esa-sceva/satcom-chunk-collection huggingface dataset:
# Pipeline Damage Identification Based on Additional Virtual Masses
## 2 Damage Identification Method Based on Additional Virtual Masses
### Damage Identification Based on Sensitivity
\begin{array}{c}\mathbf{R}=[\mathbf{R}_{1}^{\mathrm{T}},\mathbf{R}_{2 }^{\mathrm{T}},\dots,\mathbf{R}_{n}^{\mathrm{T}}]^{\mathrm{T}}\\ \mathbf{R}_{i}=[\mathbf{R}_{1,i}^{\mathrm{T}},\mathbf{R}_{2,i}^{\mathrm{T}},\dots,\mathbf{R}_{ qi,t}^{\mathrm{T}}]^{\mathrm{T}}\\ \mathbf{R}_{ki}=[\mathbf{R}_{ki,1},\mathbf{R}_{ki,2},\dots,\mathbf{R}_{ki,n}]\\ \Delta\tilde{\mathbf{\omega}}=[\Delta\tilde{\mathbf{\omega}}_{1}^{\mathrm{T}},\Delta \tilde{\mathbf{\omega}}_{2}^{\mathrm{T}},\dots,\Delta\tilde{\mathbf{\omega}}_{n}^{ \mathrm{T}}]^{T}\\ \mathbf{\Delta}\tilde{\mathbf{\omega}}_{i}=[\Delta\tilde{\mathbf{\omega}}_{1,i},\Delta \tilde{\mathbf{\omega}}_{2,i},\dots,\Delta\tilde{\mathbf{\omega}}_{qi,i}]^{T}\\ \Delta\mathbf{\mu}=[\Delta\mathbf{\mu}_{1},\Delta\mathbf{\mu}_{2},\dots,\Delta\mathbf{\mu}_{n} ]^{T}\end{array} \right. \tag{9}\]  
Damage factor \(\mathbf{\mu}\) can be approximated by Equation (10), which can be the initial value \(\mathbf{\mu_{0}}\) in the next iteration. Then, the new sensitivity matrix \(\mathbf{R}\) is constructed, and the above steps are repeated until it converges. \[\mathbf{\mu}=\mathbf{\mu_{0}}+\mathbf{R}^{+}\Delta\tilde{\mathbf{\omega}}, \tag{10}\]  
In the equation above, \(\mathbf{R}^{+}\) denotes the generalized inverse matrix of \(\mathbf{R}\), which can be calculated by a singular value decomposition.
## 4 Conclusions  
To further enhance the performance of knowledge graph embedding models, this paper proposes a novel knowledge representation model called TransRFT, based on a relational neighborhood and flexible translation. This model improves its ability to handle rare entities by incorporating relational neighborhood information, and enhances the handling of complex relationships through flexible translation. This method first projects the triplet vectors into the relational hyperplane, and then employs the flexible translation to relax the strict constraints in the triplet, which enhances the model's ability to deal with complex relationships. The relational neighborhood information is then included to better utilize the data already present in the graph structure and boost the model's performance. Finally, Bernoulli sampling is used to replace the entities in the positive-case triplet, which improves the model training effect. In addition, we conducted tests on link prediction and triplet classification using the WordNet and FreeBase databases, and found that the TransRFT model outperformed other models on both sparse and dense datasets. This indicates that our model can better handle complex knowledge graph data, thereby enhancing the practical value of knowledge graphs.  
