esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Distance-Based Energy Aware Routing (DEAR) Algorithm
### DEAR Algorithm
#### 4.2.1 Basic Assumptions  
We make the following basic assumptions similar to [17]:  
* All sensor nodes are static after deployment.
* The communication links are symmetric.
* Each sensor node can control its power level to the neighbors.
* Each sensor node can know the distance to its neighbors and to the sink node.
* We assume ideal MAC layer conditions.  
Here, we make no assumption of the uniform distribution of sensor nodes or the knowledge of global network topology. Based on the received signal strength index (RSSI) or other positioning and localization techniques, each node can get the distance to its neighbors and to the sink node and then adjust its transmission power level.
## 2 Materials and Methods
### Outline of Computational Approach
#### 2.5.3 Artificial Bee Colony (ABC) Algorithm
If the generated \(V_{i,k}\) exceeds the predefined parameter boundaries, it is adjusted to either the lower or upper limit using Equation (13), maintaining feasibility within the search space [45]. \[V_{i,k}= \begin{cases}\Lambda_{low,k},&V_{i,k}<\Lambda_{low,k}\\ V_{i,k},&\Lambda_{low,k}\leq V_{i,k}\leq\Lambda_{up,k}\\ \Lambda_{up,k},&V_{i,k}>\Lambda_{up,k}\end{cases}  \tag{13}\]  
The new solutions \(V_{i}\) generated by employed bees represent potential food sources. The quality of these sources is evaluated, and their fitness values are assigned using Equation (14). \[fit_{i}= \begin{cases}\frac{1}{1+S_{i}},&g_{i}\geq 0\\ 1+abs(g_{i}),&g_{i}<0\end{cases}  \tag{14}\]  
Here \(g_{i}\) represents the cost value of the solution \(V_{i}\). Employed bees perform a greedy selection between their current solutions \(Q_{i}\) and potential solutions \(V_{i}\) based on fitness values. If \(V_{i}\) is better, the bee replaces \(Q_{i}\) in its memory and resets the failure counter. Otherwise, it keeps \(Q_{i}\) and increases its failure counter by one. This process ensures efficient exploration and refinement of solutions [45]. When employed bees return to the hive, they share food source information through dances, which onlooker bees observe to assess nectar quality. Onlooker bees then select food sources based on probabilities calculated using Equation (15), where selection is proportional to nectar amount [45]. \[p_{i}=\frac{fit_{i}}{\sum\limits_{n=1}^{N}fit_{n}} \tag{15}\]Here, \(fit_{i}\) represents the nectar amount (fitness) of the \(i\)th source, and \(N\) denotes the total number of food sources. After selecting a source, onlooker bees generate a candidate food source using Equation (12), similar to the employed bees search process. The new candidate \(V_{i}\) is then compared with the existing solution \(Q_{i}\). If the nectar amount of \(V_{i}\) is greater than or equal to that of \(Q_{i}\), the new source replaces the old one in memory; otherwise, the previous source is retained [45]. In the ABC algorithm, at the end of each cycle, both employed, and onlooker bees increment the failure counter for solutions that could not be improved. If an improvement occurs, the counter is reset to zero. Each solution has an associated failure counter, which increases if neither the employed nor the onlooker bee enhances it. When this counter exceeds a predefined threshold (limit parameter), the solution is considered depleted, and the employed bee abandons it. The bee then becomes a scout, generating a new random food source using Equation (11) to replace the exhausted one [45].
## 5 Robust Control
### Robust Stability Analysis  
We assume that the controller \(C(s)\) was designed such that the closed loop with the nominal plant Equation (4) is stable. Let \(L(s)\) denote the corresponding (nominal) open loop transfer function, i.e.,  
\[L(s)=C(s)\cdot\widetilde{G}(s). \tag{20}\]  
Furthermore, let us define \(\widetilde{S}(s)\) and \(\widetilde{T}(s)\) as the (nominal) sensitivity and complementary sensitivity function  
\[\widetilde{S}(s)=\frac{1}{1+L(s)}\quad\text{and}\quad\widetilde{T}(s)=\frac{L( s)}{1+L(s)}. \tag{21}\]  
Then, assuming an additive uncertainty Equation (8), the closed loop system is robustly stable if and only if  
\[\|W\,C\,\widetilde{S}\|_{\infty}<1, \tag{22}\]  
see [34]. The inequality Equation (22) can be adjusted into:  
\[\left\|\frac{W\,C}{1+C\,\widetilde{G}}\right\|_{\infty}<1. \tag{23}\]This condition can be interpreted graphically in the Nyquist diagramm: The envelope of the frequency response with the center \(L(j\omega)\) and the radius \(W(j\omega)\widetilde{G}(j\omega)\) cannot include or circumvent the critical point \(-1\in\mathbb{C}\). Then, the closed loop system is stable because of the small gain theorem [32].  
