esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Macroscopic Failure  
As illustrated in Figure 14, the macroscopic failure images of coarse- and fine-grained granite in variable-angle shear tests demonstrate that failure of the specimen primarily  
Figure 13: \(T_{2}\) spectral curves of granite under different cooling methods: (**a**) coarse-grained granite after high-temperature water-cooled impact, (**b**) coarse-grained granite after high-temperature liquid-nitrogen-cooled impact, (**c**) fine-grained granite after high-temperature water-cooled impact, (**d**) fine-grained granite after high-temperature liquid-nitrogen-cooled impact.  
occurs along the shear plane. Under identical heat treatment conditions, when \(\theta=45^{\circ}\), a significant number of fragments are ejected during failure, and the surface of the specimen forms pronounced spall craters, with only a few cracks around the shear failure plane. Conversely, at an angle of 65\({}^{\circ}\), the specimen predominantly fails along the shear plane, exhibiting a relatively flat shear failure surface, a hallmark of pure shear failure. This phenomenon can be attributed to the increasing shear stress, which, at higher angles, causes the variable-angle shear test to approach a direct shear test. Conversely, as the shear angle decreases, the proportion of compressive stress increases and the compressive stress inhibits shear failure, rendering the variable-angle shear test more analogous to a uniaxial compression test [25]. With increasing heat treatment temperature, the degree of fracture of coarse- and fine-grained granite at the same shear angle generally increases, and this phenomenon is more pronounced in the samples treated with liquid-nitrogen-cooling shock. This is related to the decrease in cohesion with increasing heat treatment temperature, as mentioned in Section 3.2.2. The high temperature reduces the cohesion within the specimen and weakens the cementation between the particles. When the specimen is loaded, the thermal cracks join and propagate rapidly, resulting in higher fracture levels at failure.
# Test-Retest Intra-Session Reliability of Isokinetic Knee Strength Measurements in Obese Children
## 2 Materials and Methods
### Statistical Analysis  
Reliability analyses were performed following the recommendations of [PERSON] [24]. Relative reliability was determined using the ICC 3,1 (2-way mixed, single measures) with 95% confidence intervals for the 2 repetitions [25]. Absolute reliability was estimated by calculating the Standard Error of Measurement (SEM) and the Smallest Real Difference (SRD). The SEM was calculated as follows: SEM = SD-\(\sqrt{1-\text{ICC}}\) where SD is the mean SD of the 2 repetitions. The SRD was 1.96-SEM-\(\sqrt{2}\). SEM and SRD were converted into percentages to facilitate further comparisons with other studies.  
The following classification was used for the interpretation of the ICC [18]: An ICC lower than 0.5 corresponded to poor reliability, an ICC from 0.5 to 0.7 corresponded to moderate reliability,  
\begin{table}
\begin{tabular}{c c c c} \hline  & **All Children (_n = 25_)** & **Boys (_n = 11_)** & **Girls (_n = 14_)** \\ \hline Age (years) & \(8.00\pm 1.32\) & \(7.90\pm 1.57\) & \(8.07\pm 1.14\) \\ Height (cm) & \(136.60\pm 9.10\) & \(136.70\pm 10.45\) & \(136.52\pm 8.29\) \\ Bone Mass (kg) & \(1.05\pm 0.17\) & \(1.08\pm 0.15\) & \(1.02\pm 0.18\) \\ Fat Mass (kg) & \(17.04\pm 4.65\) & \(15.63\pm 4.87\) & \(18.14\pm 4.32\) \\ Lean Mass (kg) & \(24.83\pm 4.15\) & \(25.37\pm 4.96\) & \(24.41\pm 3.53\) \\ Lean Mass and Bone Mass (kg) & \(25.89\pm 4.28\) & \(26.45\pm 5.09\) & \(25.44\pm 3.67\) \\ Total Mass (kg) & \(42.96\pm 8.08\) & \(42.09\pm 9.34\) & \(43.64\pm 7.23\) \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the children who have been included in the study.  
an ICC from 0.7 to 0.9 corresponded to good reliability, an ICC greater than 0.9 corresponded to excellent reliability.  
Bland-Altman analysis was performed to show the level of agreement between test and retest in both peak torque and work. Plots show \"the bias\" and limits of agreement (LOA) calculated to 95%. Bias values close to zero represented strong agreement, and the smaller range between these 2 LOA were interpreted as better agreement.  
PT and TW were also analyzed as mean values divided by total mass (kg) and lean mass (kg). The significance level was determined at \(p<0.05\).
