esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Genericity: Quantitative Analysis
Moreover, there is a significant difference between Kubernetes on the one hand and Mesos and Marathon on the other hand:  
* Docker Swarm stand-alone and Aurora are indeed clearly less generic in terms of offered features than the other CO frameworks. After all, Aurora is specifically designed for running long-running jobs and cron jobs, while Docker Swarm stand-alone is also a more simplified framework with substantial less automated management. We only recommend Docker Swarm stand-alone as a possible starting point for developing one's own CO framework. This is a relevant direction because 28% of surveyed users in the most recent OpenStack survey [4], responded that they have built their own CO framework instead of using existing CO frameworks (see also Figure 4). We make such recommendation because the API of Docker Swarm stand-alone is the least restrictive  
Figure 6: The number of common feature implementation strategies supported by each CO framework is shown for each of the 7 CO frameworks. Figure 7: Resulting \(p\)-values of the Nemenyi multiple comparison test. For \(p\)-values <= 0.05, we can reject the null hypothesis, i.e., there is no significant difference in overall ranking between a pair of CO frameworks). in terms of the range of offered options for common commands such as creating, updating and stopping a container. For example, Docker Swarm stand-alone is the only framework that allows to dynamically change resource limits without restarting containers. Such less restrictive API is a more flexible starting point for implementing a custom developed CO framework. * The significant difference between Kubernetes and Mesos can be partially explained by the fact that Mesos by itself is not a complete CO framework as Mesos enables fine-grained sharing of resources across different CO frameworks such as Marathon, Aurora and DC/OS. * The significant difference between Kubernetes and Marathon can be explained by the fact that very few new features have been added to Marathon since the start of DC/OS. After all, DC/OS is the extended Mesos+Marathon distribution that has also an enterprise edition. An important conclusion is that there are no significant differences between the three most generic CO frameworks: Docker Swarm integrated mode, Kubernetes and DC/OS. However, for 13 sub-aspects, a specific CO framework distinguishes itself by offering the most common features in that sub-aspect. In particular, Kubernetes, Docker Swarm integrated mode, DC/OS and Mesos are the most distinguishing frameworks:
## 2 Materials and Methods
### Sampling and Analytical Procedure  
Agricultural soil samples were collected during February 2010 to April 2011 from the surface layer (15-20 cm) of soil with a stainless steel Ekman Grab Sampler from twenty (20) different locations adjacent to DEPZ (Figure 1) in two different seasons--dry and wet season. The sampler was inspected for possible cross-contamination and cleaned with ambient water for individual sample collection. The difference from one sampling point to another was approximately 100 m. About 250-300 g of the soil was sampled from the central part of the grab sampler by avoiding contact with the inside materials of the sampler and transferred to a pre-cleaned plastic container. Collected soil samples were air dried for several days over pre-cleaned Pyrex petry dishes. Then 2-3 g dry soil samples were digested in about 15 mL of aqua-regia (HCL:HNO3 = 3:1) for approximately 4-5 hours using a hotplate maintaining a heating temperature of approximately 110 \({}^{\circ}\)C. The samples were next placed in a 100 mL Pyrex glass beaker and diluted with distilled water up to 50 mL. The solution was filtered and the filtrates were analyzed in the Analytical Research Division, BCSIR Laboratories, Dhaka by Atomic Absorption Spectrophotometer (AA-6401 F, Shimadzu, Japan). The working standard solutions for each metal were prepared before every analysis. The analytical procedures were verified with National Institute of Standards and Technology (NIST) traceable certified reference standards. Concentrations of Fe, Mn, Cu, Ni, Zn, Cr and Cd were measured by an air acetylene flame AAS, with As determined by hydride vapor generation AAS, Hg by hydride generation with cold vapor AAS and Pb by graphite furnace AAS.
