esa-sceva/satcom-chunk-collection huggingface dataset:
# DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval
## 5 Experiments
### Experimental Results
#### 5.4.2 Results on Nus-Wide Dataset  
The mAP scores on NUS-WIDE of DA-GAN and competitors are reported in Table 3. Compared with the results on Wikipedia, the precision of all these methods are relatively higher. The proposed method performs well on this dataset, which defeats CM-GANs (IZT mAP = 78.1%, T2I mAP = 72.4%, Aver. mAP = 75.3%) and DSCMR (IZT mAP = 61.1%, T2I mAP = 61.5%, Aver. mAP = 61.3%) by 2T mAP = 79.7%, T2I mAP = 75.2%, Aver. mAP = 77.5%. It indicates that the dual attention mechanism can discover more important semantic features between different modalities to generate more discriminant representations. On the other hand, we observe that the performance of other traditional and deep learning-based approaches are far behind our method even though the precisions of them are obviously higher than the results on Wikipedia.  
The PR curve of DA-GAN and the state-of-the-arts are presented in Figure 7b,c. We can find that the trends of the precisions on NUS-WIDE are different from the situations  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Traditional Method** & **IZT** & **T2I** & **Aver.** \\ \hline CCA [69] & 37.8 & 39.4 & 38.6 \\ KCCA [11] & 36.2 & 39.4 & 37.8 \\ MCCA [70] & 44.8 & 46.2 & 45.5 \\ MVDA [71] & 50.1 & 52.6 & 51.3 \\ MMDA-VC [72] & 52.6 & 55.7 & 54.2 \\ JRL [73] & 58.6 & 59.8 & 59.2 \\ \hline
**Deep Learning-Based Method** & **IZT** & **T2I** & **Aver.** \\ \hline DCCA [42] & 53.2 & 54.9 & 54.0 \\ DCCAE [24] & 51.1 & 54.0 & 52.5 \\ CCL [74] & 50.6 & 53.5 & 52.1 \\ CMDN [75] & 49.2 & 51.5 & 50.4 \\ ACMR [57] & 58.8 & 59.9 & 59.3 \\ DSCMR [44] & 61.1 & 61.5 & 61.3 \\ CM-GANs [76] & 78.1 & 72.4 & 75.3 \\ \hline
**The Proposed Method** & **IZT** & **T2I** & **Aver.** \\ \hline DA-GAN & **79.7** & **75.2** & **77.5** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison results (mAP@50 in %) with 13 competitors on NUS-WIDE dataset. The best performance values are in bold-font.  
Figure 7: The PR curve of our method DA-GAN and the competitors on Wikipedia, NUS-WIDE and Pascal Sentences dataset. (**a–c**) show the PR curves of IZT task on Wikipedia, NUS-WIDE and Pascal Sentences; (**d–f**) report PR curves of IZT task.  
on Wikipedia. For the I2T task (shown in Figure 6(b)), the precision of DA-GAN and the competitors decline obviously in the interval \([0.0,0.2]\). After that, the downward trend tends to be gentle. When the recall is larger than 0.8, fast performance degradation occurs, except for three traditional methods, i.e., CCA, KCCA, and MCCA. At all levels of recall, the precision of DA-GAN is higher than all the rivals. For the T2I task (shown in Figure 6(c)), the performance of all these approaches shows a gradual downward trend. Although the precision of CM-GANs is slightly higher than our method in the interval \([0.1,0.2]\), it cannot defeat DA-GAN when the recall is larger than 0.2. The retrieval accuracies of other approaches, as expected, are much lower than DA-GAN.
## 2 Model and Test Setup
### Setup for Signal Generation and Acquisition  
Artificial AE signals were generated by using the Hsu-Nielsen (H-N) pencil-lead break method, in which the pencil lead is pressed against the surface until the lead breaks [25]. For the consistent  
Figure 2: A schematic diagram of (**a**) a scaled PRF model, (**b**) the location of pile foundation and accelerometers, and (**c**) setup for data acquisition (not to scale).  
AE signal generation, the pencil lead with 3 mm length and 0.5 mm diameter was firmly pressed against a surface at 30\({}^{\circ}\) degree to the surface until the lead broke. The H-N pencil lead break generated an intense acoustic signal, which had similar characteristics with the actual AE signal in concrete, and this is considered as a point AE source [26; 27]. The H-N pencil lead break method is widely used in AE experiments to verify coupling between AE sensors and a specimen surface, AE attenuation, and source localization capabilities, due to its good reproducibility [27; 28; 29].  
