esa-sceva/satcom-chunk-collection huggingface dataset:
# Sparse Logistic Regression: Comparison of Regularization and Bayesian Implementations
## 3 Methods  
Formally, a linear logistic regression model is described in terms of the logistic linking function:  
\[\sigma_{\mathbf{\theta}}(x_{i})=\frac{1}{1+e^{-x_{i}\mathbf{\theta}}} \tag{1}\]  
where \(\mathbf{\theta}\in\mathbb{R}^{p}\) is a \((p\times 1)\) vector containing the coefficient of the linear model, \((\mathbf{x_{i}},y_{i})\) are the input-output data points, where \(x_{i}\in\mathbb{R}^{p}\) and \(y_{i}\in\{0,1\}\) for \(i=1,\ldots,n\) so that \(\mathbf{y}=[y_{1},\ldots,y_{n}]^{T}\) is a \((n\times 1)\) vector and \(\mathbf{X}=[x_{1},\ldots,x_{n}]^{T}\) a \((n\times p)\) matrix. Moreover, we consider the input \(\mathbf{X}\) scaled to have zero mean such that we can consider the intercept term to be zero. We refer to linear logistic regression because there is a linear relationship between the data \(x_{i}\) and the model coefficients \(\mathbf{\theta}\).  
Estimation of the model coefficients \(\mathbf{\theta}\) in (1) is obtained by Maximum-Likelihood (ML). Assuming that the output variable \(y\) is distributed in logistic regression according to a Bernoulli distribution, the probability of the outcome for the \(i\) data point can be written in compact form as:  
\[p(y_{i}|x_{i};\mathbf{\theta})=\sigma_{\mathbf{\theta}}(x_{i})^{y_{i}}(1-\sigma_{\mathbf{ \theta}}(x_{i}))^{1-y_{i}} \tag{2}\]  
The likelihood \(L(\mathbf{\theta})\) of the model given the data is then obtained considering the contribution of all the \(n\) data points:  
\[L(\mathbf{\theta})=p(\mathbf{Y}|\mathbf{X};\mathbf{\theta})=\prod_{i=1}^{n}p(y_{i}|x_{ i};\mathbf{\theta})=\prod_{i=1}^{n}\sigma_{\mathbf{\theta}}(x_{i})^{y_{i}}(1-\sigma_{ \mathbf{\theta}}(x_{i}))^{1-y_{i}} \tag{3}\]  
The parameter vector \(\mathbf{\theta}\) is finally estimated by maximizing the logarithm of the likelihood (3):  
\[l(\mathbf{\theta})=logL(\mathbf{\theta})=\sum_{i=1}^{n}y_{i}log(\sigma_{\mathbf{\theta}}( x_{i}))+(1-y_{i})log(1-\sigma_{\mathbf{\theta}}(x_{i})) \tag{4}\]  
In other words, the estimated model coefficients are those that maximize the probability (or likelihood) of having generated the data. The ML estimate is prone to overfitting [33], especially in high dimensional problems where there are many and possibly correlated input variables. For this reason, sparse classification techniques are useful because they select only a limited number of input variables which generally yields better generalization performance. In the next Sections, we briefly describe the LASSO and RVM implementations of the logistic model (1).
## 3 Results and Discussion  
### Comparison of Measured and Simulated Data in Heat and Mass Simulation  
Comparison between the measured data and the simulated data for micro-environment temperature and RH shows relative errors within \(\pm\)4% for temperature and \(\pm\)4.5% for RH, and absolute errors below 0.7 \({}^{\circ}\)C for temperature and 2.5% for RH (Figure 9). In detail, the accuracy of the simulation is demonstrated through a series of comparative figures. The first figure (a) illustrates the data for the tight control condition, where the errors are  
Figure 8: Framework of parallel prediction (”n” predictors for “n” sensors).  
kept below 3.5% for temperature and 4.5% for RH. The subsequent figures present the conditions under more relaxed control. The second figure (b) depicts a scenario with a 5 \({}^{\circ}\)C fluctuation over a 60-min period at a fixed 50 %RH, where the relative errors are under 4% for both temperature and RH. The third figure (c) represents a 10% RH fluctuation over a 60-min period at a fixed 20 \({}^{\circ}\)C, with errors below 3% for temperature and 1% for RH. These results indicate the high accuracy of the simulation, validating its effectiveness in replicating real-world conditions.  
