esa-sceva/satcom-chunk-collection huggingface dataset:
# A Versatile Shared-Aperture Antenna for Vehicle Communications
## 3 Results  
Based on the design process, a tri-band shared-aperture antenna covering the GPS L1 band, 5-band, and 5.9 GHz V2V band was designed using ANSYS HFSS 2021. Subsequently, it was fabricated and measured. During the measurements, the 5-parameters were obtained using a Keysight Network Analyzer N5244A (Keysight, Santa Rosa, CA, USA), and the antenna's radiation pattern and gain were measured in an anechoic chamber. The prototype of the antenna, the testing environment, and the port definition are depicted in Figure 12. The antenna test architecture is shown in Figure 13. It mainly consists of a transmitter, a receiver, and a turntable. The transmitter includes a signal generator and a transmitting antenna, and the receiver includes a receiving antenna and a spectrum analyzer. The standard gain horn and the antenna under test are measured on the turntable separately, and the gain and pattern of the antenna under test can be calculated based on the power values received by the standard gain horn and the spectrum analyzer. In addition, the gain values of the standard gain horn at 1.5 GHz, 3 GHz, and 5.9 GHz are 9 dBi, 12 dBi, and 14.5 dBi, respectively.  
Figure 11: The S11 and realized gain results of proposed pattern reconfigurable antenna.  
Figure 10: Equivalent circuit of BAP70-03. **(a)** PIN on; **(b)** PIN off.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Para.** & \(LF_{1}\) & \(LF_{2}\) & \(LF_{3}\) & \(LF_{4}\) & \(LF_{5}\) & \(LF_{6}\) \\ \hline Value & 20 & 15 & 10 & 12 & 52 & 20 \\ \hline
**Para.** & _WF_ & \(L_{SLIB}\) & \(Slot_{W}\) & \(Slot_{L}\) & \(Slot_{S}\) & \\ \hline Value & 2 & 200 & 5 & 33 & 20 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pattern reconfigurable antenna dimensions (mm).
## 2 Materials and Methods
### Flow Cytometry  
Single-cell suspensions were prepared from spleen, draining lymph node (dLN), and muscle samples. The spleen and dLN samples were mechanically dissociated using a 100-\(\upmu\)m cell strainer and a syringe plunger, while muscle tissue was enzymatically digested. The muscle samples were transferred to tubes containing 500 \(\upmu\)L of digestion buffer (1.6 mg/mL collagenase D and 60 U/mL DNase I in RPMI), chopped into small fragments, and incubated at 37 \({}^{\circ}\)C for 30 min with an additional 1 mL of digestion buffer. Single-cell suspensions were then obtained by passing the digested tissue through a 100-\(\upmu\)m strainer, collected in 5 mL of ice-cold FACS buffer, centrifuged at 1500 rpm at 4 \({}^{\circ}\)C for 5 min, and filtered through a 40-\(\upmu\)m strainer before staining. The cells were stained with fluorochrome-conjugated antibodies against CD45 (30-F11), CD11b (M1/70), CD11c (N418), CD8 (53-6.7), CD169 (3D6.112), F4/80 (BM8), Ly6G (1A8), MHC class II (M5/114.15.2), and IFN-\(\gamma\) (XMG1.2) (BioLegend, San Diego, CA, USA). Live/dead staining was performed using the Fixable Violet Dead Cell Stain Kit (Invitrogen, Carlsbad, CA, USA). For restimulation, the cells were incubated with the COVID-19 spike peptide library (Miltenyi Biotec, Bergisch Gladbach, Germany) at 100 ng per sample for 48 h. Subsequently, the supernatant was collected, and cytokine analysis was performed using the CBA assay. The remaining cells were used for flow cytometry staining. Data acquisition was performed using a CytoFLEX analyzer (Beckman Coulter, Brea, CA, USA). The cell numbers presented in the figure were calculated using CytExpert software version 2.4 (Beckman Coulter, Brea, CA, USA) and FlowJo software version 10.7.2 (BD Biosciences, Franklin Lakes, NJ, USA). Specifically, the total cell number was determined and multiplied by the gating percentage of each immune cell population to estimate absolute cell counts.
## 6 Conclusions  
This study developed a stochastic queuing framework to evaluate the IoT-SBL coordination strategy under time-varying traffic demand. By modeling vehicle arrivals via a Markov Arrival Process and incorporating real-time bus priority and lane-switching logic, the proposed method demonstrated significant advantages in managing intersection queues and improving overall efficiency.  