## 2 Spacecraft Attitude Dynamics, Determination and Control
This assumption is motivated by the slow rotation of the Sun, as seen from the ECL, when compared with the characteristic time of the rotational dynamics of the spacecraft. The PD control structure is motivated since the proposed algorithms are inherently proportional to the Sun angle, with derivative terms given by the angular rates. Being the proportional term direction not completely known in three-dimensional space, the steady-state error cannot be asymptotically eliminated. Thus, the integrator term cannot be directly included in the present control structure. Moreover, the presence of the integrator term would reduce the stability of the closed-loop system.
## 5 Experimental Validation
### TUM-VI Benchmark Dataset  
The TUM-VI Benchmark dataset was captured by a handheld fisheye stereo Kinect camera, which contains hallway, lobby, outdoor, indoor, and slide scenes. The entirety of the room sequence carries a true value of motion capture and the background of the proposed method is an indoor environment, so the room1 to room6 sequences were selected for testing in this experiment. The complexity of the scene is also increased by selecting the slide1 sequence of the slide scene, for which the state estimates are computed from the truth values of the start and end phases.  
In the section, the proposed method was compared with VINS-Mono and UV-SLAM. The ATE values of each algorithm were calculated separately as shown in Table 5. UV-SLAM utilizes both point and structured line features, but due to the lack of unstructured line constraints in the irregular Manhattan scene, the algorithm induces a large directional drift with an average RMSE of 0.157. As can be seen from the comparison results in Table 5, the proposed method achieves the minimum translation error, and the trajectory accuracy is improved by 42.80% and 8.65%, respectively.  
Figure 9 plots the local detail comparison trajectories for room2 and room5 where the three systems are able to extract a maximum of 150 point features and 30 line features from the input image frame. The narrow and violently rotating indoor sequence scene poses a greater challenge to the stability of the algorithm, but the figure visualizes that the two sequences have less fluctuation and lower cumulative drift compared with the two representative VIO systems, VINS-Mono and UV-SLAM.  
Figure 10 shows the APE evolution plots of VINS-Mono proposed on room1_512_16, room3_512_16, and room5_512_16 sequences with respect to Table 2. Each set of exper  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **VINS-Mono** & **UV-SLAM** & **Proposed** \\ \hline room1\_512\_16 & 0.094 & 0.079 & **0.071**1 \\ room2\_512\_16 & 0.069 & 0.068 & **0.067** \\ room3\_512\_16 & **0.248** & 0.127 & 0.121 \\ room4\_512\_16 & 0.041 & 0.042 & **0.042** \\ room5\_512\_16 & 0.340 & 0.216 & **0.210** \\ room6\_512\_16 & 0.071 & 0.069 & 0.070 \\ slide1\_512\_16 & 0.583 & 0.495 & **0.426** \\ Average & 0.206 & 0.157 & **0.144** \\ Enhance & 42.80\% & 8.65\% & — \\ \hline \hline \end{tabular} \({}^{\dagger}\) Values in bold indicate the closest true value.  
\end{table}
Table 5: RMSE APE (meter) of VINS-MONO, UV-SLAM, and the proposed method on the TUM-VI Benchmark dataset.  
Figure 9: Comparative paths with respect to room2_512_16 (**left**) and room5_512_16 (**right**) (blue dashed lines: the proposed method; green solid lines: UV\(-\)SLAM; red solid lines: VINS\(-\)Mono; and red thick dashed lines: ground truth).  
iments has a smaller error result for the proposed method. From the above results, it can be concluded that the proposed method effectively improves the performance of motion estimation.
## 3 Results and Discussion
### Hydroxymethylofurfural Content  
The confitures prepared in the vacuum evaporator compared to the ones made at normal pressure in the thermomix had significantly lower HMF content directly after production, as well as after 30, 120 and 180 days of cold storage (Table 2). This is undoubtedly an advantage of the vacuum method and confirms the relevance of the research conducted in the context of obtaining a better-quality product. The crucial factor for such significant discrepancies in the content of this compound was the level of temperature applied during a similar heating time. Right after production, the HMF content in our confitures from the vacuum evaporator and thermomix was 0.14 and 0.55 mg/100 g, respectively. [PERSON] et al. [13] noted, in strawberry jams cooked in vacuum and atmospheric conditions, the HMF quantity ranges of 1.44-2.14 mg and 4.94-12.90 mg in 100 g, respectively. In another study, nine times more HMF was found in jujube jam prepared using an open-pan evaporation method than by vacuum evaporation [14].  
