esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### D2D Communication Assisted RF Fingerprint Positioning  
From the test database, 1860 test fingerprints were chosen to evaluate the proposed method, since all of them are not suitable for the experiment carried out in this work. Training database remained unaltered, containing 697 fingerprints as in Zenodo repository. From several communications with a secondary device (see Section 3.4) minimum \(\delta\) containing coordinate were selected as the test fingerprint's position according to the proposed method. On the other hand, the traditional k-NN method has been applied to compute the same test fingerprint's coordinate to compare the effectiveness of the proposed method. If the UE (the primary device that needs to know the position) is stationary, then it can not get the benefit from the proposed method to select the best result out of several options since it would experience the same positioning information all the time. UE needs to change its positioning, but the other secondary devices can either be in motion or stationary. Figure 8 shows the comparison between our proposed method and the traditional fingerprint positioning method. This comparison is based on the empirical Cumulative Distribution Function (CDF) of the 3D positioning error highlighting the median error (50%) and the spherical error (95%) as suggested by the ISO18305:2018 standard for real-time locating systems [56], and the third quartile (75%) as suggested by the EvAAL framework [57]. For this experiment, we considered two scenarios for the behavior of the secondary devices: Motion and Stationary. Physical distance among the devices was chosen randomly. CDF of 3D positioning error in Figure 8 shows that compared to the traditional positioning method, our proposed method provides a significant amount of better accuracy. The mean 3D positioning accuracy through traditional method is 8.68 m, that is similar to the results provided in [13]. Our proposed method managed to rectify the error and significantly reduce the mean 3D error  
Figure 7: Visual representation of the reference fingerprints location. Fingerprints are colored according to the elevation (floor level).  
The results indicate that the proposed method provided better results if the other secondary devices are stationary, with a mean 3D error of 5.34 m. Nonetheless, our proposed method also provides good results, with a 5.59 m mean positioning accuracy, when the primary device exchanges information with devices which are in motion. It is worth to highlight that the proposed method prevents very large positioning errors. With the traditional method, almost 10% of operational samples have a positioning error higher than 20 m, whereas the presence of such kind of errors is marginal when D2D communications are considered.
# An Analysis of Differential Evolution Population Size
## 2 Background and Related Work
### Population Size as a Fixed Value  
Besides setting the population size as a dimensionality-related factor, numerous studies on DE have employed a fixed value for the population size [82], irrespective of problem dimensionality or any other problem characteristics. For instance, studies have used 10 individuals [86], 20 individuals [87], 30 individuals [88], 50 individuals [6], 60 individuals [89; 90], 75 individuals [91], 100 individuals [20], 200 individuals [92], and 250 individuals [93]. The selection of a fixed population size has been typically applied based on trial-and-error strategies. Such strategies are subjective and have rarely been justified. The general guidelines to set the population size either as a problem dimensionality-related factor or a fixed value imply that the user has previous knowledge about the problem being considered, although such knowledge is scarcely available.
# Compact 15 mJ Fiber-Solid Hybrid Hundred-Picosecond Laser Source for Laser Ablation on Copper
## 4 Conclusions  
In summary, we developed a 10 mJ-level fiber-solid hybrid hundred-picosecond laser source with a stable performance and compact structure. It consists of an all-fiber master oscillator, a QCW side-pumped Nd:YAG regenerative amplifier, and a Nd:YAG double-pass amplifier. In this laser system, pulse stretcher, beam shaping, and thermal effect compensation are used to realize high-energy hundred-picosecond laser output. First of all, we use fiber components such as a fiber Bragg grating, bandpass filter, and fiber stretcher to effectively suppress nonlinear effects, thereby controlling the width of the spectrum, and achieving effective matching of the gain linewidth of fiber and the solid amplifier. Then, we theoretically and experimentally study the self-focusing effect and thermal effect compensation in the solid-state Nd:YAG laser amplifier. With this system, we obtain a single pulse energy of 14.58 mJ and a pulse width of 228 ps at a repetition rate of 500 Hz. It has a 3 dB spectral width of 0.47 nm at 1064.1 nm. The beam quality factors are 1.49 and 1.51 along the \(x\)- and \(y\)-axis directions, respectively. Lastly, we theoretically simulated the laser ablation process of metallic copper material, thus confirming the practicability of this hundred-picosecond laser source. This compact laser source is capable of generating millijoule-level hundred-picosecond laser pulses, which is perfect light source for studying thermal damage and plasma damage of the laser-induced materials. Due to the advantages of the interaction between the millijoule-level hundred-picosecond laser and the materials, we are eager to further enhance the output characteristics of laser system and apply it in industrial processing and laser-induced damage.  
