esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review and Motivation of This Work
### Literature Review
** & **Topic** & **HO** & **TO** & **Limits** & **Contribution** \\ \hline
[41] & Task offloading & Yes & Yes & The energy consumption of UEs are not considered. & - & Task offloading decision was formulated as MDP to minimize delay considering handover, migration, communication and computation; - & - \\ \hline
[41] & Joint optimisation & Yes & Yes & - & Only one handover is considered for each UE; Not applicable for 3D random mobility (limited for 1D road) & - & Mobility-aware computation offloading was proposed in MEC-based vehicular networks. \\ \hline
[24,46] & Joint optimisation & No & Yes & - & No handover; No mobility model. & Service caching placement and computation offloading is considered. \\ \hline
[25,29] & Proactive network association & No & Yes & - & No handover; No mobility model; No UE energy constraint. The conventional MEC-based offloading methods are not powerful enough, and advanced optimisation methods are required. [PERSON] presents a method, the Markov decision process (MDP), for reducing the amount of delay in high mobility vehicular networks [41]. Another method of joint optimisation is presented in [50; 51] for vehicle random mobility. In [52], the boundless simulation area (BSA) model for configuring the vehicles's mobility is presented, which is based on the Markov chain. For the IoT technology, a proactive network association can be a sufficient solution for reducing latency in MEC systems [53; 54]. As a solution for reducing the handover in MEC-based networks, in [55], a region partitioning approach is presented, where this method is evaluated for both random and real traces. In another work [56], the dynamic service placement is employed, where offline algorithms are used for determining the optimal service for minimizing the average cost. To support the energy-constrained devices such as IoT sensors, the wireless powered MEC networks are integrated with simultaneous wireless information and power transmission (SWIPT) techniques in [57]. In another work [58], the
# Performance Evaluation of GPS Auto-Surveying Techniques
###### Abstract  
With the increase in the widespread use of Global Navigation Satellite Systems (GNSS), increasing numbers of applications require precise position data. Of all the GNSS positioning methods, the most precise are those that are based in differential systems, such as Differential GNSS (DGNSS) and Real-Time Kinematics (RTK). However, for absolute positioning, the precision of these methods is tied to their reference position estimates. With the goal of quickly auto-surveying the position of a base station receiver, four positioning methods are analyzed and compared, namely Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), using only pseudorange measurements, as well as the Hatch Filter and position thresholding. The research results show that the EKF and UKF present much better mean errors than LS and WLS, with an attained precision below 1 m after about 4 h of auto-surveying. The methods that presented the best results are then tested against existing implementations, showing them to be very competitive, especially considering the differences between the used receivers. Finally, these results are used in a DGNSS test, which verifies a significant improvement in the position estimate as the base station position estimate improves.  
GNSS; GPS; base-station; auto-surveying +
Footnote †: journal: Sensors  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
# Smart Cities Big Data Algorithms for Sensors Location
## 3 Process Implementation
### Matrices for the Dynamic Map  
The objective of the frequency matrix is to provide an environment variable in shared memory. This variable content is the sum of incidents specifying time intervals with high risk. Frequent readings to this matrix will be performed to draw on a georeferenced map with the current sensors.  
The frequency matrix accumulates the number of quality levels labeled (see Figure 5) during a specific time. In this way, the sensors with the highest levels of pollution are easily identified--Table 3  
Figure 6: Flowchart for level classification.  
Figure 7: CART Algorithm from data training.  
depicts one of these periods for five sensors. In Table 3, it can be observed that sensors 3, 4, and 5 have the highest frequency in Level 5, meaning that they are classified as extremely bad.  
In order to locate or relocate sensors, a matrix indicating sensors in neighboring areas is required. See, for example, Table 3, where the name to identify the sensor implies its coordinates. In Table 4 can be observed, for instance, that Sensor 1 (Oblatos) in the first row is in the surrounding area with Sensors 2, 4, and 5 (Centro, Tlaquepaque, and Loma Dorada) indicated by a number '1' in those columns.  
