esa-sceva/satcom-chunk-collection huggingface dataset:
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement  
This section will give an overview of the field of measurement technology for HV battery systems. The measurement technology is a key component of a battery management system and enables the determination of state variables such as the SOC, State of Health (SOH) or State of Function (SOF). Measured variables usually include cell voltages, the total voltage of the battery system, the total current and the temperature. By knowing the state variables, the battery system can be protected for  
Figure 4: (**a**) battery pack of Mitsubishi i-MiEV; (**b**) battery pack of _VW e-Up;_ (**c**) battery pack of _smart_ _forvo electric drive._ Note: scaled differently.  
Figure 5: (**a**) battery module of Tesla Model-S, top view; (**b**) battery module of _VW e-Up,_ module with 6s2p, top view. Note: scaled differently.  
example against overcharging or deep discharging. In addition to providing safety related functions, the state variables allow for an optimized utilization of the battery system.  
If the application of the battery storage is known, necessary requirements on the sensors can be derived. Typical requirements to be considered can be costs, bandwidth, precision, measurement range or size. A detailed overview of the requirements was given in Section 2.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 5 Simulation Results and Discussion
### Complexity Analysis  
In this section, the computational complexity of the proposed model is described. The computational complexity time is measured by considering the number of floating-point operations (FLOPS). In the proposed model, the Bi-LSTM network has four gates that need to be processed at the same time. The complexity of the detection network is the sum of the four gate parameters and considers the forward and backward transfer process. Accordingly, the computational complexity of the proposed network can be expressed as \(\mathcal{O}(4\times L_{s}\times O_{s}\times 2(h_{l}\times h_{d}\times n_{l}))\). Here, \(L_{s}\) is the number of received input packets, \(O_{s}\) is the OFDM block size, \(h_{l}\) is the input size of LSTM, \(h_{d}\) is the number of hidden layers, and \(n_{l}\) is the neuron size in the LSTM layer.
## 4 Performance Analysis
### Simulation Performance Analysis
#### 4.1.3 Simulation Signal Analysis Conclusion  
It can be concluded through the above simulation data results that the power frequency magnetic field can effectively detect the magnetic disturbance caused by the movement of ferromagnetic targets within the simulated conditions. Compared with other methods, the Synchrosqueezing Transform can also effectively extract the magnetic anomaly signals induced by the movement of ferromagnetic targets and retain the temporal frequency features of the magnetic anomaly signals. Therefore, it can be used as an effective method for magnetic anomaly signal detection.
## 5 Simulation Experiment and Analysis
### Path Planning Analysis  
#### 5.1.1 Multi-UAV Cooperative Search Trajectories Based on Knowledge Base Scenario 1  
We consider five searched targets moving with a square formation at sea, where the central point with position (5000, 0, 0) is the water target, and its motion model is as follows: \(v_{tx}=-19.6\sin(0.225k)\),\(v_{ty}=19.6\cos(0.225k)\) and \(v_{tz}=0\), where \([v_{tx},v_{ty},v_{tz}]^{T}\) is the motion vector. Suppose that the start points of two UAVs are given as (600, \(-1000\), 500) and (\(-500\), \(-300\), 300), respectively.  
The trajectories of two UAVs are shown in Figure 5. It can be observed that the UAVs initially approach the target formation with an irregular flight path during the search phase. Upon first detecting the water target (i.e., target 1), two UAVs activate search rule 1 in the knowledge base and simultaneously move along a spiral track centered on the water target. Subsequently, upon discovering the second target (i.e., target 4), search rule 2 is triggered  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Hyper-Parameter** & **Value** \\ \hline
1 & Learning rate \(\lambda_{Q},\lambda_{\pi},\lambda\) & 5.6 \(\times\) 10\({}^{-4}\) \\
2 & Discount factor \(\gamma\) & 0.99 \\
3 & Hidden layers & 2 \\
4 & Number of hidden units & 256 \\
5 & Minibatch size & 256 \\
6 & Smoothing factor \(\delta\) & 0.005 \\
7 & Playback pool capacity & 10\({}^{8}\) \\
8 & Target entropy \(\overline{H}\) & \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameter setting of KD-SAC algorithm.  