The generated artificial AE signals were captured at six accelerometers (353B18, PCB piezotronics) which had a sensitivity of 10 mV/g and the operating and resonant frequencies of 0.0035-30 kHz and >70 kHz. In fields, it is difficult to install a sensor array on the buried foundation in the subsurface. Therefore, we attached the accelerometers on the upper surface of the raft by using vacuum grease to provide acoustic coupling and mechanical fixtures. The array of accelerometers was spatially arranged to cover the whole area of the raft foundation, as shown in Figure 2b.  
The AE signal captured by each accelerometer was simultaneously conditioned and amplified with the gain level of 40 dB through a signal conditioner (482C series, PCB piezotronics). The conditioned AE signal was recorded at a sampling interval \(\Delta t\) of 60 ns, and a total of 31,250 data points per signal was acquired by the 8-bit A/D converter (or digital oscilloscope, DSO-X 3024A, Agilent technologies). We used two oscilloscopes and two signal conditioners to cover six accelerometers at the same time. Thereby, the signals simultaneously captured at the six accelerometers were synchronized to share the same time stamp for source localization. Our testing condition in the laboratory had low levels of mechanical and electronic background noises. Therefore, we directly used the acquired raw signals for AE source localization without any signal filtering and preprocessing.
## 5 Implementation Details  
While it is possible to implement SOMM on top of any operating system, current implementation of SOMM is based on TinyOS [14] that is the _de facto_ standard operating system for WSNs. In the following sub-sections, we describe the key points of design and implementation of SOMM.
# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
## 2 A New View Synthesis Technique Based on Latent Diffusion Models
### Transfer Learning Tasks Based on Latent Diffusion Models  
To generate views of the same object from different angles with limited initial perspectives, [PERSON] et al. [27] utilized over 10 million images and corresponding camera poses from the Objavverse dataset [28]. They employed the intrinsic image generation capabilities of latent diffusion models (LDMs) for large-scale training, enabling the generation of new views based on input pose information. The objective can be expressed as follows:  
\[\widehat{x_{R,T}}=f(x,R,T) \tag{4}\]  
where \(\widehat{x_{R,T}}\) represents the newly synthesized image, \(f\) denotes the latent diffusion model, \(x\) represents the input image, and \(R\) and \(T\) refer to the camera's rotation and translation information, respectively.  
To achieve rapid convergence of the network model with limited data [29] and to optimize issues related to texture and morphology deviations in new view synthesis for generalized data [30], a transfer learning approach is employed. This involves fine-tuning the model using pre-trained weights on the target task dataset. The process utilizes various datasets of ore blocks to refine the entire model, resulting in the intelligent ore block shape prediction model based on the latent diffusion model. The detailed fine-tuning process is as follows:  
1. Input the original ore block image x and the target view ore block image x(R, T) (both with dimensions 128 pixels \(\times\) 128 pixels) along with their corresponding camera pose data;
2. Using the encoder \(\varepsilon\), downsample the full-size images by a factor of 4, encoding them into lower-dimensional latent data of 32 pixels \(\times\) 32 pixels (compressed data);  
Figure 3: Latent diffusion model (LDM) inference process.  
3. Randomly sample a time step \(t\) from K noise levels (commonly set to K = 1000);
4. Conduct the forward diffusion process by progressively adding Gaussian noise, corresponding to the sampled time step \(t\), to the target image x(R, T);
5. Input the noise-added ore block image into the U-Net for reverse diffusion to predict the noise. Use the CLIP model to extract embedding information from the original input image and combine this information with the camera's pose data R and T to form an embedding vector c(x, R, T). This vector, along with the original image, helps guide the generation of the target image;
6. Gradually compute the loss between the actual noise and the predicted noise during the reverse diffusion process;
7. Use the decoder D to upsample the compressed image from 32 pixels \(\times\) 32 pixels back to the full-size image of 128 pixels \(\times\) 128 pixels, decoding it back into the pixel space.
