esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 2 Materials and Methods
### Flight Collections  
An airborne demonstration of CMIS was conducted in January-February 2021 on the NASA Langley Research Center (LaRC) Gulfstream-III with four round-robin flightsout of LaRC. The first flight on 22 January 2021 was an 'engineering flight' designed to demonstrate that the airborne instrument setup collected and successfully stored data. The objective of the engineering flight was to verify that the flight procedure allowed sufficient time for the instrument to reach its operating temperature of 150 K and that the various instrument commands were executed correctly by the support equipment and software on aircraft. The flight plan for the engineering flight was chosen to cover relatively long, linear ground features to allow the calculation of a high fidelity (pixel-level) lens distortion model. Another objective of this flight was to collect measurements from both cold and warm targets to verify that the instrument meets its designed dynamic range. All objectives of the engineering flight were achieved.  
Three science flights were conducted on 27 January, 29 January, and 8 February as shown in Figure 5. The case studies and aircraft holds analyzed for this effort are depicted with arrows. Each hold consists of a racetrack flight pattern intended to cover the scene with two passes (e.g., Hold D1 and D2, or collectively Hold D). The red line segments and boxes show the overpasses of satellites used for validation. Selected cases were limited to periods with stable flight profiles without significant yawing maneuvers or turbulence.  
Each flight was designed to collect on cloud features during specific underflights of earth-observing satellites. For example, the purpose of Science Flight #1 was to collect on daytime marine and land stratocumulus and to underfly CALIPSO and MODIS.  
As discussed below, the capability to produce AMVs and CTHs for the free troposphere and PBL from CMIS data is demonstrated with these aircraft flights. The flights successfully achieved their primary objectives, which were to show that the sensitivity of the focal plane array and the quality of the images met requirements specified for the instrument (Table 3).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Criteria** & **Requirement** \\ \hline Multi-spectral SWIR/MWIR & 2.25, 3.75, 4.05 \(\upmu\)m \\ Field of view & \(\geq\)40\({}^{\circ}\) \\ Ground sample distance & \(\leq\)1 km \\ Distortion & \(<\)10\({}^{\circ}\), software-correctable \\ Multi-angle & \(\geq\)20\({}^{\circ}\) \\ Sensitivity & NEdT \(\leq\) 1 K at 270 K, 230 K for 3.75, 4.05 \(\upmu\)m \\ SNR \(>\) 100 for 2.25 \(\upmu\)m \\ Size, weight, and power & 6-U CubeSat compatible \\ \hline \hline \end{tabular}
\end{table}
Table 3: Instrument specification.  
Figure 5: Depictions of the flight paths and analysis regions for (**a**) Science Flight #1, (**b**) Science Flight #2, and (**c**) Science Flight #3. The red boxes and lines denote EOS satellite overpasses within approximately 15 min of the CMS aircraft flight.
# Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections
## 2 Materials and Methods
### Approach for Deriving Evaluation Indices  
The average delay and CO emissions at an intersection are chosen as the two evaluation indexes here. The VISSIM can output the average delay [34], travel time, velocity, and acceleration data. Then, based on the relationship between vehicle specific power (VSP) and acceleration and velocity v, following [35], the vehicle specific power (VSP) can be calculated. In addition, for the vehicle type of emission calculation, the traffic flow is divided into two categories: light vehicles and heavy vehicles. Light vehicles account for 70% of the total, and heavy vehicles account for 30% of the total. The speed of the light vehicle is set to 50 km/h, and the speed of the heavy vehicle is set to 40 km/h. In addition to the settings mentioned in the article, other settings use the default values in the VISSIM4.3 software.  
