esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 6 Conclusions
### Coordinates and Clock Comparison  
In general coordinate and clock differences have been demonstrated to be discontinuous and show an oscillating trend with a period of about 12 h (except BeiDou IGSO satellites which have a period of about 24 h). The discontinuities affect mostly the along-track component, in which they are about 1 m for GPS, Galileo and BeiDou IGSO and up to 3 m for GLONASS and BeiDou MEO, and to a lesser extent the radial and cross-track components, in which they are at dm-level.  
GPS and Galileo broadcast ephemeris are closer to the precise orbits than those of GLONASS and BeiDou: for GPS and Galileo the average values of RMS are about 0.15 m and 1 ns whereas for GLONASS the average RMSs are 0.48 m and 5.6 ns and for BeiDou 0.43 m and 2.2 ns.
## 1 Introduction
### Gerschgorin Disk Theorem  
The definition of Gerschgorin disk [18]  
\[\mathbf{A}=\left(a_{ij}\right)_{n\times n} \tag{1}\]  
\[R_{i}=\sum_{\begin{array}{c}j=1\\ j\
eq i\end{array}}^{n}\left|a_{ij}\right|=\left|a_{i1}\right|+\cdots+\left|a_{ i(i-1)}\right|+\left|a_{i(i+1)}\right|+\cdots+\left|a_{in}\right|\left(i=1,2, \cdots,n\right) \tag{2}\]  
The circular regions on a complex plane, shown as (1) in the following, are named after the \(i\)-th Gerschgorin disk of the matrix \(\mathbf{A}\), and \(R_{i}\) is the radius of Gerschgorin disk \(G_{i}\). Gerschgorin disk theorem can be illustrated in Figure 1.  
\[G_{i}=\left\{z||z-a_{ii}|\leq R_{i},z\in C\right\}\quad(i=1,2,\cdots,n) \tag{3}\]  
As Figure 1 shows, each Gerschgorin disk means the possible location of the corresponding eigenvalue of the matrix \(\mathbf{A}=\left(a_{ij}\right)_{n\times n}\). The center of the \(i\)-th disk is the diagonal element \(a_{ii}\); its radius is the sum of the modulus of the non-diagonal elements in \(i\)-th row, which can be calculated as Equation (1).  
Gerschgorin Disk Theorem:  
The \(n\) eigenvalues of the matrix \(\mathbf{A}\in\mathbf{C}^{n\times n}\) locate in the union of relevant \(n\) Gerschgorin disks. \(\lambda_{i}\in\cup G_{i}\), \(\lambda_{i}\) is the \(i\)-th eigenvalue of the matrix \(\mathbf{A}\), where \(I=1,\cdots,n\). Abbreviate this as the disk theorem.
# Research on Image Feature Extraction and Environment Inference Based on Invariant Learning
## 5 Discussion  
In the context of deep learning facing challenges in out-of-distribution generalization, this paper proposes an image invariant feature extraction method based on invariant learning specifically for image datasets and introduces, for the first time, an environment partitioning algorithm for mixed image datasets using Grad-CAM. Validation of this algorithm is conducted using the iWildCam 2019 dataset, leading to the following conclusions:  
1. The proposed image invariant feature extraction method based on invariant learning significantly outperforms methods that do not utilize invariant learning in terms of accuracy and stability on the test set, achieving consistent and stable high-accuracy predictions across different environments. This demonstrates a substantial enhancement in the model's out-of-distribution generalization capability, providing a novel solution for image analysis in complex environments.
2. A comparison of the heatmaps generated by Grad-CAM indicates that the IRM-ResNet18 effectively extracts key invariant features of the classification target. Further validation of IRM-ResNet18's superior feature extraction performance is achieved by training support vector machines and random forest models with the extracted features.
3. By integrating invariant learning with Grad-CAM theory, this paper proposes an environment partitioning algorithm capable of clustering spurious features within mixed image datasets. The results from the environment partitioning experiments further substantiate the algorithm's effectiveness in addressing the issue of unknown environmental labels.  
The proposed image environment partitioning method resolves the challenge of requiring multiple heterogeneous environments for training invariant models, providing essential conditions for image classification and invariant feature extraction. This method is applicable to target recognition, image classification, and segmentation tasks in complex environments, with potential applications in practical scenarios such as fault detection in power systems and defect identification of insulators in transmission lines. Future research will apply the proposed iterative feature separation system to the classification and recognition of electrical equipment images under complex background conditions, aiming to achieve more precise environment partitioning outcomes.  
