esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Polymerization Reactions within AAO Templates
### ATRP Polymerization
The study was carried out by checking the diffusional permeation of vitamin B12 at temperatures above and below the lower critical solution temperature. ATRP Copolymerization of NIPAm-AAm. [PERSON] et al. [74] studied the synthesis of poly(N-isopropylacrylamide-acrylamide) (PNIPAM-AAm) microgels of different chemical compositions by surface initiated-atom transfer radical polymerization (SI-ATRP) on anodized aluminum oxide (AAO) templates with pore diameter and length of 200 nm and 700 nm, respectively. Previously, AAO walls were silanized, and AAm was used as a co-monomer in order to tune not only the lower critical solution temperature (LCST) but also the surface mechanical properties and the hydrophilicity of the copolymer, as shown in Figure 12. The hypothesis of a possible local molecular rearrangement of PNIPAm-AAm nanostructures at the LCST was proposed. Below the LCST, the copolymer nanopillars would present a random distribution of monomers in the polymer network, but, when the  
Figure 11: Schematic illustration of the fabrication of PNIPAM nanotubes in a porous AAO membrane [137]. Copyright 2005. Reproduced with permission from the publisher: [PERSON]. temperature increased, the aggregation of NIPAm segments would occur near the center of the nanopillars, displacing hydrophilic AAm units towards the surface. Molecular simulation results supported the hypothesis that as temperature increased above the LCST a higher concentration of hydrophilic units near the surface would lead to enhanced hydrophilicity. In a later work, [PERSON] et al. [35] studied the polymerization of NIPAm-AAm in the presence of Fe\({}_{3}\)O\({}_{4}\) nanoparticles within AAO templates with the objective of obtaining thermo-responsive PNIPAm nanopillars displaying enhanced responsiveness through the incorporation of nanoparticles. The final objective was to obtain a material that combines more than one stimulus-responsive property into a single material. When increasing the temperature above the volume phase transition temperature, the incorporation of magnetic nanoparticles into the PNIPAm-AAm nanopillars sharply increased the stiffness and hydrophobicity of the samples, the magnetic response being proportional to the amount of nanoparticles incorporated.
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 4 Experimental Evaluation
### Dataset and Experiment Setup  
**Target ASR model**: The target model of this paper for the experiments covers two types of logits. For sparse logits, the DeepSpeech model with the CTC loss function is selected, and for dense logits, the Lingvo model with the cross-entropy loss function is selected.  
**Source dataset**: (1) LibriSpeech (Libri) [39]. The LibriSpeech dataset is from the LibriVox project. It is composed of English speech data with a sampling rate of 16 kHz, and the recording environment is relatively stable. This paper randomly selects speech data on the \"test-clean\" branch. (2) Mozilla Common Voices (MCV) [40]. The MCV dataset is a public speech dataset contributed by volunteers worldwide. The recording environment is inconsistent, and the volume levels vary. To be consistent with LibriSpeech, this paper uses the \"sox\" tool to downsample each speech from 48 kHz to 16 kHz, and randomly select speech data from it.  
**Dataset in our experiment**: This paper randomly selects 600 speeches from each dataset as the original speech. The number of transcribed words ranges from 3 to 10, so the speech durations are approximately the same as those in the original papers [9]. However, only five target phrases are set in that study, leading to monotonous logits distribution of the adversarial example, thus making the success rate of our attack unrealistically high. To truly reflect the strength of our attack against the detection method, this paper randomly selects 100 transcriptions from 600 speeches as target phrases. Then, using the remaining  
Figure 6: Logits value statistics in Lingvo.  
500 speeches as original speeches, the C&W adversarial example dataset and logits-Traction adversarial example dataset were created on DeepSpeech and Lingvo models, respectively. The success rate of detection based on three features is evaluated by C&W adversarial example dataset. The decline in detection success rate is evaluated on the LT adversarial example dataset.  
This paper adopts accuracy, false-positive rate (FPR), and false-negative rate (FNR) as evaluation indicators. The accuracy rate is used to evaluate the quality of the detection method; FPR represents the proportion of the original speech detected as an adversarial example; FNR represents the proportion of adversarial examples detected as the original speech. FPR and FNR are negatively correlated due to the usage of threshold division for judgment.