During the 180 days of storage, HMF content increased slightly to 0.17 and 0.67 mg/100 g in the evaporator and thermomix confitures, respectively. In addition, similar dynamics of changes in HFM content were observed between the different storage periods: 0-30 days, 30-120 days and 120-180 days. Other researchers also report increased HMF levels in fruit preserves stored under various conditions. In some cases, the increase in HMF in the product was significantly higher than that recorded by us. A 120-day cold storage of bitter orange jam and orange jam resulted in a 32.9% and 112.7% increase in HMF content [30], while our blue honeysuckle berry confitures showed only 14.5% (thermomix) and 14.3% (evaporator) increases over the same period of time. [PERSON] and [PERSON] [31] observed an increased content of HMF during 30-day storage of orange jam. Its levels increased by 144% and 169% during storage at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C, which is about a 20-fold change compared to our own study. This indicates that not only the temperature of the processing, but also the temperature of the storage can significantly affect this HMF quantity in fruit preserves.
# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 4 Experiment Setup
### Neural Network Models
#### 4.2.5 BERT Model  
BERT [14] is a novel transformer approach that has achieved state-of-the-art results on various NLP tasks. Therefore, we aim in this work to evaluate how the BERT pre-trained model performed in the Arabic hate speech detection task. BERT was released in multilingual versions, pre-trained on monolingual corpora (Wikipedia) in 104 languages including Arabic. In this experiment, we fine-tuned the cased version of the multilingual pre-trained model by adding a simple classification layer that performs a binary classification to classify the tweets into hate or non-hate. To prepare our data for BERT, we examined 2 different preprocessing procedures. The first one followed the same steps described in Section 5. The second one is a lite version of the first one in which stop words, punctuation, and non-Arabic words are kept. The lite preprocessing showed a better result in terms of F1 score on fivefold cross validation on the training set. The next step was to prepare our pre-processed data to be in BERT format. To do so, we added a special token to mark the beginning (CLS) and separation/end (SEP) of each tweet. Next, we tokenized our inputs into tokens that corresponded to BERT's vocabulary. This input was fed into BERT and then into the final classification layer, where all of the parameters were fine-tuned end-to-end using our labeled data.  
Figure 3: CNN + GRU architecture.
# Remote Sensing for Irrigation of Horticultural Crops
###### Abstract  
This paper reviews the literature on applications of remote sensing for monitoring soil- and crop- water status for irrigation purposes. The review is organized into two main sections: (1) sensors and platforms applied to irrigation studies and (2) remote sensing approaches for precision irrigation to estimate crop water status, evapotranspiration, infrared thermography, soil and crop characteristics methods. Recent literature reports several remote sensing (RS) approaches to monitor crop water status in the cultivated environment. Establishing the right amount of water to supply for different irrigation strategies (maximization of yield or water use efficiency (WUE)) for a large number of crops is a problem that remains unresolved. For each crop, it will be necessary to create a stronger connection between crop-water status and crop yield.  
crop water status; water use efficiency (WUE); platforms; sensors; evapotranspiration (ET); Crop water stress index (CWSI); time-temperature threshold (TTT); vegetation indices (VIs) +
Footnote †: journal: _Horticultural_
## 2 Experimental Procedures
### Current Efficiency Measurement  
In ECM, an oxygen evolution might simultaneously occur as a side reaction in parallel with the anodic metal dissolution, due to the formation of a passive film. The efficiency for anodic dissolution can reflect the localization effect for materials in ECM. Generally, large current efficiency can be obtained in a high-current-density region, leading to efficient anodic dissolution. However, to avoid - stray corrosion, small current efficiency is expected at low current density. Thus, the current efficiencies at different current densities are essential to reflect the localization effect for anodic dissolution in ECM.  