Matching frequency matrix to the neighbor sensors with high pollution levels can be deduced. Locating new sensors in the highest levels of pollution will help getting a better understanding regarding the contamination source or the type variants that are affecting the area. The dynamic model might change the location requirements by distinguishing a new sensor's location or the relocation of those that are already in the system.  
In our example, new sensors could be located where the two arrows point in Figure 8. These are between sensors 3 and 4, and sensors 4 and 5 (corresponding to Miravalle, Tlaquepaque and Loma Dorada). According to the frequency matrix in Table 3, these three sensors have a high number of records at level 5, or Extremely bad, and they also correspond to neighboring areas according to the neighboring matrix in Table 4, where it can be observed that sensor 3 is adjacent to sensor 4, and sensor 4 is adjacent to sensor 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sensor**} & **Sensor 1** & **Sensor 2** & **Sensor 3** & **Sensor 4** & **Sensor 5** \\  & **Oblatos** & **Centro** & **Miravalle** & **Tlaquepaque** & **Loma Dorada** \\ \hline
2 & 1 & & 1 & 1 & \\
3 & & 1 & & 1 & \\
4 & & & 1 & & 1 \\
5 & & 1 & 1 & 1 & \\ \hline \end{tabular}
\end{table}
Table 4: Neighboring sensors matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sensor**} & **Level** & **Level** & **Level** & **Level** & **Level** \\  & **Quality 1** & **Quality 2** & **Quality 3** & **Quality 4** & **Quality 5** \\  & **(Good)** & **(Fair)** & **(Bad)** & **(Very Bad)** & **(Extremely Bad)** \\ \hline
1 & 452,567 & 6,298,653 & 7,302,451 & 3,245,121 & 1,012,563 \\
2 & 452,567 & 543,765 & 983,432 & 393,592 & 754,832 \\
3 & 1,902,345 & 4,210,213 & 4,329,034 & 3,290,546 & 7,554,901 \\
4 & 761,432 & 845,789 & 904,7
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 4 Results  
In this section, the CNN-BiConvLSTM model and baseline models were evaluated for the test datasets with comparative analysis. Section 4.1 evaluates the results for the case of interpolation, corresponding to several successive or discrete intermediate missing frames in a radar sequence. Because the extrapolation can also be used to reconstructing missing frames, Section 4.2 further evaluates and compares the results for the case of extrapolation to verify whether the effectiveness of a model was better for interpolated cases. The influences of the quality of input radar sequences and model sizes on the performance of the models are investigated in Section 4.3.
## 2 Materials and Methods
### Ray-Tracing Analysis  
The time difference in the various echoes relative to the first arriving component (the one corresponding to the direct path in an LOS situation) can give some information about where they have been produced. Multipath components in the PDP are associated with a certain delay and thus with a certain travelled distance. Using these delays and the dimensions and geometry of the environment, each multipath component can be mapped to a certain scattering element in the environment, wherefore a  
Figure 6: Frequency correlation function and estimation of coherence bandwidth at 40 GHz in the auditorium.  
very simple ray-tracing tool has been developed only calculating the origin of the different multipath components and not the amplitude of them. Using image theory [13], first order reflections on the most significant elements were computed.  
Figure 7 shows, as an example, the fit between the PDP obtained from measurements and the delay of different propagation paths computed by the above-described ray-tracing tool. Up to four different paths considered by the ray-tracing tool superimpose to the measured PDP, whereas the LOS component and the floor reflection fit perfectly the empirical results, the reflection caused by the back door seems just a bit ahead of the measured one. This may be due to small measurement errors, a more complex multipath propagation than that used in the simulations, or even some additional delaying elements not considered in this analysis.