## 3 Result and Discussion
### Optimization Process Using Response Surface Methodology (RSM)  
The optimal value for each of the studied variables was predicted by Equation (2) in order to obtain the highest rate of biodiesel conversion. Equation (2) is based on the polynomial regression equation, which is derived from the response surface methodology (RSM) model. The optimal value for each of the variables is as follows: molar ratio (methanol to oil) equal to 10.18:1; reaction time of 7.33 min; and catalyst concentration of 1.15 wt %. The polynomial regression equation predicted that if the variables acquire these values, the highest rate of biodiesel conversion (99.51%) will be obtained. In order to verify the predicted value, the optimized variables were experimentally studied. The experimental test showed biodiesel conversion equal 98.91%. The biodiesel conversion predicted by response surface methodology (RSM) is in excellent agreement with the experimental measurements.
## 3 Simulation Results and Discussion
This study employs a decentralized computational architecture to address the computational complexities associated with traditional centralized control frameworks. The total computational complexity of the proposed work can be divided into three distinct parts. Firstly, the \"Real-Time Vehicle Speed Calculation\" module at a RSU continually updates the average vehicle speed as the autonomous vehicle travels through managed segments within specific time intervals. When an autonomous vehicle begins its journey, it utilizes the \"Real-Time Driving Speed and Time Calculation\" module to determine its driving route. Following this, when a passenger initiates a streaming service application within the autonomous vehicle, the remaining three modules collaborate to allocate the necessary bandwidth for the streaming service. Specifically, the \"Streaming Service Support\" module invokes the \"Real-Time Streaming Bandwidth Allocation\" and \"Video Clip Preloading\" modules to perform real-time bandwidth allocation or preload planning of video segments for the streaming service application, based on the characteristics and specification demands of the streaming service. Therefore, the maximum computational complexity in this context should be determined as outlined in the last paragraph of the \"Streaming Service Support\" module. It is noteworthy that this study introduces an innovative approach to address the potential bandwidth limitations faced by upcoming vehicular networks, especially during periods of high demand. The proposed approach involves a unique strategy: by accurately calculating the estimated arrival times of autonomous vehicles at different segments of their routes, and by leveraging V2V communication, it becomes possible to efficiently distribute bandwidth resources. This redistribution of bandwidth occurs from base stations strategically positioned in less congested road sections to the autonomous vehicles that require additional bandwidth support. To be specific, the proposed algorithm effectively facilitates the delivery of preloaded video clips to autonomous vehicles facing bandwidth inadequacies while navigating congested roads, utilizing V2V communication. This results in a substantial increase in the allocated bandwidth for streaming applications used by autonomous vehicle passengers. Furthermore, it substantially narrows the gap between the acquired and required bandwidth. Last but not least, the proposed algorithm further boosts overall bandwidth utilization by employing bandwidth relay from less congested base stations.
## 3 Implementation of the Proposed BIM-GIS Utility Infrastructure Framework
### Evaluation  
There are several diverse methodologies based on project benefits for evaluating BIM- and GIS-based systems. To evaluate the developed BIM-GIS-based integrated framework for an underground utility management system for earthwork operations, we adopted quantitative and qualitative approaches [50; 51]. A practical case study evaluation of a 1.5 km section of installing a new underground utility was evaluated. The qualitative evaluation metrics were based on this developed framework's goals and advantages (Table 2). Considering the proposed framework's objectives, we compared this system's qualitative superiority with traditional underground utility earthwork operations. The quantitative evaluation metrics were based on the actual implementation of this developed system during the case study project (Table 3). The evaluation metrics identified the importance, applicability, and superiority of a developed BIM-GIS-based underground utility management system.  
The errors and uncertainty of the actual utility location were measured during the trial excavation by determining the actual utility location using a Leica Robotic Total Station and compared with the location of utility in BIM model. It was found that the error was within the range of \(\pm\)100 mm (horizontal) and \(\pm\)250 mm (vertical) with respect to actual utility locations.
