esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 5 Conclusions  
In this paper, we introduced a new reversible data hiding scheme for AMBTC-compressed images by depending on the adjacent relation of mean values. To achieve a high embedding capacity and efficiency rate, the high correlation of values in two mean tables was explored and used to embed a secret message. The experimental results demonstrated that the proposed scheme obtained a high embedding capacity while guaranteeing the same PSNRs as the traditional AMBTC algorithm. In addition, the proposed scheme achieved a higher embedding capacity and efficiency rate than those of previous schemes while maintaining an acceptable bit rate. Obviously, the experimental results confirm that the proposed scheme can be used not only to protect secret messages but also as a practical algorithm for real-time applications.  
Conceptualization, C.-C.L. and T.-S.N.; methodology, C.-C.L. and T.-S.N.; software, T.-S.N. and W.-C.C.; validation, C.-C.L., and C.-C.C.; writing--original draft preparation, T.-S.N.; writing--review and editing, C.-C.L.; project administration, C.-C.C.; funding acquisition, C.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan (grant number MOST 109-2410-H-167-014).  
Not applicable.  
**Data Availability Statement**: All test data can be accessed in public repository.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multirow{2}{*}{**Performance**} & \multicolumn{3}{c}{**Proposed Scheme**} & \multicolumn{2}{c}{**[PERSON] et al. [20]**} \\ \cline{3-6}  & & \(m=3\) & \(m=4\) & \(m=5\) &
\begin{tabular}{c} **Best Combination** \\ **of \(\alpha\) \& \(\beta\)** \\ \end{tabular} & \((\alpha,\beta)\) \\ \hline \multirow{4}{*}{Lena} & PSNR (dB) & 33.97 & 33.97 & 33.97 & 33.97 & \\ \cline{2-6}  & EC (bits) & 60,010 & 75,128 & 87,199 & 64,516 & \\ \cline{2-6}  & Embedding Efficiency & 0.23 & 0.29 & 0.33 & 0.25 & \\ \hline \multirow{4}{*}{Baboon} & PSNR (dB) & 26 & 26 & 26 & 26 & \\ \cline{2-6}  & EC (bits) & 40,048 & 64,882 & 86,902 & 64,516 & \\ \cline{2-6}  & Embedding Efficiency & 0.15 & 0.25 & 0.33 & 0.25 & \\ \hline \multirow{4}{*}{Peppers} & PSNR (dB) & 33.40 & 33.40 & 33.40 & 33.40 & \\ \cline{2-6}  & EC (bits) & 63,010 & 78,799 & 88,771 & 64,516 & \\ \cline{2-6}  & Embedding Efficiency & 0.24 & 0.3 & 0.34 & 0.25 & \\ \hline \multirow{4}{*}{F16} & PSNR (dB) & 31.95 & 31.95 & 31.95 & 31.95 & \\ \cline{2-6}  & EC (bits) & 71,233 & 81,097 & 88,648 & 64,516 & \\ \cline{1-1} \cline{2-6}  & Embedding Efficiency & 0.27 & 0.31 & 0.34 & 0.25 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed scheme and [PERSON] et al.’s scheme [20] on PSNR, EC, and embedding efficiency.
## 4 Results
### Model Accuracy Validation
From Figures 10-15, it can be observed that, in the low-density region, the predictions of population density by all six methods are relatively close to the true values compared to the high-density region, with smaller absolute errors. Overall, the scatter points in the graphs are evenly distributed on both sides of the line, indicating the reliability of the model's accuracy. From the regression fitted line, it can be observed that the blue solid line and the red dashed line obtained from Bayesian optimization methods (Figures 14 and 15) are closer to the other methods. This indicates that these two Bayesian optimization methods have smaller deviations from the true values, suggesting better predictive capabilities. In the error distribution histograms, the horizontal axis displays the range of model errors, where a larger numerical range indicates a greater deviation in model prediction. The vertical axis represents the number of the prediction error. The majority of the errors of the model predicted based on the six mentioned optimization methods are concentrated around zero. It indicates that random forest models have good prediction accuracy and stable prediction ability. From Figures 10-12, it can be seen that the models optimized with random search, grid search, and genetic algorithms have a wider prediction error range. The model optimized with these methods have relatively unstable prediction capabilities, and there may be large prediction errors in individual counties. From Figures 13-15, it can be observed that the models optimized with simulated annealing, Bayesian optimization based on Gaussian process, and Bayesian optimization based on gradient boosting regression trees have narrower horizontal ranges, implying smaller prediction error ranges and more stable prediction capabilities. In conclusion, models constructed using parameters from the Bayesian optimization method exhibit higher coefficients of determination and smaller average errors, indicating better fitting performance and accuracy. This indicates that the Bayesian optimization algorithm is superior to commonly used methods for selecting random forest modeling parameters.
