esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Experimental Validation  
The experimental response was established to compare with the numerical response to verify the accuracy of the finite element model. The results are shown in Figure 11. In the pre-damage region, the reaction force increases rapidly at the beginning of compression and slows down suddenly, and the structure starts to experience progressive damage. The initial yield strength and strain hardening modulus at the plateau stage showed a large deviation. The yield strength of the simulated curves is lower, and the hardening modulus is higher. These deviations may be due to the limited accuracy of the elements, and the contact between the struts near the nodes was modeled with an underestimation of the node stiffness, resulting in a lower yield strength at the initial stage. However, the simulation model still ensures acceptable accuracy in energy absorption.  
Figures 12 and 13 show the results of the uniform gyroid and Diamond TPMS based cellular structure deformation behavior through numerical simulation and experiment. When \(\varepsilon=0.3\), an \"X\" shaped band was observed in the Sk-G samples and a 45\({}^{\circ}\) shear band was found in the Sk-D structures. This is due to the uniform formation of plastic hinges at the nodes during compression, leading to the rotation and collapse of the cell pillars until densification. Thus, the skeletal TPMS cell samples show an unstable deformation pattern.  
Figure 10: SEM micrographs of (**a**) Sh-G (**b**) corrugation(**c**) Sh-D (**d**) pore.  
Figure 11: Comparison diagram of simulation and experiment (**a**) Sk-G (**b**) Sk-D (**c**) Sh-G (**d**) Sh-D.  
Figure 12: Deformation comparison by simulation and experiment of skeletal TPMS structures.  
Figure 13: Deformation comparison by simulation and experiment of sheet TPMS structures.  
phenomenon. Therefore, the sheet-TPMS samples showed a more stable deformation pattern during axial compression than the skeletal TPMS samples.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
###### Abstract
Energy consumption models have been proposed and evaluated for various communication protocols in WSNs [5; 6; 7; 8], also including BLE [9; 10; 11]. However, actual sensor uptime and power consumption are affected by many parameters that are difficult to be generalized to all available connectivity solutions and implementations. Power consumption depends on the actual implementation of the communication solution, such as the physical RF implementation, power management, and the most important factor: the actual software running on a wireless sensor device. Having in mind the above said, in the paper, we demonstrate how a general model can be put in place and evaluated for a special case of low-power sensor for long-term ECG monitoring. Moreover, in this paper, we also shortly illustrate the task of providing guidelines for further development of the ECG sensor, based both on the experience gathered from real-life usage of the sensor and on the analysis of the production version of the sensor circuitry. The real-life experience was gathered from volunteer users [12] and from health-care centers [13]. In particular, the contributions of the paper can be summarized as follows:  
* We perform an in-depth analysis of the power consumption sources, particularly those connected with the BLE communication protocol, in order to model and then tune the autonomy of the given wireless low-power body sensor for long-term ECG monitoring. Power consumption estimations are the basis for the sensor autonomy estimation and we thoroughly analyzed the sensor from this angle. * Based on the findings, we propose analytical models that enable the power consumption estimations for all wireless operating modes of the ECG sensor. The proposed models are validated with the measured power consumption of the ECG sensor at different ECG sensor settings, such as sampling rate and transmit power. * The resulting models and autonomy estimations enable the engineers to find the optimal wireless ECG sensor settings to optimize the autonomy as well as the hardware of future sensor revisions, given the possible scenarios of sensor use. The paper is organized as follows. In the next section, the related work from the area of modeling and analyzing energy consumption models is overviewed. In that section, the wireless body sensor for continuous ECG measurements is described as well. Following this, in Section 3, we present the sensor's operating modes important for power autonomy estimation, including also energy consumption measurements during those modes. The power consumption models are presented in Section 4. Their validation and sensor autonomy analysis subsequently follow in Section 5. The paper concludes with a critical summary of the presented results, limitations of the work, and recommendations for increased autonomy of wireless sensors.