Conceptualization, Y.N., C.C. and B.W.; methodology, Y.N.; software, B.W.; validation, B.W. and Z.Z.; formal analysis, Y.N. and C.C.; investigation, Y.N. and C.C.; resources, Y.N., C.C., B.W. and Z.Z.; data curation, B.W.; writing--original draft preparation, B.W.; writing--review and editing, Y.N., C.C.; visualization, Y.N. and C.C.; supervision, Y.N. and C.C.; project administration, Y.N.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (NSFC grant: U19 bB2014).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **WN11** & **FB13** & **FB15K** \\ \hline SE [40] & 53.0 & 75.2 & — \\ SME (bilinear) [24] & 73.8 & 84.3 & — \\ NTN [19] & 70.4 & 87.1 & 66.5 \\ TransE [30] & 75.8 & 81.5 & 79.7 \\ TransH (unif) [31] & 77.7 & 76.5 & 74.2 \\ TransH (berm) [31] & 78.8 & 83.8 & 87.7 \\ TransRFT (unif) & 86.7 & 88.7 & 91.4 \\ TransRFT (berm) & **88.6** & **90.4** & **92.5** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Triplet classification accuracy of different models.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 4 Results  
### System Parameters  
In this section, the performance of OCDM is compared against the conventional OFDM system over static and dynamic UWAC channels. The evaluation will be based on three performance metrics: Mean Square Error (MSE) between \(x_{n}^{k}\) and \(\hat{x}_{n}^{k}\), Bit Error Rate (BER) after Viterbi decoding and Effective Data Rate (EDR), which is defined as:  
\[\text{EDR}=(1-\text{PER})\cdot D_{r}, \tag{18}\]  
where the Packet Error Ratio (PER) denotes the percentage of the erroneous frame w.r.t. the total transmitted frames (a frame is erroneous if one bit is not properly decoded), and the raw data rate \(D_{r}\) is defined as:  
\[D_{r}=\frac{N_{d}N_{u}}{N(1+N_{d})(1+R_{CP})}BR_{c}\log_{2}M\quad\text{bps} \tag{19}\]  
The system parameters for both the water tank and Watermark NOF1 replayed channel experiments are summarized in Table 2. Some of these parameters are shared by both OCDM and OFDM systems so as to provide a fair comparison.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 4 Numerical Simulation  
Numerical simulations were conducted to evaluate the performance of PS-FDE when applied to MSK and GMSK, comparing it with traditional FDE employing simple phase detection [15]. The system parameters used in the simulation (Table 2) were configured as follows: a 48 kHz sampling frequency, a 6 kHz center frequency, a 2 kbps transmission rate, a 4 kHz bandwidth, a signal sequence length of 1024 bits, and a CP length of 512 bits. The designed signal sequence ensured compliance with the modulated signal phase and maintained phase continuity after CP insertion. Consequently, no additional tail symbols were required.
## 3 Results
### Effects of Mixing Feldspathic Sandstone and Sand on Soil Microbial Biomass Indices  
Figure 1 shows that soil MBC, MBN, MBP, MBC:MBN, MBC:MBP, MBN:MBP, and MBC:SOC were present in significantly higher quantities in mixed soil than in sand soil by 222.8%, 112.0%, 58.2%, 54.3%, 106.3%, 33.1%, and 23.0%, respectively. Notably, changes in the MBC, MBN, MBP, MBC:MBP, and MBC:MBN varied more than changes in MBN:MBP and MBC:SOC.  
Effects of Mixing Feldspathic Sandstone and Sand on Soil Extracellular Enzymes Activities and Its Stoichiometry  
For individual enzymes, the values of BG, NAG, AKP, CBH, and BX in mixed soil were 2.4, 1.0, 3.1, 1.5, and 3.0 times higher than those in sand soil, respectively (Figure 2). For the ecoenzymatic stoichiometry, the BG:NAG ratio increased significantly in mixed soil, with an increase of 65.4%. However, the BG:AKP and NAG:AKP ratios showed significant downward trends, decreasing by 19.0% and 50.3%, respectively (Figure 2). In addition, a quantitative vector analysis method for estimating resource allocation strategies of microbial communities was shown visually in Table 3. Compared with sand soil, we found the BG/(BG + AKP) was lower, while the BG/(BG + NAG), vector length, and vector angle were higher in mixed soil. Notably, all vector characteristics of enzyme activity had significant differences between two soils.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristics** & **Sand Soil** & **Mixed Soil** & **F (1,5)** & **_P_ \\ \hline Texture & sand & sandy loam & / & / \\ pH & \(8.34\pm 0.07\) & \(8.16\pm 0.17\) & 1.07 & 0.36 \\ EC (\(\mu\)s/cm) & 181.73 \(\pm\) 14.98 & 201.37 \(\pm\) 35.52 & 0.26 & 0.64 \\ BD (g/cm\({}^{3}\)) & 1.63 \(\pm\) 0.05 & 1.47 \(\pm\) 0.02 & 9.13 & 0.04 * \\ SWC (\%) & 2.66 \(\pm\) 0.08 & 4.61 \(\pm\) 0.04 & 479.31 & \textless{}0.01 ** \\ STC (g/kg) & 1.61 \(\pm\) 0.21 & 3.25 \(\pm\) 0.24 & 26.17 & \textless{}0.01 ** \\ SOC (g/kg) & 1.06 \(\pm\) 0.09 & 2.79 \(\pm\) 0.06 & 252.95 & \textless{}0.01 ** \\ SIC (g/kg) & 0.55 \(\pm\) 0.18 & 0.46 \(\pm\) 0.18 & 0.13 & 0.74 \\ Porosity (\%) & 25.31 & 33.46 & 17.03 & 0.02 * \\ Field capacity (\%) & 6.88 & 16.51 & 216.39 & \textless{}0.01 ** \\ \hline \hline \end{tabular} Values are expressed as mean \(\pm\) SE, \(n\) = 3. * and ** indicate statistical significance at the 0.05 and 0.01 probability levels, respectively. EC, BD, STC, SOC, SIC, and SWC are the abbreviations of soil electrical conductivity, soil bulk density, soil total carbon, soil organic carbon, soil inorganic carbon, and soil water content, respectively.  
