esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions  
### Interpretation of InSAR Results  
Average ground deformation velocities within the study area are presented in Figure 5. As shown in the velocity map in Figure 5a obtained by SBAS-InSAR method, the deformation velocity of the study area ranges from \(-58.10\) to \(78.58\) mm/yr. Most of the area is stable with an average deformation velocity of \(5.69\) mm/yr, while the region north of Chatian Town is observed to show an overall movement away from the satellite apparently. It may be due to the regional overall ground surface movement. In the figure, a zoom region at Huangtian Town is presented, and two local abnormal areas can be observed at the bottom left and center of the figure in red color. In the present study, the focus is on the slope along the highway; therefore, only the area A (approximately located on an artificial slope) is taken into consideration in the following analysis.  
According to the abnormal displacement shown in the ground deformation velocity map, 22 potential hazards as shown in Figure 5b can be initially identified. In addition, the historical slope hazards are also drawn in Figure 5b. By comparing above data, it can be found that the hazards identified in this study are mainly distributed to the south of Chatian Town, which is basically consistent with the spatial distribution of historical hazards. Furtherly, it can be suggested that the granite residual layer and the Quaternary soil layer widely distributed south of Chatian Town are more vulnerable to slope hazards.
## 3 Results
### Structural Effects
the atoms are slightly off their lattice positions around the 7 nn and 9 nn shell distance from the Ga (Figure 7). A summary of formation energies of the various mono-vacancies studied in this work are shown in Table 2.  
The above results suggest that the monoclinic distortion is induced in all Ga concentrations, and the formation energy and orientation of this distortion is dependent on the distance the mono-vacancy is from the Ga atoms. We would like to note that a variation of this distortion does form (not shown) when the mono-vacancy was far from the Ga in the 0.926 and 1.852 at. % Ga concentrations, but the change in formation energy was not as significant, as calculated in the 2.778 at. % Ga concentration. Another general trend is the increase of the formation energy when the mono-vacancy is located at 2 nn \begin{table}
\begin{tabular}{c c c} \hline \hline
**at. \% Ga** & **Distance from Ga** & \(\mathbf{E_{f}^{\text{vac}}}\) \\
**at. \% Ga** & **Distance from Ga** & **(eV)** \\ \hline
0.926 & 1 nn & 1.39 \\  & 2 nn & 1.48 \\  & 6 nn & 1.25 \\
1.852 & 1 nn & 1.49 \\  & 1 nn between 2 Ga & 1.58 \\  & 2 nn & 1.50 \\  & 2 nn between 2 Ga & 1.61 \\  & 7 nn & 1.22 \\
2.778 & 1 nn & 1.39 \\  & 1 nn between 2 Ga & 1.57 \\  & 2 nn & 1.44 \\  & 2 nn between 2 Ga & 1.53 \\  & 2 nn between 3 Ga & 1.75 \\  & 4 nn & 1.10 \\  & 7 nn & 1.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Formation energies (\(E_{f}^{\text{vac}}\)) of mono-vacancy at varying distances from the Ga.  
Figure 7: Radial distribution function (RDF) of a mono-vacancy from first (1 nn), second in between three Ga atoms (2 nn bw 3 Ga), fourth (4 nn), and seventh (7 nn) nearest neighboring distance from the Ga atom for 2.778 at. % Ga. RDF is calculated from the perspective of the Ga atom. distance from the Ga atom when compared at 1 nn distance. This implies that the Ga interaction is still influential up to the 2 nn shell suggestive of superexchange interactions. This trend is also present in a previous all-electron DFT study of hydrogen-vacancy complexes in 3.125 at. % Ga, where the calculated formation energies of a vacancy from the Ga increased from 0.76 eV for 1 nn to 1.05 eV for 2 nn distance and then decreased to 0.76 eV at 4 nn distance from the Ga [25], and this trend did not change with the inclusion of spin-orbit coupling.
