esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Safron Crops with UAVs
## 3 Field Study Design Methodology
### Selection of Photogrammetry Techniques  
Photogrammetry is the method of acquiring reliable information about an object or a location through analyzing two-dimensional photographs. This information includes precise measurements of the three-dimensional features of the terrain or the object of interest. More specifically, photogrammetry allows for coordinate calculation; the quantification of distances and heights; and the creation of topographic maps, digital elevation models, and orthophotos.  
There are two general types of photogrammetry, aerial and terrestrial, depending on whether the camera is in the air or on the ground. The terrestrial type is also called close-range photogrammetry because it concerns object distances up to 200 m. There is also a hybrid type called small-format aerial photogrammetry that combines the aerial vantage point with close object distances and high image detail. Aerial photogrammetry, especially UAV photogrammetry, has been widely used for Precision Agriculture for the reason that it has many advantages, such as high-resolution images, low cost, and flexible survey planning [19].  
Using UAV photogrammetry, we generated the following types of products [20]:  
* **Orthomosaic:** Orthomosaics, or orthophotos (Figure 3), correct any geometric distortion that is inherent in aerial images. By using a process called orthorectification, a highly detailed map referenced to the real world can be created. Orthorectification removes perspective from each individual image to create consistency across the whole map, while keeping the same level of detail from the original image. The final product is a single mosaic built through edge matching and color balancing.
* **Normalized Difference Vegetation Index (NDVI):** NDVI (Figure 4) is an indicator that calculates the vitality of vegetation based on UAV captured data. Live vegetation (where chlorophyll is present) reflects more infrared and green (in the electromagnetic spectrum) radiation than other wavelengths. Vegetation absorbs more blue and red radiation, so the human eye observes vegetation as green. The NDVI uses near infrared and red imaging channels to measure healthy vegetation. In mathematical terms, the formula is worded as follows: \[\textbf{NDVI}=(\text{NIR}-\text{RED})/(\text{NIR}+\text{RED})\] where NIR is the reflection in the near infrared range, and RED is the reflection in the red range of the spectrum. Multispectral images were used for NDVI calculation.
* **Ground-truth image:** \"Ground truth\" stands for the objective observation, verifiable by humans, of the state of an object or information that can be considered a fact. The term \"ground truth\" has recently gained popularity, as it has been adopted by machine learning and deep learning approaches. In this context, we refer to a \"ground truth image\"--human-generated classifications of image data on which algorithms are trained or evaluated. This image can be created through a process called annotation, where we label the objects on an image by using an appropriate software tool.  
Figure 3: Orthophoto example.
# BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n
## 3 Experiments  
### Evaluation Metrics  
To ensure an objective and comprehensive evaluation, we adopted core evaluation metrics, commonly used in the field of object detection, namely _precision_, _recall_, _F1 score_, and _mAP_ at Intersection over Union (IoU) threshold of 0.5 (_mAP_@0.5). _Precision_ reflects the proportion of true positive (_TP_) samples among all _TP_ and false positive (_FP_) samples, according to (17). _Recall_ reflects the proportion of correctly detected samples among all true positive and false negative (_FN_) samples, according to (18):  
\[precision=\frac{TP}{TP+FP} \tag{17}\]  
\[recall=\frac{TP}{TP+FN} \tag{18}\]  
Higher _precision_ means fewer FPs (mistakenly judging background and other interferences as targets). Higher _recall_ means fewer FNs (undetected targets). _Precision_ and _recall_ focus on measuring detection performance under a single threshold.  
_F1 score_ is the harmonic mean of _precision_ and _recall_, providing a balanced measure of both metrics. It is particularly useful when there is an uneven class distribution or when _precision_ and _recall_ are equally important. _F1 score_ is calculated as follows:  
\[F1\ score=2\times\frac{precision\times recall}{precision+recall} \tag{19}\]  
_F1 score_ reaches its maximum value of 1 when both _precision_ and _recall_ are perfect, and approaches 0 when either _precision_ or _recall_ is poor. This metric provides a single score that balances the trade-off between _precision_ and _recall_.  