\end{table}
Table 2: Soil characteristics in sand vs. a mix of sand and feldspathic sandstone.  
Figure 1: Soil microbial biomass and microbial biomass C:N:P stoichiometry and microbial quotient in sand vs. a mix of sand and feldspathic sandstone. * and ** indicate statistical significance at the 0.05 and 0.01 probability levels, respectively. Error bars are the standard errors (_n_ = 3).
# Temporal Continuity Expression for Network Topology of Space Information Systems
###### Abstract  
The main functions of the space information system, such as providing the backbone transmission, broadband access, and global connectivity, are realized based on the network topology. Thus, it is necessary to recognize the temporal dynamics of the network topology. A temporal continuity expression method is proposed to describe the topological dynamic characteristics of the network in space information systems. Based on orbit dynamics, a time-dependent adjacency matrix of the space information system can be established by introducing the geometric linkable factor, the link distance intensity factor, and the relative angular velocity factor of the node. The adjacency matrix describes the dynamic characteristics from two layers: one is the physical layer using a time-dependent function, which represents the feasibility of inter-satellite link construction in the system cycle; the other one is the transport layer, described by a piecewise continuous function that varies with time, which characterizes the link quality during the connection period between two satellites. The results show that compared with the existing network topology description methods, the proposed method describes the network topology more accurately, which can distinguish the network topology characteristics at any time, and is more conducive to the understanding and application of the network topology of the space information system.  
space information system; network topology; inter-satellite link; dynamic characteristic; temporal continuity  
**2024**, _13_, 2824. [[https://doi.org/10.3390/electronics13142824](https://doi.org/10.3390/electronics13142824)]([https://doi.org/10.3390/electronics13142824](https://doi.org/10.3390/electronics13142824))
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
###### Abstract  
Data collection problems have received much attention in recent years. Many data collection algorithms that constructed a path and adopted one or more mobile sinks to collect data along the paths have been proposed in wireless sensor networks (WSNs). However, the efficiency of the established paths still can be improved. This paper proposes a cooperative data collection algorithm (CDCA), which aims to prolong the network lifetime of the given WSNs. The CDCA initially partitions the \(n\) sensor nodes into \(k\) groups and assigns each mobile sink acting as the local mobile sink to collect data generated by the sensors of each group. Then the CDCA selects an appropriate set of data collection points in each group and establishes a separate path passing through all the data collection points in each group. Finally, a global path is constructed and the rendezvous time points and the speed of each mobile sink are arranged for collecting data from \(k\) local mobile sinks to the global mobile sink. Performance evaluations reveal that the proposed CDCA outperforms the related works in terms of rendezvous time, network lifetime, fairness index as well as efficiency index.  
