esa-sceva/satcom-chunk-collection huggingface dataset:
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technology (Standard)** & **Coverage Range** & **Theoretical Data Rate** & **Maximum Number of Nodes** & **Power Consumption** & **Market Adoption** \\ \hline Ethernet (IEEE 802.3) & 100 m & 10 Mbps-100 Gbps & 1 per wire (254 on a subnet) & Low & High \\ \hline PLCC (Insteon, IEEE 1901, CE bus, LonWorks) & 300–3000 m & 13 kbps–200 Mbps & 500–1000 & Low–High & Low \\ \hline Serial Comm. and Modbus (RS232, RS422, RS485) & 15–1200 m & 1–10 Mbps & 32 typical. Up to 256 with some ICs & Low & High \\ \hline Zigbee (IEEE 802.15.4) & 10–100 m & 250 kbps & 255 & Low & High \\ Bluetooth (IEEE 802.15.1) & 10 m & 2–24 Mbps & 8 & High & High \\ BLE & 50 m & 2 Mbps & 8 & Low & Low \\ \hline WiFi (IEEE 802.11 a,b,g,n,ac) & 50–70 m & 11–1300 Mbps & 255 & High & High \\ \hline EncOcean (EnOcean standard) & 20–200 m & 125 kbps & 2\({}^{32}\) & Low & Low \\ \hline BACnet (ANSI/ASHRAE 135) & 1200 m & 9.6–115.2 kbps & \begin{tabular}{c} 32 typical. Up to 128 Master Nodes \\ on same Segment \\ \end{tabular} & Low & Low \\ \hline \begin{tabular}{c} GLOWPAN \\ (IEEE 802.15.4) \\ \end{tabular} & 10–100 m & 250 kbps & 2\({}^{64}\) & Low & Low \\ \hline \
\begin{tabular}{c} Z-Wave \\ (Z-Wave Alliance) \\ \end{tabular} & 30–300 m & 100 kbps & 2\({}^{32}\) & Low & Low \\ \hline
\begin{tabular}{c} LoRaWAN \\ (LoRa Alliance) \\ \end{tabular} & 10,000 m & 0.3-50 kbps & 2000–3000 & Low & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key characteristics of prominent wired and wireless technologies for building applications.
# Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
## 3 System Model
### Node Distance  
In the edge-side WSN, the distances between nodes mainly include the distance between sensor nodes and edge nodes, the distance between cluster head nodes and common nodes, and the distance between cluster heads.  
* Sensor node to edge node In the WSN at the edge end, the edge end needs to manage and monitor the sensor nodes, and the distance between them will affect the energy consumption of the nodes. Therefore, when selecting the cluster head, the node closer to the edge end should be selected as the cluster head node as much as possible. Specifically, the distance \(D_{i}^{e}\) between the edge end and the \(i\)-th sensor node can be defined as \[D_{i}^{e}=\frac{D_{i}}{D_{\text{max}}}\] (7) where \(D_{i}\) is the distance between the \(i\)-th sensor node and the edge node, and \(D_{\text{max}}\) is the distance between the furthest sensor node and the edge node.
* Inter-cluster node distance When selecting the cluster head nodes, ensure that the cluster head nodes in the network are evenly distributed as much as possible, so that the energy consumption of the cluster head nodes can be as uniform as possible, which can improve the network life cycle. Therefore, in the process of cluster head selection, the distribution of cluster head nodes and intra-cluster nodes needs to be considered. Specifically, we define the inter-cluster node distribution factor \(D_{i}^{c}\). When it is smaller, the distribution of each intra-cluster node is more uniform. The specific definition is as follows: \[D_{i}^{c}=\frac{\sum\limits_{i\in\mathcal{C}_{i}}\left(C_{i}^{avg}-C_{avg} \right)^{2}}{Q}\] (8) where \(C_{i}^{avg}\) represents the average number of neighbor nodes of all nodes in the \(i\)-th cluster, \(C_{avg}\) represents the average number of neighbor nodes of \(N\) nodes in the entire network, and \(Q\) represents the total number of clusters.