## 4 Conclusions  
The benefit of NaOH combined CaO for the CS pretreatment was investigated to enhance the biogas production. In short, Treat-CSB was chosen as the optimum pretreatment option, since it could obtain high lignin removal efficiency and biomass recovery. In addition, when Treat-CSB was co-digested with swine manure, higher methane yield could be obtained, which was similar to that of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Chemical Loading**} & \multirow{2}{*}{
\begin{tabular}{c} **CH\({}_{d}\)/Com** \\ **Sover** **(mL/g VS)** \\ \end{tabular} } & **Gross Biomass** & **Benefit of** & **Revenue** & **Cost for** & **Cost for** & **Net Benefit** \\  & & **Energy** & **Electricity** & **due to** & **NaOH** & **CaO** & **from** \\  & & **Production** & **Generation** & **Pretret-ment** & **Input** & **Input** & **Pretreatment** \\  & & **(k/kg TS)** & **(\$/ton TS)** & **(\$/ton TS)** & **(\$/ton TS)** & **(\$/ton TS)** & **(\$/ton TS)** & **(\$/ton TS)** \\ \hline RCS & 237 & 4690 & 137 & 0 & & & \\ \hline Treat-CSA 0.1 & 328 & 6170 & 180 & 43.0 & 45.0 & 0.0 & -2.0 \\ NaOH & 328 & 6040 & 176 & 39.0 & 33.8 & 3.6 & 1.9 \\ \hline Treat-CSC 0.05 & & & & & & \\ NaOH + 0.05 C CaO & & & & & & \\ \hline Treat-CSD 0.025 & & & & & & \\ NaOH + 0.1 C CaO & 249 & 4680 & 136 & –0.38 & 11.3 & 7.2 & -18.8 \\ \hline \hline \end{tabular}  
* * Gross biomass energy production = methane yield \(\times\) VS\(\times\) solid recovery \(\times\) lower heating value of methane \(\times\) effective electric efficiency.  
\end{table}
Table 3: Effect of CaO and Na(OH) pretreatments on net benefit of electricity generation from methane of CS digestion.  
the co-digester fed with CS pretreated with NaOH only. This result means that CaO addition could effectively reduce the use of expensive NaOH in the CS pretreatment while not deteriorating biogas yield; a net benefit of $1.9/ton TS could be earned. In short, pretreatment of CS with NaOH combined CaO is a cost-effective pretreatment option for the CS anaerobic digestion.
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 2 Materials and Methods
### Topology of Artificial Neural Networks  
A single neuron is unable to address problems of significant complexity independently. However, when neurons are aggregated, as observed in the human brain, they create a multilayer network capable of solving more intricate problems once trained. The structure of this network, referred to as the topology of an artificial neural network, arises from the arrangement of neurons organized into layers [37; 38; 39]. Expanding on this concept, three types of layers can be identified, each of which will be elaborated on below:* Input layer: receives the data directly, typically associated with input dimensions or data points. In this study, lung pathology images represent this input data, where each pixel in the image corresponds to a dimension or variable. Through the preprocessing proposed in this research, the images are adjusted to a resolution of 30 \(\times\) 30 pixels, resulting in an input layer composed of 900 neurons.
* Hidden layers: play a pivotal role in shaping the network's architecture by connecting neurons. Striking the right balance in terms of layer count, neurons per layer, and connectivity levels is crucial. While increasing these parameters can offer benefits, it may also prolong training duration and heighten the risk of overfitting. Hidden layers retain critical information linked to input data, accumulating insights in synaptic weights during training. This knowledge is indispensable for the network to discern patterns in new data. In this study, neural networks ranging from 1 to 4 layers are examined, assessing configurations of 10 to 500 neurons per layer to determine optimal settings for achieving high accuracy in processing lung pathology images.
