esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### P-SBAS InSAR Processing
Across the large expanses of rural and vegetated land in the northwestern sector of the processed region, the phase-unwrapping step was more challenging, as typically happens across these land cover types, where errors are more likely to occur due to widespread phase decorrelation. Least square solution retrieval was used for coherent targets using the singular value decomposition (SVD) method, and residual orbital ramps were estimated (as the linear components of phase artifacts; [78]) and then removed from the wrapped interferograms [19]. A second iteration of temporal and spatial phase-unwrapping was implemented for the orbit error-free interferograms, followed by SVD, as per the P-SBAS Sentinel-1 workflow [54]. Temporal coherence was computed, and the threshold on the minimum allowed temporal coherence to select coherent targets was set at 0.85. Atmospheric phase components were identified and removed. The output datasets including geolocation (i.e., latitude and longitude in the WGS84 system, and elevation above the reference ellipsoid), annual LOS velocity, and date-by-date displacement histories (time series) for each coherent target were exported in comma-separated values (.csv) ASCII format, according to the European Plate Observing System-Implementation Phase (EPOS-IP) project specifications. The corresponding metadata, key properties, and low-resolution quick-looks of the retrieved annual LOS velocity in raster (.png) and Google Earth (.kma) format (Figure 4) were also generated for each output dataset. The stable point for both ascending and descending P-SBAS processing was set at the same location within the city of Crotone, at 39.078\({}^{\circ}\)N, 17.120\({}^{\circ}\)E, and the annual LOS velocity values and time series were referenced accordingly. The use of a common reference point allowed internal calibration of the two output datasets, and enabled their combination as described in Section 3.3.
## 7 Engineering Plant-Microbe Interactions for Sustainable Agriculture  
One of the most exciting developments in agricultural microbiology is the construction of SynComs designed to enhance specific plant traits, such as drought tolerance, nutrient use efficiency, and disease resistance. These SynComs are assembled from functionally compatible microbial strains selected based on genomic and phenotypic traits, such as the ability to fix nitrogen, solubilize phosphorus, or produce phytohormones. For instance, PGPR strains like _Bacillus, Pseudomonas_, and _Azospirillum_ are commonly included in drought-focused SynComs due to their ability to produce osmoprotectants, exopolysaccharideacharides, and ACC deaminase, which modulate plant stress responses [146,147]. Such engineered communities can enhance root development, increase water use efficiency, and maintain photosynthetic activity under water-deficit conditions when applied to crops. Microbial vaccines or inoculants represent a specialized approach within SynCom strategies. Unlike classic biopreparations or microbial fertilizers, which are broader formulations aimed at improving soil fertility, nutrient availability, and general plant growth through nutrient cycling and soil health enhancement [148,149], microbial vaccines are typically formulated to introduce specific beneficial microbes, such as nitrogen-fixing bacteria like _Azotobacter_, directly to the plant or soil [150]. Their goal is to stimulate plant immunity, enhance resistance to pathogens, or deliver targeted growth benefits, thereby offering a more precise and responsive solution to environmental stressors and plant health challenges.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 14 Enhancements
#### 2.2.5 Multicarrier Operation  
To enable the massive NB-IoT deployment, in Release 14, NB-IoT can monitor paging and perform random access on non-anchor carriers. With this feature, one or more non-anchor carriers are added to the anchor carrier to carry out the synchronization and mobility measurements by using the NRS. Non-anchor carriers should also perform random access or paging when needed. Therefore, paging occasions and hence paging load will be spread over the anchor and non-anchor carriers and all carriers can then monitor paging.
## Data and Methods
### Data  
#### 2.2.1 Land Surface Properties  
The MODIS MCD43B4 Aqua and Terra combined Nadir BRDF-Adjusted Reflectance (NBAR) product corrected for sun and view angle effects was used to generate a dynamic wetland dataset for the UZRB [39]. These data are available at a 1 km spatial and 8-day temporal resolution with seven wavebands in the infrared and visible ranges of the electromagnetic radiation spectrum (Table 1). Other data sets include the quality control product, MODIS MCD43B2, at the same spatial and temporal resolutions [40], and land cover classes following the International Geosphere-Biosphere Programme (IGBP) system, MODIS MCD12Q1 land cover type product [41]. The land cover data is a stationary product available for each year from 2002 to 2013 at 500 m resolution. Vegetation phenology information retrieved from the MODIS Terra MOD15A2 product provides Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area Index (LAI) reconstructed from surface reflectance also at 1 km resolution every 8 days [42]. Finally, the MODIS MOD17A2 GPP product (version 55) was obtained for comparison against model results [43].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Band** & **Light** & **Wavelength (nm)** & **Brightness (\(w_{p}\))** & **Greenness (\(w_{g}\))** & **Witness (\(w_{w}\))** \\ \hline
1 & Red & 620–670 & 0.4395 & \(-\)0.4064 & 0.1147 \\
2 & Near-infrared (NIR\({}_{1}\)) & 841–876 & 0.5945 & 0.5129 & 0.2489 \\
3 & Blue & 459–479 & 0.2460 & \(-\)0.2744 & 0.2408 \\
4 & Green & 545–565 & 0.3918 & \(-\)0.2893 & 0.3132 \\
5 & Near-infrared (NIR\({}_{2}\)) & 1230–1250 & 0.3506 & 0.4882 & \(-\)0.3122 \\
6 & Mid-infrared (MIR\({}_{1}\)) & 1628–1652 & 0.2136 & \(-\)0.0036 & \(-\)0.6416 \\
7 & Mid-infrared (MIR\({}_{2}\)) & 2105–2155 & 0.2678 & \(-\)0.4169 & \(-\)0.5087 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MODIS instrument specifications and tasseled cap coefficients from [44].  
