esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reverification of the Multicomponent Hydration Model
### Formulation of Extensive Modeling and its Verification  
Based on the above discussion, it was hypothesized that only part of condensed water is available for hydration due to discontinuous fine pore structures, which are peculiarly formed in a low W/C mixture. Specifically, this paper proposes an extensive model in which the amount of free water available for hydration is reduced according to a new evaluation parameter of discontinuity of pore structures. The proposed formulation is shown as follows:  
\[\psi=\frac{\phi_{cp}}{\phi_{gl}}\ \text{(if }\psi\geq\psi_{\text{lim}}\,,\ \psi=\psi_{\text{lim}}\,,\ \text{if not, }\psi=\psi) \tag{7}\]  
\[w^{\prime}_{free}=w_{red}\times w_{free} \tag{8}\]  
\[w_{red}=exp\left\{-a\cdot\sqrt{1-S_{cp}}\left(1-\frac{1}{\psi_{lim}}\times \psi\right)\right\}\left(\text{if }S_{cp}\geq 0.8,\ S_{cp}=S_{cp}\,,\ \text{if not, }S_{cp}=0.8\right) \tag{9}\]  
where \(\Psi\) is the porosity ratio of capillary to gel, which is an indirect parameter simply representing discontinuity of pore structures, \(\Psi_{lim}\) is the critical value of \(\Psi\) at which existing condensed water is fully available wherever it is, \(w^{\prime}_{free}\) is the modified amount of free water available for hydration, \(w_{free}\) is the amount of free water, \(w_{red}\) is the reduction factor against the amount of free water, \(S_{cp}\) is the average saturation degree of capillary pores, and \(a\) is the constant that stands for an impact of \(S_{cp}\) on the reduction factor, \(w_{red}\). In short, by using the above extensive model, the available free water is limited with \(w_{red}\), only when both of the following conditions are met; \(\Psi\) is smaller than \(\Psi_{lim}\), and \(S_{cp}\) is unsaturated (less than 1.0). Herein, \(\Psi_{lim}\) and \(a\) were finally determined through various sensitive analyses and regarded as 0.5 and 10.0, respectively, in this paper. Figure 22 displays the relationship between \(\Psi\) and \(w_{red}\), defined in Equation (9). In the range of \(\Psi\geq\Psi_{lim}\) (= 0.5), this extensive model can be considered the same as the original one, because \(w_{red}\) remains 1.0. On the other hand, as the lower \(\Psi\) falls in the range of \(\Psi<\Psi_{lim}\), the amount of free water available for hydration is more limited according to \(S_{cp}\). In the case of \(S_{cp}\) = 1.0, condensed water naturally exists around unreacted cement particles, enabling hydration to proceed continuously without limitation regardless of \(\Psi\).  
Figure 21: Modeling scheme on redistribution of trapped “ink-bottle” water and increase in relative humidity at high temperature [24].  
These results reveal that the proposed extensive model works properly to enhance simulation accuracy, covering the peculiar hydration in a low W/C mixture. In other words, this extensive modeling can be applied to arbitrary W/C mixtures in a unified approach.
## 3 ATON Framework
### Modifying Published Scenes  
Once a 3D scene is published (i.e., it has a unique ID assigned), updating or manipulating its items _at runtime_ using a front-end or presenter is possible through proper interfaces (e.g., transforming objects, adding annotations, etc.) but making these changes persistent requires direct intervention on the corresponding JSON descriptor (see Section 3.2). It is quite natural to expect routines that allow authenticated users to change, update, annotate, fine-tune or modify their scene in a persistent manner. Scene patches allow client web-apps to send compact partial edits (patches) to the DN through the REST API in order to modify a given scene and its corresponding JSON file. Such approach is based on _JSON patches_[52], a format for describing changes to a JSON document, that fully suits the scene descriptors of the ATON framework.  
This enables authenticated clients to perform scene updates at runtime by sending over the network small patches that persistently modify the JSON file on the server (see Figure 6). Common examples include applying scene-graphs modifications (adding/removing/transforming scene nodes); adding/modifying scene description or semantic annotations; modifying lighting or environment and more in general, anything in the JSON scene descriptor. A web-application can thus provide usable interfaces allowing users to transparently perform such tasks and apply changes to a scene. This approach not only allows web-apps to perform arbitrary edits to a scene through compact JSON patches exchanged over the network, but it is also a robust approach for future changes involving the JSON scene descriptor, custom JSON descriptors or to modify other JSON files. Furthermore,the DN may easily keep track of 3D scene modifications performed on the descriptor by creating lightweight snapshots, also usable for _undo_ operations.
