esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 8 Conclusions and Future Work  
In this paper, we have obtained closed-form expressions for the duty cycle and initial energy storage that guarantee self-sustained operation in TinyOS solar-based EH-WSN devoted to periodic monitoring. To achieve these results, first we have developed an energy consumption model, whose deviation from simulation estimates is 4.8% in the worst case. Then, we have formulated the condition for energy neutral operation by combining an approximate version of the energy consumption model with a well-known solar irradiance model. This model assumes a periodic pattern for the irradiance level that is based on the average prediction at given spatial and temporal coordinates. The approximate energy consumption model omits the details of the LPL mechanism implemented in TinyOS sensor nodes, while at the same time it retains the essential components. For this reason, we postulate that the obtained formulation can be extended to other software (with corresponding hardware) platforms, and thus it can be used as a starting point to develop adaptive schemes that dynamically adjust the duty cycle of nodes according to changing traffic and weather conditions. We leave these issues for further research. We also suggest that the energy-harvesting model considered in this paper be refined according to the geographic latitude and meteorological conditions of the deployment scenario.  
Conceptualization, S.G. and S.E.; Validation, S.G. and S.E.; Formal Analysis, S.G.; Investigation, S.G. and S.E.; Writing and Original Draft Preparation, S.G. and S.E.; Writing, Review & Editing, S.G. and S.E.  
This research received no external funding.  
The authors wish to thank [PERSON], from People Power Company, for his valuable comments on much of the subject matter of this paper, particularly with regard to the implementation of LPL in TinyOS, to which development he contributed mostly.  
The authors declare no conflicts of interest.
## 3 Mathematical Model
### Model Formulation
#### 3.2.8. Headway Constraints  
Headway constraints could ensure safe operation at stations and maintain a safe distance between adjacent trains in sections. In actual operation of high-speed railways, the train headway is not a fixed value; it is different due to the stop status of adjacent trains at the station. Therefore, we handle the train headway more precisely in this model. Figures 3 and 4 illustrate the different types of headway with four types each of departure and arrival headway. The corresponding relationship between different types of headway and the stop status of trains is shown in Tables 6 and 7.  
Figure 3: Departure headway in different situations of train stops.  
As shown in Figures 3 and 4, the above headway only takes effect when train \(i\) departs before train \(j\); however, the sequence of trains in the model is not fixed. To ensure the reasonableness of the constraint, we introduce a binary variable \(y_{ij}^{m}\), which equals 1 when train \(i\) departs from station \(m\) earlier than train \(j\); otherwise, it is 0.  
Therefore, the headway constraints are expressed as Constraints (14) and (15). The variable \(y_{ij}^{m}\) is expected to satisfy Constraint (16). Theoretically, the right side of the Constraints (14) and (15) is one minimum headway only when \(y_{ij}^{m}\) is 1. Moreover, this set of constraints can also avoid conflicts between trains in sections.  
\begin{table}
\begin{tabular}{c c c} \hline \(p_{i}^{m}\) & \(p_{j}^{m}\) & **Departure Headway Type** \\ \hline
1 & 0 & \(h_{dp}\) \\
0 & 1 & \(h_{pd}\) \\
0 & 0 & \(h_{pp}\) \\
1 & 1 & \(h_{dd}\) \\ \hline \end{tabular}
\end{table}
Table 6: Four types of departure headway according to the value of \(p_{i}^{m}\) and \(p_{j}^{m}\).  
