esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 2 Materials and Methods
### Evaluation Metrics
Equation (5) shows how to calculate the Dunn index for clustering with \(K\) partitions. \[Dunn=\frac{\min_{1\leq i\leq j\leq K}d(i,j)}{\max_{1\leq k\leq K}\hat{d}(k)} \tag{5}\]  
where \(d(i,j)\) is the distance between cluster \(i\) and \(j\) (measuring separation) and \(\hat{d}(k)\) is the intra-cluster distance of cluster k (measuring compactness). As separation should be maximized and compactness minimized, it results in that Dunn index must be maximized. Silhouette width estimates the average distance between clusters considering how well an observation is clustered, in particular, how close each element in one cluster is to elements in the neighboring clusters. To calculate Silhouette width, it is necessary to first calculate the average dissimilarity \(a_{i}\) between the element \(i\) and all other elements of the cluster \(k\) to which \(i\) belongs (\(C_{k}\)) using Equation (6). \[a_{i}=\frac{1}{|C_{k}|-1}\sum_{j\in C_{k},j\
eq j}d(i,j), \tag{6}\]  
representing the compactness of an element to the cluster to which belongs. Secondly, for each element \(i\), the average dissimilarity \(d(i,C)\) of \(i\) to all elements of \(C\) are calculated and the minimum is computed, as enunciated in the following Equation (7). \[b_{i}=min_{1\leq l\leq K,i\
otin C_{i}}\frac{1}{|C_{l}|}\sum_{j\in C_{l}}d(i,j) \tag{7}\]  
where \(K\) in the number of clusters. This metric represents the separation of an element from the rest of the clusters. Lastly, using results from Equations (6) and (7), the Silhouette width for an element \(i\) is calculated applying Equation (8). \[S_{i}=\left\{ \begin{array}{ll}\frac{b_{i}-a_{1}}{\max(a_{i},b_{i})}&if\quad|C_{ k}|>1\\ \\ 0&if\quad|C_{k}|=1\end{array} \right. \tag{8}\]  
It is important to note that, the Silhouette coefficient of clustering is the mean of the Silhouette width of all the elements. Therefore, the objective is to maximize this index. There are other internal validation metrics available to be used in the validation of an unsupervised learning algorithm [42; 43; 44; 45; 46; 47] that could be alternatives to the selected ones. However, the chosen measures cover the three clustering criteria in order to evaluate and compare the trained clustering models [37].
## 4 Calculation Example of Slope Stability under Freeze-Thaw Conditions
### Analysis of Calculation Results
#### 4.4.2 Effect of Initial Moisture Content on Slope Stability under Freeze-Thaw  
Figure 4 shows the law of the influence of initial moisture content on the safety factor of the cutting slope under freeze-thaw. It can be seen that as the initial moisture content of the soil increases, the safety factor decreases in sequence and the decreasing amplitude increases gradually. The reason for this is that the porosity and water content of the slope increases after freezing and thawing, and the cohesive force decreases with the increase in the water content, resulting in a decrease in the shear strength of the slope, which gradually reduces the slope safety factor.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 2 Related Works
### Deblurry Dataset  
The development of high-quality datasets is crucial for model training in deep learning, as they significantly influence training outcomes and their applicability in real-world scenarios. In the field of high-level computer vision, prominent datasets such as ImageNet [24], COCO [25], and PASCAL VOC [26] have been developed. These datasets focus on a broad spectrum of real-world objects and scenes, encompassing categories like animals, everyday objects, and complex environments. Similarly, in the medical domain, critical datasets include fastMRI [27], which consists of knee and brain MRI scans; the BraTS [28] dataset for brain tumor segmentation; and the MIMIC-CXR [29] dataset for chest X-ray images, aimed at predicting conditions such as atelectasis, edema, and cardiomegaly. In the realm of remote sensing, datasets like DOTA [30] for object detection in aerial images and HRSC2016 [31] for ship detection and segmentation are vital for advancing vision tasks. These datasets are indispensable for high-level vision tasks, including image classification, detection, and segmentation, providing extensive annotations and a broad range of categories that enhance the accuracy and robustness of models across various applications.  