Conceptualization, Y.D.; software, X.M.; validation, B.Y.; writing--original draft, X.M. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by the Beijing Outstanding Youth Science Fund under Grant JQ22009.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 4 Recognition of the Coefficient of the Vibration Error  
In order to recognize the coefficients in the nonlinear model, the Kalman filter (KF) method should be used. The Kalman filter is an optimized autoregressive data processing algorithm [27]. The basic principle of the algorithm is to use the minimum mean square error as the best estimation criterion, and use the state-space model of signal and noise to introduce the relationship between state variables and observed variables by establishing state equations and observation equations model [28]. KF is a linear filter, which is only suitable for linear systems. EKF (Extended Kalman Filters) and UKF (Unscented Kalman filters) are both developed to work with nonlinear systems. As the EKF linearizes the nonlinear system using Taylor expansion while the UKF propagates the sigma points by function evaluations using the unscented transformation [29]. Several studies have compared these two methods [30].  
In this paper, we choose to use the EKF method. UKF is complicated and has a big computation burden. It costs a significant amount of time to select the sigma points. Moreover, the set of the weight for every sigma point is random because it was settled artificially. It often leads to the filter result divergent. Therefore, compared to EKF, UKF does not perform well as it is expected in theory. In order to calibrate more error items, we set 36 parameters in the state vector. If UKF is used, the computation will be greatly increased. In addition, the EKF accuracy is enough for the FOG-based MWD. Therefore, we choose to use EKF.
# Research on a Wi-Fi RSSI Calibration Algorithm Based on WOA-BPNN for Indoor Positioning
## 2 Methods and Algorithms
### Algorithms
#### 2.2.1 Calibration Algorithm Based on WOA-BPNN
Obtain the predicted output value using the nonlinear sigmoid activation function, as shown in Equations (8) and (9), as follows: \[I_{j}=\sum_{i=1}^{N_{sample}}w_{ij}RSSI_{x,i}+\theta_{j}\] (8) \[O_{j}=\frac{1}{1+e^{-I_{j}}}\] (9) where \(w_{ij}\) is the connection weight between the neuron \(i\) and the neuron \(j\), \(\theta_{j}\), \(I_{j}\) and \(O_{j}\) are the threshold, input value and output value of the neuron \(j\), respectively, and \(RSSI_{x,i}\) is the input value of the neuron \(i\) and \(N_{sample}\) is the number of samples. * **Step 8:** With the forward propagation process, we obtain the predicted value of the output layer neuron. According to the error between the predicted output value and the real output value, obtain the loss function of the current iteration number \(E_{j}\), as shown in Equation (10); the error is propagated back to the upper layer of neurons to obtain the error in this layer, and then passes layer by layer until the top hidden layer is reached. Update and adjust the connection weights and thresholds based on the gradient descent method, as shown in Equations (11) and (12), as follows: \[E_{j}=\frac{1}{2}\sum_{j=1}^{N_{sample}}\ (RSSI_{y,j}-RSSI_{y,j}^{t})^{2}\] (10) \[f(X_{i})=\sum_{i=1}^{N_{sample}}\left|y_{i}-y_{i}^{\prime}\right|\]0 (11) \[f(X_{i})=\sum_{i=1}^{N_{sample}}\left|y_{i}-y_{i}^{\prime}\right|\]1 (12) where \(RSSI_{y,j}\) is the real output value of the output layer neuron \(j\), \(RSSI_{y,j}^{t}\)  
* **Step 9:** After repeated learning and training, when the current number of iterations of the BPNN model \(T\) reaches the maximum number of iterations of the BPNN model \(T_{\text{max}}\), select the BPNN model with the smallest loss function \(E_{j}\) as the final calibration model, and save the parameters of the current WOA-BPNN calibration model and corresponding mobile terminal model. * **Step 10:** Repeat **Step 1** to **Step 8** for multiple mobile terminals, then establish the calibration model database.