* Distance between cluster heads In the process of cluster head selection, when the distance between cluster heads is large, the data need to go through more hops to reach the destination, which increases the delay of data transmission. Therefore, the distance between cluster heads is an important parameter in the cluster head selection algorithm, which determines whether the WSN can achieve the best routing performance. Therefore, the distance factor \(D^{h}\) between cluster heads can be defined as \[D^{h}=\frac{\sum\limits_{i\in M}D_{i}^{e}}{\sum\limits_{\forall i,j\in M}D_{i, j}^{h}}\] (9)where \(D_{i,j}^{h}\) represents the distance between cluster heads \(i\) and \(j\), which is used to adjust the distance between cluster heads. \(D_{i}^{e}\) represents the distance from the cluster head node \(i\) to the edge node. In order to make \(D^{h}\) smaller, it is necessary to reduce the distance between cluster heads and edge nodes and, at the same time, increase the distance between cluster heads so that the distribution of cluster heads in the entire network is more dispersed.
## 4 Factors Influencing Proppant Transportation
### Fracturing Fluid Viscosity Ratio  
In this study, four different fracturing fluid viscosity ratios of 100, 10, 5, and 2 were selected to study the effect of the viscosity ratio on proppant transport and proppant placement. Fracturing fluid is injected with a viscosity of 100 mPa-s, and then fracturing fluid with a lower viscosity is injected.  
Under different viscosity ratio conditions, the shape of the sand dunes is significantly altered (Figure 5). Comparing the lengths of sand dunes under different viscosity ratios, it was found that  
Figure 4: Proppant placement under different fracturing fluid viscosities: (**a**) 10, (**b**) 50, and (**c**) 100 mPa-s.  
when the viscosity ratio was greater than or equal to 5, the length of the sand banks was equal to 1000 mm, and the length of the sand banks did not increase after lower viscosity fracturing fluid was injected. When the viscosity ratio was 2, because the fracturing fluid injected later still had a good carrying capacity, the length of the sand dune increased (to greater than 1000 mm). With regard to the height of the sand bank, when the viscosity ratio increased from 2 to 5, the height of the sand bank increased from 29 to 40 mm. In the case of viscosity ratios of 10 and 100, although the sand dune height increased dramatically, its height increase mainly occurred at the fracture inlet, and the proppant was unevenly distributed in the fracture. In particular, when the viscosity ratio was increased to 100, the proppant settled in the fracture inlet almost as soon as it entered the fracture, increasing the risk of sand plugging.  
When the variable viscosity injection method was used, the viscosity ratio increased within a certain range, the proppant transport distance increased, the height of the sand bank was higher, and the proppant was more uniformly laid. Therefore, to form an effective proppant placement shape, the viscosity ratio should be maintained between 2 and 5.
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 4 Carrier-To-Noise Density Ratio
### Comparison of BDS-3 and GPS, GLONASS and QZSS Satellites at the Same Frequency  
Since the B1C and B2a signals of the BDS-3 MEO satellites have good interoperability with other GNSS signals at the same frequency, they are compared. Since the performance of the GNSS satellites declines with the aging of the payload, the authors chose three satellites from BDS-3, GPS and Galileo launched in 2018. Meanwhile, the authors selected the J01 satellite with better visibility in China because QZSS has no new satellite after 2018. In this contribution, the C/N\({}_{0}\) of the same-frequency signals of the four satellites C25 (BDS-3), G04 (GPS), E13 (Galileo) and J01 (QZSS) are compared. The C/N\({}_{0}\) of B1C, L1, E1 and L1 (QZSS) is shown in Figure 7, from which we can be found that the signal strength of BDS-3 and GPS is slightly higher than Galileo and QZSS. Since the J01 satellite of QZSS is more than 10 years old, its performance inevitably deteriorates with the aging of the onboard load. Therefore, in this contribution, the performance of the J01 satellite deviates from other satellites.  