Our findings align with those of [PERSON] and [PERSON] [PERSON] and [PERSON] (2019), who reported that intermittent bus lanes reduced average delays by 15-30% under moderate saturation. However, the proposed IoT-SBL strategy achieved up to 71% queue length reduction at V/C = 0.97 due to its real-time responsiveness and integrated coordination via IoT. At low saturation levels (V/C! 0.5), our results show that the advantage of IoT-SBL diminishes, in agreement with observations by [PERSON] (2019), where traditional bus lane strategies perform comparably.  
Practically, IoT-SBL is most beneficial in corridors where saturation exceeds 0.5, especially under peak-hour congestion. For intersections with V/C ratios above 0.7, the strategy can reduce average delays by 40-70% and maintain intersection throughput above 90%. These benefits are particularly evident in urban settings with frequent bus operations (e.g., departure intervals less than 10 min) and high private vehicle demand.  
The IoT-SBL strategy is inherently adaptable to diverse urban environments. Key parameters (e.g., lane capacity, signal phases) can be calibrated to local traffic patterns, infrastructure capabilities, and policy frameworks. Future work will explore case studies in cities with varying congestion profiles and IoT readiness to validate universal applicability. While the IoT-SBL strategy demonstrates efficacy under fixed saturation levels, real-world implementation requires adaptation to dynamic traffic fluctuations. Future work will incorporate time-varying parameters to address this gap.  
Conceptualization and methodology, B.Y.; methodology and writing--original draft preparation, H.Y.; software, validation and visualization, S.G.; resources and funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62103443.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company CRRC Nanjing Puzhen Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations
## 2 Materials and Methods
### Determination of the Second EWL/WL Ambiguity  
Since the wavelength of EWL is long enough, the float ambiguities obtained by the least square method can be fixed by rounding directly. The addition of the EWL carrier phase observations and the fixed ambiguities can be regarded as accurate pseudo-range observations [23]. Then, the fixed EWL combinations are used as known observations to estimate second EWL (for quad-frequency) or WL combinations, and the corresponding equations are as follows (taking WL as an example):  
\[\left[\begin{array}{c}\mathbf{v}_{\textit{{EWL}}}^{\prime}\\ \mathbf{v}_{\textit{{W}}}\end{array}\right]=\left[\begin{array}{cc}\mathbf{B}&-\eta_ {\textit{{EWL}}}I_{\textit{s}}&\mathbf{0}\\ \mathbf{B}&-\eta_{\textit{{W}}}I_{\textit{s}}&\lambda_{\textit{{W}}}I_{\textit{s}} \end{array}\right]\left[\begin{array}{c}\mathbf{x}\\ \textit{{ion}}\\ \mathbf{N}_{\textit{{W}}}\end{array}\right]-\left[\begin{array}{c}f_{\textit{ {EWL}}}^{\prime}\\ I_{\textit{{W}}}\end{array}\right] \tag{10}\]  
where \(\mathbf{v}_{\textit{{EWL}}}^{\prime}\) and \(\mathbf{v}_{\textit{{W}}L}\) represent the residual vector of DD pseudo-range observations of precise EWL and DD WL observations, respectively; \(-\eta_{\textit{{W}}L}\) is the ionospheric scalar factor of the WL signal; and \(\mathbf{l}_{\textit{{EWL}}}^{\prime}\) and \(\mathbf{l}_{\textit{{W}}L}\) are OMC vectors of the corresponding observations, respectively. The ambiguities of WL can be determined by the method of Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) [24]. When quad-frequency is used, the WL of Equation (10) is changed to a second EWL. The wavelength of a second EWL is shorter than the first EWL, which can be fixed using least squares or the LAMBDA method. Then, fixed second EWL observations are used to constrain WL ambiguities.
## 4 Experiments
### Evaluation Metrics  
In the IEMOCAP and CMU-MOSEI datasets, the applied evaluation metrics were consistent with those outlined in [1]. For the IEMOCAP dataset, these metrics include the average binary accuracy and average F1 score, whereas the average binary weighted accuracy and average F1 score were applied for the CMU-MOSEI dataset. The predictions of  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Item** & **IEMOCAP** & **CMU-MOSEI** & **CH-SIMS** \\ \hline Modality & Text, visual, audio & Text, visual, audio & Text, visual, audio \\ Language & English & English & Chinese \\ Task & Multi-class classification & Multi-label classification & Multi-class classification and regression \\ Annotation & Multimodal labels & Multimodal labels & Multimodal and unimodal labels \\ Evaluation & Accuracy, F1 & Weighted accuracy, F1 & Weighted F1, Corr, MAE, Acc-2 \\ \hline Training size & 5162 & 14,524 & 1368 \\ Validation size & 737 & 1765 & 456 \\ Testing size & 1481 & 4188 & 457 \\ Total samples & 7380 & 20,477 & 2281 \\ \hline Distribution of & Angry: 1103 & Angry: 4600 & Negative (\(-1.0\), \(-0.8\)): 754 \\ labels & Excited: 1041 & Disgusted: 3755 & Weakly negative (\(-0.6\), \(-0.4\), \(-0.2\)): 484 \\  & Frustrated: 1849 & Fear: 1803 & Neutral (0.0): 345 \\  & Happy: 595 & Happy: 10,752 & Weakly positive (0.2, 0.4, 0.6): 346 \\  & Neutral: 1708 & Sad: 5601 & Positive (0.8, 1.0): 352 \\  & Sad: 1084 & Surprised: 2055 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed characteristics and statistics for IEMOCAP, CMU-MOSEI, and CH-SIMS datasets.  
the proposed method for each emotional category and overall performance were evaluated by averaging the scores from all emotional annotations.  
Regarding the CH-SIMS dataset, this work follows the approaches by [PERSON] et al. [29] and [PERSON] et al. [28], recording the experimental results in two formats: multiclass classification and regression. For multiclass classification, this work reports the weighted F1 score and multiclass accuracy (Acc-2). For regression, this work reports the mean absolute error (MAE) and [PERSON]'s correlation. Higher values indicate better performance for all metrics except for the MAE.
## 5 Data Collection for GHG Emissions  
GHG emissions from PI can be measured directly onsite or estimated using EFs and models, with the choice depending on the purpose, cost, and feasibility. EFs and models, which use data like fuel consumption, are commonly employed for GHG data collection [12, 31, 39], and organizations like the Intergovernmental Panel on Climate Change (IPCC) and USEPA develop these factors for accurate emissions estimation. While direct measurements are precise, alternative data sources, such as global CO\({}_{2}\) emission databases and national inventories, are also used [31, 39, 40]. Advanced systems like Geographic Information System (GIS), remote sensing (RS), and optical measurements are increasingly integrated for comprehensive monitoring [31, 41]. Satellites and projects like NASA's \"Vulcan\" are improving accuracy for tracking GHG sources [31, 42, 43]. For efficient GHG data collection in PI projects, accuracy, reproducibility, and verifiability are crucial. Since trenchless technologies were introduced in the 1980s, they have resolved many issues of traditional ETs [31].
## 2 Research Subjects and Methods
### Set-Pair Analysis (SPA) Theory  
The Set-Pair Analysis (SPA) theory, first proposed by [PERSON] [10], is now widely used in security assessments as a systematic method for quantifying both deterministic and uncertain problems [11; 12]. SPA analyzes the common (same), differing (different), and opposing (opposite) characteristics of two sets under a given precondition. This method metrically inscribes these characteristics and calculates the degree of same-different-opposite linkage. It can also be extended to situations involving more than two sets, allowing for new, in depth research that evaluates objectively existing uncertainties [13]. The degree of linkage is expressed by the following equation:  
\[\mu=\frac{S}{N}+\frac{F}{N}i+\frac{P}{N}j \tag{1}\]  
where  
* \(\mu\) is the degree of association;
* \(N\) is the total number of features in the two sets;
* \(S\) is the number of common features in the two sets;
* \(P\) is the number of similar features in the two sets;
* \(F\) is the number of opposing features in the two sets;
* \(i\) is the coefficient of the degree of dissimilarity, ranging from \(-1\) to \(1\);
* \(j\) is the coefficient of opposition, with a constant value of \(-1\).  
For Equation (1), \(\frac{S}{N}\) is called the degree of congruence, \(\frac{F}{N}i\) is called the degree of difference, \(\frac{P}{N}j\) is called the degree of opposition, and a, b and c are their respective linkage components [14]. Therefore, Equation (1) can be simplified to Equation (2).  
\[\mu=a+bi+cj \tag{2}\]  
In practical use, scenarios often involve more than one set. The ternary linkage function of Equation (2) may not adequately address the complexity of such situations. Therefore, to accommodate this complexity, the linkage degree is expanded to different levels, resulting in Equation (3), which represents a multivariate linkage number function and can be expressed as follows:  
\[\mu=a+bi_{1}+bi_{2}+\cdots b_{n-2}i_{n-2}+cj \tag{3}\]  
Set-Pair Analysis (SPA) theory provides a robust framework for quantifying relationships between deterministic and uncertain variables within two or more sets. By extending the degree of same-different-opposite linkage across multiple sets, SPA allows for a comprehensive analysis of complex situations, which is crucial for accurately assessing security risks and uncertainties. This expanded model lays the foundation for more refined evaluations in subsequent sections.