collection point; network lifetime; rendezvous time; energy consumption +
Footnote †: journal: Sensors
## 2 Background and Article Organization  
According to [16; 17; 18], Cyber-Physical Systems (CPSs) can be defined as multi-dimensional and complex systems that integrate computing and networks to govern physical actuators or machines, such as robots. CPSs are a core foundation of Industry 4.0 [19] based on the 3C (computing, communication, and control) approach. The main objective of CPSs is to provide different services (e.g., sensing, information feedback, and dynamic control) to enable reliable, safe, and efficient monitoring and control of physical entities [19]. In this context, digital twins represent an engineering category focusing on virtual models more than 3C capabilities [19]. In addition, ref. [20] demonstrated a proof of concept of digital twin usage in manufacturing. The proposed modules estimate a worker's physical load in a parts-picking scenario and perform dynamic scheduling based on the predicted working progress under ergonomic constraints. In this context, the digital model and digital shadow are two related concepts. On the one hand, a digital model is described in [21] as a representation of reality constructed manually that remains static when reality changes. Therefore, there is no data exchange between the physical and virtual worlds. On the other hand, ref. [21] describes a digital shadow as \"an automatically derived model that changes when reality changes\". However, in a digital shadow, \"there is no automated real-time feedback loop\". This is because a digital shadow only provides a one-way data flow from the physical environment to the virtual model [22]. Digital twins go further than digital models and digital shadows, as their results directly trigger or impact reality. Therefore, digital twins provide a two-way data flow from the physical system, both to the virtual system and the other way around [22]. The primary interest of this review is to identify those software artifacts used in HRI applications where the virtual and physical systems can communicate with each other. Therefore, digital models and digital shadows are outside the scope of this article. Most recently, emergent human-centered paradigms, such as Society 5.0 and Industry 5.0, have been envisioned to promote the creation of Human-Cyber-Physical Systems (HCPs), where humans play a central position as \"masters\" of CPSs [23]. Therefore, the highest right to make decisions always comes from humans, no matter how powerful or sophisticated an intelligent system is. Novel perspectives on these human-centered paradigms have been described in [24; 25]. The main focus of the present article is to review those human-robot interaction systems proposed in the literature that use game engines as keystone elements for the creation of complex systems that integrate software frameworks and libraries enabling computing (e.g., object and human recognition), communication between the different modules (e.g., using a socket library or middleware), and control of real robots.
# Grouping Sensors for the Key Distribution of Implicit Certificates in Wireless Sensor Networks
## 5 Conclusions  
There is an increasing number of sensor nodes in WSNs. The sensor node grouping directly affects the communication efficiency of WSNs. Herein, we developed a grouping method for the key distribution of implicit certificates in WSNs to enhance computing performance. Further, we propose a novel key distribution mechanism for WSN applications. Following the management mechanism along with its above-average performance on storage requirement, the resilience of node capture, key connectivity, and scalability, we developed an efficient network grouping strategy, ABG, which can minimize encryptions and decryptions by combining data aggregation and inter-group communication (both of which require encryptions and decryptions for the sensed data) in the same node. We investigated the influence of the number of aggregators in the network on the number of encryptions and decryptions. We found that ABG can reduce EN compared to other grouping algorithms. Moreover, the average storage of group key parameters for ABG with a neighboring aggregator arrangement is also smaller than that of TDG and BUG. The performance evaluation results suggest that the proposed scheme can be employed in ubiquitous WSN application domains. Experimental design conditions include same power, memory capacity, CPU processing power, and communicational capability, and the results depend on these conditions. Thus, if the conditions are changed, the results may differ: there are the experiment's limitations.  
In our future studies, in addition to investigating the encryption and decryption operations issues, we will investigate other issues, such as the average storage of group key parameters and the effect of non-neighboring and neighboring aggregators on encryption and decryption. This will provide a more efficient way to communicate and secure data in WSN. This study proposed an experimental environment that may not adapt to the actual network environment. In the future, a system simulation method, such as steady-state simulation, is expected to be employed in the experiment to make the investigation fit the realistic network environment.  