Figure 4: Arrival headway in different situations of train stops.  
\[d_{i}^{m}-d_{j}^{m}\geq p_{i}^{m}\cdot h_{dp}+p_{j}^{m}\cdot h_{pd}+ \left(1-p_{i}^{m}-p_{j}^{m}\right)\cdot h_{pp}+p_{i}^{m}\cdot p_{j}^{m}\cdot \left(h_{dd}-h_{dp}-h_{pd}+h_{pp}\right) \tag{14}\] \[-U\Big{(}1-y_{ij}^{m}\Big{)},\forall i,j\in K,i\
eq j,\forall m\in \{1,2,\ldots,|S|-1\}\] \[a_{i}^{m+1}-a_{j}^{m+1}\geq p_{i}^{m+1}\cdot h_{ap}+p_{j}^{m+1} \cdot h_{pa}+\left(1-p_{i}^{m+1}-p_{j}^{m+1}\right)\cdot h_{pp}+p_{i}^{m+1} \cdot p_{j}^{m+1}\cdot\left(h_{aa}-h_{ap}-h_{pa}+h_{pp}\right)\] (15) \[-U(1-y_{ij}^{m}),\forall i,j\in K,i\
eq j,\forall m\in\{1,2,\ldots,|S|-1\}\] \[y_{ij}^{m}+y_{ji}^{m}=1,\forall i,j\in K,i\
eq j,\forall m\in\{1,2,\ldots,|S|-1\} \tag{16}\]
## 1 Introduction
### Organization of the Paper  
The remainder of this work is organized as follows. Our first contribution is detailed in Section 2. First, we present the need for message replication techniques in OppNets, identify network characteristics that need to be observed when replicating messages, and overview existing message replication techniques. Then, we investigate the current state of message replication in OppNets with respect to sensed data collection, and suggest design guidelines for message replication techniques. For our second contribution, we follow our design guidelines to propose a technique for improving message replication in Section 3. In particular, we consider the unique characteristics of OppNets for sensed data collection to propose a set of mechanisms and an algorithm that constitute LARep. In Section 4, we evaluate our proposed LARep through simulation experiments. Finally, Section 5 concludes the paper and discusses future work.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 4 Experiments
### Detector Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multicolumn{6}{c}{**Classic**} & \multicolumn{3}{c}{**Generalized**} \\ \cline{2-13}  & \multicolumn{3}{c}{**hite1**} & \multicolumn{3}{c}{**hite05**} & \multicolumn{3}{c}{**hite10**} & \multicolumn{3}{c}{**hite11**} & \multicolumn{3}{c}{**hites**} & \multicolumn{3}{c}{**hite10**} \\  & **per-dis** & **per-ins** & **per-dis** & **per-ins** & **per-dis** & **per-ins** & **per-dis** & **per-ins** & **per-dis** & **per-ins** & **per-dis** & **per-ins** \\  & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** & **(A\%)** \\ \hline ConSE (baseline) & 14.99 & 31.62 & 34.45 & 57.64 & 45.42 & 68.18 & 0.05 & 0.29 & 13.97 & 32.28 & 23.63 & 46.21 \\ \hline ConSE + CI & **15.34** & 32.26 & **34.95** & 58.45 & 46.28 & 69.06 & 0.2 & 0.81 & 14.39 & 33.58 & 24.15 & 46.94 \\ (YOLOv2-80) & **(2.33!)** & (2.02!) & **(1.4!5!)** & (1.4!1!) & (1.89!) & (1.29?) & (300!) & (179!) & (3.01!) & (4.03!) & (2.2!) & (1.58!) \\ \hline ConSE + CI & 15.22 & 32.32 & 34.55 & 58.15 & 45.63 & 68.79 & **0.69** & 1.11 & 14.14 & 33.8 & 23.91 & 47.4 \\ (YOLOv2-9k) & (1.53!) & (2.21!) & (0.29!) & (0.88!) & (0.46!) & (0.89!) & **(1280!)** & (282!) & (1.22!) & (4.71!) & (1.18!) & (2.58!) \\ \hline ConSE + CI & 15.14 & **32.94** & 34.67 & **59.12** & **46.81** & **69.65** & 0.17 & **1.3** & **14.4** & **33.81** & **24.34** & **48.22** \\ (\(\text{GT}_{\text{S}}\)) & (1!) & **(4.12!)** & (0.64!) & (2.57!) & (3.06!) & (2.16!) & (240!) & (**34.8!) & (**3.08!) & (**4.74!)** & (**3.7!) & (**4.3!) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparative evaluation in the differentiation of sources for surrounding information with the \(\text{HM}_{path}\) method. our results generally validate that our methods based on the contextual inference positively affect ZSR in an advanced way, as shown by the previous evaluation results.