###### Abstract  
Creep and shrinkage of Cement and Concrete Composites (CCC) are significant properties that need to be considered to use these materials in practice. Many previous scientific studies revealed CCC creep characteristics under sustained compression and shrinkage, using traditional test methods from design standards. Because of the complexity of experimental procedures, CCC creep in tension has not been studied as close. Furthermore, there is no unified standard that proposes applicable testing methods or specific testing apparatus. This study examines the suitability of 2D--Digital Image Correlation (DIC) to observe the creep deformations of specimens under tension. Ordinary Portland cement (OPC) mortar with 1% polyvinyl alcohol (PVA) fibres has been investigated in the research. Compact tension (CT) specimens 150 \(\times\) 150 \(\times\) 12 mm (with a notch) were used. Creep deformations under sustained uniaxial tension (applied loading corresponding to 60% of the ultimate strength) were measured. DIC images were captured using an entry/mid-level DSLR camera. Results show that DIC is suitable for studying uniaxial tensile creep of cement and concrete composites. Deformation of specimens in tension was similar to that measured using the conventional method (using surface-attached gauges).  
c cement and concrete composites; creep; direct tensile test; digital image correlation; 2D-DIC
# S-Band Class-C-F Power Amplifier with 2 nd Harmonic Control at the Input
## 3 Design and Measurements of Class-C-F PA
The simulated results of class-C-F PA without harmonic control at the gate node, i.e., with a classical sinusoidal input drive, are also reported in the same figure for comparison. The realized class-C-F PA performed a peak efficiency of 63% with a saturated output power of 37 dBm (5 W) around 2.4 GHz. Moreover, compared to the attainable performance resulting without harmonic control, the obtained results show the critical role played by phase relationship between the 2 nd harmonic and the fundamental one in shaping of the input voltage, allowing the possibility to also exploit such harmonics for a class-C biasing condition. The results reported in Figure 12d show the performance over frequency range driven by input power 18.4 dBm. An output power over 36 dBm from 2.2 GHz to 2.43 GHz was obtained, with a similar flattening behavior result for the efficiency and gain, which remained close to 60% and 18 dB, respectively, from 2.28 GHz to 2.43 GHz. The dropping behavior is related to the the deterioration of harmonic matching at higher frequencies. Figure 9.4 Comparison of simulated load lines among class-C-F PA, class-C-Tuned Load PA, and class-C-F PA without proper gate harmonic control. Figure 9.5 Comparison between simulated and measured drain efficiency (DE) (**a**), output power (**b**), and power gain (**c**) for the designed class-C-F PA, compared with tuned load and improper harmonic control results. Measurement performances over frequency for a fixed input power level of \(P_{in}~{}=~{}18.4\) dBm are reported in (**d**).
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 2 Reactive Power Optimization Based on Load-Curve Segmentation
### Segmentation in Case of Multiple Load Curves  
Since the power consumption characteristics of different power consumers are not the same, it is not applicable to assume all consumers follow the single load curve. In case of multiple load curves, two strategies can be applied. One is to conduct segmentation for each load curve first, and use all separation points to do final segmentation.  
Another alternative strategy is to use an integrated load curve. Suppose there exist \(K\) users, each corresponding to independent load curve. Let \(L_{t,k}\) be the load curve of \(k\)-th user, \(P_{k}\) be the its capacity in kW, the integrated load curve \(L_{total}\) can be calculated as below.  
\[L_{total}=\sum_{k=1}^{K}P_{k}L_{t,k} \tag{4}\]  
Afterwards, the segmentation methods mentioned in previous section can be applied to the integrated load curve.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Evaluation Methodology
#### 3.1.3. WV-2 Fusion Evaluation  
As shown in Table 4, the gray mean values in B1, B3, and B4 (GS fusion) are larger and contain more information. The gray mean value in each band of the NNDiffuse results is closer to each band of the original multispectral data, indicating the information content is closer to the original multispectral images. The standard deviation in the B1, B2 (PCA), B3, and B4 (NNDiffuse) bands is high, indicating the grayscale divergence is also high. The contrast is relatively high, particularly in the B2, B3, and B4 bands where the average gradient, layering, and image sharpness are high. The information entropy value in each PCA band is also higher than in other images, indicating more information content. The correlation coefficients in B1 and B2 are higher and the information is more uniform.  
