esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Findings
### Core Elements Underpinning SCM in the Construction Industry
#### 3.2.3 Relationships
In an industry increasingly exposed to uncertainty, where unexpected problems are almost inevitable, trust liberates participants to pursue courses of action with confidence in each other's, enhancing greater agility, informality and morale in relationships [72]. Collaboration is the basis for good working relationships among participants and ensures supply chain efficiency and responsiveness [10]. It implies leaving behind adversarial relationships to embrace an integrated team culture [18], in which an emphasis on group effort prevails over individual efforts and rewards [6]. If things go wrong, energy is not wasted on looking for someone to blame. Instead, solutions are developed in a concurrent style [17]. In a collaborative supply chain, partners work together in an integrated and coordinated way in order to accomplish collective goals and to achieve mutual benefits [1]. This does not mean that relationships with every partner have to be collaborative, but efforts should focus on developing stronger ties with the more strategic ones and managing weaker ties through standardized practices [6]. The development of a more collaborative culture strongly depends on effective communication among SC partners, ensuring good and reliable flows of information [14]. However, under traditional contracts, there is often an unwillingness among SC participants to share information [21], affecting the formation of long-term relationships [10]. Information sharing reduces supply chain uncertainty and provides the possibility to anticipate situations [28], driving all the project phases and enabling correct and in time decision making [20]. An open and effective communication is crucial to obtain competitive advantages. It minimizes errors, reworks and schedule delays [10]. Additionally, it facilitates eliminating learning barriers, allowing participants to acquire knowledge, to innovate and to learn from the best business practices [74; 80]. Conflicts among supply chain partners, which generally bring tension and lead to bad relationships, are hardly avoidable in the construction industry [10]. Therefore, early warning mechanisms should be established in order to anticipate potential problems. Furthermore, developing protocols for effective joint problem solving, continuous improvement and sharing learning mechanisms are essential not only to solve problems as early as possible but also to avoid the same problems to come again [14; 18]. Unclear risk allocation is one of the roots problems of much litigation. Sharing risk and benefits is an SC commitment to share either profits or losses, being a motivation to achieve common goals [10]. A deep understanding of the key SC risks is pivotal to formulate more realistic plans and expectations. After estimating the impacts that they may cause on project objectives, partners can better allocate the risks through the entire supply chain [44]. Risks should be allocated to the parties who can best manage it, offering appropriate rewards. That is, return should be always be balanced against the effort [18].
## 4 Security Requirements in Current Offline Payment Functions
### Offline Payment Function with Cryptocurrency Proposed by [PERSON]  
Among the security requirements mentioned, [PERSON] et al. [93] reviewed the fulfillment of no double spending and unforgeability for the offline payment function of Bitcoin. The risk of double spending on offline payment functions comes from the fact that the paper can use the public key more than once. In an online payment situation using blockchain, a record of double spending can be stored in a block to prevent double spending. However, it is difficult to prevent double spending from transactions via offline payment functions. In addition, since Bitcoin is traded through a distributed ledger method, it is even more difficult to use a solution introducing a trusted third party, as stated in [PERSON] et al. [1]. Thus, a different approach is required to satisfy the security requirements of no double spending and unforgeability in offline payment functions.  
The method proposed by [PERSON] et al. [93] creates an offline wallet using tamper-proof secure hardware. However, unlike the security mechanism for the online payment, where no double spending is satisfied through several surveillants, this method is less stable due to the reliance on technology-based hardware support.  
As an alternative to the method above, [PERSON] et al. [93] proposed the utilization of the deposit system. This enables only wallets that have received the certificate from the certification authority to participate in offline payment functions. At this time, the certification authority must provide a certificate to the wallet to prove the amount of cryptocurrency sent. Through these two methods, the security requirements on no double spending can be satisfied even in an offline payment situation.  
To satisfy the security requirements of unforgeability, the verification and confirmation mechanism based on the time-variant transaction mentioned in Section 3 could be introduced. This method imposes a time limit on the transaction confirmation verification in existing Bitcoin, and the transaction becomes recognized only when a block is created within the time limit specified. The probability of the occurrence of coin forgery \(\alpha\) can be reduced by adjusting the time \(\delta\) required for offline payment. If the coin forgery cannot be prevented for a specific time limit \(\delta\), the forgery can be resolved by lowering the limit \(\delta\). For instance, if the attacker has 40% hashing power and the time limit \(\delta\) is set to 1600 s, the coin forgery attack cannot be prevented. However, when \(\delta\) is set to 800 s, the probability of coin forgery \(\alpha\) can be reduced down to a probability lower than a certain probability that can satisfy unforgeability even in offline payment situations.  
