esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 3 Hierarchical Age Estimation with Semantic Attention
### Swin Transformer Based Global Regression
#### 3.1.2 Local Adjusted Decision  
Although Swin Transformer effectively captures global semantic information, the inherent ambiguity and visual similarity between neighboring age labels make sole reliance on global prediction insufficient. To address this, we incorporate a local adjustment module to refine the global age prediction and improve estimation accuracy. Motivated  
Figure 2: Network architecture of proposed approach.  
by [30], errors mainly occurred around the global estimation. To enable more precise local regression, we construct a dynamic age range around the global estimation based on the local adjustment decision strategy. This local range is defined as:  
\[R=[y-r,\;y+r] \tag{2}\]  
where \(R\) is the predicted global age and \(r\) is the neighborhood radius. The radius is dataset-dependent and can be adaptively tuned. For instance, the optimal radius for Morph II and CACD is 10, while for MegaAge-Asian it is 15. More detailed analysis of the impact of the radius are discussed in the experimental section. This flexible adjustment strategy narrows the search space for local estimation, mitigates the global prediction errors and enhances the model's focus on relevant age intervals.
## 2 Results and Discussion
\begin{tabular}{l l c c c} \hline
24. & Camphene & 958 & 0.2 & 0.4 & - \\
25. & Benzaldehyde & 965 & 0.6 & 20.4 & - \\
26. & Hexanoic acid (caproic acid) & 994 & 0.1 & 4.6 & - \\
27. & Octanal & 1007 & - & 0.7 & - \\
28. & \(p\)-Cymene & 1032 & 0.1 & 2.7 & - \\
29. & Limonene & 1036 & - & 0.5 & - \\
30. & 2-Hydroxybenzaldehyde & & & & \\  & (salicylaldehyde) & 1052 & - & 0.2 & - \\
31. & 1-Phenylethanone (acetophenone) & 1074 & - & 0.7 & - \\
32. & Heptanoic acid & 1093 & - & 0.8 & - \\
33. & Fenchone & 1094 & & 0.2 & \\
34. & Nonan-2-one & 1097 & - & 1.3 & - \\
35. & 1-Ethyl-4-(1-methylethyl)-benzene & 1110 & - & 1.6 & - \\
36. & 2-Phenylethanol & 1116 & 1.2 & 3.3 & - \\
37. & Camphor & 1153 & - & 0.9 & - \\
38. & Pentybenzene & 1165 & - & 0.4 & - \\
39. & Bromeol & 1175 & 1.1 & 4.5 & 0.2 \\
40. & Naphthalene & 1190 & - & 1.2 & - \\
41. & Octanoic acid & 1191 & 0.5 & 1.4 & - \\
42. & 2-Methoxy-4-methylphenol & & 1198 & 0.3 & 0.9 & - \\  & (\(p\)-cresol) & & & & \\
43. & 3-Isopropylbenzaldehyde & 1229 & 0.4 & 4.7 & - \\
44. & 4-Ethyl-2-methoxyphenol & & & & \\  & (4-ethylgaiaicol) & & 1286 & 0.4 & 0.9 & - \\
45. & Nonanoic acid & 1288 & - & 0.5 & - \\
46. & Bornyl acetate & 1291 & - & 0.6 & - \\
47. & 1-Methylnaphthalene & 1316 & - & 0.4 & - \\
48. & \(p\)-Acetylacetophenone & 1325 & - & 0.7 & - \\
49. & 2-Methoxy-4-propylphenol & & 1375 & 1.2 & 1.5 & - \\
50. & 4-Hydroxy-3-methoxy- & & & & \\  & benzaldehyde (vanillin) & 1406 & 0.3 & - & - \\
51. & 1,7-Dimethylnaphthalene \({}^{**}\) & 1427 & 0.4 & 0.7 & - \\
52. & 3,5-Dimethoxybenzaldehyde & & & & \\  & (syringaldehyde) & & & & \\
53. & 4-Hydroxy-3-methoxy- & & & & \\  & acetophenone (acetovanillone) & & & & \\
54. & \(\alpha\)-Muurolene & 1507 & 0.6 & 0.9 & - \\
55. & Calamenene & 1531 & 1.0 & 1.3 & - \\
56. & \(\gamma\)-Cadinene & 1535 & 0.1 & - & - \\
57. & \(\alpha\)-Calacorene & 1551 & 0.1 & - & - \\
58. & 2,3-Dimethylbenzofuran & 1578 & 0.3 & - & - \\
59. & 1-Methyl-7-(1-methylethyl)- & & & & \\  & naphthalene (eudalin) & & 1587 & 0.6 & 0.5 & - \\
60. & 1,6,7-Trimethylnaphthalene \({}^{*}\) & 1590 & 0.3 & - & - \\
61. & 1,6-Dimethyl-4-(1-methylethyl)- & & & & \\  & naphthalene (cadalin) & & 1684 & 1.1 & 0.7 & - \\ \hline \end{tabular}   
