esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Attitude Algorithm
### Theoretical Background of the Attitude Algorithm
Under the assumption that is valid the commutation equation [75]:  
\[\dot{\omega}\cdot e^{-\int_{0}^{t}\dot{\omega}\mathrm{d}t}=e^{-\int_{0}^{t} \dot{\omega}\mathrm{d}t}\cdot\dot{\omega}~{}, \tag{3}\]  
i.e., \(\omega_{x}\), \(\omega_{y}\), \(\omega_{z}\) are considered to be constant during a step time (\(\Delta t=t_{n+1}-t_{n}\)), the angular increments around the attitude axes result in [75; 82; 83; 84]:  
\[\Delta\phi_{x}(t_{n}) =\int_{t_{n}}^{t_{n+1}}\omega_{x}(t_{n})\mathrm{d}t=\omega_{x}(t_ {n})\Delta t; \tag{4}\] \[\Delta\phi_{y}(t_{n}) =\int_{t_{n}}^{t_{n+1}}\omega_{y}(t_{n})\mathrm{d}t=\omega_{y}(t_ {n})\Delta t;\] \[\Delta\phi_{z}(t_{n}) =\int_{t_{n}}^{t_{n+1}}\omega_{z}(t_{n})\mathrm{d}t=\omega_{z}(t_ {n})\Delta t;\]  
\(\dot{\omega}\) denoted the matrix:  
\[\dot{\omega}=\frac{1}{2} \begin{bmatrix}0&\omega_{z}&-\omega_{y}&\omega_{x}\\ -\omega_{z}&0&\omega_{x}&\omega_{y}\\ \omega_{y}&-\omega_{x}&0&\omega_{z}\\ -\omega_{x}&-\omega_{y}&-\omega_{z}&0\end{bmatrix} . \tag{5}\]  
The values of \(C_{m}\) and \(S_{m}\) coefficients depend on the algorithm order \(m\) but also by the norm \(\phi_{0}\)[75; 82; 83; 84],  
\[\phi_{0}(t_{n})=\sqrt{\Delta\phi_{x}^{2}(t_{n})+\Delta\phi_{y}^{2}(t_{n})+ \Delta\phi_{z}^{2}(t_{n})}. \tag{6}\]  
Developing Equation (2), the current parameters of the attitude quaternion result by using the relations:  
\[\begin{array}{l}q_{1}(t_{n+1})=C_{m}q_{1}(t_{n})+S_{m}\Delta\phi_{z}(t_{n})q _{2}(t_{n})-S_{m}\Delta\phi_{y}(t_{n})q_{3}(t_{n})+S_{m}\Delta\phi_{x}(t_{n})q _{0}(t_{n}),\\ q_{2}(t_{n+1})=-S_{m}\Delta\phi_{z}(t_{n})q_{1}(t_{n})+C_{m}q_{2}(t_{A simplest numerical algorithm for the quaternion ortho-normalization recalculates its current parameters with the next equations [2; 3; 75; 80; 81; 82; 83; 84]:  
\[ \begin{array}{l}q_{1 ortho}(t_{n+1})=\frac{q_{1}(t_{n+1})}{|Q(t_{n+1})|},\,q_{2 ortho }(t_{n+1})=\frac{q_{2}(t_{n+1})}{|Q(t_{n+1})|},\\ q_{3 ortho}(t_{n+1})=\frac{\alpha_{1}(t_{n+1})}{|Q(t_{n+1})|},\,\,q_{0 ortho}(t_{ n+1})=\frac{q(t_{n+1})}{|Q(t_{n+1})|},\end{array}  \tag{8}\]  
where the norm of the current attitude quaternion results from:  
\[|Q(t_{n+1})|=\sqrt{q_{0}^{2}(t_{n+1})+q_{1}^{2}(t_{n+1})+q_{2}^{2}(t_{n+1})+q_{ 3}^{2}(t_{n+1})}; \tag{9}\]  
\(q_{1 ortho}(t_{n+1}),\,q_{2 ortho}(t_{n+1}),\,\,q_{3 ortho}(t_{n+1})\,\) and \(q_{0 ortho}(t_{n+1})\) are the components of the attitude quaternion after the ortho-normalization procedure. Considering the equivalence of the attitude quaternion with the NED (north-east-down frame) to the body rotation matrix \(C_{n}^{b}\)[2; 3; 75; 80; 81; 82; 83; 84], the current values of the \(C_{n}^{b}\) matrix elements are calculated with:  
\[\begin{array}{l}c_{11}(t_{n+1})=q_{0}^{2}(t_{n+1})+q_{1}^{2}(t_{n+1}
# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 4 Analysis of Simulation and Experiment Results
