esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
In the process of data services, data compression and indexing algorithms play a crucial role in data processing and management. Data compression algorithms reduce storage costs and improve transmission efficiency to cope with the increasing volume of data. Indexing algorithms, on the other hand, play a key role in quickly retrieving and querying large-scale datasets, making data access more efficient and fast.  
However, the current field of data compression and indexing still faces some challenges. For example, in terms of data compression, traditional compression algorithms often cannot simultaneously guarantee high compression ratios and data accuracy. Error-bounded compression algorithms face a balance issue between compression efficiency and data precision. In terms of indexing algorithms, designing efficient index structures to support fast queries is a pressing problem as data volumes continue to increase.  
At the same time, existing compression algorithms and indexing technologies are designed independently and often do not fully consider service requirements, leading to suboptimal service effects. Therefore, this section will introduce some common data compression algorithms and tree-based indexing algorithms, and compare and analyze them.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.19 Cortex  
The main purpose of the tool is ML Model serving (as a web service) and Monitoring. It is built on top of Kubernetes and has the capability of autoscaling. It requires a lot of effort for setting up and currently supports AWS only (therefore, the tool can be seen as an open-source alternative to Amazon's Sagemaker). Cortex includes Prometheus [80] for metrics collection and Grafana [82] for visualization.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 1 Introduction
The UFI module employs a multi-disparity window scan-and-select method to expand the perceptual range of the initial disparity field, and improve the initial disparity accuracy. The MGA module propagates the adaptive masked reliable hidden state globally, thus introducing long-range dependencies to enhance the disparity refinement efficiency. The advantages of proposed MUIR are as follows:  
1. The proposed MUIR requires no additional mask supervision or dedicated training for uncertainty prediction. It can jointly predict disparity, matchability mask, and disparity uncertainty using only the disparity ground truth for single-stage joint training, without compromising disparity accuracy. 2. The MUD module is integrated into the iterative framework to decode the matchability mask and disparity uncertainty from hidden state at any iteration, ensuring the model's scaling flexibility. 3. The model with the UFI and MGA modules achieves more efficient disparity refinement per iteration, which is more important for refinement in regions with large disparity.
# An Oversampling Method for Class Imbalance Problems on Large Datasets
## 4 Experimental Results
### Synthetic Objects Generated by SMOTE, NDO and Fast-SMOTE  
This experiment applies SMOTE, NDO, and Fast-SMOTE over three real datasets to show the synthetic objects generated by each method.  
For this experiment, we used the mammography dataset that was also used in [54] for this purpose, and we included the glass6 and ecoli1 datasets from the KEEL repository [55]. For a better appreciation of the plotted objects, for this experiment, we selected only 10% of majority class objects and 10% of minority class objects. In this way, the IR of each dataset is preserved.  
To show the synthetic objects generated by each method, in Figures 1-3 we plotted, in a 2-D projections, the mammography, glass, and ecoli datasets respectively, together with 10 synthetic objects generated by each method. The mammography dataset was projected on the fourth and fifth features, the glass6 dataset on the \(aluminum\) and \(silicon\) features, and the ecoli1 dataset on the \(Mcg\) and \(AIm2\) features. In all three figures, green circles represent the objects of the majority class, and red crosses represent the objects of the minority class. Also, we show in Table 1 the mode values computed by the proposed method on each dataset (see rows) for axis \(x\) and \(y\) (see columns) for the Figures 1-3.  
Figure 1: Synthetic objects (blue diamonds) generated by SMOTE (**a**), NDO (**b**), and Fast-SMOTE (**c**) over a 2-D projection of the dataset Mammography. Green circles represent objects of the majority class, and red crosses represent objects of the minority class.  
Figure 2: Synthetic objects (blue diamonds) generated by SMOTE (**a**), NDO (**b**), and Fast-SMOTE (**c**) over a 2-D projection of the dataset Glass. Green circles represent objects of the majority class, and red crosses represent objects of the minority class.  
In Figures 1-3, we can see that Fast-SMOTE, as well as SMOTE and NDO generate synthetic objects close to the objects of the minority class.
