esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### System Modelling and Availability Analysis
#### 3.3.2 Fuzzy Markov Model of the Wellhead Connector
The Markov model formula of the wellhead connector can be obtained:  
\[\left\{ \begin{array}{c}P_{0}(t)\left[-\sum\limits_{i=1}^{18}\lambda_{i}- \lambda_{22}\right]+P_{19}(t)\lambda_{23}+P_{21}(t)\lambda_{21}=dP_{0}(t)/dt\\ P_{0}(t)\lambda_{i}-P_{i}(t)\lambda_{19}=dP_{i}(t)/dt\ (i=1,2\cdots 18)\\ \left[P_{20}(t)+\sum\limits_{i=1}^{18}P_{i}(t)\right]\lambda_{19}-P_{19}(t)( \lambda_{20}+\lambda_{23})=dP_{19}(t)/dt\\ P_{19}(t)\lambda_{20}-P_{20}(t)\lambda_{19}+P_{21}(t)\lambda_{24}=dP_{20}(t)/dt \\ P_{0}(t)\lambda_{22}-P_{21}(t)(\lambda_{21}+\lambda_{24})=dP_{21}(t)/dt\\ \sum\limits_{i=0}^{21}P_{i}(t)=1\\ P_{0}(0)=[1,0,\cdots,0]\end{array} \right.. \tag{20}\]  
Solving the above formula can get the transient probability of the system in various states. When the system is in the installation test state S\({}_{19}\) or the periodic test state S\({}_{21}\), the wellhead connector can still work normally, thus, the transient availability of the system is:  
\[A(t)\ =P_{0}(t)+P_{19}(t)+P_{21}(t). \tag{21}\]  
Let \(P_{i}\) (\(I=0,\,1,\,2\,\ldots\,\,21\)) be the steady state probability that the system is in various states, \(P_{i}=\lim\limits_{x\rightarrow\infty}P_{i}(t)\); the steady state probability equation of the wellhead connector is obtained as follows:  
\[\left\{ \begin{array}{c}P_{0}\left[\sum\
olimits_{i=1}^{18}\lambda_{i}+ \lambda_{22}\right]=P_{19}\lambda_{23}+P_{21}\lambda_{21}\\ P_{0}\lambda_{i}=P_{i}\lambda_{19}(i\ =\ 1,2\cdots 18)\\ \left[P_{20}+\sum\
olimits_{i=1}^{18}P_{i}\right]\lambda_{19}=P_{19}(\lambda _{20}+\lambda_{23})\\ P_{19}\lambda_{20}+P_{21}\lambda_{24}=P_{20}\lambda_{19}\\ P_{0}\lambda_{22}=P_{21}(\lambda_{21}+\lambda_{24})\\ \sum\
olimits_{i=0}^{21}P_{i}=\ 1\end{array} \right.. \tag{22}\]  
Therefore, the steady state availability of the system for normal operation is:  
\[A=P_{0}+P_{19}+P_{21}. \tag{23}\]  
Mean time to failure (MTTF) is an important reliability index of the system. It refers to the average time from normal operation to fail. In order to solve the MTTF of the wellhead connector, the first step is to determine the Q-matrix. The system fails when in states S\({}_{1}\)--S\({}_{18}\) and S\({}_{20}\), these states are defined as absorbing states [42], these elements related to the absorbing states are discarded to obtain the matrix Q:  
\[Q\ =\ \left[ \begin{array}{cc}1-\sum\
olimits_{i=1}^{18}\lambda_{i}-\lambda_{ 22}&\lambda_{22}\\ \lambda_{21}&1-\lambda_{21}-\lambda_{24}\end{array} \right]. \tag{24}\]  
MTTF can be expressed as:  
\[MTTF\ =\ \left[ \begin{array}{cc}1&0\end{array} \right][(I-Q)^{-1}\left[ \begin{array}{c}1\\ 0\end{array} \right]]. \tag{25}\]
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 2 Existing Methods for Detection and Identification of Objects in an Image
### Contour Searching  
The basic idea of using contours in image processing is to produce a curve that encloses the objects contained in the image. Successful usage of this object bounding method is dependent on applied image preprocessing methods such as image smoothing and morphological operations. The only condition for using contour searching is to divide the image into so-called positive and negative regions [10], whose boundaries can be considered as bounded objects. The correct setting of the parameters in this method guarantees the correct detection of contours in the image. The term contour is defined as a list of points that represent a curve in an image. These curves are represented as sequences in which the record encodes information about the next point on the curve. Due to its structure, the contour searching function can construct a so-called contour tree. Therefore, it can determine which contour is a root contour and which contours represent the child contours [10; 13]. In the case of detection of objects in the image, in most cases, it is necessary to delimit only the root contour of the object.  