## 2 Materials and Methods
### Endpoint Localization Method  
The vibration signals obtained are usually continuous with multi-steps controlled by several commands. The start and end positions of each step in the vibration signals are defined as the endpoints of the action. Endpoint localization is to identify the start and end positions of a segment in the continuous time-series signal, by which the subsection of the moving process can be obtained and further scored by the scorecard model.  
Usually, the wavelet transform (WT) [22] and the double threshold method [23] are utilized to detect the end position of a series of signals. WT can identify the step position, and the double threshold method can detect endpoints through the calculation of the zero-crossing rate and local energy. However, in the case of endpoint localization for manipulator vibration signals, these two methods are susceptible to noise interference. Based on the commonly used methods for endpoints detection, we propose a scheme to automatically identify the endpoints of the vibration signals, including the process of step transformation, wavelet transformation, and localization.
## 4 Findings and Discussion
### Web Graph Analysis
#### 4.1.1 Web Graph Analysis for Higher Star-Rated Hotels  
Figure 5 is a web graph analysis of online complaint attributes for higher star-rated hotels. A clear difference was observed between the networks that formed. For instance, Location Accessibility and F & B Issue exhibited a strong direct connection (98.73%), meaning that these two attributes have a strong relationship. From this, it could be inferred that if hotel guests staying at higher star-rated hotels do not complain about Location Accessibility, they might complain about F & B Issue. Clearly, this is a relationship that could benefit from further research. The graph also shows several medium connections between various pairs of complaint attributes; for example, Location Accessibility and Customer Service (34.62%). This connection could suggest that if hotel guests complain about Location Accessibility, they might also complain about Customer Service or vice versa. Room Space and Room Facility (33.15%) and Miscellaneous Issue and F&B Issue (31.21%) also were among the top three medium connections. Some examples of weak connections include Safety and Cleanliness (14.75%) and Hotel Facility and F&B Issue (14.65%) (see Table 5).  
Figure 5: Web graph analysis of online complaint attributes for higher star-rated hotels.
## 3 Results and Discussions  
### Stick Slip Vibration Effect on the Bit  
The stick-slip effect on the bit in both the upscale and downscale models was analyzed over an 80-s duration at varying torque values, as shown in Figure 2. In this simulation, the upscale bit operated with a torque of 500 kgfm, while the downscale bit functioned at a significantly lower torque of 0.01 kgfm. Both bits were run under the same top RPM to compare their behavior under identical operating conditions. From the graph, it is evident that the upscale bit initially rotated at a higher RPM than the top RPM, but it soon experienced stick-slip oscillations. After the first 5 s, the bit overcame the initial sticking phase and exhibited torsional oscillations, attempting to synchronize with the top RPM. As the top RPM increased, the upscale bit's rotation gradually stabilized and became more easily associated with the top RPM over time to allow the severity of oscillations to reduce. On the other hand, the downscale bit had somewhat dissimilar behavior. At the start of the run, an apparent sticking at 0 RPM was observed. This probably means some level of instability at the start-up. However, after this happened, the downscale bit also experienced a moment of stick-slip. Eventually, quite similar to the upscale bit, the downscale bit overcame the stick-slip effect and began to rotate in sync with the top RPM as the system stabilized.  
The torque values for both setups were increased to evaluate their response under higher torque conditions. Specifically, the torque on the upscale bit was raised to 1500 kgfm, while the downscale bit's torque was increased to 0.04 kgfm. Figure 3 illustrates the behavior of both bits at these elevated torque levels. As observed, both bits initially exhibited similar dynamics to their performance under the lower torque values with stick slip at the onset. However, the stick-slip periods became more pronounced for both the upscale and downscale bits, indicating that higher torque amplified torsional oscillations (especially for the upscale bit). Despite the extended stick-slip phases, both bits eventually synchronized with the top RPM over time, stabilizing their rotational speeds in alignment with the system's overall rotation.  