The other uncertainties have different versions of such conditions. Table 1 summarizes the robust stability conditions for the uncertainty models.
## 2 Materials and Methods
### AoR Estimation
#### 2.2.2. Symmetrical Axis of Rotation Approach (SARA)  
The SARA algorithm used in this work is based on the original work of [PERSON] et al. [26] and considers two marker sets that are simultaneously moving relative to the GCS. To run the algorithm, an LCS is assigned to each marker cluster, that is, to each segment. The same shank LCS used to run the IHA algorithm was used, whereas the foot LCS was defined as shown in Figure 3b and includes:  
1. \(\rm O_{F}\), the origin located at the centroid of the foot marker cluster;
2. \(a\), antero-posterior axis oriented from FProxLat to FDistLat;
3. \(b\), medio-lateral axis orthogonal to \(a\) axis, oriented toward FDistMed;
4. \(c\), axis orthogonal to the plane (\(a\),\(b\)).  
By mathematical formulation, SARA considers that the estimated AoR, used to represent the relative motion between each LCS and the GCS, remains stationary with respect to the corresponding segment. Rigid body transformations are performed to evaluate the rotation matrices and translation vectors associated with each segment, which relate the \"fixed\" reference marker set represented in each LCS to the corresponding time-varying configuration in the GCS. This means that, for the shank segment, a rotation matrix \(R_{\rm i}\) and a translation vector \(T_{\rm i}\) must be estimated, whereas, for the foot segment, separate matrix \(S_{\rm i}\) and vector \(D_{\rm i}\) are considered. Each matrix and vector are evaluated for any time, similarly to the IHA parameters. As detailed in the literature [26; 40], this calculation is performed by minimization of an objective function. This problem, which is equivalent to a linear least squares one, is solved via SVD and yields the functional axis parameters for each LCS, that is, a position vector \(c\), which defines a reference point on the axis, and an orientation vector \(v\). These parameters, due to the nature of the algorithm, are time-independent; therefore, two couples of constant vectors (\(\epsilon_{\rm shank}\), \(v_{\rm shank}\)) and (\(\epsilon_{\rm foot}\), \(v_{\rm foot}\)) are obtained as a consequence of the SVD. Each couple identifies the position and the orientation of the functional axis in the corresponding LCS. Due to measurement noise, when transformed in the GCS by means of the (\(R_{\rm i}\), \(S_{\rm i}\)) matrices and the (\(T_{\rm i}\), \(D_{\rm i}\)) vectors, the axes identified by each couple do not coincide, and therefore an average is performed for each time frame.  
To ease the comparison between SARA and IHA approaches, the axes calculated by both methods were represented in the shank LCS. Unlike the IHA approach, which is subjected to an angular velocity threshold to avoid ill-posed estimation, SARA is able to perform the AoR evaluation for all the time frames available.
## 2 Materials and Methods
### Performance Metrics  
Evaluating a machine learning model's performance requires more than just accuracy and loss. While these metrics give an initial idea of the model's learning and generalization capabilities, they do not provide a complete picture, especially when dealing with class imbalances or when different types of errors have varying consequences. To gain a comprehensive understanding of the model's effectiveness, it is essential to validate it using a broader set of performance metrics.  
In the context of this research, which involves detecting tomato leaf diseases across 11 different classes, the criteria used for calculating performance metrics are extended to accommodate the diverse range of classes. These criteria include True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN):  
* True Positive (TP): Instances where the model correctly predicts images belonging to a specific disease category or as healthy;
* True Negative (TN): Instances where the model correctly predicts that a sample does not belong to any of the other 10 classes;
* False Positive (FP): Instances where the model incorrectly predicts that an image belongs to a specific class when it actually belongs to another class;
* False Negative (FN): Instances where the model incorrectly predicts that a sample does not belong to a particular class when it actually does.  