In Figure 1 the segmented object is shown on the left and the object whose contours are outlined is shown on the right. It is also possible to see several other contours of different colors that represent other objects inside of the root contour. This phenomenon can also occur after morphological operations and image smoothing since the recorded objects are never homogeneous.
# Evaluation of Network RTK Positioning Performance Based on BDS-3 New Signal System
###### Abstract  
The BeiDou navigation satellite system (BDS-3) has been deployed and provides positioning, navigation, and timing (PNT) services for users all over the world. On the basis of BDS-2 system signals, BDS-3 adds B1C, B2a, B2b, and other signals to realize compatibility and interoperability with other global navigation satellite systems (GNSS). Network real-time kinematic (RTK) technology is an important real-time regional high-precision GNSS positioning technology. Combined with the network RTK high-precision service platform software developed by the author's research group and the measured data of a provincial continuously operating reference station (CORS) in Hubei, this paper preliminarily evaluates the network RTK service performance under the new signal system of BDS-3. The results show that single BDS-3 adopts the new signal combination (B1C+B2a) and transition signal combination (B1I+B3I) when providing virtual reference station (VRS) services, the RTK fixation rate of the terminal is above 95%, and the horizontal and elevation accuracies are within 1 cm and 2 cm, respectively, which meets the requirements of providing high-precision network RTK services by a single BDS-3 system. In addition, the positioning accuracy of BDS-2 is relatively poor, while the accuracy of BDS-3 is better than global positioning systems (GPS) and BDS-2. The combined processing effect of the B1I+B3I transition signal of BDS-2/3 is optimal, the accuracy of E and N directions is better than 0.5 cm, and the accuracy of U direction is better than 1.5 cm. It can be seen from the atmosphere correction accuracy, regional error modeling accuracy, and network RTK terminal positioning accuracy that the service effect of the B1C+B2a combination is slightly better than that of the B1I+B3I combination. When a single BDS-3 constellation provides network RTK services, it is recommended to use the B1C+B2a combination as the main frequency solution, and when the BDS-2/3 constellation provides service, it is recommended to use the B1I+B3I combination as the main frequency solution.  
BDS-3; network RTK; double-difference atmospheric correction; atmospheric modeling and interpolation; VRS +
Footnote †: journal: remote sensing
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 4 Proposed System Setup
### Iterative Source Channel Decoding Setup:
Furthermore, the received symbols are first presented to the \(DSTS\) demapper and are further demodulated into \(L_{M,a}(\hat{y}_{i})\) by \(SP\) demodulation as mentioned in Figure 3. At first iteration only the LLR values \(L_{M,a}(\hat{y}_{i})\) are presented to the URC decoder, but, in the consequent iterations, both the LLR values \(L_{M,a}(\hat{y}_{i})\) and \(A\)--priori information \(L_{URC}^{apr}\) are presented to the URC decoder. \(A\)-posteriori information \(L_{M,a}(\hat{y}_{i})\) is then subtracted from \(A\)-priori information \(L_{URC}^{apr}(\hat{x}_{i})\) to obtain the extrinsic information \(L_{URC}^{apr}(\hat{x}_{i})\), which is further deinterleaved and presented as input \(A\)-priori soft bit sequence \(L_{URC}^{apr(\hat{x}_{i})}\) to the IRCC decoder, which, in turn, uses the Maximum A-posteriori Probability (MAP) decoder for decoding. The output \(A\) -posteriori LLR \((L_{D,p})\) of MAP decoder is subtracted from the soft bit sequence \((L_{IRCC}^{apr})\), which results in the extrinsic information \(L_{IRCC}^{extr}\). The extrinsic information is further interleaved to become the \(A\)-priori information \((L_{URC}^{apr})\) for the next iteration. Furthermore, the performance of IRCC and URC completely depends on the number of iterations of the iterative process and it completely depends on residual redundancy that remains in the coded bit-stream.
