esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper has developed an efficient GEER-DLAD technique for IoT applications. The presented model permits the IoT devices to exploit the energy efficiently in such a way to rise the network span. Primarily, the IoT devices continuously gather the data. Then, they are compressed by the use of ELC technique. Subsequently, the IoT devices executed the routing technique using MSO algorithm and select the optimal route to destination. Upon the election of routes, the compressed data will be forwarded towards the time. At the time of data transmission, the DLAD technique is applied to detect the occurrence of anomalies in IoT communication networks. A comprehensive experimental validation process takes place and the results guaranteed the improvement of the GEER-DLAD model interms of energy efficiency and detection performance. In future, the detection performance of the DLAD can be improved by the use of learning rate scheduling approaches. Besides, the advanced deep learning architectures can be used instead of RNN-LSTM model for anomaly detection. Furthermore, the local optimal and inefficient global exploration ability of the MSO algorithm can be resolved in our future work. At last, the computational complexity analysis of the proposed model can be investigated as a part of future extension.  
Conceptualization, E.L.L. and A.A.J.; data curation, E.L.L.; formal analysis, E.L.L. and A.A.J.; funding acquisition, C.S. and G.P.J.; investigation, A.F.S.D.; methodology, A.F.S.D. and C.S.; project administration, C.S.; resources, G.P.J.; software, A.A.J.; supervision, G.P.J.; validation, C.S.; visualization, G.P.J.; writing--original draft, A.F.S.D.; writing--review and editing, G.P.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean Government (MSIT) (Research on AI-Based Cryptanalysis and Security Evaluation) under Grant 2020-0-00126.  
Not applicable.  
**Data Availability Statement**: The data presented in this study are openly available in [[https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)]([https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)) (accessed on 1 March 2021) at [doi: 10.23721/100/1478792].  
Figure 10: The Comparative results analysis of existing with RNN-LSTM model.
# Transmission Condition Monitoring of 3D Printers Based on the Echo State Network
## 4 Results and Discussion
### Comparison with Peer Methods  
As shown in Figure 6, SVM, LPPSVM, and PCASVM were adopted for comparison with the ESN. As can be seen from the bar graph in Figure 6, the monitoring accuracy of SVM, LPPSVM, and PCASVM will not increase when the proportion of training samples increases. The highest accuracy was 48.87% using these three methods to monitor 3D printers. Furthermore, compared to the ESN, the monitoring accuracy of the three methods was not stable.  
Figure 5: The accuracy of the proposed method under different proportions of training samples: (**a**) using different channels’ data and (**b**) all nine channels’ data.
## 4 Discussion
### \"Letter\" Segmentation  
The first step in the proposed analysis involved segmenting individual letters based on the y-component of velocity and selecting valid letter segments. In certain instances, participants deviated from consistent letter formations such as making errors, pausing, or lifting the pen--which resulted in atypical handwriting data. These segments were excluded from further analysis according to the segmentation algorithm, which assessed letter length (LL) against the median LL and verified the presence of a local minimum in Vy during potential letter segments.  
To avoid analytical bias from unequal data contributions across participants, we ensured equal representation by selecting the minimum number of valid letter segments (N\({}_{\text{L}}\)) identified across all subjects. This consistent N\({}_{\text{L}}\) value was used for subsequent data analysis. Furthermore, selecting the first N\({}_{\text{L}}\) valid segments, rather than a random subset, provided more reliable input by helping to mitigate potential fatigue effects [53; 54].
# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
## 4 Results Analysis and Downstream Task Application  
### Analysis of Experimental Results  
#### 4.1.1 Comparative Analysis of Image Synthesis Results  
To facilitate a more intuitive comparison of new view generation effects, visual comparisons of new views generated from different styles of ore blocks without post-processing are shown in Figure 6. Sections that significantly differ from the actual results or contain certain discrepancies in the details are highlighted with red boxes. It is evident that traditional methods based on neural radiance fields (NeRF) have poorer generalization capabilities and slower generation speeds in new view generation tasks. In contrast, the latent diffusion model exhibits a more accurate inference of texture, shape, and color in the generation of new views of ore. For predictions with smaller deviation angles (Groups A, B, and C), the model, after transfer learning on ore block data, provides more precise detail compared to the original latent diffusion model, especially in areas such as edge shadows, the smoothness of ore block contours, and the accuracy of shape generation (e.g., Group A). For predictions with larger deviation angles (Groups D and E), the latent diffusion model exhibits inherent shortcomings. This issue arises because, when the specified angle for the generated image differs significantly from the input image, the model faces increased difficulty in leveraging prior knowledge and current input information, which in turn reduces its ability to accurately infer missing details. Although this problem can be somewhat mitigated by covering a wider range of scenarios in the training data, the objective reality remains that greater loss of detail leads to larger prediction deviations. Nevertheless, the latent diffusion model, after being adapted through transfer learning on ore block data, still achieves generated results that are closer to the ground truth compared to the original model. Experimental results based on the test set used in this study are shown in Table 1. The method proposed in this paper achieved an average PSNR of 23.02 dB, an average SSIM of 0.754, and an average LPIPS of 0.268. Compared to the traditional PixelNeRF model and the original model, the average PSNR increased by 12.48 dB and 1.66 dB, respectively, the average SSIM increased by 0.186 and 0.052, respectively, and the average LPIPS decreased by 0.211 and 0.050, respectively. Therefore, although the transfer learning model based on the ore block dataset still shows some biases and limitations when predicting at significantly shifted angles, which may not fully represent real-world objects, it still outperforms the original model. The new model is better adapted to the task of predicting different perspectives of ore blocks, and its higher accuracy is more conducive to guiding autonomous shoveling and remote-controlled shoveling operations.
## 4 Simulation Results
### Comparison of the Different Working Pairs
Furthermore, in this case, there are some working pairs able to achieve GTLs up to 30 K, specifically AC SRD 1352-2/ammonia and AC Carbotech A35/methanol, which, when the ambient temperature is \(-10\)\({}^{\circ}\)C, can achieve COP higher than 0.4 with specific energy higher than 100 kJ/kg. Even if these conditions can be considered useful only under some specific cases, they still represent an interesting option to enlarge the operation of the AdHT at higher delivering temperatures. The last investigated operating condition is the one exploiting 80 \({}^{\circ}\)C of waste heat source to upgrade heat that can be re-used in industrial processes. Figure 8 represents the COP and Table 5 specific energy calculated for the different working pairs for three different GTLs, namely, 20 K, 40 K, and 50 K. As can be seen in Figure 8a and in Table 5, in the case of GTL = 20 K, all the selected working pairs can achieve the expected target even exploiting 30 \({}^{\circ}\)C of ambient temperature. The three composite sorbents based on LiCl, using water and methanol as working fluids, are the ones showing the highest specific energy, above 1000 kJ/kg, irrespective of the ambient temperature at which they are operating. Differently, regarding the COP, the most effective working pairs are the ones using ammonia as a working fluid. Indeed, for these working pairs, the COP remains above 0.5. Interestingly, as highlighted in Figure 8b,c and in Table 5, for the other two investigated QTLs, namely 40 K and 50 K, the composite MWCNT-LiCl/methanol cannot be operated, since under these conditions, it is not able to produce sufficient heat upgrading effect. This of course represents a critical factor to consider for the working pair selection, indeed if higher temperature is requested by the industrial process, other pairs must be considered. In particular, the one showing the highest COP and specific energy is the SGST-LiCl-35/water. It has to be highlighted that, under these QTLs, the AdHT can be operated only up to 20 \({}^{\circ}\)C of ambient temperature, specifically at GTL = 50 K, only the above-mentioned working pair can be operated up to 20 \({}^{\circ}\)C.  
Figure 8: Thermal COP obtained by the different investigated working pairs, ex-ploiting 80 \({}^{\circ}\)C as waste heat source, with a GTL of 20 K (**a**), 40 K (**b**), and 50 K (**c**), as a function of the ambient temperature. As expected, for these operating conditions, the pure adsorbents are also characterized by lower performance, since the overall working fluid exchange per cycle is much lower than that of the composites, thus causing a reduction of the achievable specific energy.