Using these criteria, the following metrics are calculated: accuracy, precision, recall, F1-score, and ROC-AUC. Accuracy measures the proportion of correctly classified instances, while precision and recall provide insights into the model's performance in distinguishing between true positives and false positives/negatives. The F1-score offers a balance between precision and recall, especially useful in imbalanced datasets. ROC-AUC quantifies the model's ability to differentiate between classes, with values closer to 1 indicating better  
Figure 7: Model performance heatmap for learning rate and batch size combinations.  
performance. These metrics collectively provide a comprehensive evaluation of the model's effectiveness in tomato plant disease classification.
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 4 Measuring the LT Precession via Pulsar Timing
### Lense-Thirring Precession in \(\dot{x}\)
#### 4.1.2 PSR J1757\(-\)1854, the Most Accelerated Binary Pulsar  
PSR J1757\(-\)1854 is a 21.5 ms pulsar in a highly eccentric (\(e=0.61\)) 4.4 h orbit around an NS companion. This pulsar was discovered in 2016 with the 64 m Parkes radio telescope Murriyang as part of the High Time Resolution Universe Pulsar Survey [83]. The orbit of this system resembles a compact version of the Hulse-Taylor pulsar PSR B1913\(+\)16, with one of the shortest merger times of any known DNS (\(\sim\)76 Myr) and the highest relative velocity (1060 km s\({}^{-1}\)) and acceleration (\(\sim\)680 m s\({}^{-2}\)) at periastron [34]. This system was believed to be promising for measuring the LT precession to \(\dot{x}\). As shown in Section 2.2,  
Figure 7: The absolute ratio of the contributions to \(\dot{x}^{\text{SO}}\) from \(\dot{x}^{\text{LT}}\) and \(\dot{x}^{\text{QPM}}\), \(R=|\dot{x}^{\text{LT}}/\dot{x}^{\text{QPM}}|\), plotted as a function of the unknown spin period of the WD (\(P_{\text{WD}}\)). (**A**,**C**) Marginalised posterior distributions with their 68% confidence intervals shaded, defined as the combination of the two 34% confidence regions on either side of the 2D maximum of the likelihood function. (**B**) Two-dimensional probability distribution with contours defining the 68%, 95%, and 99% likelihood confidence intervals. The grey-shaded regions and dotted contours are constraints using only the radio observations of the pulsar, whereas the red regions and solid contours include additional binary evolutionary constraints from simulations. Figure from [75]. Reprinted with permission from AAAS.  
this contribution depends on the misalignment angle and geodetic precession phase. In the recent study [34], the preferred geometry solutions obtained from \"precessional Rotating Vector Model\" analysis [84] indicate an upper limit for \(\dot{x}^{\rm LT}\) (see Table 1) two orders of magnitude smaller than the uncertainty of \(\dot{x}^{\rm obs}\) from a 6 yr campaign with the Green Bank Telescope (GBT) and Parkes telescopes. With simulated observations for GBT and MeerKAT, it was concluded that a 3-\(\sigma\) measurement of \(\dot{x}^{\rm obs}\) may be possible by 2031, and other contributing terms in Equation (10) also need to be determined and deducted so as to measure the LT contribution, which is a challenging task.