## Appendix A  
First, we derive a general result on the moments of \(y_{k}\) when in the signal model (1) \(x_{k}\) is assumed as a generic random signal. Then, we provide the general formula for the moments of \(y_{k}\) when \(x_{k}\) is a deterministic sinusoidal signal.  
We start from the definition of even-order moments  
\[\mu_{2m}=E\left[y_{k}^{2m}\right]=E\left[\left(Ax_{k}+w_{k}\right)^{2m}\right]. \tag{11}\]  
The application of the binomial theorem leads to  
\[\mu_{2m} =E\left[\sum_{n=0}^{2m}\binom{2m}{n}A^{n}x_{k}^{n}w_{k}^{2m-n}\right] \tag{12}\] \[=\sum_{n=0}^{2m}\binom{2m}{n}A^{n}E\left[x_{k}^{n}\right]E\left[w _{k}^{2m-n}\right]. \tag{13}\]  
Since the odd-order moments of \(w_{k}\) are zero, we have that only terms corresponding to even-order moments of \(w_{k}\) remain in the summation  
\[\mu_{2m}=\sum_{n=0}^{m}\binom{2m}{2n}A^{2n}E\left[x_{k}^{2n}\right]E\left[w_{ k}^{2(m-n)}\right]. \tag{14}\]  
The even-order moments of a real Gaussian random variable are  
\[E\left[w_{k}^{2n}\right]=(2n-1)!!\sigma^{2n},\qquad n=1,2,\ldots \tag{15}\]  
and then, we obtain  
\[\mu_{2m}=\sum_{n=0}^{m}\left(\binom{2m}{2n}\left(2m-2n-1\right)!!\right)A^{2n }E\left[x_{k}^{2n}\right]\sigma^{2(m-n)}. \tag{16}\]  
In the case of real sinusoids, under the assumption of uniform random initial phase in \((0,2\pi)\), by definition the even-order moment of \(x_{k}\) is  
\[E\left[x_{k}^{2n}\right]=\frac{1}{2\pi}\int_{0}^{2\pi}\left(\cos\left(2\pi vk +\phi\right)\right)^{2n}d\phi. \tag{17}\]  
By symmetry the integral above can also be written as  
\[\int_{0}^{2\pi}\cos^{2n}\theta d\theta=4\int_{0}^{\pi/2}\cos^{2n}\theta d\theta, \tag{18}\]  
where the integral on the interval \((0,\pi/2)\) is given by  
\[\int_{0}^{\pi/2}\cos^{2n}\theta d\theta=\frac{\pi}{2}\frac{(2n-1)!!}{(2n)!!}. \tag{19}\]  
Therefore,  
\[\frac{1}{2\pi}\int_{0}^{2\pi}\cos^{2n}\theta d\theta=\frac{(2n-1)!!}{(2n)!!}. \tag{20}\]  
Putting (20) in (16) we finally obtain the general formula (3) for even-order moments of the signal (1).
# Semantic Segmentation Based on Depth Background Blur
## 5 Conclusions  
In this paper, we propose a semantic segmentation method based on background blurring, which adaptively processes the input image background via the fuzzy factor \(\sigma\), without changing the original network structure or introducing additional parameters, to expand differences between background and foreground and guide the network segmentation. The selection of \(\sigma\) is determined by the overall background rate \(R^{B^{*}}\) of the dataset and the background rate \(R^{B}\) of the current image. The former determines the approximate range of its value, while the latter determines its specific value. Compared to the attention mechanism in the network layer, bokeh plays the same role in the dataset, by weakening the background information to highlight the features of the foreground. Moreover, our approach can be lightly embedded into the existing segmentation network. As our experiments show, our method achieves competitive performance on PASCAL VOC 2012 and CamVid, with mean IOU increased by 5.2 and 3.7, especially for the small proportion category in the dataset. The main limitation of this study is that our bokeh method relies on the existing segmentation network, and the performance of the existing segmentation network directly determines whether we can accurately trace the background. Different segmentation networks selected may result in diverse results. Therefore, a natural progression of this work is how to efficiently segment the foreground and background without relying on the current network. In addition, adding classical image processing methods and how to encode and decode contour information effectively will be the focus of this paper in the future.  
Writing--original draft, H.L.; Writing--review and editing, C.L. and A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by Sichuan Science and Technology Program grant number 2019 YJ0541, the Open Project of the Key Lab of Enterprise Informationization and Internet of Things of Sichuan Province grant number 2021 WZY02, the Open Fund Project of Artificial Intelligence Key Laboratory of Sichuan Province grant number 2018 RYJ02, Postgraduate course construction project of Sichuan University of Science and Engineering grant number YZ202103 and Graduate innovation fund of Sichuan University of Science and Engineering grant number Y2021099.  
