esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
###### Abstract  
Fifth-Generation (5G) networks deal with dynamic fluctuations in user traffic and the demands of each connected user and application. This creates a need for optimized resource allocation to reduce network congestion in densely populated urban centers and further ensure Quality of Service (QoS) in (5G) environments. To address this issue, we present a framework for both predicting user traffic and allocating users to base stations in 5G networks using neural network architectures. This framework consists of a hybrid approach utilizing a Long Short-Term Memory (LSTM) network or a Transformer architecture for user traffic prediction in base stations, as well as a Convolutional Neural Network (CNN) to allocate users to base stations in a realistic scenario. The models show high accuracy in the tasks performed, especially in the user traffic prediction task, where the models show an accuracy of over 99%. Overall, our framework is capable of capturing long-term temporal features and spatial features from 5G user data, taking a significant step towards a holistic approach in data-driven resource allocation and traffic prediction in 5G networks.  
5G networks; user allocation; traffic prediction; deep learning; long-short term neural networks; Transformers 2025
## 3 Overview of LPWAN
### Security and Privacy  
As numerous cyberattacks and intrusion menaces have continued to plague the deployment of wireless devices, security and privacy vulnerability concerns have been identified as a major challenge in IoT applications [15; 41]. To avoid intrusion, security measures and features such as authentication,encryption (AES, RSA, etc.) and hardware security (e.g., tamper-proofing) are characteristics attributed to devices connected within LPWA network [42].
## 2 Modifications for Hoek-Brown Yield Criterion and the Pseudo-Dynamic Method on Seismicity Analysis  
### Nonlinear Hoek-Brown Strength Criterion  
At the end of the last century, the nonlinear H-B strength criterion was devised by [PERSON] and [PERSON] (2010), and the characteristic that the failure envelope shows significant nonlinearity during the destruction of rocks was further elucidated. It has been widely used in practical geotechnical engineering, due to its material parameters that can clearly characterize the state or quality of the rock. The nonlinear H-B failure criterion is expressed by the following equation of the major and minor principal (effective) stresses \(\sigma_{1}\) and \(\sigma_{3}\)  
\[\sigma_{1}=\sigma_{3}+\sigma_{c}\bigg{(}m\frac{\sigma_{3}}{\sigma_{c}}+s\bigg{)} ^{n} \tag{1}\]  
where \(\sigma_{c}\) is the uniaxial compressive strength of intact rock. And the material parameters \(m\), \(s\), and \(n\) are defined as  
\[m=m_{i}e^{\left(\frac{CSI-100}{28-14\Omega}\right)} \tag{2}\]  
\[s=e^{\left(\frac{CSI-100}{9-3D}\right)} \tag{3}\]  
and  
\[n=\frac{1}{2}+\frac{1}{6}\left(e^{-\frac{CSI}{13}}-e^{-\frac{20}{3}}\right) \tag{4}\]  
where \(CSI\), \(m_{i}\), and \(D\) are the Geological Strength Index, which describes rock quality, the parameter associated with the rock material type, and the rock perturbation coefficient characterizing blast effects and stress relief, respectively. More detailed definitions of these parameters are presented by [PERSON] et al. (2010). The homogeneity and isotropy of the rock mass are the primary condition for the H-B failure criterion. More precisely, it should be presupposed that the rocky slope is either intact or composed of strongly fractured rock when the H-B strength criterion is employed for stability analysis of the rocky slope.
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 4 Discussion
### Complement to Active 3D Winds  
Lidar-derived winds from Aeolus presented in Figure 18 show that the swath width from an active system is very narrow compared to an EO/IR passive system. The advantage of lidar is that it has the potential to retrieve winds over clear and cloudy regions, while CMIS is limited to cloudy regions (at present). The horizontal resolution for Aeolus data is approximately 90 km for Rayleigh clear-sky winds and 10 km for Mie cloudy sky winds [12]. CMIS could effectively complement Lidar observations by providing higher horizontal and vertical resolution datasets over wider swaths. The GSD as a function of orbit altitude is shown in Table 6 above.