## 3 Approach
### Temporal Feature Pyramid  
Inspired by resnet [41] and FPN [42], we designed a temporal feature pyramid by stacking multiple pyramid levels to enhance the expressiveness of the network. Specifically, we applied a temporal downsampling operation at each pyramid level to accommodate action instances with various durations, where the downsampling rate is between d, d \(\in\) {1, 2, 2, 2, 2}. Each pyramid level consists of MLC, WSA, and GA modules, and the output of each module is independent. Therefore, the network has the ability to capture local, window, and global range features simultaneously. In order to aggregate these features, a MAX operator is adopted along the temporal dimension, which is used to filter out the strongest features. The aggregation strategy can be expressed as  
\[\overline{S}_{\tau} =YW^{\tau}\parallel YG^{\tau}\parallel YM^{\tau},\quad\tau=1,2,3, \cdots,L \tag{8}\] \[\widetilde{S}_{\tau} =MAX(\overline{S}^{\tau}),\quad\tau=1,2,3,\cdots,L\] (9) \[S_{\tau} =\downarrow\left(\widetilde{S}_{\tau}\right),\ \tau=2,3,\cdots,L,\ S_{\tau}\in\mathbf{R}^{T/ 2(\tau-1)\times D} \tag{10}\]  
where \(\parallel\) denotes the concatenation operation, \(\tau\) represents the pyramid level, and \(\downarrow\) represents the downsampling operation, which is implemented with a 1D convolution with a stride of 2. Finally, we combined the results of all stages and obtained the temporal feature pyramid set S = {\(S_{1},S_{2},\cdots,S_{6}\)}. We designed two lightweight prediction branches with the same structure, but independent of each other, for action classification and boundary regression, respectively. This structure consists of three layers of 1D convolution with a kernel size of 3. We considered every location i \(\in\) {1, T} in the sequence as an action instance candidate. For the classification branch, a sigmoid function was followed to predict the probability \(C_{t}^{i}\) of action categories. Similar to the regression branch, in order to ensure that the distance \((d_{t}^{s},d_{t}^{s})\) to the left and right boundary is a positive number, a Relu activation function is attached at the end.
## 4 River Blockage Criteria
### Discrimination of Statistical Landslide Cases  
[PERSON] et al. [37] collected a large database that includes three hundred landslide dams from the Alps to the Southern Apennines and Sicily. Fifty-seven information fields, such as the geometric parameters of the landslide, the dam body, the valley, and the lake, were recorded in detail. Among these landslides, 60 landslides with the fully recorded geometric parameters of the landslide and the stream were chosen to verify the proposed discriminant model. As presented in Appendix A, these parameters are the height difference between the maximum and minimum values of landslide variable \(H\), steepness of landslide slope \(\alpha\), total length of landslide \(L_{\rm T}\), length of landslide body \(L_{\rm L}\), maximum width of landslide \(W_{\rm max}\), thickness of landslide \(T\), volume of landslide \(V\), valley width \(W_{\rm V}\), and steepness of riverbed \(\theta\).  
Figure 18: Discriminant results of the experiments.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{2}{c}{**Experiment Results**} & \multicolumn{2}{c}{**Prediction Results**} \\ \hline
**Experiment** & **Total Number** & **Number of** & **Number of** & **Accuracy** \\
**Blockage State** & **of Groups** & **Partial Blockage** & **Complete Blockage** & **Accuracy** \\ \hline Partial blockage & 27 & 25 & 2 & 92.6\% \\ Complete blockage & 18 & 2 & 16 & 88.9\% \\ Overall accuracy & & & & 91.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction accuracies of the improved probability model based on the experimental results.  