The C/N\({}_{0}\) statistical results of different GNSS satellites are shown in Table 6. We can find that, except for the QZSS satellite which has a higher average elevation, the average elevation of the other three GNSS satellites are in the range of 33-40 degree. The signal strengths of different GNSS systems on the two frequencies are relatively close, and the B2a frequency point is 3-4 dB/Hz higher than that of BIC.  
Figure 6: Comparison of Beidou System (BDS) signal carrier-to-noise ratio on different frequencies.  
Figure 7: Comparison of C/N\({}_{0}\) values of BIC, L1, E1 and L1 (QZSS).  
Except for GEO satellites, all BDS-3 satellites can transmit B1C and B2a signals. The signal strength of B1C and B2a signals is significantly higher than other signals, especially B2a signals are about 3 dB/Hz higher. Moreover, the signal strength of B1C and B2a signals is very close to other GNSS signals at the same frequency. Therefore, when using BDS-3 signals for positioning and navigation, BIC and B2a have priority.
## 3 Results
### Multi-Classification Result Scenario  
The multiclass classification results demonstrate great accuracy in detecting and classifying different types of attacks.  
As detailed in Table 2, it is clear that our model has achieved outstanding results in detecting various cyber-attacks. For multiclass classification scenarios, the model achieved 100% specificity and accuracy while achieving a precision of 99.365%, a recall of 99.957%, and an F1-Score of 99.6604%, crucial for preventing unnecessary alarms and ensuring the seamless operation of IoHT devices.  
As represented in Figure 3, the confusion matrix represents the true positive and false positive rates across different classifications. A noteworthy observation is the model's success in distinguishing between 'No Attack' instances and 'Smurf Attack' scenarios, with only 15 cases misclassified from a substantial dataset. It indicates the model's high sensitivity and specificity in operational environments.  
The weighted average results in Table 3 show the model's overall performance with accuracy, precision, recall, and F1-Score above 0.9985, 0.997 specificity and 0.001439 error rate. The aggregate analysis ensures the model's consistent strength across various attack vectors, solidifying its role as a comprehensive solution for cybersecurity in the IoHT landscape.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Specificity (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score** \\ \hline No Attack & 100 & 100 & 99.365 & 99.957 & 99.6604 \\ ARP Spoofing & 100 & 100 & 100 & 100 & 100 \\ Nmap Port Scan & 100 & 100 & 100 & 100 & 100 \\ Smurf Attack & 99.86 & 99.55 & 99.987 & 99.807 & 99.897 \\ DoS Attack & 0.9944 & 99.44 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-classification results for each class.
## 2 System and Channel Model
where \(\psi=w_{eq}/2\sigma\) is proportional to the amount of pointing error strength with \(w_{eq}\) denoting the equivalent beam radius in the detector aperture and \(\sigma\) representing the spatial jitter, i.e., the pointing error displacement. Specifically, larger \(\psi\) parameter values imply stronger pointing errors and, thus, more severe misalignment-induced fading. Indeed, increased \(\psi\) values practically refer to smaller spatial jitter values, which means that the beam footprint overlaps the detector aperture to a greater extent, thus describing smaller amounts of pointing mismatch. Consequently, when \(\psi\rightarrow\infty\) or