esa-sceva/satcom-chunk-collection huggingface dataset:
# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 5 Conclusions  
In this paper, a Searched Binary Network, SBNN, for SAR ship classification is proposed. Experimental results show our network achieves good results in different tasks with a strong robustness. The proposed SBNN outperforms other networks for the model size and the computational complexity. In particular, the number of MAdds in SBNN is very small, which means SBNN has a strong potential for being implemented on the tiny-size mobile devices.  
Conceptualization, H.Z., W.S. and S.G.; methodology, H.Z.; software, H.Z.; validation, H.Z., L.X., S.G. and W.S.; formal analysis, H.Z. and L.X.; investigation, H.Z. and L.X.; data curation, H.Z. and S.G.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z, S.G. and L.X.; visualization, H.Z.; supervision, S.G.; project administration, W.S.; funding, S.G. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operations** & **Network** & **Accuracy** \\ \hline
7 & Searched Floating Point CNN & 79.72\% \\ (Original) & by PC-DARTS (\(\mathcal{C}=8\), \(\mathcal{L}=8\)) & 78.63\% \\ \hline
4 & Searched Floating Point CNN & 82.14\% \\ (Only Weight-equipped Operations) & by PC-DARTS (\(\mathcal{C}=8\), \(\mathcal{L}=8\)) & 80.03\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of different candidate operations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & \
\begin{tabular}{c} **Binary** \\ **MAdds** \\ \end{tabular} &
\begin{tabular}{c} **Floating** \\ **Point MAdds** \\ \end{tabular} & **MAdds** & **Weights** \\ \hline Bi-RealNet18 [25] & 76.76\% & 307.89 M & 10.77 M & 15.58 M & 11.18 M \\ ReActNet [26] & 70.83\% & 884.74 M & 0.67 M & 14.49 M & 28.33 M \\ AdamBNN [27] & 78.16\% & 884.74 M & 0.67 M & 14.49 M & 28.33 M \\ SBNN (ours) & 80.03\% & 95.51 M & 3.18 M & 4.67 M & 0.37 M \\ \hline \hline \end{tabular}
\end{table}
Table 9: The comparison with modern computer vision binary networks in the three-categories SAR ship classification task.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 62001227.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within this article.  
**Acknowledgments:** The authors thank the reviewers for their help on the article during review progress.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Results and Discussion
### Latency and Resource Overhead  
While detection accuracy is essential, real-time operation in edge environments depends critically on latency and system efficiency. Table 11 reports the inference latency, model update time, and runtime memory usage for each model and training type.  
All configurations remained comfortably within real-time boundaries, with an inference latency below 20 milliseconds in all cases. The federated autoencoder, for example, maintained a latency of just 13.1 ms, compared to 12.4 ms for the local model. The LSTM model had a slightly higher latency (19.6 ms federated and 18.7 ms local) but delivered superior detection performance, as shown earlier.  
Memory usage was modest: the autoencoder remained under 30 MB, while the LSTM required approximately 42 MB during inference--both feasible for modern edge nodes. Federated training introduced only minor overhead in update time (3-4 s per round), validating its efficiency for periodic training in production environments.
# Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka +
## 8 Outlook
However, at the edge and fog layer, which are common deployments for smart city applications, the situation can be very different: reaching remote state management services can be costly, in terms of access fees and latencies, and cumbersome, due to the limited bandwidth available to reach the Internet and public cloud services. Think for instance of the archetypal multi-modal analytics application illustrated in Figure 4: the inference components may need a semi-persistent state to process the audio-video across a time window and the whole neural network model can be considered an internal state during the training phase of the analytics applications. Research is ongoing on how to optimise state management in serverless platforms in the edge and fog layers: for instance, [PERSON] et al. proposed different models to achieve this goal in [40], but the studies in the literature are still in their infancy compared to the maturity level of the serverless-in-the-cloud technology. In the long term, with specific reference to the multi-modal smart city analytics applications in the H2020 MARVEL project a possible deployment scenario could be the following:  
* **Edge tier**: only deploy stateless functions, so that there is no latency due to accessing state management services; the orchestration platform has maximum flexibility to autoscale function instances, which leads to the highest gains in terms of the reduced overprovisioning of (costly) resources and energy consumption. * **Fog tier**: deploy stateless and stateful functions as needed. The stateful functions can retrieve/update the state residing in a local service at the fog layer itself. If the state of an application is _ephemeral_, i.e., it is only relevant for a limited amount of time and a given location, then there is no need to synchronise it periodically with a global state of the system in the cloud, but such an operation is required in general for a persistent state (e.g., a neural network model under training). * **Cloud tier**: state-of-the-art serverless platforms can be used to efficiently deploy and run all kinds of functions. A further optimisation to be considered is that the stateless vs. stateful behaviour of a function can be temporary. For instance, with _continual learning_ applications, which are of interest for smart cities when the analytics function has to be adapted to the environment over time, a component can be stateless at times (during the inference phases) and stateful at other times (when there is a need to retrain the model). In this situation, a smart orchestration can obtain full benefits in terms of performance while keeping the overprovisioning of the resources low.
