esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Regional Analysis of Western Arabia  
We discuss here the impact of each of the corrections we considered for the InSAR time series. First, we corrected our phase time series for solid earth tides by removing the calculated solid earth tide displacement in the LOS from our observation. The resultant map (Figure S4) shows regional long wavelength anomalies as the displacement increases gradually westward. However, short wavelength anomalies appear within western Arabia, distributed away from the Red Sea coast, within the plate interior. Second, we remove the tropospheric phase residual, and the resultant displacement map of this correction shows the observed displacement field corrected for the tropospheric component (Figure S5). Comparing this displacement field with the displacement field corrected for solid earth tides (Figure S4) suggests minor changes, as the long wavelength anomalies still sweep across western Arabia. Third, removing the delay corresponding to DEM error resulted in a displacement field (Figure S6) that shows very minor effects on the observed regional long wavelength signal.  
A long wavelength phase residual still exists, which may be caused by ionosphere effects, or inaccuracies in the assumed orbits. We remove this long wavelength signal empirically by fitting and removing a bilinear ramp from the phase [42]. We also estimate annual (Figure S7) and semi-annual (Figure S8) components from our displacement time series. Our annual and semi-annual magnitude maps show potential long wavelength signals between latitudes 22N and 26N. These signals are spatially correlated with the observed phase residual magnitude transition from east to west. These observed signals are not validated nor well understood and are thus simply removed from our velocity since we are primarily concerned with short length scale displacements.  
The resultant velocity map (Figure 2A), which excludes the annual and semi-annual signals, shows long wavelength velocity variations with small magnitudes. On the other hand, this velocity map also shows many prominent short wavelength velocity variations with larger magnitudes. The uncertainty of our results increases proportionally with distance away from the chosen spatial reference point as shown in the standard deviation of our velocity map (Figure S9). We show three examples of observed subsidence within western Arabia, the Alyutamah, Dhaa, and Shajwah Valleys (Figure 2B-D respectively). The observed subsidence within all the reported examples exceeds 2 cm/y, which is much larger than the noise threshold (Figure S9). Our focus in this paper is on the Al-Yutamah Valley (Figure 2B). For this region, we manually reprocess the InSAR data to increase the resolution and enhance the spatial and temporal coverage.
# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 4 Experiments and Analysis
### Comparative Experimental Results and Analysis
#### 4.4.3 Ablation Experiments  
From Table 6, it can be seen that, first, in the original RT-DETR model, mAP50 reached 0.886, with a parameter quantity of 19,875,612 and GFLOPs of 56.9. This indicates that the RT-DETR module itself already has a relatively high detection capability, but there is still room for improvement. When we improved RepaNet based on RT-DETR, the model's mAP50 increased to 0.902, with the parameter quantity remaining unchanged and GFLOPs  
Figure 8: Scatter plots comparing different models.  
also remaining at 56.9. This shows that the RepaNet module significantly improves the detection performance of the model without increasing computational resources. Further, when the EHFPN module was added to RT-DETR, the model's mAP50 slightly decreased to 0.871, but the parameter quantity was reduced to 17,821,836 and GFLOPs decreased to 47.5. This indicates that the EHFPN module reduces the computational resource requirements of the model to a certain extent, but the improvement in detection performance is limited. The EHFPN module reduces the parameter quantity and computational cost through a more efficient feature pyramid network design but may not be as good as RepaNet in feature extraction and fusion. When the EUCB module was added to RT-DETR, the model's mAP50 increased to 0.901, the parameter quantity increased to 20,189,856, and GFLOPs increased to 57.7. This shows that the EUCB module further improves the detection performance of the model by increasing computational resources, enhancing the model's ability to locate and classify targets. When RepaNet and EHFPN were added to RT-DETR simultaneously, the model's mAP50 reached 0.899, the parameter quantity remained at 17,821,836, and GFLOPs remained at 47.5. This indicates that the combination of the RepaNet and EHFPN modules can still maintain high detection performance while reducing computational resources. The RepaNet module optimizes feature extraction and fusion, while the EHFPN module reduces the parameter quantity and computational cost through a more efficient feature pyramid network design. Finally, when the RepaNet, EHFPN, and EUCB modules are used simultaneously, our EER-DETR model is formed. The model's mAP50 reached 0.905, the parameter quantity was reduced by 9.7% compared to the original RT-DETR model, and GFLOPs were significantly reduced. In conclusion, by gradually adding RepaNet, EHFPN, and EUCB modules, the detection performance of the model was significantly improved, and computational resource requirements were also optimized. This proves that our EER-DETR model achieved high accuracy and lightweight efficiency.