### Experiment Result Analysis  
We built an indoor NLOS 5G positioning test platform based on 5G positioning signal transmitters, clock synchronization modules, atomic clocks, and 5G positioning terminals. The transmitters shared the same atomic clock and achieved time synchronization through fiber optic synchronization modules. The positioning terminals measured TDOA values and transmitted them back to a laptop for position calculation. In Figure 7, the left side shows the physical structure of the 5G positioning signal transmitter used in the experiment, which consists of a power supply module, power amplification module, and positioning signal generation module. The right side shows the structure of the 5G positioning terminal. The terminal performs baseband signal processing and data demodulation in the FPGA, algorithm control and position calculation in the ARM processor, and information transmission through a Bluetooth module.  
Figure 8 shows the experimental test scene. A total of six 5G positioning signal transmitters were deployed. There were four pillars in the center of the test area used as wireless signal obstructions. The blue circle in the figure indicates the location of the positioning signal transmitter, and the yellow square indicates the location of the test point where the positioning terminal is placed. The distance between Tx-1 and Tx-3 was 5 m, and the distance between Tx-1 and Tx-2 was also 5 m. The distance between test points was 2.5 m.  
Figure 6: The execution times of different NLOS error-elimination methods.  
In Figure 9, the CDF of the position error is displayed. The performance of each algorithm in the actual environment is slightly lower compared to that in the simulation results. The NM-SLE algorithm proposed in this paper outperforms the other four algorithms significantly. The positioning error was below 4 m for over 90% of the cases. Additionally, the theoretical average values of the positioning error calculated from the derived CDF are presented in Table 2. Compared to the other four algorithms, the proposed NM-SLE algorithm exhibits the best positioning accuracy in field experiment.  
Figure 8: Schematic of field experiment.  
Figure 7: The hardware platform used in the experiments.  
Figure 9: A comparison of the CDFs of the location estimation errors in the field experiment.
## 4 TSN for Industrial Communications
IEEE 802.1 Qcc specifies three TSN network configuration models: fully centralized, hybrid, and fully distributed [60]. Several data flows (streams) between end devices (acting as talker/listener) co-exist in a TSN. The User/Network Interface (UNI) deals with the exchange of configuration information, such as join/leave requests, resource configuration/releases for a particular stream, or status responses, between end users and the network. In a fully centralized model, end devices send their requirements to an entity called the Centralized User Configuration (CUC), which passes these details to the Centralized Network Configuration (CNC) device. Accordingly, the CNC manages all the streams and schedules in the network. In a hybrid model, the user's requirements are passed directly to the network, and the CNC does the configuration without the existence of the CUC in the network. In a fully distributed model, neither the CNC nor CUC exists. The configuration data exchanges between bridges use YANG models (a data modeling language), as specified in IEEE 802.1 Qcp. IEEE802.1 Qcx specifies the YANG models associated with fault management in the connectivity. Currently, both of these are part of the active IEEE 802.1Q-2022 standard [61].