All MODIS data products were projected into Universal Transverse Mercator using the MODIS Reprojection Tool [45]. Except for MOD17A2 GPP which is already corrected for cloud contamination, the non-static data were then gap-filled, cloud corrected and smoothed using a Savitzy-Golay filter in TIMESAT [46]. The static MCD12Q1 land cover data were spatially interpolated using a nearest neighbor algorithm to the same 1 km grid as the MCD43B4 data before selecting the training data. FPAR and LAI data were interpolated to the 5 km and 1-h resolution of the DCHM-V (Duke Coupled Hydrology Model with Vegetation).
# JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation
###### Abstract  
Nowadays, we can observe the applications of machine learning in every field, ranging from the quality testing of materials to the building of powerful computer vision tools. One such recent application is the recommendation system, which is a method that suggests products to users based on their preferences. In this paper, our focus is on a specific recommendation system called movie recommendation. Here, we make use of user reviews of movies in order to establish a general outlook about the movie and then use that outlook to recommend that movie to other users. However, a huge number of available reviews has baffled sophisticated review systems. Consequently, there is a need to find a method of extracting meaningful information from the available reviews and use that in classifying a movie review and predicting the sentiment in each one. In a typical scenario, a review can either be positive, negative, or indifferent about a movie. However, the available research articles in the field mainly consider this as a two-class classification problem--positive and negative. The most popular work in this field was performed on Stanford and Rotten Tomatoes datasets, which are somewhat outdated. Our work is based on self-scraped reviews from the IMDB website, and we have annotated the reviews into one of the three classes--positive, negative, and neutral. Our dataset is called JUMRv1--Jadavpur University Movie Recommendation dataset version 1. For the evaluation of JUMRv1, we took an exhaustive approach by testing various combinations of word embeddings, feature selection methods, and classifiers. We also analysed the performance trends, if there were any, and attempted to explain them. Our work sets a benchmark for movie recommendation systems that is based on the newly developed dataset using a three-class sentiment classification.  
movie recommendation system; sentiment analysis; feature selection; IMDB; Word2 Vec; GloVe +
Footnote †: journal: Applied Sciences
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 2 Related Works
### Joint Learning  
SOSD-Net [30] was developed as a joint monocular depth estimation and semantic segmentation neural network that could explore the geometric relationship between monocular semantic segmentation and depth estimation to embed semantic objectness to simultaneously learn geometric cues and scene parsing. The authors of [32] proposed a collaborative deconvolutional neural network that could perform depth estimation and semantic segmentation. It consisted of depth and semantic deconvolutional neural networks. A point-wise bilinear layer integrated feature maps from the two deconvolutional neural networks to fuse the semantic and depth information. Then, the integrated features were used for semantic segmentation and depth estimation by two sibling classification layers. The authors of [31] proposed a constrained monocular self-supervised depth estimation method in which depth prediction could be aligned to its segmentation counterpart. It used segmentation and depth estimation to estimate object boundaries. Then, it adopted the distance between the segmentation and depth edges to measure consistency for optimizing the depth estimation model. DSPNet [25] used a ResNet50 network to extract multi-level feature maps with multi-task learning to achieve high GFLOPs-to-accuracy ratios and simultaneously perform object detection, depth estimation, and image segmentation. The TRL framework [33] adopted the historical experiences of features from the previous time steps of two tasks to improve the estimation of the two tasks in the next time step. The authors designed a task-attentional model to propagate the features stream and correlate the two tasks, in which useful features were enhanced while task-irrelevant features were suppressed. The authors of [26] proposed a single real-time model to perform depth estimation and the semantic understanding of scenes simultaneously. They modified a real-time semantic segmentation network to reduce the number of floating point operations.
# Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm
## 2 Materials and Methods
### Validation and Performance Measurement  
We performed some steps in our experiment to develop the accident prediction model. The first step was to partition the dataset in the ratio of 70% training and 30% testing data. The accuracy was assessed using a 10-fold cross-validation technique during the second stage. The entire dataset was divided into 10 subsets at random, with each subset being used as testing data along with the other nine subsets.
## 4 Experiments and Results  
To evaluate the effectiveness of our proposed models, we designed comparative experiments on a computer with an Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40 GHz, equipped with GeForce GTX 1080, and running Ubuntu 20.04.2 LTS. Our experiments were designed in Python. The main Python libraries that we used are Tensorflow and Keras. We also used Matterport's MaskRCNN framework [40] to build our models. The dataset has 5546 images in total, of which 4436 images are the training set and 1110 images are the test set. The training set contains 9477 samples and the test set has 2359 samples.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 4 TBD Algorithms Based Deep Learning
### TBD Algorithm Based on Deep Learning Object Tracking
#### 4.2.2 LSTM-Based Object Tracking  
Multiple studies in the literature have demonstrated that LSTM-based methods have the potential to ensure the correct processing of long-term dependencies while successfully resolving the issue of gradient disappearance and explosion in neural networks through its \"gate units.\" LSTM-related data association techniques have been applied to the field of multi-object tracking [60].  
Most LSTM-based techniques for tracking objects combine several classifiers that deal with space and shape with LSTM modules that take temporal consistency into account [61; 62; 63]. A long-term tracking solution based on the characteristics collected by the LSTM layer was provided by MILAN et al. [64] who employed an LSTM-based classifier to track objects in a video sequence and realized the re-tracking of items that disappeared and reappeared in the video frequency. While the algorithm performed favorably to other techniques, including the combination of a Kalman filter with the Hungarian algorithm, the results on the MOT15 test set did not quite reach top accuracy; however, the algorithm was able to run much faster than other algorithms (~165 FPS) and did not use any kind of appearance features, leaving room for future improvements. This idea was further explored by [65] who employed a bilinear LSTM network, where one LSTM network tracks motion and the other handles information about object interactions, to identify numerous cues for evaluating long-term relationships. The results of the performance comparison reveal that the approach has stronger robustness and better performance than conventional methods, such as the Hungarian and JPDA algorithms. These two elements are combined to determine the similarity score between frames. Another method of using multiple LSTMs is [66]. Ran et al. proposed a triple-stream network based on pose, which combines three other affinity outputs of three LSTMs to calculate affinity: one for appearance similarity, using CNN features and pose information extracted by AlphaPose [67], one for motion similarity, using posture joint speed, and one for interactive similarity, using an interactive grid. Then, a custom tracking algorithm is used to correlate the detection. When tracking multiple objects, the tracker remembers each object's appearance and motion information. This memory is used to compare trajectory and prediction matching and is updated as a result. In order to overcome the issue of simultaneously taking into account all tracks in the memory update process, Kim et al. [68] added a novel multi-track pooling module to the original model structure, which solved the problem and only added a minor amount of cost.
## 2 Study Areas and Data  
### Study Area  
The Loess Plateau is located at latitude of 34\({}^{\circ}\)-40\({}^{\circ}\)N and longitude of 103\({}^{\circ}\)-114\({}^{\circ}\)E, stretching across a region west to the Riyue Mountains, east to the Taihang Mountains, south to the Qinling Mountains, and north to the Yinshan Mountains in Inner Mongolia (Figure 1). The Plateau is approximately 1000 km long from east to west, and 700 km wide from north to south, spanning a total area of approximately 6.4 \(\times\) 10\({}^{5}\) km\({}^{2}\). The Plateau is full of hills, basins, and alluvial plains where elevation ranges between 1000 and 1600 m above sea level, and is covered with continuous loess of 100-300 m in thickness. The loess was geologically transported from the northwestern Gobi desert by wind and has accumulated on the Loess Plateau since the beginning of the quaternary [29]. It is the largest area of loess deposition in the world, and lies in a semi-humid and semi-arid transitional zone that has an annual average temperature of 4.3 \({}^{\circ}\)C in the northwest and 14.3 \({}^{\circ}\)C in the southeast. Limited annual, rainfall, approximately 250 to 780 mm, is highly variable both spatially and temporally and concentrated during the summer months. Frequent storms, steep landscape, low vegetation cover, and highly erodible soil have led the Loess Plateau to be one of the most severely eroded regions globally [30; 31]. After human disturbances over thousands of years, forest only primarily exists in some montane areas of the Plateau, with the majority of the Plateau undergoing heavy soil erosion.  