in the knowledge base to explore other targets along the mid-vertical lines connecting these two discovered targets. The effectiveness of the event-triggering strategy we mentioned is demonstrated by this experiment. The UAV can select its search mode based on the fundamental search rules (refer to Table 1) to adapt to different cases, such as employing a spiral search or conducting a search along the central vertical axis. Specifically, the UAV employs different search rules from the Knowledge Base based on the number of targets discovered, such as utilizing rule 1 when one target is detected and rule 2 when two targets. When event attributes and related conditions are associated with an action template in the knowledge base, the UAV can invoke and execute the corresponding action template from the knowledge base. The search rules stored in the knowledge base can serve as experiential guidance for the UAV, enabling it to directly acquire self-service decision outcomes, which may encompass actionable actions or search trajectories (such as spiral search trajectories).
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 5 Conclusion
Thus, we should perform the inter-comparison of CDR and reanalysis products and promote replacing satellite EDRs with CDRs in the future reanalysis production. Climate data records are becoming increasingly important for numerous applications and societal benefit areas. This requires increased coordination and collaboration of CDR development, production, archive, dissemination, application, and service among scientists and organizations. Moreover, extending climate data records to broader long-term reference environment data records to benefit more user sectors will be a strategic step forward. In this way, we may achieve the goal of developing and implementing a robust, transparent, sustainable, and scientifically defensible approach for developing, producing, preserving, and provisioning CDRs and REDRs from satellite observations and _in-situ_ measurements and thus providing an end-to-end data service to various user sectors and decision makers. We acknowledge Remote Sensing for hosting the Special Issue on Satellite Climate Data Records and Applications. [PERSON], [PERSON] and [PERSON] acknowledge the support from the NOAA Climate Data Record Program. The research of [PERSON] is jointly supported by the National Basic Research Program of China (grant No. 2015 CB953703) and the National Natural Science Foundation of China (41371328 and 91537210). Three anonymous reviewers' comments and suggestions are helpful for the improvement of the manuscript. The views, opinions, and findings contained in this paper are those of the author(s) and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision. [PERSON] contributed to the write-up of Sections 1, 4 and 5; [PERSON] contributed to the write-up of Sections 3.1 and 4; [PERSON] contributed to the write-up of Sections 1, 2 and 5; [PERSON] contributed to the write-up of Sections 2 and 3.2; [PERSON] improved and polished the whole paper. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AAI & Absorbing Aerosol Index \\ AE & Angstrom Exponent \\ AMSU & Advanced Microwave Sounding Unit \\ AOT & Aerosol Optical Thickness \\ AVHRR & Advanced Very High Resolution Radiometer \\ \end{tabular}
## 1 Introduction
To further improve its performance and lay a foundation for future multi-DOF measurements, the alignment tolerance of spatially separated heterodyne grating interferometer was analyzed on the structure with a mechanical fixture of our design. Qualitative analysis, quantitative simulations and experiments are implemented and described in the following sections.
# SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance
## 4 Results and Validation
### Evaluation Experiment  
To test the model's efficiency and experiment, the experiment uses sample test images to make predictions. Figure 9 shows the improved and baseline models' detection results. The figure shows that the improved model exhibits better detection performance for pedestrians and vehicles in crowded environments. Even in cases where vehicles and pedestrians are partially occluded, the improved model still performs remarkably well. Furthermore, it can be observed that the improved model exhibits higher sensitivity to variations in image scale. This result is particularly crucial for subsequent deployment on the web for real-time monitoring.  
To further validate the theoretical effectiveness of the model in practice, this study employs Grad-CAM for heatmap visualization. Grad-CAM is a computer vision technique for interpretability. It generates a gradient-weighted-class activation map to represent the key contributing regions of the neural network's prediction on the image.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & **Model** & **Precision** & **Recall** & **mAP0.5** & **mAP0.50.95** & **Parameter** & **GFLOPs** \\ \hline
1 & YOLOv8 & 0.874 & 0.711 & 0.811 & 0.577 & 3,157,200 & 8.9 \\
2 & +Soft-NMS & 0.862 & 0.718 & 0.816 & 0.586 & 3,157,200 & 8.9 \\
3 & +GSConv & 0.874 & 0.710 & 0.812 & 0.580 & 2,947,792 & 8.1 \\
4 & +Triplet\_A 1 & 0.877 & 0.705 & 0.810 & 0.578 & 3,190,168 & 9.0 \\
5 & \(1+3+4\) 2 & 0.875 & 0.735 & 0.826 & 0.598 & 2,980,760 & 8.2 \\
6 & \(1+2+3+4\) & 0.886 & 0.742 & 0.831 & 0.609 & 2,980,760 & 8.2 \\ \hline \hline \end{tabular}  
* 1+Triplet_A is short for Triplet Attention. 2 Here is the baseline model YOLOv8 introducing modules such as GSConv and Triplet Attention.  