## 4 Testing and Comparison of Error Detection Capability
### Simulation and Comparison of Error Detection Capabilities  
In order to analyze the error detection capability of the proposed systolic MD architecture, we first verified the proposed secure architecture using the C++ program, then simulated fault injection and got the error detection capability of the architecture based on 100,000 testing cases. The testing results were compared with the result of the error detection scheme in [40] in Table 2. In addition, we also applied [PERSON]'s multi-column parity prediction scheme to our MFM, and using the same way, investigated its error detection capability. Here, we need to clarify a fact--in this paper we borrowed [PERSON]'s multi-column parity prediction idea and applied it to MD over a prime field, but in [36], Mozaffari Kermani applied it to MD over GF(2\({}^{\text{m}}\)). The MD operations over a prime field and GF(2\({}^{\text{m}}\)) are different, thus the cost of error detection is different. Figure 6: Five types of error models.
## 3 Results and Discussion
### Small-World Network Characteristics  
Many real-world networks exhibit the structural characteristics of small-world network. Compared with the same-scale random network, it has a similar average path length and higher clustering coefficient [29]. According to the aforementioned, the number N of nodes in the co-occurrence network of high-frequency words in bioinformatics literature in Figure 2 was 50. The average path length of the network was 1.9 and the clustering coefficient was 0.363. Compared with the corresponding random networks, the co-occurrence network of the high-frequency words of the bioinformatics literature had the same level of average path length and higher level of clustering coefficient, implying a clear small-world phenomenon. The results showed that any two high-frequency words of bioinformatics literature were connected at most by another high-frequency word. More than  
Figure 2: Co-occurrence network of high-frequency words within the bioinformatics literature (\(K=500\), \(E=200\)).  
half of high-frequency words had a direct co-occurrence relationship with each other, indicating a clear co-occurrence relationship among high-frequency words of the bioinformatics literature.  
According to the small-world characteristics of the co-occurrence network of high-frequency words in the bioinformatics literature, any two high-frequency words have a direct or indirect co-occurrence relationship. The information in Figure 2 can provide readers or researchers with certain searching suggestions. For example, if a researcher wants to query the literature related to \"genetics\", the platform should also automatically recommend the literature related to the high-frequency words \"protein\" and \"metabolism\" which have a direct co-occurrence relationship with \"genetics\".
# Determination of Effective Parameters for Hydropower Plants' Energy Generation: A Case Study
## 4 Results and Discussion
### Energy Generation vs. Precipitation  
It is known that the energy generation of hydropower plants is heavily dependent on precipitation, given that these facilities operate by harnessing the flow of water. Therefore, the deviations in patterns of precipitation have the potential to induce fluctuations in river flow. Extensive periods of insufficient precipitation have the potential to diminish river flow, thereby exerting an influence on the energy generation capacity of hydropower facilities. On the contrary, substantial precipitation has the potential to enhance river flow, thereby optimizing the generation of hydropower. Figure 10a-d illustrate the variation in daily energy generation of Bagisli HPP in contrast with daily precipitation in the studied field for the given time interval. It should be noted that the given figures have been produced using the daily precipitation data and daily energy generations, which are the sources of the data mentioned before. It can be concluded from the figures that the daily precipitation has no impact on daily energy generation, which means there is no correlation between them. As can be seen in Figure 10a-d, energy generation can be achieved at a high level even on days when precipitation is 0 mm. The reason for this situation is that when the monthly total precipitation characteristics are examined, it takes time for the precipitation to be transmitted to the power plant as flow rate. As can be seen in Figure 8a-d, the effects of snowfall in winter and rainfall in spring are observed in the following days and months. In addition, when Figure 10a-d are analyzed, it can be seen that on some days there is no energy generation even though there is precipitation. This outcome does not state that precipitation has no impact on energy generation. Because, as given in Figure 8a,b evaluated before, which represent the variation in energy generation, flow rate, and precipitation using monthly mean data, the peak of flow rate occurs with the peak of energy generation after the peak of precipitation, which means monthly flow rate and energy generation follow the precipitation that occurred in the studied region. The highest energy generation among the studied years was attained in 2019, which had the highest precipitation value. This result clearly shows that precipitation has a direct effect on energy generation through river flow. Therefore, the unpredictability of precipitation and the possibility of alterations as a result of seasonal variations may incorporate unpredictability into the energy generation of hydropower plants over the long term.