## 1 Introduction
Section 4 introduces the methodology for implementing LET and TDMA, detailing how these models combine to improve predictability and data consistency in multicore systems, and presents a real-time implementation using the CYT2B75 Traveo II Entry Family platform. Section 5 discusses the results obtained from applying the LET-TDMA method in different processor configurations, illustrating the latency and data transfer behavior. Section 6 discusses how the implementation of LET and TDMA benefits communication in multicore systems by reducing latency and improving determinism and analyzes the results obtained. Finally, Section 7 presents the conclusions, emphasizing this study's contributions and suggesting areas for future research.
## 4 Case Study  
A case study was conducted to evaluate the fault-detection performance and correction effect of the virtual sensor. To achieve this, a commercial building and faults in an SCWT sensor were modeled using EnergyPlus, and simulations were performed. The virtual sensor proposed in this study was trained using a dataset produced via simulations.
The only difference is in the structure of the information: (i) for the document, we know a word occurring at some position in the document, we know its neighbours but we do not know its next occurrence until we scan all the following words between the current and the next occurrence of the word; (ii) similarly for the inverted list, we know a word occurring at some position in IL, we know its previous and its next occurrence, however, it is not known its neighbouring word in the text until we scan possibly all ILs of the positional inverted index. Considering the second case and the _gap encoding_ for the ILs, we have realized that after permeation of single ILs, we obtain analogous sequence of pointers/integers as in the case of the compressed document using some word-based substitution compression method. The only difference is that pointers do not point to vocabulary entries, however, to a previous occurrence of the same word in the sequence of integers (the pointer is represented by the encoded gap). Our Positional Inverted Self-Index (PISI) brings the following contributions. It exploits the extremely high search speed of the traditional positional inverted index [11]. Searching for a single word, the resulting time depends only on the number of occurrences of the word. The text is composed as a sequence of pointers (pointing to the next occurrence of a given word). The pointers are encoded using the byte coding [19]. It implies that the search algorithm can easily and fast decompress the pointers and traverse all the occurrences by simple jumping the sequence of the pointers. Furthermore, it applies the idea of self-indexing which means that the indexed text is stored together with the index. Thus, the necessary space is significantly reduced and the achieved compression ratio is lower. All the word forms and the separators are stored in the separated presentation layer proposed in [17]. PISI provides another appreciable property. It is based on the time-proven concept of the inverted indexing that is well known and widely tested in the field of the Information Retrieval. Furthermore, PISI can be easily incorporated into some existing document-level search algorithm and shift its search capability to the level of single words and their positions in the document.