Conceptualization, R.-I.C.; data curation, C.-W.C.; formal analysis, C.-W.C.; funding acquisition, R.-I.C. and [PERSON]-H.H.; investigation, R.-I.C., C.-W.C. and [PERSON]-H.H.; methodology, R.-I.C. and C.-W.C.; software, R.-I.C. and C.-W.C.; supervision, [PERSON]-I.C. and [PERSON]-H.H.; validation, Y.-H.H.; writing--original draft, [PERSON]-I.C. and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-I.C. and [PERSON]-H.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Taiwan National Science and Technology Council (NSTC), under Grant no. 110-2410-H-002-094-MY2, 111-2221-E-224-033-MY2 and MOE \"Teaching Practice Research\" Subsidies Program grant number PBM1110139.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Small Analysis with Electronic Nose (E-Nose)  
The electronic nose (E-nose), a pivotal tool for assessing the concentration and presence of odor molecules, plays a significant role in determining product quality and consumer acceptability [20]. The results of the E-nose analysis (Figures 1 and S1, and Table S3) offer insights into the conductivity of sample gas and zero gas to the sensors [43]. Sensor response, denoted by the ratio of conductance (G0/G or G/G0, where G0 and G represent sensor conductance before and after exposure to gas samples, respectively), exhibited diverse trends across different sensors. Sensors W1W (responsive to organic sulfur compounds and terpenes), W5S (broad range), W1S (broad range for methane), and W2S (broad range for alcohols) displayed an initial sharp rise in the first 10 s, followed by a gradual decline to a steady value after 20 s. Conversely, sensors W1C (aromatic), W3C (aromatic), W6S (hydrogen), W5C (aromatic-aliphatic), W2W (sulfur-organic), and W3S (methane-aliphatic) maintained relatively constant values close to 1, indicating minimal responses [15]. The sensorgram output (Figure S1) revealed similarities between the control responses and the bicultures consisting of _Lactobacillus helevticus_, Lh 43, and _Aectobacter pasteurianus_, Ap-As.1.41. Both treatments exhibited a sharp rise in sensors W1W, W5S, W1S, and W2S during the first 10 s, almost maintaining these elevated levels throughout the analysis. Fermented pures, including _Lacticaseobacillus casei_ Lc-122 and _A. pasteurianus_ Ap-As.1.41, as well as _Lactiplantiobacillus plantarum_, Lp-28 and _A. pasteurianus_, Ap-As.1.41, displayed a similar trend with slight modifications. Notably, sensors W1S and W2S showed persistent elevated levels after 20 s, indicating lingering concentrations of these volatiles.
## 2 Ionospheric TEC Extraction  
Ionospheric total electron content (TEC) is the integrated ionospheric electron density along the ray path between GNSS satellites and receivers. The TEC is commonly extracted from dual-frequency GNSS receivers through methods of carrier phase smoothed pseudorange [42] and uncombined precise point positioning (UPPP) [43; 44]. The TEC estimated by the method of carrier phase smoothed pseudorange is affected by the code-delay multi-path and the length of every continuous arc [45]. The UPPP method proposed by [PERSON] et al. [44] is used to improve the accuracy of ionospheric TEC extraction by precise GNSS orbit and clock products. In general, the accuracy of TEC estimated by the UPPP method is superior to that of the carrier phase smoothed pseudorange method. We use the UPPP method to extract ionospheric TEC from BDS-GEO observations. The estimated ionospheric delay \(I_{ion}\) by the UPPP method includes not only the real ionospheric delay, but also the differential code biases from both receivers and satellites [46], as shown in the following equation:  
\[I_{ion}=I+\frac{1}{1-\gamma_{2}}(\text{DCB}^{r}-\text{DCB}^{s}) \tag{1}\]  
where DCB\({}^{r}\) and DCB\({}^{s}\) are the differential code biases of receivers and satellites, respectively; \(I\) is the real ionospheric delay; and \(\gamma_{2}=f_{1}^{2}/f_{2}^{2}\).  
In Equation (1), the DCBs should be separated from the ionospheric delay in the UPPP model. The first order of ionospheric refraction is only considered to estimate the ionosphere delay and assume that all electrons of the ionosphere are concentrated in a thin layer at a certain height. The vertical TEC can be translated from slant TEC using the single-layer model. In addition, the vertical TEC is modelled by a spherical harmonic function defaulted as fourth-order here, and then the DCBs of receivers and satellites are estimated by the least square method. As the DCBs of receivers and satellites are correlated to each other, a zero-mean constraint condition of all BDS satellites is needed to separate receiver and satellite DCBs [47].  
\[\begin{split}&\text{VTEC}=\cos(\arcsin(\frac{R}{R+H}\sin(\partial z )))\times\text{STEC}\\ &=\cos(\arcsin(\frac{R}{R+H}\sin(\partial z)))\times[\frac{f_{1}^{ 2}}{40.28}I_{ion}+\frac{f_{1}^{2}f_{2}^{2}}{(f_{1}^{2}-f_{2}^{2})40.28}(\text{ DCB}^{r}-\text{DCB}^{s})]\end{split} \tag{2}\]  
where \(z\) is the satellite elevation angle; \(R\) is the earth's radius; \(H\) is the height of the SLM; and its values are taken as \(R=6371\) km, \(H=506.7\) km and \(\partial=0.9782\), respectively.