The current efficiencies for Inconel 718 at different current densities were measured on the basis of weight-loss measurements. According to Faraday's Law, the theoretical weight loss \(M_{theo}\) can be calculated as follows:  
\[M_{theo}=\frac{It}{F(\frac{n_{1}}{A_{1}}a_{1}+\frac{n_{2}}{A_{2}}a_{2}+\cdots+ \frac{n_{j}}{A_{j}}a_{j})} \tag{1}\]  
where \(I\) is the current value, \(t\) is the dissolution time, \(F\) is the Faraday constant, \(A_{j}\) is the atomic weight of the dissolving atoms type, and \(n_{j}\) is the valence of dissolution. The valences of the metallic elements are assumed to be Ni\({}^{2+}\), Fe\({}^{2+}\), Cr\({}^{3+}\), Nb\({}^{3+}\), Ti\({}^{4+}\), Mo\({}^{4+}\) and Al\({}^{3+}\).  
A specific flow channel was designed [25], and the machining current and time were precisely controlled. To obtain the experimental weight loss \(M_{exp}\) accurately, the specimen was rinsed, dried and weighed carefully before and after each experiment. The current efficiency \(\eta\) in each run can be determined to be:  
\[\eta=\frac{M_{theo}}{M_{exp}} \tag{2}\]
## 4 Experimental Setup
### Experiments Results
#### 4.1.7 Real-World Case Study  
In order to verify the ability of SA-MPCMOEA to solve practical application problems, we conduct verification respectively in RW1 (Pressure Vessel Design), RW2 (Simply Supported I-beam Design), RW3 (Cantilever Beam Design), RW4 (Crash Energy Management for High-speed Trains), and RW5 (Water Resources Management). The mathematical expressions of these five engineering design problems can be found in [35]. In each problem, it is necessary to find the minimum value of different objective functions.  
From Table 8, it can be seen that SA-MPCMOEA has achieved the optimal solution or near-optimal solution in all practical engineering problems, demonstrating its applicability in solving practical engineering problems.
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 5 Conclusions  
In this paper, a radar systematic error correction method based on cooperative targets is studied, and the measurement error model of the radar is established. In order to solve the problem of low accuracy of the radar systematic error estimation, an improved sparrow search algorithm is proposed to solve the systematic error, so as to avoid the accuracy loss caused by the linearization of nonlinear equations. At the same time, a variety of strategies are used to improve the sparrow search algorithm, which greatly improves the convergence speed, real-time performance, and stability of the algorithm, while taking into account the solving accuracy. The simulation results show that the method can significantly reduce the influence of systematic error on the radar measurement, and the target track accuracy is improved by 83%, 77%, and 78% in the triaxial directions, which verifies the effectiveness of the proposed method.  
The method proposed in this paper is based on the assumption that the detection accuracy of different targets is consistent with that of the same radar system, but, in fact, the radar system displays slightly different processing of detection echoes of different targets. Some characteristics of targets (such as extremely strong radar cross-sectional area changes, special scattering characteristics, etc.) often require unconventional processing through the radar system, thus introducing additional errors, which then causes the radar to detect different targets with inconsistent accuracy. In the future, this error should be analyzed to improve the radar measurement error model, and a more universal error correction method should be studied.  
Conceptualization, Y.L. and B.F.; methodology, Y.L.; software, Y.L.; validation, Y.L., Z.S. and H.X.; formal analysis, Y.L.; investigation, Y.L.; resources, Z.S.; data curation, B.F.; writing--original draft preparation, Y.L.; writing--review and editing, Z.S.; visualization, H.X.; supervision, Z.S.; project administration, B.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors would like to thank all the reviewers for their constructive comments. The program for the simulation of the proposed algorithm in this article was written with the MATLAB 2021b produced by Math-Works, Inc.  
The authors declare no conflicts of interest.
