esa-sceva/satcom-chunk-collection huggingface dataset:
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
###### Abstract  
The kinetic coefficient of friction \(\mu_{\mathrm{k}}\) was measured for sea ice, stainless steel, and coated steel sliding on a natural sea ice cover. The effects of normal stress (3.10-8.11 kPa), ice columnar grain orientation (vertical and parallel to the sliding direction), sliding velocity (0.02-2.97 m-s\({}^{-1}\)), and contact material were investigated. Air temperature was higher than \(-5.0\,^{\circ}\mathrm{C}\) for the test duration. The results showed a decline of \(\mu_{\mathrm{k}}\) with increasing normal stress with \(\mu_{\mathrm{k}}\) independent of ice grain orientation. The \(\mu_{\mathrm{k}}\) of different materials varied, partly due to distinct surface roughnesses, but all cases showed a similar increasing trend with increasing velocity because of the viscous resistance of melt-water film. The velocity dependence of \(\mu_{\mathrm{k}}\) was quantified using the rate- and state- dependent model, and \(\mu_{\mathrm{k}}\) was found to increase logarithmically with increasing velocity. In addition, \(\mu_{\mathrm{k}}\) obtained at higher air temperatures was greater than at lower temperatures. The stick-slip phenomenon was observed at a relatively high velocity compared with previous studies, which was partly due to the low-stiffness device used in the field. Based on the experimental data, the calculation of physical models can be compared.  
sea ice; kinetic friction coefficient; in situ test; sliding velocity; ice navigation +
Footnote †: journal: Applied Physics Letters
## 3 Background
### Classifiers
#### 3.3.1 Support Vector Machine  
Support Vector Machines (SVMs) are a set of supervised machine learning algorithms that are hugely popular in classification tasks. In order to maximize the distance between classes, SVM tries to construct a hyperplane that can act as a separator. In [19], the authors used SVM for the classification of malware features generated using word embedding techniques and achieved promising results. SVM can help us identify subtle changes in malware samples as the hyperplane is capable of working in a higher dimensional space with the help of a kernel trick in cases where the data are not linearly separable. There are a number of kernel functions that we can use to achieve this high-dimensional mapping. Another important part of the algorithm is the cost regularization parameter C which helps avoid overfitting. It does so by allowing some classes into the boundary of the hyperplane. Once the hyperplane is set, the algorithm predicts classes by mapping data points to the high-dimension space and judging the relative position with the hyperplane. More information on SVM and its mathematical proof has been provided in the following sources [24; 25; 26].
## 5 Federated Task Offloading in the Proposed Scheme
\(b_{k}\) is the data size on which \(E_{ser}\) forms output prediction \(y_{i}\) for data point \(x_{i}\). In such cases, every \(E_{ser}\) in local zone \(Z\) computes a local model \(L_{m}\). To present the same, consider that \(h\) zones are demarcated, and each zone has a local \(E_{ser}\) which operates on \(b_{k}\). The edge server nodes act as clients and download the global model from the cloud server, denoted as \(E_{cs}\). The global model \(G_{m}\) has the following initial conditions. \[G_{m}=\{w_{0},N(R),G_{b_{k}},G_{\eta},L_{fg}\} \tag{10}\]  
where \(w_{0}\) denotes the initial weight of model, \(N(R)\) denotes the number of iterations for training, \(G_{b_{k}}\) denotes the global batch size, \(G_{\eta}\) is the global learning rate, and \(L_{fg}\) is the global loss function. Based on downloaded \(G_{m}\), any \(h^{th}\) local \(E_{ser}\) forms the local models \(L_{m}\) with the following parameters. \[L_{m}^{h}=\{w_{0}^{h},N(L),L_{b_{k}},L_{\eta},L_{lg}\} \tag{11}\]  
where \(L_{b_{k}}\) denotes the local batch size, \(L(R)\) is the local iterations, \(L_{\eta}\) is the local learning rate, and \(L_{lg}\) is the local loss function. The aim is to minimize \(L_{lg}\) with successive iterations. At local nodes, each edge server (client) splits \(L_{b_{k}}\) into small batches denoted by \(\beta\) and runs local epochs from \(1\) to \(q\). For every batch \(\beta\in L_{b_{k}}\), the weight function is updated as follows. \[T_{t_{U_{k}}} =b_{k}/c(U_{k}) \tag{7}\]0  
where \(D_{Lm}\) denotes the local gradient. These \(h\) client nodes return \(w_{c}\) to \(E_{cs}\), which updates its weight function for the next iteration as follows. \[T_{t_{U_{k}}} =b_{k}/c(U_{k}) \tag{7}\]1  
The global loss function is denoted as \(f(w)=\frac{\eta_{b}}{n}F_{h}(w)\), where \(F_{h}(w)=\frac{1}{n_{b}}\sum_{i\in P_{h}}f_{i}(w)\).