###### Abstract
Excess adipose tissue despite a normal BMI has been termed normal weight obesity (NWO) syndrome [11] and studies conducted in adults have shown that obese individuals with normal body weight are insulin resistant, suffer from hyperinsulinemia, and are prone to type 2 diabetes and premature coronary artery disease [11]. Body mass index (BMI) is commonly used to categorize body weight. In children, the interpretation of BMI is based on percentile charts [12; 13]. However, BMI does not provide information on body fat mass (FM) [12; 13]. There are several methods for assessing FM, with densitometric techniques, primarily dual-energy X-ray absorptiometry (DXA), considered the \"gold standard\" [14]. DXA is considered a \"reference\" for body composition assessment because of its safety, non-invasiveness, scanning speed, and precision. However, its accuracy varies by manufacturer and software. This method is stationary and not suitable for field research outside the laboratory. Therefore, one of the most commonly used methods of estimating body composition, including FM, is bioelectrical impedance analysis (BIA). BIA does not measure FM but estimates it with algorithms that consider the parameters of resistance and reactance. BIA is characterized by high accuracy and repeatability [12; 13]. This method enables a non-invasive and quick estimation of body composition, including fat tissue, lean body mass, skeletal muscle mass, water content, and others [15]. The body composition of children and adolescents changes with age. During puberty, girls usually show a significant increase in fat mass, while boys show a significant increase in muscle mass [16; 17; 18]. In obesity, too high body fat content may lead to serious health threats such as hyperlipidemia, diabetes, hypertension, lower cardiorespiratory efficiency, and reduced muscle strength and physical fitness [1; 2; 17]. The primary task of the program for the treatment and prevention of obesity in school-aged children should be not only the reduction in body weight but, above all, the normalization of fat tissue. Increasing PA is one of the most important strategies to achieve this goal [19]. School-age children spend a significant part of the day at school. Therefore, the level of PA during school time, especially the number of hours of physical education (PE) lessons, may have a significant impact on children's BMI and body composition [20; 21; 22]. The purpose of this pilot study was to evaluate the effectiveness of school-based PA intervention on the anthropometric indicators and body composition of overweight and obese children during a two-year observation period, from the age of 10 to 12.
# Development of Energy Storage Systems for High Penetration of Renewable Energy Grids
## 3 Frequency Regulation Technology Model
### Dynamic Regulation Control Model  
The characteristics of the dynamic regulation control model are generally divided into the frequency detection, deadband range, frequency operating range, and droop gain of active power. For the dynamic regulation control model based on the WECC second  
Figure 6: The block diagram of the REGC_A model [27,28].  