To comprehensively evaluate the model performance under different confidence levels, the average _precision_ (_AP_) and _mAP_ are used. Reflecting a model's overall detection accuracy under all confidence thresholds, _AP_ is defined as the area under the _precision_-_recall_ curve, which is the integral area of _precision_ values (_P_) at various _recall_ levels (_R_), calculated as follows:  
\[AP=\int_{0}^{1}P(R)dR \tag{20}\]  
_mAP_ is obtained by averaging the _AP_ values across all target categories, as follows:  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{21}\]  
Additionally, to comprehensively measure a model's actual deployment performance, we used two other indicators--the model's parameters counts and computational complexity.
# Design of a Device Based on an LED Matrix for Water Sterilization
## 3 Results
### The Impact of Bacterial Concentration and Sample Volume on the Sterilization Efficacy of LED 2  
To determine the limits of LED 2 sterilizing capacity against increasing concentrations of microorganisms, these were progressively increased by five orders of magnitude, from \(5\times 10^{3}\) to \(5\times 10^{8}\) CFU/mL. Under static conditions, an increase in levels of surviving viable microorganisms related to the concentration used was observed, although the increases were dependent on each bacterial species (Figure 4). However, when tests were conducted under stirring conditions, the sterilization efficiency experienced a drastic improvement, with viability close to zero even at the highest concentrations (Figure 4).  
Figure 4: Influence of bacterial concentration on the sterilizing capacity of LED 2. (**A**), _E. coli_; (**B**), _K. pneumoniae_; (**C**), _E. faecalis_; (**D**), _S. enterica_; (**E**), _C. perfringens_. The tests were conducted for 5 min under static conditions (black, solid lines) or with stirring (gray, dashed lines). All tests were performed in triplicate.  
Figure 3: Effect of LED 2 on the viability of different microorganisms. (**A**), _K. pneumoniae_; (**B**), _E. faecalis_; (**C**), _S. enterica_; (**D**), _C. perfringens_ (vegetative cells); (**E**), _C. perfringens_ (spores); (**F**), _C. albicans_; (**G**), _S. cerevisiae_. Tests were carried out under static conditions (black, solid lines) or with stirring (gray, dashed lines). All tests were performed in triplicate.  
The experiments described up to this point were conducted by placing each LED unit in a volume of 10 mL of water. To explore the limits of the device's sterilizing capacity, tests were performed using each bacterial species at a concentration of 5 \(\times\) 10\({}^{3}\) CFU/mL in increasing volumes up to 320 mL. Under static conditions, the volume increase resulted in an increase in the number of viable microorganisms, following patterns dependent on each bacterial species. Conversely, the same series of tests conducted under stirring conditions showed total mortality for all microorganisms (Figure 5).  
Using _E. coli_ at a very high concentration (5 \(\times\) 10\({}^{8}\) CFU/mL), the sterilizing capacity was explored in the same range of sample volumes. The results showed partial mortality dependent on the volume when tests were carried out under static conditions, but total mortality in volumes up to 80 mL when agitation was used in the tests. Under these latter conditions, mortality was also very high in larger volumes, with only 8% viable at 320 mL (Figure 6).
## 6 Case Study
### Process Robustness Analysis
#### 6.3.4 Process Robustness Comparison of Three Substitution Strategies under RB Failure Mode  
As shown in Figure 14, (1) in the RB failure mode, as the number of node attacks increases, the RoS values under all three substitution strategies rapidly decline and finally decrease to 0, at which point all cloud orders fail to be processed. This indicates that the RB failure mode is more destructive to the robustness of the CMP than either the ID or IB modes. (2) The \"external\" curve is always located above the \"internal\" curve, and the \"inter&exter\" curve is nearly always located above the \"external\" curve. This indicates that the RoS value is largest under the internal-external integration replacement strategy, followed by the external substitution strategy, and then the internal substitution strategy. Further, under the three strategies, the number of node attacks required to make all cloud order processing fail (i.e., when the process robustness decreases to its lowest) are 110 (\"internal\"), 150 (\"external\"), and 160 (\"inter&exter\"). Therefore, to obtain robustness of the CMP under the RB failure mode: internal-external integration strategy > external substitution strategy > internal substitution strategy.  