## 3 Results and Discussion
### Physicochemical Properties
#### 3.2.2 Water Absorption Capacity  
The water absorption capacity (WAC) of food products generally denotes the flour's ability to interact with water [32]. Based on Table 2, WAC ranges from 1.20 g/g to 1.26 g/g. The WAC values among the sample show no significant differences (\(p>0.05\)) from each other. The highest WAC value was presented by rice flour made from Bario Adan Halus (1.26 g/g), and the lowest was from TOR (1.20 g/g). Similar values of the WAC rice flour have been stated in previous studies, which is approximately 1.2 g/g [59; 60]. Different WACs of rice flour have been associated with starch composition, specifically the amylose and amylocetin ratio; a higher WAC indicates a high amylose content in rice [61]. In this study, Bario Tuan showed the highest amylose concentration; however, Bario Tuan rice flour did not follow that pattern. [PERSON] et al. [62] reported that a high lipid content might decrease WAC because the hydrophobic group of lipids can interfere with the hydration capability of starch granules within rice flour. Based on Table 1, Bario Tuan exhibited the  
Figure 1: The colour diversity of (**a**) TQR (reference sample) and Bario rice flour, (**b**) [PERSON] _Adan Halus_, (c) [PERSON] _Tuan_, (**d**) [PERSON] _Celum_, and (**e**) [PERSON] _Merah Sederhana_.  
highest crude fat content; thus, it can be concluded that the possibility of a high crude fat content within Bario Tuan might impedes starch granules' hydration, consequently reducing the WAC. It has been concluded that lower-WAC rice flour (0.99 g/g to 1.07 g/g) provided better rice bread based on its physicochemical properties [63]. Conversely, a study by [PERSON] and [PERSON] [64] discovered that higher-WAC rice flour (1.28 g/g and 1.38 g/g) produced the best gluten-free bread features. According to [PERSON] et al. [63], high water absorption decreases stickiness and produces solid dough, and at the bread level, rice flour with low water absorption generates fresh bread.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 2: Develop and Down-Select Nanogrid Architectures  
#### 4.2.1 Step 2.1: Evaluate Potential Nanogrid Architectures  
An analysis of potential nanogrid infrastructure is performed in order to determine the most beneficial system architectures for Naval Support Activity Monterey including the benefits and drawbacks of DC and AC based nanogrids, centralized and decentralized control systems, and other important metrics. The primary viable nanogrid structures are: (1) DC-based nanogrid with centralized control, (2) AC-based nanogrid with centralized control, (3) AC-based nanogrid with decentralized control, and (4) DC-based nanogrid with decentralized control. A Pugh Matrix is provided in Table 4 that shows the different potential nanogrid structures and their benefits and drawbacks.  
The analysis in Figure 4 indicates that an AC nanogrid would be better than a DC nanogrid with centralized control. However, the need for energy security and survivability is paramount to military installations. This outweighs the other existing criteria. Therefore, a DC nanogrid with a decentralized controller is chosen as the best design solution for the research objective and stakeholder concerns. This design optimizes power balance by independently controlling each load, DER, and ESS [65].