Meanwhile, [PERSON] et al. [93] did not deal with non-repudiation, verification, anonymity, and prevention from DDoS attacks. Additional examination is required to satisfy the above conditions, considering that the transaction details of Bitcoin are difficult to connect to the blockchain in the situation of offline payments.
# Research on Influence of Switching Angle on the Vibration of Switched Reluctance Motor
## 3 Influence of Switching Angle on Vibration
### Influence of Switching Angle on Current
The curve of the current harmonic components with the turn-off angle can be obtained by Fourier transform of the current waveform at different turn-off angles. Figure 7 shows the current harmonic variation rule when the reference current is 5A, the turn-on angle is fixed at 15\({}^{\circ}\), and the turn-off angle varies from 120\({}^{\circ}\) to 150\({}^{\circ}\). As can be seen from the figure, as the harmonic order increases, the harmonic amplitude shows a decreasing trend. From the variation trend of current harmonics of each order, it can be seen that the influence of the turn-off angle on current harmonics has obvious regularity. With the increase of the harmonic order, the current harmonic amplitude gradually presents the characteristics of periodic oscillation with the change of the turn-off angle, and with the increase of the harmonic order, the oscillation period gradually shortens. It can be read from the figure that the oscillation period of the 18 th order is 20 degrees, and the oscillation periods of the 36 th and 48 th order of current harmonics are 10 degrees and 5 degrees, respectively. After comparing and analyzing the harmonic amplitudes and their vibration periods of other orders, it is found that the oscillation period of the current harmonic amplitudes is related to the current harmonic orders, and the following relationship is approximately satisfied between them:  
Figure 4.1  
Figure 4.2 Current curves under constant turn-on angles and different turn-off angles. \(T_{k}\) is the oscillation period of the \(k\)-th order current harmonics changing with the turn-off angle. For low-order harmonics, due to the long oscillation period, the oscillation period cannot be observed in the limited range of off-angle variation. In the actual operation of the switched reluctance motor, for the same working conditions, there are various combinations of switching angles to achieve the speed regulation and torque control requirements. Under the angle control strategy, different switching angle combinations will generate different current waveforms, which will affect the performance parameters of the motor, such as vibration, torque ripple, and energy efficiency. Figure 4.3 Influence of turn-off angle to current harmonic magnitude (**a**) DC (**b**) 3 rd order (**c**) 9 th order (**d**) 18 th order (**e**) 36 th order (**f**) 48 th order.
## 4 Field Data Case
are a priori unknown and, therefore, a prior PDF with mean of zero and a relatively large STD of 100 ppm was defined (corresponding to an average of 9.3% of the magnitude of the measurement values). For the two types of data, different noise levels were estimated from repeated VES and FDEM measurements. These noise levels were used for defining the PDFs that provide the basis for the construction of data matrix **D**. We chose an ensemble size of 50,000 for this study based on the results of the synthetic sample size study and the fine discretization chosen for this study. The total computation time (on the same personal computer) for the separate VES inversion was 53.06 s, and for the separate FDEM inversion 724.36 s. The total computation time of the joint inversion was 770.49 s using the same prior and and forward response ensemble. The difference in the update step computation time between VES/FDEM inversion and the joint inversion was 0.07 and 0.15 s, respectively. In Figure 7, we show the results for separate and joint KEG inversion of VES and FDEM data. All of the profiles result in EC estimates in the same order of magnitude. The best fit models of both separate inversion both approximately indicate an increase of EC with depth. However, in the best fit from joint inversion, a different soil EC profile is derived. First, the top is estimated as relatively resistive, followed by a distinct increase of soil EC up to a depth of approximately 0.5 m. Below 0.5 m, the EC value decreases again down to a depth of 1 m. For the lower most part of the profile below 1 m, the EC is increasing again. Although smooth results of multi-layer inversion are difficult to interpret in terms of distinct soil or ground water layers, the joint best fit EC profile is in better agreement with the soil profile from auger drilling. Especially, the increase in clay content for the profile section between 0.15 m and 0.7 m agrees with the higher EC in the best fit joint profile. In both best fit profiles from separate inversion, this section of increased EC cannot be observed. Besides the difference in shape of the best fit profiles, the magnitude of uncertainty can be compared for the three posterior models. Whereas the profiles from the separate inversions only show a small reduction of the prior uncertainty for the upper, most sensitive, profile parameters,  
Figure 6: Output from auger drilling arranged according to approximate depth in the subsurface. the joint posterior model results in a comparably larger reduction of uncertainty. With increasing depth, i.e., with a decrease in measurement sensitivity, the uncertainty magnitude increases until, finally, the joint STD is in the same order of magnitude as for the other posterior models. Overall, the supposed properties of the joint posterior model, as described in the previous two paragraphs, are in agreement with the expectations for joint inversion as compared to the separate inversions.
