1.1\), \(F^{2}=1.7\), \(Q^{1}=10\), \(Q^{2}=20>Q^{1}\), \(H=1\), \(R^{1}=20\), and \(R^{2}=80\). \(F^{2}=1.7\)represents a greater kinematic maneuverability of the CT dynamic model, which has a higher measurement noise variance \(R^{2}=80\). The detection probability \(p_{\text{d}}\) is 0.8. There are no measurement costs (\(h=0\)). Consider two types of smart targets, called reckless and cautious, where reckless targets have a higher probability of maneuvering (using the CT model) when tracked than cautious targets. Hence, the dynamic model probability matrix \(\mathbf{U}=\left[\boldsymbol{u}^{0},\boldsymbol{u}^{1}\right]\) differs with the target types. Specifically, this subsection selects an arbitrary parameter instance to conduct the simulation experiment.  
\[\mathbf{U}=\begin{cases}[[0.90,0.10]^{\prime},[0.20,0.80]^{\prime}],&\text{ for reckless targets}\\,&\text{for cautious targets}.\end{cases} \tag{52}\]  
where, if target \(n_{1}\) belongs to reckless targets, target \(n_{2}\) belongs to cautious targets, then \(u_{n_{1}}^{0,m}>u_{n_{2}}^{0,m}\) and \(u_{n_{2}}^{1,m}>u_{n_{2}}^{1,m}\), \(m=2,\ldots,M\). It is noted that the TEC states are linearly related to the matrix \(\mathbf{U}\), and the absolute values of the indices vary as \(\mathbf{U}\)  
Regarding PCL-indexability condition (PCLI2), namely that the MP index defined by \(\text{mp}^{*}(P)\triangleq f(P,P)/g(P,P)\) be bounded below, continuous and monotone non-decreasing, Figure 4 plots the MP index for the two target types and the two weights \(d=1,\ 5\). The plot is clearly consistent with condition (PCLI2), and results have been observed in other instances not reported here. Due to the higher probability of a cautious target acquiring a lower cost with the same state \(P\) under action 1, cautious targets obtain a higher index value than reckless targets in all states, which indicates that the MP index policy takes the tracking action prudently for reckless targets.  
In summary, the above partial numerical evidence shows that the model of concern satisfies PCL-indexability conditions (PCLI1) and (PCLI2), and hence the MP index shall be further used as a surrogate of the Whittle index and referred to as the Whittle index.
## 3 Reinforcement Learning
The term _Q(s,a)_ represents the expected value of the total return for the state \(s_{t}=s\) (the present state), which is the sum of the reinforcements, taking into account the rate of discount (\(\gamma\)), as described in expression:  
\[Q^{\pi}(s,a)=E_{\pi}\left\{\sum_{k=0}^{\infty}\gamma^{k}r_{t+k+1}|s_{t}=s,a_{t} =a\right\} \tag{14}\]  
Two central questions in relation to reinforcement learning are presented in [32]:  
* Given a policy \(\pi\)_(s,a)_, what is the best way to estimate _Q(s,a)_? * Given an affirmative response to the preceding question, how can this policy be modified so that _Q(s,a)_ approaches the optimum value of this function, and how can the consequent corresponding optimum policy be obtained? Figure 2: Scheme of interaction between the agent and environment. The literature suggests various algorithms that can be used in replying to these questions. However, in the present work it was decided to use the Q-learning algorithm developed by [35], which offers advantages including the fact that it can directly approach the optimum value of _Q(s, a)_, irrespective of the policy employed. The values of _Q(s, a)_ are updated according to:  
\[Q(s,a)=Q(s,a)+\alpha[r_{t+1}+\ \gamma.max_{a}Q(s_{t+1},a)-Q(s,a)] \tag{15}\]  
where \(\alpha\) is the learning rate (\(0\leq\alpha<1\)) and \(\gamma\) is the discount rate _(\(0\leq\gamma<1\))_. The Q-learning algorithm is presented in Algorithm 1. The episode mentioned in this algorithm is characterized by a sequence of states ending in a final state. ```
1:Initialize _Q (s, a)_ randomly;
2:Repeat (for each episode)
3:Initialize \(s\);
4:Repeat
5:Choose \(a\) for \(s\) using the policy \(\pi\);
6:Given the action \(a\), watch \(r\), _s'_;
7:\(Q(s,a)=Q(s,a)+\ \alpha[r_{t+1}+\gamma.max_{a}Q(s_{t+1},a)-Q(s,a)]\)
8:\(s\to s\)';
9:until the final state is reached;
10:until the number of episodes is reached
```  
**Algorithm 1** Q-learning algorithm. Given that convergence of the algorithm can only be guaranteed if all the state-action pairs are visited an infinite number of times, selection of the policy to be used in the Q-learning algorithm must ensure that all the pairs have non-null probability of being visited. This can be achieved using an \(\varepsilon\)-greedy policy, defined by:  
\[\pi(s,a)= \begin{cases}1-\varepsilon+\dfrac{\varepsilon}{|A(s)|},if\ a=\ a^{*}= arg\ max\limits_{a}Q(s,a)\\ \dfrac{\varepsilon}{|A(s)|},a\
eq a^{*}\end{cases}  \tag{16}\]  
This policy consists in a choice of the associated action to the highest value of Q with \(1-\varepsilon+\dfrac{\varepsilon}{|A(s)|}\) probability and random selection of any other action with \(\dfrac{\varepsilon}{|A(s)|}\) probability, where \(|A(s)|\) is the number of possible actions to be executed from \(s\), and \(\varepsilon\) is the control parameter between greed and randomness.
## 3 Order Tracking Based on the Proposed GAD Method  
Figure 2 depicts the flowchart of the rotational speed estimation method based on GAD; there are mainly three steps in rotational speed estimation: data acquisition, decoupling of gravity acceleration and rotational speed evaluation. In step 1, a local in situ accelerometer is mounted on the rotating disc, and then, the signal is transferred from a wireless data transmission system; after that, the acquired signals are stored in the data acquisition system. In step 2, the GAD method is applied to derive the rotational phase for speed estimation. A low-pass filter is firstly adopted to suppress the high-frequency component of the torsional, horizontal and vertical vibration, and then, phase compensation is presented; after that, the mode decomposition method is used to improve the accuracy of the gravity acceleration component. In step 3, the rotational phase and speed are estimated based on Hilbert transformation and the phased unwrapping method. Furthermore, the estimated rotational speed is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions.