esa-sceva/satcom-chunk-collection huggingface dataset:
# Beam Dynamics Design of a Multi-Ion RFQ for Medical Application
## 4 Error Analysis
### Input Errors Analysis
The tolerance error of the input emittance is 0.042 \(\pi\) mm\(\cdot\)mrad. This indicates that to improve transmission efficiency and beam quality, strict control of the emittance of the injected beam is essential. Ideally, the RFQ accelerator can directly accept and accelerate a direct current (DC) beam with no energy spread from the ion source. However, it is limited by the stability of the ion source, the injected beam always has some energy spread. The RFQ dynamics design structure should be able to accommodate a certain level of beam energy spread. Figure 9(d) illustrates the variation in beam transmission efficiency with energy spread. From the figure, it can be seen that the RFQ has a good tolerance to energy spread, with transmission efficiency remaining stable above 95% for beams with an energy spread of less than 5.1%. Additionally, the degradation in beam quality due to the energy spread remains within an acceptable range. The electromagnetic lenses in the LEBT section (including sputtering lenses and quadrupole lenses) are subject to current noise and voltage drift, which can result in spatial displacement and divergence-coordinate displacement of the beam centroid injected into the RFQ. Since the RFQ input beam is a DC beam, with symmetric error effects in the \(x\) and \(y\) directions, it is sufficient to analyze errors in one direction. As illustrated in Figure 9(e), the beam transmission efficiency shows a wide tolerance for variations in spatial displacement and divergence-coordinate displacement. To maintain a transmission efficiency greater than 95%, the spatial displacement should be less than \(\pm\)0.035 cm, and the divergence-coordinate displacement should be within \(\pm\)20 mrad.
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 2 Materials and Methods
### Precipitation Products: TAMSAT3, CHIRPS, and MSWEP
#### 2.1.1 TAMSAT3  
TAMSAT3 is a precipitation product based on TIR imagery from geostationary sensors onboard the METEOSAT platforms of the first and second generation (MFG and MSG), providing daily estimates from 1983 to the present on the African continent at a 4 km spatial resolution [7; 27]. The algorithm is based on a cloud indexing approach, where the Cold Cloud Duration (CCD) is used as a proxy for precipitation, representing the period of time during which the cloud top temperature is lower than a threshold value selected to identify the precipitating cloud systems. The peculiarity of TAMSAT3 is the exploitation of rain gauge data in the calibration phase, which is climatological and local, i.e., varying monthly and spatially, in order to capture the temporal and geographical variations of precipitation without yearly updates. This eliminates the need to refer to a rain gauge dataset acquired in real time (i.e., simultaneous to satellite acquisitions) in favor of a historical rain gauge dataset. This approach is particularly suitable to the African continent, where the density and availability of real time measurements of precipitation from ground stations varies from area to area, with consequent negative effects on satellite precipitation estimates.  
TAMSAT3 provides daily time series of precipitation since 1983, although there are several days with missing data especially in the 1980s and 1990s (the worst years are 1985, 1986, and 1989 with 73, 80, and 71 missing days, respectively). One day is considered missing when it has no TIR data for more than six consecutive hours, while a group of five days is considered missing if more than one day is missing [7]. In the present study, data gaps have been filled in with daily climatological averages obtained from the time series of the TAMSAT3 dataset.  
At the time this analysis was being carried out, the available TAMSAT release was the previously described version 3.0, which was replaced by a new version 3.1 now down-loadable from TAMSAT archive during the writing of this work. In this new version, changes include an increased number of rain gauges used in the CCD calibration, the extension of minimum threshold temperature to -65\({}^{\circ}\)C (as opposed to -60\({}^{\circ}\)C), and filling in all missing days so that the rainfall record is temporally complete (no details are provided with respect to the adopted methodology). However, as stated on the TAMSAT v3.1 web page ([[http://www.tamsat.org.uk/data](http://www.tamsat.org.uk/data)]([http://www.tamsat.org.uk/data](http://www.tamsat.org.uk/data)), accessed on 21 October 2021), there have been no significant algorithmic changes between v3.0 and v3.1; thus, the estimates are very similar and no major differences are expected from v3.0 to v3.1.
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 1 Introduction
The existing research for long time integration techniques for GNSS-based radar refers to traditional methods, such as fractional Fourier transform (FrFT) [27], Radon Fourier transform [28], etc. In [29; 30], the whole integration interval is divided into several frames. The target echoes are coherent accumulated inside the frame and noncoherent integration among different frames to obtain a focused RD map. However, the target echoes have not been accumulated effectively due to the noncoherent integration process. A modified radon Fourier transform is proposed in [31] to achieve long time integration for GNSS_based bistatic radar. However, this method needs to estimate the parameters of the target echo signal and the estimation result will affect the integration gain. In this paper, we focus on the clutter cancellation and long time integration of GNSS-based passive radar for air target detection. Firstly, the classical clutter cancellation method, ECA, which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace is applied in GNSS-based bistatic radar. As a result, the clutter has been effectively removed from the surveillance channel. After that, an integration method based on long-time integration Fourier transform (LIFT) is proposed to achieve the azimuth compression. In the new method, the Doppler frequency of the target during the CPI is described by a second-order polynomial. Additionally, the Doppler variation is described by a new variable, called relative acceleration (RA). Once the RA is obtained, the target echo can be correctly accumulated by LIFT. The coarse estimation of RA is obtained by FrFT and the accurate one is obtained through optimizing the parameter that is related to the target detection results. The remainder of this paper is organized, as follows: Section 2 describes the GNSS-based bistatic radar system consists of system geometry and signal model/Section 3 discusses the clutter cancellation algorithm and elaborates the new long time integration method that is based on LIFT. Section 4 validates the proposed method through simulations and Section 5 concludes the paper.
## 4 EGCIR Protocol
### Assumptions  
The following assumptions are considered while designing and developing the EGCIR protocol:  
1. The sensor nodes are distributed randomly;* Sensor nodes remain static after dispersal with restricted constraints;
* After network deployment, nodes cannot be added or removed;
* Neither the source nor receiver is faulty or malicious;
* The BS has unlimited resources in terms of processing, storage, transmission and energy;
* Each node has equipped with a global positioning system (GPS) to identify its position.
## 2 Overview of the Study Area
### Characteristics of Strata and Coal Seams
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Lithology**} & \multicolumn{2}{c}{**Saturated**} & \multirow{2}{*}{**Elastic Modu-**} & \multirow{2}{*}{**Tensile**} & \multirow{2}{*}{**Weight/K/km\({}^{3}\)**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Precision**} \\  & \multicolumn{2}{c}{**Compressive**} & \multicolumn{2}{c}{**loss/MPa**} & & & & & & & & & & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & & & & & & & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & & & & & & & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & & & & & & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & & & & & & \\ \hline Sand-soil layer & 0.72 & 23 & 0.03 & 17.2 & 37.0 & 0.31 & 0.07 & 0.38-0.47 & 11.9–17.3 & \(4.58\times 10^{-4}\) \\ Red soil layer & 0.85 & 69 & 0.20 & 18.6 & 37.2 & 0.30 & 0.08 & 0.42 & 17.4–18.7 & \(3.27\times 10^{-4}\) \\ Sandy & 30.4 & 2400 & 3.13 & 25.1 & 38.0 & 0.24 & 1.58 & 2.70 & 0.47 & \(3.21\times 10^{-8}\) \\ Fine-grained & 36.1 & 6100 & 2.50 & 26.1 & 41.7 & 0.20 & 5.33 & 0.8–10.2 & 0.10–2.03 & \(2.13\times 10^{-7}\) \\ Sill sandstone & 34.9 & 6300 & 2.45 & 25.5 & 37.7 & 0.21 & 6.06 & 0.4–9.34 & 0.22–2.26 & \(4.32\times 10^{-6}\) \\ \(2^{-2}\)-coal seam & 22.1 & 5100 & 0.24 & 13.6 & 38.5 & 0.22 & 5.11 & - & - & - \\ Bottom plate & 80.7 & 35,000 & 1.86 & 26.8 & 43.0 & 0.29 & 48.22 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical and mechanical parameters of various rock and soil layers used in the numerical model.
# Soft Error-Tolerant and Highly Stable Low-Power SRAM for Satellite Applications
## 2 Proposed RHSP16T Cell
### Soft-Error Recovery Analysis
(3) SEMNU at node pair Q-S0: When sufficient charge accumulates at the node pair Q-S0, it can cause the cell to flip to a different state. The feedback mechanism between the nodes prevents recovery to the initial state. However, charge sharing between transistors can be prevented by keeping a minimum spacing of 1.62 \(\upmu\)m between PMOS transistors and 0.6 \(\upmu\)m between PMOS and NMOS transistors [4]. As shown in Figure 2, the layout of the proposed RHSP16T cell maintains a spacing of 4.175 \(\upmu\)m between Q and S0, thereby preventing SEMNUs at this node pair.