# Hybrid Quantum Dot as Promising Tools for Theranostic Application in Cancer
## 2 Significance of Hybrid Quantum Dot as Cancer Theranostics
### Perspectives of QDs for Diagnostic/Imaging Utility
Carbon nanomaterials, including carbon nanotubes, graphene, and graphene derivatives, have garnered significant attention due to their potential applications in fields as diverse as fingerprinting, photocatalysis, electromagnetic shielding, and electrics [70; 71]. Due to their luminescence, versatile surface chemistry, easy cellular internalization, and high biocompatibility, CQDs are particularly promising in drug delivery. Additionally, the nano-formulation system also offers the possibility to increase drug solubility, bioavailability, and half-life. Although doxorubicin (DOX) is widely used for cancer treatment, it has many disadvantages, including a low EPR effect, low cellular internalization, and cytotoxicity to normal cells [72]. One approach to bypassing these problems is to use a multifunctional nanocarriser system for tumor-targeted drug delivery, which has the advantage of accumulating at the tumor site due to the increased EPR effect.
## 4 Discussion
### Economic Analysis and Material Selection  
The study demonstrates the importance of correlating the technical analysis of reliability with the operational and economic aspects, to ensure the efficient and sustainable operation of equipment in aggregate quarries. Decisions regarding the choice of materials, reconditioning methods, and maintenance strategies must consider not only the technical aspects, but also the costs involved, the impact on production, and the availability of resources. In the Patars quarry operation, there are three screening units, each equippedwith three tiers, featuring 6 square meters of chute area per tier. For the cost calculation of chute replacement, the following factors were considered: material cost, labor cost, and a company overhead of 7%. The fabrication of chutes from Hardox 400 steel proved to be more difficult under field conditions, resulting in a 30% increase in labor costs. Conversely, the labor cost for fabricating chutes from Pucest plates was identical to that of the original chute material, amounting to 1091 euros per unit. Cost calculations were performed for different chute materials used in the screening plant (Tables 12 and 13).  
Considering that the original chute material in the screening plant exhibits an 80% reliability at 105 operational days, the following operational lifespans are required for the new materials to be considered efficient, maintaining an 80% reliability: 157 operational days between failures for Hardox 400 steel plate and 251 operational days between failures for Pucest plate. Although Hardox 400 and Pucest materials have higher initial costs compared to the original sheet material, they promise a significantly longer operational lifespan to maintain the same level of reliability. This implies that, in the long term, investing in more expensive materials could prove more cost-effective due to the reduced frequency of replacements, and consequently, lower maintenance costs and downtime. The selection of the optimal material depends on the maintenance strategy of the operation. If the focus is on minimizing initial costs, the original sheet material is the most economical option. If the objective is to maximize operational lifespan and minimize downtime, Hardox 400 or Pucest are superior choices. Pucest plate offers the longest operational lifespan but also incurs the highest cost. Hardox 400 provides a balanced compromise between cost and operational lifespan. For a comprehensive evaluation, a life cycle analysis should be conducted, encompassing all long-term costs, such as acquisition costs, installation costs, maintenance costs, downtime costs, and replacement costs. Operational conditions must also be considered to determine the most suitable material. For example, if the material will be subjected to extreme abrasion conditions, Pucest might be the optimal choice.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Lessons Learned
#### 6.1.3 Q-Learning Algorithm Convergence  
The main concern of QL-based optimization is algorithm convergence and lack of training samples in a real-world environment. FANETs have limited energy and communication resources; thus, the algorithm should have faster convergence to optimal decision making. Additionally, it should adaptively learn the optimal policy by dealing with dynamic topology in FANETs. To overcome this issue, QMR [22] and QTAR [23] utilizes the PTS metric to choose the relay UAV. PTS is the ratio between the distance progress toward the destination and the link delay offered by the corresponding relay UAV. Performing the exploration in the initial stage of learning by using the PTS metric helps to overcome the lack of training samples, and it accelerates the algorithm's convergence to optimal decision making. However, exploration based on the PTS may not be effective in highly dynamic FANETs because it predicts the mobility of UAVs only based on the relative distance. In FANETs, the UAV mobility depends on relative distance, relative velocity, and flying direction (six DoFs), which can be precisely calculated only using the predictive link duration metric [21; 29; 59]. Thus, performing the exploration or exploitation based on the link duration metric should give better link stability and routing performance in FANETs. In addition, a proper strategy is required to perform the exploration and exploitation to obtain better reward.
