esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Performance Analysis
The estimated network lifetime \(L\) for a node using the location-based model is  
\[L=\frac{E_{0}}{E_{location}}=\frac{E_{0}\times I_{request}\times(X)}{(C+1)T_{x}+ CR_{x}+E_{s}}. \tag{19}\]  
The packet delivery latency \(L\) regarding to LPL protocol [13] is analyzed as follows:  
\[L_{h}=(h-1)(I_{W}/2+T_{pkt})+T_{pkt}, \tag{20}\]  
where \(h\) is the number of hops to the sink, \(I_{W}\) is sleep interval, and \(T_{pkt}\) is the time period to transmit a data packet. The detailed analysis and results for latency can be found in [13]. We calculate the above parameters based on default values of the TOSSIM radio model for CC2420 used in [13]. Each model has a different policy for periodic wakeup, sensing, and packet transmitting as follows. **The Periodic Model:** In the periodic model, a node periodically wakes up, performs sensing, processing, and transmits sensing packets to the cloud. In this model, WSNs may be deployed without prior knowledge of where and when a user may request sensing data. They are set beforehand with a reasonable wakeup interval \(I_{W}\) and sensing data report interval \(I_{S}\) for the trade-off between energy efficiency and requirements of general applications (i.e., in default LPL setting, \(I_{W}^{WSN}=0.5\) s is used and \(I_{S}^{WSN}=10\) s is popularly used). **The Location-Based Model:** In the location-based model, because the cloud is aware of the location of users and when sensing data is requested in a region of interest of an application, it can control when a sensor needs to provide sensing service and at which rate to meet requirements of served applications. According to state-of-the-art localization and prediction algorithms, the cloud can track and estimate the near future (i.e., a few seconds or even more) locations of mobile users. The cloud sends scheduling requests to WSNs to enable them to be available to serve mobile users when the users enter their region of interests. For example, when there is no user demand, WSNs can be set with \(I_{W}^{quiet}=1\) s and \(I_{S}^{quiet}=\infty\). With a longer sleep interval and without a need for data packet transmission when there are no serving requests, the model helps save energy for constrained sensor nodes. When a mobile user requests sensing data from a WSN, sensors can be scheduled to wake up more frequently (i.e., \(I_{W}^{serving}=0.25\) s or even always on) and perform high sensing rate (i.e., \(I_{S}^{serving}=1\) s or even lower) depending on requirements of the application, to serve mobile users. With lower sleep interval and higher sensing rate settings on demand, the model can provide a better quality of sensing information to applications and users. In summary, the main benefit of the proposed work is that based on knowledge of the cloud about where and when sensing services are required, WSNs are scheduled to (1) save as much energy as possible during quite time periods and (2) be available to serve users on demand with a high quality of information based on requirements of applications.
# An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet +
## 1 Introduction
On the other hand, _mixed-code_ text consists of using special characters to write words in such a way they would graphically resemble the intended words but from the machine point of view, it is considered a no-sense sequence of symbols. The question should be asked--What is the function of mixed-code text? The answer lies in the simplicity of the method. Mixed-code text only requires the user to use a bit of creativity and on the reader's part only the knowledge of the language as the reader in a flow would easily understand the intended text. However, a machine will take the text literally and would check for its content which in most cases would be dismissed as garbage. Consequently, there is a need for careful deliberation to re-evaluate text and to provide a method for improving its interpretation. In this panorama, this article, which is an extended version of [20], focuses on the detection of hidden propaganda in mixed-code text, by proposing an algorithm for supporting the analysis of suspicious mixed-code text, based on the optical character recognition (OCR) analogy [21]. Specifically, a segregation approach is adopted to analyse the characters' combinations, which in turn are transformed into images. A character recognition model, based on machine learning techniques, is trained in order to recognise such modified letters of the alphabet. The process iterates through all possible combinations to find the intended word. A second model deals with the text classification, to determine whether a text contains hidden propaganda messages or not. Since such problem falls within the detection field, accuracy, precision, recall and f1-score have been employed as evaluation criteria for assessing the proposal's performances for the detection of hidden propaganda in mixed-code. From the conducted experiments, it results that each of those metrics reaches more than 91%, as a symptom of good performances, that are inline in comparison to the related works described in Section 2. The rest of the paper is structured as follows: Section 2 provides an overview of the related works by distinguishing between mixed-code analysis and propaganda detection. An overview on optical character and image recognition, which represent the enabling technologies, have been introduced in Section 3. In Section 4, the most common identified mixed-code categories, the overall adopted analysis process along with the proposed algorithm are elaborated. Whereas its evaluation is presented and discussed in Section 5. Finally, the conclusion and future works are highlighted in Section 6.
