esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mixed Method with Effective Color Reduction
## 2 Materials and Methods
### Overview of the BS+ATCQ Algorithm
#### 2.1.2 The Ant-Tree for Color Quantization Method
When it is necessary to update an existing subtree \(c\), first the ant \(h_{i}\) is placed on \(S_{c}\) and then the values associated with \(S_{c}\) are updated: \(nc_{c}=nc_{c}+1\), \(e_{c}=e_{c}+d_{ic}\) and \(sum_{c}=sum_{c}+p_{i}\). When the selected ant is not on \(a_{0}\), the operations to be performed depend on the number of children of \(a_{pos}\) (Algorithm 4). There is a limit to the number of children on the nodes of the tree: \(Q_{max}\) for the root node and \(L_{max}\) for the others. Both values can be different because \(Q_{max}\) limits the size of the quantized palette. If \(a_{pos}\) has no children, \(h_{i}\) becomes the first one. If \(a_{pos}\) has two children and the second one has never been disconnected from \(a_{pos}\), first this child and all its descendants are disconnected from the tree and moved to \(a_{0}\); then, \(h_{i}\) connects as the new second child. In other cases, a child of \(a_{pos}\) is selected, denoted \(a^{+}\). If \(a^{+}\) is not sufficiently similar to \(h_{i}\), (\(Sim(p_{i},a^{+})<T_{c}\), where \(c\) is the subtree that includes the node \(a^{+}\)), and \(a_{pos}\) admits at least one more child, \(h_{i}\) connects to \(a_{pos}\); in other cases, \(h_{i}\) moves on \(a^{+}\). When the iterations of the algorithm conclude, the nodes of the second level of the tree define a quantized palette that includes \(q\) colors, \(\{color_{1},...,color_{q}\}\). Then, the quantized image is generated by replacing the color of all the ants included in the subtree \(c\) with the color of the node \(S_{c}\). ```
1:if no children connected to \(a_{pos}\)then
2: Connect \(h_{i}\) to \(a_{pos}\)
3:else if two ants connected to \(a_{pos}\) and the second one never disconnected then
4: Move to \(a_{0}\) the subtree with root in the second child of \(a_{pos}\)
5: Connect \(h_{i}\) to \(a_{pos}\)
6:else
7: Select a node connected to \(a_{pos}\), \(a^{+}\)
8:if (Sim(\(p_{i}\), \(a^{+}\))\(<T_{c}\)) and (\(a_{pos}\) has less than \(L_{max}\) children) then
9: Connect \(h_{i}\) to \(a_{pos}\)
10:else
11: Move \(h_{i}\) on \(a^{+}\)
12:endif
13:endif
```  
**Algorithm 4** Ant case operations (\(h_{i}\))-ATCQ.
## 2 Materials and Methods
### Samples
#### 2.1.1 Experimental Design  
A total of 11 different seed and cake samples, shown in Table 1, were analyzed. These samples were derived from seeds with varying hull and impurity contents to assess the effects of these parameters on the quality of the produced oil. The experiment was divided into three major groups:* Group 1--Repeated Sample (Sample 1): Sample 1 was produced with a 10% hull content and 5% impurities. This sample was repeated five times to ensure consistency and minimize variability.
* Group 2--Variable Hull and Impurity Contents: Samples 2 to 9 represented different combinations of hull content (ranging from 0% to 20% at 5% intervals) and impurities (ranging from 0% to 10% at 2.5% intervals), aimed at exploring the effects of changing seed properties on oil quality.
* Group 3--Boundary Samples (Samples 10 and 11): Sample 10 was produced using seeds with 0% hull content and 0% impurities, serving as a baseline with minimal interference from seed components. Sample 11 was produced with 20% hull content and 10% impurities, representing the maximum content of hulls and impurities to observe their most significant effects.  
Control sample was the original sunflower hybrid NS-H-6326, with its natural (original) hull and impurity content.