## 4 Empirical Analysis
### Evaluation Criteria Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Third Class** & \begin{tabular}{c} **Prevention of Life** \\ **Safety** \\ \end{tabular} & \begin{tabular}{c} **CCTV Replacement/** \\ **Complementary** \\ **Effects** \\ \end{tabular} & \begin{tabular}{c} **Conformity to** \\ **Relevant Policies** \\ \end{tabular} \\ \hline \multirow{4}{*}{Purpose compliance} & Prevention of life safety & (1, 1, 1) & (0.93, 1.17, 1.47) & (1.17, 1.47, 1.83) \\ \cline{2-5}  & CCTV replacement/complementary effects & (0.68, 0.85, 1.07) & (1, 1, 1) & (0.97, 1.30, 1.72) \\ \cline{2-5}  & Conformity to relevant policies & (0.55, 0.85, 1.07) & (0.58, 0.77, 1.03) & (1, 1, 1) \\ \cline{2-5}  & TFN (\(S_{1},\ S_{2},\ S_{3}\)) & (0.28, 0.39, 0.54) & (0.24, 0.34, 0.48) & (0.19, 0.26, 0.37) \\ \cline{2-5}  & Weight (normalization) & 0.394 & 0.337 & 0.270 \\ \hline \multirow{4}{*}{**Third Class**} & \begin{tabular}{c} **Service** \\ **Performances/Effects** \\ \end{tabular} & \begin{tabular}{c} **Competitiveness** \\ **versus Existing** \\ **Services** \\ \end{tabular} & \
\begin{tabular}{c} **Market Demand** \\ **Growth** \\ \end{tabular} \\ \hline \multirow{4}{*}{Service suitability} & Service performances/effects & (1, 1, 1) & (1.13, 1.41, 1.79) & (1.03, 1.36, 1.76) \\ \cline{2-5}  & Competitiveness versus existing services & (0.56, 0.70, 0.88) & (1, 1, 1) & (0.77, 0.98, 1.26) \\ \cline{2-5}  & Market demand growth & (0.57, 0.73, 0.97) & (0.79, 1.02, 1.30) & (1, 1, 1) \\ \cline{2-5}  & TFN (\(S_{1},\ S_{2},\ S_{3}\)) & (0.29, 0.41, 0.58) & (0.21, 0.29, 0.40) & (0.22, 0.30, 0.41) \\ \cline{2-5}  & Weight (normalization) & 0.402 & 0.295 & 0.299 \\ \hline \hline \multicolumn{5}{c}{**Third Class**} & **Economic Efficiency** & **Field Applicability** &
\begin{tabular}{c} **Operational** \\ **Sustainability** \\ \end{tabular} \\ \hline \multirow{4}{*}{Service feasibility} & Economic efficiency & (1, 1, 1) & (0.69, 0.87, 1.11) & (0.74, 0.94, 1.20) \\ \cline{2-5}  & Field applicability & (0.90, 1.15, 1.45) & (1, 1, 1) & (1.22, 1.59, 2.05) \\ \cline{1-1} \cline{2-5}  & Operational sustainability & (0.83, 1.06, 1.35) & (0.49, 0.63, 0.82) & (1, 1, 1) \\ \cline{1-1} \cline{2-5}  & TFN (\(S_{1},\ S_{2},\ S_{3}\)) & (0.22, 0.30, 0.42) & (0.28, 0.40, 0.57) & (0.21, 0.29, 0.40) \\ \cline{1-1} \cline{2-5}  & Weight (normalization) & 0.308 & 0.396 & 0.295 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The third class TFN and weight calculation results.