Not applicable.  
The authors declare no conflict of interest.
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 4 Proof-of-Concept Implementation
### Using ID6 and ID8 AIS Messages as Carriers of the SecAIS Data  
The cryptographic data of the SecAIS are transmitted over the conventional AIS infrastructure, by means of either ID6 or ID8 AIS messages. ID6 AIS messages are designed for addressed communication (i.e., to a specific MMSI), and ID8 AIS messages are designed for broadcast communication. Both offer an isolated data payload subsection of 920/952 bits, designed to carry non-AIS-related data for registered third-party applications. This approach has been time-tested by many applications and has the advantage of being completely transparent to the underlying AIS infrastructure. However, because the size of the data carried by each ID6/8 AIS message is limited, more than one ID6/8 AIS message  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **mIBC** & **PoC Implementation** \\ \hline Private Key Generator & IMO-mIBC-PKG & PKG \\ mIBC Public Parameters & IMO-mIBC-PKG-PP & Public Parameters of the RFCs \\ Public key & MMSI & ID = “2011-02\(\backslash\)0 tel: +447700900123\(\backslash\)0” \\ Private key & IMO-mIBC-PKG-SKMMSI & Secret-key (SSK) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cryptographic components.  
is required to transmit SecAIS data. In this implementation, we use ID8 AIS messages for transmitting both Authenticated-SecAIS (mode 2) and SK-IBE-SecAIS (mode 4) data. It must be noted that, depending on the SecAIS usage mode, the transmission of SecAIS cryptographic data requires the following number of ID6/8 AIS message(s):  
* The transmission of Authenticated-SecAIS (mode 2) data requires two ID8 AIS messages. The first is an initial conventional ID8 AIS message to send the public key (PVT) that is derived from the MMSI of the vessel. The second is the conventional ID8 AIS message that transfers the signed version of the original AIS message;
* The transmission of SK-IBE-SecAIS (mode 4) data requires three ID8 AIS messages per receiver of the secret key for the symmetric cipher.
# Additive Manufacturing in Underwater Applications
## 5 Areas of Application  
Each year, underwater 3D printing becomes more popular, including the usage of AM for the printing of elements for underwater applications, as well as trials for in situ printing in underwater environments. In Table 2, we present the most important areas of exploitation of this technology. It is worth noticing that, among them, only two areas--the repair of marine structures and 3D-printed marine structures--have been investigated regarding the usage of partly submerged 3D printers (nozzle area).  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Area** & **Technology Readiness 1** & **Reference(s)** \\ \hline
1 & Rapid prototyping of different components of underwater vehicles for ecological monitoring & Prototypes for scientific applications & [38] \\ \hline
2 & Replicas of living organisms for hydrodynamic tests—for example, benthic organisms and shark skin membranes & Models for investigation (prototypes) & [16] \\ \hline
3 & Investigation of biomechanics, such as mimicking efficient natural swimmers (e.g., boxfish, seahorse) & Models for investigation (prototypes) & [16; 76] \\ \hline
4 & Coral reef restoration, including artificial reefs & Prototype underwater installations & [21; 27] \\ \hline
5 & Pressure housings & Laboratory tests for small elements, computer modeling & [50] \\ \hline
6 & Repair of marine structures (underwater) & Laboratory tests for small elements, computer modeling & [49; 50] \\ \hline
7 & 3D-printed marine infrastructure (concrete) & Laboratory tests for small elements, computer modeling & [29] \\ \hline
8 & Membranes for water treatment & Prototypes and small-scale solutions & [77; 78] \\ \hline
9 & Sensors—for example, 4D whisker sensors & Prototypes and small-scale solutions & [79] \\ \hline
10 & Artificial muscles for stealthy undersea propulsion & Laboratory tests for small elements, computer modeling & [80] \\ \hline \end{tabular} \({}^{1}\) In the case of many studies on the selected topic, the one with the highest technological readiness has been chosen.  
\end{table}
Table 2: Selected applications for underwater AM.
## 4 Discussion
### Possible Reasons for the Inconsistent Performance of the Four Products
#### 4.1.2 Inconsistency of Model Structures  
The second reason for the inconsistent performance of these products comes from differences in model structures and algorithms. Firstly, BESS does not include evaporation from canopy interception, while both MOD16 and PML-V2 do. Canopy interception is an important part in ET besides canopy transpiration and soil evaporation. Previous studies showed that canopy interception could account for as much as 30% of total precipitation in tropical forests [66]. Thus, the omission of canopy interception could bring a large error in tropical forests. It is worth noting that MOD16 does not fully consider evaporation from canopy interception, as it is set to 0 if relative humidity is less than 70%.  