**T
## 4 Proposed Algorithm
### Proposed Enhanced Algorithm
#### 4.3.1 Hybrid Solution Initialization Method  
The conventional WOA uses a stochastic number generator to generate the initial population. A generated initial population via this type has the following issues. First, this method will lead to a decrease in the performance of the initial solution, thereby reducing the convergence performance of the algorithm. Second, our problem is a mixed solution problem, and a stochastic number generator can only produce continuous solutions. Due to these reasons, we propose a hybrid solution update method in this part.  
First, we generate the continuous part of the solution via chaos maps. Note that chaos maps are special mathematical structures that include periodic and stochastic continuous sequences. Because of their characteristics, they are often used in the improvement of initialization in evolutionary computation algorithms. Specifically, there are about 10 common chaotic maps, namely, Chebyshev, circle, Gauss/mouse, iterative, logistic, piecewise, sine, singer, sinusoidal, and tent. In this work, we select an iterative map to generate the continuous part of the solutions, which can be detailed as follows.  
\[p_{q+1}=\sin\biggl{(}\frac{c\pi}{p_{q}}\biggr{)},c=0.7, \tag{10}\]  
where \(p_{q}\) is the \(q\)th sequence of the iterative map. Then, the continuous part of a solution can be initialized as follows.  
\[x_{k}=lb_{k}+p_{q}(ub_{k}-lb_{k}), \tag{11}\]  
By using this method, the initial solution generated by the algorithm will have a more balanced performance, so the algorithm's performance is better.  
Second, we generate the binary part of the solution, which can be detailed as follows.  
\[\mathbf{B}=\text{Rand}_{1,0}(N_{\text{sel}}\,,N_{S}-N_{\text{sel}}\,), \tag{12}\]  
where \(\text{Rand}_{1,0}(a,b)\) can generate a vector combined with \(a\) ones and \(b\) zeros.
## 4 Dynamic Weighted Clustering Algorithm with Dual Cluster Heads
### Weight Factor
\(PL_{i}\) can be expressed as [26] \[PL_{i}=\frac{A}{1+a\cdot exp(-b\cdot arctan(\theta)-a)}+10 log(H^{2}+d_{i}^{2})+B\] (3) where \(A=\eta_{LoS}-\eta_{NLoS}\), \(B=20 log(\frac{4\pi f}{c})+\eta_{NLoS}\), \(\eta_{LoS}\) and \(\eta_{NLoS}\) are the excessivepath loss of LoS condition or non-line-of-sight (NLoS) condition, while \(f\) is the frequency of the UAV, \(c\) is the speed of light, \(\theta\) is the elevation angle satisfying \(tan\theta=\frac{H}{d_{i}}\), \(d_{i}\) is the horizontal distance between UAV \(i\) and gBS, \(d_{i}=\sqrt{(X_{i}-X_{0})^{2}+(Y_{i}-Y_{0})^{2}}\), \(X_{0}\) and \(Y_{0}\) are the coordinates of gBS, and \(a\) and \(b\) are constants related to the communication environment. * Stability. Since UAVs have strong mobility, relative mobility is used to measure the stability of nodes in the cluster. The smaller the relative moving speed of a cluster head node and its neighbors is, the longer the cluster head node stays in the cluster, i.e., the stronger the stability of the cluster is. The stability factor is expressed as \[M_{i}=\sum_{i^{\prime}\in(N_{i}\cap\mathcal{U}_{i}),\ i^{\prime}\
eq i}(\omega _{i}-\omega_{j^{\prime}})^{2}\] (4) where \(\omega_{i}\) and \(\omega_{j^{\prime}}\) are the angular velocity of UAV \(i\) and \(i^{\prime}\), respectively. Since we assume that all UAVs fly at the same height and their flight trajectories are circles centered on the gBS, the difference of angular velocity between a UAVs nodes can be used to represent the relative position change between nodes, i.e., the stability within the cluster. * Energy. The remaining energy of the UAV determines its remaining lifetime. The UAV that becomes the cluster head node needs to maintain as long of a lifetime as possible to maintain the intracluster communication. The energy factor is represented by the consumed energy \(E_{i}\), the lower \(E_{i}\) is, the longer the remaining lifetime of the node is. \(E_{i}\) is expressed as \[E_{i}=E_{ini}-E_{rm}\] (5) where \(E_{ini}\) and \(E_{rm}\) represent the initial and remaining energy of the UAV, respectively.
