esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Analysis and Discussion
### The Impact of \"Sea Toll\" on Seaport Resilience

Table 4: Tabulation of data on the assessment of seaport resilience criteria by all respondents. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Seaport Resilience** & \begin{tabular}{c} **Contingency** \\ **Plan** \\ \end{tabular} & \begin{tabular}{c} **Forecast** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Strategic** \\ **Alliance** \\ \end{tabular} & \
\begin{tabular}{c} **SC Relationship** \\ **Management** \\ \end{tabular} &
\begin{tabular}{c} **Advanced IT** \\ **System** \\ \end{tabular} \\ \hline Contingency plan & 1 & 0.33 & 0.20 & 0.33 & 0.20 \\ Forecast accuracy & 3 & 1 & 0.50 & 3 & 2 \\ Strategic alliance & 5 & 2 & 1 & 3 & 3 \\ SC relationship management & 3 & 0.33 & 0.33 & 1 & 3 \\ Advanced IT system & 5 & 0.50 & 0.33 & 0.33 & 1 \\ Total & 17 & 4.17 & 2.37 & 7.67 & 9.20 \\ \hline \hline \end{tabular} Source: Authors.  
\end{table} 
Table 7: Consistency test of port resilience criteria data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Seaport Resilience** & \begin{tabular}{c} **Contingency** \\ **Plan** \\ \end{tabular} & \begin{tabular}{c} **Forecast** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Strategic** \\ **Alliance** \\ \end{tabular} & \begin{tabular}{c} **SC Relationship** \\ **Management** \\ \end{tabular} & \
\begin{tabular}{c} **Advanced** \\ **IT System** \\ \end{tabular} &
\begin{tabular}{c} **Matrix** \\ **Matrix** \\ \end{tabular} \\ \hline Contingency plan & 1 & 0.33 & 0.20 & 0.33 & 0.20 \\ Forecast accuracy & 3 & 1 & 0.5 & 3 & 2 \\ Strategic alliance & 5 & 2 & 1 & 3 & 3 \\ SC relationship management & 3 & 0.33 & 0.33 & 1 & 3 & 0.98 \\ Advanced IT system & 5 & 0.50 & 0.33 & 0.33 & 1 & 0.74 \\ Total & 17 & 4.17 & 2.37 & 7.67 & 9.20 \\ \hline \hline \end{tabular} Source: Authors.  
\end{table} 
Table 5: The assessment criteria [78]. the seaport system in a comprehensive manner. Thus, in the application of the \"sea toll\" program, stakeholders could be interpreted as having the view that the crucial impact, with regard to the existence of collaborative programs between institutions or stakeholders, was the reduction of uncertainty and an increase in the future resilience of the ports. For Indonesia, as an archipelagic country with each local government having its own rules and policies for regulating port operations, it is very important to improve collaborative programs between the port areas. Synchronization of rules and policies will be needed to achieve optimal port resilience through Indonesia's \"sea toll\" program as shown in Figure 6.
# Fast 3D Semantic Mapping in Road Scenes +
## 5 Experiments and Results
### Quantitative Results
Optimized GPU implementation could be studied in future to realize the online CRF update. Taking the _odometry_03_ sequence as example, our approach acquires 114 keyframes with 28 million 3D points from the sequence of 801 frames, which utilizes only about 1/7 frames for mapping. Note that smaller values of the parameters _KFDistWeight_ and _KtUsageWeight_ could give more constraints between keyframes so that to achieve more accurate mapping. But it has a rather limited influence on the number of keyframes, the number of 3D points, and the size of storage. Compared to the system [28], it costs around 20 minutes on a standard desktop machine for 800 images long sequence involving about 20 million 3D points. Our system is a fast monocular vision mapping, even though it uses an offline CRF optimization. \begin{table}
\begin{tabular}{l c} \hline \hline
**Component** & **Average Consumed Time** \\ \hline Semantic segmentation & \(\simeq\)100 ms \\ SLAM & \(\simeq\)40 ms \\ Incremental fusion & \(\simeq\)50 ms \\ \hline
3D CRF 1 Iter. & 800–2000 ms \\
3D CRF 2 Iter. & 1200–2400 ms \\
3D CRF 3 Iter. & 1500–3000 ms \\
3D CRF 4+ Iter. & \(>\)2000 ms \\ \hline [PERSON] et al. [28] & \(\simeq\)20 min/800 frames \\ Our ‘baseline’ & \(\simeq\)200 s/800 frames \\ Our & \(\simeq\)80 s/800 frames \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Timing results. The table lists the operation time for different components of our system. Times of three core components are averaged over all sequences and the Conditional Random Field (CRF) timings depends on the iterations and the point cloud sizes. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Encoder** & **mIOU[0.5:0.25:1.75]** & **WITH Cityscapes** \\ \hline ResNet\_101 & 52.5 & \\ ResNet\_101 & 57.1 & \(\surd\) \\ \hline Xception\_65 & 56.0 & \\ Xception\_65 & 64.8 & \(\surd\) \\ \hline MobileNet\_v2 & 51.8 & \\ MobileNet\_v2 & 60.7 & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of 2D semantic segmentation with/without the Cityscapes. Using the pre-trained Cityscapes model, the accuracy of 2D semantic segmentation could be greatly improved on the KITTI semantic data. Lastly, we test the system with semantic segmentation on all frames as the 'baseline' pipeline. We find that for one thing it is hard to say the accuracy of 3D semantic mapping is improved. Because for the LSD-SLAM, the current keyframe is refined with its following frames until new keyframe is selected. The depth map of the current keyframe is more accurate than the depth measure on each frame. If we use the 'baseline' pipeline, we need the depth information on each frame; even though more semantic information is used in the incremental fusion, the noisy depth would lead to inaccurate semantic map. Besides, since the visual SLAM process runs faster than the semantic segmentation at present, the untreated frames would quickly exceed the buffer limit, leading to new frames blocked. The entire system cannot run in real-time and it would not simultaneously generate the semantic map.
# A High Signal-Noise Ratio UWB Radar for Buried Pipe Location Using Golay Complementary Sequences
## 6 Discussions and Conclusions  
For the UWB radar system, the frequency dispersion is an important issue, which affects the performance of radar imaging. The UWB signal traveling through a dispersive medium will arise the appearance of Brillouin precursors that broaden the pulse width and drift the power of the signal towards lower frequency bands [44]. The precursor waveforms superimposed to the traveling waveform result in inaccurate processing in the receiver system. To mitigate these impairments, two frequency response filtering can be used in the UWB radar system. Previous works have demonstrated that this filter is suitable for the structure that has correlation-based processing in the receiver end [45]. The SNR of the radar system can be further enhanced if the precursor formation is considered [46]. Further research should be accomplished, applying this filter in the system.  
High rate Golay complementary codes are applied to GPR for underground pipe location. The buried pipe is located by the correlation method and the BP algorithm. Experimental results show that metallic pipe and plastic pipe detected and the range resolution is 10 cm. Golay complementary sequences are a pair of complementary sequences. By adding the correlation of the two complementary sequences, we can obtain that the sum of the two correlation functions has a peak value of twice code length and a sidelobe level of zero. Therefore, compared with Golay complementary sequences radar with stepped frequency signal radar or chaotic signal radar, the Golay-based radar has higher SNR, which is beneficial for deep buried pipe location. Besides, the frequency and code length of the Golay complementary codes can be flexibly selected according to the requirements of the detection environment.  
Conceptualization, methodology, writing-original draft preparation, J.L.; Investigation, Y.L.; writing--review and editing, formal analysis, H.X.; project administration, L.L.; resources, B.W.; data curation, X.C.  
Figure 8: Plastic pipe location based on (**a**) stepped frequency signal radar, (**b**) chaotic signals radar, and (**c**) Golay complementary sequences radar.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant numbers 41604127, 41704147, 61601319, and 61705007; The Natural Science Foundation of Shanxi Province, grant numbers 201801D221185, 201701D221114, 201801D121140, and 201701D121009; the Key Research and Development (R&D) Projects of Shanxi Province, grant numbers 201703D321036 and 201803D31037; The Shanxi Key research and development Foundation of China, grant numbers 201803D121057; The China Scholarship Council (CSC), grant numbers 201806935029.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
Finally, a list of optimization metrics was provided in [9] to address issues with resource management and service placement. The indicators that are taken into consideration include latency, blockage probability, congestion ratio, cost, energy usage, and resource use. The authors' findings suggest that more studies have to be performed on issues pertaining to service placement problems, optimization techniques, and evaluation environments. Numerous simulation tools [10] have been developed, and a range of solutions have been proposed to address the edge computing difficulties, according to the evaluation of relevant work. But there is still an apparent gap in the literature when it comes to experimental research that thoroughly tests algorithms in simulated environments using certain performance criteria. With an emphasis on an experimental methodology that involves methodically testing algorithms in controlled environments, this work seeks to close this gap. The study compares each algorithm to see if any shows a discernible advantage over the others and assesses whether the observed results match the theoretical predictions of each method.