\[\text{VSP}=\text{v}(1.1\text{a}+0.132)+0.000302\text{v}^{3} \tag{1}\]  
After obtaining the VSP value of each vehicle in each lane, each vehicle should be distributed to a VSP interval according to its VSP value, and then the proportion of the vehicles in each interval to the total number of vehicles in each lane can be calculated. Finally, the CO emission rate of each VSP interval during the simulation period can be calculated by multiplying the CO emission rate of each vehicle shown in Table 1 and the number of vehicles in each VSP interval. The sum of the CO emission rates of all VSP intervals can obtain the total CO emissions of all vehicles per unit time. Then, the total CO emissions of all vehicles during the simulation period can be obtained by multiplying the total CO emissions of all vehicles per unit time and the total travel time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **VSP Interval** & **CO Emission Rate** \\ \hline
1 & (\(-\infty_{\text{v}}\)\(-\)2) & 0.0110 \\
2 & [\(-\)2,0) & 0.0087 \\
3 & [0,1) & 0.0047 \\
4 & [1,4) & 0.0122 \\
5 & [4,7) & 0.0167 \\
6 & [7,10) & 0.0233 \\
7 & [10,13) & 0.0293 \\
8 & [13,16) & 0.0369 \\
9 & [16,19) & 0.0495 \\
10 & [19,23) & 0.0638 \\
11 & [23,28) & 0.1054 \\
12 & [28,33) & 0.2478 \\
13 & [33,39) & 0.4131 \\
14 & [39,+\infty) & 0.6247 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The emission rate of pollutant CO corresponding to each interval (g/s).  
Figure 4: The simulation models for left turn at an intersection. (**a**) U-turn; (**b**) direct left turn.
## 7 Discussion
### Limitations  
Our framework has been tested with a single prototype in only one indoor environment. While this provided initial insights into the conceptual framework and validation of the key technologies, the results may not generalize across different types of indoor settings, such as shopping malls, university buildings, and hospitals, where space structures and user behaviors can vary.  
The current stage of our research did not include extensive usability tests with actual users, which limited our understanding of the effectiveness of the proposed geofencing design and calibration strategy and overall user satisfaction. The prototype was not subjected to public experimentation, which would provide a more robust validation of the system's effectiveness, reliability, and acceptance in the real world by different user groups, especially those who are not familiar with AR technologies.  
This study has not focused extensively on the methodologies for data acquisition and organization, which are critical for scaling the application to other venues and for further development. The registration of reference location markers, placement and tuning of geofences, and association of media content to the navigation targets can be labor-intensive. Effective data acquisition and efficient data organization can facilitate the system's scalability and maintenance.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 7 Simulations
### Impact of Frame Length  
We maintain the same scenario as in previous sections, to analyze the impact of end-to-end delays for various frame lengths. The only variation between simulations is the frame length, which is in the range of 11 to 14 slots. Figure 11 shows the empirical cumulative distribution-function of end-to-end delays of message for various frame lengths. The results indicate that there is an important end-to-end delay increase when frame length increases. We have also observed that 14 slots is the maximum admissible value, as greater values produces poor goodput ratios. For instance, the goodput ratio of a frame length of 15 is just 0.37. In Section 6 we computed the optimum frame-length with the genetic algorithm, and we concluded that 11 was the minimum admissible value. In this section, we have simulated our approach with the minimum and higher lengths, and realized that slots of 11 or 12 achieve similar results. However, there is a significant decrease in terms of performance when we consider slots greater than or equal to 13.  
Figure 10: End-to-end message delays.
# Application of Machine Learning in Electromagnetics: Mini-Review
## 4 Limitations  
It is undeniably noticeable from prior sections that the advent of ML has revolutionized the progress in EM. However, machine-learning-based approaches have limitations. As ML approaches are data driven, major limitations arise due to inadequate data to train the model effectively. Moreover, as ML facilitates predictions, accuracy can decrease in new cases and needs continuous evaluation and training of the model. Even the best-performing CNN is found to be quite inaccurate outside of its known environment [148]. Although surrogate modeling provides great opportunities, it requires sufficient empirical data from experiments and sophisticated tuning of the hyperparameters to achieve the expected results. It often lacks efficiency in the case of higher dimensionality and highly non-linear problems. Although different unsupervised algorithms are used to solve the feature reduction and classification problems, almost all require expertise in handling the design and in-depth programming of the required model of interest. Although it is important to provide an abundance of data during training as the accuracy of the model mostly depends on it [149], additional characteristics such as the learning rate, batch size, and others are also essential for optimized and improved outcomes [150].  
Although PCA and GA have shown significant optimization of ML algorithms using an intelligent search-based mechanism [151; 152], further improvement is necessary to achieve better results. Even in antenna arrays, the performance of ML techniques is not satisfactory due to the increased possibilities of antenna combinations. Sometimes, even larger datasets for fault detection results in low accuracy, which makes the selection of the appropriate ML algorithm difficult [153].  
Figure 8: Applications of Machine Learning in the field of electromagnetics.
