esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
###### Abstract  
The GRACE Follow-On satellite mission measures distance variations between its two satellites in order to derive monthly gravity field maps, indicating mass variability on Earth on a scale of a few 100 km originating from hydrology, seismology, climatology and other sources. This mission hosts two ranging instruments, a conventional microwave system based on K(a)-band ranging (KBR) and a novel laser ranging instrument (LRI), both relying on interferometric phase readout. In this paper, we show how the phase measurements can be converted into range data using a time-dependent carrier frequency (or wavelength) that takes into account potential intraday variability in the microwave or laser frequency. Moreover, we analyze the KBR-LRI residuals and discuss which error and noise contributors limit the residuals at high and low Fourier frequencies. It turns out that the agreement between KBR and LRI biased range observations can be slightly improved by considering intraday carrier frequency variations in the processing. Although the effect is probably small enough to have little relevance for gravity field determination at the current precision level, this analysis is of relevance for detailed instrument characterization and potentially for future more precise missions.  
GRACE Follow-On; instrumentation; laser ranging; microwave ranging 2022144335[[https://doi.org/10.3390/rs14174335](https://doi.org/10.3390/rs14174335)]([https://doi.org/10.3390/rs14174335](https://doi.org/10.3390/rs14174335))
# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Analysis
#### 6.4.4 Data-Fusion Locations  
Due to the development of hardware foundation, communication technology, and computing technology, data intelligence shows the development from cloud intelligence to edge intelligence and then to terminal intelligence. Different fusion technologies provide data-preparation functions with different performances for different levels of data analysis. The data-fusion location is taken as the classification standard in this paper, and the following three aspects are discussed.  
* _Terminal data fusion and analysis_: Terminal devices are the important hardware foundation of data generation and acquisition. As a computing unit exposed to data for the first time (although its storage, communication, and computing capacity are limited), the amount of data can be enriched with the use of data perception and generation technology, while the quality of data can be improved by data-cleaning and data-filtering technologies. On this basis, some analysis models established at the cloud can be used to obtain computing results efficiently and be put into the application as soon as possible;
* _Edge-cloud data fusion and analysis_: The edge cloud is the computing and resource storage unit nearest to the IoT terminals. As a higher-level management system, the edge cloud can provide higher computing, storage, and communication capabilities. Therefore, with the edge cloud, fusion and analysis can be conducted to more data, and data-integration technology provides strong support for the integration and fusion of distributed data sources. In addition, the edge cloud can prepare better quality data for the cloud and more efficient services for terminals based on data-transmission fusion (3C resource fusion) and data-fusion storage;
* _Cloud data fusion and analysis_: Because there are massive historical data and a large amount of real-time data in the cloud, in addition to using statistical methods or supervised machine-learning algorithms for the fusion and analysis of structured data, a greater proportion consists of conducting deep information mining for a large number of semi-structured and unstructured data based on AI technologies such as deep learning, reinforced learning, and label-less learning. Moreover, feature-level fusion for different data and decision-level fusion based on computing results also provide stronger decision-making and service for terminals.
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
###### Abstract  
Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.  
underwater sensor networks; green computing; whale optimization; sensor networks +
Footnote †: journal: Sensors
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 3 Results and Discussion
### Acid Leaching Effects
#### 3.4.4 Influence of Leaching Agent Concentration  
This study investigated the effect of the sulfuric acid concentrations (1 mol/L to 6 mol/L) on the leaching rate of lithium at an R\({}_{\mathrm{fs}}\) of 1:2, a roasting temperature of 850 \({}^{\circ}\)C, a roasting duration of 2.5 h, a leaching temperature of 80 \({}^{\circ}\)C, a solid-to-liquid ratio of  
Figure 10: Effect of leaching duration on the leaching rate of lithium.  
Figure 9: Effect of solid–liquid ratio on the leaching rate of lithium.  