## 1 Introduction
### Contribution and Structure of the Article  
The RRD is a major granary in Vietnam but is strongly affected annually by flooding from May to October. Until now, in the RRD the most common way to assess rice damage caused by flooding is based on GSO statistics. However, the statistical report is not updated in real-time, meanwhile, the effectiveness of satellite imagery for rice mapping and NRT flooding monitoring has been proved. In this study, we proposed a method for the rapid assessment of flood inundation and the affected rice area in the RRD using S1A imagery. An experiment was carried out using data from the tropical storm Son-Tinh in July 2018. We firstly produced the rice map in the winter-spring season, and flood maps caused by the Son-Tinh storm. The affected rice area was estimated by the timely analysis of flood maps on the rice map. After that, statistical reports for rice damage by inundation from the Vietnamese GSO were used to assess the accuracy of our proposed method. To our best knowledge, this study is the first work on the rapid assessment of affected rice areas caused by flood inundation for the RRD region using S1A images. The damage can be assessed in NRT at high spatial resolution (i.e., 10 m). The good evaluation results in comparison with GSO reports have highlighted the potential application aspects of the proposed method. In the next section, the study area and datasets will be presented. The methodology of the proposed method will be described in Section 3. The experimental results, accuracy assessment and further analysis will be presented in Section 4. Finally, Section 5 presents the conclusions and future work.
## 1 Introduction
[15] proposed a parameter estimation algorithm for updating the FE model for the damage assessment of reinforced concrete slabs using vibration-based frequency response function (FRF) data. [PERSON] et al. [16] proposed a dynamic condition assessment approach based on wavelet packet energy of cross-correlation functions from the recorded acceleration responses to identify the damage of shear studs in slab-on girders. Although the vibration-based testing methodology for the damage detection of shear studs in SCCBs has gained much interest over the past years, there remains some debate as to whether the measured modal parameters are sensitive enough and suitable for detecting multiple instances of damage. This study focuses on quantifying the damage to shear  
Figure 1: Shear connection in steel–concrete composite beam: (**a**) welded shear studs; and (**b**) perforated plate. \(N\) stands in SCCBs by novel proposed modal-shape-based indicators. The contribution of the present study is threefold: (1) a novel solid-slipping nonlinear FE model for the SCCB is developed in ABAQUS 2021 without depending on the stiffness-slip function, to guarantee the accuracy of dynamic perturbation analysis; (2) two vibration-based indicators, namely the modal curvature difference and the modal flexibility difference curvature are proposed to locate the damaged shear studs and quantify the damage degree; (3) the robustness and sensibility of the two indicators were proven to drive further practical application.
# Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models
## 5 Analytical Model Development
Five statistical parameters pertaining to the results are listed in Table 5. As shown in Figure 5b and Table 6, predictions for the dissolution rate of C\({}_{3}\)S were high-fidelity, with \(R^{2}\) of 0.92 and _RMSE_ of 9.545 umol/m\({}^{2}\)/s, respectively. The predictions of the _alkaline_ solvent scenario are superior, in terms of \(R^{2}\), to those from _generic_ solvent scenario. The high-quality prediction is expected because the _alkaline_ solvent scenario minimizes the effect from H\({}^{+}\); in other words, the input-output correlations become simpler due to the reduction of the influence of H\({}^{+}\). Therefore, the trend for the simple system can be captured by the analytical model exactly. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\(\mathbf{C_{0}}\)} & 59.7404 & \(\mathbf{C_{1}}\) & \(-\)17.0531 & \(\mathbf{C_{2}}\) & \(-\)0.3166 \\ \hline \multicolumn{1}{c}{\(\mathbf{C_{3}}\)} & \(-\)231.8133 & \(\mathbf{C_{4}}\) & 1.7087 & \(\mathbf{C_{5}}\) & 1.7798 \\ \hline \multicolumn{1}{c}{\(\mathbf{C_{6}}\)} & 0.0256 & \(\mathbf{C_{7}}\) & \(-\)0.0646 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Seven coefficients and one constant (for seven input variables corresponding to the physicochemical properties of C\({}_{3}\)S and solvents) optimized for the analytical model of the _generic_ solvent scenario. Figure 5: The analytical model’s predictions of C\({}_{3}\)S dissolution rate against experimental measurements for **(a)**_generic_ solvent and **(b)**_alkaline_ solvent. Coefficient of determination (\(R^{2}\)) is shown in the legend, providing a measure of the prediction performance. The dashed line represents the ideal prediction.