# Singing Transcription from Polyphonic Music Using Melody Contour Filtering
## 3 Note Transcription
### Pitch Line Matching  
The importance of pitch issues in note transcription has generally been ignored, and most of the criteria selected represent the mean value of pitch within the segmentation interval [35]. This method is certainly the most efficient; however, there are some deviations in the singing voice. [PERSON] [36] proposed a local pitch histogram to estimate the correct pitch. As shown in Figure 8, the existence of glide will affect the judgement of the entire pitch. Nevertheless, the stable area of singing voice mostly appears in the tail. Our proposed pitch line matching algorithm is as follows:  
1. Calculate the average pitch \(\overline{P}\) of the note and round it to an integer MIDI pitch;
2. Determine the five pitch lines \(\overline{P}\), \(\overline{P}\pm 1\), \(\overline{P}\pm 2\), as shown by the dashed blue line in Figure 8;
3. For the pitch \(P_{i}\) of each frame, if the interval between a certain pitch line and \(P_{i}\) is less than 50 cents, \(P_{i}\) will be matched to that pitch line. Lastly, the most frequently matched pitches are recorded as the final pitch of the note;
4. Repeat the above steps to determine the pitch of all notes.  
We furthermore considered the characteristics of singing voices to avoid the interference of aspiration. This subtle improvement has a huge impact on our entire experiment process. Our proposed algorithm effectively improves the accuracy of pitch by over 4% on average.  
Figure 7: Segmentation of notes using stability region: (**a**) is a fragment of the first song in the dataset “Cmedia” from singing transcription task; (**b**) shows the detailed data in the blue box, where the black box represents the sliding window and the threshold in the figure is the optimal pitch difference of the melody segmentation.
# Research on Flexible Traceability System of _Agaricus bisporus_ Supply Chain
## 3 Traceability Process Analysis of _Agaricus bisporus_ Supply Chain  
### Supply Chain Traceability Process Analysis  
The supply chain of _Agaricus bisporus_ exhibits pronounced seasonality, with its growth being significantly influenced by natural factors such as temperature and humidity, leading  
Figure 1: Blockchain-based traceability model for agricultural products.  
to fluctuations in production across different seasons. As a perishable product, it is imperative to maintain freshness, imposing higher standards on transportation, storage, and sales processes. Compared to other agricultural products, the growth cycle of _Agaricus bisporus_ is relatively short, requiring timely harvesting and market entry, thereby necessitating the supply chain to possess efficient coordination and distribution capabilities. Additionally, consumers have stringent requirements for the quality and safety of mushroom products, with high standards set for aspects such as pesticide residues and pest control. The cultivation and production stages demand a certain level of agricultural expertise and specialized knowledge, including techniques for soil treatment and temperature-humidity control. Due to the influence of seasonality and growth cycles, matching the supply and demand of _Agaricus bisporus_ proves relatively complex, calling for a supply chain equipped with flexible allocation capabilities.  
Through a more detailed examination of the _Agaricus bisporus_ supply chain traceability process, it is imperative that various stages within the supply chain facilitate the sharing and exchange of information to ensure seamless collaboration among them. Each stage comprises multiple sub-stages [16]. Addressing all stages, the system further categorizes the _Agaricus bisporus_ production process into six main modules: raw material and mushroom house management, cultivation management, storage management, processing management, logistics management, and sales management [17]. The traceability process of the _Agaricus bisporus_ supply chain is illustrated in Figure 2.
# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 1 Introduction
To overcome these issues, this paper proposes a multi-scale feature extraction neural network model based on the ConvLSTM network with convolutional long short-term memory for predicting PM2.5 concentration in air pollution prediction. Multiscale characteristics provide richer information than single-granularity characteristics. This article constructs a network with multiple branches to extract temporal features of PM2.5 with different granularities. The introduction of convolution helps memory cells capture long-term spatial dependencies.