1 g:30 mL, and a leaching duration of 120 min. The results are shown in Figure 11. As shown in Figure 11, the leaching rate initially increased and then decreased, where the maximum of 92.00% was reached at a sulfuric acid concentration of 3 mol/L. This phenomenon was related to the silicate product. When the sulfuric acid concentration is too high, the generated silicate precipitates adsorb onto the surface of the fly ash and attach to the reaction vessel walls, leading to a poor leaching. In addition, a sulfuric acid concentration beyond 4 mol/L yields a large amount of translucent gel-like precipitate inside the reaction vessel. This precipitate was identified as silicate. During reaction, the mixed precipitate of silica and silicates would hinder some roasted product reactions, thus resulting in a decrease in leaching rate when the acid concentration is too high.
# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
## 3 Results and Discussion
### Evaluating the Performance of Existing Convolutional Neural Networks
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Epochs** & **CNN** & **Accuracy** & **Precision** & **Recall** & **AUC** & **F1-Score** \\ \hline \multirow{4}{*}{50} & AlexNet & 0.688 & 0.742 & 0.848 & 0.723 & 0.792 \\  & GoogLeNet & 0.817 & 0.868 & 0.821 & 0.836 & 0.843 \\  & ResNet-50 & 0.739 & 0.848 & 0.745 & 0.755 & 0.793 \\  & ResNet-50 Pre-trained & 0.767 & 0.821 & 0.852 & 0.810 & 0.836 \\  & AlexNet & 0.707 & 0.866 & 0.670 & 0.738 & 0.755 \\
100 & GoogLeNet & 0.826 & 0.867 & 0.840 & 0.845 & 0.853 \\  & ResNet-50 & 0.806 & 0.852 & 0.834 & 0.828 & 0.843 \\  & ResNet-50 Pre-trained & 0.811 & 0.865 & 0.818 & 0.843 & 0.841 \\  & AlexNet & 0.821 & 0.885 & 0.815 & 0.836 & 0.849 \\  & GoogLeNet & 0.758 & 0.778 & 0.711 & 0.791 & 0.743 \\
150 & ResNet-50 & 0.785 & 0.887 & 0.753 & 0.835 & 0.815 \\  & ResNet-50 Pre-trained & 0.815 & 0.854 & **0.859** & 0.825 & 0.857 \\  & AlexNet & 0.749 & 0.853 & 0.736 & 0.778 & 0.790 \\
200 & GoogLeNet & 0.823 & 0.861 & 0.841 & 0.841 & 0.851 \\  & ResNet-50 & 0.813 & 0.830 & 0.747 & 0.853 & 0.828 \\  & ResNet-50 Pre-trained & 0.807 & 0.910 & 0.767 & 0.843 & 0.833 \\  & AlexNet & 0.752 & 0.841 & 0.793 & 0.760 & 0.816 \\
250 & GoogLeNet & 0.811 & 0.869 & 0.805 & 0.831 & 0.836 \\  & ResNet-50 & **0.839** & 0.894 & 0.836 & 0.856 & **0.864** \\  & ResNet-50 Pre-trained & 0.777 & 0.864 & 0.781 & 0.791 & 0.820 \\  & AlexNet & 0.814 & 0.873 & 0.818 & 0.826 & 0.845 \\
300 & GoogLeNet & 0.802 & 0.855 & 0.818 & 0.837 & 0.836 \\  & ResNet-50 & 0.798 & 0.805 & 0.756 & 0.821 & 0.780 \\  & ResNet-50 Pre-trained & 0.816 & **0.929** & 0.753 & **0.860** & 0.832 \\ \hline \end{tabular}
\end{table} 
Table 6: Performance results for existing convolutional neural networks in test dataset. Comparing Training Time of Existing CNNs for Optimal Performance  
Following the review of the obtained results, the CNNs with the best and most consistent results for the metrics were selected to compare the time spent training each CNN until it reached its best values (Table 7). Evaluating the results, it can be noted that ResNet-50 outperformed the other CNNs in almost every metric, only having a difference of 0.023 to the highest value obtained by the ResNet-50 pre-trained model at the recall metric. However, it needed 3 h and 23 min of training, which corresponds to 250 epochs, while GoogLeNet achieved competitive values in a shorter training time of 2 h and 7 min and a lower number of epochs (200 epochs). The superior performance of ResNet-50 can be attributed to its deep architecture, which allows it to capture complex patterns and features in the dataset, but the inception architecture present in GoogLeNet allows it to capture a wider range of features in the data while also reducing the number of parameters and computations needed.