## 1 Introduction
The representative mechanism (data reduction) is also engaged in these sub-clusters for more energy conservation. In our algorithm, all sensor nodes are first grouped into clusters (physical clusters) using hierarchical protocols. Subsequently, a sensor node generates a prediction model to forecast the data readings instead of reporting periodically data readings. The prediction mechanism is based on temporal data correlation demonstrated in [16]. The forecasting value is regarded as the concrete data reading. Hence, the transmissions of data reading reports are rationally reduced. Afterwards, sensor nodes within a physical cluster having strong spatial data correlations are then grouped into a sub-cluster (or say virtual cluster). Those chosen sensor nodes become the representative nodes for the sub-clusters and bear the responsibility of sensing environment around the whole virtual cluster. The main contributions of this paper are twofold. First, we use two tiers hierarchical architecture to form physical clusters and virtual clusters as shown in Figure 1. Under this kind of architecture, the energy conservation benefits by the reduced transmissions, which are dependent on the prediction model. Also, the energy can be further conserved because of the representative mechanism by sub-clustering. Only the representative sensor node of the virtual cluster turns its sensing unit on. Oppositely, the others within a virtual cluster can just power off for energy saving. Secondly, the decrement of transmitted data readings results in the possible decreased accuracy in monitoring environment. Moreover, the representative sensor nodes need to be able to represent the monitoring area well. Therefore, we try to keep the accuracy of monitoring within an acceptable level. Note that the representative sensor needs to transmit to physical cluster head (abbreviated as _pch_) its readings, which differ from the predicted readings by more than a certain pre-specified error threshold. The remainder of this paper is organized as follows. Section 2 reviews several hierarchical protocols. Also, some data reduction algorithms, based on correlation and prediction proposed previously, are surveyed in a concise manner. In Section 3, we put forward the system model of our scheme. A sub-clustering algorithm under a hierarchical architecture is presented. Also, we investigate our data reduction mechanism based on data prediction and correlation. Then, the simulation results of the proposed algorithms are presented in Section 4. Finally, conclusion is given in the last section of the paper. Figure 1: Two tiers hierarchical architecture is composed of physical and virtual clusters.
## 3 Experiments and Results  
To assess the effectiveness of MSACN, extensive experiments are evident on the ZY-3 optical satellite image dataset. In the experiment, we strictly set a series of hyper-parameters and conditions to ensure the accuracy and comparability of the experiment. We compare the MSACN with other methods using convolutional neural network (CNN) architectures and Transformer-based methods to evaluate the performance advantages of MSACN, and try to explore the different impacts of architecture on remote sensing image processing. In terms of experimental results, we record various performance indicators in detail, to comprehensively assess the performance of various methods. A detailed presentation of our experimental results will follow in the subsequent sections.
## 2 Materials and Methods
### Methodology
Values categorized in this manner can subsequently be represented on a map using suitable color-coding. Hot spot analysis can be applied in any scenario where a spatial relationship is desired to identify clusters of high or low values. Crime analysis [14; 15; 16; 17] is one of the most common fields where this method is used. It aims to identify spatial clusters with high crime rates to introduce appropriate solutions for crime prevention. In [16], the authors also analyzed crash data besides crime. The paper [17] compared the hot spot analysis method with Kernel Density Estimation (KDE). Other applications can be found in epidemiology [18; 19], economic geography, demographics, traffic incidents, analysis of voting patterns, and retail trade [17]. There are also examples of hot spot analysis applications in geology and geotechnics in the literature. Ref. [20] presents an optimized hot spot analysis method to detect landslides. In turn, ref. [21] presents an analysis of the subsidence problem caused by groundwater withdrawal using Sentinel-1 satellite data and the hot spot method to identify areas of significant subsidence. Sentinel-1 data were also used in [22] to analyze land movement in the Alpine region. The use of hot spot analysis allowed the authors to create a semi-automatic method for extracting moving areas associated with landslides and other mass destruction processes. Figure 2: Sample Moran scatterplot.
# Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
## 5 Conclusions  
In order to estimate the flow field signal change based on the flow sensing mechanism of a lateral line system, so to improve the environment adaptability of AUVs, a novel KKF-VSM was provided, using the Kautz function as the kernel function. This is a novelty compared to previous related works, which were mainly based on RBF-NNM, and VSM with the frequency response function as the kernel function. The flow field around a square target was studied, and its pressure signal time series was obtained by CFD and PIV. The predicting accuracy, sensitivity, and robustness of KKF-VSM were analyzed in this work. In addition, an RBF-NNM with a Gaussian kernel function was compared with KKF-VSM. Some interesting conclusions were made. The pressure signal of the flow field around the square target was chaotic, and KKF-VSM and RBF-NNM could predict its change trend very well. However, in comparison with RBF-NNM, KKF-VSM showed higher accuracy in  
Figure 16: Predicting results of KKF-VSM when an irregular undesired signal existed.  