## 2 Theoretical Framework and Simulation Experiment Setup
 \tag{20}\]  
where \(\mathbf{W}_{Gx}\), \(\mathbf{W}_{Gy}\), \(\mathbf{W}_{Ex}\) and \(\mathbf{W}_{Ey}\) represent the output weight matrix; \(W_{Gx,i}\), \(W_{Gy,i}\), \(W_{Ex,i}\) and \(W_{Ey,i}\) respectively represent the \(i\)th-element of \(\mathbf{W}_{Gx}\), \(\mathbf{W}_{Gy}\), \(\mathbf{W}_{Ex}\) and \(\mathbf{W}_{Ey}\); \(I_{Gx,i}(n)\), \(I_{Gy,i}(n)\), \(I_{Ex,i}(n)\) and \(I_{Ey,i}(n)\) respectively represent the \(i\)th output state of the GS-PC\({}_{x}^{\prime}\), GS-PC\({}_{y}^{\prime}\), ES-PC\({}_{x}^{\prime}\) and ES-PC\({}_{y}^{\prime}\). Here, \(I_{Gx,i}(n)=|E_{Gx}(i)|^{2}\), \(I_{Gy,i}(n)=|E_{Gy}(i)|^{2}\), \(I_{Ex,i}(n)=|E_{Ex}(i)|^{2}\) and \(I_{Ey,i}(n)=|E_{Eg}(i)|^{2}\). \(b_{out}\) is a constant and equal to \(1\). Previous studies have shown that these output weight matrices can be analytically given by [27]\[ \begin{split}\mathbf{W}_{Gx}&=\mathbf{Y}_{Gx}\mathbf{X}^{ \text{T}}\Big{(}\mathbf{X}_{Gx}\mathbf{X}_{Gx}^{\text{T}}+\mu\mathbf{\Pi} \Big{)}^{-1},\mathbf{W}_{Gy}\mathbf{=}\mathbf{Y}_{Gy}\mathbf{X}^{\text{T}} \Big{(}\mathbf{X}_{Gy}\mathbf{X}_{Gy}^{\text{T}}+\mu\mathbf{\Pi}\Big{)}^{-1}, \\ \mathbf{W}_{Ex}&=\mathbf{Y}_{Ex}\mathbf{X}^{\text{T}} \Big{(}\mathbf{X}_{Ex}\mathbf{X}_{Ex}^{\text{T}}+\mu\mathbf{\Pi}\Big{)}^{-1}, \mathbf{W}_{Ey}\mathbf{=}\mathbf{Y}_{Ey}\mathbf{X}^{\text{T}}\Big{(}\mathbf{X} _{Ey}\mathbf{X}_{Ey}^{\text{T}}+\mu\mathbf{\Pi}\Big{)}^{-1},\end{split}  \tag{21}\]  
where the superscript \(T_{r}\) represents the transpose of the matrix; \(\mathbf{\Pi}\) is an identity matrix; \(\mu\) is utilized to avoid overfitting the ridge regression parameter, which is set to \(10^{-6}\); \(\mathbf{X}_{Gx}\), \(\mathbf{X}_{Gy}\), \(\mathbf{X}_{Ex}\) and \(\mathbf{X}_{Ey}\) all are matrices and their \(l\)th columns are [\(b_{out}\); \(C_{Gx}(l-L_{Gx})\); \(I_{Gx,i}(l)\)] [\(b_{out}\); \(C_{Gy}(l-L_{Gy})\); \(I_{Gy,i}(l)\)], [\(b_{out}\); \(C_{Ex}(l-L_{Ex})\); \(I_{Ex,i}(l)\)] and [\(b_{out}\); \(C_{Ey}(l-L_{Ey})\); \(I_{Ey,i}(l)\)] respectively; \(\mathbf{Y}_{Gx}\) and \(\mathbf{Y}_{Ex}\) both are matrices, and their \(l\)th columns are [\(C_{Gx}(l-L_{Gx}+1)\)] and [\(C_{Ex}(l-L_{Ex}+1)\)], respectively; and \(\mathbf{Y}_{Gy}\) and \(\mathbf{Y}_{Ey}\) both are matrices, and their \(l\)th columns are [\(C_{Gy}(l-L_{Gy}+1)\)] and [\(C_{Ey}(l-L_{Ey}+1)\)], respectively. According to the complete lag synchronization theory (see Equations (19)), we obtain  
\[ \begin{split} S_{1}^{{}^{\prime}}(n)\approx S_{1}(n-L_{Gx}),& S_{2}^{{}^{\prime}}(n)\approx S_{2}(n-L_{Gy}),\\ S_{3}^{{}^{\prime}}(n)\approx S_{1}(n-L_{Ex}),& S_{4}^{{}^{ \prime}}(n)\approx S_{2}(n-L_{Ey}).\end{split}  \tag{22}\]
## 5 Conclusions and Future Work  
The experimental results presented in this paper confirm that lifetime of the activity recognition system with wearable sensor network can be significantly extended by using the embedded classifiers that detect useful sensor readings. The introduced approach allows us to effectively eliminate the data transmission that are not necessary for performing the given recognition task. This reduction of data transmission preserves high recognition accuracy. In this study, the presented method was applied for human activity recognition, however it can be easily adapted for different applications of wearable sensor networks that involve the use of recognition algorithms for other purposes. It is also suitable for application in Internet-of-Things environment. Comparison with state-of-the-art solutions have shown that the proposed method achieves better trade-off between the accuracy and transmission reduction. 75% of data transmissions from sensor nodes were eliminated, while the accuracy level of 95% was kept.  