## 2 Materials and Methods
### Fatigue Tests and Fractography  
The fatigue tests were performed on a test bench specifically developed for this purpose, based on the NP EN ISO 4210-8:2019 standard [15]. Figure 5a shows a real photograph of the test bench, and Figure 5b illustrates the standard suggestion.  
Figure 4: Crank arm produced by L-PBF.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Al** & **Si** & **Mg** & **Mn** & **Cu** & **Fe** & **Zn** & **Ti** & **Ni** & **Pb** & **Sn** \\ \hline Bal. & 9–11 & 0.25–0.145 & 0.45 & 0.05 & 0.55 & 0.10 & 0.15 & 0.05 & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nominal chemical composition of the used aluminum alloy (wt.%) [16].  
The NP EN ISO 4210-8:2019 standard [15] recommends testing the crank arm at an angle of 30\({}^{\circ}\) or 45\({}^{\circ}\), with the load applied at 65 mm from the longitudinal plane of the crank arm, as can be observed in Figure 5a,b. This device was attached to a servo-electric testing machine, the model Instron ElectroPuls E10000 (Norwood, MA, USA). The fatigue tests were performed at room temperature under constant amplitude (R = 0) with sinusoidal load waves, and a cyclic frequency of 5 Hz. The objective of the fatigue tests was to comply with the recommendations of the NP EN ISO 4210-8:2019 standard for a youth and adult leisure bicycle, specifying a fatigue life of 100,000 cycles with 1300 N of maximum load.  
Additionally, the morphology of the fracture surfaces and the primary failure mechanisms were analyzed using a Hirox HR-5000 3D optical microscope (Tokyo, Japan) and a Hitachi Jeol JSM-5310 scanning electron microscope (SEM).
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 6 Conclusions  
In conclusion, the Convoy VANET system at the very least accomplishes its goal of rapid message propagation in spite of high message loss rates. The key takeaway is that the ability to accurately predict the transmission behavior of ones' peers allows for more rapid decision making with respect to retransmissions. Reducing the unknown variables and their range of values reduces the uncertainty in peer behavior (e.g., the retransmission delay is largely determined by relative position). This, in turn, enables more accurate, implicit knowledge of peers' states and thus smaller delays between necessary retransmissions and faster message propagation. While the channel efficiency aspect likely has room for improvement, the results are reasonably close to intuitive expectations given the channel reliability.
## 3 Application of Harmony Search  
HS is a population-based algorithm [41]. To execute it, np number of vectors are first created, each representing a path as previously defined. These vectors form the original form of the harmony memory (HM). This is followed by finding T values of all paths and determining the best and worst among them. Normal cycles start after this introductory step. At each cycle, a new vector representing an additional path is created so that the number of vectors at hand becomes np + 1. The basic characterization of HS becomes decisive at this step. Parameters known as the HM considering rate (HMCR) and pitch adjusting rate (PAR), both with values between 0 and 1, are used. HMCR indicates that a new component of the new vector will be chosen with a probability of HMCR from those in HM  
Figure 1: Five-wall, five-joint example. (**a**) Numberings of walls and nodes; (**b,c,d**) nozzle movements for Candidates 1, 2, and 3, respectively.  
otherwise, it is arbitrarily in the feasible range. PAR indicates that the component chosen will not be taken as is but instead with a slight change with PAR probability. In the original study on HS [41], these values were given as 0.95 and 0.10, respectively. After the np + 1 st vector is determined, its T value is calculated as with previous vectors. The last step in the cycle is to eliminate the vector with the worst T value such that np vectors in HM remain. These cycles continue until a decision is taken regarding the convergence of the procedure.  
In the present study, a parameter search is conducted to find HMCR and PAR values that are particularly suitable for the current problem. This search is in fact enhanced to include two additional parameters. The first, called PARrange, is used to find the range in which slight changes would be made when pitch adjustment is applied. The second, called PARsign, is very specific to the problem considered, namely, the probability of changing the direction of pouring concrete into a wall from + (from start to end joint) to - (from end to start joint), or vice versa.  