## Processing Setup and Description
### Data and Tools
The GPS observation data from the LEO receiver are acquired from COSMIC Data Analysis and Archive Center CDAAC ([[https://cdaac-www.cosmic.ucar.edu/cdaac](https://cdaac-www.cosmic.ucar.edu/cdaac)]([https://cdaac-www.cosmic.ucar.edu/cdaac](https://cdaac-www.cosmic.ucar.edu/cdaac))) for CHAMP and GRACE, JPL ([[https://podaac.jpl.nasa.gov](https://podaac.jpl.nasa.gov)]([https://podaac.jpl.nasa.gov](https://podaac.jpl.nasa.gov))) for GRACE, and EUMETSAT ([[https://eoportal.eumetast.int](https://eoportal.eumetast.int)]([https://eoportal.eumetast.int](https://eoportal.eumetast.int))) for Metop. The attitude data are acquired from CDAAC (CHAMP: as measured and provided by GFZ; Metop: nominal type) and JPL (GRACE). The conversion from binary to RINEX format might include a pre-processing and screening of the data, as it is the case for GRACE. In the current setup two different sources for GRACE observation data are exploited: (1) CDAAC: L1A raw data which show inferred clock biases reaching up to 0.5 us before reset (native to the GRACE clock oscillator [34; 49]); (2) JPL: L1B screened data, where the clock trend is eliminated in the screening process but at the same time the phase observations are altered [67]. Primarily, the latter data are used among highly precise geodetic applications. For the application of RO, however, it is important to note that the native clock offset is required for the correction of the occultation time stamps and range calculation (for removal of GPS receiver oscillator drift in zero differencing). Optimally handled, both data (L1A and L1B) should achieve closely similar results in POD. However, the processing of the large clock trends poses challenges to the POD software packages that might not be optimally handled in the software versions used for this study and somewhat decreases the quality of the resulting orbits. The internal quality assessment and uncertainty estimation is based on two independent software packages: Bernese GNSS software v5.2 [30], a widely used multi-GNSS data processing software capable of LEO-POD developed at AIUB, and NAPEOS v3.3.1 [31], another state-of-the-art GNSS data processing software developed and maintained by the European Space Operations Centre (ESOC) of ESA. Both software packages are capable of multi-GNSS and support the SLR tracking system for orbit validation. The SLR results presented in this paper are calculated with the Bernese software. A similar overall processing strategy for LEO POD is inherent to the individual software packages but they vary in detail (Appendix A illustrates the setups used). Furthermore, Bernese was used to propagate random error estimates based on software extensions by AIUB [68; 69]. The different models and processing setups of the Bernese and NAPEOS packages as used in this study are summarized in Table 1. The subsequent uncertainty calculations are carried out by separate routines of the rOPS.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 6 Conclusions
\begin{tabular}{l l} BF & Beat Frequency (SEE test method) \\ BiCMOS & Bipolar CMOS \\ BIT & Bipolar Junction Transistor \\ BOX & Buried Oxide \\ CER & Code Error Rate (SEE test method) \\ COTS & Commercial Off-The-Shelf (ADCs) \\ CMG & Control Moment Gyroscope \\ CMOS & Complementary Metal-Oxide Semiconductor \\ CS & Coherent Sampling (SEE test method) \\ DAC & Digital-to-Analog Converter \\ DC & Direct Current \\ DD & Displacement Damage \\ DDC & Digital Down-Converter \\ DDD & DD Dose \\ DGO & Dual Gate Oxide \\ DIS & Dynamic Input Signal (SEE test method) \\ DNL & Differential Non-Linearity \\ DOI & Digital Object Identifier \\ DR & Dynamic Range \\ DS or \(\Delta\Sigma\) & Delta-Sigma (ADC architecture) \\ DT & Dynamic Threshold (SEE test method) \\ DTI & Deep Trench Isolation \\ ELDRS & Enhanced Low Dose Rate Sensitivity \\ ELT & Enclosed Layout Transistor \\ EM & Engineering Model \\ ENOB & Effective Number Of Bits \\ ESA & European Space Agency \\ F & Flash (ADC architecture) \\ FDSOI & Fully Depleted Silicon On Insulator \\ FIF & Folded-Interpolated Flash (ADC architecture) \\ FinFET & Fin-shaped Field Effect Transistor \\ FOM & Figure Of Merit \\ GNSS & Global Navigation Satellite Systems \\ HDR & High Dose Rate \\ HEP & High Energy Physics \\ IC & Integrated Circuit \\ IEEE & Institute of Electrical and Electronics Engineers \\ INL & Integral Non-Linearity \\ IP-core & Intellectual Property core \\ ISSCC & International Solid-State Circuit Conference \\ LC\({}^{2}\)MOS & Linear Compatible CMOS \\ LDR & Low Dose Rate \\ LEO & Low Earth Orbit \\ LET\({}_{th}\) & Linear Energy Transfer Threshold \\ LOCOS & LOCAL Oxidation of Silicon \\ LSB & Least Significant Bit \\ MDPI & Multidisciplinary Digital Publishing Institute \\ MOS & Metal-Oxide Semiconductor \\ NASA & National Aeronautics and Space Administration (USA) \\ NB & Number of Bits \\ NSREC & Nuclear and Space Radiation Effects Conference \\ P & Power (consumption) \\ PIP & Pipelined (ADC architecture) \\ PLL & Phase-Locked Loop \\ \end{tabular}
## 3 Four-Port MIMO Antenna
### Results and Discussion
#### 3.2.7 Mean Effective Gain  
By examining the mean effective gain, the received power of a MIMO antenna system in a fading environment may be examined (MEG). The ideal instance would have an MEG value of less than 3 dBi. According to Figure 17, the suggested ultra-wideband MIMO antenna gives MEG \(-8\) dBi at an operating bandwidth of 26.5-42.7 GHz. Moreover, the following Equation (7), provided in [45; 46], may be used to compute the MEG of a MIMO antenna:  
\[MEG_{i}=\frac{P_{rec}}{P_{inc}}=\oint\left[\frac{\mathit{XPR.\ G_{\theta i}( \Omega)+G_{i}(\Omega).P(\Omega)}}{1+\mathit{XPR}}\right]\mathit{d}\Omega \tag{7}\]  
where \(P_{\emptyset}\) represents an angular density function of incident power and \(\mathit{XPR}\) is the cross-polarization power ratio.  
Figure 16: CCL of the proposed work.  
Figure 17: MEG of the proposed MIMO antenna.
# Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries
## 2 Materials and Methods  
### Electrical and Thermal Models  
The electrical model applied in this work is the Thevenin model, as described by Equations (4)-(6), extracted from [PERSON] et al. [39]. They express the variation over time of the voltage of an RC Thevenin circuit (\(U\)), the variation over time of the \(SoC\), and \(V\).  
\[\frac{dU(t)}{dt}\ =\ \frac{-1}{R_{1}C_{1}}U(t)+\frac{I(t)}{C_{1}}, \tag{4}\]  
\[\frac{dSoC(t)}{dt}\ =\ \frac{-I(t)}{C_{BOL}}, \tag{5}\]  
\[V(t)\ =\ OCV-R_{0}I(t)-U(t), \tag{6}\]  
The Thevenin model for LFP cells is the model proposed by [PERSON] et al. [35], which uses hysteresis. For NMC cells, the formulation can be found in the study by [PERSON] et al. [38], but also in other studies, for example, that by [PERSON] et al. [36]. The convention used in this work is that \(I\) is positive while discharging.  
In the work presented in this paper, the thermal model used is based on reversible and irreversible heat generation and heat transfer from the cell to the ambient atmosphere. The model is derived from [PERSON] et al. [10], [PERSON] et al. [42], and [PERSON] et al. [31]. The values used in the model are cell mass (\(m\)), the specific heat capacity of the cell (\(C_{p}\)), \(T\), cell-ambient heat transfer coefficient (\(h\)), heat transfer surface area of the cell (\(A\)), \(t\) in seconds, ambient temperature (\(T_{a}\)), \(OCV\), \(V\), and cell current (\(I\)). The whole model is expressed in Equation (7).  
\[mC_{p}\frac{dT}{dt}\ =\ hA(T_{a}-T)+I(OCV-V)+IT\frac{\partial OCV}{\partial T}, \tag{7}\]where the term \(I\) (\(OCV-V\)) is the irreversible heat generation, and \(I\)\(T\) (\(\partial OCV/\partial T\)) is the reversible heat generation due to entropy variation. Heat interchanged between the cell and the atmosphere is modeled by the term \(h\)\(A\)(\(T_{a}\)\(-\)\(T\)). The thermal model used is based on Equation (7). The reversible term is always lower than the irreversible term, as stated by [PERSON] et al. [42], and for high \(CR\), the irreversible term is much higher than the reversible one. The reversible heat generation due to entropy variation is ignored.  