## 5 Object Detection Application Domains
### Pedestrian Detection  
An important application area of object detection is pedestrian detection. It is used extensively in complex applications, including video surveillance, self-driving cars, etc. Earlier pedestrian detection methods used for object detection, such as the HOG detector [13] and the integral channel features (ICF) detector [89], rely purely on terms of feature representation [13; 89], classifier design [90], and acceleration detection [91]; others, such as \"detection by components\" [92; 93; 94; 18], gradient-based representation [95; 16; 96], and the deformable part-based model (DPM) [79; 17; 27] are used for pedestrian detection. In pedestrian detection the main difficulties and challenges faced are (a) small pedestrian detection; (b) hard negatives; (c) real-time pedestrian detection from HD video; (d) dense and occluded pedestrians. The first CV task that applied deep learning was pedestrian detection [97]. The recently improved Tiny-YOLOv3 [98] implementing pedestrian detection on the Pascal-Voc 07 dataset achieved better accuracy compared to Tiny-YOLOv3. Table 8 shows possible directions to overcome the major \begin{table}
\begin{tabular}{l l} \hline \hline
**Year** & **Main Challenges** \\ \hline
2012 & Fine-grained Classification, Classification, Classification, and localization \\
2013 & Detection, Classification with localization \\
2014 & Detection, Classification, and localization \\
2015 & Object localization, Scene Classification, Object detection from video \\
2016 & Object localization, object detection from video, Scene Classification and parsing \\
2017 & Object localization, Object detection, Object detection from video \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ILSVRC image classification dataset statistics. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & **PASCAL VOC 2012** & **ILSRVC 2013** & **ILSRVC 2014** \\ \cline{2-5}
**Number of Object Classes** & **20** & **200** & **200** \\ \hline Training & No. of images & 5717 & 395909 & 456567 \\  & No. of objects & 13609 & 345854 & 478807 \\ \hline Validation & No. of images & 5823 & 20121 & 20121 \\  & No. of objects & 13841 & 55502 & 55502 \\ \hline Testing & No. of images & 10991 & 40152 & 40152 \\  & No. of objects & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of object detection datasets between PASCAL VOC and ILSRVC. challenges and difficulties faced in pedestrian detection. Table 9 shows various papers applied deep learning-based techniques to handle dense and occluded pedestrian detection. The widely used datasets for evaluating the pedestrian detection performance are PASCAL-VOC [83], INRIA [13], KITTI [47], CalTech [81], ETH [75], and CityPersons [73].
## 3 System Building Blocks
### Acquisition
#### 3.1.2 Set-Up for Image Acquisition Conditions  
Light represents one of the major components in the acquisition of an image, and it directly interferes with the obtained results. In order to improve image quality, which is highly important for the reconstruction of the 3D model, it is necessary to consider artificial light in the scene. Tests have been conducted that take this parameter into consideration; namely, acquisitions made with different light conditions, both artificial and natural. For the artificial light, we used two softboxes with 5500K lights, which are commonly used for photography lighting.  
Figure 3: Stereo camera: ZED.  
Figure 2: Plenoptic and time-of-flight cameras (left to right: Lytro Illum, Raytrix R42, DepthSense325, RealSense).  
It is also necessary to include the background factor, which needs to be homogeneous, in accordance with the general recommendations of international standards for ID and travel documents. The use of light and a uniform background can enhance the model reconstruction owing to the fact that it decreases the existence of outliers in the reconstruction process. The tests with variations in lighting and background are presented in Figure 4, where one can easily see the importance of having good lighting and background conditions.
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
###### Abstract  
Good learning image priors from the noise-corrupted images or clean natural images are very important in preserving the local edge and texture regions while denoising images. This paper presents a novel image denoising algorithm based on superpixel clustering and sparse representation, named as the superpixel clustering and sparse representation (SC-SR) algorithm. In contrast to most existing methods, the proposed algorithm further learns image nonlocal self-similarity (NSS) prior with mid-level visual cues via superpixel clustering by the sparse subspace clustering method. As the superpixel edges adhered to the image edges and reflected the image structural features, structural and edge priors were considered for a better exploration of the NSS prior. Next, each similar superpixel region was regarded as a searching window to seek the first \(L\) most similar patches to each local patch within it. For each similar superpixel region, a specific dictionary was learned to obtain the initial sparse coefficient of each patch. Moreover, to promote the effectiveness of the sparse coefficient for each patch, a weighted sparse coding model was constructed under a constraint of weighted average sparse coefficient of the first \(L\) most similar patches. Experimental results demonstrated that the proposed algorithm achieved very competitive denoising performance, especially in image edges and fine structure preservation in comparison with state-of-the-art denoising algorithms.  