## 3 Results  
### Following the Facility Scenario  
The distribution of the seismometer network in this scenario was tailored to the mining facilities. Seismometers were deployed at three levels of elevation of the mining facility: 15 with a distribution at elevations of 1840-1880 msl (level 1), 11 at elevation levels of 1640-1680 msl (level 2) and 15 seismometers at 1500-1560 msl (level 3). The results of the forward modeling were synthetic absolute data for P- and S-wave velocities. The data were then prepared to find the event pair using ph2 dt. The ph2 dt program provided outputs in the form of data from earthquake events that were used as pairs and data on differences in travel times for these earthquake pairs. These files were used as inputs for the double-difference relocation method. The results of the ph2 dt program for the scenario following a mining facility with 41 stations were as follows: the total number of P- and S-wave phase pairs was 389,008, the number of P and S phase pairs used was 379,520 (97%), the number of earthquake pairs was 9489 and the average number of records per pair was 79.  
The output of the ph2 dt program is the catalog difference time (dtct), which is one of the input parameters in the inversion process. As explained in the section on the synthetic model, we also conducted random sorting of phase travel time data with a limit of 1 earthquake event recorded by a minimum of 10 nearest stations. The inversion input parameters were: (1) catalog difference time (dtct), (2) station coordinates, (3) velocity model, (4) sorted phases and (5) earthquake events. Other parameters that need to be determined in performing a TomoDD inversion are damping and smoothing. The smoothing parameter selected for this inversion process was 0. Determination of the damping value used depends on the condition number (CND) value, which can be seen in the output of the TomoDD program. According to [PERSON], a stable inversion has a CND value between 40 and 80 ([PERSON], 2001). The damping value used in this study was 350, with the number of iterations limited to 7 iterations. Figures 7 and 8 are Vp and Vs CRT tomograms for the scenario for the facility for each elevation. Areas with good resolution were interpreted as being in areas that have clear alternate positive and negative anomalies.  
Figure 7: **(a)** Checkerboard Resolution Test Vp following the facility for each elevation at 1540–1820 (interval 40 m). **(b)** Checkerboard Resolution Test Vp following the off-facilities area for each elevation at 1540–1820 (interval 40 m).  
Figure 8: (**a**) Checkerboard Resolution Test Vs following the facility for each elevation at 1540–1820 (interval 40 m). (**b**) Checkerboard Resolution Test Vs following the off-facilities area for each elevation at 1540–1820 (interval 40 m).
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Distribution Network Flexibility Indicators
#### 2.3.2 Distributed Generation Consumption Rate  
DG consumption rate refers to the proportion of DG systems (e.g., solar, wind, etc.) that are actually consumed or utilized after being connected to the grid. This rate reflects the actual efficiency of the use of DG and is usually expressed as a percentage. Affected by natural conditions and geographical characteristics, China's new energy distribution is extremely uneven, there is a serious phenomenon of abandonment of wind and solar energy in places where wind and solar energy are abundant, the energy utilization rate is low, and how the grid can consume the output of these new energy sources is an urgent problem to be solved. In this paper, the DG consumption rate is used to measure the ability of ADN to absorb the power from DG.  
