esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
###### Abstract  
Rockfalls and landslide events are caused by different factors among which are included geomorphological and climatic factors and also human interaction. Therefore, the economic and social impacts can be significant and the remote monitoring of such hazards has become an essential topic in various applications. Wireless sensor networks (WSNs) are well suited for the deployment of monitoring systems, benefiting from the different technologies and topologies that are available and evolving nowadays. This review paper aims to summarize and overview the up-to-date state of the art of rockfall and landslide monitoring systems based on WSNs. The implementation and methods were analyzed for each solution, along with the system architecture and relevant hardware aspects. All the retrieved data were used to analyze the current trends and future possibilities in the field of WSN geohazard monitoring.  
geohazard; IoT; landslide monitoring; risk assessment; rockfall monitoring; sensors; wireless sensor network +
Footnote †: journal: _Sensors_  
0000-0001-5000-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00  
The authors of [16] provided an assessment of global landslide susceptibility (LSS). The study was carried out at a 36 km spatial resolution of global satellite soil moisture observations, in order to prepare a combination of a global LSS map with dynamic soil moisture estimates for landslide modelling. The analysis carried out in [17] revealed the main topics, countries, and institutions that have conducted research regarding the topic of rockfall and landslides.
## 4 Experimental Verification
### Data Processing  
ASO is used to find the optimal high-quality factor \(Q_{1}\) and low-quality factor \(Q_{2}\) for the time-domain signals of the reciprocating compressor under three working conditions. The initial \(Q_{1}=3\) and \(Q_{2}=1\) are set. The range of \(Q_{1}\) is [3; 40], and the range of \(Q_{2}\) is [1; 10]. After 1000 iterations of atomic search algorithm optimization, the high-quality factor and low-quality factor in different states are obtained. In the state of the normal valve \(Q_{1}=5.0059\), \(Q_{2}=1.4033\); in the state of valve wear \(Q_{1}=25.8657\), \(Q_{2}=1.1788\); and in the state of spring failure \(Q_{1}=7.4277\), \(Q_{2}=1.4721\). Figure 8 illustrates that the optimized high-quality factor and low-quality factor are applied to the subsequent RSSD. The high-resonance components, low-resonance components, and residual components of the three working conditions are obtained by ASO-RSSD.  
Figure 6: Details of valves in three different states.  
Figure 7: Time-domain waveforms of vibration signals of reciprocating compressors under three working conditions: (**a**) Time-domain signal of normal valve; (**b**) Time-domain signal of valve wear; (**c**) Time-domain signal of spring failure.  
Due to the simulation experiments confirming that the fault information after signal decomposition is mainly contained in the low-resonance component, RCMDE is extracted only from the low-resonance components. The RCMDE optimized by GA is developed to extract features of the low-resonance components. The scale factor s of each group of RCMDE is set to 11, and the data length \(x\), embedding dimension \(m\) and time delay \(\tau\), are optimized. The range of \(x\) is [512, 5120]; the range of \(m\) is [4, 6]; and the range of \(\tau\) is [1, 6]. The finally obtained RCMDE value of each sample with different colors is shown in Figure 9a, where atrib1\(\rightarrow\)atrib11 represent scale factors 1-11. For a further evaluation of the method, RCMDE without optimization is compared with the signal length \(x=5120\), embedding dimension \(m=5\), time delay \(\tau=1\), and scale factor \(s=11\). The result is shown in Figure 9b. The RCMDE optimized by GA of each group is less prone to aliasing compared to the RCMDE without optimization. As is shown in Figure 9c, RCMDE values with optimized parameters will be smaller. It can be invented that the RCMDE values with optimized parameters have better aggregation between the same category compared with the RCMDE values without optimization, which also enables better fault classification in the next step of pattern recognition. For a more in-depth analysis of the advantages of the optimized parameter, the eigenvalues of the two methods are imported into the pattern recognition model.  
Figure 8: The RSSD results of vibration signals of reciprocating compressor under three working conditions: (**a**) RSSD results of the normal valve; (**b**) RSSD results of the valve wear; (**c**) RSSD results of the spring failure.