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 3 Ext-LOUDS
### ELOUDS-Super Algorithm  
Based on Ext-LOUDS, an efficient superset query algorithm, ELOUDS-Super, is proposed. The algorithm is implemented based on the following simple observation:  
**Observation 1**.: _Given a query \(Q\), if all the elements corresponding to by the nodes from root (exclude root) to an end node \(N\) are in \(Q\), the sets corresponding to \(N\) are qualifying results of \(Q\)._  
Based on the above observation, ELOUDS-Super works on a recursive manner and follows a depth-first traversal starting from the root. The detailed description of ELOUDS-Super is shown in Algorithm 1. In Algorithm 1, we use a parameter internalID to denote the sequence number of the current accessing internal node in the total internal nodes. At the beginning of the algorithm, both parameter level and internal ID are set to 0. The major steps of Algorithm 1 are discussed as follows:  
1. Perform a SELECT operation on IsFirstChild vector to obtain the starting position \(p_{start}\) and the ending position \(p_{end}\) of the child nodes of node indicated by node_num (lines 2 and 3);
2. Perform a binary search to obtain the position \(p\) of the current query element \(Q[level]\) in Elems vector (line 4);
3. If the node corresponding to \(p\) is an end node, perform a RANK operation on IsEnd vector to obtain the qualifying sets corresponding to the node, and merge it into the result set (lines 5-7);
4. If the node corresponding to \(p\) has child nodes, obtain the internalID and execute the algorithm recursively (lines 8-10).  
```
//Input: \(Q\), a query having the same ordering with dataset Level: the depth of the trie, starts with 0 node_num: ID of non-leaf node, the ID of root is 0 //Output: \(RS\): results of superset query
1. While(\(level!\)!=\(|Q|\))
2. \(p_{start}\)\(\leftarrow\)select\({}_{1}\)(\(IsFirstChild\), node_num+1)
3. \(p_{end}\)\(\leftarrow\)select\({}_{1}\)(\(IsFirstChild\), node_num+2)
4. \(p\)\(\leftarrow\)binary(\(Elems\), \(p_{start}\), \(p_{end}\), \(Q[level]\))
5. if(\(IsEnd[p]\))
6. \(i\)\(\leftarrow\)rank\({}_{1}\)(\(IsEnd\), \(p\))
7. \(RS\)\(\leftarrow\)\(RS\) U ENIL[i]
8. if(\(Haschild[p]\))
9. \(node\_num\)\(\leftarrow\)rank\({}_{1}\)(\(HasChild\), \(p\))
10. Superset(\(Q\), \(level\)+1,node_num)
11. level\(\leftarrow\)level+1
```  
**Algorithm 1** ELOUDS-Super algorithm
## 5 Experimental Results
### Contour Planning  
The contours in the simulations and experiments are planned by the non-uniform rational B-spline (NURBS), which is a parametric curve composed of parameters such as control point, knot vector, degree of curve, and weight [31; 32; 33]. The NURBS curves can be modified by adjusting the weights and control points, or by changing the basis function generated by the recursive calculation through different spacing of the elements in the node vector. This means a NURBS-generated curve can be divided into a string of multi-interval fitted splines. The equation for defining a NURBS curve is  
\[C(u)=\frac{\sum_{i=0}^{n}B_{i,d}(u)W_{i}P_{i}}{\sum_{i=0}^{n}B_{i,d}(u)W_{i}}= \sum_{i=0}^{n}R_{i,d}(u)P_{i} \tag{28}\]  
where \(P_{i}\) denotes the control point, \(R_{i,d}(u)\) is the rational basis function, \(B_{i,d}(u)\) is the basis function, \(W_{i}\) is the weight, and \(d\) and \(u\) are the degree and parameter of the NURBS curve, respectively. Table 2 shows the NURBS parameters of each contour. As shown in Figure 8a, the star NURBS can be designed with 12 control points. Figure 8b-d shows the NURBS control points for a circle, a bow, and a heart, respectively.