[PERSON] designed the study, conducted the experiments, contributed to the data analysis and discussion of the results, and wrote the manuscript; [PERSON], [PERSON], [PERSON] and [PERSON] participated in the numerical simulation of the theory; [PERSON], [PERSON] and [PERSON] provided theoretical ideas and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Jilin Province (20200201257 JC) and the Key Scientific and Technological Research Project of Jilin Provincial Department of Science and Technology (20190303108 SF).  
Not applicable.  
The authors thank Beijing University of Technology for providing experimental support with the picosecond laser source.  
The authors declare no conflict of interest.  
Figure 12: Simulation results of free electron concentration.
## 3 Experimental Setup  
In this section we describe the experimental setup for the analysis performed. It is necessary to introduce how OSM data is obtained and how we process this data and prepare it for analysis. OSM data can be downloaded in OSM-XML format and Shapefile (SHP) format from services such as GeoFabrik [39]. An important characteristic of these downloads is that the OSM spatial data contained within the OSM-XML files or SHP files are close to real-time representations of the spatial data stored in the global OSM database. GeoFabrik state that \"essentially any change made to the global OSM database is usually reflected in the data packages available for download within \(24\) h\" [39]. Crucially, the OSM XML data available for download from GeoFabrik, similar services, or indeed the OSM web API contains only the most recent version of each object in the selected region. These XML files contain none of the historical data.
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 4 Results and Discussion
### Results
#### 4.4.2 Multi-Regional Temperature Prediction Experiment  
Temperature prediction experiments are conducted in several cities to evaluate the performance differences of each model for temperature prediction in different regions. Harbin, Beijing, Jinan, Qingdao, Nanjing, Hangzhou, Guangzhou, and Shenzhen are selected as the experimental cities. The number of time-sliding windows is set to 30. The number of input nodes is 30 \(\times\) 8 for a total of 240, and the number of output forecast days is 1 day.  
The experimental RMSE and MAE of temperature prediction for the next day at 8 cities are shown in Tables 8 and 9, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Forecast Days** & \multicolumn{3}{c}{**RMSE/\({}^{o}\)C**} & \multicolumn{3}{c}{**MAE/\({}^{o}\)C**} \\ \hline  & **GCN-BiLSTM** & **DFN** & **LSTM** & **ARIMA** & **GCN-BiLSTM** & **DFN** & **LSTM** & **ARIMA** \\ \hline
1 & 1.534 & 2.933 & 2.245 & 2.124 & 2.414 & 3.445 & 2.982 & 4.122 \\
2 & 1.459 & 3.238 & 2.357 & 2.245 & 2.358 & 3.562 &
## 6 Research Approach
### Literature Analysis  
The review process was modeled after [PERSON] and [PERSON]'s approach [38] and consisted of two consecutive activities: gathering articles and extracting relevant information, followed by analyzing and presenting the findings. IEEE Xplore, ACM Digital Library, and Google Scholar were used to gather articles, with a search strategy that combined the terms \"Certificateless\", \"CLC\", \"VANET\", \"Vehicular\", \"V2X\", \"C-ITS\". The set of articles was then narrowed down into categories based on the search strings \"Authentication\", \"Aggregated signatures\", \"Key agreement\" and \"Signcryption\". To determine inclusion, the article titles and abstracts were examined. Also, in parallel to the search phase, we collected surveys and literature reviews into a separate set, from which we snowballed articles that was in scope with the inclusion criteria. Only English-language articles that addressed aspects falling within the definition of CLC in C-ITSs were kept. Moreover, we only considered articles published in the time frame of 2022-2023 to limit the scope into a more current view of the topics. However, we provide a lightweight analysis on articles between 2016-2023 to understand trends in the research area over time. The remaining articles were then divided among the authors and processed as follows:  
1. Each article was scored with CLC technical readiness level points (CLC-TRL) using the proposed translation table found in Table 1.