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 2 Materials for Doping ZnO Nanoparticles (NPs)
### Other Elements  
Apart from RE and TM elements, other elements have also been exploited as dopant atoms. For example, aluminium has been used mainly for the fabrication of flexible detectors. For example, a stretchable NO\({}_{2}\) gas sensor was developed by allowing the diffusion of Al ions in an array of vertically aligned ZnO nanorods [136]. A flexible pH sensor based on Al-doped ZnO nanosheets was also obtained. Due to Al doping, the sensitivity of the device was highly improved (50.2 mV/pH) with respect to the pure ZnO nanosheets (34.13 mV/pH) [137].  
Magnesium is another common choice for ZnO doping and it was used mainly for improving the optical and electromechanical properties with respect to pure ZnO. As in the case of gold, Mg doping was found to increase the photoluminescence properties of ZnO in the visible spectrum of light [138]. In the same work, the photocatalytic degradation of rhodamine B was proven to be enhanced by Mg doping. Moreover, an enhanced antibacterial activity was also obtained, which was correlated to a lower band-gap and the resulting enhanced photocatalytic activity with respect to pure ZnO. From the electromechanical point of view, in a work from [PERSON] et al., the piezoelectric coefficient of Mg\({}_{\mathrm{x}}\)Zn\({}_{\mathrm{(1-x)}}\)O appeared to be increased (d\({}_{33}\) = 54.1 pm/V) by increasing the amount of Mg up to x = 0.28 [139]. After this point, which was found to be the limit for not observing the formation of any secondary phase in the crystalline structure of ZnO, the authors found that the d\({}_{33}\) coefficient abruptly reduced values below the pure ZnO one.  
Nitrogen-doped ZnO nanowires, further coated by a thin layer of titania to form a core-shell structure, were also proposed [140]. After the thermal annealing of ZnO nanowires in a nitrogen atmosphere, the nanomaterials showed a higher absorption of UV and visible light than the corresponding air-annealed samples, due to slightly lower values obtained for the band-gap. Ultimately, the one-dimensional ZnO-TiO\({}_{2}\) core-shell nanostructures were proposed for photoelectrochemical water splitting, showing enhanced efficiencies under solar light illumination.  
Actually, the choice in terms of dopants is very wide, as witnessed by a few other works reporting the use of other elements such as chlorine [141], antimony [142], fluorine [143], lithium [144] and many others, each of which changes specific properties of pure ZnO that may be suitable for a specific application.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 2 Framework  
### Structure of an MMG  
The structure of an MMG in this paper is shown in Figure 1. The parties involved in energy trading in the MMG are MGs, the EMO and a power grid. Each MG mainly consists of a PV system, an ESS, loads, a smart meter and a local EMS. For a rational MG, the first choice of PV generation is to supply its own loads and charge its own ESS. The MG can then act as a seller when there is an energy surplus or as a buyer when there is an energy deficit. The net energy profile of the MG is optimized by the local EMS according to the benefits generated by energy consumption and the internal prices from the EMO. The EMO is responsible for stimulating energy trading in MMGs by establishing a reasonable internal buying price (\(p_{cb}^{h}\)) and selling price (\(p_{cs}^{h}\)) each hour. At the occurrence of an internal energy mismatch, the EMO trades with power grids to balance supply and demand. In order to guarantee that the profits of the MG obtained from energy sharing among MGs are better than those of the MG obtained from energy trading with power grids directly, the internal prices produced by the EMO should be between the selling price (\(p_{gs}^{h}\)) and the buying price (\(p_{gb}^{h}\)) of power girds, allowing the EMO to maximize its own profits under the constraint as follows:  
\[p_{gs}^{h}\leq p_{cs}^{h}<p_{cb}^{h}\leq p_{gb}^{h} \tag{1}\]
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 4 Results
### Sigmoidal Models
#### 4.2.3 Von Bertalanffy Model  
As illustrated in Figure 9c, the results obtained by applying the Von Bertalanffy model, as outlined in Equation (16), are presented. This model was employed to illustrate the progression of costs over time, incorporating a functional structure that facilitated the capture of the non-linear growth characteristic of progressive construction processes.  