generation generic model, the corresponding parameters must be designed. Assuming that the flexible adjustment range is not considered, the frequency of the upper threshold is designed at 59.98 Hz. The symbol of the upper threshold is \(f_{dhd2\_dReg}\), and the calculation equation can be written as follows:  
\[f_{dbd2\_dReg}=\frac{-59.98+60}{60}=0.00033333\text{ pu} \tag{4}\]  
If the lower threshold of the dynamic regulation control model is set at 60.02 Hz, the symbol of the lower threshold is \(f_{dhd1\_dReg}\), and the calculation equation can be written as follows:  
\[f_{dbd1\_dReg}=\frac{-60.02+60}{60}=-0.00033333\text{ pu} \tag{5}\]  
When the grid frequency is less than 59.98 Hz, the battery energy storage system will provide active power to the grid based on the droop gain. The under frequency of droop gain function can be written as follows:  
\[D_{up\_dReg}=\frac{P_{max}-P_{0}}{-f_{max}+f_{ref}-f_{dbd2\_dReg}} \tag{6}\]  
When the grid frequency is greater than 60.02 Hz, the battery energy storage system will charge active power from the grid based on the droop gain. The over frequency of droop gain function can be written as follows:  
\[D_{dn\_dReg}=\frac{P_{max}-P_{0}}{-f_{min}+f_{ref}-f_{dbd1\_dReg}} \tag{7}\]  
Equations (6) and (7) are defined as follows:  
\(D_{up\_dReg}\), under frequency droop gain of the dynamic regulation control model (pu);  
\(D_{dn\_dReg}\), over frequency droop gain of the dynamic regulation control model (pu);  
\(P_{max}\), maximum output power value (pu);  
\(P_{0}\), initial output power value (pu);  
\(f_{max}\), maximum under frequency value (pu);  
\(f_{min}\), minimum over frequency value (pu);  
\(f_{ref}\), grid frequency value (pu);  
\(f_{dhd2\_dReg}\), upper threshold of the dynamic regulation control model (pu);  
\(f_{dhd1\_dReg}\), lower threshold of the dynamic regulation control model (pu).  
Therefore, based on the proposed dynamic regulation control model, the droop gains of under frequency and over frequency for dReg 0.25 and dReg 0.5 are 260.8696 and 125, respectively.
## 2 Conventional Finite Control Set-Model Predictive Control for Two-Level Voltage Source Inverter
### Load Model  
Three-phase loads connected to the inverters are typically modeled as circuits comprising resistance, inductance, and back-EMF. The relationship between the inverter output and load in this three-phase model can be expressed as follows:  
\[L_{o}\frac{d}{dt}\begin{bmatrix}i_{\text{ao}}\\ i_{\text{bo}}\\ i_{\text{co}}\end{bmatrix}+R_{o}\begin{bmatrix}i_{\text{ao}}\\ i_{\text{bo}}\\ i_{\text{co}}\end{bmatrix}+\begin{bmatrix}e_{\text{a}}\\ e_{\text{b}}\\ e_{\text{c}}\end{bmatrix}=\begin{bmatrix}v_{\text{an}}\\ v_{\text{bn}}\\ v_{\text{cn}}\end{bmatrix} \tag{4}\]  
where \(i_{\text{ao}}\), \(i_{\text{bo}}\), and \(i_{\text{co}}\) denote the three-phase load current vector; \(v_{\text{an}}\), \(v_{\text{bn}}\), and \(v_{\text{cn}}\) denote the inverter output voltage vector; \(e_{\text{a}}\), \(e_{\text{b}}\), and \(e_{\text{c}}\) denote the load back-EMF; and \(R_{o}\) and \(L_{o}\) denote the load resistance and inductance, respectively.  
This equation can be transformed into the stationary \(\alpha\)-\(\beta\) coordinate system through Clarke transformation as follows:  
\[L_{o}\frac{d}{dt}\begin{bmatrix}i_{\alpha}\\ i_{\beta}\end{bmatrix}+R_{o}\begin{bmatrix}i_{\alpha}\\ i_{\beta}\end{bmatrix}+\begin{bmatrix}e_{\alpha}\\ e_{\beta}\end{bmatrix}=\begin{bmatrix}V_{\alpha}\\ V_{\beta}\end{bmatrix} \tag{5}\]  
where \(i_{\alpha}\) and \(i_{\beta}\), \(V_{\alpha}\) and \(V_{\beta}\), and \(e_{\alpha}\) and \(e_{\beta}\) denote the currents, voltages, and back-EMF in the \(\alpha\)-\(\beta\) coordinate system, respectively.  