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 2 Proposed Methodology
### Fine-Tuning and Transfer Learning  
This section outlines the steps involved in training and refining our models. Initially, pre-trained weights from the extensive ImageNet dataset, comprising 14 million images categorized into a thousand classes [34], are utilized. The Keras library facilitates the importation of these weights, accelerating convergence through the incorporation of previously learned features and improving image recognition performance. The transfer learning approach leverages the benefits of ImageNet weights, specifically tailored for image classification. This method expedites tasks by requiring less effort compared to the utilization of randomly initialized weights. Fine-tuning is then performed on the UCMerced_LandUse dataset images, focusing on adjusting the final layers of the base model while freezing all other layers to retain the initial UCMerced_LandUse dataset weights. This strategic approach optimizes training by preserving the valuable insights gained from the pre-trained ImageNet weights in the initial layers. During this process, all other layers are frozen to maintain the weights obtained from the initial training on the UCMerced_LandUse dataset [35]. Upon combining the proposed layers and classifier, the entire network is released once the final layers have been trained on the UCMerced_LandUse dataset. Evaluation of the model accuracy is then conducted using test data, incorporating weights from both the ImageNet dataset and the UCMerced_LandUse images. All DL models undergo fine-tuning with diverse hyper-parameters outlined in Table 1. Image input dimensions are 224 \(\times\) 224 \(\times\) 3, utilizing a batch size of 32. We use an SGD optimizer and employ categorical cross-entropy (CC) as the loss function. The SoftMax activation function is applied to the output layer, ensuring the robustness and effectiveness of the land-cover classification model.  
Figure 6: The architecture of ResNet-50 model with proposed fully connected layers.
## 2 Related Works
### Denoising  
Various types of noises such as salt and pepper noise, Gaussian noise, Poisson noise, speckle noise, and many other fundamental noise types can contaminate document images and deteriorate the respective image quality [41].  
Due to the ill-posed conditions of denoising, it is very important to know the original image's statistical distribution. For example, some models have been introduced to emphasize the local area of pixel-like and total variation (TV) regularization [42]. Although those methods remove some noise from images, they also remove, unfortunately, some other good or important or needed details from images [43; 44; 45].  
Recently, a patch-based approach is showing to be an up-and-coming solution to denoise images [46]. In most denoising schemes, the corrupted input image is decomposed by the denoiser into a set of patches, which will then be denoised separately, and later, those parts (i.e., denoised patches) are merged to create the final denoised image. One example of such a scheme is the so-called sparse coding method, which is trying to map each patch into a sparse coefficient matrix, in which most of the elements are zero or near to zero (sparse) [47; 48].  
A further scheme, a group method, works significantly better than the last described method, as most involved patches are Gaussian mixture models and have a multivariant correlation. Natural images are considered non-Gaussian, but they have one problem: they usually need more resources to be executed [46; 49].  