Simulations were performed applying Equations (8)-(11) with a \(\delta_{t}\) of 15 s.  
\[T_{k}\ =\ T_{k-1}+\frac{\delta_{t}[hA(T_{a}-T_{k-1})+l_{k-1}(OCV_{k-1}-V_{k-1})]} {mC_{p}}, \tag{8}\]  
\[SoC_{k}\ =\ SoC_{k-1}-\frac{l_{k-1}\delta_{t}}{3600\cdot C_{BOL}}, \tag{9}\]  
\[U_{k}\ =\ \exp\Biggl{(}\frac{-\delta_{t}}{R_{1,k-1}C_{1,k-1}}\Biggr{)}U_{k-1}+ \Biggl{[}1-\exp\Biggl{(}\frac{-\delta_{t}}{R_{1,k-1}C_{1,k-1}}\Biggr{)}\Biggr{]} R_{1,k-1}I_{k-1}, \tag{10}\]  
\[V_{k}\ =\ \ OCV_{k}-R_{0,k}I_{k}-U_{k}, \tag{11}\]  
where \(T\), \(I\), \(V\), \(SoC\), and \(U\) were discretized. While \(OCV\), \(R_{0}\), \(R_{1}\), and surface layer capacitance of cell (\(C_{1}\)) values depend on \(SoC\) and \(T\), they were fitted by using several studies on different cell technologies.
## 3 Materials and Methods
### Image Processing of Thermal Images
#### 3.3.2 Robust Principal Component Analysis (RPCA)  
While PCA is effective in reducing dimensionality and extracting dominant patterns in thermal data, it is known to be sensitive to noise and outliers [44; 45]. To overcome this limitation, RPCA offers a more resilient framework for separating meaningful thermal features from undesired background interference [45].  
RPCA decomposes the thermal video data matrix \(M\in\mathbb{R}^{T\times N}\), where \(T\) is the number of frames and \(N\) is the number of pixels per frame, into two components [44; 45; 46; 47] :  
\[M=L+S \tag{1}\]  
Figure 2: Schematic of the experimental setup. A large-diameter ultrasonic transducer is placed across the top of two CFRP specimens: one clamped vertically (lower sample) and one resting horizontally (upper sample). This configuration allows excitation of both samples simultaneously.  
where \(L\) is a low-rank matrix and \(S\) is a sparse matrix. This decomposition is obtained by solving the following convex optimisation problem [46]:  
\[\min_{L,S}\|L\|_{*}+\lambda\|S\|_{1}\quad\text{subject to}\quad M=L+S \tag{2}\]  
Here, \(\|L\|_{*}\) denotes the nuclear norm (sum of singular values) that promotes low-rankness, and \(\|S\|_{1}\) is the element-wise \(\ell_{1}\)-norm that encourages sparsity in \(S\). The regularisation parameter \(\lambda\) balances the trade-off between the low-rank and sparse components and was selected based on established recommendations in the literature [23; 45].  
In this study, RPCA was applied to enhance defect visibility in CFRP thermographic video sequences. After decomposition, the low-rank component \(L\) preserved the dominant thermal feature associated with the defects, while reducing background variations and random noise. As a result, the \(L\) component was selected for further analysis and compared with the PCA outputs.  
To solve the decomposition efficiently, the inexact augmented lagrangian multiplier (IALM) method was employed due to its balance between computational speed and convergence reliability [36; 47].
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 7 Discussion
Despite pre-programmed missions making the use of AUVs, a more autonomous solution for WQM activities, none of the studies reviewed so far in FPV applications employed this solution. The proper use of AUVs in WQM requires advanced robotic skills, equipment, and higher costs, not justified so far for use in FPV plants. A further increase in complexity can be achieved when a team or swarm of AUVs is used. This future solution may be considered an interesting option when large water bodies need to be monitored, and vertical profiles of many water columns are required, or when a contemporary measurement of parameters at different locations is required. As the commercial offer of AUV swarms is still lacking, this solution belongs to a future scenario.