The model demonstrated a satisfactory representation of the observed behavior in the experimental data, particularly during the initial and intermediate phases of the construction process. The iteration curves were consistently distributed around the main curve of the model, indicating stability in the simulations under different input conditions. The upper and lower bounds delineated the dispersion of iteration trajectories. The lower limit of the range represented a scenario with low costs in the early phases of the project, followed by a sustained and gradual growth, until reaching maximum levels towards the end of the standardized period. This behavior was consistent with typical patterns of physical and financial progress in infrastructure works.  
In contrast to earlier logistic models, the Von Bertalanffy model exhibited a more gradual transition between growth stages, a property that rendered it well-suited for representing constructive processes that do not necessitate abrupt accelerations. However, this same characteristic may have constrained its capacity to capture dynamics with rapid changes in slope, such as those that occur in critical phases of execution.
# Energy Efficiency of D2D Multi-User Cooperation
## 3 Analysis of System Energy Efficiency
### System Energy Efficiency
\begin{array}{ccc}(^{g})b_{1,1}^{\prime}&\cdots&(^{g })b_{1,x_{d}}^{\prime}\\ \cdots&\cdots&\cdots\\ (^{g})b_{x_{d},1}^{\prime}&\cdots&(^{g})b_{x_{d},x_{d}}^{\prime}\end{array}  \right]_{(x_{d}\cdot x_{d})},g\in(1,\cdot\cdot\cdot,a) \tag{15}\]\[B^{\prime}_{a+1}=\left[ \begin{array}{cccc}{}^{(a+1)}b^{\prime}_{1,1}&\cdots&{}^ {(a+1)}b_{1,b}\\ \cdots&\cdots&\cdots\\ {}^{(a+1)}b^{\prime}_{b,1}&\cdots&{}^{(a+1)}b^{\prime}_{b,b}\end{array} \right]_ {b\cdot b} \tag{16}\]  
There is one 1 in each row and each column. In other words, \({}^{(\delta)}b^{\prime}_{m,j}=1\) and \({}^{(\delta)}b^{\prime}_{i,n}=1\) only when \(m\
eq i,j\
eq n\), \(g\in(1,\cdots,a+1)\). The \(B^{\prime}_{a+1}\) will be an empty matrix if \(b=0\). Thus, the energy consumption matrixes of paired D2D users of \(G_{g}\left(Y_{g},N_{g}\right)\) can be expressed as:  
\[E_{g}=\left[ \begin{array}{cccc}{}^{(\delta)}E^{1}_{1}&\cdots&{}^{(\delta)}E^ {1}_{K-g\cdot x_{d}}\\ \cdots&\cdots&\cdots\\ {}^{(\delta)}E^{x_{d}}_{1}&\cdots&{}^{(\delta)}E^{x_{d}}_{K-g\cdot x_{d}} \end{array} \right]_{(x_{d}\cdot(K-g\cdot x_{d}))},g\in(1,\cdots a+1) \tag{17}\]  
There is only one member bigger than zero in each row and each column and there are \(x_{d}\) columns, where there is only one member bigger than zero in each column. The rest of the \(|(K-g\cdot x_{d})-x_{d}|\) columns are all zeros. In other words, \({}^{(\delta)}E^{m}_{j}\
eq 0\) and \({}^{(\delta)}E^{i}_{n}\
eq 0\) when \(m\
eq i,j\
eq n\). The total energy consumption of cooperation between \(Y_{1}\) and \(N_{1}\) can be expressed as:  
\[{}^{2}_{d}E_{U\to U}=\sum_{g=1}^{a+1}\left(\sum_{m=1}^{x_{d}}\sum_{j=1}^{K-g \cdot x_{d}}{}^{(\delta)}E^{m}_{j}\right) \tag{18}\]  
\({}^{(\delta)}E^{m}_{j}\) represents the energy consumption of the cooperation between user \(U_{m}\) and \(U_{j}\). \(U_{m}\) and \(U_{j}\) are paired by using the \(g\)th Kuhn-Munkres. Hence, the total energy consumption of sharing one packet \(D_{d}\) among \(K\) users can be expressed as:  
\[E_{d}=\left\{ \begin{array}{ll}{}^{d}E_{S\to U}+\frac{1}{d}E_{U\to U},\;x_{d} \geq K-x_{d}\\ {}^{d}E_{S\to U}+\frac{2}{d}E_{U\to U},\;x_{d}<K-x_{d}\end{array} \right. \tag{19}\]  
If \(S\) divides data \(L\) into \(n\) packets and the length of each packet is \(D_{d}\), the total energy consumption of sharing \(L\) with \(K\) users can be expressed as  
\[E_{L}=\sum_{d=1}^{n}E_{d}\]  
The energy efficiency will be expressed as:  
\[EE=E^{\prime}_{L}/E_{L} \tag{20}\]  
where \(E^{\prime}_{L}\) represents the total energy consumption when all the outage probabilities of links are 0.