## 3 Performance Analysis
### Outage Probability  
The CDF of e2e instantaneous SNR can be calculated from (13) as  
\[F_{\gamma_{e2e}}(z)=\Pr[\min(\gamma_{R},\gamma_{D})\leq z]. \tag{14}\]  
It can be observed that \(\gamma_{R}\) and \(\gamma_{D}\) in (14) are two dependent random variables (RVs), since the \(\mathcal{P}_{R}\) in (10), which is the harvested energy at the relay, depends on the channel gain \(h_{R}\).  
**Lemma 1**.: _The CDF of \(\gamma_{e2e}\) is given as_  
\[F_{\gamma_{e2e}}(z) =\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\frac{\Phi_{1}\phi_ {2R}}{\
u_{0}^{\Theta_{1}}}G_{4,8}^{7,1}\Bigg{(}\frac{\psi_{1R}^{2}z}{2^{4}\mu _{R}}\Bigg{|}\frac{1,\kappa_{1R},1+\varrho_{1}}{\varrho_{1},\kappa_{2R},0} \Bigg{)}+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\sum_{n=0}^{N}\frac{\Phi _{2R}}{\mu_{0}^{\Theta_{2}}}G_{4,8}^{7,1}\Bigg{(}\frac{\psi_{1R}^{2}z}{2^{4}\mu _{R}}\Bigg{|}\frac{1,\kappa_{1R},1+\varrho_{2}}{\varrho_{2},\kappa_{2R},0} \Bigg{)}\] \[+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\sum_{n=0}^{N}\frac{ \Phi_{3}\phi_{2R}}{\
u_{0}^{\Theta_{3}}}G_{4,8}^{7,1}\Bigg{(}\frac{\psi_{1R}^{ 2}z}{2^{4}\mu_{R}}\Bigg{|}\frac{1,\kappa_{1R},1+\varrho_{3}}{\varrho_{3}, \kappa_{2R},0}\Bigg{)}+\sum_{k=1}^{\beta_{R}}\left(1-F_{\gamma_{D}^{\prime}}(1 )\right)\phi_{2R}G_{3,7}^{6,1}\Bigg{(}\frac{\psi_{1R}^{2}z}{2^{4}\mu_{R}} \Bigg{|}\frac{1,\kappa_{1R}}{\kappa_{2R},0}\Bigg{)}, \tag{15}\]  
_where \(\mu_{0}\) is given in (A1), \(\varrho_{1}=\frac{\xi_{D}^{2}}{2}\), \(\varrho_{2}=\frac{\eta+\alpha_{D}}{2}\), and \(\varrho_{3}=\frac{\eta+1}{2}\). Also, \(\Phi_{1}\), \(\Phi_{2}\), and \(\Phi_{3}\) are given in (A7). Further, \(F_{\gamma_{D}^{\prime}}(1)\) can be obtained from (A5)._  
Proof.: Please refer to the Appendix A for the proof of (15).  
The outage probability of the communication system is defined as the probability that the received e2e instantaneous SNR is smaller than a predefined threshold, \(\gamma_{\text{th}}\), and it can be expressed as  
\[P_{\text{out}}=\Pr[\gamma_{e2e}\leq\gamma_{\text{th}}]=F_{\gamma_{ e2e}}(\gamma_{\text{th}}), \tag{16}\]where \(F_{\gamma_{t2e}}(\gamma_{\rm th})\) can be obtained from (15).
## 5 Evaluation
### CPCROK Implementation  
#### 5.2.1 Data Preparation  
By generating a large number of vehicle trajectories passing through the mix zone, as mentioned earlier, we have successfully collected a substantial amount of data. This data have been carefully organized into a dedicated dataset, specifically designed for offline model training in the RNN-FGTGM module. The dataset comprises four features: the x-coordinate, y-coordinate, and velocity components of the x-axis and y-axis, which are fed chronologically. Similarly, the output dataset also contains the same four features, which are used to form fake beacons. To ensure comprehensive RNN training and cover all possible steering angles, random noises are introduced to both entrance and exit. This ensures that even vehicles following the same route will have slightly different steering actions, covering the range of angles from \(-90^{\circ}\) to \(90^{\circ}\) and enhancing the diversity and effectiveness of the training process.