# Data Compression with a Time Limit
## 4 Lossy Compression
### Test 3  
The dataset \(D\) to be compressed consisted of 1000 images, for a total size of 692.2 MB (691,215,000 bytes). Single runs of the compression tools with the indicated quality setups yielded the following results shown in Table 10:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tool** & **Size of Output in Bytes** & **Time in ms.** &
\begin{tabular}{c} **Quality** \\ **Parameter** \\ \end{tabular} & **SSIM** \\ \hline JPG1 & 43,432,750 & 5672 & 95 & 0.972077 \\ WEBP2 & 25,647,408 & 4728 & 95 & 0.980147 \\ JPEG-NO OPTIMIZE3 & 45,836,232 & 5911 & 95 & 0.972077 \\ JPEG4 & 13,738,395 & 5795 & 48 & 0.912076 \\ JPEG5 & 9,696,851 & 5493 & 25 & 0.8863093 \\ JPEG6 & 8,613,749 & 5974 & 20 & 0.8786752 \\ JPEG7 & 8,198,962 & 5741 & 18 & 0.8743648 \\ WEBP8 & 3,603,432 & 4914 & 1 & 0.8735347 \\ JPEG-NO OPTIMIZE9 & 14,969,285 & 6431 & 48 & 0.912082 \\ JPEG-NO OPTIMIZE10 & 11,228,397 & 6629 & 25 & 0.886300 \\ JPEG-NO OPTIMIZE11 & 10,266,529 & 6980 & 20 & 0.878630 \\ JPEG-NO OPTIMIZE12 & 99,131,536 & 6999 & 18 & 0.874339 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test 3.  
Figure 14: Optimal mix.  
From Figure 15 it is possible to observe that the fastest tool was WEBP2 with a quality setup of 95, while the slowest was JPEG_NO_OPTIMIZE12 with a quality setup of 18.  
The best tool for output size was WEBP8 with a quality setup of 1, while the worst was JPEG_NO_OPTIMIZE3 with a quality setup of 95.  
Figure 16 shows the useful setups.  
Figure 17 shows the optimal mix.  
Figure 16: Useful setups.  
Figure 15: Setups.  
In the case where the time budget is t\({}^{\prime}\) = 4800 and the SSIM quality limit is 0.873483 ms, the optimal mix is given by the mix of the WEBP2 and WEBP8 setups.  
The number of images to be compressed with WEBP2 is equal to 613 with a quality of 95, while for WEBP8 the images are 387 with a quality of 1.  
Compressing D as suggested by the algorithm resulted in a compression time of 4868 ms, while the output size was 16,623,908 bytes and an SSIM of 0.927159.  
The time budget was used practically entirely.  
In this case, there was a very small margin of error of about 68 ms.
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 4 A DFT-Based Query Running Time Prediction Algorithm
### Query Coefficients  
To predict the query running time, we compute the query descriptor \(\Psi_{q}\) as a six-dimensional vector \(<\)\(x_{0},x_{1},x_{2}\), \(x_{3},x_{4},x_{5}\)\(>\) as follows. For each term \(t\in q\), we add the corresponding descriptors \(t_{\psi_{0}},t_{\psi_{1}},t_{\psi_{2}}\) and \(t_{\psi_{3}}\) of each term in \(q\), so we obtain an initial query vector with dimension four. Then, we include two additional descriptors computed as the \(\max\{t_{\psi_{2}}\}\) and \(\max\{t_{\psi_{4}}\}\) for each \(t\in q\). All vectors \(\psi_{t}\) are calculated off-line, while \(\Psi_{q}\) is obtained at query run time.  
For a given query \(q\), the descriptors \(<\)\(x_{0},x_{1},x_{3}\)\(>\) represent the sum of integrals obtained with the DFT and the descriptor \(x_{2}\) represents the sum of all documents where the query terms appears, which gives an approximation to the search space of \(q\). We do not compute the sum of \(\psi_{4}\) for each term of the query because it is a lower bound of the score of the top-\(k\) and if there are several term lists with high scores, the sum of those scores will increase the value of \(\psi_{4}\) and it will lose its characteristic of lower bound. We also use the maximum values of \(\psi_{1}\) and \(\psi_{4}\) that are _minimum bounds_.  