\end{table}
Table 3: Accuracy results for each scenario of the ablation experiment.  
Utilizing Grad-CAM for analysis, it is evident that in complex regions where targets are detected, the improved model shows greater sensitivity within corresponding heatmap areas than the baseline YOLOv8 model (Figure 10).  
The improved model can analyze images of varying scales and extract useful information from images to the greatest extent. In contrast, the baseline model focuses on large areas containing vehicles and pedestrians in complex scenes and fails to capture information about pedestrians in distant regions. The results showed that the improved model is more effective in capturing the characteristics of the detection target in a complex environment.
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 6 Results and Discussion  
### Evaluation of the Speech Estimator  
This section consists of assessing the system performance using two speech estimators: LSA and OMLSA. The objective of this experiment is evaluating the impact of the SPP in the enhancement performance. OMLSA speech estimator applies the Wiener filter according to the spectral distribution of speech and silence. This is defined by a map of the probability of speech for the t-f regions, called speech presence probability (SPP) [24]. In this paper, the SPP feed from the same \(G_{MMSE}\) obtained with the deep structured network. Then it is applied according to the \(Gmin\) (Equation (6)), which is a boundary for the minimum gain that translates into a regulatory term for the strongest of the enhancement. The following experiment tests different values of \(Gmin\) to evaluate its impact in the performance. On the other side, the LSA speech estimator does not use the SPP, and instead it directly employs the Wiener filter estimation as described in Equation (4).  
Figure 3 shows the results of quality metrics for the speech estimation based on LSA and OMLSA with different \(Gmin\) values. The variation among \(Gmin\) values allows seeing the best trade-off between enhancement and distortion throughout the objective quality metrics. The highest SNR was obtained by the system using OMLSA with the lowest \(Gmin\) (0.00562). However, in this case, the corresponding signal distortion is also highest among all. Conversely, the system based on LSA achieved the lowest signal distortion in terms of LLR, while the noise reduction is moderated since the SNR was in the middle of cases. The lowest SNR improvement was for \(Gmin=0.562\) as expected, where the enhancement is slightly considering the noise dominant bins (see Equation (6)), so the signal remains very noisy. The desirable result is a reasonable trade-off between distortion and SNR level, where the SNR improvement indicates the noise reduction but at the same time, the signal distortion does not increase too much.  
On the other side, the best performance for objective quality by PESQ was achieved by the system based on OMLSA with \(Gmin=0.0562\). STOI results indicated that the intelligibility was not so variable among all conditions. The best performance was for moderated values of \(Gmin\) and also for the system based on LSA. Note that these results are consistent with the system with lowest signal distortion. This is reasonable since we could expect better understanding when the speech is clearer and without robotic sounds. Finally, the best results for MOS-related metrics were again for the system based on OMLSA \(Gmin=0.0562\). These results, together with a good improvement of SNR with moderated distortion, confirm that the best trade-off among all quality metrics evaluated belongs to the system based on OMLSA with \(Gmin=0.0562\).
## 4 Results
### Single-Objective Optimization
#### 4.1.3 Exergy Efficiency Optimization  
This was carried out by solving Equation (26), using the decision variables and their values shown in Tables 2 and 3, respectively, on the GWO algorithm. The results obtained as shown in Table 6 depict how, in order to maximize the exergy efficiency, the following should take place:  
* Minimize the compression ratio, pinch point temperature difference and inlet turbine temperature;
* Maximize the inlet combustion chamber temperature.
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 4 Results and Discussion  
### Experimental Data and Settings  
We anonymously collected data from 11 different people who agreed to share their videos for this research to publish the results while preserving their identity, and the demographic data are presented as:  
* Males: 7 (63.64%);
* Females: 4 (36.36%);
* Age: 20-50 years old at the measurement time;
* Recording location: Budapest, Hungary;
* Nationality: Hungary (8), Argentina (2), Algeria (1).  