## 2 Effectiveness of Eddy Current Damping in Seawater
### Experimental Results  
The free vibration of the cantilever beam is measured in four cases to study the additional damping provided by the eddy currents. Figure 3 plots the free vibration response of the cantilever beam in these four cases. The average damping ratios of the cantilever beam and descriptions of the four cases are given in Table 1. It can be seen that the original damping ratios of the cantilever beam without eddy current damping are 0.55% and 2.87% in the air and in the seawater environment, respectively. The damping ratios with eddy current damping are increased from the original values to 7.33% and 8.98%, respectively. The increased damping ratio provided by eddy current damping is 6.78% in air and 6.02% in the seawater environment. It is shown that the influence of the seawater environment on the eddy current damping is limited and the feasibility of using eddy current damping in seawater is validated.  
Moreover, the damping ratio in the first five vibration cycles are obtained from the logarithmic decrement method and plotted in Figure 4. It can be seen that the damping ratio of the cantilever beam at first one or two cycles is larger than that at last few cycles. This phenomenon indicates that the structural damping is much higher during large-amplitude vibration.  
Figure 3: Free vibration response of cantilever beam in each case.  
Figure 2: Experimental setup for free vibration tests: (**a**) global view; (**b**) side view of seawater container; (**c**) front view of seawater container; (**d**) eddy current damper.
## 5 Conclusions  
Two kinds of beads, alginate-based and carrageenanan-based algal beads (LD and CC beads, respectively) gelled by Ca-alginate and K-carrageenanan, respectively, and then modified with polyethyleneimine (PEI) and glutaraldehyde (GA) by using the impregnation method, were compared for Pd(II) recovery. Results show that due to the Ca-alginate barrier, functional material-PEI can hardly get access to the sites in the core of LD beads: a lower amount of PEI is incorporated in LD/PEI beads compared to CC/PEI beads, where the PEI diffusion barrier is negligible. This phenomenon leads to a lower sorption efficiency but faster sorption kinetics of LD/PEI compared to the CC/PEI composite. Moreover, when applied to treat synthesized leaching liquors from a spent catalyst and a waste car catalytic converter, the two sorbents are stable, but CC/PEI beads have a higher Pd recovery efficiency.  
The following are available online at www.mdpi.com/2076-3417/8/2/64/s1, Figure S1: The preparation process of LD/PEI and CC/PEI beads, Figure S2: The sorption capacity of the beads prepared at two different times (Dose: 0.5 g L\({}^{-1}\); pH: 1-adjusted by H\({}_{2}\)SO\({}_{4}\) or NaOH; Contact time: 72 h; T: 20 \({}^{\circ}\)C), Figure S3: EDX analysis of LD/PEI and CC/PEI beads before and after Pd(II) sorption, Figure S4: FTIR spectra of the raw biomass (_L. digitata_ and _C. crispus_), beads (LD/PEI and CC/PEI) and the beads after loading with Pd(II), Figure S5: pH change after sorption process, Figure S6: Plots of q\({}_{t}\)/q\({}_{eq}\) as a function of the time for Pd(II) sorption onto LD/PEI and CC/PEI beads, Table S1: Experimental frequencies of the bands observed for the raw biomass (_L. digitata_ and _C. crispus_), beads (LD/PEI and CC/PEI) and the beads after loading with Pd(II). Unit: cm\({}^{-1}\), Table S2: Pd(II) sorption properties of a series of sorbents, Table S3: Palladium and anion species when 15 mM SO\({}_{4}\)\({}^{2-}\) (i.e., 5 mM Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\), 15 mM CuSO\({}_{4}\), or 5 mM Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\)) were added.  
**Acknowledgments:** [PERSON] acknowledges the China Scholarship Council (CSC, Grant N\({}^{\circ}\) 201566600002) for providing a PhD fellowship. [PERSON] and [PERSON] acknowledge the European Union's Seventh Framework Programme (FP7/2007-2013) since these results are indirectly derived from a research project (BIOMETAL-DEMO) funded by the FP7 Programme managed by REA-Research Executive Agency ([[http://ec.europa.eu/research/rea](http://ec.europa.eu/research/rea)]([http://ec.europa.eu/research/rea](http://ec.europa.eu/research/rea))) under Grant N\({}^{\circ}\) 699101. Authors thank [PERSON] (IMT Mines Ales, C2 MA) for his technical support for SEM and SEM-EDX analyses.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] acquired and analyzed the data; [PERSON] drafted the manuscript; [PERSON] and [PERSON] helped perform the analysis with constructive discussions and revised the manuscript.  