# On End-to-End Intelligent Automation of 6G Networks
## 3 5G Orchestration: Related Works and Limitations
### Related Works
This highlighted the proposed framework's effectiveness, efficiency, and suitability for 5G metro-scale RAN networks [79]. [PERSON] et al. again focused on the optimal network slicing problem as part of the 5G network orchestration process [80]. However, the authors in this case focused on the resiliency and latency awareness of the orchestration decisions. To that end, the authors formulated the problem of resilient and latency-aware 5G network slice orchestration in a mobile edge computing (MEC)-enabled environment as a binary integer programming (BIP) problem. The objective of the BIP model was to reduce the deployment cost of the different slices while taking into consideration the bandwidth, latency, and placement requirements/constraints. Moreover, since the BIP problem is shown to be NP-hard, the authors proposed a GA-based solution for the aforementioned problem [80]. Simulation results showed that the proposed GA-based solution reduced resource wastage, improved network throughput, and provided high resiliency against failures [80]. Note that these works can be categorized using one of two points of view: the first being the methodology adopted to solve the orchestration problem and the second being the level at which the orchestration decisions are made. Table 1 summarizes the related works in terms of the methodology adopted, including \"Mathematical Optimization-Based\", \"Game-Based\", \"Metaheuristic-Based\", or \"Heuristic-Based\" solutions. In a similar manner, Table 2 summarizes the works in terms of the orchestration decisions' level being at the \"Core\", \"Transport\", or \"Radio Access\" network level.
## 5 Prototype: A Proof of Concepts
### EHR Based on the Hyper-Ledger Indy  
We started by implementing the solution that manages EHRs based on Hyperledger Indy as summarized in Figure 2 and detailed in Section 4.2. Here are the various interfaces of the prototype involved during the different steps of the approach.  
Suppose that patient [PERSON] visits a laboratory L for the first time to receive some medical analysis. Usually, after his visit, a new record is created containing an association between the [PERSON] public key and his set of medical attributes. [PERSON] is not requested to provide his name; he can stay anonymous by only proving that he owns the private key associated with his provided public one. This verification is conducted using the protocol of Table 4. At the end of this visit, the local database of laboratory L is updated by the following entry, where \(v_{i}\) is the value of the attribute \(a_{i}\). The public key \(kb_{0}\) will be an identifier of the patient [PERSON] in laboratory L. It is not necessarily unique and can change from one visit to another.  
Similarly, [PERSON] saves an association between his keys and the laboratory name in his secure local wallet. His wallet will be updated using the following entry:  
Now suppose that [PERSON] visits doctor [PERSON], and he wants to show her some of his attributes provided by laboratory L:  
1. [PERSON] sends a request to the laboratory to ask for a subset of attributes in \(a_{1},\ldots,a_{n}\) that are associated with \(kb_{0}\). This request should contain the name of the attributes, his owner (\(kb_{0}\)), a fresh public key \(kb\), and fresh random DID as shown in Figure 5. First, the laboratory checks the identity of [PERSON] by asking him to prove that he owns public key \(kb_{0}\) using the protocol of Table 4. Then, the laboratory asks [PERSON] to prove that he is also the owner of public key \(kb\) using the protocol of Table 4.
2. The laboratory creates a certificate attesting that DID belongs to the owner of \(kb\) and saves it in the hyper-ledger Indy as shown in Figure 6.  
Figure 11: [PERSON] provides [PERSON] with the DID and list of his attributes.  
3. Then, the laboratory creates a signed document (patient credentials) for [PERSON] containing the DID and the requested attributes with their values. After that, the laboratory sends these credentials to [PERSON], who saves them in his wallet as shown in Figure 7.
4. Patient [PERSON] forwards the credential containing the attributes and their value to [PERSON].
5. Then, doctor [PERSON] accepts [PERSON]'s invitation as shown in Figure 8 and obtains the public key Kb associated with DID from the hyper-ledger Indy.
6. [PERSON] requests [PERSON] to prove that he is the owner of kb as shown in Figure 9. This proof is performed using the challenge-response protocol shown in Table 4.
## 3 Methodology and Materials
### The Establishment of the ANN Model  
An artificial neural network is an adaptive and nonlinear processing model composed of many interconnected neurons. Nowadays, the most widely used ANN type is back propagation (BP) artificial neural network, which includes the input layer, the hidden layer and the output layer [16]. Prior to application of prediction, the ANN model needs to be trained to learn the relationships between inputs and outputs. During the training process, the input signal is processed layer by layer from the input layer, through the hidden layer and finally transmitted to the output layer. If the error between the target output and the network output is too large at the output layer, error back propagation will be carried out. In this procedure, the connection weights and biases will be adjusted to reduce the error to a defined level, so that the network output is closer to the target output [17].  