## 4 Dataset Acquisition
### Image-to-Text Transition Process in Report Generation  
The transition process from image features to text generation in the model is orchestrated by a combination of CNN and Transformer architectures, working together to translate visual information into coherent textual descriptions. This process can be subdivided into several key stages, as described below:Initially, visual features are extracted from the images using four pre-trained CNNs, specifically the Inception-V3 model. These models process four images from different views (frontal and lateral), with each learning to extract intrinsic and important features from the view it was trained on, each passing through a series of convolutional layers, which extract rich hierarchical representations of the visual inputs. The outputs of these convolutional layers are then converted into feature vectors through the Flatten operation for each image view;
2. These vector representations of each view are then concatenated to form a single combined representation that encapsulates the information from all views of the exam images. This combined vector is then passed through a Transformer-based encoder, which applies layer normalization and multi-head attention to further process and refine the visual features. The encoder uses normalization and attention layers to maintain feature integrity while capturing contextual dependencies between different parts of the image;
3. After encoding the visual features, the resulting vector is fed into the decoder, also Transformer-based. The decoder starts generating the textual description word by word, using a causal attention mask to ensure that sequence generation is autoregressive, meaning each generated word depends only on the previous words in the sequence. The decoder incorporates word embeddings, applies multiple attention layers to integrate information from the encoder and previously generated words, and uses dense feed-forward layers to transform these integrations into probabilities over the output vocabulary;
4. During training, the decoder's input includes the sequence of words up to the current word, and the model is trained to predict the next word in the sequence. The loss is calculated by comparing the generated sequence with the true target sequence, and accuracy is assessed based on the match between predicted and true words, adjusted for the attention mask. During inference, the model uses the generated output at each step as input for the next, continuing to generate words until it encounters the end-of-sequence token.  
This complex mechanism allows the model to effectively capture the transition from visual features to textual representations, ensuring that the generated descriptions are not only accurate, but also contextually rich and coherent.
# Dual-Band Light Absorption Enhancement in Hyperbolic Rectangular Array
## 4 Conclusions  
Dual-band light absorption enhancement in the HRA is presented, and double absorption bands with peak absorption of \(\sim\)95% in the visible-to-near-infrared region are obtained. The enhanced light absorption of the HRA resulted from the PSP resonance, and the absorption channels at short and long wavelengths are identified as the second-order and first-order PSP modes, respectively. The impedance theory is used to evaluate the absorption properties of the HRA, which shows that both the real and imaginary parts of input impedances are varied abruptly around the absorption bands so as to satisfy the impedance matching. The absorption spectra of the HRA can be estimated by using the EMT, and the accuracy of the EMT can be improved as the number of film stacks is increased. The dual-band absorption is very robust to the variations of the width and the number of film stack, and good refractive index sensing performances can be achieved by using the two absorption bands. The proposed strategy of the HRA can, in principle, be applied to other HMMs systems and could be useful in diverse applications, including thermal emitters, photovoltaics and multi-analyte sensors.  