## 3 Results and Discussions
### Uniform Twist Control with Multi-harmonic Inputs  
The uniform multi-harmonic active twist control is obtained with the GA optimization framework established in Section 2. The optimization variables are the amplitudes of sine and cosine components of twist rate ranging from \(-1.5^{\circ}\)/m to \(1.5^{\circ}\)/m. A saturation limit of the active twist rate is set as \(1.0^{\circ}\)/m. When compared with the single-harmonic inputs, the multi-harmonic inputs obtain higher power reductions of 3.59%, 6.86%, 5.84%, and 9.89% for the four flight conditions.  
For condition B, Figures 16 and 17 show the optimal multi-harmonic input. The components of 0/rev and 1/rev are dominant. Figure 18 shows that the blade shaft moment decreases in majority azimuth, but still increases near 55\({}^{\circ}\) and 235\({}^{\circ}\). Figure 19 shows the distributions of load increments with the optimal multi-harmonic input. When compared with the optimal 2/rev input, the multi-harmonic input generates larger pitch up deformations from 20\({}^{\circ}\) to 100\({}^{\circ}\). With the further decrease in negative-lift area in the tip region on the advancing side, the induced drag loads in the root region become reduced. The pitch down deformations from 120\({}^{\circ}\) to 360\({}^{\circ}\) impel a shift of loading zones towards the root region, which averts the increase of drag loads at the tip region and alleviates the blade shaft moment from 180\({}^{\circ}\) to 270\({}^{\circ}\).  
Figure 16: Amplitudes of the 0–5/rev harmonic component of the optimal input for condition B.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 4 Conclusions  
This study focused on investigating the effects of using MGWP, which is a blend of marble waste powder (MWP) and granite waste powder (GWP) in equal ratios, as a partial replacement for OPC at various proportions (5%, 10%, 15%, 20%, 25%, and 30%). The study aimed to evaluate the impact of MGWP on the workability, physical, mechanical, durability, and microstructural properties of mortar. Before conducting the investigation, the MWP and GWP were characterized. From the results obtained, the following conclusions can be drawn:  
* As per the ASTM C618 standard, GWP can be categorized as a Class N natural pozzolan material, which signifies its pozzolan properties. However, MWP does not meet the requirements to be classified as a pozzolan material according to the same ASTM standard.  
Figure 16: DTA curves of mortar samples at two curing ages: (**a**) 7 days, and (**b**) 28 days.  
* As the percentage of MGWP increased, the workability of the mortar mixes decreased, which can be attributed to the higher fineness of the MGWP particles.
* The addition of up to 15% MGWP led to an improvement in the bulk density, compressive strength, and homogeneity of the mortar, with the mortar containing 10% MGWP showing the best performance in these tests. Similar improvements were also observed in properties related to durability, specifically resistance to sulfate attack, water absorption, and porosity.
* A microstructure analysis confirmed that the addition of MGWP resulted in changes in the structure of C-S-H gels in the mortar samples. The morphological structure of the mixes became denser up to a 10% content of MGWP. However, a slight increase in mass loss was observed with an increase in MGWP content when exposed to high temperatures.
* Replacing up to 10% of cement with MGWP in mortar can improve its physical, mechanical, and durability properties. Incorporating the MGWP, which would otherwise be landfilled, in the mortar allows for a reduction in the usage of OPC. This reduction could contribute to achieving sustainability in the concrete industry.  
Conceptualization, M.D.Y.; methodology, D.M.N.; software, Y.K.A.; validation, B.W.Y.; formal analysis, W.Z.T.; investigation, D.M.N.; resources, M.D.Y.; data curation, Y.K.A.; writing--original draft preparation, B.W.Y.; writing--review and editing, W.Z.T.; visualization, B.W.Y.; supervision, M.D.Y.; project administration, M.D.Y.; funding acquisition, W.Z.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data supporting the findings of this study are available upon request.  
The authors declare no conflict of interest.