## 2 Related Works
In order to identify cyber threats in smart Internet of Things (IoT) systems, including smart homes, smart e-healthcare systems, and smart cities, [PERSON] et al. [22] suggested a federated deep learning (DL) intrusion detection system utilizing GAN, named FEDGAN-IDS. In order to train the GAN network using augmented local data and serve as a classifier, they distributed it among IoT devices. They demonstrated that their model performed better and converged earlier than the most standalone IDS by comparing the model's convergence and accuracy. [PERSON] et al. [23] described a framework based on FL for detecting cyberattacks in vehicular sensor networks (VSNs). The proposed FL approach makes it possible to share computing resources and train with devices. For better performance in attack detection, a Random Forest (RF)-based ensemble unit is used in the suggested approach in conjunction with a group of Gated Recurrent Units (GRU). [PERSON] et al. [24] highlighted that vehicle IoT devices feature sensors that produce device-specific information that can affect device security if leaked. GPS, cameras, radar, etc., must be shared in cooperative driving. The authors proposed using FL to improve system security and performance by integrating massive IoT networks with various devices. [PERSON] et al. [25] proposed a new intrusion and virus detection system to secure the healthcare system's whole network. The proposed approach consists of two parts: an IDS for medical devices installed on the healthcare network and a malware detection system for data servers and medical staff devices. The goal was to protect the entire network, regardless of the devices and computers installed. The limitation is that it demands the installation of numerous systems across the healthcare network. Additionally, a correlation procedure is needed to compile the outcomes. We have summarized the key contributions of the similar works in Table 1. As can be seen, we have included the publication year, experiment dataset, used classifiers/algorithms and key findings in the table. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Reference** & **Dataset** & **Classifier/Algorithm/Framework** & **Findings** \\ \hline \hline \multirow{7}{*}{2017} & \multirow{7}{*}{[12]} & & & This paper proposed a system that detects anomalies in IoT networks. Dimension reduction was performed with the help of principal component analysis to prevent the performance of fault diagnostics from declining and to stop real-time demand threats from complex computations. This article lacked examples of how to calculate the memory size. \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related works.
## 6 Experimental Verification
### Learning Phase  
The data set exploited to train the proposed structure was created by collecting real-time data from the actual work of the MLS system. The data included a reference signal that determined the desired position of the levitation object in each time step and correlated control signal that the default LQ controller generated. The sample time of exploited signals was pre-defined by the manufacturer of the system; it was 0.01 s by default, and empirically was determined that 10,000 samples per variable (100 s of measurements) would provideoptimal results. Following Figure 3, a minimal number of inputs was two, a specified input signal was \(Y(t)\), and a delayed version of the specified input signal was \(Y(t-n)\). Following that rule, for building the dataset for this research, \(Y(t)\) represented the position signal, while for the delayed signal \(Y(t-n)\) used delayed signal \(Y(t)\) with a delay equal to 1, or \(Y(t-1)\) was formed. The recorded control signal of the default LQ control logic was used as the training output of the neural network. After building the set of training data for performing training processes of the OEI controller, DNN, and OENN + AOG networks, the training set was split in the following manner: 70% of the data were used for the training, 15% for validation purposes, and 15% for test cases. The test set was used to evaluate the performance of the trained model, while the validation set was used to follow the convergence of the error signal toward zero. If the validation error was equal in six consequent iterations, the network could not improve further, and the training stopped.