Figure 9: _Cont._  
To assess the agreement between measured and simulated data, Table 3 shows the KGE values in these three validation conditions. All values exceed the baseline (KGE \(\geq\) 0.5), which indicates an acceptable agreement in simulating the heat, air, and moisture transfer between the micro-environment and macro-environment. The model effectively reproduces the variation trends and fluctuation periods in the measurement conditions for a subsequent study about the determination of an acceptable macro-environment.  
However, the results show relatively high values in the last two measurement conditions with the relaxing control compared to the first condition with tight control. This suggests that the model exhibits greater agreement in the relaxing control than in the tight control.
## 3 Methodology
### The Proposed MDDA Algorithm  
The proposed MDDA algorithm introduces a distinctive weighted matrix designed to enhance the accuracy of low-cost GNSS receivers in smartphones. This weighting matrix is then integrated into the downdate SS algorithm to provide the best set of satellites that can be utilized in better positioning calculations.  
The proposed weighting matrix is constructed by combining Equations (1), (4) and (6) to factor in the influence of both satellite elevation (\(EI\)) and carrier-to-noise ratio (\(C/N_{0}\)). This integration is expressed as follows:  
\[\sigma_{Pr}^{2}=k+cl\left(\frac{10^{-\frac{C/N_{0}}{10}}}{\sin^{2}(EI)}\right) \tag{17}\]  
where \(\sigma_{Pr}^{2}\) is the proposed variance value, \(k\) represents the RMS of pseudorange observation residuals, while \(cl\) corresponds to the chipping length of pseudorange-coarse-acquisition (C/A-code), which is defined as 293 m for L1 measurements, based on standard GNSS specifications that optimize signal processing. Moreover, the weighting matrix \(R\) is formulated as follows:  
\[R=diag[\sigma_{Pr1}^{2}\;\sigma_{Pr2}^{2}\;\ldots\;\sigma_{Prm}^{2}] \tag{18}\]  
where \(\sigma_{Pr1:m}^{2}\) is the proposed variance value from satellite 1 to satellite m.  
Additionally, this weighting matrix is integrated with the downdate SS algorithm to filter out satellites with noisy measurements. The selection process entails picking satellites that demonstrate both 3D position RMSE values lower than the average of the \(C/N_{0}\) values on the horizon and elevation angles below a designated threshold. Moreover, in situations where the receiver has a restricted number of channels, the MDDA selects the most optimal satellite while disregarding the rest.  
Assessing the efficacy of the MDDA algorithm, it transcends its exclusive application with a GNSS receiver. Furthermore, its effectiveness is observed when incorporated into the integration framework of a GNSS receiver and an inertial navigation system, encompassing both loosely coupled and tightly coupled integration schemes.
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Maintenance Periods on Battery Performance
#### 3.2.2 Effects of Maintenance Periods on Battery Power  
In this study, the effects of different maintenance periods on the change rate of battery charge-discharge power were tested with different SOC values--80%, 50%, and 20%. The test results are shown in Figures 11-13. We can see that, with the 120-day maintenance period, the charge power reduction rate dropped 3.8% more and the discharge power reduction rate reduced 5.8% more, compared to those values resulting from the 90-day maintenance period. Thus, it can be considered that these two maintenance periods had equivalent effects on the charge-discharge power of Ni-MH battery modules.
###### Abstract  
This paper presents a method of altitude control of the powered parafoil with uncertainties and disturbances based on sliding-mode backstepping control combined with a linear extended state observer (LESO). First, the dynamics of a powered parafoil is derived in the longitudinal plane using its inclination angle. The problem of altitude control is converted to the issue of angle control. Next, uncertainties and disturbances are considered as a total disturbance. An LESO is used to estimate the total disturbance and form an inner-loop compensation. Backstepping control is employed to regulate the inclination angle to follow the desired value. A fractional sliding surface is introduced to the backstepping control. This ensures the transient performance of altitude control of the powered parafoil. Then, stability analysis shows that the observation errors of the LESO are bounded and the control system is uniformly ultimately bounded. Simulation results of an 8 degree-of-freedom powered parafoil illustrate that the LESO can effectively estimate the states of the system and demonstrate the validity and the superiority of the presented method.  