## 2 Research Object
### Research Object and Mathematical Model  
This article aims to minimize the total input power of the pump station under different water diversion conditions as the objective function, with the total water diversion of the pump station and the working head of the pump unit as constraints, and determines the optimal allocation of the required number of operating units and flow within the station under the premise of a certain water diversion of the pump station. The total water intake of the pump station is determined by the total workload of each operating unit. Under a certain water intake, the head of the pump station is obtained from the external characteristic curve of the pump station. By optimizing the flow distribution between each unit, the input power of the entire pump station is minimized.  
The mathematical model of optimization problems can be described as follows:  
Optimization variables: \(Q=(Q_{1},Q_{2},Q_{3},\cdot\cdot\cdot\cdot\cdot,Q_{i})\).  
Objective function: The aim is to minimize the total input power, or energy consumption, inside the pump station. The expression is as follows:  
\[\text{min}P=\sum_{i=1}^{n}P_{i} \tag{1}\]  
Constraint function: Taking the operating head of the water pump as the constraint, the total water intake \(Q\) of the pump station is the sum of the working flows of \(n\) operating units. Ensuring the safe and effective operation of the unit necessitates a certain head range  
Figure 3: Genetic algorithm operation flow chart.  
as boundaries. This head constraint of the water pump unit determines the flow range for single pump operation. The expression is as follows:  
\[Q=\sum_{i=1}^{n}Q_{i} \tag{2}\]  
\[H_{\text{min}}\leq H_{i}\leq H_{\text{max}} \tag{3}\]  
In the above equations, \(P\)--total input power of the pump station, in MW; \(n\)--number of units started; \(P_{i}\)--input power of the \(i\)th operating unit, in MW; \(Q\)--total water intake of the pump station, m\({}^{3}\)/s; \(Q_{i}\)--working flow rate of the \(i\)th operating unit, m\({}^{3}\)/s; \(H_{\text{min}}\)--minimum head of the water pump unit during operation, m; \(H_{\text{max}}\)--maximum head of the water pump unit during operation, m; and \(H_{i}\)--working head of the \(i\)th operating unit, m.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Input Feature Extraction Based on Trajectory Features Correlation  
In AIS data, not every variable can have a positive impact on improving the accuracy of the trajectory restoration model. Therefore, the multidimensional feature processing of AIS data is needed to select highly correlated input features. Correlation analysis and correlation coefficient calculation are commonly used methods to effectively assess the degree of association between features. Through this method, those features that are most correlated with trajectory restoration data can be selected, thus ensuring model efficiency and accuracy and reducing data redundancy. This correlation-based feature selection strategy enhances the understanding of the intrinsic structure of the data and helps to improve the repair performance of the model.
## 3 Recent Advances on IEEE 802.15.4-Based WMSN Approaches
### WMSN Solutions Developed on Top of the IEEE 802.15.4 PHY and MAC (Non-beacon Mode) Layers
#### 3.2.3 Ip500(r) Alliance  
Recently, another IP-based mesh solution was provided by the IP500(r) Alliance [15], an industrial association driven by important Original Equipment Manufacturers (OEMs) such as DORMA, Gunnebo, JOB Detectomat and Honeywell, among others. The IP500(r) Alliance is focused on diverse market segments such as home, industrial or building automation. Its goal is to provide a solution that guarantees interoperability and low-power consumption to end-users (_i.e._, OEMs or system manufacturers) by means of a full protocol stack based on well-accepted standards, regulations and industry protocols for WMSNs. In particular, the IP500 specification employs the MAC and PHY layers (only in the 433 MHz, 868 MHz and 915 MHz bands) of the IEEE 802.15.4-2006 standard and the operation of 6 LoWPAN. Furthermore, as an added-value function, this approach offers support to the application layer for building automation and control network solutions through implementations as the BACnet(tm) protocol [37]. IP500 implements a 16-bit IEEE addressing scheme to support communications between WMSN nodes. Additionally, IP500 also offers the network layer the complete functionality of 6 LoWPAN for providing direct connectivity among WMSN nodes and IP-based devices. In contrast, 6 LoWPAN/RPL always employs IPv6 addresses regardless of the type of device. As a result, in peer-to-peer WMSN