Considering these factors in the discrimination of landslide cases, the improved probability model was fitted as follows:  
\[S=\frac{1}{1+e^{-3.172-0.014H+0.064a+0.003L_{7}-0.002L_{z}+0.004W_{mm}-0.0447-5 5\times 10^{-3}V+0.023W_{V}-0.026\theta}}, \tag{4}\]  
Figure 19 presents the discriminant results of the real landslide cases in a scatter plot. Most points are located above the line of \(S\) = 0.5, so most landslide cases are complete blockages. The discriminant results are also marked in different colors to indicate the actual blockage state. Compared with the actual blockage state, seven cases are not correctly classified by the improved probability model. The detailed prediction accuracies are listed in Table 4. The prediction accuracies of partial blockage and complete blockage are approximately 56.3% and 93.2%, respectively, and the overall prediction accuracy is approximately 83.3%. Thus, the improved probability model is accurate enough to predict the blockage state of the landslide dam. Since Equation (4) is obtained from real landslide cases, it can be applied to forecast the blockage state of other potential landslides.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 4 Performance Verification and Analysis
### Single Base Station Sensing Validation
This demonstrates that the theoretical model accurately predicted the maximum sensing height. The horizontal and vertical positioning accuracies within the CDF 95% range were statistically analyzed within the sensing range. The horizontal accuracy varied slightly across different test points, ranging between 2.41 m and 3.34 m. The horizontal positioning accuracy slightly decreased as the distance increased, but it still met the specified requirements. The primary reason, as indicated by Equation (1), was that the increase in distance led to a decrease in the SNR, further reducing sensing accuracy. The vertical accuracy  
Figure 14: Single base station vertical test route. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Point (m)**} & **Measured** & **Theoretical** & \multirow{2}{*}{**Error**} & **Measured** & \multirow{2}{*}{**Theoretical**} & \multirow{2}{*}{**Error**} & **Horizontal** & **Vertical** \\  & **Minimum** & & & & & **Maximum** & **Exor** & **Positioning** & **Positioning** \\  & **Height (m)** & & & & **Height (m)** & & **Percentage (\%)** & **Accuracy at CDF** \\ \hline
50 & 30.6 & & 2 & 73.1 & 72.0 & 1.53 & 2.41 & 2.34 \\
100 & 30.7 & & 2.33 & 115.2 & 113.9 & 1.14 & 2.45 & 4.23 \\
150 & 30.5 & & 1.67 & 156.6 & 155.9 & 0.45 & 2.58 & 4.98 \\
200 & 30.5 & & 1.67 & 199.9 & 197.8 & 1.06 & 3.26 & 3.93 \\
300 & 31.1 & & 3.67 & 281.8 & 281.7 & 0.04 & 3.11 & 2.94 \\
350 & 30.6 & & 2 & 301.0 & 300 & 0.33 & 3.34 & 3.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Single base station vertical direction test results. fluctuated between 2.34 and 4.98 m, showing no strong correlation with distance. The poorest performance occurred at the 150 m horizontal distance point, primarily because the flight trajectory near the base station height formed a small angle with the radial vertical tangent of the AAU, resulting in an unstable sensing performance. However, both the horizontal and vertical positioning accuracies were significantly below the upper limit of the specified requirements, aligning with the expectations. By comprehensively traversing the sensing range in multiple directions, the measured results and the theoretical analysis exhibited the same trend. Thus, the theoretical model proposed in Section 2.3 effectively replicated the real-world sensing capabilities with an accuracy rate exceeding 92%.
## 2 Literature Review
In [32], images are generated by the synchrosqueezing wavelet transform, which are then used as the input of a pretrained model namely ResNet-18 for binary emotion classification. Similarly, the authors of [33] consider a fused 2-dimension image combined from the connectivity measures and different overlapping time windows, which represent effectively brain connectivity. Then, a hybrid DL technique of CNN and LSTM is adopted for the regconition of four emotions using the above images. Moreover, the CWT is considered in [34; 35] for the construction of the scalogram images, which improve the representation of the input EEG signals.Then, the final emotion recognition is decided by the majority voting algorithm [34] using the output of different transfer learning models and by the multiclass SVM model [35] using the best feature layers of various transfer learning models. In [36], the binary gray wolf optimizer is implemented for the selection of the most relevant input features from statistic, wavelet, and Hurst exponent features. Additionally, the differential evolution algorithm is also employed to identify the optimal hyperparameters for the bi-directional LSTM model structure proposed as the final algorithm. Channel optimization is also considered in [37], where NSGA-II is implemented to balance the two-objective problem in terms of detection performance and channel number. Besides, the CNN is applied for the proposed algorithm namely EEGNet, which uses the feature extracted from the sub-bands using the DWT. The authors of [38] introduce binary many-objective particle swarm optimization with cooperative agents in combination with a ConvLSTM model as the channel selection method. Here, the proposed model is based on the autoencoder, which shows better performance in capturing the spatio-temporal information of the preprocessed EEG signals. From previous studies, two key research gaps have been identified: (1) the lack of proper channel selection for model optimization and (2) a small number of extracted features used as input. Many studies have not adequately considered channel selection, which may compromise the efficiency of the EEG-based emotion recognition system. In addition, a small number of extracted features can limit the model's ability to capture comprehensive patterns in EEG signals. To address these limitations, this study implements an optimized channel selection process using GA to identify the most relevant EEG channels, thereby improving signal quality and model efficiency. Furthermore, we expand the extracted feature set to 42 features across time-frequency, entropy, and complexity domains. Increasing the number of extracted features is essential because EEG signals exhibit intricate variations that require a richer feature set for comprehensive pattern recognition.