Although each fused image (Figure 6) exhibited a certain degree of spectral deviation and information loss, GS and PCA fusion generally produced the least spectral distortion.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**WV-2** & **Band** & **Gray Mean** & **Standard** & **Information** & **Average** & **Correlation** & **Spectral** & **Bias Index** \\  & & **Value** & **Deviation** & **Entropy** & **Gradient** & **Coefficient** & **Distortion** & **Bias Index** \\ \hline \multirow{4}{*}{MS} & Band1 & 242.78 & 47.58 & 6.24 & 10.51 & 0 & 0 & 0 \\  & Band2 & 413.81 & 77.75 & 7.18 & 18.17 & 0 & 0 & 0 \\  & Band3 & 230.29 & 106.68 & 7.71 & 21.73 & 0 & 0 & 0 \\  & Band4 & 239.77 & 216.10 & 7.80 & 30.44 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{GS} & Band1 & 241.04 & 40.81 & 6.76 & 5.26 & \(-\)0.06 & 39.24 & 0.15 \\  & Band2 & 319.48 & 71.53 & 7.58 & 7.58 & \(-\)0.04 & 66.97 & 0.20 \\  & Band3 & 250.67 & 100.53 & 7.93 & 7.36 & 0.01 & 96.35 & 0.41 \\  & Band4 & 285.53 & 218.18 & 8.07 & 9.58 & 0.25 & 180.01 & 1.06 \\ \hline \multirow{4}{*}{NNDiffuse} & Band1 & **241.30** & 35.11 & 6.26 & 5.15 & \(-\)0.01 & **32.94** & 0.12 \\  & Band2 & **320.56** & 67.18 & 7.17 & 7.60 & 0.01 & **59.79** & 0.18 \\  & Band3 & **247.81** & **104.89** & 7.63 & 7.62 & 0.07 & **91.09** & 0.38 \\  & Band4 & **252.25** & **224.58** & 7.61 & 10.34 & 0.25 & **168.68** & 0.85 \\ \hline \multirow{4}{*}{PCA} & Band1 & 238.53 & **47.76** & **6.98** & **8.44** & \(-\)0.06 & 42.04 & 0.16 \\  & Band2 & 315.84 & **72.57** & **7.70** & **10.74** & \(-\)0.04 & 68.03 & 0.20 \\ \cline{1-1}  & Band3 & 248.23 & 97.65 & **7.97** & **9.83** & 0.01 & 96.15 & 0.  
This effect was minimal in NNDiffuse results, which is evident from the visual content interpretation and spectral retention analysis. As such for WV-2, PCA fusion is optimal for identifying and extracting coastal zone aquaculture.
## 4 Experiments and Results
### Comparison with Other Methods

Table 2: The AUC score of different detection methods including the KD-based method, the LID-based method, the NSS-based method, and the IACS-based (our method) method on MNIST and SVHN datasets. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**CIFAR**} \\ \cline{2-5}  & **KD** & **LID** & **NSS** & **IACS** \\ \hline
**FGSM** & 0.7355 & 0.9950 & **0.9999** & 0.9832 \\
**PGD** & 0.9774 & 0.9950 & **0.9995** & 0.9898 \\
**DeepFool** & 0.6434 & 0.9109 & 0.5214 & **0.9837** \\
**JSMA** & 0.5847 & 0.7575 & 0.5248 & **0.9869** \\
**CW\({}_{2}\)** & 0.716 & 0.9292 & 0.5239 & **0.9842** \\ \hline \end{tabular}
\end{table} 
Table 3: The AUC score of different detection methods including the KD-based method, the LID-based method, the NSS-based method, and the IACS-based (our method) method on the CIFAR10 dataset. The best results are highlighted in bold. Figure 4: Crossing attacks performance: The horizontal axis represents the training set, and the vertical axis represents the test set. The closer the color is to yellow, the better the detector’s performance is (DP refers to DeepFool). Figure 5: The IACS value with different attacks. Green denotes normal examples’ box plots, and black denotes adversarial examples’ box plots. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Resnet** & **Convnet** \\ \hline
**FGSM** & 0.10 & 0.28 \\
**PGD** & 0.00 & 0.16 \\
**DeepFool** & 0.00 & 0.85 \\
**JSMA** & 0.09 & 0.81 \\
**CW\({}_{2}\)** & 0.01 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy of different adversarial examples in Resnet (target model) and Convnet.