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 2 Related Work
### Transformer-Based Image Segmentation Methods  
The transformer, initially developed for sequence modeling and transformation tasks, is highly effective at capturing long-range dependencies in data. Its success in natural language processing (NLP) has motivated its adoption in computer vision. [PERSON] et al. [16] proposed the swin transformer, a versatile backbone for vision tasks. This model generates hierarchical feature maps and achieves linear computational complexity with respect to image size. Building on this work, [PERSON] et al. [30] further introduced Swin-Unet, a U-shaped architecture comprising an encoder, a bottleneck, a decoder, and skip connections. Swin-Unet uses the swin transformer for feature extraction and employs skip connections during upsampling to integrate encoder features with decoder features, enabling accurate image segmentation predictions.  
[PERSON] et al. [31] developed the ConViT model, which integrates the inductive bias of CNNs into the transformer to better focus on local positional and content information. [PERSON] et al. [32] introduced ScaleFormer, a model that combines CNN-derived local features with transformer-based global representations at multiple scales. This approach emphasizes detailed spatial information (contextual cues) alongside long-range dependencies (positional cues), effectively addressing the challenges of scale variation in image segmentation.  
In the domain of volumetric medical image segmentation, [PERSON] et al. [33] proposed nnFormer, which integrates interleaved convolutions and self-attention mechanisms to learn volumetric representations. The model employs skip attention to replace traditional concatenation or summation operations in U-Net-style architectures, ensuring efficient local and global feature extraction. Similarly, [PERSON] et al. [6] introduced TransUNet, which incorporates a transformer-encoder to capture long-range dependencies critical for segmentation tasks.  
[PERSON] et al. [34] proposed MCTrans, a model embedding multi-scale convolutional features into token sequences. By leveraging a self-attention mechanism, MCTrans facilitates cross-scale pixel-level context modeling and employs learnable proxy embeddings to capture class dependencies. The combination of self-attention and cross-attention mechanisms allows the model to model semantic relationships and enhance feature representations effectively.  
Transformer-based segmentation models use the transformer's strength in capturing global relationships between distant pixels, making them highly effective at extracting global image features. However, their intricate architectures often face challenges in efficiently capturing fine spatial details. To address this, we propose using dense skip connections to repeatedly fuse multi-scale features, thereby capturing more detailed information.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 3 Methods
### Calculation of the Grid Anchor Labels
#### 3.2.2 Single Intersection  
When a lane line intersects the boundary of an anchor at only one point (Statements 16-21), and the intersection occurs exclusively at the anchor's vertex (e.g., Anchor 2 and Anchor 4 in Figure 4a and Anchor 1 and Anchor 3 in Figure 4b), classification is determined based on the horizontal flooring rule. In this case, Anchor 2 in Figure 4a and Anchor 1 in Figure 4b do not contain lane line features and are classified as background, with all attribute values set to 0 (Statements 17-19). Conversely, Anchor 4 in Figure 4a and Anchor 3 in Figure 4b contain lane line features and are classified as lane lines. The single intersection point is denoted as \(\left(x_{i,j}^{p},y_{i,j}^{p}\right)\), and the direction attribute of the anchor is determined using Table 2. Since there is only one intersection, the slope attribute value is calculated as the reciprocal of the slope of the lane line segment formed by the two adjacent lane line coordinate points that define this intersection (Statement 6). The formula for the offset attribute values is as follows:  