## 1 Introduction
For instance, criteria such as accessibility, suitability, operability, learnability, client personnel requirements, installability, transparency, and understandability are used to further enhance usability. The consumers of today encounter difficulties when trying to select the most appropriate service provider with respect to various considerations, such as available budgets, operational needs, and performance duration, due to the rapid rise of online cloud services and providers. The most suitable service provider should be determined by a consumer's evaluation of various QoS attributes, such as dependability, security, cost, and performance. The consumer's requirements must be matched with the features of the cloud services provided by different service providers to determine which CSP is most suitable. A thorough investigation and a reliable ranking methodology are needed to select an appropriate CSP. Since this incorporates the inherent linkages among several QoS criteria, this can be classified as a multi-criteria decision-making (MCDM) problem. Recent years have seen a tremendous increase in investigations of CSP selection while relying on the MCDM method. For example, [PERSON] et al. [9] applied K-means algorithms with a DEMATEL-analytic network process (ANP) to rank CSPs with respect to the interdependence among the performance measurements. Furthermore, [PERSON] et al. [10] applied a hybrid method by integrating the analytic hierarchy process (AHP) with the Induced OWA (IOWA) operator to assist cloud consumers with selecting the optimal service provider. The Conditional Preference Networks (CP-nets) framework was proposed by [PERSON] and [PERSON] [11] to select the optimal CSP while considering a group of attributes with complex interdependencies. This study explains how to evaluate all parties (CSP, resources, and tasks) by using the MCDM method. Then, the tendencies of tasks based on upper and lower thresholds are defined, and these are calculated based on the results of applying the MCDM method. Finally, a simple mechanism for optimizing the process of assigning customers' tasks to the best available CSP/resource is adopted.
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
###### Abstract  
Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.  
fault diagnosis; convolution neural networks; wavelet packet transform; data-to-image conversion; bearing +
Footnote †: journal: Applied Sciences
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
## 4 Four Heterogeneous Signcryption Schemes for V2I Communication Scenarios
### IBC\(\rightarrow\)PKI One-to-Many Signcryption (OMHSC-II)  
The **Setup** is the same as the above MOHSC-I algorithm. **PKI-KG** and **IBC-KG** algorithms have slight changes, i.e., the sender RSU's key pair is (\(pk=H_{1}(ID)=Qsk=sQ\)) in IBC and the receivers \(V_{i}\)'s \((i=1,2,\ldots,n)\) key pairs (\(pk_{ri}=x_{ri}P,sk_{ri}=x_{ri}\)) in PKI.  
**Signcrypt**: Taking **params**, a random message \(m\), RSU's secret key \(sk\), and multiple receivers' public keys \([pk_{ri}]_{i=1}^{n}\) as input, the algorithm performs as follows (repeat 2, 3 steps \(n\) times):  
1. Randomly choose \(r\in Z_{q}^{*}\) and compute \(R=r\cdot P_{pub}\);
2. Calculate \(k_{i}=e(pk_{ri},r\cdot sk)\), \(c_{i}=m\oplus H_{2}(k_{i})\);
3. Calculate \(h_{i}=H_{3}(pk,pk_{ri},R,c_{i})\), \(S_{i}=sk\cdot h_{i}\);
4. Broadcast \(\sigma=(R,c_{1},\ldots,c_{n},S_{1},\ldots,S_{n},\Omega)\) to multiple receivers \(\{V_{i}\}_{i=1}^{n}\), where \(\Omega\) is a label, which includes a message about how \(c_{i}\) and \(S_{i}\) are associated with the receivers.  
**Unsigncrypt**: Any receiver \(V_{i}\) takes (\(R\), \(c_{i}\), \(S_{i}\)) from \(\sigma\). Take \(V_{i}\)'s secret keys \(sk_{ri}\), the sender's \(pk\), and **params** as inputs, then this algorithm performs the following steps:  
1. Compute \(h_{i}=H_{3}(pk,pk_{ri},R,c_{i}),1\leq i\leq n\);
2. Verify \[e(S_{i},P)=e(h_{i}\cdot pk,P_{pub});\] (7) If (7) is held, emits _true_, which means \(\sigma\) is valid. Or else, emits _false_ and abort.
3. Calculate \[k_{i}^{\prime}=e(pk,sk_{ri}\cdot R);\] (8)
4. Calculate \(m=c_{i}\oplus H_{2}(k_{i}^{\prime})\), and get the message \(m\).  
**5. Security Proof**  
The correctness and security of our proposed schemes will be discussed in this section.