## 4 Discussion
### Impact of LUE on MODIS GPP  
Regarding the MODIS GPP algorithm, \(\epsilon_{\max}\) represents the maximum light use efficiency of the vegetation in photosynthesis. The value of \(\epsilon_{\max}\) will vary with the type of biome. For a given biome type, \(\epsilon_{\max}\) is set to a constant by BPLUT. Encouragingly, BPLUT corrected and updated the value of \(\epsilon_{\max}\) several times. However, because of the immense diversity of earth surface environments and climate conditions, assigning a constant value of \(\epsilon_{\max}\) to the same biome type does not conform to the truth [52]. The improved \(\epsilon_{\max}\) that we inferred for the eight sites in this paper greatly differed from the MOD17A2 \(\epsilon_{\max}\) that was specified by BPLUT (Figure 12 and Table 6). Because the value of \(\epsilon_{\max}\) provides the foundation for the actual light use efficiency, the misestimation of \(\epsilon_{\max}\) will inherently decrease the accuracies of the GPP estimates.
# File-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection
## 5 Experimental Results and Discussion
### Experimental Systems and Testing in Construction Sites
#### 5.3.2 Visualization Effect of Object Detection Algorithm  
As shown in Figure 16, a comparison of detection performance between the YOLO series algorithms and the algorithm proposed in this paper under various conditions within the system is presented.  
It is demonstrated through detection results that the FLE-YOLO algorithm proposed is applicable to images of hooks under different backgrounds, lighting conditions, and angles, accurately detecting the two-dimensional position information of the hook. Furthermore, compared with other algorithms in the same series, it effectively addresses issues of missed detection, false detection, and over-detection under conditions such as darkness, blurriness, dimness, complex backgrounds, and exposure (indicated by blue ellipses and rectangular textboxes in the figure). Moreover, the detection confidence of this model is higher than that of other algorithms.  
Figure 14: Visual monitoring interface. (**a**) Monitoring Area I; (**b**) Monitoring Area II; (**c**) Monitoring Area III; (**d**) Monitoring Area IV.  
Figure 15: FLE-YOLO algorithm hook detection and tracking experimental flowchart.  
5.3.3. The Implementation of Automatic Hook Tracking  
This study centers on the target detection of tower crane hooks and transmits hook position information to a spherical camera for corresponding automatic tracking. As illustrated in Figure 17, the tracking effect in the experimental field is demonstrated.  
Figure 16: Visualization results of tower crane hook detection using different algorithms.  
Figure 17: The presentation of tracking results.  
The figure demonstrates the tracking effects under vertical and horizontal tracking. With an increase in frames, it becomes evident that the camera accurately captures the position of the hook during vertical or horizontal operations of the tower crane. By rotating the camera, the hook remains consistently within the camera frame and as close to the center of the frame as possible. Tower crane operators on the ground or in the cabin utilize a PC or Pad to observe and operate the crane remotely, achieving unmanned operation of the tower crane, as shown in Figure 18. The interface is the tower crane information management system interface, and the right side of the interface shows the visual monitoring interface. The lower part of the interface displays real-time measurements, with the first row showing Temperature, Humidity, Illuminance, Wind Speed, Tilt Angle X, and Tilt Angle Y. The second row displays Tilt Angle Z, Height, Rotation Angle, Amplitude, and Measurement Value.
## 5 Conclusions  
A new security mechanism, MSA, is proposed in this paper. Compared with some related research, location-based keys are bound with the IDs of the mobile nodes to mitigate malicious attacks, such as replication attacks, Sybil attacks and wormhole attacks. The parameter \(\Delta\), which is the radius of the MSA, is a user-defined value that can be set to satisfy different security levels. Local detection is distributed and performed by the ordinary nodes inside the MSA without the participation of the sink node. However, in overall detection, detection tasks are transferred to the sink node and performed along with the key updating operations. Simulation results reveal that proposed mechanism performs well when it resists malicious attacks and displays better results in terms of detection accuracy and energy balance.