\[T_{n+3,i,j}^{s}=x_{i,j}^{p}-\frac{(2i+1)W}{2W_{s}} \tag{7}\]  
\[T_{n+4,i,j}^{s}=y_{i,j}^{p}-\frac{(2j+1)H}{2H_{s}} \tag{8}\]
# A Simple Alternative Method for Preservation of 2-Methylisoborneol in Water Samples
## 2 Materials and Methods
### Analysis Methods  
The analytical method for 2-MIB was modified from the headspace solid-phase microextraction (HS-SPME) method of [PERSON] et al. [6; 31] and [PERSON] et al. [33]. A 30/50 mm DVB/CAR/PDMS (No. 57348-U) fiber from Supelco (Bellefonte, PA, USA) coupled with a fiber holder (No. 57330-U, Supelco) was employed for the extraction and desorption of all the samples. The fiber was outgassed by baking at 270 \({}^{\circ}\)C for 0.5 h at the inlet of a gas chromatograph (GC) prior to its use for 2-MIB analysis. Twelve-milliliter amber glass vials with Teflon-silicone septa were used for the 2-MIB extraction. A sample volume of 4.5 mL, containing 1.54 g of added sodium chloride to saturate the solution, was used for analysis.  
The sample vial was placed on a magnetic stirrer and agitated at 320 rpm. A fixed quantity of 2-isopropyl-3-methoxy pyrazine (IPMP) and 2-isobutyl-3-methoxy pyrazine (IBMP) (100 ng/L) was added to all the water samples and a blank, and calibration standards served as the internal standards. The extraction temperature was controlled at 80 \({}^{\circ}\)C using an aluminum heating block (Shinko Electronic Co., Taipei, Taiwan). For the extraction of the analytes, the SPME fiber was placed in the headspace of the sample vial for a predetermined time span, typically 30 min. After 30 min of exposure, the SPME fiber was injected into the GC for 2 min to desorb the trapped analytes.  
A GC (Agilent 7890, Santa Clara, CA, USA) equipped with a mass spectrometer (MS) (Agilent 5975) was employed for the separation and quantification of the 2-MIB. The MS was operated in a positive chemical-ionization mode with methane (99.9995%, Yun Shan, Tainan, Taiwan) as the reagent gas to quantify the compound. A 30 m \(\times\) 0.25 mm I. D. \(\times\) 0.25 \(\upmu\)m thick film HP-5 MS column (Agilent) was used for separation of the T&O compounds. The sample transfer line to the mass spectrometer and the MS ion source were set at 260 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. The injection port of the GC was installed with a 0.75-mm-inner-diameter SPME liner, operated in splitless mode, with the port heated to 260 \({}^{\circ}\)C. The GC oven temperature was initially held at 45 \({}^{\circ}\)C for 2 min, increased to 260 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, and then held at 260 \({}^{\circ}\)C for 2 min. The total run time was 25.5 min. The carrier gas was ultrahigh-purity helium (99.9995%, Yun Shan, Taiwan) at a constant flow of 1.5 mL/min.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 3 System Model and Design Goal
### Node Contact Model and Delivery Delay Calculate  
In the CBCAIS system model, messages will be forwarded to nodes with smaller delivery delay, so each node needs to calculate the message delivery delay to destination node. Some studies [23; 24] found that the pair-wise node inter-contact time (the time gap separating two contacts of the same pair of nodes) in realistic traces follows an exponential distribution. So we also assume that the distribution of pair-wise node inter-contact time in DTNs follows the exponential distribution with mean value \(1/\lambda_{ij}\), where \(\lambda_{ij}\) represents the contact frequency between nodes \(N_{i}\) and \(N_{j}\). The value of \(\lambda_{ij}\) can be calculated by the time average method in Equation (1):  