The excellent prediction performance of the ANN model can be carried out if its architecture parameter of the number of neurons in hidden layer (\(N\)) have been selected correctly. However, there are no scientific formulas to determine the optimal number of neurons in the hidden layer [18]. Therefore, a very intuitive trial-and-error method was used to determine the \(N\) value.  
In this work, inputs of an ANN were the cement content, water-cement ratio, sand-coarse aggregate ratio, water reducer content and curing time, while the output was the UCS value. The ANN-PSO integrated model was developed based on MATLAB programming language. Based on trial tuning and experience, the range of \(N\) value was set to 5-12. The Levenberg-Marquardt algorithm was adopted as the training algorithm. The logsig function and the purelini function were adopted as transfer function at hidden layer and output layer, respectively. The 10-fold cross validation was used as the validation method. The learning rate was 0.2 and the momentum coefficient was 0.7 [19,20].  
To assess the performance of the ANN model, the mean relative error MRE and the coefficient of determination R\({}^{2}\) were adopted. The lower the MRE and the closer R\({}^{2}\) is to 1, the better prediction performance of the ANN model. All formulas can be described as below:  
\[\text{MRE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\left(y_{i}-p_{i}\right)}{y_{ i}}\right|\times 100 \tag{3}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-p_{i}\right)^{2}}{\sum_{i=1}^{ n}\left(y_{i}-\overline{y}_{i}\right)^{2}} \tag{4}\]
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
## 3 Proposed V2V Channel Model
\tag{11}\]  
In (11), \(\textbf{q}_{v}^{i,C_{u,m}^{i}}(t)\), \(\varphi_{v}^{i,C_{u,m}^{i}}(t)\), \(\alpha_{v}^{i,C_{u,m}^{i}}(t)\) represents the direction vector, azimuth angle, and elevation angle, respectively, where \(i\in\{\text{Tx},\text{Rx}\}\). Combining (10) and (11) with the help of [33], the Doppler frequency for NLoS path \(f_{n,m}(t)\) can be written as:  
\[f_{n,m}(t)=\frac{1}{\lambda} \begin{bmatrix}\|\textbf{v}\|^{Tx_{c}C_{u}^{Tx}}( 0)+\int_{0}^{t}\|\textbf{a}\|^{Tx_{c}C_{u}^{Tx}}(t^{\prime})dt^{\prime}\end{bmatrix}  \tag{12}\]  
Hence, the transceivers and the scatterers are moving, so the calculation of the Doppler frequency is complicated; for the sake of simplicity, the overall Doppler frequency of this proposed model can be represented as (12). The movement of transceivers and scatterers comes into consideration in (12) where the mathematical term of the AoAs, AoDs, and the velocity vectors of the Tx, Rx, and clusters are also shown. By substituting (12) into (2), we can obtain the channel fading coefficient, as well as the parametric V2V channel model with respect to the 3D velocity variations.
## 1 _Article_  
### Effect of Lyophilization Process on Nutritional Value of  
Meat By-Products  
[PERSON],*(r), [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
1 Department of Food Safety and Quality, Veterinary Academy, Lithuanian University of Health Sciences,  
2 Institute of Animal Hearing Technologies, Faculty of Animal Sciences, Lithuanian University of Health Sciences, Mickeviciaus Str. 9, LT-44307 Kaunas, Lithuania  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +370-68-114-982  
The meat industry generates large amounts of by-products, and their mass represents approximately one-third of the live weight of animals. Most by-products are disposed of and not used for processing, although they meet hygiene requirements and are suitable for human consumption or for the processing of food supplements. The aim of this study was to investigate the effect of lyophilization on the nutritional value and retention of functional ingredients in ovine and porcine by-products: liver, kidneys, hearts, and lungs. For this purpose, meat by-products of the third category were selected in X and Y slaughterhouses and divided into two parts: one part was freeze-dried at \(-80\)\({}^{\circ}\)C for 72 h, and the other part was left raw. Fatty acid composition was determined by gas chromatography-mass spectrometry (GC-MS), and amino acid analysis was performed by AccQ Tag technology (Waters Corp., Milford, MA, USA) and HPLC. Our study shows that the lyophilization process did not significantly affect protein and fat content. The largest decrease in the amount of proteins was determined in samples of ovine kidneys, at 0.8%, while the difference in protein in ovine liver samples before and after lyophilization was 0.38%. The composition of essential amino acids did not change after lyophilization, except a decrease in Leu and Thr in porcine samples and Leu, Thr, and Met in ovine samples (\(p<0.05\)). The lyophilization process did not significantly affect the polyunsaturated fatty acid content, including the amounts of omega 3 and omega 6 fatty acids. The optimal ratio of omega 6 and 3 fatty acids was determined in samples of lyophilized ovine livers (2.65), and the largest ratio was found in samples of lyophilized porcine hearts (16.67). The study results show that, after lyophilization, meat by-products of the third category (according to Regulation (EC) No. 1069/2009, Categorization, Article 10), especially ovine liver, can be used as a source of amino acids and polyunsaturated fatty acids for functional food processing. The process of lyophilization is also appropriate for preserving meat by-products without losing the nutritional value and beneficial components.