Equations (4) and (5) express the continuous-time state-space equations; however, discretization is necessary in actual digital control systems. The load model can be represented as a discrete-time state-space equation by applying the forward Euler method. The load current at the next sampling point is expressed as follows:  
\[\begin{bmatrix}i_{\alpha}(k+1)T_{s}\\ i_{\beta}(k+1)T_{s}\end{bmatrix}=\left(1-\frac{R_{o}T_{s}}{L_{o}}\right) \begin{bmatrix}i_{\alpha}(kT_{s})\\ i_{\beta}(kT_{s})\end{bmatrix}+\frac{T_{s}}{L_{o}}\begin{bmatrix}V_{\alpha}(kT_ {s})\\ V_{\beta}(kT_{s})\end{bmatrix}-\frac{T_{s}}{L_{o}}\begin{bmatrix}e_{\alpha}(kT_ {s})\\ e_{\beta}(kT_{s})\end{bmatrix} \tag{6}\]  
where \(T_{s}\) denotes the sampling period; \(i_{\alpha}(k+1)\) and \(i_{\beta}(k+1)\) denote the predicted load current values at the next sampling point; \(i_{\alpha}(k)\) and \(i_{\beta}(k)\) denote the load currents at the current sampling point; \(V_{\alpha}(k)\) and \(V_{\beta}(k)\) denote the inverter output voltages; and \(e_{\alpha}(k)\) and \(e_{\beta}(T_{s})\) denote the load back-EMF.  
Figure 2: Space voltage vector diagram based on the switching states of the three-phase two-level inverter.
## Materials and Methods
### Thermal Analyses of the Samples  
The thermal behavior of _n_-hexadecane, _n_-eicosane, PCL microparticles and mPCM was recorded using a DSC 3+ (Metller Toledo, Columbus, OH, USA) piloted on PC with STARe software. The sample space was purged with nitrogen flow during the experiments. The onset temperatures of solid-liquid and liquid-solid transitions (_T\({}_{onset}\)_), end temperatures of phase transitions (_T\({}_{end}\)_), and maximum temperatures of melting (_T\({}_{m}\)_) and crystallization (_T\({}_{c}\)_), as well as melting (_\(\Delta H_{m}\)_) and crystallization (_\(\Delta H_{C}\)_) enthalpies of each transitions were determined. 3-8 mg of microcapsules (or _n_-alkanes) were placed in an aluminum pan and placed in DSC apparatus, heated from \(-20\)\({}^{\circ}\)C to 80 \({}^{\circ}\)C at 5 \({}^{\circ}\)C-min\({}^{-1}\), held constant at 80 \({}^{\circ}\)C for 10 min, and then cooled to \(-20\)\({}^{\circ}\)C at the same scanning rate, and held for 10 min. Each test was conducted twice and carried out with a least three independent experiments. Values were taken on every second cycle. On the base of phase transitions enthalpies, the loading content (LC) as well as the encapsulation efficiency (EE) were calculated from the Equations (1) and (2) [43],  
\[LC=\frac{\Delta H_{mPCM}}{\Delta H_{PCM}} \tag{1}\]  
\[EE=\frac{LC}{LC_{th}} \tag{2}\]  
where, \(\Delta H_{mPCM}\) and \(\Delta H_{PCM}\) are the melting enthalpies of microparticles, and _n_-alkanes (_n_-hexadecane \(\sim 199\) J-g\({}^{-1}\), and _n_-eicosane \(\sim 205\) J-g\({}^{-1}\)), respectively. _LC\({}_{th}\)_ is the theoretical loading content.  
The relative crystallinity of the PCL matrix in the samples (_X\({}_{c}\)_) was calculated according to Equations (3) and (4),  
\[X_{c(m)}=\frac{\Delta H_{m}\ (PCL\ \mathit{phase\ in\ mPCM})}{\Delta H_{m(PCL)}^{0} \big{(}1-w_{m}\ (m)\big{)}}\times 100 \tag{3}\]  
\[X_{c(th)}=\frac{\Delta H_{m}\ (PCL\ \mathit{phase\ in\ mPCM})}{\Delta H_{m(PCL)}^{0} \big{(}1-w_{m}\ (m_{th})\big{)}}\times 100 \tag{4}\]  
where, \(\Delta H_{m}\) is the specific melting heat, \(\Delta H_{m}^{0}\) is the theoretical specific heat of 100% crystalline PCL, which was taken as 139.5 J-g\({}^{-1}\)[44], and \(w_{m}\) was the weight fraction of PCM in the samples.  
The thermal stability of _n_-alkanes, PCL microparticles and mPCM were assessed by thermogravimetric analysis, carried out on a TGA 3+ (Metller Toledo, Columbus, OH, USA) under inert atmosphere at a purge rate of 50 mL-min\({}^{-1}\). A heating rate of 10 \({}^{\circ}\)C-min\({}^{-1}\) was applied, and the temperature raised from 20 to 700 \({}^{\circ}\)C. For each experiment, 3 to 8 mg was used in the TGA test. In addition to the percentage weight (TG) curves, the derivative weight loss percentage (DTG) was calculated for each sample.