Figure 1: Location of the study area. The location of the Loess Plateau in China and elevation (**a**), vegetation type (**b**), and annual average temperature and precipitation in the study area (**c**). The elevation data was based on the advanced space borne thermal emission and reflection radiometer (ARTER) ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))) with a resolution of 1000 m. The vegetation type data is courtesy of the Institute of Botany, Chinese Academy of Sciences of 1:100 million Chinese Vegetation Map (2000), and the climate data was from the WordClim website (www.worldclim.org/) with a resolution of 1 \(\times\) 1 km [32].
# Modularity-Based Incremental Label Propagation Algorithm for Community Detection
## 3 Results
### Modularity  
To test the ability of MILPA to detect the community structure of real networks, eight real-world datasets are applied, including four small networks and four large networks. To better show the effect of community division, three small networks are selected and their results of community detection are shown in Figure 4. The nodes in a community are marked with the same color and form a circle. It can be seen that most edges are inside the community and a few of edges are between the communities.  
Figure 4: The results of community detection for three networks. Left: Dolphins. Middle: Football. Right: Karate.  
Figure 3: NMI of five methods in LFR networks of Group **A** and **B**.  
Then, to show the stability of the proposed MILPA, twenty independent tests are performed on these networks with LPA, LPAMP, and MILPA, and the results of three small networks are shown in Figure 5. Firstly, it can be clearly seen from this figure that the average modularity of MILPA is much higher than that of LPA and LPAMP. Secondly, the modularity distribution of LPA is relatively scattered, and the difference between the maximum and minimum of its modularity in each network is much larger than MILPA. Besides, while the modularity of LPAMP may sometimes be greater than that of MILPA, the overall stability of MILPA is better than that of LPAMP. The same observations can be obtained from other networks.  
For a further comparison, three other methods are also used for community detection, i.e., Fastgreedy, GN, and Louvain. To ensure the effectiveness of the results, every experiment is performed ten times independently and the average modularity of each method in each network is shown in Figure 6. These methods are sorted according to their modularity in descending order in each chart of this figure. Note that in four large networks, GN and LPAMP take a lot of time, so they are not considered. It is evident that, among these networks, MILPA shows outstanding performance compared to LPA, LPAMP, GN, and Fastgreedy, and it is only second to the best method Louvain. Moreover, in four small networks, MILPA is almost as good as Louvain and always better than other methods. Another significant phenomenon is that with the rise of network size, the performance gap between methods becomes larger. For instance, the performance of MILPA is improved by no more than 14% in four small networks compared with LPA, while it achieves 16.6%, 23.9%, 32.3% and 58.1% improvement in four large networks, Email, Facebook, DM-CX, and Hamsterster, respectively. There is no doubt that LPA is the worst of these methods since its modularity is the lowest in six of eight networks.  
It can be observed from above results that, no matter in a small network or a large network, MILPA can achieve a high modularity value and greatly improve the stability of the standard LPA in community detection.  
Figure 5: The modularity distribution of twenty community partitions of three networks by LPA, MILPA and LPAMP.
## 5 Conclusions  
This paper firstly proposes additional damping structures with EMWM (free damping structure, constrained damping structure, and insertion damping structure). The preparation process of different additional damping structures is studied. Finally, tensile, shear and compressive static mechanical tests are performed based on the principle of energy dissipation in different additional damping structures. The following conclusions are mainly obtained:  
1. According to the existing preparation technology of EMWM, the preparation process of plate additional damping structures with EMWM was explored. A series of specimens required for the test were prepared based on different process parameters.
2. When the specimens were subjected to tensile, shear, and compression loads, the filament diameter and density had the greatest influence on tensile strength, the filament diameter had the greatest influence on shear strength, and the thickness had the greatest influence on the loss factor. Through the orthogonal test, the optimal combination of performance under different mechanical tests was obtained, and under the evaluation index, its performance was improved by more than 30% compared with the optimal group of performance in the orthogonal test.
3. An increase in rate accelerates the fracture failure under shear and tensile tests, but the rate is not affected in compression. The loss factor tends to increase with tensile displacement, while the cut-line stiffness tends to decrease with tensile displacement. The change in loss factor and cutline stiffness at shear displacement is opposite to that at the tensile test. When the compression displacement increases, the loss factor increases and then decreases, and the secant stiffness increases.  
Figure 25: Loss factor and stiffness curves with different loading displacements in compression tests.  
**Author Contributions:** Conceptualization, L.C. and Y.W.; methodology, L.C.; validation, Y.T., T.L. and J.L.; investigation, L.C. and Y.W.; data curation, Y.T., T.L. and J.L.; writing--original draft preparation, L.C.; writing--review and editing, Y.W. and Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key R&D Plan of China, grant number 2021 YFC2802600, funder [PERSON].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.