esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA
## 6 HW/SW Implementation
### Block Diagram  
A block diagram containing the final arrangement of the involved components inside the biometric system is represented in Figure 9.  
Figure 9: Block diagram representing the integration of the biometric system, obtained in Vivado IDE.  
The Zynq's PS, located at the bottom right of the diagram, is the diagram's main block. This component is the software interface responsible for managing the data flow between the cores.  
The AXI buses are represented by two AXI Interconnect blocks connected to the HP ports of the PS. These blocks establish a bridge between PS and PL ports. In the diagram of Figure 9, AXI Interconnects link the PS HP ports to the AXI4-Stream port of AXI direct memory access (DMA) blocks located in the PL. Additionally, AXI connects the PS GP ports to the AXI DMA AXI4-Lite ports.  
AXI DMA provides a direct high-bandwidth access to the external memory for a AXI4-Stream port. This feature allows to transfer volumes of data without the control of the PS, speeding up data transfers. The block diagram contains two AXI DMA block with different configurations. The bottom one provides a one-way channel to transfer EEG features from the memory to the EEG_CALCDIST IP core via AXI4-Stream. The top AXI DMA block is a two-way channel that allows the transfer of PPG samples from the memory to the PPG IP core but also to return the PPG IP core's products to the PS.
## 5 Evaluation
### Congestion Resilience  
Figures 20 and 21 show the packet size distribution as a result of coding the two test video clips, _Paris_ and _Stefan_, with CIF at 30 frame/s. The sequences were this time coded with a CBR target of 500 kbps. In this distribution, 5.6% intra-coded MBs were randomly added to each frame, equivalent to one row of MBs in CIF. (In the Joint Model (JM) reference software of H.264/AVC used, this results in a gradual decoding refresh within 18 frames, because there is no replication of previously allocated random MB positions in successive frames.) From Figure 20 for _Paris_, about 63% of packets are moderately sized, with 11% relatively large. The remaining 26% are small. The average absolute length of motion vectors in the _Stefan_ sequence is 5.5, while for _Paris_, it is only 2.2, implying that there is much more motion activity in _Stefan_. Partially as a result, the more active _Stefan_ sequence has fewer smaller packets (Figure 21), than _Paris_ at this bitrate.  
Figure 19: Packet mean delay (s) for streaming (**a**) _Football_ and (**b**) _Paris_ with 5%, 10% and adaptive redundant data.  
Figures 22 and 23 present the result of streaming _Paris_ and _Stefan_ respectively across the WiMAX link, before and prior to turning on the congesting sources mentioned at about frame number 125. During this period, approximately 2% of the CBR stream packets are dropped through buffer overflow. From the figures, it is apparent that there is a content-dependent effect, as the more active sequence, _Stefan_, does not benefit from the scheme as much as _Paris_ does. The reason for this is apparent from looking back at the packet size histograms of Figures 20 and 21. As _Stefan's_ packet sizes are predominantly in the range 500-900 B, they are more likely to pose a risk of overflow at the WiMAX BS output buffer. However, for both types of content there is a positive gain from turning on the packet scheduling and, in the case of _Paris_, there is a very definite gain in video quality, which remains stable.  
Figure 21: Packet size (bytes) distribution for _Stefan_ CIF@30 Hz, coded at a CBR of 500 kbps, with 5.6% RIMR.  
Figure 20: Packet size (bytes) distribution for Paris CIF@30 Hz, coded at a Constant Bit-Rate (CBR) of 500 kbps, with 5.6% Random Intra Macroblock Refresh (RIMR).
## A Layered Fault Tree Model for Reliability Evaluation of Smart Grids  
**[PERSON] *, [PERSON] and [PERSON]**  
School of Information System and Management, National University of Defense Technology,  
Changsha 410073, China; E-Mails: chenhao1040(@gmail.com (H.C.); [EMAIL_ADDRESS] (B.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-152-4369-2469.  