In summary, from the perspective of multi-agent simulation, all three resource substitution strategies significantly improved the process robustness of the CMS. In the four failure modes (i.e., ID, IB, RD, and RB), the process robustness levels under all three strategies were higher than those with no strategy. Further, the internal-external integration replacement strategy brought the greatest robustness enhancement, followed by the external replacement strategy, and then the internal replacement strategy. This is reasonable because the three strategies provided different amounts of alternative resources: the internal substitution strategy can provide fewer alternative resources, which corresponds to lower robustness; the external substitution strategy can provide five resources of the same type because five external cloud service providers are involved in this paper; and the internal-external integration replacement strategy can provide more than five resources of the same type or alternative resources, which corresponds to higher robustness. In addition, in the failure modes based on the initial topology (i.e., ID and IB), only the process robustness under the no-replacement strategy and the internal replacement strategy significantly decreased. In contrast, in the failure modes based on the recomputed topology (i.e., RD and RB), the process robustness under all strategies significantly decreased, indicating that the failure modes based on the recomputed topology were more destructive to process robustness. However, for all four failure modes, all three resource substitution strategies could protect the process robustness of the CMS to some extent.  
Figure 14: Variation of RoS under RB failure mode.
# An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields
## 4 Experimental Evaluation
### Experimental Results
#### 4.3.2 Quantitative Analysis
We used the provided partitions of training and validation sets, and the obtained results are reported in Table 3. It can be observed that the evaluation on the Cityscapes validation set is similar to that on the VOC dataset. Using our method, the highest mIoU score can reach up to 65.4%. The IoU, mIoU, and PA scores of FCN-8s, DeepLab-v2, and our method are given in Figure 14. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FCN-8s** & **DPN [40]** & **CRF-RNN [20]** & **DeepLab-v2** & **Our Method** \\ \hline road & 95.9 & 96.3 & 96.3 & 96.8 & **97.2** \\ sidewalk & 71.5 & 71.7 & 73.9 & 75.6 & **78.9** \\ building & 85.9 & 86.7 & 88.2 & 88.2 & **88.8** \\ wall & 25.9 & 43.7 & **47.6** & 31.1 & 35.1 \\ fence & 38.4 & 31.7 & 41.3 & 42.6 & **43.3** \\ pole & 31.2 & 29.2 & 35.2 & **41.2** & 40.2 \\ traffic light & 38.3 & 35.8 & **49.5** & 45.3 & 44.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Per-class IoU score on Cityscapes dataset. Best performance of each category is highlighted in bold. Figure 13: The improvement by our method compared with FCN-8s and DeepLab-v2. It can be observed from Figure 14 that the proposed method has significantly improved the IoU, mIoU, and PA scores in every category with the comparison of FCN-8s. Similar to the results in VOC 2012 dataset, our method is better than DeepLab-v2 in most categories (except for pole, traffic light, and bicycle). The detailed improvement statistics are shown in Figure 15.  
As shown in Figure 15, our method can get a significant improvement compared with FCN-8s, which reach up to 9.3% in mIoU and 9.1% in PA, respectively. Moreover, Figure 15 intuitively shows the improvement level using our method with the comparison of FCN-8s and DeepLab-v2. The improvement can be clearly observed in 100% (19/19) of categories when compared with FCN-8s and 84.21% (16/19) of categories when compared with DeepLab-v2, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FCN-8s** & **DPN [40]** & **CRF-RNN [20]** & **DeepLab-v2** & **Our Method** \\ \hline traffic sign & 52.3 & 47.4 & **59.7** & 58.8 & 59.3 \\ vegetation & 87.3 & 88.4 & 90.6 & 91.6 & **93.5** \\ terrain & 52.1 & 63.1 & **66.1** & 59.6 & 61.6 \\ sky & 87.6 & 93.9 & 93.5 & 89.3 & **94.2** \\ person & 61.7 & 64.7 & 70.4 & 75.8 & **79.3** \\ rider & 32.9 & 38.7 & 34.7 & 41.2 & **43.9** \\ car & 86.6 & 88.8 & 90.1 & 90.1 & **94.1** \\ truck & 36.0 & 48.0 & 39.2 & 46.7 & **53.4** \\ bus & 50.8 & 56.4 & 57.5 & 60.0 & **66.0** \\ train & 35.4 & 49.4 & **55.4** & 47.0 & 51.8 \\ motorcycle & 34.7 & 38.3 & 43.9 & 46.2 & **47.3** \\ bicycle & 60.6 & 50.0 & 54.6 & **71.9** & 70.4 \\ mIoU & 56.1 & 59.1 & 62.5 & 63.1 & **65.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 14: IoU, mIoU, and PA scores on the Cityscapes dataset.