powered parafoil; altitude control; sliding mode backstepping; fractional calculus; LESO
## 2 System and Channel Model
### Joint Impact of Attenuation, Turbulence and NZB Pointing Errors  
The joint PDF of the total channel state for each hop can be deduced from the integral below  
\[f_{h_{n}}(h_{n})=\int f_{h_{n}|h_{n,n}}(h_{n}|h_{n,n})f_{h_{n,n}}(h_{n,n})dh_{n,n}, \tag{15}\]  
where \(f_{h_{n}|h_{n,n}}(h_{n}|h_{n,n})\) stands for the conditional probability of \(h_{n}\) given \(h_{n,n}\)[67]. The latter can be expressed as [57]  
\[\begin{array}{ll}f_{h_{n}|h_{n,n}}(h_{n}|h_{n,n})&=\ \frac{d\tau_{h_{n} \rho_{n}}(h_{n}|h_{n,n})}{dh_{n}}=\frac{\psi_{n}^{2}}{(A_{0,n}g_{n}h_{n,n})^{ \prime\frac{n}{2}}h_{n,n}}\Big{(}\frac{h_{n}}{h_{n,n}h_{n,n}}\Big{)}^{\psi_{n}^ {2}-1},\ 0\leq h_{n}\\ &\leq A_{0,n}g_{n}h_{n,n},\end{array} \tag{16}\]  
where \(F(.)\) stands for the cumulative distribution function (CDF).  
By substituting (8) and (16) by (15) and by utilizing the analysis performed in [63], we obtain  
\[\begin{array}{ll}f_{h_{n}}(h_{n})\!=\frac{a_{n}g_{n}\psi_{n}^{2}}{A_{0,n}g_ {n}h_{n,n}\Gamma(a_{n})\Gamma(\beta_{n})}\\ \\ \times G_{1,3}^{3,0}\!\left(\frac{a_{n}g_{n}h_{n}}{A_{0,n}g_{n}h_{n,n}}\left| \begin{array}{c}\psi_{n}^{2}\\ \psi_{n}^{2}-1,\ \alpha_{n}-1,\ \beta_{n}-1\end{array}\right.\right),\end{array} \tag{17}\]  
where \(G_{P,q}^{m,n}(.)\) denotes the Meijer \(G\)-function, [71].
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 2 Materials and Methods
### Employing SAM to Assist in Unsupervised Model Counting  
SAM, as a visual meta-model proposed by Meta AI, has achieved remarkable performance in class-agnostic segmentation by utilizing contextual attention. This study reveals that the essence of few-shot counting is actually similar to querying features in images that resemble the distribution of counting instances in a class-agnostic manner. Therefore, in summary, the main workflow of this model is as follows: first, the powerful generalization capability of the SAM meta-model is utilized for semantic segmentation. Then, based on the semantic features, matching is performed to identify the instances that require counting in the image. Finally, once the instances in the image are obtained, counting is performed using the few-shot counting model, as done in previous approaches. The following section will provide a detailed description of how SAM is used to extract image vector features:  
Step 1: Semantic segmentation using SAM. Thanks to the training of SAM with an image encoder, prompt encoder, mask decoder, and a large-scale dataset, it supports three schemes for segmentation: point prompt, box prompt, and automatically generated mask prompt. To achieve an unmanned automated system, this study chooses the approach of automatically generating masks for the initial segmentation of the images in the first step and then converts the results into vector form in the second step.  
Step 2: Similar to the popular use of LangChain in 1.0.0 version with large models like GPT, this study employs vectorization to perform semantic similarity sorting of the model's knowledge and memory. Specifically, a small amount of sample data is stored in vectorized form as external knowledge for the model. Different vectorization methods are tested to store the samples as knowledge vectors, including FLAT [20], HNSW [21], and SWAV [22]. After experimenting with various methods, SWAV is ultimately chosen as the vectorization approach. The specific process is illustrated in Figure 4.  
Figure 4: SAM conducts the preliminary data processing stage.  
Figure 3: Images of different pests.
Photonic Generation of Reconfigurable Ternary Modulated Microwave Signals with a Large Frequency Range  
Yishi Han  
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China; [EMAIL_ADDRESS] (C.Z.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (G.G.); z  
radar system for joint data communication and radar sensing [17]. As for high-speed communication and high-resolution radar sensing application [18], the QAM-LFM signal has been designed, which is generated by superposition of ASK and LFM signals [19]. Moreover, a combined LFM and PSK signal can be applied in the Wuhan ionospheric oblique back-scattering sounding system to achieve higher range resolution and larger signal processing gain [20]. Nowadays few schemes may generate two kinds of modulated signals in the same structure. For example, ASK and PSK signals can be obtained by a dual-polarization quadrature phase-shift keying modulator [21], and PSK and LFM signals are generated through a dual-polarization dual-drive Mach-Zehnder modulator [22]. A structure of flexibly generating ternary modulated signals may meet more comprehensive scenarios. Sadly, such a scheme hasn't been reported.  