The recorded videos can be found in Kaggle at the following link: [[https://www.kaggle.com/datasets/abdallabhenhamida/mc-evm-videos-dataset](https://www.kaggle.com/datasets/abdallabhenhamida/mc-evm-videos-dataset)]([https://www.kaggle.com/datasets/abdallabhenhamida/mc-evm-videos-dataset](https://www.kaggle.com/datasets/abdallabhenhamida/mc-evm-videos-dataset)) (accessed on 24 October 2024).  
The recorded videos for the testing and validation of the whole workflow consists of two parts for each subject person:  
* A recorded video of different lengths ranging from minimum 10 s to a maximum of 30 s;
* The current measured pulse at the same time when recording the videos.  
The recorded videos were collected with a white background using the following devices:  
* The videos were recorded using a \"Dell Latitude 5310\" laptop with a 960 \(\times\) 540 resolution;
* The actual pulse value which is measured with an \"Amazfit Band 7\" device.  
A series of tests were performed on the recorded videos and were validated against the actual pulse values which were measured at the same time while recording the videos. All the measurements were taken in the same minute with a maximum time difference of 22 s (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Video Time** & **Device Time** & **Time Difference** \\ \hline
1 & 24 May 2023 13:22:55 & 24 May 2023 13:23:09 & 00:00:14 \\ \hline
2 & 24 May 2023 13:29:28 & 24 May 2023 13:29:41 & 00:00:13 \\ \hline
3 & 24 May 2023 13:34:37 & 24 May 2023 13:34:49 & 00:00:12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time difference between the video recording time and the device time.
# A High-Throughput In Vitro Radiobiology Platform for Megavoltage Photon Linear Accelerator Studies
## 3 Results
### No Radiation Beam Inhomogeneity between and within Wells of Multiwell Tissue Culture Plates  
For MV irradiations (open-field and homogeneous), the mean radiation doses for the central wells were 2.00, 4.00, 8.09, 16.08, and 24.07 Gy, and for the edge wells, they were 2.01, 4.01, 8.02, 16.04, and 24.07 Gy. No consistent pattern of difference in cell viability signal between the central and the edge wells was observed for either A375 or LN18 cell lines or with either MV or KV irradiation. For 10 MV irradiations, the differences in cell viability signal between the central and the edge wells were not statistically significant (\(p\) = 0.41 for A375 and \(p\) = 0.88 for LN18). For 250 KV irradiation, no significant differences in cell viability signal between the central and the edge wells were observed (\(p\) = 0.71 for A375 and \(p\) = 0.12 for LN18; Figure 6A-D). The \(\gamma\)-H2 AX foci signal analyses showed a homogeneous pattern of \(\gamma\)-H2 AX foci for 8 Gy KV irradiation (Figure 6E).  
Figure 6: **(A–D)** Comparison of cell viability signal between central and edge wells showing no significant differences. **(A,B)** MV irradiations, \(p\) = 0.41 for A375 cell line and \(p\) = 0.88 for LN18 cell line. **(C,D)** KV irradiations, \(p\) = 0.71 for A375 and \(p\) = 0.12 for LN18. **(E)** Homogeneous distribution of \(\gamma\)-H2 AX foci (in green) for A375 cell line. CellTag700 stain (in red) was used to stain nucleus and cytoplasm. Superimposed images showing homogeneous \(\gamma\)-H2 AX foci distribution related to cells (in yellow). Radiation doses are in Gy. Cell viability signal measured at 490 nm. Error bars indicate standard deviation.