## 5 Conclusions  
We have proposed a clusterization method to suppress fading noise in the satellite-mediated GMCS protocol. In detail, the measurement data are divided into several clusters for separate security analysis, of which the data have relatively similar transmittance. The optimal upper and lower bounds of each cluster are based on the link's total PDT, whereas the optimal cluster number should balance the reduction in fading noise and increase the composable finite-size effect caused by the clusterization method. We found that the clusterization method can improve the composable finite-size rate when the fading noise is large enough, but may damage the performance by increasing the composable finite-size effect if the fading noise is at a relatively low level. Moreover, increasing the frequency of the signal preparation and detection of the system can suppress the composable finite-size effect, thereby allowing the proposed method to work effectively in a lower fading noise case.  
Conceptualization, S.X. and W.P.; writing--original draft preparation, Z.Z.; writing--review, Y.G. and H.L.; writing--editing, H.X. and W.J. All authors have read and agree to the published version of the manuscript.  
This work was supported by the key research and development project in Hunan Province (Grant No. 2022 GK2016), the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 22C0446), Key project of Scientific Research of Hunan Provincial Education Department (Grant No. 21A0470 and 22A0669), and the Natural Science Foundation of Hunan Province (Grant No. 2023 JJ50268 and 2023 JJ50269).  
All data generated or analyzed during this study are included in this published article.  
We would like to thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their helpful discussions.  
The authors declare no conflict of interest.
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 4 Experiment and Analysis
### Comprehensive Performance Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Two-Stage Trained Model**} \\ \hline
**Label Category Completeness** & **1-Label** & **5-Label** & **10-Label** & **20-Label** \\ \hline
25\% & 0.804 & 0.808 & 0.816 & 0.822 \\
50\% & 0.826 & 0.852 & 0.861 & 0.86 \\
75\% & 0.873 & 0.898 & 0.902 & 0.905 \\
100\% & 0.9 & 0.915 & 0.945 & 0.942 \\ \hline \hline \multicolumn{5}{c}{**Directly Trained Model**} \\ \hline
**Label Category Completeness** & **1-Label** & **5-Label** & **10-Label** & **20-Label** \\ \hline
25\% & 0.744 & 0.759 & 0.773 & 0.77 \\
50\% & 0.763 & 0.819 & 0.836 & 0.84 \\
75\% & 0.869 & 0.884 & 0.885 & 0.9 \\
100\% & 0.9 & 0.903 & 0.905 & 0.933 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Purity results. clustering performance obtained by training with a large amount of unlabeled data, so the overall distribution of the obtained results was more reasonable. In summary, the algorithm in this study effectively dealt with the two problems of insufficient numbers and missing categories of labels. Under the corresponding conditions, the clustering purity was higher.
## 4 Discussion
### The Impacts of Nearby Waterbodies on AMD Mapping
The use of a GNSS system that provides the extraction polygons a lower 2 DRMS circular error is, therefore, recommendingduring ground verification processes to avoid extracting data values overlapping with nearby waterbodies--often a presence within derelict mining areas. The choice of spatial resolution is also important, as when spatial resolution increases, the number of mixed pixels decreases [65]. Variations in the FWP\({}_{total}\) percentage were accompanied by a contrast in image clarity amongst the instruments. An FWP analysis is, therefore, recommended when mapping AMD in mining environments inundated with water features, especially using handheld spectrometers. Samples taken by handheld spectrometers containing just traces of moisture cause influential outliers during statistical analysis. Pixels between land and water bodies are prone to spectral mixing, including every spectral resolution, especially at the pixel boundary between land and water [66; 67]. Identifying mixed pixels during spatial data extraction is, therefore, a crucial step, as this can suppress the data extraction of areas of interest such as river areas [66], or in this case, AMD/ Fe(III) iron distribution on former mine sites. The FWP thresholds were successful at identifying the mixed pixel boundary found between land and water bodies for all sensors.