Secondly, both PML-V2 and BESS couple with photosynthesis to constrain canopy surface resistance in the PM equation, while MOD16 does not. A missing carbon and water coupling process can bring large errors in estimating ET, especially when vegetation is subject to environmental stress (e.g., drought, heatwave, cold, etc.) [67]. Although both PML-V2 and BESS have considered the process of carbon and water coupling, their idea for calculating leaf photosynthesis is different. PML-V2 adopts the \"big-leaf model\" while BESS adopts the \"two-leaf model\" in which leaves are divided into shaded and sunlit leaves. In addition, BESS induces a clumping index, rather than LAI, to distinguish the structure of the canopy.  
Thirdly, the method of calculating canopy surface resistance is different in MOD16, PML-V2, and BESS. Both PML-V2 and BESS apply the Ball-Woodrow-Berry equation [68] to calculate stomatal conductance, while MOD16 applies an empirical equation constrained by air temperature and atmosphere pressure. It should also be noted that PML-V2 uses relative humidity to constrain stomatal conductance in the Ball-Woodrow-Berry equation, while BESS uses vapor pressure deficit.  
Fourthly, MOD16 considers ET in the night while both PML-V2 and BESS do not. Previous study suggested that leaf stomata are closed in the night and, thus, the leaf does not have a transpiration process in the night [11]. However, recently, more and more research has supported the existence of stomata opening in the night for some plants [69; 70; 71]. Thus, MOD16 seems be more reasonable in this aspect.  
It should be noted that we are unable to compare the input data and model structure differences between GLASS and the other three products, as it is produced by fusing several products instead of having independent model input and algorithms.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model  
As illustrated by Figure 1, a hybrid sensor network is an IoT infrastructure that includes four different types of nodes: (1) conventional sensor node (CSN); (2) sensor tag node (STN); (3) hybrid sensor node (HSN) that combines RFID reader and sensor functionalities; and (4) a base station connected to a gateway (BSN). The CSN nodes and HSN nodes are organized into clusters with each cluster having a cluster head (CH) node that collects, aggregates and forwards the sensor readings from cluster member (CM) nodes, which might be either CSN or HSN nodes to the gateway. On the other hand, the STN nodes located in groups within reading range of an HSN form an IoT island of RFID slave (RS) nodes, which are interrogated periodically by the RFID reader side of the HSN nodes. The HSN nodes that are closer to the base station relay data for other HSN nodes further from the base station. The CSN nodes may be distributed in the network randomly or based on geo-spatial requirements and constraints.
###### Abstract  
This paper addresses the communication issue encountered by a hybrid controller when finding consensus in terms of the rendezvous target point in a broadcast and communication environment. This issue may result in a high level of computation and the utilization of agent resources when a continuous communication is required by agents to meet convergence requirements. Thus, an event-triggered system was integrated into the design of a broadcast and distributed consensus linear controller using the simultaneous perturbation stochastic algorithm (SPSA). The agent's movement towards the rendezvous point is based on the broadcast value, whereas the next agent's state position depends on the distributed local controller output. The communication error obtained during communication between the agent and neighbors is only added to the gradient approximation error of the SPSA if the event-triggered function is violated. As a result, in our model, the number of channel utilizations was lower and the agents' performances were preserved. The efficiencies and effectiveness of the proposed controller have been compared with the traditional sampling broadcast time-triggered (BTT) approach. The time and iterations required by the broadcast event-triggered (BET) system were less than 40.42% and 21% on average as compared to BTT. The trajectory was not the same--the BET showed scattered movements at the initial stage, whereas BTT showed a linear movement. In terms of the number of channels, 28.91% of channels were preserved during the few hundred iterations. Consequently, a variety of hybrid controllers with event-triggered mechanisms can be proposed for other multi-agent motion coordination tasks.  
multi-agent system; consensus control; broadcast; stochastic approximation; event-triggered 4.0
## 4 Systematic Review of the Research Pillars
### Sustainability in Urban Development
#### 4.3.4 EEG for Sustainable Building Environment  
Electroencephalogram (EEG) technology integration in sustainable building environments provides significant data about how occupants' cognitive performance and well-being are impacted by these spaces. Researchers can better understand the connection between the built environment and human experience by using EEG to detect brain activity objectively in reaction to different building designs. When compared to conventional buildings, EEG research has demonstrated that residents of sustainable buildings have better attentional focus and greater visual system involvement [89]. A decrease in cognitive load is shown by the regulation of brain activity, especially increased theta/delta activities, which foster awareness and improve mental health [89]. Another advantage is that Real-time feedback on how various spatial settings affect human perception and work efficiency is possible when EEG and virtual reality are combined [90].  