In this paper, we propose a photonic scheme to generate ternary modulated microwave signals, including ASK, PSK, and LFM signals. This signal generator is compact because the generation of ternary modulated signals is accomplished by using only a DP-DPMZM, and there is no costly multi wavelength light source. In simulation, this system finally generates an 8 GHz bandwidth LFM signal, and 2.5 Gbit/s ASK and PSK signals at eight wavebands. Benefiting from the independent modulation configuration of two DPMZMs, the large frequency range covers from 10 GHz to 80 GHz and the operation of switching ternary signal is flexible.
# Research on Image Denoising in Edge Detection Based on Wavelet Transform
## 4 Results and Verification  
It has been more than thirty years since the canny edge detection algorithm was proposed, and it is still one of the classic image edge detection algorithms. Compared to other edge detection algorithms, canny has significant improvements in both non-extreme value suppression based on the direction of the edge gradient and lag thresholding with double thresholding. The process generally consists of four stages: noise removal, calculation of gradient amplitude and direction, non-maximum suppression and hysteresis thresholding [27; 28; 29].  
Figure 5: Hard threshold function working process.  
Figure 6: Image denoising: (**a**) noise-laden images; (**b**) denoised images.  
The bridge images before and after noise removal by wavelet transform were subjected to edge detection, and the results obtained are shown in Figure 7. From Figure 7a, it can be seen that the edges of the bridge are initially revealed, but the noise has affected the accuracy of obtaining the relevant detail information of the bridge, and the quality of the whole image is not good enough to provide more reference and support. Compared to Figure 7a, the effect of noise on the results has been significantly reduced in Figure 7b; the bridge edges are basically clear and the presentation of details has been improved, which shows that the removal of noise with wavelet transform before edge detection is obvious and necessary.
# A Computational Study on Magnetic Nanoparticles Hyperthermia of Ellipsoidal Tumors
## 3 Computational Results and Discussion
The thermal damage at the tumor center as a function of treatment time is shown in Figure 9. At the beginning of the treatment, the thermal damage of the malignant tissue at the tumor center is low and relatively unaffected by the tumor aspect ratio and whether it is a prolate or oblate spheroid. As the treatment time progresses, the highest thermal damage is observed in the tumors with AR = 1 and the lowest for tumors with AR = 8. For example, at 20 min of treatment the Arrhenius damage index is \(\Omega\approx 2.75\) for AR = 1 and \(\Omega\approx 1\) for AR = 8. The thermal damage evolution in the tumor center is generally independent of the tumor prolateness or oblateness, but it is strongly affected by the aspect ratio. Also, after approximately 22 min of procedure, \(\Omega\) appears to be relatively stable for each aspect ratio. If we select the frequently quoted \(\Omega=1\) threshold value above, which has a relatively high chance of causing irreversible damage the malignant tissue, we find that for AR = 1 this value is reached after approximately 10 min. of treatment. However, for AR = 8, more than 20 min of treatment are required to reach the \(\Omega=1\) threshold value, and shorter times are required for tumors with smaller AR values. Figure 10 shows the Arrhenius integral \(\Omega\) in the tumor for three representative geometries. Figure 10a is for AR = 1, Figure 10b for a prolate tumor with AR = 2 and in Figure 10c for an oblate one with AR = 2. For each case, the thermal damage in the central tumor region is higher than regions near the tumor surface, which is the direct result of the temperature distribution (see Figures 5 and 6). To determine the extent of the damaged region in the ellipsoidal tumors, the frequently quoted \(\Omega=1\) criterion is used as carried out by [PERSON] et al. [106] and [PERSON] et al. [110]. In these studies, the boundary between the necrotic tumor area (\(\Omega>1\)) and the undamaged tumor region (\(\Omega<1\)) was determined using the \(\Omega=1\) iso-contour. If we follow this approach in the sample tumor cases shown in Figure 10, we find that the location of this boundary is significantly affected by the aspect ratio and by whether the tumor is a prolate or oblate spheroid. Interestingly, for an oblate tumor of AR = 2, a small portion of the surrounding healthy tissue may necrose, which is not the case for a prolate tumor of the same aspect ratio. Figure 9: Evolution of thermal damage at the center of ellipsoid tumors for all analyzed cases.