## 3 Models and Assumptions
### The Environmental Context  
The environmental context consists of the industry's scale and structure, the company's competitors, technical service provider support, macroeconomic context, and regulatory environment [44]. Ref. [29] divides environmental factors into the inter-organizational environment and socio-political environment. In the inter-organizational environment, the adoption of innovation by organizations is usually caused by the competitive pressure caused by the adoption or imminent adoption of similar organizations [75]. The literature suggests that competitive pressure has always been regarded as the driving force for the adoption of organizational innovation, prompting organizations to seek competitive advantage through the adoption of innovation [48; 76]. SMEs tend to postpone innovationadoption due to a lack of expertise in innovation adoption [77, 78]. In general, innovation providers can help organizations bridge the knowledge gaps associated with innovation adoption. For instance, vendor support affects the decision to use accounting software [79], IS vendor support has a positive impact on the adoption of Internet/e-commerce technology by Canadian SMEs [80], and vendor support helps SMEs implement ERP systems [81].  
In the socio-political environment, government support is considered to play a significant role in the adoption of innovation by organizations [82, 83]. The government supports the adoption of organizational innovation by providing organizations with government subsidies [84], public technology development [85], technical training [86], etc. Based on the above, the following hypotheses are proposed:  
**H8**: _The competitive pressure of collaborative robot innovation is positively correlated with the adoption intention of SMEs._  
**H9**: _The vendor support for collaborative robot innovation is positively correlated with the adoption intention of SMEs._  
**H10**: _The government support for collaborative robot innovation is positively correlated with the adoption intention of SMEs._
## 3 Results and Discussion
### Resin Durability and Reproducibility  
A cycling experiment was conducted to verify the durability and reproducibility of the H-TTAPA resin. In particular, after the metal ions were adsorbed onto the resin, they were desorbed using mineral acids of different strengths. Figure 8b shows that, even after five adsorption-desorption cycles, there was only a very slight change in the exchange capacity of the H-TTAPA resin with each of the metal ions.  
Figure 7: Results for the (**a**) adsorption analysis, (**b**) effect of the contact time, and (**c**) effect of pH (experimental conditions: adsorbent dose = 1 g; contact time = 4 h; pH = 7; temperature = 25 \({}^{\circ}\)C; and agitation rate = 150 rpm).
## 5 Experimental Setup
### Case Study  
The proposed solution has been applied to a real test case composed of four actors that, when combined, allow for the creation of an infrastructure that can be replicated in many other industrial scenarios. The experimental setup consists of four actors. The first is the production line. It is the main system to be monitored in order to analyze the electrical parameters of the AD. The second actor is a smart meter module, which is directly connected both to the three-phase electrical connections of the industrial machine and to the third actor, the Raspberry Pi, using a Modbus TCP/IP protocol. The results of the AD are then sent to the fourth actor, the cloud, for further analysis and notification. In addition to this, to test and debug the proposed solution on the edge, the interconnection between the embedded device and the $604 module is not established directly but instead passes through the local area network (see Figure 7).  
In the next paragraphs, the production line and the smart monitoring module are described.
## 3 Results and Discussion
### Nanoscale Structural Properties
Dislocation lines can only be terminated on the inner surface of the interface or on the free crystal surface, but they may not stop inside the crystal and appear in the form of isolated sections. Therefore, inside the crystal, any dislocation must be closed to form a dislocation ring or be connected to form a dislocation network, or move toward a surface/edge to form a threading dislocation. All TEM images of the \(\varepsilon\)-Si layer, Ge\({}_{0.2}\)Si\({}_{0.8}\) layer and Si substrate of the sample are clean and flat, without visible defects/dislocations. In contrast, dislocations are concentrated in the Ge\({}_{\mathrm{x}}\)Si\({}_{1-\mathrm{x}}\) layer with the highest dislocation intensity near the Ge\({}_{\mathrm{x}}\)Si\({}_{1-\mathrm{x}}\)/C-Si interface. The dislocations first arose in the beginning of the Ge\({}_{\mathrm{x}}\)Si\({}_{1-\mathrm{x}}\) epitaxial growth. With an increase in thickness during the epitaxial growth, some (existing and/or new born) dislocation lines connected with others, to form dislocation networks, and remained within the crystal. Some connected and became dislocation loops, or they continued extending to the sample edges. Because of the existence of dislocation in the Ge\({}_{\mathrm{x}}\)Si\({}_{1-\mathrm{x}}\), especially near the Ge\({}_{\mathrm{x}}\)Si\({}_{1-\mathrm{x}}\)/C-Si interface, the average lattice quality of monocrystalline silicon deteriorated rapidly in the beginning of the graded buffer layer, which leads the FWHM of the Raman peak to broaden (as shown in Figure 6). With the gradual reduction in dislocations, the lattice quality improved. Each type of dislocation helps to relax internal/residual stresses that are induced by the change in lattice dimension [25; 26]. However, the threading dislocation is harmful to the structural reliability and performance of semiconductor devices, so it is necessary to avoid the appearance of threading dislocations. By using the manufacturing process to produce a strained silicon, dislocation  
Figure 8: Low-resolution TEM images of sample cross-section (**a**) around Ge\({}_{0.2}\)Si\({}_{0.8}\) layer, (**b**,c**) at Ge\({}_{\mathrm{x}}\)Si\({}_{1-\mathrm{x}}\)/C-Si interface and (**d**) inside Ge\({}_{\mathrm{x}}\)Si\({}_{1-\mathrm{x}}\) layer with a dislocation loop. lines mainly form rings or networks inside the crystal structure, which avoids the generation of threading dislocations.