## 1 Introduction
These findings align with the theoretical foundations of Agile and MBSE, which emphasize iterative development cycles, continuous stakeholder engagement, and adaptive planning. These practices collectively contribute to the observed improvements in development speed, cost efficiency, and innovative capacity. The requirements for the 3 ColStar satellite mission changed dynamically throughout its development, reflecting its iterative and flexible nature and the need for an iterative approach such as the Arcadia methodology. This strategy enabled our team to respond quickly to evolving technological, operational, and stakeholder requirements. For example, the initial technical specifications were modified through subsystem tests and simulations, resulting in changes to payload configurations and communication protocols. The adaptability provided by the Arcadia technique was critical in successfully accepting these adjustments. By encouraging regular contact and collaboration among geographically distributed team members, Arcadia facilitated streamlined workflows and faster decision-making. This allowed us to maintain technical accuracy while reducing errors throughout mission development. In comparison, without an Agile methodology like Arcadia [11], the iterative nature of requirement changes and their implications for design, integration, and validation phases would have been significantly more difficult to manage within the recommended project planning durations outlined by the European Cooperation for Space Standardization (ECSS) [43], NASA [44], and JAXA [45] standards. The capacity to respond swiftly to changing needs highlights the value of Agile techniques in optimizing resource allocation and enhancing overall project outcomes in small satellite missions. This article is organized as follows: Section 2 provides a description of the \({}^{3}\)ColStar KiboCUBE Colombia mission and the Concept of Operations. Section 3 contains the concepts of MBSE, Arcadia Method, and Capella software (version 6.1). Section 4 explains the \({}^{3}\)ColStar KiboCUBE Systems Engineering Structure by applying the Agile methodology with Arcadia Method and Capella modeling tool. Finally, Sections 5 and 6 summarize the discussion and conclusions. This proposal has been pre-selected as a finalist for the 8\({}^{th}\) KiboCUBE round [46].
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Machine Learning and Deep Learning-Based Techniques
#### 3.7.5 Accuracy Measures  
The efficacy of the MLC and SVM classifiers was assessed using the kappa coefficient, the producer accuracy (PA), and the user accuracy (UA), accuracies of the confusion matrix [84]. The ratio of accurate pixels to all of the pixels in the error matrix is known as the overall accuracy (OA), or pixels associated with class Y but mistakenly recognized as class Y. When pixels are categorized as class Y but are not connected to class Y, the UA provides commission errors as a sign of the algorithm's reliability. The PA additionally includes the number of times an algorithm incorrectly labels a pixel as Y and omission mistakes for specific classes. Using random classification analyses to account for the possibility that agreements could emerge by accident, the kappa coefficient is a statistical indicator of how well a classified map agrees with reference data [85]. The kappa coefficient ranges from 0 to 1, with values closer to 0 indicating more categorization uncertainty and values closer to 1 indicating less. In order to verify the accuracy of the findings produced by these ML algorithms, fieldwork for ground truth observations was also conducted in the research region.
###### Abstract  
The detection of tomatoes is of vital importance for enhancing production efficiency, with image recognition-based tomato detection methods being the primary approach. However, these methods face challenges such as the difficulty in extracting small targets, low detection accuracy, and slow processing speeds. Therefore, this paper proposes an improved RT-DETR-Tomato model for efficient tomato detection under complex environmental conditions. The model mainly consists of a Swin Transformer block, a BiFormer module, path merging, multi-scale convolutional layers, and fully connected layers. In this proposed model, Swin Transformer is chosen as the new backbone network to replace ResNet50 because of its superior ability to capture broader global dependency relationships and contextual information. Meanwhile, a lightweight BiFormer block is adopted in Swin Transformer to reduce computational complexity through content-aware flexible computation allocation. Experimental results show that the average accuracy of the final RT-DETR-Tomato model is greatly improved compared to the original model, and the model training time is greatly reduced, demonstrating better environmental adaptability. In the future, the RT-DETR-Tomato model can be integrated with intelligent patrol and picking robots, enabling precise identification of crops and ensuring the safety of crops and the smooth progress of agricultural production.  
tomato target detection; RT-DETR; Swin Transformer; attention mechanism; feature fusion; small target detection; computer vision +
Footnote †: journal: Applied sciences