## 2 Experimental Model and Facilities
### Wind Tunnel  
As demonstrated in Figure 3, the FD-09 wind tunnel is a single-reflux closed low-speed wind tunnel. The experiment section is 14 m long, and the effective cross-sectional area of the experiment section is 8.7854 m\({}^{2}\). The left and right tunnel walls of the experiment section are parallel to each other, and the upper and lower tunnel walls have a 0.2\({}^{\circ}\) expansion angle, in order to eliminate the influence of the increase of boundary layers on the downstream wind tunnel wall and minimize the axial static pressure gradient. The turbulence of the wind tunnel is less than 0.1%, and the dynamic pressure stability is less than 0.003.  
As the angle of attack changes, the blockage of the wing in the wind tunnel duct is portrayed in Figure 4. The curve in the figure indicates that when the angle of attack exceeds 6\({}^{\circ}\), the blockage will be close to 6%, and the experimental results will become unreliable [24]. On the one hand, the expansion and compression of the channels on the upper and lower surfaces will affect the flow itself and make the experiment results deviate from the reality. On the other hand, the change of air pressure will bring additional aerodynamic load to the membrane and further affect the performance of the wing. Therefore, with respect to mechanism research, the wind tunnel experiment results can be used to study the deformation and flow behavior in the range of small and medium angles of attack. With respect to method research, the wind tunnel experiment can help explore numerical calculation methods with high accuracy and provide technical means for the study in a wider range.
## 2 Materials and Methods  
### Material and Outdoor Test Site  
The material used in this work was as-received AISI 4135 high-strength steel produced by Beijing Shougang New Steel Co., Ltd., Beijing, China, and its chemical composition is provided in Table 1. The mechanical properties include a tensile strength of 830 MPa, a yield strength of 780 MPa and an elongation of 10.4% at ambient temperature (20 \({}^{\circ}\)C). To observe the microstructure of the steel, a steel sample of 10 mm in diameter and 10 mm in height was ground with a series of silicon carbide (SiC) papers (from 400 to 1000 grade) and then polished with a 2.5 \(\upmu\)m diamond abrasive. Then, the sample was etched in a nital etchant (4% nitric acid and 96% anhydrous alcohol) for 10 s, treated with deionized water, swabed with alcohol and finally dried in a cold air stream. The microstructure of the AISI 4135 high-strength steel is shown in Figure 1, where pearlite and ferrite can be observed. The specimens are hollow cylindrical steels with open ends, an inner diameter of 34 mm and a wall thickness of 0.5 mm (See Figure S1 for more information on the dimensions of the specimens). Both the inner and outer surfaces of the cylindrical wall were grinded, with a final roughness average (Ra) of 0.4. A palladium (Pd) film of ca. 0.6 \(\upmu\)m in thickness was electroplated on the inner surface of the hollow cylindrical steel for facilitating hydrogen detection, inhibiting corrosion, etc. [25; 26; 27]. The effective hydrogen diffusion coefficient, \(D_{\mathit{eff}}\), of the specimen was determined through the breakthrough-time method [28], the value of which was 4.13 \(\times\) 10\({}^{-7}\) cm\({}^{2}\)-s\({}^{-1}\). The outdoor hydrogen permeation test was conducted in Xuejia Island Harbor (36\({}^{\circ}\)06\({}^{\prime}\) N; 120\({}^{\circ}\)10\({}^{\prime}\) E), Qingdao, China. The tide in the sea area is of typical semidiurnal one, and the record shows that the annual average tidal range and temperature were -2.7 m and -12 \({}^{\circ}\)C, respectively.
## 4 Experiments and Results
### Results and Analysis
#### 4.2.1 The Results of a Poisoning Attack  
In this type of attack, the attack poisons the training data in order to access the proposed model, so as to manipulate the output status of the patient and alter abnormal behaviors to normal behaviors, which will affect the patient's status. The attacker uses data modification, which poisons the training data directly by modifying them before they are used for training, in order to manipulate the model's decision (this is referred to as Data Poisoning Attack 1), as shown in Table 4. Another type of poisoning attack entails logic corruption, which affects the overall learning process of the proposed model (this is referred to as Data Poisoning Attack 2), as shown in Table 5. The results in Tables 4 and 5 show a decrease in the detection accuracy, as well as in the other performance metrics. Two data subjects, namely, subject 1 and subject 2, were examined. Table 4 shows that the accuracy of the model regarding subject 1, without the attacks, was 97%. This decreased to 74% after employing Data Poisoning Attack 1. For subject 2, the model without attacks achieved an accuracy of 99.9%; after applying Data Poisoning Attack 1, the accuracy dropped to 75.59%. Similarly, Table 5 reports the results of Data Poisoning Attack 2, which also shows a drop in all performance metrics. It reveals that the accuracy drops to 59% for subject 1 and to 75% for subject 2. As a result, the poisoning attacks were successful in compromising the proposed model's performance and causing data misclassification, which would impact the status of both patients and their treatments.  