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
## 3 Results
### Fungal Growth Dynamics on Plastic Substrates
#### 3.3.3 Comparison of Fungal Behavior at 20 \({}^{\circ}\)C
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{3}{c}{**Weathered Samples**} & \multicolumn{2}{c}{**Unweathered Samples**} & \multicolumn{2}{c}{**Control Samples**} \\ \hline \multirow{2}{*}{**Sample (25 \({}^{\circ}\)C)**} & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\  & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** \\ \hline
1 & 43.88 \(\pm\) 6.87 & 754.65 \(\pm\) 188.26 & 25.63 \(\pm\) 5.04 & n.a. & 42.3 \(\pm\) 24.32 & n.a. \\
2 & 64.01 \(\pm\) 15.76 & 1640.63 \(\pm\) 1044.26 & 23.79 \(\pm\) 7.49 & 781.98 \(\pm\) 214.36 & 42.3 \(\pm\) 24.32 & 587.02 \(\pm\) 313.9 \\
3 & 47.88 \(\pm\) 8.18 & 1344.78 \(\pm\) 723.93 & 35.55 \(\pm\) 19.23 & 598.39 \(\pm\) 352.26 & 54.21 \(\pm\) 32.73 & 1538.96 \(\pm\) 949.21 \\
4 & 47.88 \(\pm\) 8.18 & 1344.78 \(\pm\) 723.93 & 34.49 \(\pm\) 17.11 & 1039.12 \(\pm\) 728.00 & 53.86 \(\pm\) 31.24 & 1628.69 \(\pm\) 899.63 \\
5 & 72.19 \(\pm\) 27.19 & 1437.97 \(\pm\) 260.99 & 38.17 \(\pm\) 17.47 & 814.66 \(\pm\) 737.94 & 55.14 \(\pm\) 13.24 & 1268.75 \(\pm\) 126.77 \\
6 & 74.19 \(\pm\) 25.08 & 1437.97 \(\pm\) 260.99 & 45.24 \(\pm\) 26.37 & 870.22 \(\pm\) 984.13 & 49.15 \(\pm\) 23.26 & 1229.19 \(\pm\) 174.54 \\  & Colony area & Halo area & Colony area & Halo area & Colony area & Halo area \\ \hline \end{tabular}
\end{table} 
Table 3: Total colony and halo areas on samples of weathered HDPE gray plastic contaminated with diesel fuel (weathered samples), unweathered white HDPE plastic contaminated with diesel fuel (unweathered samples) and unweathered and uncontaminated gray HDPE plastic samples (control samples), incubated at 25 \({}^{\circ}\)C.
# The LHAASO PeVatron Bright Sky: What We Learned
## 3 Overview of the 12 LHAASO Sources
### LHAASO J2018+3651  
LHAASO J2018+3651 has a maximum energy of about 270 TeV, and it is one of the three LHAASO UHE detections located in the Cygnus region (the others are LHAASO J2032+4102 and LHAASO J2108+5157), and it has two potential TeV candidates: VER J2016+371 and VER J2019+368 [43; 131].  
These two VERITAS sources are both associated with MGRO J2019+37, an extended \(\gamma\)-ray emission detected the first time by the Milagro observatory [185]. This region, one of the brightest of the Cygnus region, overlaps many different sources, but the PWN of the PSR J2021+3651 together with the star-forming region Sharpless 104 (Sh 2-104) were suggested as the main contributors [186; 187] (see Figure 13, left). Later, VERITAS resolved this TeV region into the two different source candidates for the LHAASO J2018+3651 counterpart, even if the extension of the LHAASO emission cannot be resolved with the same angular resolution.  
The first candidate, VER J2016+371, is an unidentified point-like source and spatially coincident with part of MGRO J2019+37 [188] and also confirmed by HAWC (HAWC J2016+371) [189]. The most likely counterpart is the PWN in the SNR CTB 87, both because of the co-location of VHE and X-ray emissions and its luminosity in the two bands [189].  
Figure 12: LHAASO J1956+2845. [Figure from [184]] Fermi-LAT off-pulse TS map for the region of PSR J1954+2836 for 0.5–500 GeV, where all the catalog sources have been removed. The cyan contour is the approximate radio shape of the SNR G65.1+0.6.  
The other candidate, VER J2019+368, is a bright extended source (1 degree), with an energy dependent morphology, and it represents the bulk of the emission from MGRO J2019+37. Its emission was also confirmed by HAWC (HAWC J2019+368) [189], with a high integrated flux above 56 TeV. The morphological and spectral features in both the TeV and X-ray bands point towards a PWN origin for this VHE/UHE emission [189]; particularly from the Dragonfly PWN (G75.2+0.1) powered by the PSR J2021+3561. This hypothesis was supported by a recent multiwavelength work from radio to X-ray to UHE \(\gamma\)-ray, also taking into account the LHAASO spectral point [190] (see Figure 13, right).  