Image restoration tasks, considered as low-level vision challenges, encompass image dehazing [32], deblurring [20], denoising [33], deraining [34], and desnowing [35]. Datasets like SOTS [36] and Dense-Haze [37] for dehazing, Snow100K [35] for desnowing, GoPro [38] and HIDE [39] for deblurring, and Rain100L and Rain100H [40] for deraining are predominantly derived from everyday scenarios featuring common subjects like people, vehicles, and buildings.  
In the field of material science, particularly regarding metal corrosion, there is a notable lack of specific datasets. This gap highlights the necessity for tailored datasets in this domain, as existing ones are inadequate for tasks such as metal corrosion image deblurring. Therefore, in this paper, we have created a blurry metal corrosion dataset. This dataset can be used to train models for removing blurriness from corrosion images, facilitating subsequent tasks such as corrosion level prediction, corrosion type detection, and corrosion area segmentation.
## 3 Methods
### Significance Testing  
In order to see if the observed differences between the evaluated models is statistically significant or occurred due to chance, we perform significance testing. More specifically, we employ [PERSON]'s test, which is also referred to as \"within-subjects chi-squared test\". It is a statistical test that is used when comparing two models or interventions on binary outcomes. The test analyzes paired dichotomous measurements in a \(2\times 2\) contingency table and assesses whether there is a significant difference in the marginal frequencies of two related variables. In machine learning, [PERSON]'s test can be employed to evaluate and compare the predictions of two models to each other by examining the instances where their predictions differ. By comparing the number of discordant pairs (cases where one model is correct while the other is incorrect), [PERSON]'s test helps to identify whether the observed differences are statistically significant.  
In our experiments, we apply [PERSON]'s test to compare the performance of selected pairs of models. Similar to Recall@3, for each query, if the recommended top three sections include our TRUE-labeled section, it returns 1, otherwise it returns 0. The \(2\times 2\) contingency table is calculated as in Table 2, with 1 representing OK and 0 representing FAIL.  
Here, the null hypothesis for [PERSON]'s test is that the content recommender performs equally well to hit the True-labeled section in the top three recommendations with model 1 and model 2 (i.e., there is no difference between the outcomes of the two models). The alternative hypothesis, then, is that there is a statistical difference in the performance of the two tested models' outcomes. The [PERSON] test statistic (\"chi-squared\") is computed as follows:  
\[\chi^{2}=\frac{(B-C)^{2}}{B+C} \tag{7}\]  
A significance level of \(\alpha=0.05\) was used for hypothesis testing. This means that if [PERSON]'s test gives a \(p\)-value below 0.05, we have less than a 5% probability of erroneously rejecting the null hypothesis.
# Spirulina Supplements as a Source of Mineral Nutrients in the Daily Diet
###### Abstract  
Spirulina is a microalgae cultivated in many countries. It is a source of valuable protein, polyunsaturated fatty acids, vitamins, antioxidants and elements. We have not found studies that address the effect of supplement form or cultivation method on the mineral content of spirulina supplements. The aim of this study was to determine whether supplement form (tablet and powder) and cultivation method (organic and conventional) of spirulina have a bearing on the mineral nutrients content. Such an approach accounts for the innovation of our research. The material used in the study was spirulina in tablets and powder form, marketed as a dietary supplement. Samples were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES). In turn, selenium (Se) content was determined by spectrofluorimetry. Overall, in terms of mean values, the most abundant mineral in spirulina supplements was phosphorus (P) (15,149 mg/kg) and the least abundant was Se (0.31 mg/kg). Our findings show that both supplement form and cultivation method affect the mineral content of spirulina. Supplements in powder form had a significantly higher content of important elements, such as iron (Fe) (673 mg/kg), magnesium (Mg) (4151 mg/kg) and potassium (K) (16,686 mg/kg), while at the same time containing significantly less sodium (Na) (9868 mg/kg). In terms of the cultivation method, organic spirulina supplements turned out to be a richer dietary source of Fe (703 mg/kg) and K (14,893 mg/kg). In turn, conventionally grown supplements had higher contents of calcium (Ca) (11,269 mg/kg), phosphorus (P) (16,314 mg/kg) and strontium (Sr) (47 mg/kg). Spirulina can therefore be a valuable addition to the daily diet, helping people to achieve the required intake of micronutrients.  