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 3 Experiment and Analysis
### Ablation Experiments  
We conducted ablation experiments on the Berne dataset to verify the effectiveness of the D-DRUNet fusion semantic segmentation network, and the experimental results are shown in Figure 14 and Table 3. Methods 1 and 2 are the change-detection results with unimodal difference images as input, while methods 3 and 4 are the change-detection results with bimodal difference images as input. Moreover, method 1 utilizes the conventional CNN convolutional kernel, and methods 2, 3, and 4 use the dynamic convolutional kernel. Table 4 shows the performance metrics of the change-detection results of the four methods. The comparison results in Figure 14a with the ground truth revealed that the segmentation results of method 1 had serious false alarms and blurred boundaries of the change region. However, method 2, which employed a single encoder, detected a more comprehensive change region, with significantly reduced false alarms. Method 3, with an asymmetric encoder and jump connection structure, destroyed the change-region structure and resulted in obvious leakage detection in the detection results. In contrast, method 4, which utilized a dual encoder and a dual jump structure, effectively reduced the leakage alarm, with more refined change boundaries. It can be seen that the D-DRUNet network incorporates multimodal difference information, and the detection effect is significantly improved.
# Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification
## 1 Introduction
Moreover, based on the extracted feature vectors, a novel multi-class SVM classification model is developed, which incorporates custom and efficient training and testing algorithms and decision making structures to classify the grid locations accurately. The classification models and decision making structures represent another novel premise of the framework. For performance analysis, power and audio recordings are localized separately. Also, the system consisting of both types of recordings is tested for location authenticity. The proposed system yields to a very efficacious location-stamp verification framework, which presents location authenticities of both power and audio recordings comparable with and in most of cases superior to those reported in the earlier works. Moreover, along with reliability for location forensics analysis the proposed system yields to a computationally cost-effective framework for cyber-security applications. The proposed location forensic application system is developed and tested in MATLAB(r) and the training and testing accuracies to locate the regions of the digital (power + audio) recordings are obtained as 97.42% and 92.00% respectively. The remainder of the manuscript proceeds as follows. Section 2 describes the ENF extraction method and ENF database formation process. Section 3 explains the extracted features from the ENF signals, developed classification model and its performance evaluation. Section 4 concludes the article.
## 4 Discussion
In conjunction with passive EPR-based accumulation and favorable electrostatic interaction between positively-charged nanoparticle surface and cell membrane, LHRH-MNPs are highly likely to attach to TNBC tissues, due to specific receptor-ligand interactions. Previously, LHRH-MNPs had been found to demonstrate excellent binding specificity to a human TNBC cancer cell line (MDA-MB-231) which overexpresses LHRH receptors [25; 26]. Specifically, the adhesion measured between LHRH-MNPs and MDA-MB-231 TNBC cells was fourteen-fold the adhesion between unmodified MNPs and TNBC cells [26]. As a result of receptor-ligand specificity, the enhanced adhesive interaction leads LHRH-MNPs to enter into TNBC cells lead via receptor-mediated endocytosis, with faster internalization kinetics [25; 26]. In vitro results showed that LHRH-MNPs are both thermodynamically and kinetically more favorable to enter into TNBC cells compared to MNPs [25]. In vivo imaging (Figures 8 and 9) demonstrated that the presence of LHRH ligands enabled specific delivery of the particles to the tumor and increased their ability to target TNBC tumors through an active pathway, thus making the tumors appear darker compared to the non-LHRH targeted group (MNP-treated group). Importantly, the TNBC tumors can be detected more readily with LHRH-MNPs than with MNPs. The LHRH-directed interaction between LHRH-MNPs and TNBC allows the LHRH-MNPs to stay in the tumor for longer durations, leading to the potential feasibility for long-term monitoring of the tumors. Therefore, LHRH-MNPs have the potential to be developed into multifunctional nanocarriers for the early detection and localized treatment of TNBC. Lastly, it is important to note that the overexpression of LHRH receptors has been demonstrated in several human malignant tumors, including breast cancer, ovarian cancer, endometrial cancer, pancreatic cancer, and other types of cancer [42; 43]. Since LHRH receptors are not expressed in the majority of normal tissues [43], these receptors play a critical role in targeted therapy of tumors. Studies have demonstrated the effectiveness of LHRH-targeted drug delivery platforms for cancer therapy by using LHRH peptides to direct anticancer drugs and imaging agents to the cancer site, while minimizing collateral damage to healthy tissues [23; 44; 45; 46; 47]. Taking advantage of overexpressed LHRH receptors by the tumor cells, LHRH targeting ligands can be exploited to guide the delivery of LHRH-MNPs to treat a variety of cancers in addition to TNBC, in addition to detecting the metastasis. In this study, the MR imaging was performed at 28 d post tumor implantation. Further studies are clearly needed to investigate the efficacy in the early detection and localized treatment of TNBC in the context of LHRH-MNP based targeting motifs. Imaging at 7 d, 14 d, and 21 d would be necessary. To better understand the long term imaging efficacy of LHRH-MNPs and their systemic response, experiments lasting one month are needed. Histopathology is also needed to investigate the potential toxicity of these nanoparticles on different organs, such as lungs, livers and kidneys.