Studies show that EEG \(\beta\) rhythms and task performance are significantly correlated, indicating that neurophysiological responses might be used to optimize building design [90]. Broad Implications for Sustainability: in line with the larger objectives of sustainable development, sustainable building methods consider not only environmental efficiency but also the psychological and social effects on inhabitants [91,92]. By encouraging healthier living conditions, the incorporation of EEG data can guide design procedures that improve occupant satisfaction and help achieve the UN Sustainable Development Goals [92].
## 3 Dynamic Network Layout Algorithm  
Given a dynamic network \(N=\{N_{0},N_{1},\ldots,N_{n}\}\), our SIPA algorithm aims to continuously online compute the corresponding layout sequence \(L=\{L_{0},L_{1},\ldots,L_{n}\}\), ensuring the overall layout quality while maintaining layout stability between adjacent networks. When the network transitions from \(N_{i-1}\) to \(N_{i}\), the network updates \(U_{i}\) may involve the addition or removal of nodes and edges. For the unchanged portions of \(N_{i-1}\), the node positions are directly copied from the layout \(L_{i-1}\). Figure 1 depicts the workflow of our algorithm. The input of our algorithm is a streaming graphs. The objective of the SIPA algorithm is continuously compute node-link-diagram-based layouts when changes of the upcoming graph is unknown, and try to preserver the layout stability while ensuing the layout quality. The main steps of SIPA dynamic network layout algorithm consist of: (1) initial placement of newly added nodes; (2) determining structural influences on nodes and propagating influences within the network; (3) combining the node influence with node aging strategy to further preserving the stability; and (4) updating layout with combination of the former factors.
## Materials and Methods
### Data Extraction and Pretreatment of Spectra  
In order to extract the actual spectral response of the samples, the influence of both the white and dark current image was removed and thus the calibrated image, \(I_{R}\), was achieved by the following equation [28].  
\[I_{R}=\frac{I_{r}-I_{d}}{I_{w}-I_{d}}, \tag{1}\]  
where \(I_{r}\) is the sample image, \(I_{d}\) is the dark current image and \(I_{w}\) is the reference image.  
The corrected hypercube for each plate (100 samples) was 500 \(\times\) 502 pixels in the spatial dimension with 128 wavebands spanning 396 to 1004 nm. For the analysis, region of interest (ROI) selection was conducted by a simple thresholding method to remove the background effect of the sample holder so as to visualize only seed pixels. It was not possible to visually select and identify partial ROIs within individual seeds as being a diseased or healthy ROIs, since the number of pixels within the seed area is a small (average of 170 pixels/seed) and the boundary of the diseased region is ambiguous. Therefore, the mean spectrum of each individual seed was calculated to represent the sample. As the next step, an ROI for each seed sample was selected to obtain an averaged spectra for the seed, for further analysis.  
In general, spectroscopic data can be affected by baseline shift, light scattering and low signal-to-noise of the system [29]. To mitigate these artifacts, the averaged spectral data of each rice sample was subjected to five different pretreatment methods: standard normal variate (SNV), normalization (mean, maximum and range) and smoothing with three windows sizes. A summary of the equations used in these pretreatment methods is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Normalization} & Maximum & \(S_{(j)k}=\frac{X_{min(j)k}}{X_{max}}\) \\ \cline{2-3}  & Mean & \(S_{(j)k}=\frac{X_{min(j)k}}{X_{max}}\) \\ \cline{2-3}  & Range & \(S_{(j)k}=\frac{X_{(j)k}-X_{min}}{X_{max}-X_{min}}\) \\ \hline \multirow{2}{*}{SNV} & \multirow{2}{*}{\(S_{(j,k)}=\frac{X_{(j,k)}-A_{0}}{A_{1}}\)} & \(A_{0}\): average value of the sample spectrum \\  & & \(A_{1}\): standard deviation of the sample-spectrum \\ \hline Smoothing & \(S_{(j)k}=\frac{x_{(j-k)k}+x_{(j+1)k}}{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pretreatment methods and equations.  
Figure 1: Schematic of the hyperspectral imaging system.