\[\lambda_{ij}=n/\sum_{l=1}^{n}Time_{N_{i}-N_{j}}^{l}, \tag{1}\]  
where \(Time_{N_{i}-N_{j}}^{1}\), \(Time_{N_{i}-N_{j}}^{2}\),..., \(Time_{N_{i}-N_{j}}^{n}\) are inter-contact time samples between nodes \(N_{i}\) and \(N_{j}\). So when node \(N_{i}\) receives a message \(m\) whose destination node is \(N_{j}\), the delivery delay of node \(N_{i}\) for message m is calculated by Equation (2):  
\[Time_{N_{i}}^{delivery}(m)=1/\lambda_{ij}+t_{rec}-t_{gen}, \tag{2}\]  
where \(t_{gen}\) and \(t_{rec}\) are the message generation time and message receipt time, respectively.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation  
Two-dimensional (2D) human pose estimation aims to automatically localize anatomical keypoints from images or video frames. These keypoints typically include the head, torso, arms, and legs, which together form a skeletal representation used for motion analysis and behavioral understanding. Early methods relied on handcrafted features and traditional machine learning techniques but exhibited limited generalization capability. With the advent of deep learning, convolutional neural networks (CNNs) have become the dominant paradigm for pose estimation. Their ability to automatically learn hierarchical features has significantly improved the accuracy and robustness of pose estimation models. This section reviews representative approaches in 2D human pose estimation and outlines key advancements and technical developments in the field. A taxonomy of existing methods is illustrated in Figure 1.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 2 System Model  
### RIS-Assisted Wireless Network Model  
This paper is based on a downlink wireless network scenario with a single 5G base station (gNB) serving \(E\) eMBB users and \(U\) URLLC users. A RIS system with \(N\) tiny reflective elements is also deployed, and the base station controls and regulates the RIS through a microcontroller. Let \(e\in\{1,\cdots,E\}\) denote the eMBB service slice, \(u\in\{1,\cdots,U\}\) means the URLLC service slice, \(n\in\{1,\cdots,N\}\) represents the RIS reflection element, and \(b\in\{1,\cdots,B\}\) means the RBs in the network, where the bandwidth of each RB is \(W\). Each time slot \(t\in\{1,\cdots,T-1\}\) has a duration of \(\tau\).  
Assume that all communicating nodes' channel state information (CSI) is completely known at the gNB. Assume that eMBB users and URLLC users are receivers (R\({}_{\text{x}}\)) and base stations are transmitters (T\({}_{\text{x}}\)), as shown in Figure 1. \(\theta_{n}\in[0,2\pi)\) denotes the phase shift of the nth RIS reflective element, \(\theta=[\theta_{1},\cdots,\theta_{N}]\) denotes the RIS phase shift vector, and \(\Phi=diag(e^{\text{i}\theta_{1}},\cdots,e^{\text{i}\theta_{N}})\) denotes the phase shift matrix of the RIS. The RIS-assisted end-to-end channel matrix \(C\) is expressed as follows: \(C=G\Phi H+D\), where \(G\) is the channel coefficient matrix between T\({}_{\text{x}}\) and RIS, and \(D\) matrix between RIS and R\({}_{\text{x}}\), \(H\) is the channel coefficient matrix between T\({}_{\text{x}}\) and R\({}_{\text{x}}\). Then, the channel gain of eMBB user \(e\) in RB \(b\) can be expressed as \(C_{e}=1\,G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e}\,^{1}\)2, where \(\Phi_{e}\) denotes the RIS phase shift matrix of eMBB, \(G_{RIS,e}\) denotes the channel coefficient between RIS and eMBB user \(e\), \(H_{gNB,RIS}\) denotes the channel coefficient between gNB and RB, and \(D_{gNB,e}\) denotes the channel coefficient between eMBB user \(e\) and gNB. Similarly, the channel gain of URLLC user \(u\) in RB \(b\) can be expressed as \(C_{u}=1\,G_{RIS,u}\Phi_{u}H_{gNB,RIS}+D_{gNB,u}\,^{1}\)2.  
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