###### Abstract
Moreover, even though they were designed to reproduce pulsations and anatomical structures, they cannot reproduce blood pressure waveforms in a controllable manner. To improve this, cardiovascular simulators of some previous studies reproduce the blood pressure waveform by regulating the input waveform of the ventricular pump and the resistance valve of the vein [12; 13; 14; 15; 16]. Among these, it tried to reproduce the change in stiffness of the vascular wall by pressing the fluid with an air spring to control compliance, but did not actually change the compliance of the vascular wall [16]. However, there is no detailed study on adjusting the compliance of the blood vascular wall. Having considered the previous works, this study aims to develop a mock circulatory platform for reproducing blood pressure waveforms in a controllable manner by adjusting the structural parameters of key components. The contribution of this work is to design, develop and evaluate a simulator that mimics the following human physiological behavior: firstly, the blood pressure waveforms in the left ventricle and the ascending aorta are the same as those of the human body; secondly, it mimics the physiological characteristics of the pressure waveform that changes with peripheral resistance and compliance of ascending aorta. Hence, the mock circulatory platform is able to reproduce various blood pressure waveforms according to different conditions (e.g., gender, age, and BMI) by adjusting resistance and compliance. Through this simulator, the correlation between the pressure waveform and the change of each parameter is evaluated and verified to see whether a realistic physiology is reproduced in the proposed approach. This paper is organized as follows. Section 2 describes the human blood pressure waveforms and the windkessel model, which is one of the simplest mathematical models of the cardiovascular system. Section 3 is about the design and simulation of the mock circulatory platform. Each component of the platform is designed to reproduce the physiological behavior of the human body and the designed platform is validated through the lumped model simulation. Section 4 describes the results of experimental validation and parametric study on the developed platform. Section 5 concludes with a discussion and suggestions for future works.
# Reaction Time and Postural Control Under Dual-Task Conditions in Brazilian Jiu-Jitsu Athletes
## 3 Results
### Comparison and Correlations Within Brazilian Jiu-Jitsu Athletes  
In the Brazilian Jiu-Jitsu group, as in the control group, the Friedmann test identified statistically significant differences across all linear (CoP: F(\(N\) = 16, df = 3) = 41.09, \(p\) = 0.0001) and nonlinear parameters within directions (SampEn_ML: F(\(N\) = 16, df = 3) = 36.36, \(p\) = 0.0001; SampEn_AP: F(\(N\) = 16, df = 3) = 37.80, \(p\) = 0.0001; FD_ML: F(\(N\) = 16, df = 3) = 36.36, \(p\) = 0.0001); FD_AP: F(\(N\) = 16, df = 3) = 36.04, \(p\) = 0.0001; LyE_ML: F(\(N\) = 16, df = 3) = 36.20, \(p\) = 0.0001; LyE_AP: F(\(N\) = 16, df = 3) = 35.08, \(p\) = 0.0001). The Dunn-Bonferroni post hoc test identified significant differences between the following trial pairs: 2 eo vs. 1 eo, 2 eos vs. 1 eos, 1 eos, vs. 2 eos, and 2 eos vs. 1 eos, with all differences highly significant (\(p\) = 0.0001). Notably, linear and nonlinear parameter values were consistently higher during the two lower-limb standing trials.  
In this group, nine significant correlations were identified (Figure 3). Negative correlations were observed between the number of lamps switched off in the standing position and both LyE_AP_1 eo (R = \(-\)0.62, \(p\) < 0.05) and LyECoP_ML_1 eos (R = \(-\)0.61, \(p\) < 0.05). Similarly, negative correlations were found between the number of lamps switched off in the sitting position and both LyE_AP_1 eo (R = \(-\)0.80, \(p\) < 0.05) and LyECoP_ML_1 eos (R = \(-\)0.58, \(p\) < 0.05). Additionally, a significant positive correlation was found between the number of lamps switched off in the standing position and LyECoP_ML_2 eos (R = 0.54, \(p\) < 0.05) (Figure 3A).  
Positive correlations were observed between the reaction time (in both standing and sitting positions) and LyE_AP_1 eo (R = 0.57, \(p\) < 0.05), as well as between LyECoP_ML_1 eos and the reaction time (R = 0.64, \(p\) < 0.05 for standing and R = 0.81, \(p\) < 0.05 for sitting) (Figure 3B).  
Figure 3: The statistically significant correlations between postural control parameters and (**A**) the number of lamps switched off in both standing and sitting positions and (**B**) the reaction times in both standing and sitting positions in the Brazilian Jiu-Jitsu group, where AP—anterior-posterior direction; ML—mediolateral direction; LyE—Lyapunov exponent; 1e—single-leg standing; 2 eos—bipedal standing with visual tracking; 1 eos—single-leg standing with visual tracking.