The same analogy applies to adversarial Data Poisoning Attack 2. As shown in Figure 3, the epsilon values were used for subject 1 and achieved different accuracy values of (75%, 75%, 75%, and 75%) and (82%, 81%, 84%, and 84%) for subject 2.  
The results reported in Figures 2 and 3 clearly show that as the amount of adversarial content increases (the epsilon values increase), the accuracy decreases; it drops sharply after epsilon = 0.3. This outcome can be generalized for both data subjects and for both poisoning attack types.
# Multilevel Coding for the Full-Duplex Decode-Compress-Forward Relay Channel
## 2 Preliminaries
### Multilevel Coding  
In the point-to-point channel, multilevel coding (see Figure 2) divides the data stream into \(m=\log_{2}(q)\) binary sub-streams for a \(q\)-ary constellation. Each sub-stream \(i\) is encoded independently with rate \(R_{i}\). At each time instance, the outputs of the (binary) encoders are combined to construct the vector \([B_{1},B_{2},\ldots B_{m}]\), which is then mapped to a constellation point \(X\). The channel is described by the conditional distribution \(P_{Y|X}(y|x)\), where \(Y\) is the output of the channel. The mutual information between the input and output is given by  
\[I(X;Y)=I(B_{1},B_{2},\ldots,B_{m};Y)=\sum_{i=1}^{m}I(B_{i};Y|B^{i-1}), \tag{3}\]  
where we use the chain rule of mutual information, one-to-one relationship between \(X\) and \([B_{1},B_{2},\ldots,B_{m}]\), and the notation \(B^{i-1}\equiv[B_{1},B_{2},\ldots,B_{i-1}]\). This equation suggests a multistage decoding where the codeword of level \(i\) is decoded using the output of the decoders of the preceding levels. A necessary and sufficient condition for multilevel coding achieving the constellation constrained capacity is that the optimal distribution \(P_{B_{1},\ldots,B_{m}}^{*}(b_{1},\ldots,b_{m})\) must be independent across its components [32]:  
\[P_{B_{1},\ldots,B_{m}}(b_{1},\ldots,b_{m})=\prod_{i=1}^{m}P_{B_{i}}(b_{i}). \tag{4}\]
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
###### Abstract  
Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial sensing using unmanned aerial vehicles (UAVs) or ground-based sensing approaches. Ground-based sensing provides high data accuracy, but it involves large grids of ground-based sensors with high operational costs and complexity. On the other hand, while the cost of aerial sensing is much lower than ground-based sensing alternatives, the data collected via aerial sensing are less accurate and cover a smaller period than ground-based sensing data. Despite the contrasting virtues and limitations of these two sensing approaches, there are currently no hybrid sensing IoT solutions that combine aerial and ground-based sensing to ensure high data accuracy at a low cost. In this paper, we propose a Hybrid Sensing Platform (HSP) for PA--an IoT platform that combines a small number of ground-based sensors with aerial sensors to improve aerial data accuracy and at the same time reduce ground-based sensing costs.  
IoT; precision agriculture; smart farming; remote sensing; hybrid sensing +
Footnote †: journal: _Future Internet_  
of ground-based sensors to automatically validate, calibrate, and improve the accuracy of aerial data; and (4) investigation of HSP and aerial data calibration benefits through field experiments.  
The results of field experiments using the proposed IoT-based HSP show that the statistical and machine learning models used in this research remarkably improve the aerial data accuracy. The improvement rate in aerial data accuracy compared with ground truth is 34% for humidity data, 51% for temperature data, 66% for infrared data, and 90% for spectrometer data. This indicates that IoT-based HSP have potential to transform the data collection process in PA by improving the data accuracy and at the same time reducing the costs for the procurement, deployment, and maintenance of sensing nodes.  
The rest of this paper is organised as follows: Section 2 presents the related work in IoT-based sensing for PA. Section 3 presents the materials and methods for IoT-based HSP. Section 4 discusses the results of HSP field experiments. Section 5 discusses the findings of this research. Section 6 concludes the paper and describes the potential future research directions.