# SSDLiteX: Enhancing SSDLite for Small Object Detection
## 2 Object Detection CNN
### Object Detection  
An object detection CNN can be constructed from a classification CNN in a two-stage architecture or a one-stage architecture. In the two-stage architecture, the regions of interest (RoIs) are proposed first and the detection is performed on the proposed regions. The representative networks are R-CNN [8], Fast R-CNN [9], and Faster R-CNN [10]. The one-stage networks do not have the region proposal stage. They have low detection capability than the two-stage networks, but can be processed faster. In the category are SSD series [12; 17] and YOLO series [18; 19; 20]. To overcome the low performance of the one-stage networks, feature pyramid structures were proposed like Feature Pyramid Networks [21], RetinaNet [22], and EfficientDet [23].  
Among the various object detection CNNs, the MobileNet and SSD combination is still widely used in the resource-limited environments because of its low complexity and fair detection capability. SSD is constructed by adding additional layers to a base image classification CNN like MobileNet. When the size of the input is 300, the SSD adds four stages consisting of a 1 \(\times\) 1 convolutional layer and a 3 \(\times\) 3 convolutional layer with the stride of two, reducing the horizontal and vertical size of the data to 1. The object detection is performed with extracted outputs of the stages, as shown in Figure 1. The size of the output of the base CNN is modified to be 1/16 scale and 1/8 scale data are extracted in the middle of the base CNN. The 1/8 and 1/16 scale data are also used for the object detection. SSDLite is basically constructed by decomposing the 3 \(\times\) 3 convolutional layers in the auxiliary stages of SSD into a depthwise convolutional layer and a pointwise convolutional layer.
# The Development of Current Collection in Micro-Tubular Solid Oxide Fuel Cells--A Review
## 6 Current Collector Shape and Structure
### Conductive Pastes, Paints and Inks  
Wires (and other current collector types) are seldom used on their own; they are often used with the addition of conductive metal-based pastes/paints/inks (from here on jointly referred to as \"pastes\"). The pastes can be applied to the electrode, to the current collector or to both [104]. The paste acts to decrease the interfacial contact resistance between the electrode and current collector and acts as a bridge to ensure a continuous path for electron flow. The pastes are more conductive than typical anode cermets and cathode materials and so the paste will increase the overall conductivity of the cell in the axial and radial directions, acting as a current collection layer, which is particularly important for larger cells [105; 106]. Pastes can also be used to secure the electrode to the current collector and are sintered to enhance their mechanical adhesion once applied [107]. Pastes are usually hand-painted, which is a cause for concern in terms of w.r.t reproducibility and manufacturability [104].  
The paste materials are most commonly silver, gold, platinum or nickel, the latter being reserved for the anode side [108; 109]. Manufacturers sell variants with varying final porosity to control the gas distribution and conductive properties. Denser variants are favoured when the contact patch is small in order to maximise the conductivity of the joint, or where sealing is also required, meaning the paste has dual functionality. More porous paints, which are typically less viscous (generally referred to as paints or inks) can be spread more liberally, covering the entirety of the active area if desired, which may lend itself to ease in manufacturing [40]. The conductive pastes can also infiltrate the electrodes and improve electron mobility in percolation pathways. Silver's functionality as a cathode material, acting as a catalyst for oxygen reduction, will contribute to electrochemical cell performance [81].