# Margin CosReid Network for Pedestrian Re-Identification
## 2 Related Work
### Metric-Based Learning Re-Identification Methods  
Studies that focus only on feature-based learning may have difficulties on distinguishing similar appearance, which can be solved through metric learning methods. Traditional metric-based re-identification methods, such as cross-view quadratic discriminant analysis (XQDA) [6] and keep it simple and straightforward metric (KISSME) [30], learn feature subspace with resolution abilities. [PERSON] et al. [8] combined SVM and ANN for data mining analysis to cope with the low accuracy and high processing time of ANN. To increase the interclass distance, [PERSON] et al. [16] used hinge loss together with SVM to enhance the generalization ability of the model. To force the distance between dissimilar sample pairs to be greater than that between similar sample pairs, [PERSON] et al. [31] proposed an improved triplet loss, and [PERSON] et al. [12] proposed quaternion loss and introduced new constraints based on it. As the network selects samples randomly, samples that are too simple will lead to poor generalization ability of the model, and samples that are too complex will lead to gradient explosion of the network during training. Therefore, selecting triplet and quaternion samples is still a major problem [11; 32]. [PERSON] et al. [11] proposed hard sample mining to solve the poor generalization ability caused by random sample selection. [PERSON] et al. [17] applied the softmax loss function to pedestrian re-identification by using the cosine similarity or Euclidean distance of the last layer of the network for distance query. Due to its strong classification ability, the softmax function remains widely used in applications of multiclassification, such as image classification, face recognition, and object detection [33; 34]. [PERSON] et al. [35] trained both softmax and center losses in ANN to classify the extracted spectral features. This combination makes the intraclass features more compact and the interclass features more expandable, but results in parameter redundancy simultaneously. Thus, [PERSON] et al. [36] combined center loss with softmax loss to supervise CNN to minimize the deep feature distance between classes. To approximate the classification boundary in the metric-based learning re-identification, a linearization method [37] was proposed on a deep network and to enhance the robustness of the loss function by the activation function. The margin of the loss function in this method is imposed on any network layer but may lead to the expansion of parameters and longer training time.
## 5 Routing over the Hierarchical Virtual Topology
### Routing Tables  
In this section, we describe the tables that need to be managed by each sensor node for forwarding the data packets. To realize the routing algorithm mentioned in Section 5.1, three types of tables are needed. One is a routing table used to decide a forwarding module from neighbors in each tier; another is a connection table used to manage the \((N-1)\)-th-tier module identifiers, which are assigned to the endpoint modules of \(N\)-th-tier virtual links; the last is a long-link table used to choose a forwarding node to be assigned to a path for a virtual long-distance link. Each node requires \(N\) routing tables, \((N-1)\) connection tables and at most one long-link table in order to determine a forwarding node, where \(N\) is the number of tiers to which the node belongs. Nodes not belonging to any physical paths that are part of a virtual long-distance link do not need a long-link table.  
The \(N\)-th-tier routing table is constructed for routing between \((N-1)\)-th-tier modules belonging to the same \(N\)-th-tier module. When node \(n\) belongs to the \(N\)-th-tier virtual module \(M_{A}^{N}\), it records entries of all of the \((N-1)\)-th-tier modules belonging to \(M_{A}^{N}\) in its \(N\)-th-tier routing table. As a particular consequence, this permits routing between any pair of nodes belonging to the same first-tier module by using the zeroth-tier routing table.  
Figure 3: Example of a hierarchical topology.  
The \(N\)-th-tier connection table converts \(N\)-th-tier forwarding module identifiers to the \((N-1)\)-th-tier module identifiers associated with the \((N-1)\)-th-tier module belonging to the \(N\)-th-tier forwarding module. Thus, when we talk about a \"connected module\" of the virtual module \(M_{A}^{N}\), we mean an \((N-1)\)-th-tier virtual module that belongs to the \(N\)-th-tier virtual module \(M_{A}^{N}\) and is connected via an \((N-1)\)-th-tier virtual link to an \((N-1)\)-th-tier virtual module belonging to a different \(N\)-th-tier virtual module. Because there may be more than one \((N-1)\)-th-tier connected module pointing toward a single \(N\)-th-tier virtual module, multiple entries for a single \(N\)-th-tier virtual module are allowed in the \(N\)-th-tier connection table.  
The long-link table is used for routing between nodes that are connected by a virtual link, but cannot communicate directly with each other due to the distance between them. We assign a physical multi-hop path to this kind of virtual long-distance link. Then, each node along the assigned path holds a long-link table for use in choosing a forwarding node toward the destination end of the virtual long-distance link.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.7 Accuracy  
In the process industry, wireless sensor networks are deployed to obtain accurate measurements of different process variables at different sampling rates. For example, in chemical and biochemical processes, temperature and pressure are measured more frequently whereas, molecular weight and concentration are measured less frequently. In [68], the authors have proposed a multi-objective algorithm to obtain a trade-off between the quality of measurement and the cost of the measurement. A trade-off between the two conflicting objectives of maximization of measurement accuracy and minimization of energy consumption has been achieved by using a lossy compression technique in [69]. The proposed design technique facilitates the node to transmit less amount of data after compression and hence can save energy during transmission. The design of intermittent fault detection in sensor nodes has been proposed in [70]. A trade-off has been obtained between the accuracy of fault detection and the detection latency by using a multi-objective optimization technique in there.