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 5 Discussion, Lessons Learned, and Open Issues
It is desirable that future approaches can make public their implementations, simulation scripts, and trace logs, whenever possible. * Self-adaption to the application context: Some applications can present different needs according to the time or type of data message. As an example, at a given time, an application can require low latency and may continue to need high reliability or lower energy consumption. Thus, routing solutions should self-adapt to the temporary context of applications. This feature can be obtained with the creation of cross-layer solutions in which the application layer indicates their particularities to the routing layer. However, it is important to highlight that some application can never require this characteristic. In this case, they can adopt a straightforward approach.
## 2 OCC System Overview
### Illumination Model for Camera Pixels  
An OCC data transmission model is illustrated in Figure 2. The channel for optical signal transmission has two components: line-of-sight (LOS) and non-line-of-sight (NLOS). The asperity of the chip faces and the geometric conditions of the encapsulating lens affect the radiation pattern of the LED. The luminous intensity model is represented by the Lambertian radiant intensity [14; 16] which is expressed as  
\[\mathrm{R}_{0}(\psi_{\mathrm{ir}})=\frac{(1+\mathrm{m}_{\mathrm{l}})\cos^{ \mathrm{m}_{\mathrm{l}}}(\psi_{\mathrm{ir}})}{2\pi}, \tag{1}\]  
where \(\psi_{\mathrm{ir}}\) denotes the angle of irradiance of the LED, and \(\mathrm{m}_{\mathrm{l}}\) is the Lambertian emission index originating from the radiation angle, \(\zeta_{\frac{1}{2}}\), at which the radiation intensity is half of that in the main beam's direction. \(\mathrm{m}_{\mathrm{l}}\) is defined as  
\[\mathrm{m}_{\mathrm{l}}=-\frac{\ln 2}{\ln(\cos\zeta_{\frac{1}{2}})}. \tag{2}\]  
Assuming that the Euclidean distance between \(\alpha\) and \(\beta\) is \(\mathrm{d}_{\alpha,\beta}\), the overall DC gain per pixel can be expressed as  
\[\mathrm{H}_{\alpha,\beta}=\mathrm{g}_{\mathrm{op}}\cos(\psi_{\mathrm{in}}) \Delta\frac{\mathrm{R}_{0}(\psi)\mathrm{A}_{\mathrm{c}}}{\mathrm{ad}_{\alpha, \beta}^{2}}, \tag{3}\]  
\[\mathrm{d}_{\alpha,\beta}=\sqrt{\mathrm{d}_{\alpha,\mathrm{x}}^{2}+\mathrm{d }_{\alpha,\mathrm{h}}^{2}} \tag{4}\]  
where \(\psi_{\mathrm{in}}\) signifies the corresponding angle of incidence, \(\mathrm{g}_{\mathrm{op}}\) represents the gain of the optical filter, \(\mathrm{A}_{\mathrm{c}}\) is the entire area of the image projected onto the image sensor, \(\sigma\) is the total number of camera pixels illuminated by the LED, and \(\Delta\) is a rectangular function whose value is implied as  
\[\Delta=\left\{\begin{array}{ll}0,&\psi_{\mathrm{in}}\geq\partial_{\mathrm{ aov}}\\ 1,&\psi_{\mathrm{in}}<\partial_{\mathrm{aov}}\end{array}\right., \tag{5}\]  
where \(\partial_{\mathrm{aov}}\) is the AOV of the camera.  
Figure 2: The System model.
## 5 Discussion
### Limitations and Future Work  
As described in Section 3.4, we adopt different configurations for the object detection and semantic segmentation tasks compared to the image classification task. This is due to the high computational cost to build global connections in the GSA module when the input resolution becomes very large. Therefore, for the proposed LGViT, when applying it on a new resolution, the configuration should be well-designed to achieve a trade-off between cost and performance. The progress to find the best configuration may be a little time-consuming but is of great value to the training phase. A well-designed configuration of the LSA and GSA modules may result in a great performance. Because of the DCPE mode in LGViT, the configurations can be changed at any time, showing high flexibility by comparison with other window-based self-attention methods [23; 49].  