Even if we cannot say if the LHAASO J2018+3651 UHE detection corresponds with VER J2016+371 or VER J2019+368, according to the models and considerations developed up to now, the contribution from the star-forming region Sh 2-104 seems not to be the dominant one.  
No neutrino constraints are present so far for this source.
## 3 Results and Discussion  
### Performance Decay Prediction of the DEA Mode  
In this section, the performance decay prediction of the PEM fuel cell in the DEA mode is evaluated. Firstly, the prediction performance of the ANN and M-ANN is compared to select the better modeling method. Then, the MPR method is employed as the benchmark model to demonstrate the necessity of ANN in modeling. Lastly, the effect of hidden layer numbers and activation functions on the prediction performance is discussed. We introduce two errors to evaluate the prediction performance, mean relative error \(\delta_{\text{mean}}\) and maximum relative error \(\delta_{\text{max}}\):  
\[\delta_{\text{mean}}=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|I_{i}-\hat{I}_{i} \right|}{I_{i}}\times 100\% \tag{12}\]  
\[\delta_{\text{max}}=\text{max}\Bigg{\{}\frac{\left|I_{i}-\hat{I}_{i}\right|}{ I_{i}}\Bigg{\}}\times 100\% \tag{13}\]  
where \(n\) is the number of data points; \(I_{i}\) (A\(\cdot\)cm\({}^{-2}\)) is the true value of current density; \(\hat{I}_{i}\) (A\(\cdot\)cm\({}^{-2}\)) is the predictive value of current density.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Train Set**} \\ \hline Current density, A\(\cdot\)cm\({}^{-2}\) & 1.6 & 1.5 & 0.4 & 1.2 & 1.2 & 1.1 \\ Anode stoichiometry & 1.3 & 1.5 & 2.0 & 1.5 & 2.4 & 1.8 \\ \hline  & **Train Set** & **Validation Set** & **Test Set** \\ \hline Current density, A\(\cdot\)cm\({}^{-2}\) & 0.8 & 1.0 & 0.8 & 1.3 & 1 & 0.6 \\ Anode stoichiometry & 1.2 & 3.0 & 2.4 & 1.4 & 1.6 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operating conditions of the twelve processes in the anode recirculation mode.
## 10 Research Challenges and Future Directions
### Data Privacy and Security  
Many annotation workflows involve sensitive information, including user data, proprietary content, and confidential documents. Transmitting this data to third-party LLM APIs introduces significant privacy vulnerabilities. Even when service providers enforce non-retention policies, which differ considerably across vendors, concerns remain about potential data leakage through model outputs. For example, an LLM is capable of inadvertently revealing elements of confidential input through its reasoning processes or becomingsusceptible to prompt-based extraction of sensitive information during later interactions involving similar content.  
Deploying models on-premises and using encrypted processing techniques are promising strategies for reducing privacy risks [170]. Techniques such as homomorphic encryption support computation on encrypted data but often result in significant performance trade-offs as demonstrated in visual AI applications [171]. In high-sensitivity environments, organizations find it justifiable to run LLMs locally within secure infrastructure. Other privacy safeguards include data anonymization and selective field masking before agent processing, although these measures occasionally alter the core annotation objective. Regulatory compliance and data governance frameworks, including HIPAA for healthcare data and GDPR for personal information in the EU, increasingly influence how annotation agents are deployed in enterprise systems. Future annotation architectures will prioritize secure environments controlled by the data owner or apply privacy-preserving transformations before processing.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### Visual Navigation Algorithms for Grasping Targets
#### 4.2.3 Visual Guidance Algorithm  
We propose a novel grasping method for aerial manipulators based on a visual guidance algorithm. This model utilizes a front-mounted depth camera D435 on a rotary-wing UAV to detect and recognize the positional information of the target object, enabling the adjustment of the aerial manipulator's pose angles. The implementation process of the visual guidance algorithm is depicted in Figure 8, and the specific steps are as follows:  
Step 1: Utilize the VideoCapture class in OpenCV to capture the video stream from the camera and read the images.  