\[H_{xn}=\frac{\int_{0}^{T}|P_{GL,t}|dt}{\int_{0}^{T}\left|P_{GL,t}^{pre}\right|dt }\times 100\% \tag{13}\]  
\[\int_{0}^{T}|P_{GL,t}|dt=\int_{0}^{T}\min\Bigl{\{}\left|P_{DG,t}^{pre}\right|_{ \prime}|P_{t,t}|\Bigr{\}}dt \tag{14}\]  
where \(H_{xn}\) is the rate of renewable energy consumption; \(P_{GL,t}\) is the actual power consumption of ADN; \(P\) is the output power of DG; \(P_{t,t}\) is the load power.  
When the load demand is low, to avoid backward transmission, the amount of DG consumed by ADN will be lower than the amount of DG that can be generated, and the consumption rate will be lower than 100%. As the consumption rate approaches 100%, the amount of DG consumed increases, thereby demonstrating the effectiveness of ADN in consuming DG.
## 1 Introduction
### Statement of Novelty  
The current research was carried out to work with uncommon speech signals of the Punjabi language, i.e., speech signals in the form of tonal and emotional cues. In this work, we designed a new dataset consisting of some additional background noises (instrumental music and other noises) considered while recording 11 different input signals from 11 speakers of different ages, accents, and environments. Each recording/input speech signal was continuously spoken by the speakers and consisted of 38 additional stanzas.  
In contrast to the existing systems for common speech signals and other languages. The existing systems attains a low WER and high accuracy. Therefore, we implemented the ML-CNN method with a new dataset of input speech signals and parameters. In addition, we observed the outcome of the work and found that although CNNs are being selected by many authors for our new dataset, CNNs outperform the existing datasets and techniques. The work concludes that CNNs can work well with prominent, minor, and tone-oriented datasets.  
The paper is categorized into the following sections. Section 2 describes essential studies related to speech recognition. Section 3 describes the prevailing algorithms of DNN and also discusses the significance of speech type considered in the present work. Section 4 describes the framework for the current system and the experimental study and result analysis. Section 5 describes the results analysis of proposed system with existing works. Section 6 concludes the entire paper with its outcome and findings.
## 2 Materials and Methods
### Analytical Approach
#### 2.1.4 Reflection FT-IR Spectroscopy  
Non-invasive infrared spectra (a total of 142 measurements) were acquired through the compact portable Bruker ALPHA II spectrophotometer. This instrument is equipped with a dedicated external reflectance module with specular optics (22\({}^{\circ}\)/22\({}^{\circ}\)) which allow contactless analyses. The system consists of a CenterGlow source, a RockSolid interferometer (with gold mirrors) and a temperature controlled DTGS detector. An integrated USB video camera provides the view of the sampling area of ca. 3 mm \(\O\). The reflection FT-IR spectra were recorded in the spectral range of 8000-350 cm\({}^{-1}\) (medium and partially near infrared region), with a resolution of 4 cm\({}^{-1}\) and using an average of 186 scans (few minutes of acquisition time). Background correction was performed employing a spectrum collected from a flat gold mirror. Data are shown as pseudo-absorption spectra (A\({}^{\prime}\) = Log (1/R); R = reflectance). The use of a wide IR spectral range that includes NIR radiations  
\begin{table}
\begin{tabular}{c c c} \hline
**Imaging Method** & **Radiation Source** & **Filters** \\ \hline
1. Visible-reflected (Vis) & Flash SB-910 Nikon * & Hot mirror + UV/IR CUT \\ \hline
2. Raking light (Rak) & Flash SB-910 Nikon * & Hot mirror + UV/IR CUT \\ \hline
3. Infrared-reflected (IRr) & Flash SB-910 Nikon * & Kodak Wratten 87 \\ \hline
4. Ultraviolet-reflected (UVr) & CR500 LED Madatec & B + W 403 UV pass filter Hot \\  & & mirror Kolari \\ \hline
5. Ultraviolet-induced visible & CR500 LED Madatec & Hot mirror Better Light UV \\  & & cut Schott KV418 \\ \hline
6. Visible-induced infrared & LED visible & IR Pass 850 nm \\  & & \\ \end{tabular}  
* Flash modified by removal of covering filter.  