* Output layer: receives information from the hidden layers and externally transmits it. In this study, the objective is to classify lung pathology images into three categories: normal, viral, and bacterial. Each category is represented by a specific neuron in the output layer, allowing the network to specialize during training. When presented with an unknown image, the neuron corresponding to the appropriate class activates to identify it, and the highest produced value is associated with the predicted label. Additionally, the option of using a single output neuron is considered, where each class is assigned a unique value, facilitating the inference of the associated label.
## 2 Related Work  
Over the past few years, the development of MAC protocols for UWSNs has advanced considerably, with growing emphasis on overcoming three core challenges: optimizing energy consumption, mitigating data collisions, and adapting effectively to the unpredictable nature of underwater environments. Researchers have explored various protocol structures, broadly categorized into contention-based, schedule-based, and hybrid models--each presenting their own balance of benefits and trade-offs across key performance indicators. For instance, UW-ALOHA-Q [27] employs reinforcement learning to support adaptive channel access, though it falls short in delivering a comprehensive evaluation of energy efficiency. RA-MAC [28], on the other hand, integrates a relay mechanism that supports both real-time and short-range transmissions. While this enhances energy performance, it introduces added complexity in managing relay nodes. Many of these protocols show potential under dynamic conditions but tend to suffer from issues, such as hidden terminal interference and limited scalability, especially in densely populated network scenarios. Protocols like OCMAC applies synchronous duty cycling with RTS scheduling to reduce energy overhead, while O-ED-MAC attempt to address collisions through depth-aware scheduling in [29; 30], respectively. These approaches, although efficient in relatively stable environments, often assume fixed or minimally mobile node topologies, limiting their effectiveness in mobile UWSNs. CA-MAC [31] attempts to integrate time synchronization and power control into a unified framework, yet the complexity of its implementation presents practical barriers. More recently, hybrid designs, such as TDTSPC-MAC [13], have been proposed, incorporating TDMA structures alongside power control strategies. Similarly, DC-MAC [32] utilizes game-theoretic approaches to facilitate collision-free scheduling among nodes. While learning-based solutions like RL-MAC [33] bring adaptiveness to the forefront, they often require considerable computational resources, which may not be suitable for energy-constrained nodes. RAP-MAC [34], for example, has demonstrated strong performance in real-world underwater testing environments, but it still lacks depth in its handling of energy optimization strategies. The evolving research in UWSNs has driven notable innovation in MAC protocol design, with particular focus on addressing spatial fairness, managing long propagation delays, and mitigating hidden terminal issues. One notable contribution is the SFM-MAC protocol [35], which introduces a multichannel approach for a single transceiver system. This solution utilizes a Markov chain-based reservation model to assess control channel
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 5 Dual- and Triple-Frequency Multi-GNSS PPP Atmospheric Constraining with AR  
This section presents analyses on the impact of atmospheric constraints in multi-GNSS PPP processing. The purpose is to outline the progression of improvement in the usage of atmospheric constraints. While the aim is to improve the solution quality and accuracy, further insight is also provided on how different processing modes are impacted by atmospheric constraining. It must be noted that post-processed products are used for the analyses presented in this section. These post-processed products include the orbits and clocks as well as GIM. Real-time analyses were not considered in this paper given the unavailability of real-time GIM delays and ZPD estimates.