Step 2: Apply Kalman filtering to denoise the captured image, reducing image distortion caused by UAV vibrations.  
Step 3: Process the denoised image by converting it to grayscale and performing binary thresholding. Utilize the Canny algorithm to obtain the edge information of the image.  
Step 4: Obtain the two-dimensional coordinates of the target object image and integrate the height data acquired from the No. 2 TFmini to obtain the camera position information. Subsequently perform coordinate transformation to obtain the target object's coordinate information, enabling real-time adjustment of the aerial manipulator's attitude.
## 3 Results and Discussion  
### Characteristics of the A-CF/EP/PEF Composites  
#### 3.1.1 Modification of the Carbon Fiber  
SEM images show that the pristine CF has a loose woven structure with gaps between fibers (Figure 2a,b), which provides the necessary space to form composites with EP. After chemical etching in the nitric acid solution (Figure 2c), the surface of CF became rough. This rough structure is conducive to enlarging the surface area and improving the bonding strength of resin with CF. On this foundation, after the APDMS modification of CF (A-CF, Figure 2d), the E-CF is wrapped in a thin wrinkled film, which will reinforce the strength between the inorganic CF and the organic EP [27]. In another word, the A-CF shall be very compatible with organics, such as EP.  
Figure 2: SEM images of pristine CF (**a,b**), E-CF (**c**), and A-CF (**d**).  
The FTIR spectra of CF, E-CF, and A-CF are shown in Figure 3. Obviously, the stretching vibration peaks of -OH and C=C appear in CF at about 3442 and 1630 cm\({}^{-1}\), respectively (Figure 3a). In addition to these two peaks, the spectrum of E-CF (Figure 3b) shows new peaks at 2920, 2850, and 1380 cm\({}^{-1}\) corresponding to the stretching vibration of -CH\({}_{2}\) and -CH\({}_{3}\), and an apparent peak at 1738 cm\({}^{-1}\) assigned to the C=O stretching vibration of ester groups (-COOR) that originates from nitrate-acid-etched CF [28]. The spectrum of A-CF (Figure 3c) shows a new peak of -CH at 2967 cm\({}^{-1}\) from APDMS, and the peaks of -CH\({}_{2}\) and -CH\({}_{3}\) at 2920 and 2850 are more intense than those of E-CF and A-CF. In addition, the sharp peaks at 1256 and 807 cm\({}^{-1}\) stand for Si-CH\({}_{3}\). The main broad band from about 880 to 1190 cm\({}^{-1}\) is attributed to Si-O-C, and the bands at 1096 and 1024 cm\({}^{-1}\) are assigned to Si-O-Si. All these results confirm the introduction of APDMS to the carbon fiber surface.
## 2 Results and Discussion
### Hydration of Other Alkynes  
Under the optimal reaction conditions, the scope and limitation of the hydration with SnCl\({}_{4}\cdot\)5H\({}_{2}\)O were investigated with using a series of terminal aromatic alkynes with different substituent groups on the aromatic ring, aliphatic alkynes, and internal alkyne as substrates, and the results are listed in Table 3.  
From Table 3, it can be seen that liquid aromatic alkynes were efficiently converted to the corresponding ketones. Electron rich aromatic alkynes (Table 3, entries 2-4) showed better conversion in comparison to alkyne that contain electron withdrawing group (Table 3, entry 5). When 4-fluorophenylacetylene was used as a substrate, the corresponding ketone was yield to 49%. The results in Table 3 also indicate that the steric hindrance of substituent on benzene had very little influence on the catalytic efficiency in the hydration of terminal aromatic alkynes, and a substituent on the \(m\)-position of benzene, with more steric hindrance, showed 98% yields for the corresponding product (Table 3, entry 2). To our delight, not only aromatic alkyne, but also aliphatic alkyne (Table 3, entry 6) was converted effectively to the corresponding aliphatic ketone in 81% yield.  
Hydration of internal alkyne was also investigated under the optimal reaction conditions. Unfortunately, the corresponding ketone was not found (Table 3, entry 7) in the reaction mixture.