Arthrosipra; conventional growing; elements; organic growing pills; powdery +
Footnote †: journal: Applied Sciences
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 2 Related Work
### Clustering of Trajectories
#### 2.1.1 Distances between Trajectories  
An important aspect in the clustering of trajectories is the computing of the distances among them. The first point to be accounted for is that moving objects travel the Earth in most cases. Therefore, the distance metric may consider the curvature of its surface. This is the case for the great circle and ellipsoids geodesic distance. In other cases, the effect of the curvature of the Earth is negligible, so the most used metric is the euclidean, but there are others like the Hausdorff or the longest common subsequence [9].  
Distances between two points on the Earth: in geometry, the ancient Greeks defined the geodesic as the shortest distance between two points on a surface. There are several formulas to calculate the geodesic for the surface of the Earth, with lesser or greater error depending on the position on the Earth's surface. In this regard, the most simple model is to approximate the Earth as a sphere; then, the geodesic distance is also called the great-circle or orthodromic distance [15]. Another approach is to consider an ellipsoidal model of the Earth; then again, there are several models of ellipsoids with variable error depending on the position. The WGS-84 ellipsoid is accepted as the most globally accurate.  
In many cases, trajectories are constrained to spatial locations of the size of an area small enough to assume it is flat; these cases neglect the curvature of the planet. This case the most common in the literature with examples like taxi routes analysis; see [6] for a review of different approaches.
## 2 Analytical Solution of Multiple Contact Pairs for the MLTWC
### Relationships between Interference and Temperature  
The interference fit for torque transmission often works within certain temperature range. The contact pressure changes when the temperature changes if the coefficients of linear expansion of the contacted pair disagree, and leads to sliding failure. Therefore, the temperature effect has to be taken into account when designing the tolerance system of the MLTWC. Suppose the interference of the contact surface is \(\delta_{0}\), and the working temperature is \(\Delta T\) higher than the design temperature. If the linear expansion coefficient of inner cylinder wall is larger than that of outer cylinder wall, the interference increases; otherwise, the interference decreases. The relationship between the interference and the temperature rise is shown in Equation (3),  
\[\delta=\delta_{0}+\delta_{T}=\delta_{0}+(\alpha_{i}-\alpha_{o})\times R\times \Delta T \tag{3}\]  
where \(\delta_{T}\) is interference change resulting from temperature rise; \(\alpha_{i}\) and \(\alpha_{o}\) represent the coefficients of inner and outer cylinder walls, respectively; and \(R\) is the radius at the contact surface.  
Figure 1: Calculation principle for interference: based on the minimum transmitted torque and maximum internal stress in need, minimum and maximum interference are obtained by solving inverse problems, and then the design interference as well as design tolerance can be derived.
# Unmanned Aerial Vehicles Rescue System Design and Traffic Model Planning
## 4 Discussion
### Remote Destinations  
For delivery locations that are outside the maximum range for UAVs (but still within the one-way maximum range, as shown in Figure 6), equipped with UAV B (the longest distance and fastest flight speed in containers) and UAV F (the best carrying capacity), the appropriate types and quantities are allocated based on actual distances and required doses. In this case, the UAV flies in one direction to the destination and then recovers uniformly when conditions are appropriate.