2. The aggregated scoring of CLC-TRL for each specified sub-field in CLC was computed where the final score was a majority scoring, i.e., if the majority of the articles had score \(n\), that would be the final scoring.
3. Each author processed step 1 and step 2 independently, then cross-checked the results and summarized the final CLC-TRL assessment.  
The reason for step 2, to use majority scoring instead of mean values, is to avoid large discrepancies between very small and very large scores, since the focus is on identifying how far the most mature research has reached, regardless of the quantity of published articles. In practice, this also meant the authors examined each extraction and discussed the motivation thereof to resolve any differences.
## 6 Conclusions  
To address the problem of the variable number of the visible satellites of the satellite navigation system in a high-orbit environment, this paper discusses the observability of the navigation system and proposes an adaptive navigation filtering optimization method based on the observability analysis. Firstly, the observability of the navigation system is analyzed, and a novel observability calculation method is proposed based on minimizing the objective function of error loss. Then the observable degree of different state variables can be derived. Secondly, according to the relationship between the observability and the filtering accuracy, a mapping value of the observable is applied as the feedback adjustment coefficient to construct an adaptive filter method. This method can combine the information of the previous period and the next period simultaneously to obtain feedback on the filter process. Through this approach, the estimation error can be effectively reduced. Simulation experiments on the high-orbit satellites of the Beidou navigation system show that the proposed adaptive EKF achieves better performance compared with traditional EKF, and verify the effectiveness and practicability of the novel method.  
In order to further improve the navigation accuracy, the next main work will focus on the effective method of combining observability with other filter methods, and combining the optimization methods to improve the filter accuracy.  
[PERSON] proposed the main idea, finished the draft manuscript; [PERSON] and [PERSON] conceived of the experiments and drew the figures and tables; Methodology, [PERSON]; [PERSON] analyzed the data; [PERSON] conducted the simulations. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [grant number 61773021, 61903086 and 61903366], the National Natural Science Foundation of Hunan Province [grant number 2019 JJ20018, 2019J]J50745 and 2020J]4280], Department of Education of Guangdong Province [grant number 2019 KTSCX193] and Fundamentals and Basic of Application Research Foundation of Guangdong Province [grant number 2019A1515110136].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
GNSGlobal Navigation Satellite System  
EKFExtend Kalman filter  
AKFadaptive Kalman filter  
IGSOinclined geo-synchronization orbit  
UTCUniversal Time  
Figure 9: This is a comparison chart between error and observable degree at portion y.
# Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model
## 4 Experimental Validation
### Resulting Analysis of RFODL-MGEC Technique on Prostate Cancer Dataset  
Figure 3 illustrates a set of confusion matrices generated by the RFODL-MGEC model on the test prostate cancer dataset. For the entire dataset, the RFODL-MGEC model categorized 47 images as tumor and 49 images as normal. Similarly, for 70% of the training dataset, the RFODL-MGEC model categorized 32 images as tumor and 34 images as normal. In addition, for 30% of the testing dataset, the RFODL-MGEC model categorized 15 images as tumor and 15 images as normal.  
Table 2 shows a brief classification performance report for the RFODL-MGEC model on the prostate cancer dataset. The experimental results indicated that the RFODL-MGEC model demonstrated effective results on the test dataset. For instance, with the entire dataset, the RFODL-MGEC model obtained an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 94.12%, 94.33%, 94.19%, and 94.12%, respectively. Moreover, with 70% of the training dataset, the RFODL-MGEC technique obtained an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 92.96%, 93.22%, 93.02%, and 92.95%, respectively. With 30% of the testing dataset, the RFODL-MGEC system obtained an average \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 96.77%, 96.88%, 96.88%, and 96.77%, respectively.  