Parameter search is performed by making runs with different choices for all these four parameters, where the range searched becomes increasingly narrower. The results of these runs for HMCR are shown in Figure 2. Figures 3-5 show the runs for PAR, PARrange, and PARsign, respectively. The parameters found are 0.95 for HMCR, 0.34 for PAR, 0.6 for PARrange, and 0.16 for PARsign, which can also be estimated from figures given. The value of 0.6 for PARrange initially seems to  
After the parameters of the algorithm are determined, HS is continued by eliminating the worst vector in HM until a certain number of cycles are completed. In all runs, the aforementioned parameters are used.
## 5 Simulation and Analysis
### Performance Analysis of Hybrid TOA/AOA Virtual Station Localization Algorithm  
MS localization accuracy of the proposed algorithm is compared with the other hybrid TOA/AOA algorithms, improved grid search algorithm (IGSA) [35], hybrid TOA/AOA algorithm (HTA) [36] and new TOA/AOA constrained algorithm (NTA-CA) [37], with cumulative distribution function (CDF) curve of MS localization error. The error of TOA and AOA satisfies \(N\big{(}0,1^{2}\big{)}\) and \(N\big{(}0,0.5^{2}\big{)}\), respectively. As shown in Figure 8, for the case of the DOS model, at a probability of 90%, the localization error of the proposed algorithm is about 5 m, whereas that of IGSA, HTA and NTA-CA is about 30 m, 45 m and 60 m for the same probability, respectively. Clearly, the proposed algorithm outperforms all other  
Figure 6: \(P_{IE}\) versus \(\delta_{T}\).  
hybrid TOA/AOA algorithms greatly. The case of the ROS model is similar to that of the DOS model as shown in Figure 9.  
In the proposed localization algorithm, the MS localization is based on the scatterer localization, the localization error of the scatterer will propagate to the procedure of MS localization. The MS localization error can be divided into two parts: the localization error derived from the scatterer localization stage and the localization error derived from the MS localization stage. MS localization error should be greater than that of the scatterer, this conclusion roughly agrees with Figures 8 and 9. Moreover, it can be observed that with the increasing localization error, the CDF curve of MS gradually approaches and eventually almost coincides with that of the scatterer, which demonstrates that, as the localization error increases, the localization error derived from the MS localization stage is almost gradually eliminated by the averaging of the rest three estimated positions of MS in MS localization stage.
# Accuracy of Code GNSS Receivers under Various Conditions
## 4 Results  
The obtained fixed solutions were characterized by deviations at the level of 2 cm for the horizontal component and 5 cm for height, so for further analysis, they were assumed as error-free; due to the lower accuracy of the remaining receivers by about 2 orders of accuracy, all measurement points acquired from code receivers were used for analysis. For each of the solutions, deviations in the horizontal components (NE) and a line graph of changes in the height component (U) were presented from the precise RTK solutions for Garmin receivers. In order to best compare the solutions against each other, the scale in each figure of the NE and U deviations was the same. Moreover, the authors did not find relations between specific models of the antenna in results.