The idea of a patch-based solution comes from the fact they are inside (i.e., parts of a bigger) one image. We can find patches at different locations within the image, which can have similarities, and these similarities can be used by the denoiser to better reconstruct the original clean images. These similar patches are referred to in what is called non-local self-similarity (NSS). This method was first used in non-local mean denoising [50] and later, because of its precision and effectiveness, extended by different studies [46; 49; 51; 52; 53; 54; 55; 56].  
Denoising by using a convolutional neural network (CNN) was introduced by [PERSON] et al. [57]. They argued that their algorithm or scheme achieved better results than traditional denoising methods such as total variation, wavelet approach, and other analytical techniques. Later, this CNN-based model was enhanced by introducing therein linear rectifier function(s) [58] and batch normalization [59].  
Most of the above methods work well for white noise, but they hardly remove other kinds of noise such as salt noise, pepper noise, or speckle noise [53; 60]. In addition, some of them introduce new distorting artifacts to the images (during the denoising process), which would need to be corrected or removed.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 2 Motion and Distortion Induced Artefacts
### Involuntary Eye Motion
#### 2.2.4 Pulsing and Respiration  
Retinal blood pulsation has also been reported as a possible cause of OCT imaging distortion. [PERSON] et al. [33] reported that, in the case of imaging retinal blood vessels, each acquisition is associated with a unique position of the blood vessels compared to the background tissues. [PERSON] et al. [34], following the same research line, reported that the heartbeat causes an arterial blood pressure pulse in the arteria carotis, inducing a head movement (average peak-to-peak amplitude of 81 \(\upmu\)m \(\pm\) 3.5 \(\upmu\)m per second) that is translated to the eye and it is visible during the retinal OCT scanning. Moreover, it is mentioned in their work that respiration also causes a slow and small amplitude movement of the head, with a frequency of 0.2 Hz. Both motions clearly affect volumetric OCT data but may in addition cause distortions of B-scans.  
Figure 2: Schematic of the involuntary eye movements drawn on an in vivo AO-OCT en face image showing individual cone photoreceptors (width \(\sim\)1–1.2 \(\upmu\)m [32]), expected to affect the OCT image quality. Slow drifts are represented by the dotted lines, whereas the fast microsaccades are shown by the green arrows. The black dots represent individual cone photoreceptors. The white curved line is caused by shadowing of anterior retinal vessels. The distance between the cones at the specific eccentricity from the fovea (\(\sim\)4\({}^{\circ}\)) is approximately 3–4 \(\upmu\)m.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 1 Introduction
In this work, a novel SP-SSA approach is proposed that performs object-specific reconstruction. 2. Superpixel segmentation and 2D-SSA are combined together for the first time for accurate spatial-spectral feature extraction. Using SP-SSA, each superpixel, i.e., object-specific spatial region is reconstructed. 3. Superior classifier performance is achieved with the proposed method in comparison to other state-of-the-art methods, even with a comparatively small number of training samples. The remainder of this paper is organized as follows. A detailed description of the proposed method is presented in Section 2. The experimental setup, results, and analysis are described in Section 3. Finally, some conclusions and future work are discussed in Section 4.