image priors; denoising; superpixel clustering; dictionary learning; weighted sparse coding Article
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Crossband Filtering--Length Extension
#### 4.4.2 Optimal CD (\(L_{\text{bb}}=14\))  
The obtained results are demonstrated in Figure 3a-c. Notably, a remarkable improvement in performance is observed from the first sample of the crossband component, as evidenced by the enhancement in all measured metrics. Furthermore, as indicated in Table 1, the optimal performance, both in terms of FWSegSNR and CD, is achieved when \(L_{\text{cb}}\) is set to 2. Similarly, for optimal performance in terms of PESQ, a value of \(L_{\text{cb}}=3\) is identified. It is worth highlighting that for values of \(L_{\text{cb}}\) exceeding 7, a noticeable decline in performance is observed compared to the conventional WPE method. This observation further underscores the importance of carefully selecting an appropriate value for \(L_{\text{cb}}\) to achieve optimal dereverberation results. The presented findings shed light on the effectiveness of integrating the first crossband component and its significant impact in improving the dereverberation performance across various evaluation metrics.  
Figure 2: Performance evaluation (Ext.) for \(L_{\text{bb}}=15\) (optimal FWSegSNR) in terms of (**a**) FWSegSNR, (**b**) CD, and (**c**) PESQ.
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.2.4 Diabetes  
[PERSON] et al. investigated the impact of amorphous PVP and semicrystalline (polyethylene glycol 6000, polyethylene-polpropylene glycol 188) polymeric additives on the crystallization behavior of piogltazone solid dispersions (drug applied in the treatment of diabetes mellitus) [80]. All polymers used in experiments were observed to reduce crystal growth rates of piogltazone compared with the drug alone. However, amorphous polymers were more effective at inhibiting crystallization rates, while semicrystalline polymers were less effective. [PERSON] and [PERSON] proved in their experiments that the diblock copolymer, poly(ethylene glycol)-_block_-poly(lactic acid) (PEG-_b_-PLA) modulated the crystal growth of totazamide (drug applied in the treatment of diabetes mellitus) [81]. They found that polymers capable of forming strong hydrophobic and van der Waals interactions might be more effective in inhibiting crystallization of poorly water-soluble and hydrophobic drugs in aqueous media than those with hydrogen-bonding capacities.
# Battery Management System Hardware Concepts: An Overview
## 3 Topologies of BMS
### Communication and Data Transfer  
Usually, the front-end ICs can be daisy-chained to use them in a larger cell stack. The MAX11068, e.g., features two I\({}^{2}\)C ports to connect to the next chip lower in the stack and the next one, which is higher in the stack [3]. Like the MAX11068, the TI bq76 PL536A also provides level-shifted interfaces to connect to ICs higher and lower in the stack. Furthermore it provides a dedicated host-interface, to connect to a microcontroller, while, when using the MAX11068, this is done using the lowest I2C port in the stack [3,9]. The LT6802G-2 can be connected to using the Serial Peripheral Interface (SPI) bus. The chip's interface does not allow daisy-chaining multiple chips directly, but connecting multiple chips to the same SPI bus using additional digital isolators [25] is possible.  
In many cases, the daisy-chain connections are used to connect multiple ICs on one PCB to each other. To implement the connection to the rest of the system and control the BMS ASIC's functions, usually a low-cost microcontroller is used (see examples _i-MiEV_ and _smart fortwo ed_ below). For the connection to further modules on other PCBs and to connect to the BMS master, often a field bus like CAN is used to provide a more robust communication. The same goes for the connection between the master and the rest of the system (also see Section 4.4).