All of the DFT descriptors are based on the use the fundamental frequency \(F\) which depends on the period \(P\) of the input signal. That is, the distributions of \(w(d,t)\) with period of \(P=10\), the distributions of the cumulative density with period of \(P=4\), and the  
\begin{table}
\begin{tabular}{l} \hline \hline
**Descriptors for Term \(t\)** \\ \hline
1. \(\psi_{0}\): PSD of \(\Phi_{t}\) at a frequency \(1/10\). \\
2. \(\psi_{1}\): DFT magnitude of Rank-Score at a frequency \(1/4\). \\
3. \(\psi_{2}\): \#Postings \\
4. \(\psi_{3}\): DFT magnitude for the processing times. \\
5. \(\psi_{4}\): score value for the \(k\)-th document in the term list (score \((k,t)\)). \\ \hline \hline
**Query Descriptors \(\Psi_{q}\)** \\ \hline
1. \(x_{0}\): \(\sum_{t\in q}t_{\psi_{0}}\) \\
2. \(x_{1}\): \(\sum_{t\in q}t_{\psi_{1}}\) \\
3. \(x_{2}\): \(\sum_{t\in q}t_{\psi_{2}}\) \\
4. \(x_{3}\): \(\sum_{t\in q}t_{\psi_{3}}\) \\
5. \(x_{4}\): \(\max_{t\in q}\{t_{\psi_{1}}  
distributions of processing time with period of \(P=4\) where the fundamental frequency is \(F=1/P\). As we explained above, we use the fundamental frequency to quantify how is the distribution of the high values of the input signal. In our case, high values of the magnitudes of the DFT represent a higher list processing cost.
# GA-Based Optimization Method for Mobile Crane Repositioning Route Planning
## 2 Current State of Mobile Crane Operation Planning
Numerous studies have been conducted on three research themes regarding mobile crane operation planning (crane selection, feasible working area identification, and lift path search for the crane). However, as mentioned earlier, studies on solving RCP are insufficient. Only some studies have mentioned the RCP. [PERSON] et al. [21] proposed an algorithm that finds the walking path of the mobile crane. The algorithm is similar to the method for solving RCP as it considers the movement of the crane, unlike previous studies. However, [PERSON] [15] defined the walking path as the path required for a crane to move, while the load is suspended from the hook to a location where lifting can be performed easily. However, the method proposed by [PERSON] cannot be used for solving RCP because it does not search an installation location for the mobile crane according to the construction sequential order. Studies directly involved in solving the RCP were conducted by [PERSON] and [PERSON] [8] and [PERSON] et al. [22]. Both studies mentioned the importance of travel route planning of the mobile crane because relocating a mobile crane is time-consuming. [PERSON] and [PERSON] [8] developed an algorithm that minimizes the frequency of relocating the mobile crane according to the construction sequential order and working range of the crane. On the other hand, [PERSON] et al. [22] developed an algorithm that finds the optimal walking path by identifying the coincident point of feasible locations according to the lift locations and comparing the working speed of the crane. However, existing studies on solving RCP have the following limitations. First, existing studies suggested heuristic-based methods. The heuristic method is widely used because it is easy to understand, and the computational speed is fast. However, it cannot guarantee the accuracy of the computational results. An improper crane operation plan can incur additional costs and schedule delays. Therefore, heuristic-based methods are not suitable for crane travel route planning, and a global solution must be found. Second, the realistic constraints of RCP and their accompanying variabilities of crane productivity have not been presented mathematically. Third, when a mobile crane is relocated from a particular position to another, the moving constraints are not considered. Obviously, construction sites have sections that are inaccessible to cranes with large volumes. Therefore, when a crane has to move from a particular position to another, the travel routes should be found quickly by excluding the inaccessible sections, and the travel time for each route should be estimated and used as a criterion in the travel route planning of the crane. Fourth, an integrated system that can increase the practical applicability has not been provided. In this study, we propose a method that overcomes the limitations observed in existing RCP studies while facilitating optimal mobile crane travel route planning.
# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
## 3 Experimental Results
### Experimental Datasets
#### 3.1.3 Low Light Synthesis Dataset  
To evaluate the generalization capability of the model, we extend the ExDark dataset by incorporating the Microsoft Common Objects in Context (COCO) dataset [49]. The COCO dataset is a large-scale computer vision dataset containing over 300,000 images and annotations across 80 object classes. However, most of the images in the COCO dataset were captured under standard lighting conditions. To address this, we design a method for synthesizing low-light images by manipulating pixel intensity and applying gamma transformations to COCO images. Under low-light conditions, the noise introduced by the image acquisition sensor primarily follows Gaussian and Poisson distributions [50]. Therefore, the process of synthesizing low-light images involves four key steps:  
1. Initial Evaluation: Calculate the average gray value of the image and evaluate whether it needs to be darkened. Only process the image whose brightness is higher than the threshold value.