# The Optimization of Covert Communication in Asymmetric Jammer - Assist Systems
## 5 Numerical Results
We select the transmission rate at the globally optimal allocation ratio \(\alpha^{*}=0.3\) and compare it with samples at \(\alpha=0.5\) and \(\alpha=0.8\). As \(\beta\) increases, \(\alpha=0.8\) exhibits the lowest transmission rate. However, \(\alpha=0.5\) achieves a higher transmission rate than the globally optimal allocation ratio \(\alpha^{*}=0.3\). Considering the substantial degradation in covertness at \(\alpha=0.5\), further comparison under the same covertness constraints is necessary. Figure 6 depicts the covert transmission rate versus covertness constraint \(\epsilon\). We select the results at the globally optimal allocation ratio \(\alpha^{*}=0.3\) and compare them with samples at \(\alpha=0.5\) and \(\alpha=0.8\). With an increasing covertness constraint \(\epsilon\), the covert transmission rate gradually increases. Under the same covertness constraint, \(\alpha^{*}=0.3\) attains the highest covert transmission rate. Combining the results from Figure 4(a),b, our proposed optimization method achieves the globally optimal noise allocation ratio, realizing the maximum covert transmission rate under the same covertness constraints. Simulation results validate the effectiveness and superiority of our proposed method. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Scenario** & \(\mathbf{P_{I}}\) & \(\sigma_{w,x,0}\) & \(\sigma_{w,y,0}\) & \(\mathbf{h_{w,x}}\) & \(\mathbf{h_{w,y}}\) & \(\mathbf{\eta}\) & \(\sigma_{b,x,0}\) & \(\sigma_{b,y,0}\) & \(\mathbf{h_{b,x}}\) & \(\mathbf{h_{b,y}}\) \\ \hline
1 & 5 & 3.5 & 2.5 & 0.46 & 0.38 & 0.3 & 3.8 & 2.2 & 0.22 & 0.56 \\
2 & 5 & 4.5 & 3.5 & 0.45 & 0.44 & 0.3 & 3.5 & 2.0 & 0.41 & 0.52 \\
3 & 5 & 5.5 & 1.5 & 0.15 & 0.13 & 0.3 & 2.8 & 4.2 & 0.15 & 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for comparative experiments. Figure 4: Transmission rate versus jammer noise ratio in three scenarios.
# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 5 Conclusions and Future Work  
In this paper, we proposed an effective approach called the multi-label weighted contrastive cross-modal hashing (MLWCH) method for high-quality cross-modal hash code generation. A novel compact consistent similarity representation module is designed to accurately preserve the multi-label semantic similarity of original instances. Furthermore, a multi-label weighted contrastive learning strategy is introduced to minimize the gap of the semantic similarity of original instances and more accurately preserve the semantic relevance of the learned hash representations. Extensive experiments on three well-known cross-modal datasets show that the proposed method significantly outperforms other baselines and achieves state-of-the-art performance.  
Moving forward, we will endeavor to extend the contrast learning strategy of assigning different weights to different positive samples to unsupervised cross-modal hashing. As an exploratory work, we will investigate the allocation of distinct weights to positive samples through unsupervised learning in scenarios where label information is unavailable.  