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
## 6 Conclusions  
In this paper, we derived new expressions to assess the BER performance of CSS in the essential fading scenarios, i.e., Rayleigh, Rician, and Nakagami-\(m\) fading channels. All the results are in terms of LoRa parameters such as spreading factor \(SF\), bandwidth \(B\), and symbol to noise ratio \(E_{s}/N_{0}\). The derived bit error probability expressions will be handy for any IoT provider that aims to predict the network coverage for different scenarios where the fading may vary. Also, we have addressed the limiting cases where \(m\to\infty\) (Nakagami parameter) and \(\kappa\to\infty\) (Rice parameter), showing that they converge to the non-fading case or the case under the influence only of the additive white Gaussian noise. All the expressions have been validated by simulation and when applicable, compared with previous results.  
C.F.D. participated in the tasks: conceptualization, formal analysis, research, software, visualization and writing--original draft. E.R.d.L. participated in the tasks: conceptualization, funding acquisition, project administration and writing--review & editing. G.F. participated in the tasks: visualization, project administration and writing--review & editing.  
**Funding:** This work was partially supported by R&D ANEEL--PROJECT COPEL 2866-0366/2013. This work was partially funded by EMBRAPII--Empresa Brasileira de Pesquisa e Inovacao Industrial. E.R.d.L. was supported in part by CNPq under Grant 313239/2017-7. G.F. was supported in part by CNPq under Grant 304946/2016-8.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their constructive comments, which certainly helped them to improve the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Results
### Analysis of the Contextual Relevance of Distractors
#### 4.1.3 Performance Differences by Generation Method Across Different Knowledge Types  
Figure 5 visually displays the performance differences among the generation methods across different knowledge types. From Figure 5, we can observe the following: (1) The Multi-Examiner has a more concentrated score distribution across all knowledge types, with median scores generally higher than the other two methods, especially pronounced in factual and conceptual knowledge. (2) GPT-4 shows a more dispersed score distribution, particularly in factual and conceptual knowledge, where its performance is relatively poorer. (3) The score distribution for human-generated distractors is close to that of the Multi-Examiner, performing well especially in procedural and metacognitive knowledge.  
Figure 6 further illustrates the relationship between generation methods and knowledge types. From Figure 6, we can make the following observations: (1) The Multi-Examiner outperforms both GPT-4 and human-generated methods across most knowledge types, with a particularly strong advantage in factual knowledge. (2) GPT-4 generally performs poorly across all knowledge types, especially in procedural knowledge. (3) The performance differences among the three generation methods are relatively small in metacognitive knowledge, suggesting that the impact of the generation method might be less significant for these higher-order cognitive tasks.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Source of** **Variation** & **Sum of Squares** & **Degrees of Freedom (DF)** & **Mean Square** & **F-Value** & **\(p\)-Value** & **Partial \(\eta^{2}\)** \\ \hline Generation Method & 37.62 & 2 & 18.81 & 19.85 & \(<\) 0.001 & 0.08 \\ Knowledge type & 12.33 & 3 & 4.11 & 4.34 & 0.005 & 0.03 \\ Interaction & 32.93 & 6 & 5.49 & 5.79 & \(<\) 0.001 & 0.07 \\ Error & 329.733 & 348 & 0.95 & & & \\ \hline \end{tabular}
\end{table}
Table 4: Results of two-way ANOVA on distractor relevance scores.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Comparison** & **Mean Difference** & **Standard Error** & \(p\)**-Value** & **95\% Confidence Interval** \\ \hline Multi-Examiner vs. GPT-4 & 0.71 & 0.16 & \(<\) 0.001 & [0.41, 1.02] \\ Multi-Examiner vs. human & \(-\)0.07 & 0.16 & 0.870 & [\(-\)0.38, 0.25] \\ GPT-4 vs. human & 0.65 & 0.16 & \(<\) 0.001 & [0.34, 0.96] \\ Factual vs. conceptual & \(-\)0.42 & 0.21 & 0.039 & [\(-\)0.83, \(-\)0.01] \\ Factual vs. procedural & \(-\)0.23 & 0.21 & 0.453 & [\(-\)0.64, 0.18] \\ Factual vs. metacognitive & 0.03 & 0.21 & 0.997 & [\(-\)0.38, 0.44] \\ \hline \end{tabular}
\end{table}
Table 5: [PERSON]’s HSD Post-Hoc Test Results for Distractor Relevance (After Bonferroni Correction).