## 5 Discussion
### Provision of Quality-of-Service (QoS)  
When the V2P system predicts that a vehicle-VRU crash is probable, it is necessary to prioritize the communication between the crash-prone pair over other V2P communications. This is required in order to be more certain about the crash so that both the devices (vehicle unit and VRU device) can warn their users about the crash. However, 802.11p-based systems do not provide such a mechanism despite being especially designed for V2X communications [14]. We discuss two possible solutions to address this challenge for 802.11p-based systems.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 2 Related Works
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Year** & **Papers** & **Area/Key Points** \\ \hline
2020 & [1, 10] & • [1]—Global trends in energy consumption of communication technology, electricity intensity, 5G, forecasting, networks and operations, video streaming \\
2020 & [1, 10] & • [10]—Base station sleep modes, discontinuous transmission, energy efficiency, reinforcement learning \\ \hline
2021 & [3, 14, 20, 21, 24, 25] & • [3]—Strategy optimization of 5G wireless network energy consumption based on artificial intelligence and other emerging technologies, forecasting algorithms for network traffic load, service-aware energy saving for a 5G base station \\
2021 & [14, 25]—Open-air interface platform based RAN sharing prototype, network slicing, SLA-based RAN sharing architecture, radio resource allocation algorithm, multi-operator spectrum sharing, multi-objective optimization \\
2021 & [3, 21, 20, 21, 24]—Traffic forecasting, overview of network optimization strategies, power modeling \\
2020—Q-learning-based dynamic adaptation of radio resource, advanced sleep modes, bandwidth and antenna adaptation, energy efficiency \\
2024—HRL approaches, challenges, domains for evaluating HRL, temporal abstraction and task decomposition, multidimensional state space \\
2025—Cognitive cellular networks, energy-harvesting base stations, spectrum sensing and leasing scheme in a multi-operator scenario, reinforcement learning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the related works in the years 2020–2023.
## 3 Proposed Method  
The proposed system extracts features through an autoencoder from the voice data in a user's mobile device. After that, the extracted features are transmitted to the server and input into the classifier, and deep voice detection and user authentication are performed. Figure 2 is a schematic diagram of the proposed method, which is shown in a comprehensive manner. As for the specific process, after receiving the voice data as the input, the size of the voice signal is unified through data preprocessing. The preprocessed data are input into the autoencoder. During the encoding, meaningful features are extracted. The encoder can be deployed on users' devices. As shown in Figure 2, when the user's mobile and server environments are divided, only the preprocessed data and not the original voice are stored in the server. Since the original voice is not exposed, privacy can be protected.  
The extracted feature vector is input into the classifier, and the classifier classifies the input voice data as users or deep voices. Through this process, user authentication and deep voice detection are possible simultaneously.
## 2 Materials and Methods  
### Catalyst Preparation  
Ni catalysts were prepared by incipient wetness impregnation. First, the proper amount of the Ni precursor (Ni(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O) (Sigma-Aldrich, Madrid, Spain) was dissolved in water to achieve a 5 wt.% Ni in the final material. Then, this solution was added dropwise to the zeolite used as a support. In each case, the total volume of the solution was adjusted to the pore volume of the zeolite. After drying, the samples were calcined at 450 \({}^{\circ}\)C for 3 h.  
Regarding the zeolite materials, ITQ-2 were synthesized starting from the corresponding zeolite precursors: ITQ-1 for pure silica ITQ-2 and MCM-22 for ITQ-2 with Si/Al ratio of 15. The synthesis of these precursors is described elsewhere [27]. The first step was the swelling of the precursor. Typically, 5 g of the precursor was dispersed in 100 g of an aqueous solution of hexadecyltrimethylammonium (CTMA) (Acros Organics, Madrid, Spain) bromide/hydroxide (40 wt.%, exchange of bromides by hydroxide of 50%) and 30 g of tetrapropylammonium (TPA) (Sigma-Aldrich, Madrid, Spain) bromide/hydroxide (25 wt.%, exchange of 30%) and the mixture was refluxed for 16 h at 52 and 80 \({}^{\circ}\)C for ITQ-2(\(\infty\)) and ITQ-2(15), respectively. Then, the layers were forced apart by placing the slurry in an ultrasound bath (50 W, 40 kHz) for 1 h. After the ultrasound treatment, the solid was collected by acidification with concentrated hydrochloric acid (Fisher Scientific, Madrid Spain) until pH was below 2 and then by centrifuging. Finally, the organic material was removed by calcination at 540 \({}^{\circ}\)C, yielding ITQ-2.  
Commercial ZSM-5, CBV3024E and CBV28014 (Zeolyst International, Delfzijl, Netherlands) with Si/Al ratios of 15 and 140, respectively, were used as starting materials. These two zeolites with ammonium as compensating cation were calcined at 500 \({}^{\circ}\)C for 3 h to obtain the acidic form.  
The nomenclature of the catalysts used in this study is: xNi/YYY(z), where x is the Ni content, YYY the type of zeolite and z the Si/Al ratio.