However, LSA and GSA do introduce extra computational and memory costs, since for each window, the key set and value are extended by its neighbors or the whole image patches. When applying the model to a very high-resolution application may be difficult. For example, the resolution of an ultra wide field fundus fluorescein angiography (UWFFA) can be \(3k\times 4k\). The computational cost of transformers for retinal vessel segmentation using UWFFA may be unaffordable. Developing some practical techniques like global information aggregation [74] to reduce the cost would be necessary to make it more applicable in realistic scenarios. Moreover, our work mainly focus on visual tasks, the great potential of Transformer-like architectures for unified modeling between vision and language has not been explored in this work. We hope to investigate this promising direction in the future.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **\#Params** & **FLOPs** & **Metrics** \\ \hline \multirow{2}{*}{Imagenet1K} & DCPE & 30M & 4.8G & 82.1 \\  & RPB & 30M & 4.7G & 82.0 \\ \hline \multirow{2}{*}{COCO2017} & DCPE & 40M & 261G & 43.4 \\  & RPB & 40M & 261G & 43.2 \\ \hline \multirow{2}{*}{ADE20K} & DCPE & 63M & 946G & 47.1 \\  & RPB & 63M & 946G & 47.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effectiveness of the proposed dynamic contextual positional encoding mode. Metrics on Imagenet1K is top-1 accuracy, on COCO2017 is \(AP^{b}\), and on ADE20K is mIoU.
## 4 Discussion
### CMF External Load  
The CMFs (Central Midfielders) in the present study covered the greatest total distance (TD) and at the highest rate. In a previous study, it was observed that CMFs covered the greatest distance compared to all other positions [25]. [PERSON] et al. [24] report that CMFs covered the greatest TD compared to other playing positions in the 1-4-3-3 formation. In a study comparing formations, it was observed that CMFs covered the greatest distance among all positions in the 1-4-4-2 and 1-4-3-3 formations, while in the 1-3-5-2 formation, they had the second-best performance behind WDFs (Wide Defenders) [16]. In a study conducted in the Chinese football league, it was found that CMFs covered the greatest TD among all positions in the formations 1-4-2-3-1, 1-4-1, 1-4-3-3, 1-3-5-2, and 1-3-4-3 [11]. From the above, it is clear that the active role of CMFs in all phases of the game (defense,attack, and transitions) and their connecting role between the other two lines (defense and attack) requires them to be highly active regardless of the formation.  
As previously mentioned, in high-speed distances, CMFs covered more distance than CDFs (Central Defenders), while in maximum speed distances, they surpassed both CDFs and FWDs (Forwards). Furthermore, CMFs executed the most accelerations and decelerations compared to all other playing positions. In a study of Under-17 players in the 1-4-3-3 formation, it was observed that CMFs covered the least distance at maximum speed among all positions [25]. In a previous study, it was noted that CMFs covered a shorter distance than WDFs at speeds of 19.8-24 km/h and similar distances at maximum speed compared to FWDs and CDFs [24]. [PERSON] et al. (2020) [16] observed that in the 1-3-5-2 formation, CMFs covered the greatest high-speed distance, while in other formations, they moved around the team average. [PERSON] et al. (2024) [11] observed that in the formations 1-4-2-3-1, 1-4-3-3, 1-4-4-2, and 1-4-1-4-1, CMFs had some of the lowest performances in high-speed and maximum-speed distances. Previous studies have shown that wide players perform more accelerations and decelerations [33; 34], although there are also reports that found no positional differences [25]. From this review, it appears that findings are not consistent regarding CMF workload in relation to formation. Players operating centrally tend to have more limited space due to a higher number of players (formations strengthen the center), thus this limited space restricts the intense actions they can perform (e.g., it is more difficult to reach speeds >25.2 km/h in the center than on the flanks).
## 3 Robustness and Reliability: New Challenges in CMR Segmentation
### Challenges to Reliable Segmentation
#### 3.2.1 Overfitting  
The first and most basic condition that a reliable segmentation model should meet is that its performance is consistent from training to testing. Failing to do so is commonly referred to as _overfitting_ or poor generalization. Two main factors are linked to overfitting: model complexity and data collection. Model complexity is related to the number of parameters in a model (e.g., the number of weights in a network), whereas data collection refers to the task of collecting and pre-processing data to train a model. In this study, we assume that the best architectures for fulfilling segmentation in the presence of an adequate number of training samples have already been identified. Therefore, we consider that overfitting can only be caused by poor data collection. In other words, the CMR segmentation methods presented in Section 2 should have a consistent training vs. testing performance as long as good data collection is guaranteed.  
The data collection process that can guarantee the reliability of the model during testing needs to meet two conditions. First, it requires collecting a large number of samples. Being CMR segmentation typically fulfilled in a supervised manner, this also implies that the collected samples require annotations. Second, the collected data should be representative of the phenomenon under study. Failing to do so is commonly known as _data bias_.