\end{table}
Table 1: Description of the radiation source and filters for the different imaging methods and in parenthesis the abbreviation used in the text to indicate the type of image.  
allows obtaining the chemical composition not only at the surface, but also from deeper layers of the paintings.
# A QR Code-Based Approach to Differentiating the Display of Augmented Reality Content
## 2 Design of Differentiated AR System Based on QR Markers
### Secret QR Tag Generation  
We designed a secret QR tag concealment algorithm to derive the marked QR tag by applying the concept of the steganography technique [23; 24; 25]. Let \(Q\) be a given QR tag generated by a barcode generator. Here, the data content of \(Q\) can be a link or a uniform resource locator (URL) of the general virtual object. Public users can receive the ordinary virtual object from the QR tag.  
According to the QR barcode standard, let \(n\) be the number of QR data and error correction modules, as the red region in Figure 2. To distinguish the general user and the specific users, let there are \(m\) classifications of members, \(ID_{i}\) and \(s_{i}\) be the identification number and the authentication code of the \(i\)-th member classification, \(1\leq i\leq m\). Here, the value of \(s_{i}\in\{0,1\}\) and the length of \(s_{i}\) is \(\lceil\log_{2}m\rceil\).  
The stream \(c_{i}\) corresponding to \(ID_{i}\) thereby can be generated by  
\[c_{i}=H_{K}(ID_{i})\bmod n. \tag{1}\]  
Here, \(K\) is the secret key of the system/dealer, and \(H_{K}(\bullet)\) is the one-way hash function with the key \(K\). The stream length of \(c_{i}\) is \(\lceil\log_{2}m\rceil\). The \(c_{i}\) indicates the concealed positions of the QR modules for the \(i\)-th member. Note that the generated values of \(c_{i}\) are limited within \(n\), and the values in \(c_{i}\) cannot be selected more than once to avoid overlapping.  
Assume \(q_{i}\) are the selected modules in \(Q\) according to the assigned positions \(c_{i}\). The authentication codes \(s_{i}\) for differential members thereby are concealed into the corresponding \(q_{i}\) modules by  
\[q_{i}{}^{\prime}=q_{i}\oplus s_{i}, \tag{2}\]  
where \(q_{i}{}^{\prime}\) are labeled as the modified modules. Note that, to conceal \(s_{i}\) into the QR tag without degrading the readability of the content, the value of \(m\) should be restricted,  
\[m\times\lceil\log_{2}m\rceil\leq\lfloor EC/2\rfloor\times 8. \tag{3}\]  
Here, \(EC\) be the number of error correction codewords of the given \(Q\).  
With the concealment process, the embedded QR tag \(Q^{\prime}\) consequently can be derived. Figure 3 demonstrates the result of the marked QR tag generation procedure for eight classifications of members (i.e., \(m=8\)). The original QR tag and the marked QR tag are depicted in Figure 2(a),2, respectively. The red modules in Figure 2(c) show the difference between Figure 2(a),2.  
Figure 2: The modules of data and error correction codewords of QR tag.
## 3 Results
### Model Performance and Evaluation
#### 3.2.2 Comparative Analysis  
In this study, the performance of the XGBoost model for predicting milk components (protein and fat) was compared with other machine learning models commonly used in similar spectral analysis studies. The comparison included Gradient Boosting Machine (GBM), Support Vector Regression (SVR), decision trees, random forest, and Multilayer Perceptron (MLP). The performance metrics (MSE and R\({}^{2}\)) for each model's predictions on protein and fat are presented in the following Table 4.  
Table 4 includes the Mean Squared Error (MSE) and R\({}^{2}\) values, showing the performance of the various models in predicting protein and fat content and demonstrating the effectiveness and success of each model on different targets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Target** & **MSE** & **R\({}^{2}\)** \\ \hline XGBoost & Protein & 0.004754 & 0.933 \\ XGBoost & Fat & 0.004795 & 0.997 \\ GBM & Protein & 0.006330 & 0.912 \\ GBM & Fat & 0.004304 & 0.998 \\ SVR & Protein & 0.030675 & 0.573 \\ SVR & Fat & 0.061659 & 0.967 \\ Decision Tree & Protein & 0.015863 & 0.779 \\ Decision Tree & Fat & 0.015433 & 0.992 \\ Random Forest & Protein & 0.025198 & 0.649 \\ Random Forest & Fat & 0.031136 & 0.984 \\ MLP & Protein & 0.050418 & 0.297 \\ MLP & Fat & 0.078021 & 0.959 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model performance comparison table.  