# NewSQL Databases Assessment: CockroachDB, MariaDB Xpand, and VoltDB
## 4 NewSQL Databases: CockroachDB, MariaDB Xpand, and VoltDB
### Summary of NewSQL Databases Characteristics  
This section presents a summary of the main characteristics of CockroachDB, MariaDB Xpand, and VoltDB.  
In Table 1, regarding partitioning, each solution uses its own method, whether it is by range or sharding. These methods are present in many NewSQL solutions and support several replication methods. The three solutions have multiple parallel transactions, which ensures that every single database is synchronized.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CockroachDB** & **MariaDB Xpand** & **VoltDB** \\ \hline Developer & Cockroach Labs & Cluxtrix Inc. & VoltDB Inc. \\ \hline Creation Date & 2015 & 2018 & 2010 \\ \hline License & Open Source & Commercial & Open Source \\ \hline Latest Release & v22.1.3, 11 July 2022 & v6.0.3, 10 March 2022 & v11.3, 8 February 2022 \\ \hline Initial Release & 2015 & 2020 & 2010 \\ \hline Data Model & Relational & Relational & Relational \\ \hline Server Operating Systems & Linux, macOS, and Windows & Linux, Windows, macOS & Linux, macOS \\ \hline Data Schema & Dynamic & Yes & Yes \\ \hline Partitioning method & Horizontal, by key range & Range & Sharding \\ \hline Replication Method & Multi-source using RAFT & Parallel Streaming & Source and Multi-Source Replication \\ \hline \hline \end{tabular}
\end{table}
Table 1: NewSQL databases summary.  
Moreover, VoltDB does not support Windows, and regarding supported languages, each database has a variety of programming languages that allows us to create applications to support each database. It is important to notice that all the latest releases are from 2022. Overall, each solution was designed to support large dataset volumes, without losing ACID properties.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 2 Related Works
### Data Augmentation for Mobile-Based Biometrics  
Data augmentation is a common approach to address the class-imbalance problem [29]. Typical augmentation methods for time series data are categorized into random transformation, pattern mixing, and generative model-based methods [32].  
The random transformation-based methods generate new time series using some transformation function on a minority class data. Representative methods are jittering, scaling, permutation, warping, rotation, cropping, etc., commonly used in mobile-based biometrics (Table 2). The authors of [3,7,15,16,17] applied jittering, scaling, permutation, etc. for motion sensor data.  
The pattern mixing-based methods produce a new time series by combining multiple time series. Interpolation and the SMOTE [33] are popular methods. The SMOTE interpolates a minority class sample and its \(k\)-nearest sample. [PERSON] et al. applied this method for touch screen and motion sensor data [13]. Our proposed random pattern mixing belongs to this category; however, it differs because existing methods combine class-constrained seen patterns, while ours does class-unconstrained random patterns.  
Several researchers attempted the generative model-based methods. For example, the authors in [11,27] utilized a GAN for hand-crafted touch screen data. However, this  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Sensor** & **Feature** & **Data Augmentation Methods** \\ \hline [PERSON] et al. [11] & Touch. \({}^{1}\) & Hand-crafted & GAN generator \\ SWIPEGAN [27] & Touch. \({}^{1}\), Acc. \({}^{2}\) & Hand-crafted & GAN generator \\ DAKOTA [13] & Touch. \({}^{1}\), Acc. \({}^{2}\), Gyro. \({}^{3}\), Mag. \({}^{4}\) & Hand-crafted & SMOTE \\ SensorAuth [15] & Acc. \({}^{2}\), Gyro. \({}^{3}\) & Hand-crafted & Jittering, scaling, permutation, sampling, cropping \\ [PERSON] et al. [16] & Acc. \({}^{2}\) & Raw & Jittering, scaling, warping, rotation \\ [PERSON] et al. [17] & Acc. \({}^{2}\), Gyro. \({}^{3}\) & Raw & Jittering, scaling, warping \\ [PERSON] et al. [3] & Acc. \({}^{2}\), Gyro. \({}^{3}\) & Raw & Jittering, scaling, permutation \\ CAuSe [7] & Acc. \({}^{2}\), Gyro. \({}^{3}\), Mag. \({}^{4}\) & Raw & Jittering, scaling, permutation, cropping, warping, rotation \\ \hline \end{tabular}  
\({}^{1}\) Touch screen, \({}^{2}\) accelerometer, \({}^{3}\) gyroscope, \({}^{4}\) magnetometer.  