###### Abstract  
Damage to buried gas pipelines caused by mining activities has been frequently reported. Based on a case study from the Central China coal mining area, this research employs a scaled model experiment to investigate the movement of overlying strata in a room-and-pillar mining goal. Distributed optical fiber strain sensors and thin-film pressure sensors were used to simultaneously measure the stress variations in the pipeline and changes in the soil pressure surrounding it. As the mining recovery rate increased from 50% to 86%, the maximum displacement of the overburden sharply escalated from 33.55 mm to 79.19 mm. During surface subsidence, separation between the pipeline and surrounding soil was observed, leading to the formation of a soil-arching effect. The development of the soil-arching effect increased soil pressure on the top of the pipeline, while soil pressure at the bottom of the pipeline increased on the outer side of the subsidence area and decreased on the inner side. Three critical sections of the pipeline were identified, with the maximum stress reaching 1908.41 kPa. After the completion of mining activities, pipeline collapse occurred, leading to a weakening of the soil-arching effect. Consequently, both stress concentration in the pipeline and soil pressure decreased. The probability integral method was corrected by incorporating the fracture angle, which enabled the determination of the location of maximum surface subsidence curvature, found to be close to the three failure sections of the pipeline.  
gas pipeline damage; coal mining area; scaled model testing; overlying strata movement; measured pipeline responses; soil-arching effect +
Footnote †: journal: Applied sciences  
0000-0001-9123-3401][PERSON]  
1 School of Mechanics and Civil Engineering, China University of Mining and Technology,  
Xiuzhou 221000, China; [EMAIL_ADDRESS] (J.-H.); [EMAIL_ADDRESS] (J.-S.); [EMAIL_ADDRESS] (P.G.); [EMAIL_ADDRESS] (M.X.); 173366150200163.com (T.S.)
### Existing Research and Limitations
#### 1.2.1 Blockchain-Based Data Management  
Several studies have proposed combining blockchain with off-chain storage or managing data access rights through smart contracts to enhance data security and privacy. For instance, [PERSON] et al. improved processing performance using a lightweight consensus protocol and allowed only authorized nodes to participate, thereby enhancing confidentiality with asymmetric encryption. However, these methods still face issues related to storing original data directly on the blockchain and involving all nodes in the transaction verification process, which raises concerns about privacy and efficiency.  