# Distributed Authentication and Authorization Models in Cloud Computing Systems: A Literature Review
## 3 Results
### Cloud Access Control Mechanisms Included in the Review
#### 3.2.9 A Multi-Tenant Access Control (MTAC)  
Multi-tenancy is a cloud architecture, which is applied to SaaS, PaaS, and IaaS. When numerous tenants share the same resources in the cloud, multi-tenancy access control becomes critical. Each tenant applies its configuration based on the hardware or software needed, with no effect on the privacy and security of other users [15]. In the multi-tenant architecture, tenants refer to the customers using the software/hardware resources [18]. The reserving of the privacy and security of the customer's data is essential in a multi-tenancy cloud environment, especially to avoid a tenant from accessing other's resources. Thus, multiple tenants can share a single application without breaching the security of the others.  
Several multi-tenancy access control models for cloud computing have been proposed. Some of them exploited the security features in the traditional access control models to support a multi-tenancy environment. To manage and separate tenants in a cloud environment [42], RBAC was extended by using users' roles and an identity management scheme. However, to manage resource sharing in a multi-tenant environment, an extended RBAC and task access control model was proposed [43]. An ABAC model was introduced for cloud IaaS where the access decision depends on the attributes of tenants, networks, storage, and other resources in the cloud [44, 45, 25, 46].  
To facilitate the management of access control in federated multi-cloud applications, a new mechanism was proposed in [45, 46, 47, 48, 49, 50, 51]. This approach used the Representational State Transfer (REST) services interfaces for managing access to different XACML component functionality. To cope with a wide range of access control policies, a cloud access control model was proposed for heterogeneous multi-tenancy architectures [18]. The proposed model used the concepts that existed in RBAC, TRBAC, and ABAC.  
Table 3 summarizes the results of access control requirements and techniques included in the current review. The requirements are used as assessment criteria for access control models in cloud environments.
# Relay Selection Schemes for FSO Communications over Turbulent Channels
## 3 The Average Bit Error Rate (ABER) Analysis  
### LN Channels  
The ABER at the receiving node over LN channels can be derived as [11]  
\[ABER_{H}\approx\frac{1}{2\sqrt{\pi}}\sum\limits_{i=1}^{S}\frac{\omega_{i}}{2} \exp\big{(}\xi^{2}\overline{\psi}-2\sigma_{x}^{2}\xi^{4}\big{)}erfc\big{(} \frac{\overline{\psi}}{\sqrt{8}\sigma_{x}}\big{)}+\frac{1}{2\sqrt{\pi}}\sum \limits_{i=1}^{S}\frac{\omega_{i}}{2}erfc\big{(}\frac{\omega_{i}^{2}\xi^{2}- \overline{\psi}}{\sqrt{8}\sigma_{x}}\big{)}\approx\frac{1}{2\sqrt{\pi}}\sum \limits_{i=1}^{S}\omega_{i}I_{H},I_{H}\in\{1,2\} \tag{21}\]  
\[\overline{\psi}=\ln\Big{(}\frac{\sqrt{4\pi}i}{\Delta_{\rho}\sqrt{\gamma}} \Big{)}+2\sigma_{x}^{2}\big{(}1+2\xi^{2}\big{)},\]  
where \(S\) is the order of the approximation, \(\overline{\gamma}\) is the average SNR of transmitter, \(\omega_{i}\), and \(x_{i}\) are the weights and the roots of the generalized La guerre polynomial, respectively [11].  