# A Software-Defined Radio Platform for Teaching Beamforming Principles
###### Abstract  
This paper presents the development and validation of a hybrid beamforming system based on software-defined radio (SDR), designed for telecommunications engineering education. The system provides an agile and user-friendly platform that allows students to observe, test, and evaluate beamforming techniques in real time. The platform integrates a multichannel SDR device (USRP N310) with traditional radiofrequency equipment and open-source software, facilitating hands-on learning experiences. The paper details the proposed hardware and software architecture and documents the calibration and validation phases. The testing and validation processes were conducted using a 3.5 GHz antenna array in both indoor and outdoor environments. The results demonstrated the system's effectiveness in achieving the desired beam orientations, with experimental results aligning closely with simulation and theoretical predictions. Significant differences in the radiation patterns observed between the indoor and outdoor measurements were documented, highlighting the impact of environmental factors on beamforming performance. The insights gained from this research provide valuable contributions to the education of future telecommunications engineers, enhancing their understanding of practical beamforming applications and the integration of modern SDR technology.  
software-defined radio (SDR); beamforming telecommunication education; phase calibration 20241410386[[https://doi.org/10.3390/app142210386](https://doi.org/10.3390/app142210386)]([https://doi.org/10.3390/app142210386](https://doi.org/10.3390/app142210386))
# A Novel Shape-Adjustable Surface and Its Applications in Car Design
## 3 Automobile Body Design with the SQ-Coons Surface
### Range of Shape Parameters
Each surface of the models has four different degrees \(d\), and all \(d\) values are divided into three types according to their various characteristics in this article and displayed in Figure 10. This classification also describes the structural characteristics of automobile surface modeling: the surfaces on the symmetrical plane are sequentially linked and left-right symmetrical; the other surfaces are arranged symmetrically on the left and right sides of the symmetrical plane. There are nine surfaces on the symmetrical plane, of which the difference degrees \(d\) in the driving direction in Figure 10a are defined as type-1; the difference degrees \(d\) in the left and right direction of the body in Figure 10b are defined type-2. There are eight surfaces on the left or right side of the symmetrical plane, of which all the difference degrees \(d\) in Figure 10c are defined as type-3. For all the models in the sample library, the difference degrees in various types have similar tendencies. There are 900 type-1 \(d\) values in the sample bank. After performing calculations and obtaining statistics according to Equations (11) and (12), the distribution of these \(d\) values is shown in Figure 11a. According to the statistics, 90% of the data are less than 0.91. Figure 10: The difference degrees \(d\) of the whole body are divided into three types according to the different surfaces and directions: (**a**) In the nine central surfaces, the type-1 difference degrees \(d\) in the driving direction; (**b**) The type-2 difference degrees \(d\) in the left–right direction; (**c**) The type-3 difference degrees \(d\) on the other surfaces. Figure 9: Part M1–M15 in the car sample library built from the model template. Considering the symmetry, there are 450 type-2 \(d\) values in the sample library, which are illustrated in Figure 11b. According to the statistics, 90% of the data are less than 0.98. Figure 11c indicates the distribution of 1600 type-3 \(d\) values. According to the results, 90% of the data are less than 1.31. To summarize, the statistical results from Figure 11 are the reference criteria for setting the range of shape parameters in this paper, from which \(d_{\text{type-1}}\in[0,\,0.91]\), \(d_{\text{type-2}}\in[0,\,0.98]\), and \(d_{\text{type-3}}\in[0,\,1.31]\) are defined as the ranges of the shape parameters of each surface, respectively. As long as the four \(d\) values of a surface conform to their ranges, the surface is evaluated as qualified. The range could be adjusted according to the strictness of the surface deformation requirements.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 2 H.264 Compression Standard and Watermark Embedding Location Selection
### Watermark Embedding Position Selection  
The watermark is embedded in the encoding process of H.264. The embedding position can be selected from I frame or P frame, chroma block or brightness block. I frame will participate in the encoding process of P frame as a reference frame, and the experiment found that embedding watermarking on the chroma subblock can be more resistant to recompression than the brightness subblock, so embedding watermark on the QDCT coefficient of I frame chroma subblock has less impact on the video.  