practically when \(\psi\) obtains very large values, pointing errors can be considered as a negligible effect. Furthermore \(w_{eq}=\left[\frac{\sqrt{\pi}\text{erf}(v)w_{eq}^{2}}{2\text{t}vexp(-v^{2})} \right]^{1/2}\), where erf(.) stands for the error function, (Equation (8.250.1) in [46]), and \(v=\frac{\sqrt{\pi}\rho}{\sqrt{2}w_{s}}\), where \(\rho\) represents the radius of the circular receiver aperture and \(A_{0}=\text{erf}^{2}(v)\) isthe fraction of the collected power at \(\rho=0\)[16; 24]. Additionally, \(w_{\delta}\), which represents the corresponding beam waist on the receiver plane at a propagating transdermal distance \(\delta\) along with a transmitter divergence angle \(\theta\), is obtained as follows [10; 38]:  
\[w_{\delta}=\delta\tan(\theta/2). \tag{6}\]  
By using Equations (2) and (5), the PDF of the random variable \(h\)can be obtained asfollows [16; 19]:  
\[f_{h}(h)=\psi^{2}A_{0}^{-\psi^{2}}h_{l}^{-\psi^{2}}h^{\varphi^{2}-1},\ 0\leq h \leq A_{0}h_{l}. \tag{7}\]  
Moreover, by using Equations (2) and (3), the instantaneous electrical SNR, \(\gamma=\eta^{2}h^{2}P_{s}N_{0}^{-1}\), is expressed asfollows:  
\[\gamma=\eta^{2}h_{l}^{2}h_{p}^{2}P_{s}N_{0}^{-1}=\eta^{2}h_{p}^{2}\exp[-\alpha (\lambda)\delta]P_{s}N_{0}^{-1}, \tag{8}\]  
where \(P_{s}\) and \(N_{0}\) represent the signal and noise optical power spectral density (PSD), respectively [34]. Consequently, the average electrical SNR, \(\mu\), is obtained asfollows [35]:  
\[\mu=\frac{(\eta E[h])^{2}P_{s}}{N_{0}}=\frac{\eta^{2}\psi^{2}A_{0}^{2}P_{s} \exp[-\alpha(\lambda)\delta]}{(\psi^{2}+2)N_{0}}, \tag{9}\]  
where \(E[. ]\) denotes expectation. Next, by using Equations (7) and (8), along with applying the standard technique of transforming random variables \(f_{\gamma}(\gamma)=\frac{f_{h}(h)}{[\sigma_{\gamma}/\delta h]}\bigg{|}\ \ h=\sqrt{\frac{ \gamma}{\eta^{2}P_{s}N_{0}^{-1}}}\)[47], the PDF of random variable \(\gamma\) is obtained asfollows:  
\[f_{\gamma}(\gamma)=\frac{\psi^{2}}{2}A_{0}^{-\psi^{2}}h_{l}^{-\psi^{2}}\Big{(} \eta^{2}N_{0}^{-1}P_{S}\Big{)}^{-\frac{\psi^{2}}{2}}\gamma^{\frac{\psi^{2}-2} {2}},\ 0\leq\gamma\leq A_{0}^{2}h_{l}^{2}\eta^{2}N_{0}^{-1}P_{S}. \tag{10}\]  
By substituting Equation (9) into Equation (10), we get  
\[f_{\gamma}(\gamma)=\frac{\psi^{2}}{2}\bigg{(}\frac{\psi^{2}}{\psi^{2}+2}\bigg{)} ^{\frac{\psi^{2}}{2}}\mu^{\frac{-\psi^{2}}{2}}\gamma^{\frac{\psi^{2}-2}{2}}, \ 0\leq\gamma\leq A_{0}^{2}h_{l}^{2}\eta^{2}N_{0}^{-1}P_{S}. \tag{11}\]
## 1 Introduction
The main contributions of this paper can be summarized as follows. 1. We apply the clustering algorithm based on the spatial distribution to divide the surface points for determining the initial bubbles. 2. A procedure based on the distance of nearby bubbles to determine the initial size of bubbles. 3. We evaluate the quality of the tetrahedral mesh in the geological model using three indicators. The structure of this paper is organized as follows. Section 1 introduces related research about mesh generation for surfaces and three-dimensional domains. Section 2 introduces the background of bubble meshing method. Section 3 introduces the method for geological model discretization based on the physical analogy between nodes and bubbles. In Section 4, mesh quality indices are introduced to evaluate the resultant meshes and applications to demonstrate the effectiveness are provided. In Section 5, the advantages and shortcomings are analyzed. Finally, the presented work is concluded in Section 6.