The authors declare no conflict of interest.
# Using Machine Learning for Quantum Annealing Accuracy Prediction
## 3 Experimental Results
### Fixed Annealing Time and Fixed UTC Prefactor
#### 3.1.2 Regression  
Next, we consider the prediction of the clique size that the D-Wave 2000Q annealer will find on certain MC instances, as a function of the features outlined in Section 2. All results in this section were obtained with the gradient boosting regressor of _scikit-learn_.  
Figure 3 shows both the predicted D-Wave clique size versus the one actually found by the annealer (left plot), as well as the permutation importance ranking of the features returned by the gradient boosting algorithm (right plot). Permutation importance ranking is a means to compute the importance of each feature [24]. It works by measuring the increase in the prediction error of the model after we permuted the feature's values, which breaks the relationship between the feature and the true outcome (since breaking the relationship between a feature and the outcome makes classification easier).  
We observe that, indeed, the model allows for a relatively accurate forecast of the found clique size, with a root mean square error of 0.696 which is quite low considering the predicted values lay in the range of up to 50. The most important features seem to be the density of the unembedded graph, the density of the original input graph, and the smallest eigenvalue of its adjacency matrix.
# State Analysis of the Water Quality in Rivers in Consideration of Diffusion Phenomenon
## 2 Materials and Methods
### Water Quality Indicators
DO is an important indicator for the living organisms that make up a river ecosystem [24; 25; 26]. Aerobic processes occurring in natural waters are essential for reducing pollution in water. These processes take place in the presence of oxygen, and the lack of oxygen in the water leads to anaerobic processes in which odorous substances are produced. The solubility of oxygen in water depends on the temperature, as the temperature increases the dissolved oxygen decreases. Another factor in the reduction of dissolved oxygen is an increase in salinity. Furthermore, the drop in pressure causes the dissolved oxygen to decrease, which is why the solubility of oxygen is usually quoted at a pressure of one atmosphere (101,325 Pa). Oxygen deficiency is the loss of dissolved oxygen in water. This is the difference between the maximum and current oxygen concentration. In other words, the deficit is the amount of oxygen that must be introduced into the water to achieve maximum saturation. The rate of water deoxygenation is directly proportional to the BOD, which is represented by the following relation:  
\[\frac{dD}{dt}=k_{1}x_{1} \tag{4}\]  
where \(\frac{dD}{dt}\)--rate of change of oxygen deficit [mg/L], \(k_{1}\)--BOD, and \(x_{1}\)--BOD over time \(t\). In order to take into account the BOD value of the water at the wastewater discharge point, denoted as \(x_{10}\), the relation (2) must be inserted into relation (4):  
\[x_{1}=x_{10}e^{-k_{1}t} \tag{5}\]  
in which \(x_{10}\) is the BOD concentration at time \(t_{0}\). From the above, the following relationship is obtained:  
\[\frac{dD}{dt}=k_{1}x_{10}e^{(-k_{1}t)} \tag{6}\]  
In parallel with the deoxygenation of the water, oxygenation (re-aeration) occurs as a result of constant contact between the water surface and atmospheric air. Oxygen uptake by the water surface takes place at a rate proportional to the actual oxygen deficit, according to the relationship:  
\[\frac{dD}{dt}=-k_{2}D \tag{7}\]  
where \(k_{2}\)--oxygen uptake (re-aeration) rate coefficient \(\left[\text{d}^{-1}\right]\). The uptake re-aeration coefficient \(k_{2}\) depends on the water temperature and flow characteristics of the river, such as: flow rate and river bed depth. The influence of temperature on the value of the re-aeration coefficient \(k_{2}\) is shown in the relation:  
\[k_{2(T)}=k_{2(20^{\circ}C)}\theta^{(t-20^{\circ}C)} \tag{8}\]where the \(\theta\) thermal coefficient is equal to 1.047. The value of the \(k_{2}\) coefficient is lower in deep and slow-flowing waters than in shallow and fast-flowing waters. The basis for determining the \(k_{2}\) coefficient is the analysis of the watercourse profile:  
\[k_{2}(20\,^{\circ}\mathrm{C})=2.833\Big{(}V/H^{3/2}\Big{)} \tag{9}\]  
where \(V\)--average flow velocity of the watercourse [m/s]; \(H\)--average depth of the watercourse [m]; and 2.833--correction factor related to roughness changes of the bottom surface of the watercourse.