\end{table}
Table 2: Data augmentation methods applied in mobile-based biometrics.  
approach demands a large set of training data from the minority class, which is unavailable in FSAD as we use only a few impostor data.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 5 Conclusion and Shortcomings
### Shortcomings  
In order to enhance the performance of IFNPMH in the future, it is necessary to explore the integration of physical mechanisms with IFNPMH. (2) Perform empirical investigations on authentic datasets to further substantiate the efficacy of IFNPMH in real-world scenarios; (3) Augment the network's capacity to generalize across diverse contexts.  
Conceptualization, S.W. and J.P. ([PERSON]); methodology, J.P. ([PERSON]); software, J.P. ([PERSON]); validation, B.Y. and J.P. ([PERSON]); formal analysis, J.P. ([PERSON]). and Y.L.; investigation, B.Y.; resources, H.L.; data curation, J.P. ([PERSON]); writing--original draft preparation, J.P. ([PERSON]); writing--review and editing, Z.Y.; visualization, S.W.; supervision, Z.Y.; project administration, H.L.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
The authors declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the State Key Project of National Natural Science Foundation of China-Key projects of joint fund for regional innovation and development (U22A20620; U21A20108), Double First Class Discipline Creation Project of Surveying and Mapping Science and Technology (GCCRC202402) and Department of Natural Resources of Henan Province Natural Resources Science and Technology Project ([PERSON] (2) 20190450-7).  
Not applicable.  
The data used in this study is available from the first author upon reasonable request.  
**Acknowledgments:** We would like to express our gratitude to the anonymous reviewers and their valuable feedback, as well as the editors and comments. Furthermore, all authors have read and agreed to the published version of the manuscript.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 6 Conclusions and Future Work  
In this article, an application of a control methodology that uses a multi-agent consensus theory was presented. The convergence of the states to both a fixed and a time-varying reference was demonstrated on a three-agents system with cyclic information flow topology. This methodology was applied in order to synchronize the speed of three DC motors. Through a robustness analysis of the uncertainty of the parameters of DC motors and the delays in communication between agents, the consensus was ensured. In addition, experimental results on a three DC motor system with a wireless communication system were presented. Most of the reviewed works presented just two motors. It was implemented by using a Bluetooth wireless agent communication system and an embedded micro-controller based control platform. It was noticed that the proposed control approach allows us to synchronize the multiple DC motor system. The small agent consensus error concerning the desired reference is due, besides the motor dynamics, to wireless communication systems, and the information flow topology.  
From the information above, we identify three different research lines that may be addressed as future work; the first is to perform an independent robustness analysis in each of the parameters of the _Laplacian matrix_ prioritizing the parameters that may present most variation. The second research line is to consider in the analysis of the time delay system a higher number of parameters with uncertainty since this paper only considers one of them. Regarding the latter, it is clear that the maximum time delay that the close loop system may withstand without losing the stability property will be smaller than the one obtained when considering only one parameter. Finally, another future work is to perform experimental testing considering different topologies of information exchange such as chain topology or balances graph topology, adapting the experimental platform for this purpose.  
Conceptualization, D.O., G.R., J.A.G., and R.L.; methodology, G.R. and J.A.G.; software, D.O., G.R., and J.A.G.; validation, D.O.; formal analysis, D.O., G.R., J.A.G., and R.L.; investigation, D.O., G.R., J.A.G., and R.L.; resources, D.O., G.R., J.A.G., and R.L.; writing--original draft preparation, D.O., G.R., and J.A.G.; writing--review and editing, D.O., G.R., and J.A.G.; visualization, D.O., G.R., and J.A.G.; supervision, G.R. and J.A.G.; project administration, G.R.; funding acquisition, D.O. and G.R. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported by Universidad Autonoma de Tamaulipas, under grants PROINNOVA-2018-250105, PROINNOVA-2018-250586, PROINNOVA-2018-250113, PROINNOVA-2018-250117, PROINNOVA-2018-250160, and PROINNOVA-2018-250255.  
[PERSON] appreciates the support and use of facilities of the Tecnologico Nacional de Mexico--Instituto Tecnologico de Nuevo Laredo.  
The authors declare no conflict of interest.