## 1 Introduction
[PERSON] [23] compared the three different data-driven methods, including multiple linear regression, ANN and SVM, to estimate the influences of both atmospheric and hydrological drivers on monthly NEE using three years of measurements at a peatland site in northwestern Turkey and obtained satisfactory predictive accuracy. On the other hand, in recent years, another two advanced machine learning modeling techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN), have been successfully applied in other fields, such as estimation of reference evapotranspiration [24, 25], evaporation prediction [26, 27], stream-flow forecasting [28, 29], and groundwater level prediction [30, 31]. These two methods have been widely acknowledged, to a large extent, due to their capability and efficiency in solving complicated nonlinear problems with only a few variables. Moreover, ANFIS method is designed through integrating fuzzy inference rules into adaptive neural networks with the intention of extracting useful knowledge involved in the trained networks in the form of fuzzy logic expression [32] and thus can conquer the weakness of knowledge interpretation for neural networks. Unlike both ANN and SVM approaches, GRNN method has the advantage of quick speed of learning, because it does not need a large number of iterations to determine optimal inner algorithms or parameters [33]. To the best of the authors' knowledge, no research has been undertaken to date that applies both the GRNN and ANFIS approaches to predict the carbon fluxes at the ecosystem level using the EC-based data. The present study attempts to model and predict the carbon fluxes based on the continuous six-year EC measurements in three old-growth forests under different climates. The major goal of this research is to investigate the feasibility of GRNN and ANFIS in terms of estimating the carbon flux exchanges between the biosphere and the atmosphere. Further, the results of both the well-established GRNN and ANFIS models are compared with those of commonly used ANN and SVM models and traditional empirical model (multiple linear regression, MLR). The secondary objective of the study is to examine the modeling differences among the three primary components of carbon exchanges, including gross primary production (GPP), ecosystem respiration (\(R\)) and net ecosystem exchange (NEE).
## 3 Case Study
### Comparison with Other Algorithms
#### 3.6.2 Comparison of Prediction Results  
The evaluation indicators for the prediction results of the improved Informer algorithm, RF, and LSTM are shown in Figure 13. The improved Informer algorithm achieved MSE, MAE, RMSE, and R\({}^{2}\) values of 4.13, 3.65, 2.03, and 0.86, respectively, significantly outperforming the RF and LSTM algorithms. RF exhibited the worst performance among the algorithms tested in this study. This is because its decision tree-based structure is not well-suited for predicting long-time-series data, such as shield tunneling data. The LSTM algorithm, as a learning model designed for long-time-series data, achieved far higher prediction accuracy than the RF algorithm. However, it lagged behind the improved Informer algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & **Hyperparameter** & \multirow{2}{*}{**Optimal Value**} \\  & **Optimization Range** & \\ \hline units & [50, 200] & 155 \\ activation & [’relu’, ’tanh’] & ’tanh’ \\ recurrent\_activation & [’sigmoid’] & ’sigmoid’ \\ use\_bias & [True, False] & True \\ kernel\_initializer & [’glorot\_unifor’, & ’glorot\_uniform’ \\  & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimal hyperparameter settings for LSTM algorithm.  
Figure 13: Comparison with other prediction algorithms (about evaluation indexes).  
Figure 14 presents a comparison of the improved Informer algorithm with other machine-learning algorithms. During the prediction of surface settlement from rings 340 to 370, the RF algorithm produced large errors, highlighting its limitations in accurately predicting large variations in settlement. The prediction results of the LSTM algorithm generally agreed with observed settlement trends. However, they showed considerable deviations from the monitoring data at rings 215, 240, 280, 335, and 360. Compared with the RF and LSTM algorithms, the improved Informer algorithm provided predictions that were closer to the actual settlement values in terms of overall trends and responses to significant variations. It could accurately capture the actual surface settlement induced by shield tunneling. Compared with the RF and LSTM algorithms, the improved Informer algorithm is better suited for predicting surface settlement in shield tunneling operations.
# EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid
## 2 Smart Grid Network Architecture with EV Access  
### Network Architecture Design  
In this section, we introduce the smart grid network architecture based on cloud computing and fog computing, which is the hardware foundation for implementing intelligent algorithms. The smart grid network architecture consists of three layers, namely the user layer, service layer, and network layer. The user layer is used to collect user information, the service layer is responsible for data analysis, requesting responses, and providing data-driven charging and discharging scheduling determined by cloud computing, fog computing, and AI technology, and the network layer integrates multiple communication technologies to ensure the interactions of the user layer and service layer. The smart grid network architecture based on cloud computing and fog computing is the hardware foundation, which can realize various intelligent algorithms.  