## 3 Results
### Plant Above-Ground Structure and Leaf Traits  
The number of leaves was significantly higher in RL (10.8 \(\pm\) 0.37 leaves per plant) compared to non-inoculated lettues (9.6 \(\pm\) 0.24 leaves per plant; \(p\) \(<\) 0.05) and L (9.6 \(\pm\) 0.24 leaves per plant; \(p\) \(<\) 0.001), while the number of leaves in R (10 \(\pm\) 0.32 leaves per plant) was similar to C, L, and RL (Table 2). The average leaf area was affected by the inoculation procedure only in L-inoculated plants (13.4 \(\pm\) 1.1 cm\({}^{2}\)), being on average significantly lower (\(p\) \(<\) 0.01) compared to non-inoculated plants (18.4 \(\pm\) 0.8 cm\({}^{2}\)) (Table 2). In contrast, in R (15.8 \(\pm\) 0.7 cm\({}^{2}\)) and RL (16.4 \(\pm\) 0.7 cm\({}^{2}\)), intermediate values were observed (Table 2). The total leaf area was significantly lower in L (128.8 \(\pm\) 12.3 cm\({}^{2}\)) compared to non-inoculated plants (177.4 \(\pm\) 11.0 cm\({}^{2}\); \(p\) \(<\) 0.05) and RL (176.2 \(\pm\) 12.3 cm\({}^{2}\); \(p\) \(<\) 0.05) but was not affected by inoculation in R (157.36 \(\pm\) 5.95 cm\({}^{2}\)) (Table 2). The leaf mass per area was significantly higher in RL (29.5 \(\pm\) 0.9 g m\({}^{-2}\)) compared to non-inoculated plants (22.0 \(\pm\) 0.5 g m\({}^{-2}\); \(p\) \(<\) 0.001), to R (23.1 \(\pm\) 0.8 g m\({}^{-2}\); \(p\) \(<\) 0.05) and L (25.0 \(\pm\) 1.7 g m\({}^{-2}\); \(p\) \(<\) 0.05) (Table 2).  
Figure 3: Total lettuce dry weight **(a)**, leaf biomass **(b)**, root biomass **(c)**, and root-to-shoot ratio R/S **(d)** estimated at DAT9 in non-inoculated lettues (C) and in plants where the PGPR synthetic consortium was applied to the roots (R), to the leaves (L), or both roots and leaves (RL). Distinct uppercase letters denote statistically significant variations (\(p\) \(<\) 0.05) among treatments for individual parameters, assessed independently for each parameter (\(n\) = 5).
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 3 Star Identification
### Impact of Magnitude Errors on Identification Rate  
Affected by the meteorological conditions, optical spectrum, and image sensor, it is possible in the star image that the gray value of a star does not correspond to the apparent magnitude, which is called a magnitude error. This means that stars with low magnitude  
Figure 8: Identification rate with different position errors.  
and high brightness may appear to have a low gray value, while stars with high magnitude and low brightness may appear to have a high gray value. To evaluate the impact of magnitude errors on the identification rate of the proposed algorithm, different brightness errors are added to 1000 frames of actual star images. The average identification rate under various magnitude error conditions is then recorded and compared with other subgraph isomorphism algorithms. The experimental results are shown in Figure 9.  
As shown in Figure 9, as the magnitude error increases, the identification rates of the proposed algorithm as well as that of the You, Liu, and Wang algorithms remain largely unaffected by the magnitude error, while the Mortari algorithm shows a significant decline in identification rate. The analysis of the experimental results is explained below. The Mortari algorithm selects the brightest star in the star image as the primary star and the next three brightest stars as accompanied stars to form a matching group. During the process of star identification, the algorithm also selects stars from the navigation feature library according to the strategy of primary and accompanied stars, which is highly susceptible to the brightness error of the primary star. In contrast, the proposed algorithm, as well as the You, Liu, and Wang algorithms, does not use brightness as a feature during the process of star identification and thus would not reference the brightness when selecting stars. As a result, these algorithms are less affected by the magnitude error and exhibit stronger robustness.