The cumulative distribution function (CDF) of the lognormal channel, while taking into consideration the pointing error effect, is substituted by signal to noise ratio threshold, where, with a signal to noise ratio above this threshold, no outage will happen and the signal can be decoded with an arbitrarily low error probability, is derived in [11], as  
\[F_{\gamma}(\gamma_{th})=I_{H}=\frac{1}{2}\bigg{[}\exp\Big{(}\xi^{2}\overline{ \psi}-2\sigma_{x}^{2}\xi^{4}\Big{)}erfc\bigg{(}\frac{\overline{\psi}}{\sqrt{8 }\sigma_{x}}\bigg{)}+erfc\bigg{(}\frac{4\sigma_{x}^{2}\xi^{2}-\overline{\psi}} {\sqrt{8}\sigma_{x}}\bigg{)}\bigg{]} \tag{22}\]An approximated ABER expression of the best relay selection for LN channels can be calculated by substituting the CDF for S-R-D scheme, as given in [11]  
\[ABER_{S-R-D}\approx\frac{1}{2\sqrt{\pi}}{\sum_{i=1}^{S}{\omega_{i}[1-(1-I_{1})(1- I_{2})]}^{N}} \tag{23}\]  
In the case of S-R scheme, multiple links exist and only one link is chosen according to the channel coefficient, so a diversity of order \(N\) will exist only on the first hop. According to this reason, an approximated ABER for S-R scheme is investigated as  
\[ABER_{S-R}\approx\frac{1}{2\sqrt{\pi}}{\sum_{i=1}^{S}{\omega_{i}\Big{[}1- \Big{(}1-I_{1}^{N}\Big{)}(1-I_{2})\Big{]}}} \tag{24}\]  
While for the case of R-D scheme, in the second hop multiple links exist and only one link is chosen according to the channel coefficient. According to this reason, an approximated ABER for the R-D scheme is investigated as  
\[ABER_{R-D}\approx\frac{1}{2\sqrt{\pi}}{\sum_{i=1}^{S}{\omega_{i}\Big{[}1-(1-I_{ 1})\Big{(}1-I_{2}^{N}\Big{)}\Big{]}}} \tag{25}\]
## 4 Conclusions  
The current study evaluated the AcoD of FW and MA combined at different mixing ratios. The co-digestion of FW and MA yielded an obvious synergistic effect in biogas production, especially at mixing ratios of 1:1 and 1:3, with maximum biogas potential improvements of 9.03% and 26.86%, respectively. In addition, chemical parameters (pH, VFAs, NH4\({}^{+}\)-N) confirmed that co-digestion of FW and MA regulates the stability and performance of AcoD. The microbial community analysis found the relative abundances of Methanospirillum, Methanocorpusculum, and Methanomethyllovorans were selectively enriched in the co-digestion (FW/MA = 1:1) system, indicating that co-digestion of FW and MA induced better biomethane production performance. Given the increase in FW and frequency of MA blooms worldwide, millions of tons of these biomass wastes urgently need to be recycled and reutilized. Co-digestion of FW and MA is a promising potential strategy for biomass waste co-disposal and bioenergy production.  
Conceptualization, C.Q.; data curation, Z.P.; investigation, X.S. and Y.H.; methodology, T.L.; resources, J.L. and L.Z.; writing--original draft, Z.P.; writing--review and editing, Z.P. and C.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province (BK20210676); the Jiangsu Provincial Agricultural Science and Technology Innovation Fund (CX213069); the National Natural Science Foundation of China (41971043); the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX22_1707, SJCX23_0656); and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors would like to acknowledge the insight provided by [PERSON] regarding the preparation and operation of the dataloggers used in the tests. Without his assistance, this research would not have been possible.  
The authors declare no conflicts of interest.