Figure 2: Upscale and downscale bit RPM at torque values of 500 kgfm and 0.01 kgfm.  
The torque on both setups was further increased to assess their behavior under more extreme conditions. Specifically, the upscale torque was raised to 4500 kgfm, while the downscale torque was increased to 0.09 kgfm. Figure 4 illustrates the effect of these higher torque values on both bits. It is evident from the figure that the stick-slip period became significantly longer, with both bits experiencing more prolonged sticking phases before resuming rotation. This behavior emphasizes that higher torque amplifies the intensity and duration of torsional vibrations, making it increasingly difficult for the bits to achieve smooth, continuous rotation. This further highlights the critical role torque plays in influencing stick-slip severity, requiring more effort and precise control to stabilize the system at higher loads.
# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
## 3 Experimental Results and Discussion  
### Experimental Data  
In order to measure the performance of the proposed method in this paper, we used MWIR 640 \(\times\) 512 pixels Integrated Detector Dewar Cooler Assembly to obtain experimental data. Table 2 provides an overview of its specifications. In this letter, IRPT we mainly study is space target or aircraft. Therefore, we obtained sky background and deep space background with their IRPT as experimental data, as shown in Figure 5. In Figure 5, the scenarios from the top row to bottom row are sky background, sky background with IRPT, deep space background, and deep space background with IRPT, respectively. Each data set has five sequences, and each sequence includes 150-200 frames. Figure 6 shows the details about the acquisition of experimental data. In these data sets, the sky background and its IRPT are the real shots of the sky and the aircraft as shown in Figure 6a; However, the deep space background and its IRPT are simulated by blackbody and target board as shown in Figure 6b. In Figure 6c, the blackbody penetrates the target hole to form an IRPT. Then, we can control the field of view movement in a certain direction by moving the camera. In the image, the IRPT will move in the opposite direction of the camera movement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step Size of Updating CFP (\(c\))** & **0.01** & **0.03** & **0.05** \\ \hline Forgetting factor & 0.98 & 0.97 & 0.96 \\ Th\({}_{\rm V}\) & 0.17 & 0.30 & 0.37 \\ Th\({}_{\rm D}\) & 0.95 & 0.97 & 0.96 \\ Th\({}_{\rm ne}\) & & 0.5 \(c\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical selection of parameters and thresholds.  
Figure 4: The distribution of Q\({}_{90\%\text{CFP}}\) with different blinking frequency (BF) and step size of updating CFP (**a**) forgetting factor = 0.96, (**b**) forgetting factor = 0.97, (**c**) forgetting factor = 0.98.  
Due to the limited number of flickering pixels of real experimental data, the simulated flickering pixels with random blinking frequency were added to the real experimental data to make a quantitative analysis on the effect of flickering pixel suppression.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Format** & **640 \(\times\) 512** \\ \hline Pixel size & 15 μm \\ Spectral Range & 3–5 μm \\ F-number & 4 \\ Noise Equivalent Temperature Difference & 30 mk \\ (NETD) & 50 Hz \\ Framerate & 14 bits \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of the MWIR detector used to obtain experimental data.  
Figure 5: The real experimental data (**a**) sky background, (**b**) sky background with IRPT, (**c**) space background, (**d**) space background with IRPT.
## 3 Review Results
### Applications in Gliomas  
The most promising use of DTI in gliomas as discussed is to define the CTV in areas of tumour infiltration not visible on anatomical MR sequences or to predict areas of tumour reoccurrence. Several studies already discussed have developed methods to perform this with one getting an AUC accuracy of 0.9 for predicting tumour recurrence sites [27]. These methods have been applied to producing SRT treatment CTVs. Virtual dissection would additionally be of benefit for gliomas in order to reduce radiation dose to the eloquent  
Figure 5: Diagram showing glioma cells migrating along white matter tracts and how a DTI-based anisotropic clinical target volume (CTV) could better capture areas of tumoral spread than isotropic CTV and potentially reduce the overall radiation dose.  
WM tracts with a small number of theoretical SRT planning studies making use of this technique, showing that it would be effective [9, 29]. Unfortunately, there have not been any prospective studies using a DTI-guided SRT treatment plan to treat patients with gliomas, as found by a recent systematic review [19]. As far as the authors are aware, there have not been studies on DTI-guided radiotherapy in any primary brain malignancies other than gliomas [19], although there are studies using DTI to differentiate gliomas from other primary brain malignancies, which is an important step for management planning [16, 17].