Figure 17: Predicting results of RBF-NNM when an irregular undesired signal existed.  
filtering regular and irregular noise. Furthermore, KKF-VSM appeared to capture weak undesired signals more easily, and its filter ability was more robust than that of RBF-NNM. In future work, the authors intend to apply this procedure considering an experimental setup in a natural underwater environment.  
Conceptualization, X.L. and X.W.; methodology, X.L.; software, X.L. and Q.Q.; validation, X.W., J.Z. and Q.Q.; formal analysis, X.L. and Q.Q.; investigation, X.L. and X.W.; resources, X.W.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.W. and Q.Q.; visualization, X.L. and Q.Q.; supervision, X.W. and J.Z.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the TIANJIN ENTERPRISE SCIENCE AND TECHNOLOGY COMMISSIONER PROJECT, grant number 19 JCTPJC52100.  
Not applicable.  
Data Availability Statement: Data are available from the corresponding author upon request.  
The authors acknowledge the generous support provided by the TIANJIN ENTERPRISE SCIENCE AND TECHNOLOGY COMMISSIONER PROJECT in funding this research.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
References
## 3 Test Results and Analysis
### Vertical Compression Performance of Bearings
#### 3.1.2 Vertical Pressure  
Figure 6 listed the load-displacement curves of the three bearings under different vertical pressures (2900 kN, 3400 kN, and 3700 kN) at a loading frequency of 0.1 Hz. It explains that the hysteretic curve slopes of the bearings, namely the vertical compression stiffness, increases significantly with the growth of vertical pressure. Among them, the curve shape for LRB is less sensitive to vertical pressure compared to TLRB and HDRB-LSF, which have thick rubber layers. This is due to the fact that as the vertical pressure increases, the thickness of the rubber layer for TLRB and HDRB-LSF diminishes, and the constraint effect of the steel plate is strengthened, ultimately leading to the rapid enlargement of compression modulus of the rubber in the triaxial compression state.
# Multi-Unit Coupled Motion Hybrid Generator Based on a Simple Pendulum Structure
## 2 Materials and Methods  
### Fabrication of MCM-HG  
The MCM-HG has a rectangular shape, assembled from acrylic plates, with an effective volume of 15 cm \(\times\) 10 cm \(\times\) 20 cm. The simple pendulum structure was manufactured using stereolithography resin (SL resin) through 3D printing technology. The pendulum shaft length is 100 mm, and the magnet holder measures 55 mm \(\times\) 55 mm \(\times\) 13 mm with a wall thickness of 3 mm. The hybrid generator comprises eight power generation units, including four TENG units (two S-TENGs and two P-TENGs; to facilitate differentiation, we refer to the TENG that is arranged in a fan shape at the lower layer as S-TENG, and the TENG that is integrated with PENG as P-TENG), two EMG units (P-EMG and C-EMG; to facilitate differentiation, we refer to the EMG composed of PCB coils as P-EMG and the EMG composed of coils as C-EMG), and two PENG units. The two S-TENGs are identical and symmetrically distributed on the lower layer. Each S-TENG consists of six contact-separation TENGs, measuring 50 mm \(\times\) 60 mm. The friction layer is composed of a 0.1 mm copper sheet and a 0.08 mm PTFE film. The friction layer is attached to a 1.6 mm-thick acrylic plate measuring 60 mm \(\times\) 80 mm. Each acrylic plate is adhered to a 0.2 mm-thick PTFE film and folded into a fan-shaped structure. The two P-TENGs are identical and symmetrically distributed on the upper layer, each comprising five contact-separation TENGs, measuring 50 mm \(\times\) 50 mm. The friction layer consists of a 0.04 mm weakly magnetic silicon steel sheet and a 0.08 mm PTFE film. The EMG units are composed of coils and magnets. The P-EMG consists of eight custom-designed PCB coils, while the C-EMG contains two ordinary wound coils. Each wound coil has an inner diameter of 3.5 mm, an outer diameter of 20 mm, and 1500 turns. Both EMG units share a magnet measuring 50 mm \(\times\) 50 mm \(\times\) 10 mm. The two PENG units are identical, each containing five PZT piezoelectric ceramic plates measuring 30 mm \(\times\) 50 mm \(\times\) 0.6 mm, adhered to a weakly magnetic silicon steel sheet measuring 50 mm \(\times\) 60 mm \(\times\) 0.04 mm.