## 2 Automatic Geometric Corrections
### Geometric Modelling
#### 2.2.1 Rational Function Model  
The geometric corrections are done with a rational function model (RFM). The RFM is an empirical model and it provides a good alternative to the physical model with equivalent results [30; 31]. Although the model is not so sensitive to GCPs distribution and their number, it is still advisable to only use high quality points that are scattered over the whole image. The RFM uses a ratio of two polynomial functions to compute the row and column image coordinates (\(r\), \(c\)) from the object space ground coordinates (usually latitude (\(\Phi\)), longitude (\(\lambda\)), and height (\(h\))) [32]. To compute the coordinates, the functions use vendor-provided rational polynomial coefficients (RPCs), which are estimated using the physical model parameters (the satellite image vendors usually supply 20 RPCs for polynomials in both numerators and denominators). The implemented geometric correction can work with a single image or a block of images with a joint adjustment. In the latter case, GCPs that are common to all images are selected during the extraction algorithm that always targets the same crossroads. In this study, the single-image workflow (Figure 6) is used to ensure that each image results are unbiased and not correlated to the others.
# Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data
###### Abstract  
Al-Hassa city, located in Eastern Saudi Arabia, boasts the world's largest basis and the most expansive naturally irrigated lands. Historically, a total of 280 natural springs facilitated significant groundwater discharge and irrigation of agricultural land. Furthermore, the water in certain springs formerly had a high temperature. The spatial variability of the water quality was evident. At the same time, Al-Hassa Oasis is situated on the northeastern side of the Ghawar field, which is the largest conventional onshore oil field in the world in terms of both reserves and daily output (approximately 3.8 mmb/d). The aforementioned traits suggest an intricate subsurface that has not yet been publicly and thoroughly characterized. Due to the presence of significant cultural noise caused by agricultural and nearby industrial activities, a robust, easy-to-use, and accurate geophysical method (gravity) was used to cover an area of 350 km\({}^{2}\), producing the 3D subsurface model of the study area. A total of 571 gravity stations were collected, covering the whole Al-Hassa Oasis and parts of the nearby semi-urban areas. The gravity data were corrected and processed, and a 3D inversion was applied. The resulting 3D geophysical subsurface modeling unveiled an intricate subterranean configuration and revealed lateral variations in density, indicating the presence of a potential salt dome structure, as well as fracture zones that serve as conduits or obstacles to the flow of the subsurface fluids. This comprehensive modeling approach offers valuable insights into the subsurface dynamics of the broader study area, enhancing our understanding of its qualitative tectonic and hydraulic features and their impacts on the area's natural resources, such as groundwater and hydrocarbons.  
gravity survey; 3D modeling; Ghawar oilfield; subsurface complexity; Saudi Arabia 2024143707[[https://doi.org/10.3390/app14093707](https://doi.org/10.3390/app14093707)]([https://doi.org/10.3390/app14093707](https://doi.org/10.3390/app14093707))
## 3 Conclusions  
Using a standard model, we have calculated the pulse duration, pulse energy and repetition rate of the Q-switched pulse train generated in a transversally-diode-pumped short-length cavity Nd:YLF laser using a graphene or CNT saturable absorber mirror. Higher energy pulses can be obtained for high values of the modulation depth \(q_{0}\); this is for graphene. However, pulse duration reaches a minimum for a certain low \(q_{0}\) value; this is for CNTs. Hence, when designing the laser, the choice of the proper \(q_{0}\) must be done with a compromise between the desired energy and duration for the pulse. We have also observed that these two magnitudes do not depend significantly on the pump \(g_{0}/l\) for the geometry studied. The repetition rate of the train Q-switched pulses is higher for lower \(q_{0}\) values and increases with the pump value \(g_{0}/l\). The results also show that Q-switch can be hindered using CNTs with very low modulation depth (\(\sim\)0.001), the relaxation oscillations being the temporal regime in these cases. The results have certain universality and can be expected for other types of transversally-pumped short-length cavity lasers with an active medium with a lifetime in the order of hundreds of microseconds.
## 3 Data and Methodology
### Data Source and Processing
#### 3.1.3 Land-Use and Land-Cover Data  
Land-use and land-cover data, encompassing 2000, 2005, 2010 and 2015 in Heilongjiang and Jilin provinces, were provided by the Resources and Environmental Science Data Center of the Chinese Academy of sciences ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/))). These data were obtained based by manual visual interpretation, and they were classified in more than 43 categories. In this study, the land-use and land-cover data were regrouped into five categories, according to a new classification logic, resulting in (i) temperate coniferous forests, (ii) cold temperate and temperate mountain coniferous forests, (iii) temperate deciduous broad-leaved forest, (iv) mixed forest of temperate coniferous and deciduous broadleaf, and (v) temperate deciduous shrub.