The model ensemble used in this study for activity recognition is suitable for the wearable sensor network, in which a few nodes are used to monitor the person. Future works will be devoted to other types of the sensor network, where a larger number of sensor nodes is connected in one cluster, and different ensemble learning methods will be used. For instance, a separate recognition sub-model can be trained based on data from each sensor node. In this case, after receiving data from sensor nodes, the recognition is performed independently using many sub-models in parallel. Subsequently, a common decision is made in a voting procedure, by taking into account the recognition results obtained from individual sub-models. This method requires only \(n\) sub-models to be trained, where \(n\) corresponds to the number of sensor nodes. Another possibility is to consider group of sensor nodes to simplify the model.  
An interesting topic for further research is also the investigation of multi-layered transmission reduction with integration of the proposed method and dedicated MAC layer or network layer protocols.  
Conceptualization, M.L. and B.P.; Data curation, M.L. and B.P.; Formal analysis, M.L. and B.P.; Funding acquisition, M.L. and B.P.; Investigation, M.L. and B.P.; Methodology, M.L., B.P. and M.B.; Project administration, B.P. and M.B.; Resources, M.L. and B.P.; Software, M.L. and B.P.; Supervision, B.P. and M.B.; Validation, M.L., B.P. and M.B.; Visualization, M.L. and B.P.; Writing--original draft, M.L., B.P. and M.B.; Writing--review & editing, M.L., B.P. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Publicly available dataset was analyzed in this study. This data can be found here: [[http://biometrics.us.edu.pl/request/database/activity](http://biometrics.us.edu.pl/request/database/activity)]([http://biometrics.us.edu.pl/request/database/activity](http://biometrics.us.edu.pl/request/database/activity)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Alternative low-cost sources (e.g., balloons, gun fires) are used for impulse response measurements when a dodecahedron speaker is not available. This study sets to explore the applicability of a method utilizing a common directional loudspeaker as a sound source. For this purpose measurements were performed in three spaces with three different common directional loudspeakers. Different placements of the loudspeakers were performed (twelve positions similar to the twelve positions of the faces of a dodecahedron speaker, different rotations of the loudspeakers for a total sum of twenty six and fourteen positions). The impulse responses obtained were added up creating a single impulse response for each case. Comparisons of the acoustic parameters measured with the proposed method and with a dodecahedron speaker are presented and suggest the expected mean absolute error and standard deviation for similar measurements. Reverberation time measurements show a mean absolute error of less than 0.08 s, as compared with measurements with a dodecahedron speaker. The proposed method can be the primary method for measuring impulse responses when a dodecahedron speaker is not available. Suggested improvements may lead to better omnidirectionality as compared with a dodecahedron loudspeaker, and set the method applicable to be utilized for auralization purposes.  
low-cost impulse response measurement; omnidirectional source; acoustic parameters; impulse response; sound source; reverberation time; auralization +
Footnote †: journal: Applied
# Deep Learning-Enhanced Spectrogram Analysis for
## 3 Methodology
### Training and Optimization  
The dataset was divided in an 80:20 ratio for training and testing. The model was optimized using categorical cross-entropy loss.  
\[L=-\sum_{i=1}^{N}y_{i}\log(\hat{y}_{i}) \tag{2}\]  
where \(y_{i}\) represents the ground truth and \(\hat{y}_{i}\) denotes the expected probability [29].  
The Adam optimizer was employed with an initial learning rate of \(1\times 10^{-4}\), and early stopping was used to prevent overfitting [30].  
The hyperparameters were selected through empirical tuning and previous studies of biomedical signal classification. A learning rate \(1\times 10^{-4}\) was selected to ensure stable convergence while preventing vanishing gradients. The batch size was set to 32 to optimize computational efficiency and generalization. The number of epochs was established via early halting, with a patience parameter of 5, averting superfluous training once the performance stabilized. Weight decay and dropout regularization were incorporated to mitigate overfitting and ensure robust feature extraction across anatomical regions. Figure 4 shows the training and validation accuracy curves.  