Figure 3: Confusion matrices of RFODL-MGEC technique for prostate cancer dataset. (**a**) Entire dataset, (**b**) 70% of training dataset, and (**c**) 30% of testing dataset.  
Figure 4 illustrates the training and validation accuracy inspection of the RFODL-MGEC model with the prostate cancer dataset. Figure 4 conveys that the RFODL-MGEC model offered maximum training/validation accuracy for the classification process.  
Figure 5 exemplifies the training and validation loss inspection of the RFODL-MGEC model with the prostate cancer dataset. Figure 5 shows that the RFODL-MGEC model offered reduced training/accuracy loss for the classification process of the test data.  
\begin{table}
\begin{tabular}{l l l l l} \hline \multicolumn{5}{l}{**Prostate Cancer Dataset**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline
**Entire Dataset** & & & & \\ \hline Tumor & 94.12 & 97.92 & 90.38 & 94.00 \\ \hline Normal & 94.12 & 90.74 & 98.00 & 94.23 \\ \hline
**Average** & **94.12** & **94.33** & **94.19** & **94.12** \\ \hline
**Training (70\%)** & & & & \\ \hline Tumor & 92.96 & 96.97 & 88.89 & 92.75 \\ \hline Normal & 92.96 & 89.47 & 97.14 & 93.15 \\ \hline
**Average** & **92.96** & **93.22** & **93.02** & **92.95** \\ \hline
**Testing (30\%)** & & & & \\ \hline Tumor & 96.77 & 100.00 & 93.75 & 96.77 \\ \hline Normal & 96.77 & 93.75 & 100.00 & 96.77 \\ \hline
**Average** & **96.77** & **96.88** & **96.88** & **96.77** \\ \hline \end{tabular}
\end{table}
Table 2: Resulting analysis of RFODL-MGEC technique with various measures on prostate cancer dataset.  
Figure 4: Accuracy analysis of RFODL-MGEC technique on prostate cancer dataset.
## 5 Frequency Hopping Techniques
### Channel Aware, Probabilistic-Channel-Usage Techniques  
Unlike the reduced-hop-set techniques, probabilistic-channel-usage techniques use all the available channels, but channels marked as good are used with higher probability.
## 4 Application of the SIW LWA for Radio-Over-Fiber-Based 5G Hot-Spots
### Radio-Over-Fiber Point-To-(Multi)Point Data Transmission Experiments
Due to the optical LO at the RAU, the three baseband channels were all heterodyned in the PD, which generated three RF signals with 3 GHz spacing in the V-band at 59 GHz, 62 GHz, and 65 GHz. Finally, these RF signals were amplified and radiated by the SIW LWA in three different directions. To evaluate the BERs, the receiver was then moved to the three corresponding user positions. In Figure 12, the measured BERs of the simultaneously transmitted signals are depicted. While a clear power penalty w.r.t single beam operation and a power penalty between the individual beams was observed, still all three beams could be detected simultaneously using FEC. The achieved cell capacity in this case was 3 Gbit/s. This performance could of course be further improved using OFDM waveforms and higher-order modulation formats such as QAM. Performance degradation occurs because, in contrast to a mixer, the SBD detector downconverts all incoming V-band signals to baseband. Thus, the signals incur interference to each other, which can be estimated based on the sidelobe suppression of the SIW LWA at the used frequencies. This creates an outlook for future antenna optimization for these use cases. The proposed concept of RAUs using SIW LWAs can fulfill the key requirements for a 5G hot-spot, which is to support multiple mobile users with a high data rate downlink service in a small and densely populated cell. It furthermore supports a centralized radio access architecture with fiber-optic fronthaul, as beam steering functionality is remoted to the central unit. The chosen wireless receiver technology with a simple SBD furthermore enables fabrication of low-cost mobile units. Figure 11: Measured bit error rate (BER) over received radio requency (RF) power at on-off keying (OOK) data rates from 500 Mbit/s to 2.5 Gbit/s. Insets show the quasi error free eye diagrams.