## 5 Results
### LSMs by Ensemble of Three-Models  
In the third phase, the ensemble approach was executed considering three models differently viz. ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, and RF-SVM-LR, which produced four LSMs of the district. The consecutive areal extension of VH, H, M, L, and VL susceptibility classes of derived LSMs was 15.74%, 12.67%, 19.33%, 21.97%, 30.28% of the district by the ANN-LR-RF model (Figure 8a); 18.47%, 15.99%, 17.18%, 19.83%, and 28.52% by the ANN-RF-SVM model (Figure 8c); 19.15%, 14.66%, 17.96%, 21.48%, and 26.75% area by the ANN-SVM-LR model (Figure 8b), and 20.62%, 16.34%, 17.66%, 18.48%, and 26.89% of study area by the RF-SVM-LR model (Figure 8b).  
Figure 7: Landslide susceptibility mapping using ensemble of two models: (**a**) ANN-LR, (**b**) ANN-SVM, (**c**) LR-RF, (**d**) ANN-RF, (**e**) SVM-RF, and (**f**) LR-SVM.
## 3 Results and Discussion
### Validation of Models  
Figure 6 indicates the comparison between the observed values and the predicted values calculated by multiple predictive regression models in Table 1. In order to test the goodness of the estimations of each PM concentration, the scattered diagrams show model fitting with calculated and observed PM\({}_{10}\), PM\({}_{2.5}\) and PM\({}_{1}\) concentrations. Their original observed values were reproduced by the comparison of observed values with calculated values, before, during, after the dust periods and for all periods. The reproduced observed values can be calculated by the linear regression equations in the scattered diagrams of Figure 6.  
Figure 7 shows that each observed and calculated PM value with the date denotes the behavior throughout each period. The correlation coefficient of each PM concentration in  
Figure 6: Comparison of observed with calculated PM\({}_{10}\), PM\({}_{2.5}\) and PM\({}_{1}\) values in (**a-c**) figures before, during and after the dust periods and all periods from 18 March to 21, 2015 at Gangneung city, Korea.  
the scattered diagram is slightly improved over the correlation coefficient in Table 1, in the case of the PM\({}_{10}\) concentration after the dust period and all periods.
# Symmetric Keys for Lightweight Encryption Algorithms Using a Pre-Trained VGG16 Model
## 2 Theoretical Background
### Symmetric Key
#### 2.1.1 Advanced Encryption Standard (AES)  
To address the need for enhanced protection and performance, NIST initiated a call for cipher candidates in 1997 to establish a new encryption standard. This led to the replacement of the existing DES and 3 DES encryption algorithms with the new AES encryption algorithms. The Feistel structure of the AES symmetric block cipher allows it to handle a 128-bit block size and supports three key lengths--128, 192, and 256 bits--enabling 10, 12, and 14 rounds of encryption and decryption with the same key. The vector design of Rijndael, which forms the basis of the AES, provides significant security, and its scalability of up to 256 bits offers resistance to potential attacks [7, 10]. Figure 2 illustrates the AES algorithm.  
Figure 1: Symmetric key encryption.
## 2 Materials and Methods  
### Overview of the Field Test  
The test area is located in the northern Shaanxi slope belt of the Ordos Basin, which is an ultra-low-permeability litho-logic oil reservoir. The oil reservoirs in this study have a permeability of less than 10 \(\times\) 10\({}^{-3}\)\(\upmu\)m\({}^{2}\). They have low formation pressure, gas-oil ratio (38 m\({}^{3}\)/t), porosity, permeability, and production. The P226 well group was selected as the experimental well group. The development horizon is the Chang 21 oil layer, the effective thickness of the reservoir is 6-13 m, the reservoir porosity is 4.5%, and the permeability is 9.76 mD.  
The water injection well group P226 corresponds to six first-line beneficial production wells, in which the W226 well is located at the southeastern edge of the development block and is not affected by other injection wells and oil wells (Figure 1). This well was, therefore, selected as the sampling well for microbial indigenous flooding, and the W202 well, which was closest to the development block, was selected as the control well.  
In 2020, the first round of activator (98 tons) was injected in two injections into the P226 well group. After 3 months, the oil and fluid production of the W226 well had increased significantly (Figure 1). The output of the control well W202 was stable, and there was no noticeable change.