esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Health Benefits of Superblocks across the City of Los Angeles
## 3 Results
### The Scenarios Used to Transform Residential Areas  
The Superblocks generated in Section 3.1 highlight the most feasible locations for implementing Superblock interventions, but they may not constitute residential areas. Since the implementation of Superblocks is highly linked with the residents' participation and co-responsibility and the estimation of the health and economic benefits is also highly connected with residential areas, it is crucial for us to understand the residential areas in the proposed Superblocks. We obtained the boundaries and use codes of approximately 2.4 million land parcels in LA County from the LA County Assessor's Data Portal. We  
Figure 1: Superblocks generated by Rules I–V (blue) and Superblock candidates generated by Rules I–IV but excluded by Rule V (white).  
then extracted those located in the City of LA with Residential use codes to generate the high-resolution City of LA residential area map. We first ranked the 597 Superblocks by their conversion ability defined in Rules I-V. The 597 Superblocks covered approximately 54.8% of the residential areas in the City of LA. We next chose appropriate conversion thresholds to develop five scenarios for transforming approximately 5%, 10%, 20%, 30% and 50% of the City of LA's residential neighborhoods into Superblocks. Specifically, we chose: (1) 2550 m as the conversion threshold (i.e., total length of contained road segments in the Neighborhood Enhanced Network, Bicycle Enhanced Network and Pedestrian Enhanced Districts) and selected 45 Superblocks that covered 4.99% of the City of LA's residential areas for Scenario 1; (2) 1625 m as the conversion threshold and selected 94 Superblocks (including the 45 Superblocks from Scenario 1) that covered 10.04% of the City of LA's residential areas for Scenario 2; (3) 1010 m as the conversion threshold and selected 169 Superblocks (including the 94 Superblocks from Scenario 2) that covered 20.07% of the City of LA's residential areas for Scenario 3; (4) 745 m as the conversion threshold and selected 302 Superblocks (including the 169 Superblocks from Scenario 3) that covered 30.07% of the City of LA's residential areas for Scenario 4; (5) 115 m as the conversion threshold and selected 530 Superblocks (including the 302 Superblocks from Scenario 4) that covered 50.97% of the City of LA's residential areas for Scenario 5. Figure 2 shows the Superblocks included in each scenario.  
Figure 2: Superblocks included in each of the five transformation scenarios.
## 4 Proposed Method
### Fault Diagnosis Method  
#### 4.4.1 Introduction to Principal Component Analysis  
PCA is a standard method applied to reduction and feature extraction and is the most used linear dimension reduction method. The purpose of PCA for dimensionality reduction is to reduce the original features as far as possible to ensure that \"information is not lost\" and obtain the maximum data information (maximum variance) in the dimension of the projection. In other words, the original feature is projected onto the dimension with the maximum projection information, so that the information loss after dimensionality reduction is minimized [46] and the characteristics of more original data points are retained while the data dimension is reduced.
## 5 Simulation Outcomes and Analysis
### Influence of Anchor Node Ratio  
We test the above localization algorithm with varying ratios of anchor nodes. R is 25 m, and the number of nodes is 100. It varies between 5% and 30% for anchor nodes. The curve variations of localization error for each algorithm are plotted in Figure 10.  
Figure 9: Comparison of various localization algorithms with different numbers of nodes: localization error.  
The localization error for each algorithm, as a whole, tends to decrease as the proportion of anchor nodes increases. The localization error decreases significantly and gradually with increasing segmentation factor when CMWN-DV-Hop is utilized. When the anchor node ratios are the same, the performance of CMWN-DV-Hop (a = 2) is comparable to that of CS-DV-Hop and BFO-GSO-DV-Hop, while significantly superior to DV-Hop. CMWN-DV-Hop (a = 2, 4, 6, 10) has a maximum reduction in localization error of 38.7579%, 57.3950%, 67.1837%, and 70.1403%, respectively, in comparison with DV-Hop, when the anchor node ratio varies. When the segmentation factor is greater or equal to 6, CMWN-DV-Hop features the lowest positioning error compared to the three algorithms, including CS-DV-Hop. Among them, the maximum reductions are 53.0907%, 46.2620%, and 52.2841%, respectively, when the segmentation factor is 6. CMWN-DV-Hop (a = 10) has the lowest average localization error, whatever the anchor node ratio, with a maximum reduction of 59.2644%, 55.0847%, and 59.1379% when in comparison with the other three proposed novel algorithms.