# Fully Reconfigurable Photonic Filter for Flexible Payloads
## 3 Proposed Structure Analysis
Although lower values of propagation losses have been reported in the literature for the same waveguide [23], we have considered a propagation loss value of \(\alpha=0.4\) dB/cm at \(\lambda\approx 1550\) nm for the \(\mathrm{TE}_{0}\) mode [24] to take into account additional losses associated with fabrication. The design of both the radius R and the coupling coefficient \(\mathrm{k}\) between the bus and both rings has been carried out, aimed at maximizing the stopband rejection. It has been observed that as the radius increases, the power coupling coefficient decreases, primarily due to the impact of the bending losses [25]. To ensure decoupling between the two rings, they are spaced apart by a distance greater than 1 \(\mathrm{\SIUnitSymbolMicro m}\), where the coupling is negligible. Figure 2: (a) 3D sketch and (b) cross-section of designed filter. The radius has been set to R = 10 \(\upmu\)m to avoid increasing bending losses and obtain the desired central frequency (\(\approx\)1550 nm). In the low-coupling regime of RRs, k = 2.24% ensures the BW \(-\) SBR trade-off. A gap g = 710 nm has been designed by performing the Beam Propagation Method (BPM) parametric simulations to obtain the desired coupling coefficient, guaranteeing a Lorentzian notch shape with an SBR of about 45 dB and a BW of 610 MHz. The impact of the variability in the fabrication process on the device's performance has been also evaluated. In particular, by considering fabrication tolerances of \(\pm\)10 nm on the gap, radius, and each waveguide width, an in-depth analysis has been carried out. To compare and properly understand each structure parameter's effect on the operating wavelength, bandwidth, and stopband rejection, they have been modified one at a time. The combination of w = 430 nm, R = 10 \(\upmu\)m \(-\) 10 nm, g = 700 nm and w = 450 nm, R = 10 \(\upmu\)m + 10 nm, g = 720 nm for the first RR and the second RR, respectively, represents the worst case since the difference in BW and SBR between the two RRs is the highest. However, a bandwidth of 510 MHz and stopband rejection of 49 dB have been achieved; therefore, the device is robust to the fabrication tolerances.
## 4 Intelligent Weather-Conscious AMC Scheme for Global Satellite-to-Ground Communications
### DL-Based Estimation Model
#### 4.2.1 Forget Gate  
When new information is input, the model needs to forget some of the old information, and the forget gate is used to select which information to forget and which to keep and, in this way, avoids the problems of gradient disappearance and gradient explosion:  
\[\mathbf{f}_{t}=\sigma\Big{[}\mathbf{W}_{f}s(t-1)+\mathbf{U}_{f}\mathbf{h}^{ \prime}(t-1)+\mathbf{b}_{f}\Big{]} \tag{7}\]  
where \(\mathbf{W}_{f}\) represents the weight between the input and the forget gate, \(\mathbf{U}_{f}\) is the weight between the precious hidden state \(\mathbf{h}^{\prime}(t-1)\) and the forget gate, \(\mathbf{b}_{f}\) is the bias of the forget gate, and \(\sigma(\cdot)\) is the sigmoid function.