Figure 1: Structure diagram of H.264/AVC encoder.  
Figure 2: Structure diagram of H.264/AVC decoder.  
For the selection of I frame chroma subblock embedded with watermark, this paper combines the number of NNZ (non-zero coefficients) and Ef (energy factor) as the condition of subblock selection, that is, the definition function:  
\[C(i)=NNZ(i)+Ef(i) \tag{1}\]  
where \(i\) represents the \(i\)-th \(4\times 4\) chroma block; \(NNZ(i)\) represents the number of non-zero coefficients of the \(i\)-th subblock; \(Ef(i)\) represents the energy factor of the \(i\)-th subblock and is the sum of the absolute values of the quantization transformation coefficients of the \(i\)-th subblock.  
The specific selection method is to first select the third non-zero quantization coefficient of all I frame chroma subblocks that is not 2 or \(-2\), then calculate the \(C\) value of the selected subblocks and select several subblocks with the largest \(C\) value as the watermark embedding objects.
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 3 Methodology
### Experimental Workflow  
The overall procedure is illustrated and summarized in the flowchart below (Figure 4). 'HP opt' stands for hyper-parameter optimization. The overall workflow is divided largely into two parts. The first part is to improve the general regression performance by training additional data in a semi-supervised manner. The second part is to make classification on the raw and the augmented models. To convert into a binary classification task, it is necessary to switch the continuous target variable into a discrete variable with either '0' or '1' labels based on the user-defined discrimination threshold value of MTF, and it is done after an optimized regression model has been discovered. The class-labeling process is described in Section 3.1. Optimizing the regression model is necessary because the classification accuracy is directly dependent on how closely the established regression model can predict the output. Getting into more details of the first part, given the raw data, the procedure splits into 10 branches in which different data sets from Table 3 are processed to give the best performing model in terms of regression. For synthesizing different data sets as listed in Table 3, simple random sampling on raw data is conducted. The sampled data are then appended to the raw data to make up augmented data sets. They are then given pseudo labels by extra trees regressor. Subsequently, the type of regression model used is DNN with the architecture as specified in Section 3.2 Each regression model is tested by 5-fold cross-validation. As for the second part, the aforementioned class-labeling is implemented, followed by training of a similar network with the selected model k. Finally, it is then compared with the model using raw data for the classification task.  
Figure 4: Experimental workflow illustrating the overall procedure.
## 4 Sentinel-3A SRAL and MWR Cal/Val Results
### Sea-Surface Calibration Results
Figure 20: Sea-surface height biases for the ascending Pass No.14 (**up**) and descending (**down**) Pass No.335 of Sentinel-3A, as estimated by the Gavdos Cal/Val site. Table 2 presents an example for the analysis of the uncertainty budget for sea-surface calibration for every cycle of the Sentinel-3A Pass No.14 and Pass No.335. In order to calculate the combined (root-sum squared) and the expanded uncertainty (standardized at 95% confidence level), two cases were identified based on the reference surface applied to transfer offshore the site sea heights: (a) the MSS reference and (b) the geoid plus MDT reference. In the former case, the combined uncertainty is of the order of \(\pm\)36 mm and the expanded uncertainty [95%] equals \(\pm\)70 mm (\(k_{[0,005]}\) = 1.96 at 95% with normality). When the geoid and MDT models were employed, these figures increased to: \(\pm\)98 mm (combined uncertainty) and \(\pm\)190 mm (expanded uncertainty, \(k_{[0,005]}\) = 1.96), respectively. This was the uncertainty for the altimeter bias estimated for each satellite cycle. This figure seems to be close to the \(\pm\)30 mm \"standard deviation\" presented by the other permanent Cal/Val sites [42].