## 5 User Preference Modeling
### User Preference Modeling for Deep Features
#### 5.1.2 Maximum Likelihood Estimation  
For a model that obeys a normal distribution, the model parameters can be obtained by maximum likelihood estimation. Therefore, the parameters of user preference modeling for deep features obtain the mean value and standard deviation by maximum likelihood estimation. The parameters \(a_{j}\) and \(\sigma_{j}^{2}\) of user preference modeling for deep features are \(u_{j}^{(i)}\). The \(P(H|a_{j},\sigma_{j}^{2})\) function represents the maximum likelihood calculation  
\[P\Big{(}H\Big{|}a_{j},\sigma_{j}^{2}\Big{)}\ =\ \prod_{v\in H}P\Big{(}v\Big{|}a_{j}, \sigma_{j}^{2}\Big{)} \tag{4}\]  
Intuitively, MLE is an attempt to find a value that maximizes the likelihood of data appearance in all possible values. The above equation is further optimized as  
\[LL\Big{(}a_{j},\sigma_{j}^{2}\Big{)}\ =\ \sum_{v\in H}P\Big{(}v\Big{|}a_{j}, \sigma_{j}^{2}\Big{)} \tag{5}\]  
Finally, the maximum likelihood estimates for parameters \(a_{j}\) and \(\sigma_{j}^{2}\) are  
\[\hat{a}_{j}=\frac{1}{M}\sum_{i=1}^{M}P\Big{(}v_{j}^{(i)}\Big{)} \tag{6}\]  
\[\hat{\sigma}_{j}^{2}=\frac{1}{M}\sum_{i=1}^{M}\Big{(}v_{j}^{(i)}\ -\ \hat{a}_{j}\Big{)}^{2} \tag{7}\]  
Figure 3: The flowchart of user preference modeling for deep features.  
After completing the maximum likelihood estimates \(\hat{a}_{j}\) and \(\hat{\sigma}_{j}^{2}\), the user preference modeling for deep features can be represented as  
\[U_{\text{d}}\ =\ P\Big{(}u_{\text{d}}\,|\ \hat{a}_{1},\hat{a}_{2},\cdots\cdots\hat{a }_{n};\hat{\sigma}_{1}^{2},\hat{\sigma}_{2}^{2},\cdots\cdots\hat{\sigma}_{n}^{2} \Big{)} \tag{8}\]  
where \(U_{\text{d}}\) represents the user preference for deep features and \(u_{\text{d}}\) represents the independent variable of user preference modeling. The \(P\) function is used to calculate the probability density and \(n\) represents the dimension of the parameter.
## 2 Materials and Methods
### CBCT Superimposition  
CBCT images were acquired pre- and post-treatment with approval from the Institutional Ethical Committee of the Tokyo Medical and Dental University (approval number: 1254). The purpose of using CBCT was explained to the subjects, and written consent was obtained thereafter from all subjects. All work was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). CBCT images were acquired using the following settings: normal mode (16.8 s, 4.10 mGy, 90 kV, and 4 mA); slice thickness, 0.147 mm; field of view, 81 \(\times\) 74 mm; and voxel size, 0.146 mm. Images were saved as digital imaging and communication in medicine (DICOM) files. CBCT images were reconstructed using 3D imaging software (OsiriX; Pixmeo, Geneva, Switzerland). The pre- and post-treatment CBCT DICOM dates were manually approximated by the same observer using best fits of the maxillary outlines in 3D multiplanar cross-sections using OsiriX [19; 20]. Regions corresponding to anatomically stable structures were used as landmarks, such as the bilateral zygomatic processes of the maxilla (i.e., key ridge) and the anterior nasal spine because their consistency was amenable to the 3D superimposition of the maxilla [21].
## 4 Performance of a NS RA-Modified Asphalt Mixture Based on Anti-Rutting Performance
### Determination of the Optimum NS RA Content  
To solve the rutting failure of the pavement, the optimum NS RA content was determined based on the anti-rutting performance of the NS RA-modified asphalt mixture. Gradation 5 was used, and the rutting tests of its NS RA-modified asphalt mixtures were conducted by using a rutting tester from China. The test results are shown in Figure 5.  