## 5 Overview of general scaling methods
### The scaling methods for retrieval models  
Generally speaking, we may only know the retrieval model at a specific scale. That is to say, we need to scale the retrieval models to the other scales through the appropriate assumptions and simplifications in order to not only consider the scaling effects but also provide scale invariant algorithms. [PERSON] [4] proposed a general method, the Computational Geometry Method (CGM), to reduce the scaling effects introduced by the heterogeneity of measurements and the nonlinearity of a retrieval model at a local scale. The method takes advantage of the convex hull of computational geometry to determine the interval where the distributed result may exist. Considering all possible distributions of measurements, the distributed result always falls into the interval of the lower and upper bounds of the retrieval model which measures the maximum error due to the scaling. Here, the bounding functions can be interpreted as a measure for the non-linearity of the model [16]. Subsequently, [PERSON] and [PERSON] [68] used such a concept combined with a least square method to determine the coefficients of the semi-empirical model that is validated at a small scale and upscale the model to the large scale with global radiances. If there is not more information available other than the domains of measurements and the retrieval model at the local scale, one can expect to reduce the scaling effects by employing the model spatialization method, which is the function of the convex and concave function of the retrieval model at a local scale. [PERSON] _et al_. [34] argued that the assumption that the retrieval model follows a uniform distribution in the interval between the convex and concave function is inappropriate within a moderate resolution pixel. This may be one of the reasons limiting the application of the spatialization model. For the specific retrieval model, a more specific scaling method would be used, the Physical Scaling Method (PSM) based on the radiative transfer theory. [PERSON] _et al_. [21] developed this method with an explicit spatial resolution dependent in order to upscale LAI retrieved from AVHRR data to the coarser resolutions. [PERSON] _et al_. [11] discussed PSM and gave examples of the radiative transfer scaling in a Norway spruce forest stand.
## 2 The Lozi Chaotic Map
### Fractional Lozi Map
#### 2.5.3 Complex Fractional Map  
Other authors, such as Joshi et al. [42], have considered the definition of a fractional map (13) for the case where the order of derivation \(\
u\) is a complex number with \(0<Re(\
u)<1\). They used \(L_{1}\) to denote the model defined by (13) and \(L_{2}\) to denote the one defined by  
\[x(n)=x(0)+\frac{1}{\Gamma(\
u)}\sum_{j=1}^{n}\frac{\Gamma(n-j+\
u)}{\Gamma(n-j +1)}[-\alpha[x(j-1)]+x(j-1)+1-bx(j-2)]. \tag{16}\]  
To systematically investigate the impact of complex order on fractional maps, they set \(\
u=\
u_{0}e^{j\pi\tau/2}\) with \(0<\
u_{0}<1\), which reduces to the real fractional order for \(r=0\). They studied the bifurcation diagram numerically, comparing it to the bifurcation diagram of the Henon map.  
In the case of the Henon map, they did not observe chaos for \(r\
eq 0\), indicating that the chaotic attractor is destroyed when complex order was introduced. They checked their results using a number of different initial conditions, finding that the bifurcation diagram does not change, which highlights that multistability is not very pronounced for the fractional order.  
They found, however, that the situation is different for the Lozi map. In this case, the chaos does not disappear. They computed the bifurcation diagrams of the Lozi map for \(\
u_{0}=0.8\) and \(r=0,0.01,0.1,0.5\) for model \(L_{1}\).  
The range of variable values indicates that there are parameter zones that are chaotic, or at least periodic with a very large period. They performed other tests as well, such as finding the largest Lyapunov exponent for models \(L_{1}\) and \(L_{2}\) to confirm the presence of chaos (the divergence of trajectories is exponential in the \(L_{1}\) formulation, and the calculation of the Lyapunov exponent is justified). They found that the largest exponents are 0.264 for model \(L_{1}\) and 0.326 for model \(L_{2}\) for the parameters mentioned above. These results confirm that the Lozi map shows chaos for the difference equation of the complex fractional order.  
Moreover, they noted that extremely strange bifurcations are observed in model \(L_{1}\), showing bifurcation diagrams for \(\
u=0.4+0.3i\) and \(\
u=0.4+0.5i\) (see Figure 7a,b of [42]) which clearly indicate the possibility of very large periods and a rich bifurcation structure that is not generally seen in integer-order systems.  
In addition, they noted that the system has memory; the complex fractional ordered Lozi map shows multistability, and the asymptotic attractor depends on the initial conditions.  
Finally, they studied control (see Section 5.2.1) and synchronization (see Section 7.1.6) for this map.