In response, this study proposes a partially decentralized blockchain framework for the secure and efficient management of EV black box data. Each EV encrypts its collected data using RSA-2048 encryption, and only the hash of the ciphertext is recorded on the blockchain, ensuring both confidentiality and integrity. This method significantly reduces bandwidth and storage burdens while enhancing performance through a streamlined consensus algorithm involving only pre-approved nodes.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 4 Experimental Evaluation
### Results and Analysis
#### 4.4.4 User Study  
The automatic sentence evaluation metrics may not show the quality of generated sentences. To evaluate the model with human judgment, a user study should be conducted. We got 10 people to evaluate the generated sentences of two testing subsets. These two testing subsets are randomly selected from the MSVD dataset and MSR-VTT dataset with 20 samples, respectively. In addition, the testing sentences are generated by our final model (CSA-SR). Users watch the same 20 videos of MSVD and MSR-VTT testing subsets, then select a proper sentence for the corresponding video from our generated sentence and a ground-truth sentence, where the ground-truth sentence is randomly selected. As shown in Table 6, \"pred\" denotes the number of generated sentences chosen by users, \"gt\" denotes the ground-truth sentences, and \"rate\" denotes the proportion of generated sentences. The higher \"rate\" indicates that the generated sentences are more appropriate from the user's perspective. The average rate shows generated sentences have a competitive quality, especially on the MSVD dataset.
## 3 Experimental Setup and Results
### Results and Discussions
In other words, with the SM implementation using our active transmitter detection scheme, a performance balance between SM and SSD is expected when the channel gain difference is smaller. The suppress ratio \(\tau\) in the pre-distortion technique is studied to illustrate the impact of pre-distortion in the proposed transmitter detection scheme, where \(\tau=\)1 is taken as a reference to represent a limited pre-distortion scenario, as inferred from Equations (1) and (2). As shown in Figure 4c, an improved BER performance can be obtained with a decreasing \(\tau\) from 1 to 0.5, which verifies the effectiveness of the pre-distortion technique. However, the BER improvement is much smaller when \(\tau\) is decreased from 0.7 to 0.5. That is because more signal nonlinearity is introduced with a smaller \(\tau\). In fact, a smaller \(\tau\) also reduces the signal power efficiency. Therefore, a tradeoff of \(\tau\) value must be made for optimized system performance. Here, \(\tau=0.5\) is applied in our experiment. Note that, here, our proposed SSD is provided using CAP modulation. In fact, it has also been theoretically reported that SSD can be provided using OFDM modulation [25]. However, when SM is incorporated with the power difference between channels, we found that under the transmitted optical power constraint, the received optical power  
Figure 4: BER of the SM with SSD using the proposed novel active transmitter detection scheme. (**a**) ideal detection vs. the proposed detection. (**b**) Different channel gain distributions. (**c**) Different suppress ratios \(\tau\). budget for CAP and OFDM is lower in the experiment. Compared with CAP modulation, OFDM has lower energy efficiency and higher PAPR [26], which is more demanding for implementation under a tight received optical power budget. Moreover, a customized pre-distortion technique for differentiating active transmitters based on OFDM signal characteristics needs to be further considered with equalization adopted [27].
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 4 Evaluation Parameters  
Simulation, as well as measurements were carried out based on the following parameters.  
BER is an important quality indicator of digital transmission systems. It is the number of bit errors per unit time; therefore, it is calculated as the number of bit errors divided by the total number of transmitted bits in one time period.  
MERrepresents a relationship between the signal-to-noise ratio and error vector magnitude. It is used to evaluate the performance of the transmitter and/or receiver in systems that employ digital modulations. It is mainly influenced by channel quality and path propagation, which cause the constellation points to deviate from the ideal location.  
EVM provides a comprehensive measure of the quality of the modulator and/or demodulator performance in the presence of various impairments (Figure 6). Various imperfections in implementation, such as carrier leakage, phase noise, shot noise, etc., cause the deviation of constellation points. EVM is used to quantify how far the points are from the ideal location. There is a one-to-one relationship between EVM and MER; however, MER is calculated from the average power of the signal.  
Figure 5: Block diagram of a linear equalizer as a linear FIRfilter with a transverse structure.  
The energy per bit-to-noise power spectral density ratio (E\({}_{\rm b}\)/N\({}_{0}\)) is an important parameter of digital communication systems. It is basically the SNR per bit (normalized SNR measurement). It is often used to compare the BER performance of various digital modulations without the influence of the used bandwidth. SNR is periodically obtained at the receiving universal software radio peripheral (USRP) and then calculated by a computer running LabVIEW.