Conceptualization, L.Z.; methodology, Z.Y. and L.Z.; software, Z.Y.; validation, Z.Y., R.W. and Z.Z.; formal analysis, L.Z.; investigation, X.Z. and Z.Y.; resources, X.Z. and L.Z.; data curation, R.W. and Z.Z.; writing--original draft preparation, Z.Y.; writing--review and editing, L.Z. and Y.L.; visualization, R.W. and Z.Z.; supervision, X.Z. and L.Z.; project administration, X.Z.; funding acquisition, X.Z. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (62202163, 62072166), the Natural Science Foundation of Hunan Province (2022)[40190], the Scientific Research Project of Hunan Provincial Department of Education (22A0145), and the Key Research and Development Program of Hunan Province (2020 NK2033).  
Not applicable.  
The public datasets used in this paper can be accessed through the following links: MIRFLICKR-25K: [[https://press.liacs.nl/mirflickr/mirdownload.html](https://press.liacs.nl/mirflickr/mirdownload.html)]([https://press.liacs.nl/mirflickr/mirdownload.html](https://press.liacs.nl/mirflickr/mirdownload.html)), accessed on 19 December 2023; NUS-WIDE: [[https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html)]([https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html)), accessed on 19 December 2023; MS COCO: [[https://cocodataset.org](https://cocodataset.org)]([https://cocodataset.org](https://cocodataset.org)), accessed on 19 December 2023.  
The authors declare no conflict of interest.  
Figure 10: Sensitivity analysis of \(\alpha\) on MIRFLICKR-25K, NUS-WIDE, and MS COCO.
## 5 Conclusions  
Remote sensing (RS) is a very reliable and efficient method for monitoring environmental and terrain changes. Topographic maps are crucial to science, research, planning, and administration in the modern world. It is entirely feasible to discern changes based on RS data collected at two distinct periods. In this article, we have developed a new TSADL-LULCCD technique for land cover classification in Nallamalla Forest, India. The presented TSADL-LULCCD technique mainly focuses on the identification and classification of land use in the Nallamalla forest using LANDSAT 7 images. Primarily, the presented TSADL-LULCCD technique employed an Adam optimizer with a dense EfficientNet model for feature extraction. For land cover classification, the TSADL-LULCCD technique exploited the TSA with the DBN model. The experimental validation of the TSADL-LULCCD technique is tested on LANDSAT-7-based Nallamalla region images. The experimental results stated that the TSADL-LULCCD technique exhibits better performance over other existing models in terms of different evaluation measures. In the future, we are planning to include forest classification with the help of hybrid DL fusion techniques that can be employed to improve classification performance.  
Conceptualization, K.L. and A.M.; methodology, K.L.; software, R.S.; validation, K.L., A.M. and R.S.; writing--original draft preparation, K.L., A.M. and R.S.; writing--review and editing, M.M. and J.D.H.; visualization, M.M. and J.D.H.; supervision, K.L. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 2 Key Steps in a FRB Search Pipeline
### Dedispersion
#### 2.2.3 Semi-Coherent Dedispersion  
Interstellar dispersion can also be thought of as an impulse response of the ISM as the electro-magnetic wave moves through it. If we are able to measure the electric field at the telescope, it can be written as  
\[\mathcal{S}(t)=\mathcal{S}_{t}(t)*\mathcal{H}(t), \tag{10}\]  
where \(*\) denotes a convolution operation and \(\mathcal{H}(t)\) is the impulse response function of the ISM such that  
\[\mathcal{F}(\mathcal{H}(t))=e^{-i\frac{2\pi\,D}{(v_{0}+t)\,u_{d}^{2}}\,DM\,v_{ d}^{2}}, \tag{11}\]  
where \(D\) is a constant, \(v_{0}\) is the lower edge of the band, \(v_{d}\) is the deviation from the lower edge, and \(\mathcal{F}\) is the Fourier transform. In the frequency domain, the ISM response function adds an additional phase to the signal which can be removed if the DM is known. This removal method, coherent dedispersion, fully corrects for the dispersion with no intra-channel smear. It is implemented by performing a complex multiplication by the inverse of the impulse function in the frequency domain. Typically, searches for FRBs are done by performing incoherent dedispersion over a number of DM trials where the intra-channel dispersion smear reduces the sensitivity of the search. In \"semi-coherent dedispersion\" one can use coherent dedispersion over a coarse grid of DM trials, and next perform a finer DM search for FRBs incoherently, improving the overall sensitivity [24].