As observed in Figure 5, when the NS RA content was less than 8%, the dynamic stability of its modified asphalt mixture dramatically increased with an increase in NS RA content. The dynamic stability of the modified asphalt mixture with 8% NS RA content is was 3.52-fold higher than that of the base asphalt mixture. However, it is interesting to find that when the NS RA content was more than 8%, the dynamic stability of its modified asphalt mixture had no significant increase. The dynamic stability with 12% NS RA content was only 1.03-fold higher than that of the base asphalt mixture. Therefore, considering the raw material price of NS RA, based on its anti-rutting performance, the optimum NS RA content was about 8%.  
Figure 5: Dynamic stability of modified asphalt mixtures with different NS RA contents.
# Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits
[4] adopted decision-tree techniques for analysis of the pumping unit and production records and found the reduction in well spacing to be the primary contributor to frac hits. A multilevel approach to understanding the comprehensive fracture network was tested as well [4]. The proposed approach integrated multi-resolution datasets of the well log and core data, along with completion, flowback, production, testing/monitoring (tracer data and offset pressure), and microseismic data. By applying machine learning (ML) separately to each dataset, the approach provided the well-level, segment-level, and stage-level overview of the fracture network. Then, all three levels of the network representation were aggregated via information fusion to build a multilevel framework of the holistic modeling. The multilevel fracture network can be interactively visualized to support decision making on planning of the infill drilling and well completion design, as well as improving unit operations such as choke management and pressure control [4]. The case study reported here builds on the prior efforts of developing ML models that can be used for conducting analysis of frac-hit impacts based on the data from selected pads, including drilling surveys, bottom-hole pressure, oil and water production, and fracking start time and duration information [4]. Incidentally, the economic impact of frac hits and shut-ins (closing off producing wells) was estimated in the most recent report [12] based on the same datasets as the ones used in this work. The ultimate goal is either complete prevention of the major negative economic impacts or optimization of the well operation to minimize such impacts. Physics-based modeling can assist in understanding the mechanics of unconventional reservoirs, such as stress field alterations affecting well performance [13,14]. However, traditional computational approaches based on the known and trusted concepts may not provide the most comprehensive and realistic model of heterogeneous geological systems with a variety of unknown features [15]. The proposed ML method attempts to close this gap by taking advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The principal conclusion is that such a workflow can help to capture time-variable features of frac hits. This case study was conducted at the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) in collaboration with an industrial partner, a major shale-field operator in the Permian Basin, located in West Texas, United States, to investigate the opportunities for the application of ML techniques to facilitate prompt frac-hit event detection and operational decision-making support.
###### Abstract  
Background: Capsule endoscopy (CE) improved the digestive tract assessment; yet, its reading burden is substantial. Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. Nonetheless, their application in the esophagus lacks evidence. The study aim was to develop a DL model for esophageal pleomorphic lesion (PL) detection. Methods: A bicentric retrospective study was conducted using 598 CE exams. Three different CE devices provided 7982 esophageal frames, including 2942 PL lesions. The data were divided into the training/validation and test groups, in a patient-split design. Three runs were conducted, each with unique patient sets. The sensitivity, specificity, accuracy, positive and negative predictive value (PPV and NPV), area under the conventional receiver operating characteristic curve (AUC-ROC), and precision-recall curve (AUC-PR) were calculated per run. The model's diagnostic performance was assessed using the median and range values. Results: The median sensitivity, specificity, PPV, and NPV were 75.8% (63.6-82.1%), 95.8% (93.7-97.9%), 71.9% (50.0-90.1%), and 96.4% (94.2-97.6%), respectively. The median accuracy was 93.5% (91.8-93.8%). The median AUC-ROC and AUC-PR were 0.82 and 0.93. Conclusions: This study focused on the automatic detection of pleomorphic esophageal lesions, potentially enhancing the diagnostic yield of this type of lesion, compared to conventional methods. Specific esophageal DL algorithms may provide a significant contribution and bridge the gap for the implementation of minimally invasive CE-enhanced panendoscopy.  