In this work, [PERSON] and [PERSON] performed the design, analyzed the data, and drafted the manuscript; [PERSON] and [PERSON] performed numerical simulations; [PERSON] and [PERSON] guided the idea and checked the figures. All authors read and approved the final manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 11811530052), Fundamental Research Funds for the Central Universities (Grant No. JUSRP21935), and Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology (Grant No. BM2014402).  
The authors declare no conflict of interest.
# Co-Clustering under the Maximum Norm+
## 2 Formal Definitions and Preliminaries
### Problem Definition
We further observe that the input matrix can, without loss of generality, be assumed to contain only integer values (by some rescaling arguments preserving the distance relations between elements). **Observation 2**. : _For any Co-Clustering\({}_{\infty}\)-instance with arbitrary alphabet \(\Sigma\subset\mathbb{R}\), one can find in \(O(|\Sigma|^{2})\) time an equivalent instance with alphabet \(\Sigma^{\prime}\subset\mathbb{Z}\) and cost value \(c^{\prime}\in\mathbb{N}\)._  
Proof. : We show that for any instance with arbitrary alphabet \(\Sigma\subset\mathbb{R}\) and cost \(c\geq 0\), there exists an equivalent instance with \(\Sigma^{\prime}\subset\mathbb{Z}\) and \(c^{\prime}\in\mathbb{N}\). Let \(\sigma_{i}\) be the \(i\)-th element of \(\Sigma\) with respect to any fixed ordering. The idea is that the cost value \(c\) determines which elements of \(\Sigma\) are allowed to appear together in a cluster of a cost-\(c\) co-clustering. Namely, in any cost-\(c\) co-clustering, two elements \(\sigma_{i}\
eq\sigma_{j}\) can occur in the same cluster if and only if \(|\sigma_{i}-\sigma_{j}|\leq c\). These constraints can be encoded in an undirected graph \(G_{c}:=(\Sigma,E)\) with \(E:=\{\{\sigma_{i},\sigma_{j}\}\mid\sigma_{i}\
eq\sigma_{j}\in\Sigma,|\sigma_{ i}-\sigma_{j}|\leq c\}\), where each vertex corresponds to an element of \(\Sigma\), and there is an edge between two vertices if and only if the corresponding elements can occur in the same cluster of a cost-\(c\) co-clustering. Now, observe that \(G_{c}\) is a _unit interval graph_, since each vertex \(\sigma_{i}\) can be represented by the length-\(c\) interval \([\sigma_{i},\sigma_{i}+c]\), such that it holds \(\{\sigma_{i},\sigma_{j}\}\in E\Leftrightarrow[\sigma_{i},\sigma_{i}+c]\cap[ \sigma_{j},\sigma_{j}+c]\
eq\emptyset\) (we assume all intervals to contain real values). By properly shifting and rescaling the intervals, one can find an embedding of \(G_{c}\), where the vertices \(\sigma_{i}\) are represented by length-\(c^{\prime}\) intervals \([\sigma^{\prime}_{i},\sigma^{\prime}_{i}+c^{\prime}]\) of equal integer length \(c^{\prime}\in\mathbb{N}\) with integer starting points \(\sigma^{\prime}_{i}\in\mathbb{Z}\), such that \(0\leq\sigma^{\prime}_{i}\leq|\Sigma|^{2}\), \(c^{\prime}\leq|\Sigma|\), and \(|\sigma^{\prime}_{i}-\sigma^{\prime}_{j}|\leq c^{\prime}\Leftrightarrow| \sigma_{i}-\sigma_{j}|\leq c\). Hence, replacing the elements \(\sigma_{i}\) by \(\sigma^{\prime}_{i}\) in the input matrix yields a matrix that has a cost-\(c^{\prime}\) co-clustering if and only if the original input matrix has a cost-\(c\) co-clustering. Thus, for any instance with alphabet \(\Sigma\) and cost \(c\), there is an equivalent instance with alphabet \(\Sigma^{\prime}\subseteq\{0,\ldots,|\Sigma|^{2}\}\) and cost \(c^{\prime}\in\{0,\ldots,|\Sigma|\}\). Consequently, we can upper-bound the values in \(\Sigma^{\prime}\) by \(|\Sigma|^{2}\leq(mn)^{2}\). Due to Observation 2, we henceforth assume for the rest of the paper that the input matrix contains integers.