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 3 ISO/IEC 29192-2 Standard Lightweight Ciphers
### Related Works on ISO/IEC 29192-2
Their implementation on an RTX 2070 GPU achieves notable improvements in encryption throughput, with AES reaching 310 Gbps, reflecting a 10.7% increase over the previous best result. LEA demonstrates a throughput of 2.47 Tbps, marking a 67% improvement, while CHAM achieves 3.03 Tbps, representing its first optimized implementation on GPUs. These results highlight the potential of GPU-based cryptographic acceleration for high-performance encryption in IoT and cloud computing environments.
## 3 Materials and Methods
### Methodology
#### 3.2.2 Model Accuracy  
To test model performance, the data set is dissected into a training set (in-sample) comprising 80 percent of sales transactions, and a test set (hold-out) composing 20 percent of the sample sales data. The predictive accuracy is measured using three standard measures: the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). International Association of Assessment Officers (IAAO) benchmarks, the Price-Related Differential (PRD) and the Coefficient of Dispersion (COD), are also examined to measure model accuracy for valuation uniformity and inequity.  
The RMSE can be defined as the standard sample deviation between the predicted and observed values, with lower RMSE values denoting a better fit model. The RMSE is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}} \tag{3}\]  
where \(y_{i}\) stands for actual and \(\hat{y_{i}}\) stands for the predicted.  
The MAE measures the prediction error by taking the mean of all absolute values of all errors. A MAE closer to zero means that the model predicts with lower error and its predictive capacity is superior. The MAE is expressed as:  
\[\text{MAE}=\frac{\sum_{i=0}^{n}\left|y_{i}-\hat{y_{i}}\right|}{n} \tag{4}\]  
where \(n\) is the number of samples, \(y_{i}\) is the target values, and \(\hat{y_{i}}\) is the predicted values.  
The MAPE measures the absolute percentage error in the prediction and can be defined as:  
\[\text{MAPE}=\frac{100}{n}\sum_{i=1}^{n}\left|\frac{y_{i}\ \hat{-}\ y_{i}}{y_{i}}\right| \tag{5}\]  
where \(\hat{y_{i}}\) and \(y_{i}\) stand for the predicted and actual values respectively, while n is the total number of out-of-sample observations.  
The COD, is the percentage the average deviation of the ratios from the median, and the most widely used measure of appraisal uniformity. This relative dispersion or  
Figure 2: Distribution of sales price across the Belfast Housing Market Area in 2021.  
variability of assessments from the median for improved residential property should be <15% and is as follows:  
\[\text{COD}=\frac{\frac{100}{n}\sum_{i=1}^{n}\left|R_{i}-\widetilde{R}\right|}{ \widetilde{R}} \tag{6}\]  
where \(R_{i}\) is the observed assessment ratio for each parcel, \(\widetilde{R}\) the median assessment ratio, and \(n\) the number of properties sampled.  
The PRD is a mean (valuation to selling price) ratio divided by the weighted mean ratio, which measures the regressivity or progressivity of the assessments. Regressive appraisals occur when high-value properties are under-appraised relative to low-value properties. Progressivity occurs when the opposite happens. If no bias exists, the PRD equals 1, indicating assessment neutrality. Regressivity arises when the values are greater than 1.03; progressivity occurs when values are less than 0.98. The PRD is expressed as:  
\[\text{PRD}=\frac{\overline{R}}{\overline{A}/\overline{5}}=\frac{\overline{R}} {WM} \tag{7}\]
The first stage performs baseband processing, control, and reconfiguration for the second stage and is implemented in a commercial SoC architecture. The RF stage (second stage), the central goal of this work, includes digital/analog conversion, mixer, and filters based on COTS, operating from 0.070 to 6 GHz. The rest of the paper is organized as follows: in Section 2, the RABE system architecture, the modeling and development of the PCB for the RF stage, the testbed design, and the software tools used are described; in Section 3, implementation, testing are shown, and the main results obtained are presented and discussed; and in Section 4, the conclusions are exhibited.