# An Enhanced Auxiliary Information-Based EWMA-t Chart for Monitoring the Process Mean
### EWMA-t Control Charts  
Let \(X\) be the quality characteristic of interest and assume that \(X\) is a normally distributed random variable with mean \(\mu_{X}+\delta\sigma_{X}\) and variance \(\sigma_{X}^{2}\), that is, \(X\sim N(\mu_{X}+\delta\sigma_{X},\sigma_{X}^{2})\). The process is in the control state when \(\delta=0\); otherwise, the process has shifted. Let \(X_{ij}\), \(i=1,2,\ldots\), \(j=1,2,\ldots,n\), be a random sample of size \(n\) drawn from the process at time \(i\). Let \(\overline{X}_{i}\) and \(S_{X,i}^{2}\) be the sample mean and sample variance of the \(i\)th subgroup, respectively, where \(\overline{X}_{i}=\sum_{j=1}^{n}X_{ij}/n\) and \(S_{X,i}^{2}=\sum_{j=1}^{n}{(X_{ij}-\overline{X}_{i})}^{2}/(n-1)\). \(\overline{X}_{i}\) follows a normal random variable with mean \(\mu_{X}+\delta\sigma_{X}\) and variance \(\sigma_{X}^{2}/n\), whereas \((n-1)S_{X,i}^{2}/\sigma_{X}^{2}\) is a chi-squared random variable with \(n-1\) degrees of freedom. \(\overline{X}_{i}\) and \(S_{X,i}^{2}\) are mutually independent random variables for an in-control process. Suppose that the process is in control \((\delta=0)\); then, the statistic \(T_{i}\) is defined by:  
\[T_{i}=\frac{\sqrt{n}(\overline{X}_{i}-\mu_{X})}{S_{X,i}}, \tag{1}\]  
is a Student's \(t\)-distribution with \(n-1\) degrees of freedom and \(S_{X,i}\) is the sample standard deviation. Therefore, the EWMA-t statistic \(Z_{i}\) at time \(i\) can be defined as:  
\[Z_{i}=\lambda T_{i}+(1-\lambda)Z_{i-1} \tag{2}\]  
where \(T_{i}\) is a \(t\) distributed random variable at time \(i\) and \(\lambda\) is the smoothing constant satisfying \(0<\lambda\leq 1\). The initial value of \(Z_{i}\) is usually set to zero (i.e., \(Z_{0}=0\)). The upper control limit \((UCL)\), central line \((CL)\), and the lower control limit \((LCL)\) for the well-known EWMA-t control chart based on \(Z_{i}\) are:  
\[\left\{\begin{array}{c}UCL=0+L_{e}\sqrt{\frac{\lambda}{2-\lambda}\cdot\frac {n-1}{n-3}}\\ CL=0\\ LCL=0-L_{e}\sqrt{\frac{\lambda}{2-\lambda}\cdot\frac{n-1}{n-3}}\end{array}\right. \tag{3}\]  
where \(L_{e}\) is the control limit constant chosen to match the desired in-control \(ARL\). When statistics \(Z_{i}\) exceeds the \(UCL\) or \(LCL\), the EWMA-t control chart initiates an out-of-control signal; otherwise, the process remains in-control. For more details on the EWMA-t chart, see [PERSON] et al. [22] and [PERSON] et al. [25].
## 1 Introduction  
While multichannel loudspeaker systems for home and car entertainment are becoming increasingly popular these days, the number of available multichannel audio recordings is still limited (note that multichannel audio recordings and mixed multichannel audio is meant subsequently). In contrast to movies on DVD or Blu-ray, the majority of audio recordings are only obtainable in the two-channel stereo format. In addition, the main content of digital radio and television and also the increasing significant streaming services for music and movies are only obtainable in the two-channel stereo format, too. So, it can be noted that there is a low availability of multichannel audio records. That is why a system is worthwhile, which extends an original stereo audio signal for playback over a multichannel loudspeaker system. As a result, the spatial quality and the listening experience can be enhanced compared with the pure stereo playback. For this reason, there is wide scholarly interest in novel stereo-to-multichannel upmix algorithms, e.g., [1; 2; 3; 4; 5] but there are many more recent publications on this topic. In order to determine the quality of such upmix algorithms, subjective listening tests were typically used [6; 7; 8]. [PERSON] [6] presented specific design criteria for stereo-to-multichannel upmix algorithms to enhance spatial sound quality. He used formal listening tests for subjective evaluation of the design criteria according to [9], where three general sound quality issues for the evaluation of multichannel audio systems were defined. [PERSON] and [PERSON] [7] used eight selected spatial attributes for sound quality evaluation in order to compare upmix algorithms. They derived a set of objectives measures from sound field analysis to predict auditory attributes. [PERSON] and [PERSON] [8] used subjective listening tests for the assessment of source separation-based upmixing algorithms.  