## 3 Data Processing Strategy  
The GPS L1/L2, GLONASS G1/G2, Galileo E1/E5a, and BDS B1/B2 observations were used for the ionosphere-free combination. The estimation parameters of the multi-GNSS combined PPP included the station coordinate, receiver clock offset, tropospheric zenith wet delay, ISB, and ionospheric-free combination ambiguity. The differential code biases (DCBs) of GPS and GLONASS were corrected with products from the center for orbit determination in Europe (CODE) and was not corrected for BDS and Galileo, since there are not yet official values available at present, to our knowledge. Table 1 shows the processing strategy and error model in the multi-GNSS combined PPP.  
To validate the proposed robust approach, one week's data from 32 stations from 5-11 March (day of year (DOY) 064-070) 2017 were chosen from the multi-GNSS experiment (MGEX) network. These stations are globally distributed and equipped with quad-system (GPS, BDS, GLONASS, and Galileo) receivers. The distribution is shown in Figure 2. The simulated real-time kinematic PPP was carried out with a sampling interval of 30 s for a single GPS system and multi-GNSS combination using the German research centre for geosciences (GFZ) multi-GNSS (GBM) final product, which provides GPS, BDS, GLONASS, and Galileo orbit- and clock-based daily solutions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Models** \\ \hline Observations & Undifferenced phase and code ionosphere-free combination observations \\ Elevation mask & 7\({}^{\circ}\) \\ Observation weight & Elevation-dependent weight, 1 for elevation angle \\ Adjustment & (E) \(>\) 30\({}^{\circ}\), otherwise 2\(\sin\)(E) \\ \multicolumn{2}{c}{Sequential least square} \\ Phase-windup effect & Corrected [38] \\ \multicolumn{2}{c}{Global positioning system (GPS), global navigation} \\ Satellite antenna phase center and variation & satellite system (GLONASS), Galileo: IGS14.atx; Beidou navigation satellite system (BDS): European space agency (ESA) Model [39,40] \\ Receiver antenna phase center and variation & GPS: IGS14.atx; GLONASS, Galileo, BDS: same as GPS \\ Station displacement & Solid Earth tide, pole tide, ocean tide loading: IERS \\ \multicolumn{2}{c}{Convention 2010 [41]} \\ Relativistic effects & Corrected (IERS 2003) \\ Satellite orbit & German research centre for geosciences (GFZ) multi-GNSS (GBM) final product \\ Satellite clock & GFZ GBM final product \\ Receiver clock & Estimated, white noise \\ Station coordinate & Estimated, white noise \\ \multicolumn{2}{c}{Saastamoinen model and global mapping function (GMF)} \\ \multicolumn{2}{c}{[42], and estimated as piecewise constants with a step size of one hour} \\ the intersystem biases (ISBs) & Estimated as constant parameters \\ Phase ambiguities & Estimated as a constant for each continuous arc \\ \hline \hline \end{tabular}
\end{table}
Table 1: The processing strategy and error model in the multiple global navigation satellite systems (multi-GNSS) combined PPP.