## 3 Experimental Flight and System Identification  
In this study, the system identification process was performed in the following steps: (1) experimental flight setup; (2) data acquisition for system identification via experimental flight; (3) linear model refinement in system identification using the gray box model approach; and (4) dynamic model analysis and verification.
## 4 Overlapped Discretization Concepts
### Multiple Grids Concepts  
The conventional discretization of complex geometries to consider simpler elements and approximate the underlying physical variations within the element has long been established. Although the direct discretization of numerical domains using two layers of partitioning, which include initial meshing of domains and secondary partitioning for parallel-processing, are typical practices among design engineers, the computational efficiency of the scheme is under scrutiny. To manage the numerical discretization of the engineering system in a more efficient manner, the concepts of overlapping/isolated zones having \"actual\" and \"potential\" grids are introduced [19]. Based on the evolution scenario within the numerical domain (e.g., passage of vehicle under a road structure), a zoning-plan for the numerical domain is defined and each zone is coded with a specific status number. These zones can be defined as main, void, partial overlapping, full overlapping, and high resolution zones, among others, which interact with each other. In the next step, discretization of individual zones is carried out based on their specific requirement  
Figure 3: Components of dynamic finite zone computational framework and its central unit.  
(continuous, meshless, particle, and so on) and, depending on the defined scenarios, the actual and potential interface grids are defined. These grids are assembled into a finite zone simulation system using various types of interfaces (active and passive interfaces) to be sent to the parallel computational solvers. Different instances of same and/or different solvers would be employed on different zones and an appropriate number of processors is chosen for each computational zone to achieve the best computational balance (minimum waiting time).  
For complex design scenarios, a smart auxiliary virtual unit would also be employed where AI and ML technologies are mobilized to reduce the number of design cycles in high resolution zones (e.g., detailed design zones within the main numerical domain). Figure 4 shows the schematic architecture of the numerical system with its component units.
# Towards Blockchain-Based Federated Machine Learning: Smart Contract for Model Inference
## 3 Methods
### Smart Contract Implementation Details
Afterwards, the JSON data (in the form of a byte array) from the ledger are selected (Figure 4--messages 2-3, Figure 5--lines 2-3), and if the selection is successful the validation continues. Otherwise, once the error object is populated, the _validateModel_  
Figure 4: Smart contract behaviour during model validation: UML sequence diagram. Figure 5: Validate model function: Chaincode fragment of the smart contract. function returns a failure message (Figure 4--opt fragment, message 4, Figure 5--lines 4-6). Afterwards, the JSON data and structure are mapped using the JSON library package function (Figure 4--message 5, Figure 5--line 7). Then, one more instance is instantiated--an array of float type (Figure 4--message 6, Figure 5--line 8). Once the required variable instance is constructed, a prediction is calculated for each validation data record and the model parameters. Considering that the prediction is calculated for each data validation record, a loop fragment is used. Inside a loop, a separate method for the prediction calculation is used and each calculated prediction is appended to the previously instantiated float type array (Figure 4--loop fragment, messages 7-8, Figure 5--lines 9-12). Once all the predictions for all DataWrapper records are calculated, the calculated predictions for a specific model are saved in the blockchain ledger using the _shim_ library [34], and finally, the success message is returned (Figure 4--message 9-10, Figure 5--lines 13-14). The test models function (Figure 2--UC2), which is called during the data upload process, is based on the same principles as the model validation function. The test function differs from the model validation in several aspects. During the testing, the models are selected from the ledger, and new prediction results are calculated (similar as in Figure 4--loop fragment, message 8) and updated accordingly. During the testing, the model and the data records remain unchanged, only the results are appended to the blockchain ledger. Both testing and validation functions calculate the prediction results, and the only difference is the number of models that passed to the function. The following experiment sections cover the runtime benchmarking results of the _validateModel_ function. The execution of the _validateModel_ function relies heavily on the _calculatePrediction_ function, where the logic of model inference resides. The running example of the logistic regression inference is demonstrated in Figure 6, which depicts how prediction is obtained for a single data row using a pre-trained model. As it can be noticed, the prediction got very close to the ground truth.