In addition, they used objective testing to measure the errors which could theoretically occur in source separation algorithms.  
Formal listening tests have been the only appreciable approach to assess the quality of stereo-to-multichannel upmix algorithms. More importantly, subjective quality assessments are always connected with expenditure and are both time-consuming and expensive. That is why an objective evaluation technique for stereo upmix algorithms for spatial audio is desirable.
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 3 Results
### Mapping CLQ at the Regional Scale  
The rice growth stage GPP-driven spectral model for year 2013 was used to map the CLQ for Aotou Town of the Conghua District and Zhongxin Town of the Zengcheng District to validate its  
Figure 5: Scatterplots of measured versus estimated values of CLQ using the validation data set of 126 samples from 2011 to 2015: (**a–e**) PLSR model; (**f–j**) SVR model; (**k–o**) GA-BPNN model.  
capacity of predicting CLQ at the regional scale in Figure 6. The referenced values of CLQ were grouped into five classes based on the gradation regulations on agriculture land quality in China (_Regulation for gradation on agriculture land quality_ GB/T 28407-2012). The R\({}^{2}\), RMSE, and NRMSE values of the predictions from the GA-BPNN model were calculated based on 60 sample data in Aotou and Zhongxin town, respectively (Figure 7). The prediction accuracies with RMSE of 73.32 and 104.35 and NRMSE of 10.47% and 17.75% show that the GA-BPNN model is appropriate to map CLQ at both towns.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 6 Conclusions and Future Work  
Integration of future communications in IoT has the capability to provide many effective business opportunities to meet the requirement of current as well as future needs. Future 6G-enabled IoT requires resource-efficient mobility solutions in addition to a better quality of services requirement. For supporting mobility in industrial IoT, a location-oriented network-based IPv6 solution is provided to optimize the performance of PMIPv6 protocol by effectively using the location information of network resources in the domain of industrial IoT in addition to integration with RSS measure. By avoiding additional communication, and unnecessary messages due to RSS errors, the proposed extension provides resource efficiency by accurately predicting the handover moment and avoiding additional signaling and buffering. The presented extension is evaluated in comparison with available related RSS protocol extensions and, based on the results obtained, it is observed that the proposed solution optimizes the performance by reducing handover latency, eliminating additional signaling, and requiring buffering. This research has the potential to be adapted for supporting mobility requirements in industrial IoT. Furthermore, the proposed extension can be enhanced by the inclusion of a number of aspects such as speed, direction, and mobility history for intelligent decision-making regarding resource efficiency and load distribution among network resources.  
Conceptualization, H.U.K.; Data curation, F.A. and M.Z.K.; Formal analysis, S.N. and LU.; Funding acquisition, I.U.; Investigation, S.N.; Methodology, H.U.K.; Project administration, I.U.; Resources, F.A.; Software, H.U.K.; Supervision, S.N. and I.U.; Validation, A.H. and M.Z.K.; Visualization, M.Z.K.; Writing--original draft, H.U.K. and A.H.; Writing--review and editing, A.H. and F.A. All authors have read and agreed to the published version of the manuscript.  
This work received no external funding.  
Data Availability StatementData will be provided on request.  
The authors declare that there are no conflict of interest.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
###### Abstract  
Stope design is an important part of mine planning, and it aims to balance ore recovery, ore dilution, and production costs without compromising the safety aspects. This paper summarizes the main results from the research, which aims to introduce new techniques to describe the ore body and surrounding rock mass at the tunnel face prior to stope excavation. The research comprises a literature review and a survey among mining professionals to assess current stope design practices. The study identifies geotechnical data, software improvements, and integration of design into mine planning as the most critical areas for improvement. The empirical part of the study proposes new techniques for fast data acquisition. The laser-induced breakdown spectrometry (LIBS) technique is developed for measurements at the tunnel face and from core boxes to provide mineralogical and geometallurgical data. Ground-penetrating radar (GPR) studies are conducted to improve discontinuity characterization, and rapid photogrammetric methods are proposed for efficient tunnel geometry characterization. The techniques discussed in this paper already have many industrial applications. This study reveals their potential to be adopted and further developed to serve ore and rock mass characterization for stope design.  