capsule endoscopy; panendoscopy; artificial intelligence; esophageal lesions
## 1 Introduction
Using a laser scanner to detect obstacles presents some difficulty, especially in varying weather conditions. Under conditions of fog, rain, and heavy snow, particles can cause the attenuation of signals or false echoes. The solution of these kinds of problems has been improved with multilayer laser scan planes by latter technology of laser scanners [7]. In order to reduce human-induced road accidents, this study presents a collision avoidance system (CAS) based on an approach using a state-of-the-art multilayer laser scanner mounted in front of tram. The system is designed to detect all objects (static and moving) within a 110-degree field in the front of the tram. The aim of the system is to work in all the heavy traffic scenarios. The system that uses a laser scanner sensor was first used in real-time tests for trams. This system is integrated to a Light Rail Vehicle (LRV) called \"Silkworm\", which is manufactured by Durmazlar Machine Inc. (Bursa, Turkey) and tested on trams at facilities of Durmazlar Machine Inc.
## 3 LR-Based RAS Selection in LR Aided PSM System
### Proposed Suboptimal LR-RAS Selection Algorithm  
A suboptimal LR-based RAS selection algorithm with less computational complexity than the optimal searching approach of (11) for the LR-PSM systems is presented, which is called subopt-LR-RAS and is summarized in Algorithm 1. It starts with an \(N_{R}\times N_{T}\) full channel matrix \(\mathbf{H}\) without LR. To avoid an exhaustive search, \((N_{T}-1)\) antennas among \(N_{R}\) receive antennas are selected by computing a Frobenius norm of each row of the full channel matrix \(\mathbf{H}\), which is given by \(C_{m}=\|\mathbf{h}_{m}\|_{F}^{2}\), \(m=1,2,\cdots,N_{R}\), where \(\mathbf{h}_{m}\) is the \(m\)-th row vector of the channel matrix \(\mathbf{H}\) and then finding antenna indexes, \(\{u(1),u(2),\cdots,u(N_{T}-1)\}\), corresponding to \((N_{T}-1)\) largest values. After determining \((N_{T}-1)\) antennas, the resulting submatrix can be given by \(\mathbf{H}(u(1):u(N_{T}-1),:)\)\(\in C^{(N_{T}-1)\times N_{T}}\). To find the last antenna, the remaining antennas are added to the submatrix \(\mathbf{H}(u(1):u(N_{T}-1),:)\) one by one. That is, the \(N_{T}\times N_{T}\) matrix can be formed as \(\mathbf{H}_{I-N_{T}+1}=[\mathbf{H}(u(1):u(N_{T}-1),:);\ \mathbf{H}(u(t),:)]\in C^{(N_{T}-1) \times N_{T}}\). The \(N_{T}\times N_{T}\) matrix can be formed as \(\mathbf{H}_{I-N_{T}+1}=[\mathbf{H}(u(1):u(N_{T}-1),:);\ \mathbf{H}(u(t),:)]\in C^{(N_{T}-1) \times N_{T}}\).  
\(C^{N_{T}\times N_{T}}\) where \(u(t)\), \(t=N_{T},N_{T}+1,\cdots,N_{R}\), indicates an antenna index of non-selected antennas. For each \(\mathbf{H}_{t-N_{T}+1}\), the fcCLLL operation is carried out to generate the new basis \(\widetilde{\mathbf{H}}_{t-N_{T}+1}\).  
Then, \(\widetilde{\xi}^{\prime}_{t-N_{T}+1}=\text{Tr}\bigg{(}\left[\widetilde{ \mathbf{H}}_{t-N_{T}+1}^{H}\widetilde{\mathbf{H}}_{t-N_{T}+1}\right]^{-1} \bigg{)}\), \(t=N_{T},N_{T}+1,\cdots,N_{R}\), can be computed. By assuming that the \(\mathbf{Q}_{t,t}\) matrix obtained from the  
\[C_{fcCLLL}^{RM}(N_{T})=N_{iter}\Big{(}2N_{T}^{3}+N_{T}^{2}+49N_{T}+60\Big{)}+C_{QR} ^{RM}(N_{T}) \tag{14}\]  
\[C_{fcCLLL}^{RS}(N_{T})=N_{iter}\bigg{(}N_{T}^{3}+20N_{T}-\frac{7}{3}\bigg{)}+C_{QR} ^{RS}(N_{T}) \tag{15}\]  
where the \(QR\) decomposition is performed by the modified Gram-Schmidt \(QR\) factorization and thus its complexity is  
\[C_{QR}^{RM}(N_{T})=N_{T}^{3}+3N_{T}^{2} \tag{16}\]  
\[C_{QR}^{RS}(N_{T})=2N_{T}^{3}-\frac{1}{2}N_{T}^{2}-\frac{1}{2}N_{T} \tag{17}\]  
For the LR-based exhaustive RAS selection algorithm, the complexity is given by  
\[N_{exhaustive}^{RM}=C(N_{R},N_{T})\Big{(}4N_{T}^{3}+8N_{T}^{2}+C_{fcCLLL}^{RM}(N _{T})\Big{)} \tag{18}\]  
\[N_{exhaustive}^{RS}=C(N_{R},N_{T})\Big{(}4N_{T}^{3}+6N_{T}^{2}-2N_{T}+C_{fcCLLL }^{RS}(N_{T})\Big{)} \tag{19}\]  
It is noted that the matrix operations described in lines 5 and 6 of Algorithm 1 are repeated \((N_{R}-N_{T}+1)\) times instead of \(C(N_{R},N_{T})\), which significantly reduces the complexity of the proposed suboptimal LR-based RAS selection algorithm, especially for large \(C(N_{R},N_{T})\).