communications, IP500 routing tables and message headers are lighter than under 6 LoWPAN/RPL, what guarantees most efficient communications and generates less traffic load on the network. Nevertheless, the IP500 proposal requires nodes with enough memory and processing capabilities to include all the 6 LoWPAN functionality (and other implementations as BACnet(tm)) into resource-constrained devices. On the other hand, IP500(r) Alliance _indicates that only end-devices can implement an energy-saving mechanism based on duty-cycling, while the rest of nodes remain always in active mode_. This makes the IP500 approach unfeasible as a valid solution for applications with energy-constraints (_i.e._, only battery-powered devices). However the IP500(r) Alliance is still in its early stages of design and development, therefore an open-access standard allowing a more detailed study of this proposal is not available yet.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 3 Experimental Results
### Discussion  
Based on the result, it is found that recent advances in model deployment for bearing fault diagnosis emphasize balancing accuracy and efficiency. For instance, quantization-aware training (QAT) and pruning techniques have been widely adopted to compress models for edge deployment. However, these methods often require additional fine-tuning steps to recover accuracy after compression. In contrast, our approach integrates knowledge distillation (KD) from the outset, enabling the student model to inherit the teacher's discriminative capabilities while reducing parameters by 72.8%. Unlike traditional KD methods that focus solely on logit matching, our framework incorporates feature-level alignment, ensuring the compressed model retains robust feature extraction abilities.  
Furthermore, compared to dynamic inference methods, which introduce runtime overhead, our static compressed model achieves real-time inference of 9.72 ms with consistent accuracy across varying loads and speeds. This makes it more suitable for industrial edge devices with fixed computational budgets. The S-transform preprocessing also reduces input dimensionality, further lowering memory usage without sacrificing time-frequency resolution. By prioritizing deployment efficiency, through KD-driven compression and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Parameters** & **Model Size** & **Inference Time (ms)** & **Accuracy (\%)** & **FLOPs** \\ \hline ResNet [20] & 743.62 k & 2979.9 KB & 203.56 & 98.21 & 222.24 \\ MobileNet [21] & 32.14 k & 145.2 KB & 20.41 & 83.44 & 24.79 \\ ShuffleNet [22] & 25.54 k & 126.1 KB & 13.44 & 80.36 & 20.79 \\ GhostNet [23] & 13.77 k & 67.5 KB & 9.69 & 85.43 & 17.54 \\ CGhostNet [24] & 8.36 k & 46.0 KB & 10.25 & 87.92 & 16.01 \\
**Proposed method** & **8.74 K** & **51.2 KB** & **9.91** & **97.69** & **18.52** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of deployment of different models.  
hardware-aware design, our method addresses a critical gap in the field: enabling high-accuracy diagnosis under strict resource constraints.
## 2 Materials and Methods
### Preprocessing  
We used an approach to pre-processing similar to that of our previous study [19]. Of the 167 features in the dataset, we removed eight that were not essential for predictive model building, none of which impacted the outcome of the model. The dataset has six possible outcomes, including readmission and mortality within six months, which we combined to allow greater focus on all-cause readmission and mortality prediction. For comparison purposes, we used the mean, Multivariable Imputation by Chained Equations (MICE), k-Nearest Neighbor (kNN), and Random Forest (RF) imputation techniques for the handling of missing values. We applied these imputations to continuous features that had missing values less than or equal to 50%. Mode imputations were applied to the categorical features [20]. Features with more than 50% missing values were excluded.  
One-hot encoding was used to encode the categorical features into an integer type [21]. By applying one-hot encoding, a new variable was created for each categorical output, with the variable containing a binary output of 0 or 1. To standardize the value of each continuous feature, we applied a standardization approach known as z-score normalization [22]. This approach transformed each continuous feature into a standard score with a mean of 0 and variation of 1 through the following formula [23]:  
\[z=\frac{x-\mu}{\sigma}\]  
We obtained the standardized value of the feature (z) by subtracting the original data point of feature (x) with its mean (\(\mu\)) to center it and then dividing it by the standard deviation (\(\sigma\)) to scale it.