To enhance result robustness and avoid bias from a single data partition, the full training and evaluation pipeline was repeated across five different random splits of the dataset. The reported metrics represent the average performance across these runs. In addition, 20% of the training set was used as a validation set during training to optimize hyperparameters and monitor convergence, including early stopping. GridSearchCV was employed for tuning the ensemble models (SVM and Random Forest) within the hybrid framework. While full K-fold cross-validation was not applied due to computational resource constraints, the use of repeated random splits provided a practical and statistically sound estimate of model generalizability across different data partitions.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods  
### Database  
This work uses the public Campo Verde database [30], which is a satellite imagery of crops from the municipality of Campo Verde, Brazil. It is of great interest as it is a tropical area. Figure 2 shows the class (types of crop) occurrences per image in the dataset. Notice that the crop of beans are not included as the image sequences used in this work contain no pixels of this crop.  
Campo Verde consists of a set of 14 Sentinel-1 images, a set of 16 Landsat 8 OLI images, and an ESRI Shapefile containing the daily class annotations in each and every area of the images. For further experiments, images corresponding to the time between October 2015 and July 2016 (16 Landsat images) are considered.  
To ensure that the obtained results become comparable with those of [30; 31], images covered by clouds in the Landsat are excluded (two images from January, one from January  
May, one from June, and one from July). As a matter of fact, the experiments in [30; 31] are carried out over Sentinel images on the same date and having no affectation by cloud cover.  
Specifically, the number of classes for sequences 1 and 2 was set as 9 and 10, respectively. Selected classes (namely: soybean, corn, cotton, sorghum, non-commercial crops (NCC), pasture, eucalyptus, grass, soil, and Cerrado) correspond to the annotations of the last image from each sequence.  
The bean class is contained in other months including October but not in the months analyzed in the sequences (February and July). Moreover, the classes of crops become represented in an imbalanced way as samples from some crops greatly outnumber the ones from other crops.
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 1 Introduction
The results show that the error of the annular pressure buildup model established in this paper, which considers the deformation of the sealed casing, is about 13% lower than that predicted without considering this factor and is closer to the measured value in the field. This study has important practical significance for guiding the casing string design and safety production of deepwater oil and gas wells.
## 1 Introduction
In Uganda, Caribbean pine wood from 10-year-old burned trees has a significantly lower density, MOE, and MOR than wood from unburned trees, and it should not be used for high-strength structural purposes but rather in low-strength construction works, such as shuttering and ceilings [22]. Both the direct and indirect effects of forest fire are present in wood salvage from fire areas, and their nature and severity depend on the time between the fire and harvesting [5; 14; 16]. Trees with lowered vitality are susceptible to insect and microbial attacks and wind falls; the resultant wood defects lower the basic quality of sawn timber and veneer-based products but do not ruin their potential use. The susceptibility varies a lot between individual forest stands, but the following generalizations have been presented [16; 23]: (1) blue stain occurs in the first year after the fire, lowering the visual quality; (2) heartwood starts to decay, and the deterioration of sapwood accelerates in the second year, reducing the quality for high-strength products; and (3) the sapwood of most coniferous species is unworthy for any construction purpose three years after the fire. The types and rates of deterioration of burned wood after forest fires in North America are illustrated in Figure 1.  
In practice, wood salvaged from forest fire areas has been allocated to either bioenergy or low-grade sawn timber; in North America, some wood has also been allocated to laminated veneer lumber (LVL) [16; 24; 25; 26]. The suitability of burned wood has also been studied for oriented strand board (OSB) and medium-density fiberboard (MDF) [27; 28]. Burned wood is not accepted for pulp, paper, or paperboard processes or products due to char and soot [28]. In North America, burned timber has been posited to still be usable for sawn timber if the logs are harvested within two years after the fire [29], but other sources claim that harvesting should be done at the latest one year after the fire to recover the full economic benefits of such logging [30; 31]. In Finland, the Forest Damage Prevention Act suggests that forest-damaged trees should be harvested from the forest if their volume exceeds 10 or 20 m\({}^{3}\)/ha for Norway spruce or Scots pine, respectively, counting trees with a stump diameter of more than 10 cm [32]. The objective of this study was to investigate the raw material potential of sawn wood and to select further processed products of Norway spruce and Scots pine sawn log trees from salvage harvesting in forest fire areas with northern European climatic conditions by comparing selected physical and mechanical properties of fire-affected trees and virgin reference trees. The effects of fire on the MOE, MOR, Brinell hardness (HB), moisture gradient, and wood color (CIE L*a*b*) were studied on clear wood specimens of outer sapwood and inner heartwood, and linear mixed models were constructed to analyze the sources of variation. Recommendations are provided for sawmill managers, further processors, and forest owners on wood allocation and potential products from forest fire areas in Finland.