stope design; mine planning; rock mass characterization; LIBS; GPR; photogrammetry Article
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 4 Results and Discussion
### Simulation Scenarios and Evaluation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline RoI (Region of Interest) & 400 m \(\times\) 400 m \\ Number of nodes & 800 \\ Number of rounds & 1000 rounds \\ Round Time & 20 s \\ Node deployment (topology) & Random \\ Packet size & 100 bytes \\ Packet rate & 1 packet/s \\ Initial node energy & 100 J \\ Cluster Routing Protocols & LEACH, P-LEACH and EA-CRP \\ Sink Mobility models & Static, RWP, RW, RD and GM \\ Interval of mobility (speed) & [1 m/s-10 m/s] \\ Time of move & 10 s \\ Time of pause for the RWP model & 5 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of simulations. Figure 2: Deployment of static and mobile sinks around the RoI, (**a**) Single Sink for the entire network, (**b**) 2 Sinks for two sub-networks, (**c**) four Sinks for four sub-networks, (**d**) eight Sinks for eight sub-networks. * Delay or Packet Latency  
To achieve accurate simulation results, we will use four random mobility models of the sink (GM, RW, RWP and RD) and a static model (a static sink located outside the region of interest; Fixed Sink) with three cluster-based routing protocols (LEACH, P-LEACH, and EA-CRP).
## 5 Discussion
### Paleoclimate Analysis of Carbonate Rocks  
[PERSON] found that the change in temperature will lead to variations of \(\delta^{18}\)O/\(\delta^{16}\)O in carbonate rocks and proposed that it can be used as a geothermometer to determine the paleo-ocean temperature [38; 39; 40]. The formula for calculating paleotemperature by using oxygen isotopes was first proposed by [PERSON] in 1953 and further calibrated by [PERSON] [41], as expressed by:  
\[\mathrm{T}=16.9-4.2\times(\delta^{18}\mathrm{O_{c}}-\delta^{18}\mathrm{O_{w} })+0.13\times(\delta^{18}\mathrm{O_{c}}-\delta^{18}\mathrm{O_{w}})^{2}\]  
where \(\delta^{18}\)O\({}_{\mathrm{c}}\) and \(\delta^{18}\)O\({}_{\mathrm{w}}\) is the \(\delta^{18}\)O\({}_{\mathrm{PDB}}\) value of CO\({}_{2}\) and the \(\delta^{18}\)O\({}_{\mathrm{SMOW}}\) value of H\({}_{2}\)O generated by the reaction of CaCO\({}_{3}\) and H\({}_{3}\)PO\({}_{4}\) at 25 \({}^{\circ}\)C.  
The existence of the \"age effect\" requires further correction of the \(\delta^{18}\)O values in carbonate rocks [28]. The average value of \(\delta^{18}\)O in carbonate rocks in the Upper Quilitage Formation is \(-8\)% after excluding three severely altered samples, compared with the value of \(-1.2\)% in Quaternary marine carbonate rocks. Thus, an average value of \(\delta^{18}\)O = \(-6.8\)% is to calibrate paleotemperature calculation formula as follows:  
\[\mathrm{T}=16.9-4.2\times(\delta^{18}\mathrm{O_{CaCO_{3}}}+0.22)+0.13\times( \delta^{18}\mathrm{O_{CaCO_{3}}}+0.22)^{2}\]  
The calculation results show that the paleotemperature during sedimentation of the Upper Quilitage Formation varies from 17.24 to 24.79 \({}^{\circ}\)C (Table 1), representing a warm-to-hot climate [42].  
Among the trace elements in carbonate rocks, Cu concentration favors a wet environment, while Sr enrichment is an indicator of dry conditions, and Sr/Cu value is often used to reflect paleoclimates for carbonate precipitation [43]. The Sr/Cu values of the studied carbonate rocks are relatively high, ranging from 57.5 to 560, indicating that the carbonate rocks in the Upper Quilitage Formation in the study area were formed in an arid climate. Moreover, the V/Cr value can also be used to determine the formation environment of carbonate rocks [44]. The V/Cr values of the samples collected in this study are mainly between 0.989 and 1.70, with an average value of 1.26, indicating that they were formed under oxidative conditions. In summary, the Upper Quilitage Formation is deposited in a warm, aerobic, and arid environment.