## 4 Discussion
of the reflected image of the laser pointer and the advanced control algorithm using the kinematics of the C-arm, are required. In order to make the fine-positioning step easier, we integrated an additional guiding pattern into the FAD in our study. However, due the interference between the guiding pattern and the guiding hole, they were made into blocks, and only one of them was attached to the FAD. Thus, the final goal of this study was set to aligning the center of the guiding pattern with the IMN hole using the guiding pattern. The experiments showed that this task took 342 s at maximum and 207 \(\pm\) 107 s on average, requiring X-ray imaging 10 times at a maximum and 6.67 \(\pm\) 2.65 times on average. The reason it takes a long time for a small number of X-ray images is because we took X-ray images after the subjects left the X-ray shielding room, when modifying the position of the FAD, in order to reduce their radiation exposure. We believe that the time can be reduced significantly if the surgeons use the C-arm without leaving the X-ray shielding room. As indicated in [6], the conventional distal interlocking using a freehand technique can take 19 \(\pm\) 7.1 min. Since the most challenging and time-consuming step in distal interlocking is intramedullary hole identification, it takes most of the time in distal interlocking. Although it is not fair comparison, it can be said that the proposed intramedullary hold identification method requires a shorter period of time than the conventional method. Finally, we did not perform the actual screwing operation using the guiding hole block, but it will be verified in future work. In addition, the optical tracking system was used only for the evaluation and thus was not part of our proposed system. We believe that the whole mechanism is cost-effective while guaranteeing high accuracy in the guidance task.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 3 Results
### Application of Synthetic Observations to Study the Synergy between TEMPO and GOES-R
#### 3.2.3 Case 2: Dust Detection  
On 13 May 2007 in the G5 NR simulation, wind-blown dust coming from the Sonoran Desert led to elevated AOD in the area southwest of Phoenix, along the US-Mexico border. As before, the aerosol detection algorithm code was run using the simulated TEMPO and GOES-R TOA reflectance data for this day as inputs. The contour lines in Figure 17 outline the areas that are detected as containing dust at the 20 UTC simulation time step, the model hour with the highest dust AOD. The figure shows that the algorithm tends to underestimate the extent of the dust plume. Overlaying the dust detection contours over the surface reflectance RTM inputs (Figure 18) shows that the combination of low surface reflectance spectral contrast and high surface reflectance leads to elevated AAI that is interpreted by the algorithm as absorbing aerosol. This is a known uncertainty in the retrieval methodology, and is most relevant over bright surfaces and low dust optical thickness, where light reflected by the surface contributes significantly to TOA reflectance. This case study indicates that the detection threshold of dust over land is probably higher than the value of 0.2 that has been assumed before, as the accuracy for this case study was less than 50% for this day. The suggestion in [PERSON] and [PERSON] (2018) of deriving AAI thresholds for different land surface types has merit and could improve the retrieval.  
Figure 17: _Cont._Figure 17: **(a)** Simulated 550 nm AOD, **(b)** fraction of simulated 550 nm AOD coming from dust for 13 May 2007 at 20 UTC. The purple contours represent the areas that were detected as smoke by the VIIRS aerosol detection algorithm.  
Figure 18: _Cont._