## 2 Materials and Methods
### Astaxanthin Quantification  
To determine the biomass astaxanthin content, 5 mg freeze-dried biomass (Section 2.5) was broken up with mortar and pestle and astaxanthin was extracted, adding 10 mL of dichloromethane. This was repeated three times until only colourless debris was left. The dichloromethane was evaporated using a [PERSON] (Germany) rotary evaporator. The astaxanthin extract was saponified for 3 h at room temperature in the dark with acetone (2.25 mL), methanol (0.25 mL), and 0.05 M NaOH in methanol (0.5 mL). This extract was transferred to three 15 mL Falcon tubes and mixed with 3 mL of petroleum ether. Additionally, 5 mL of 10 wt.% aqueous NaCl solution was added, mixed, and centrifuged at 5500 rpm for 5 min. The upper phase was transferred into a fresh NaCl solution. The washing step with NaCl solution was performed three times in total. Finally, the petroleum-rich phase was evaporated, and the astaxanthin extract was dissolved in solvent B (methanol, MTBE, water, 8:89:3, \(v/v\)) used for HPLC analysis and filtered with a 0.22 \(\upmu\)m disposable nylon syringe filter before HPLC analysis. Quantification of the de-esterified astaxanthin was carried out on an HPLC unit (LC-20 AB, Shimadzu, Japan) equipped with a diode array detector (SPD-M20A, Shimadzu, Japan) and a YMC carotenoid column (C30, 3 \(\upmu\)m, 150 \(\times\) 4.6 mm, YMC Co., Kyoto, Japan) according to our previous study [26]. As the mobile phase, solvent A (methanol, MTBE, water, 81:15:4, \(v/v\)) and solvent B (methanol, MTBE, water, 8:89:3, \(v/v\)) were used. The gradient of the solvent A and B was as followed: 2% solvent B for 11 min, a linear gradient from 2% solvent B to 40% solvent B for 7 min, 40% solvent B for 6.5 min followed by a linear gradient to 100% solvent B for 2.5 min, 100% solvent B for 3 min, a linear gradient to 2% solvent B for 3 min, held for 3 min. The injection volume was 10 \(\mu\)L and the flow rate of the mobile phase was 1 mL min\({}^{-1}\). DIN 32645 was used for the calculation of the limit of detection (LOD) and limit of quantification (LOQ), using the calibration curve [34]. The calculated LOD and LOQ values were 1.85 and 5.51 mg L\({}^{-1}\), respectively.
## 5 Discussion  
### Application of LAI\({}_{\text{FR}}\)  
The spatial aggregation of landslides is widespread in nature, bringing uncertainty to the assessment of landslide hazards and risks. However, it has often been ignored in past studies [19; 20; 22]. Considering landslide spatial aggregation, we proposed the DFR method with an index called LAI\({}_{\text{FR}}\). Based on the AHP and RF models, we applied the DFR method to an LHA study and obtained the landslide hazard map along the KKH. The results show that the DFR method can significantly improve the prediction performance of the LHA models. According to the ROC curve, the AUC values of models using the DFR method are significantly higher than those of the original model. This phenomenon occurs in both the AHP and RF models (Figure 8a). Compared with the original FR method, the application of the DFR method can improve the prediction accuracy of the LHA model by 1.4% to 2.1%. However, there are also distinct differences between the AHP and RF models. Compared with the RF model, the AHP model exhibits significantly greater improvements after applying the DFR method. Previous studies have also shown that methods considering landslide spatial aggregation can effectively reduce the uncertainty, but the effects may not be the same in different models [20; 22]. In addition, after the application of the DFR method, the landslides distributed in zones with very low and low landslide hazards in the prediction results also decreased conspicuously. Moreover, compared with the existing indices [19; 22], the DFR method takes into account the discrepancies in the coverage of various classes of each predisposing factor [20]. In other words, the DFR method is more applicable to the anisotropic natural conditions in terms of basic principles and therefore has a better application effect.