###### Abstract  
User experience (UX) is crucial for interactive system design. To improve UX, one method is to identify failure modes related to UX and then take action on the high-priority failure modes to decrease their negative impacts. For the UX of interactive system design, the failure modes under consideration are human errors or difficulties, and thus the risk factors concerning failure modes are subjective and even subconscious. Existing methods are not sufficient to deal with these issues. In this paper, a fuzzy failure mode and effect analysis (FMEA)-based hybrid approach is proposed to improve the UX of interactive system design. First, hierarchical task analysis (HTA) and systematic human error reduction and prediction approach (SHERPA) are combined to identify potential failure modes concerning UX. Subsequently, fuzzy linguistic variables are employed to assess the risk parameters of the failure modes, and the similarity aggregation method (SAM) is adopted to aggregate the fuzzy opinions. Then, on the basis of the aggregation results, fuzzy logic is adopted to compute the fuzzy risk priority numbers that can prioritize the failure modes. Finally, the failure modes with high priorities are considered for corrective actions. An in-vehicle information system was employed as a case study to illustrate the proposed approach. The findings indicate that, compared with other methods, our approach can provide more accurate results for prioritizing failure modes related to UX, and can successfully deal with the subjective and even subconscious nature of the risk factors associated with failure modes. This approach can be universally utilized to enhance the UX of interactive system design.  
Keywords:human-computer interaction; user experience; interactive system design; failure mode and effect analysis; fuzzy set theory +
Footnote †: journal: Applied Sciences
## 3 Results
### Validation of the Proposed DBIM-FISTA Algorithm
#### 3.1.2 Reconstructions with Experimental Data  
We conducted an experiment with the antenna array surrounding the lower half of the phantom shown in Figure 3. We inserted a cylindrical blood target with radius \(\rho=15\) mm and permittivity \(\epsilon_{r}\approx 67.3-9.3j\) at 1.0 GHz into the phantom, centered at \(O_{tg}=(-30\) mm, 30 mm) along the horizontal axes. Reconstructions in 3-D and for the 2-D slice defined by the antenna centers are shown in Figure 9, where the Debye parameters of the brain material \(\varepsilon_{\infty}=20\), \(\Delta\varepsilon=20\) and \(\sigma_{s}=0.147\) are used.  
The 2-D reconstruction has less artifacts than the 3-D image along the same x-y slice, as it solves an inverse problem with a lot fewer unknowns. The y-z and x-z slices from the  
Figure 8: Reconstructions of “Case I” when \(RE\) converges: 2-D with 200 iterations and 3-D with 120 iterations. (**a,b**) Residual error plot for 2-D and 3-D, respectively. (**c**) Contrast \(\Re(\delta_{\epsilon})\) for 2-D reconstruction. (**d–f**) Contrast \(\Re(\delta_{\epsilon})\) for the y-z, x-z, and x-y slices of 3-D reconstruction, respectively.  
Figure 9: Reconstructions of \(\Re(\epsilon_{r})\) for the experimental data at 1.0 GHz in 3-D: (**a**) y-z slice, (**b**) x-z slice, and (**c**) x-y slice, and in 2-D (**d**).  
3-D reconstructions show that the bottom part of the cylindrical target is detected more clearly than the upper half, as the eight-antenna ring is placed in the lower half. These images also suggest that reflections from the plastic container have created artifacts in the 3-D reconstruction images. Overall, both 2-D and 3-D algorithms have detected the target at the right position, albeit with artifacts that are more pronounced in 3-D for the axial slice. We have also performed reconstructions when the antenna ring is placed at different heights, and the results become worse and even fail to detect the target when the antenna ring is near the upper part of the head phantom due to that the curvature of the surface reflects signals into the air which are not captured by the antenna ring.