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 5 Discussion
### Influence of the Dataset on the Detection Results  
We trained the MFNN using an image-expanded sample dataset. When expanding the image dataset, the number of samples required for training the network and the methods for image expansion should be comprehensively considered to avoid incorrect detection results. When we annotated the slice images, only the core regions of oceanic phenomena in the images were annotated, and this region was used to represent the entire oceanic phenomenon. This procedure may introduce subjective factors that affect annotations. In future research, we plan to develop an improved method to annotate images to increase the accuracy of the results.  
We currently use images from Sentinel-1 for MFNN training and testing. All parameters are optimized according to the Sentinel-1 image dataset, and thus, the current detection network only performs well when Sentinel-1 images are used as input. Because the features of oceanic phenomena in SAR images are highly related to satellite parameters such as polarization and band parameters, the features learned by the network through the Sentinel-1 satellite training dataset cannot be directly applied to the images from other satellites. If the network is to be used to analyze images acquired from other satellites, the hyperparameters should be reselected, and the network should be trained and tuned using the corresponding satellite image dataset.  
Only five types of oceanic phenomena, namely oceanic eddies, rain cells, oceanic fronts, ship wakes, and oil spills, were included in the current study for analysis by the MFNN. In future research, we will  
Figure 11: The detection results for atmospheric front that do not exist in the training dataset.  
construct a complete dataset and improve the network structure in order to detect sea ice, internal waves, and other oceanic phenomena.
# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 4 Methodology and Experiments
### Experiments and Results
#### 4.2.2 Second Experiment: Pre-Trained ResNet-50 Model with SVM  
The learned image features from the training images were extracted and followed by SVM classifier, as shown in Figure 7. In the implementation process, we created an Image-Data store to help with data management. This was because, after reading, the images were loaded into the memory system, thus making it effective for a huge collection of images. After uploading, the data were divided into 70% training and 30% validation using random sampling to avoid biasing the results. As a pre-processing stage, ResNet-50 network can only process RGB images that are 224 \(\times\) 224; here, we resized and convert any grayscale images to RGB. Using deep layers for higher level features gives better distinct features for recognition tasks. For feature extraction, the layer before classification layer, named 'fc1000', was used to extract features by using the activation method. These features were then used to train and test SVM classifiers using a fast-linear solver. The activation outputs were arranged into columns to speed-up SVM training that follows, and the fast stochastic gradient descent (SGD) was implemented for the training. The 'MiniBatchSize' was set 32 to ensure the CNN and image data fitted into the servers' memory. With regard to results for this experiment, Figure 8 shows that the ResNet-50 model obtained a higher accuracy of 100% on GTAV face, ORL, and YTF datasets. It also obtained 98.50% and 96% recognition accuracy for both FEI and Georgia Tech face datasets, respectively.  
Figure 6: Recognition accuracy for AlexNet with features from layers ‘fc6’, ‘fc7’, and ‘fc8’.  
Figure 7: ResNet-50 convolutional neural networks with SVM.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 2 Materials and Methods
### Theory: Landslide Detection with SAR
#### 2.1.2 Absolute Coherence Method  
For two SAR images whose acquisitions span a landslide trigger event, landslide pixels are expected to have a low coherence. The occurrence of a landslide represents a significant modification of the ground surface and therefore its scattering properties. The simplest method of using coherence information to map landslides is to use this absolute coherence, assigning low-coherence pixels as 'landslide' and high-coherence pixels as 'not landslide'. For a few cases, landslides have been identified using this method, e.g., [37]. However, as low coherence may be due to factors unrelated to the trigger event, such as the presence of dense vegetation or unfavourable imaging geometry, this method is likely to result in a classification surface with many false positives--low coherence regions that are classified as 'landslide' but are not landslides.