2. Brightness Reduction: Randomly decrease the image brightness to 60% to 80% of its original level.
3. Gamma Correction: Utilize gamma transformation to simulate the dark light effect, with a random gamma parameter ranging between 2.0 and 5.0.
4. Noise Addition: Introduce random Gaussian and Poisson noise. The Gaussian noise has a mean of 0, with a standard deviation randomly varying between 0.1 and 0.3.  
Since the COCO dataset includes more categories than ExDark, we select the relevant labels based on ExDark's category list and remove any unrelated ones to create a subset. This subset is then darkened and merged with ExDark to form a new ExDark+ dataset. The extended dataset consists of 12 categories, 11,287 images, and 30,415 labels, significantly enhancing the diversity of sample characteristics. Figure 7 presents the results of synthesizing low-light images from the COCO dataset. The synthesized images closely resemble low-light images captured in real-world environments, exhibiting characteristics such as uneven lighting, low contrast, and blurred boundaries. The label distribution in the Exdark+ dataset is highly imbalanced, as shown in Figure 8. The most frequent label is \"people\" with 8279 instances, while the least frequent label is \"bus\" with only 1190 instances.  
Figure 7: Synthesized low-light image samples.
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## Materials and Methods
### Collecting Samples  
The sample collection of every class (Swamp, Water, Bog, Land, Wetland Mixture, Sediment Bogs) has been performed using random points on every class using the UAV image. Thus, 88 random points were added to the swamp class, 14 to the water class, 158 to the bog class, 73 to the land class, 187 to the wetland mixture class, and 35 to the sediment bog class, making a total of 555 points. After the points were added, LST and NDVI values were extracted for all eleven Landsat-8 images, and VH and VV values were extracted for all twelve Sentinel-1 images. The average values and the standard deviation values were calculated for every class. The relations between the LST, NDVI and, SAR values were made on an annual level based on monthly periods.
## 4 Mixed Reality for Human-Robot Interaction
### Mixed Reality-Enhanced Robotic Tele-Welding
#### 4.4.2 Mixed Reality-Enhanced Robot-Assisted Welding
This approach involves incorporating MR technology, allowing the user to be fully immersed in a virtual operating space enhanced by real-time visual feedback from the robot's working area. In this proposed robotic tele-welding system, the user's hand movements are mapped to imitate the robot's motions, enabling spatial velocity-based control of the robot's tool center point (TCP). Several design elements, including mixed reality, digital twin, and virtual fixtures, are integrated and implemented in user interfaces for intuitive comprehension of the robot's state, especially in critical welding situations. Digital twin technology was utilized to capture the real-time pose of the physical UR5 robot during its operation, enabling welders to observe the rotational status of each joint. By combining virtual twin data with onsite video streams in the MR space, a comprehensive monitoring system was created, offering real-time insights into the robot's operations. This setup facilitated accurate and efficient adjustments of welding motions based on the robot model data. To ensure optimal visualization within the MR welding workspace, the virtual UR5 robot's data and motions were scaled at a ratio of 1:5 to match the user's perspective. Within this framework, virtual fixtures (VFs) were employed and categorized into guidance and prevention fixtures. The proposed MRVF integrated both types, guiding users to the initial welding point effectively and preventing collisions between the torch tip and the workpiece. In the welding process, it is essential for the electrode to contact the molten weld pool to transfer filler metal to the workpiece. Simultaneously, contact between the torch tip and the workpiece must be avoided to prevent damage [107]. In the MRVF tele-welding workspace (as illustrated in Figure 10), a transparent prevention VF panel was overlaid on the virtual workpiece. This panel displayed a 2D representation of the actual welding process, minimizing collisions between the user-manipulated torch tip and the workpiece. Figure 10: The intuitive MR-based user interfaces for comprehending robot states in tele-welding experiments. (**a**) Typical 2D visual feedback for remote-controlled robotic welding, where the user uses a monitor to observe the welding process without the immersive HMD usage; (**b**) the virtual replica of the physical welding workpiece; (**c**) tele-welding MR platform without haptic effects, including welding virtual workpiece, overlaid RGB stream, virtual welding torch, and the scaled digital twin of UR5; (**d**) MRVF module involving hybrid guidance and prevention VFs [107].