# Hybrid Multi-Antenna Techniques for V2X Communications--Prototyping and Experimentation
## 3 Detailed Description of the Diversity Engine
A far as the integration phase is concerned, the diversity engine was implemented as a software module in C++ and executed on the communication unit that is digitally connected to the RF modules, as shown in Figure 6. Moreover, just like the RF modules, Linux operating system was also uploaded on the communication unit. The communication unit software is hosted on a Linux operating system.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Assessment of Drought Hazard and Vulnerability  
A systematic approach for the assessment of drought hazard and identification of drought vulnerability indicators for the Lam Phaniang River Basin was addressed in this study. The drought index, known as the Standardized Precipitation Evapotranspiration Index (SPEI), was used to understand the characteristics of droughts in the study area. A composite risk assessment was then carried out by considering a combined effect of drought hazard (likelihood) and drought consequence (vulnerability), which were quantified via standardized indices like the Drought Hazard Index (DHI), Drought Vulnerability Index (DVI), and Drought Risk Index (DRI). The classification scheme was employed for the aforementioned indices to be grouped into classes ranging from low to very high. Then, mapping of the DHI, DVI, and DRI classes was performed to generate drought risk maps for the Lam Phaniang River Basin.
## 4 Laboratory SSCB Model Test under the Lateral No-Flow Condition
### Infiltration Behavior in the SSCB Model Test
#### 4.4.2 Result for Case 2  
The model test for Case 2 was performed under a rainfall intensity of 50 mm/h over 6 h (Table 3). As in Case 1, the amount of water that drained from the soil tank after 6 h was found in a steady state; hence, we concluded the model test at that time. Figure 6b shows the volumetric water contents (\(\theta\)) measured by the EC-5 sensors for the model test of Case 2. Notably, the response times of sensors No. 1 and No. 2 in the sand layer under I = 20 mm/h were different from those measured in Case 1 (Figure 6b). In particular, sensor No. 1 started to respond 50 min after the start of the test and indicated a rapid increase in the volumetric water content within a short time after that. Meanwhile, sensor No. 2 started to respond 20 min earlier than sensor No. 1 (30 min after the start of the test), indicating a rapid increase in the volumetric water content until 60 min, when the saturation state was reached. The reason why sensor No. 1 reacted before sensor No. 2 in Case 1 could be that lower rainfall intensity (I) causes a predominantly vertical infiltration, and thus the increase in the volumetric water content was first detected by sensor No. 1. However, in Case 2 with higher rainfall intensity (I) at the upper boundary, it can be understood that the lateral no-flow boundary condition could cause the accumulation of water in the right corner of the model, and thus the increase in the volumetric water content was first detected at sensor No. 2 location. Since the results of both of these sensors showed high volumetric water contents at 85 min, we infer that the saturation of the sand layer in Case 2 was completed after that time. Moreover, sensors No. 3 and No. 4 in the gravel layer showed an increase in the volumetric water content at 85 min: a breakthrough should have occurred in Case 2 at that time. In Case 2, under a rainfall intensity of 20 mm/h, the saturation of the sand layer and the breakthrough occurred 95 min and 125 min, respectively, before those in Case 3.  
Figure 7: Occurrence of rainfall infiltration as a function of the elapsed time in Case 1: (**a**) initial condition; (**b**) after 60 min; (**c**) after 200 min; (**d**) after 210 min; (**e**) after 360 min.
## 2 Theoretical Background of the Uncertainty Theory
### Applications and Benefits of Uncertain Models
#### 2.4.2 Fuzzy Logic (FL)  
Fuzzy logic (FL) is used in SCM for supplier selection, risk analysis, and demand forecasting. FL models help address the ambiguity and imprecision inherent in these decisions, resulting in more flexible supply chain strategies. Recent developments include the integration of FL with other strategies to improve decision-making under uncertainty [6; 19]. Fuzzy logic deals with uncertain or vague circumstances by creating imprecise collections of data relevant to activities such as choosing vendors, predicting consumer interest, and making decisions when no single solution is definitive [6]. For instance, it might be represented as follows:  
\[A(x)=\mu_{A}(x)\geq\alpha\]  
where \(A(x)\) is a fuzzy set, \(\mu_{A}(x)\) is the membership function, and \(\alpha\) is the minimum acceptable level.