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 3 Methodology
### System Overview  
The inspection target of the proposed system is a small part that uses a reel as shown in Figure 4C. Condensers, resistors, diodes, and power coils are some examples of such targets. In this study, a coil is selected as the inspection target.  
Figure 4A shows the overall structure of the trial product that performs inspections as the system is applied, and Figure 4B shows the detailed images of the main components in the trial product such as the camera, lens, and light. The overall process of the coil defect detection system is as follows: A coil, which is an inspection target, is inserted into the equipment, in which the camera and light are installed through the rail. The inserted coil is photographed through the camera equipment, and the system determines whether the photographed coil is normal or defective through the trained model. The dataset for training the deep learning model used in this study is also created using the trial product.  
Figure 4: Experimental equipment for data acquisition and quality inspection.  
Figure 3: Diagram of the coil defect detection system.
## 3 Results
### Results Regarding Anterior Arch Length  
In the case of the anterior arch length variable, a minimum of 3.20 was observed in the case of measurements made with the new and conventional instruments. The minimum value of the mean of the measurements, i.e., 4.02, was obtained by the new instrument measurement and the maximum of 4.04 with the digital measurement. It can be seen that the minimum standard deviation of 0.422 was obtained with the conventional measurement and the maximum of 0.423 with the digital measurement.  
Concerning the anterior arch length, it was observed that the data within the sample did not deviate from a normal distribution. The resultant findings are as follows: the classical measurement of the arch perimeter yields a \(p\)-value of 0.081, surpassing the significance threshold of 0.05. Similarly, the instrument measurement produces a \(p\)-value of 0.095 (>0.05), and the digital measurement results in a \(p\)-value of 0.058 (>0.05) (Figure 7).  
For the measurement of the anterior arch length, the difference from the conventional measurement was \(-0.00025\)\(\pm\) 0.001 and from the digital measurement \(-0.02\)\(\pm\) 0.0006 (Figure 8).  
Significant differences among the three measurement types were identified through the Wilcoxon test applied in the case of anterior arch length to the following parameters: digital measurement-new instrument \(p=0.00000424<0.05\). Between conventional measurement and new instrument measurement (\(p=0.932>0.05\)), there are no significant statistical differences. The same is observed in the case of conventional measurement and digital measurement \(p=0.06>0.05\)) (Figure 8).  
Figure 7: Density and quantile–quantile plots for the measurement distributions of anterior arch length corresponding to the type of measurement.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 2 Background and Related Work
### Quantum Gates  
Quantum gates are unitary operations that change the state of qubits. Each quantum gate is associated with a specific matrix that describes its action on the state vector of qubits. A gate that acts on \(n\) qubits will be represented by a \(2^{n}\times 2^{n}\) unitary matrix. We provide a brief description of some notable gates which have been used in this work.  
* Hadamard Gate: A single-qubit gate that maps the basis state \(|0\rangle\) to \(\frac{|0\rangle+|1\rangle}{\sqrt{2}}\) and \(|1\rangle\) to \(\frac{|0\rangle-|1\rangle}{\sqrt{2}}\), thus creating an equal superposition of the two basis states.
* RX, RY, and RZ Rotation Gates: These unitary operators rotate the state vector of a qubit around a given axis by a given angle. The RX gate is one of the rotation operators. It is a single-qubit rotation through angle \(\theta\) radians around the x-axis. The RY gate is a single-qubit rotation through angle \(\theta\) radians around the y-axis. Similarly, the RZ gate is a single-qubit rotation through angle \(\theta\) radians around the z-axis.
* CNOT Gate: A universal two-qubit quantum gate that flips the state of the second qubit, the target qubit, if and only if the first control qubit is in the state \(|1\rangle\). A CNOT gate is used to entangle two qubits. Any quantum computation can be performed using only CNOT gates and single-qubit gates. The CNOT gate matrix can be derived from a unitary matrix by flipping the target states based on the control states.