# Predictive Analysis for Optimizing Port Operations
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1** & **Kappa** \\ \hline
**Extreme Gradient Boosting** & 0.9511 & 0.9798 & 0.6923 & 0.7507 & 0.7202 & 0.6935 \\ Extra Trees & 0.9498 & 0.9365 & 0.7014 & 0.7348 & 0.7176 & 0.6901 \\ Random Forest & 0.9491 & 0.9701 & 0.6853 & 0.7364 & 0.7098 & 0.6820 \\ ResNet & 0.9464 & 0.7975 & 0.6157 & 0.7484 & 0.6755 & 0.6489 \\ Decision Tree & 0.9459 & 0.8394 & 0.6907 & 0.7079 & 0.6991 & 0.6694 \\ Neural Network & 0.9460 & 0.7827 & 0.5832 & 0.7674 & 0.6627 & 0.6208 \\ Light Gradient Boosting Machine & 0.9389 & 0.9688 & 0.4375 & 0.7996 & 0.5652 & 0.5354 \\ K Nearest Neighbors & 0.9228 & 0.8741 & 0.4426 & 0.6027 & 0.5104 & 0.4695 \\ Gradient Boosting & 0.9166 & 0.8990 & 0.1295 & 0.7371 & 0.2196 & 0.1979 \\ Ridge Classifier & 0.9097 & 0.8418 & 0.0360 & 0.5570 & 0.0676 & 0.0573 \\ Logistic Regression & 0.9079 & 0.8080 & 0.0436 & 0.4363 & 0.0792 & 0.0638 \\ Linear Discriminant Analysis & 0.9059 & 0.8159 & 0.1250 & 0.4385 & 0.1945 & 0.1607 \\ Ada Boost & 0.9056 & 0.8277 & 0.0684 & 0.3893 & 0.1162 & 0.0917 \\ SVM & 0.8135 & 0.8375 & 0.2273 & 0.3695 & 0.1117 & 0.0688 \\ Naive Bayes & 0.5680 & 0.7600 & 0.8872 & 0.1606 & 0.2720 & 0.1394 \\ Quadratic Discriminant Analysis & 0.1167 & 0.5137 & 0.9978 & 0.0932 & 0.1705 & 0.0049 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing 10-fold CV results of different methods with multiple metrics (sorted using Accuracy) for Delay Time classification at port. Figure 17: Confusion matrix on test data for Delay Time classification. Figure 15: SHAP decision plot for Total Time classification of a sample observation. Figure 19 shows the SHAP analysis for Delay Class, indicating that the most significant impact on the XGBoost output is by Berth, Source, destination, and Nationality. This indicates that geographical features play a key role in Delay. Figures 20 and 21 show the Decision plot and Force plot for a specific sample, where the actual Delay Class is 0. The SHAP output is \(-5.08\), indicating the output is class 0 since it has a low value, with features such as Month, Berth, and Flag Cabotage having the most influence on this sample. Since this sample is Class 0, meaning no delay, there is no need to change any feature in this case.
## Appendix A UAS Regulations in the Arctic  
The Arctic comprises several regions with varying regulations and it is essential for UAS operators to identify and understand the regulations applicable to their UAS type, survey purpose, and pilot qualifications. Information for each Arctic region (associated with each ruling country) and authority contact references can be found in [50]. While several restrictions apply for recreational purposes, scientific research benefits from increased flexibility. Generally, UASs may not be flown over or in the vicinity of military areas, airports, embassies, or prisons, except by permission from the local persons in charge. UAS model (multirotor or fixed-wing), weight, and vehicle visibility are key parameters when assessing legislation associated with the planned survey, which makes low weight such as in the minimal set-up preferable. All authorities request extreme caution be taken to avoid any environmental damage or wildlife disturbance.  
In Svalbard (under Norwegian regulations), flying UASs does not require a special permission from the government. Any flight activity using a UAS must, however, be in accordance with the general rules pursuant to the Svalbard Environmental Protection Act--Act of 15 June 2001 No. 79 relating to the protection of the environment in Svalbard. The UAS must be clearly visible to the operator at all times so that full control of the model aircraft can be maintained and may only be flown during daylight hours with an altitude limit of 120 m above ground or water. UAS must remain 150 m from people, motor vehicles or buildings not controlled by the aircraft operator, except during take-off and landing.  
The UAS operational rules for Denmark and Greenland are found at BL 9-4 from January 2004, along with amendments AIC B 20/09 (on First Person View flight), AIC B 21/09 (on Frequencies), and AIC B 24/10 (on UAS). Restrictions tighten as the vehicle increases in weight. UASs weighing more than 7 kg require pilot certification, operational approval and set altitude limits. Large UAS (weighing between 25 and 150 kg) are prohibited. Foreign operators can obtain permissions if they are authorized in their country of origin and can meet all relevant Danish requirements. All classes of UAS are restricted to a 100 m altitude limit (above ground level). Exceptions to the various restrictions may be made for research operations and commercial operators upon request.