## 4 Discussion  
Except for Figure 6, which was simulated using CST 2016 Microwave Studio, other simulation data were obtained using the ANSYS HFSS 2014 EM simulation software. To validate the proposed design, an MS consisting of 6 \(\times\) 6 cells is fabricated. Full-wave simulations were performed on this MS prototype. Its radiation performance was tested using Ceyear-3671D vector network analyzer (VNA), Midrand, South Africa and Xiang Tong OTA microwave anechoic chamber, Guangzhou, China.
## 2 Control Structure and State Variable Observers Used in the Research
### Sliding Mode Observer
Rearranging Equation (8), the estimated electrical rotor position can be calculated as follows:  
\[\hat{\theta}_{e}=a\tan^{2}(\frac{-\hat{e}_{\alpha}}{\hat{e}_{\beta}}), \tag{13}\]  
The estimated mechanical speed can be calculated by deriving the observed rotor position:  
\[\hat{\omega}_{m}=\frac{1}{p}\frac{d\hat{\theta}e}{dt}, \tag{14}\]  
The block diagram of the reduced-order SMO is presented in Figure 5.  
Example transients of estimated and measured speed in sensor and sensorless mode are presented in Figure 6. The results confirm stable operations in a sensorless system. Figure 5: Block diagram of the reduced SMO used in the research. In order to compare the estimation accuracy of both systems, Figure 7 shows the estimation error transients (difference between measured and estimated speed) for both systems under the same operating conditions. Based on the presented results, it can be concluded that the SMO system achieves greater accuracy. Figure 6: Transients of speed, current in \(q\) axis, and rotor position measured and estimated by SMO in sensor (**a**) and sensorless (**b**) mode.
###### Abstract
In this context, the purpose of this article is to examine the traffic levels in Barcelona utilizing data from the Ajuntament de Barcelona's open data service, employing both classic and machine learning methods. The primary contributions include conducting an exploratory analysis of the traffic data with visualization techniques, developing predictive models utilizing both time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm, and examining the results along with a discussion. The specific flow of the research methodology employed in this article is depicted in Figure 1. To our knowledge, this study represents the first attempt to analyze open data on traffic levels utilizing visualization techniques, time series analysis, and the XGBoost algorithm. Next, we outline the structure of the document. Section 2 reviews related work, focusing on traffic flow studies, prediction, and models in Barcelona. Section 3 details the methodology, encompassing visualization techniques, time series analysis, and the XGBoost algorithm. Section 4 outlines the application, detailing the dataset employed and presenting and discussing the results obtained through the various components of the methodology. Section 5 provides a discussion based on the results obtained in the previous sections. Finally, Section 6 draws pertinent conclusions and outlines potential lines of future research.
###### Abstract  
This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.  