As shown in Figure 1, according to the cross-layer design, the V2G auxiliary service system framework of this model can be divided into the service layer [17], network layer, and user layer. This cross-layer design takes into account the information integration and service interaction requirements of the service network and security requirements of all levels [18].  
The V2G auxiliary service system adopts service-oriented technology, optimized control, and coordinated control theory to guide the distributed control system. The specific application of service-oriented technology is based on the actual charging demand of EV users and the real-time operating state of the power grid. Information interaction and hierarchical control can be achieved through visualization technology and user interface technology.  
Figure 1: An illustration of vehicle-to-grid (V2G) network environment.  
Figure 2 is the implementation diagram of EV grid integration, which shows the three layers of the V2G auxiliary service system mentioned above. The mesh communication between EV and EV supply equipment can be achieved through power line communication (PLC) and 5G networks. The data from electric vehicles and any other devices is stored by cloud. It could be processed for a variety of application purposes, such as optimized charging.  
As shown, the user layer could provide an interface for the user to interact with the service and collect the information of each entity in real time and upload it to the network layer. The network layer is the connection between the user layer and service layer. The service layer is responsible for dealing with user requests, making intelligent decisions, and providing macro control over the entire grid.
# Blind Image Quality Assessment with Deep Learning:
## 3 Experimental Setup  
We initiated this study by following the methodology and the source code presented by the authors at [16]. Two relevant aspects had to be embraced from early on, in order for us to be able to conduct the study. First, the code available at ([[https://github.com/subpic/koniq](https://github.com/subpic/koniq)]([https://github.com/subpic/koniq](https://github.com/subpic/koniq)), accessed on 25 November 2022) was implemented by the authors on Google Colab notebooks ([[https://colab.research.google.com/](https://colab.research.google.com/)]([https://colab.research.google.com/](https://colab.research.google.com/)), accessed on 25 November 2022). In a first attempt, we tried to run the code on a Google Colab platform, under a free plan. However, the resources of this platform are not guaranteed as static allocations and are limited under these conditions. As such, we were not able to simply execute the code as is. A first straightforward conclusion was that in order to be able to train the KonCept512 model using Google Colab infrastructures, we would have to set up a paid plan. This financial constraint, which was not explicitly addressed in the original manuscript, led us to the use of local computing infrastructure for the remainder of the study.  
Figure 1: End-to-end architecture of trained KonCept512 model to predict MOS values [9].  
Second, the authors in [9] used Python Keras library ([[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/)), accessed on 25 November 2022) with TensorFlow ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/)), accessed on 25 November 2022) as a backend and ran on two NVIDIA Titan Xp GPUs ([[https://www.nvidia.com/en-us/titan/titan-xp/](https://www.nvidia.com/en-us/titan/titan-xp/)]([https://www.nvidia.com/en-us/titan/titan-xp/](https://www.nvidia.com/en-us/titan/titan-xp/)), accessed on 25 November 2022). Nevertheless, they do not specify the versions of the library used. As a result, we had to make several assumptions, namely batch size and library versions, and adjust the code in order to replicate the author's code on our local infrastructure (one NVIDIA RTX 3080 ti GPU ([[https://www.nvidia.com/en-eu/geforce/graphics-cards/30-series/rtx-3080-3080](https://www.nvidia.com/en-eu/geforce/graphics-cards/30-series/rtx-3080-3080) ti/]([https://www.nvidia.com/en-eu/geforce/graphics-cards/30-series/rtx-3080-3080](https://www.nvidia.com/en-eu/geforce/graphics-cards/30-series/rtx-3080-3080) ti/), accessed on 25 November 2022).  
This section details all the steps explored during the experimental setup that led us to results that are very similar to the ones presented by the authors in [9]. Moreover, as previously introduced, we applied the KonCept512 models to a lifelogging dataset with the objective of predicting the MOS of thousands of images captured in a person's daily life.