Figure 8: Distribution of prediction errors.
# Structure Preserving Convolutional Attention for Image Captioning
## 3 Proposed Method  
### Overview  
We start by briefly describing the encoder-decoder image captioning framework [5; 6], and then we describe our SPCA modules.  
**Encoder-decoder framework:** Given an image and the corresponding caption, the encoder-decoder model is directly optimized by the following objective:  
\[\theta^{*} = \arg\max_{i}\sum_{(I,y)}\log p(y|I;\theta), \tag{1}\]  
where \(\theta\) is the parameters of the model, \(I\) is the given image, and \(y=y_{1},\dots,y_{n}\) is the corresponding caption words.  
We adopt LSTM for decoding image features into a sequence of words. The update for its hidden units and cells of an LSTM are defined as:  
\[h_{t} = LSTM\left([x_{t};c_{t}],h_{t-1}\right) \tag{2}\]  
where \(x_{t}\) is the embedded word representation, \(c_{t}\) is the context representation, \([\ ;\ ]\) represents concatenate. Furthermore, \(h_{t}\) is the hidden state and memory cell at time \(t\).  
Commonly, the context representation \(c_{t}\), is an important factor in the neural encoder-decoder framework, which provides visual evidence for generating caption. Attention mechanism [5] has been proven to be crucial in producing \(c_{t}\):  
\[c_{t}=ATT\left(V,h_{t}\right) \tag{3}\]  
where \(ATT\) is the attention function, \(V\in\mathbb{R}^{C\times W\times H}\) (\(C,W,H\) represents the channel, width and height, respectively) is the image feature map, which is the output of a CNN image encoder.  
As for conventional spatial attention models, \(ATT\) is a grid-based attention. In these models, the context representation \(c_{t}\) is a vector, which is formulated as:  
\[z_{ti} = \mu^{T}\tanh(W_{v}V_{i}+W_{h}h_{t-1}) \tag{4}\] \[\alpha_{t} = softmax\left(z_{t}\right)\] (5) \[c_{t} = \sum_{i=1}^{H\times W}\alpha_{ti}V_{i} \tag{6}\]  
where \(V_{i}\in\mathbb{R}^{C}\) is a vector representation corresponding to the \(i\)-th grid of the image features, \(W_{v},W_{h}\) and \(\mu\) are parameters to be learnt. \(\alpha_{ti}\) is the attention weight for \(V_{i}\).
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 7 Conclusions  
In this work, we investigated ways to find capacity limits concerning the number of users, number of BS antennas, and SNR. By assuming a dominant LoS environment in a massive MIMO scenario, it was possible to derive analytical expressions for the channel capacity. Convenient simplifications on expressions were possible working at low- and high-SNR regimes. Furthermore, it is demonstrated that the Gamma PDF can approximate the PDF of the instantaneous channel sum-capacity as the number of antennas grows for both cases of low- and high-SNR regimes. A second important demonstration is that a Gamma PDF can also approximate the PDF of the summation of the channel's singular values as the number of devices increases. Finally, the utility of such a framework is useful for a massive number of IoT devices as we show that the transmit power of each device can be made inversely proportional to the number of BS antennas.  
Conceptualization, methodology, software, validation, investigation, formal analysis, data curation, and writing, original draft preparation: F.A.P.d.F. and C.F.D.; writing, review and editing: E.R.d.L.; resources, supervision, project administration, and funding acquisition: G.F. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 732174 (ORCA project). Additionally, this work was partially supported by R&D ANEEL - PROJECT COPEL 2866-0366/2013 and EMBRAPII - Empresa Brasileira de Pesquisa e Inovacao Industrial. [PERSON] was supported in part by CNPq under Grant 313239/2017-7. [PERSON] was supported in part by CNPq under Grant 304946/2016-8.  
The authors declare no conflict of interest.