Keywords:data assimilation; Ensemble Kalman Filter; GLASS; SMAP; evapotranspiration; net ecosystem exchange +
Footnote †: journal: remote sensing
# Developing a Dead Fish Recognition Model Based on an Improved YOLOv5s Model
## 5 Discussion  
The method proposed in this paper is mainly for dead fish identification on the water surface of the outdoor farm and has a good application prospect for the detection of floating objects on the water surface in complex environments. The mAP\({}_{0.5}\) of the improved model reaches 87.5%, which is 4.5% higher than the original YOLOv5s model. However, there are still false and missed detections, and a richer dataset is needed to feed the model to improve its training. Although high-pixel images can be obtained through mobile phones and cameras, some images have problems such as reflections, blurred backgrounds, and overexposure. Therefore, designing a targeted image enhancement algorithm is also important to study. In addition, nighttime datasets are difficult to collect, the quality of images is poor, and the recognition of dead fish at night or under low illumination has not been studied much. Nighttime detection is also a difficult problem in target detection, and research can be carried out inspired by the proposal of Pe-YOLO in 2021. In terms of the complexity of the model, there is not a lot of lightweight operation in this paper but just a reduction in the parameters added in the model improvement with the amount of computation. A good model needs to be both accurate and fast at the same time. The method proposed by [PERSON] [27] et al. provides ideas for subsequent pruning algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Module** & **P/\%** & **R/\%** & **mAP\({}_{0.5}\)/\%** & **Params/10\({}^{6}\)** & **FLOPs/G** & **F1 Score/\%** \\ \hline Faster RCNN & 57.1 & 81.9 & 79.37 & 136.75 & 369.7 & 67.3 \\ SSD & 93.1 & 66.1 & 75.2 & 24.01 & 61.1 & 77.3 \\ YOLOv3-tiny & 90.9 & 74.9 & 81.6 & 9.52 & 44.9 & 82.1 \\ YOLOv5s & 89.7 & 75.9 & 83.0 & 7.02 & 15.9 & 82.2 \\ YOLOv6n & 92.0 & 73.6 & 83.0 & 4.16 & 11.5 & 81.8 \\ YOLOv8n & 93.2 & 77.2 & 84.7 & 2.68 & 6.8 & 84.4 \\ YOLO-DWM & 93.6 & 77.5 & 87.5 & 6.34 & 15.7 & 84.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of different models.  
The lightweight dead fish detection model DM-YOLO proposed by [PERSON] [12] et al. replaces the YOLOv4 backbone network with MobileNetV3, but MobileNet can lead to feature extraction in terms of information loss. In future research, we can try to prune or use a lightweight network instead of a backbone network to achieve lightweight. [PERSON] [11] et al. proposed the multi-scale feature fusion FFA-SSD algorithm, which is based on SSD, different from the YOLO series of algorithms, which is also an important idea in the research of dead fish identification.
# Study on Mechanism of Stick-Slip Vibration Based on Torque Characteristics of PDC Bit
## 4 Study on Torsion Variable Characteristics of PDC Bit Drilling Drill String
### Torsion Velocity Change in the Drill String  
When the formation lithology of the PDC bit changes suddenly, its rotational speed will change significantly through the torsion variable The influence of penetration depth on bit speed is shown in Figure 7, wherein the marked speed change is the relative change.  
We can consider the 1000 m-long drill pipe as an example and assume a rotational speed of 80 rpm (the red dotted line position marked in the figure). The overall trend of the 6\({}^{\prime\prime}\) PDC bit increases with the strength of the formation rock. When drilling in the Mimosa170 rock block, the bit speed can be reduced from 80 to 73 rpm with a variation of 3 mm intrusion depth per unit time. This is the deceleration state and then returns to 80 rpm. This shows that the change in penetration depth (or the sudden change in WOB) has a significant influence on the bit speed.  
Taking the 8\(\%\)\({}^{\prime\prime}\) PDC bit as an example, we see that this type of bit has the least influence on the speed fluctuation owing to the change in penetration depth compared with that of the 6\({}^{\prime\prime}\) and 124\({}^{\prime\prime}\) bits. A longitudinal comparison of different lithologic changes, such as an intrusion depth of 1 mm, from the Lanea50 formation block to the Mimosa170 formation block reveals that the change in rotational speed per unit time is 2.14 rpm.  
For the 12\(\%\)\({}^{\prime\prime}\) PDC bit, in the Mimosa130 formation rock block, when the initial state of the bit is at a high invasion depth (e.g., 3.0 mm) and drops to 0.25 mm per unit time, the resistance torque of the rock suddenly decreases, and the accumulated torsion variable in the drill string system is released to the bit to accelerate its speed to be \(>\)80 rpm (increasing to 82.59 rpm). This is an accelerated state.  
Figure 6: Comparison between invasion depth and the torsion variable of PDC bits of three sizes. (**a**) 6\({}^{\prime\prime}\)drill bit; (**b**) 8\(\%\)\({}^{\prime\prime}\) drill bit; (**c**) 12 4\({}^{\prime\prime}\) drill bit; (**d**) Comparison of torsion variables of three bits (Lanea50).