## 3 Results  
In this section, the performance of the developed model was analyzed through a two-stage evaluation process. First, to assess the accuracy of the model and its consistency with the literature, it was evaluated using the input parameters proposed by [PERSON] et al. and compared with reference values. This step aims to demonstrate that the model operates in alignment with existing methodologies in the literature and produces predictions consistent with the theoretical framework. In the second stage, the model's performance under real-world traffic conditions was analyzed using the CUPAC dataset. Appropriate scenarios were selected, and spatial filtering was applied to eliminate environmental effects, ensuringthat only laser intensity values originating from the vehicle object were analyzed. The distance between the vehicle and the sensor, along with vertical and horizontal angles and color information, were provided as input parameters to the model. The model's predictions were then compared with actual measurement data. This evaluation process highlights the applicability and generalizability of the model to different LIDAR systems. Initially, the colors used in the study conducted by [PERSON] et al. were analyzed at distances of 2.5 m and 5 m and at angles of 0\({}^{\circ}\) and 5\({}^{\circ}\). These distance and angle ranges align with the constraints defined in the modeling process. The number of effective laser channels and other parameters were also adjusted to match the experimental conditions described in the original study. Table 6 presents the predicted and actual intensity values, along with the percentage accuracy values, providing a quantitative assessment of the model's accuracy. As observed in the results, the predicted intensity values from the model exhibit a high level of agreement with the actual intensity values. The accuracy rates presented in Table 6 are consistently above 98%, demonstrating that the model performs reliably across different distances, angles, and surface types. For a distance of 2.5 m and an angle of 0\({}^{\circ}\), the highest accuracy was achieved on the SB-Gloss (Black) surface with 99.941%. The lowest accuracy under the same conditions was observed on the TCSRM-Gloss (Red) surface at 98.694%. The model's consistent accuracy
## 4 Conclusions  
Motivated by the promising design for a multi-PW laser with a 150-fs level based on the existing SG PW laser system, the temporal contrast and shape fidelity of the compressed pulse influenced by TOD should be compensated. Hence, finding a method to support increased energy with a large aperture, highly efficient transmission, and convenient operation is required. Therefore, a TOD compensation scheme using a temperature-tuning birefringent crystal is proposed in this paper. The GVD, TOD, and FOD of the existing SG PW laser system and their influence on the compressed pulse with different bandwidths are analyzed to provide an evaluation basis for compensating dispersions of the TOD and FOD. The results indicated that TOD compensation is necessary for a pulse duration of 150 fs. The TOD introduced by the quartz birefringent crystal was calculated, and the compensation scheme for the TOD of \(1.3\times 10^{6}\) fs\({}^{3}\) was designed using a quartz crystal. Furthermore, a principle-proof experiment was investigated offline to measure the GVD and TOD by a Wizzler, which is based on theoretical simulations. These results provide a feasible method for controlling and compensating the TOD with a temperature-regulated birefringent crystal, which to the best of our knowledge, is achieved for the first time. In addition, they are important for supporting updates of the multi-PW (5-10 PW) at the 150-fs level.  
Figure 8: (**a**) Experimental measurement results of GVD represented by the dotted black line, compared with simulated results shown by the solid red line; (**b**) the measured and simulated TOD; (**c**) the initial spectral and phase measurement with the residual GVD of \(-6\times 10^{4}\) fs\({}^{2}\) and TOD of \(5\times 10^{6}\) fs\({}^{3}\) from the laser without inserting quartz; (**d**) the spectral and phase with the GVD of \(-8\times 10^{4}\) fs\({}^{2}\) and the TOD of \(-1.3\times 10^{7}\) fs\({}^{3}\), according to the valleys in Figure (**b**).  
The large-scale laser system has higher residual TOD and needs a thicker birefringent crystal to compensate. The FSR may be affected but can be controlled by cascaded crystals. Further work is planned to use this scheme in an online laser system by using the designed parameters and judging the dispersions and FSR in detail. Furthermore, the residual TOD is lower for small-scale laser systems, such as high average power fiber lasers with a short pulse. The method can afford more FSR and will be more beneficial and convenient for achieving high time-domain fidelity for the compressed pulse after TOD compensation.  
Not applicable.  
All the data reported in this paper are presented in the main text. Any other data will be provided on request.  
The authors declare no conflict of interest. The funders had no role in the study design, collection, analyses, or interpretation of data; writing of the manuscript; or decision to publish the results.
## 2 Augmentation of Li-Fi Wireless DMT Transmission Capacity Through Sparse Coded Mask Optimization
### Impact of the Masking Ratio on the Channel Capacity  
Revisiting Our Masked Autoencoder and Signal Compression  
* If we assume that we strictly use that shorter time to send the compressed DMT waveform and can pack more compressed symbols into the same total time, the capacity can be described as Equation (10). \[C_{masked}=\frac{Bits/symbol}{(1-r)T}\] (10)
* This suggests an ideal capacity improvement factor, as shown in Equation (11). \[\frac{C_{masked}}{C_{base}}=\frac{\frac{Bits/symbol}{(1-r)T}}{\frac{Bits/symbol }{1}}=\frac{1}{1-r}\] (11)  
* If \(r=0.75\) (75% masked), then \(\frac{1}{1-r}=4\). In an ideal scenario with negligible overhead, we could quadruple throughput.
* If \(r=0.85\), \(\frac{1}{1-r}\approx 6.67\).
* Practical Considerations and the 1.85\(\times\) Factor
* Overheads and Guard Intervals
* In practice, our experiment must incorporate positional encoding, guard intervals, zero padding, and other overheads that reduce the net gain from the theoretical \(\frac{1}{1-r}\).
* We also rely on the masked autoencoder's ability to reconstruct the missing patches reliably. If the masking ratio is too high, the system may fail to meet our EVM threshold for reliable demodulation.
* Experimental Feasibility
* In our Li-Fi testbed, we found that at masking ratios of up to 85%, the error vector magnitude (EVM) for QPSK/16-QAM still remains below the first-generation GFEC limit.
* Accounting for all overhead (MAE training overhead, patch boundary, zero padding, etc.), our experiments showed an effective capacity gain of up to \(\sim\)1.85\(\times\) (instead of the pure theoretical factor near 6.67).
* This value (1.85\(\times\)) reflects a realistic balance between high masking ratios and maintaining acceptable EVM under practical Li-Fi conditions (1 m distance, \(\geq\)240 lux illumination).
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model
### The Relationship between \(\left(\sigma_{\theta,i}^{t}\right)^{2}\) and \(\omega_{i}^{t}\)  
Obviously, in the \(t\)-th time slot, the radar detection power \(p_{i}^{t}\) and communication transmission power \(q_{i}^{t}\) of each UAV satisfied \(p_{i}^{t}+q_{i}^{t}=E_{\text{max}}\). Due to the impact of the radar detection power on the variance \(\left(\sigma_{\theta,i}^{t}\right)^{2}\) of the AOA measurement error and the impact of the communication transmission power on the communication coverage radius \(\omega_{i}^{t}\), it was necessary to establish a quantitative relationship between \(\left(\sigma_{\theta,i}^{t}\right)^{2}\) and \(\omega_{i}^{t}\).  
The relationship between \(a_{r}\) and \(a_{c}\) of the \(i\)-th UAV in the \(t\)-th time slot was:  
\[\frac{a_{r}}{\left(\sigma_{\theta,i}^{t}\right)^{2}}+a_{c}\left(\omega_{i}^{t }\right)^{2}=E_{\text{max}} \tag{22}\]  
Among them, \(a_{r}\) and \(a_{c}\) represent parameters relating to the power consumption of radar systems and communication systems, respectively. The magnitude of these two values could be fitted in practical applications by measuring the power consumption of hardware systems.
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Communication  
The BMS usually has to communicate to the complete system (e.g., power electronics, energy management or Vehicle Control Unit (VCU) in a vehicle), in order to provide status information and receive instructions and parameters. For this, it has to be considered which means of communication are provided or required by the system. In addition, the required communication speed, robustness and reliability need to be checked. Does the data have to be sent at a certain minimum speed to comply with safety relevant tasks or can there be a hierarchy based communication that is slow enough to save power, but still ensures the application's timing needs? Decisions regarding these aspects may very well already have been taken on the system level, requiring the BMS to adapt to this. One example  
Figure 1: Comparison of SOC uncertainty depending on a \(\pm 1\) mV voltage accuracy.  
would be the need to provide a Controller Area Network (CAN) interface to talk to the system, which inherently sets some boundary conditions regarding speed, robustness and reliability.  
Apart from the system level, between the BMS components, communication is also necessary. For modular systems that are spread over several Printed Circuit Boards (PCBs), e.g., it needs to be defined, if there is a \"Master\" module, how it talks to \"Slave\" modules that amongst others are responsible for data acquisition or control of actors. For this, basically the same aspects as for the system wide communication are relevant. Examples can be found in Section 3.3 and implementation considerations regarding communication aspects in Section 4.4.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 4 Discussion
### Practical Considerations and Limitations
Conversely, when shooting from a distance, even though a relatively larger area of soil surface can be observed, the resolution is reduced. As a result, a review of the optimal resolution for soil density prediction is needed.