_Received: 20 March 2014; in revised form: 31 May 2014 / Accepted: 15 July 2014 / Published: 29 July 2014_  
**Abstract:** The smart grid concept has emerged as a result of the requirement for renewable energy resources and application of new techniques. It is proposed as a practical future form of power distribution system. Evaluating the reliability of smart grids is of great importance and significance. Focusing on the perspective of the consumers, this paper proposes a layered fault tree model to distinguish and separate two different smart grid power supply modes. Revised importance measures for the components in the fault tree are presented considering load priority, aiming to find the weak parts of the system and to improve the design and using. A corresponding hierarchical Monte Carlo simulation procedure for reliability evaluation is proposed based on the layered fault tree model. The method proposed in this paper is tested on a case of reliability assessment for the Future Renewable Electric Energy Delivery and Management (FREEDM) system. The proposed technique can be applicable to other forms of smart grids.  
**Keywords:** smart grid; reliability evaluation; layered fault tree; hierarchical simulation
## 4 Deployment Scenario and Prototype
### Stored Data  
One of the main services offered by SmartResidents to its users is providing real-time data of different types that can be consulted from the television as the primary interaction element. The user knows in an instant if the paddle tennis court is occupied at any given time, the temperature of the pool or the air outside, whether dumpsters in a given area are full, or the noise levels in the area, which undoubtedly improves residents' quality of life. In addition, changes in all these factors may be consulted using different time scales or dates, allowing more thorough analysis for a specific end goal. Figure 9 shows the changes in the temperature for a given day from 08:00 H to 18:00 H, in time intervals of 10 min, using graphics to show the benefits of cloud computing. SmartResidents integrates this functionality in the application.  
Figure 8: SmartResidents interface for a specific sensor (Temperature).  
Figure 9: Temperature change graph.  
Figure 10 shows the data supplied by the logical layer in JSON format, used in Figure 9.  
These analysis capabilities allow stored information to be analyzed thoroughly, using the tools provided by the logical layer. The analysis capabilities of cloud computing (through the use of information technology and communications, or more advances tools [28]), allows the provision of alternative services to improve energy management and optimize the use of existing resources to be focused on. Along these lines, a number of proposals are introduced in the next section.
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
###### Abstract  
This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on sensor and radio module effectiveness are also presented.  
IoT; smart city; waste management; LoRa; LPWAN; energy saving; ultrasound sensor
## 2 State of the Art
The authors of [17] developed a hybrid biometric template protection scheme by integrating cancelable biometrics and biocryptography. Face recognition was performed using image preprocessing methods using the Tree Structure Part Model, feature extraction using the Ensemble Patch Statistics technique, and classification using a multi-class linear SVM. The testing was conducted on three databases: CVL, FEI and FERET. The system achieved an accuracy of 99.47% for CVL, 98.10% for FEI and 100% for FERET, which confirms its effectiveness compared to existing methods. In one of the latest works, whereby the authors used a neural fuzzy extractor, a new approach for biometric authentication was implemented based on the use of correlation neurons [18]. Voice images of speakers from the RedDots database were used as biometric data. The main advantage of the proposed model was the use of feature correlation to improve authentication accuracy. The system achieved an error rate of EER = 2.64% with 4096 neurons and six synapses. Table 1 provides information about the main methods described in the current section. In publicly available scientific papers, there is no widespread use of neural fuzzy extractor for protecting voice images, but it has been sufficiently tested for such types of images as faces [24] and fingerprints [25]. However, its application to voice images is justified by the universality of working with feature vectors extracted from a \"raw\" biometric image, which makes the method independent of the method of presenting biometric data. Only the feature extraction algorithm is modality-dependent.
# CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
## 4 Experiments
### Spectrograms  
Figure 2 shows the sample spectrograms of the utterances converted by DiffVC and CycleDiffusion, respectively. As demonstrated in the figure, the spectrograms of the utterances processed by CycleDiffusion show more distinct and well-defined formant structures in comparison to those generated by DiffVC.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Conversion Direction**} & \multicolumn{1}{c}{**DiffVC**} & \multicolumn{1}{c}{**CycleDiffusion**} \\ \hline \multirow{4}{*}{Intra-gender} & F1\(\rightarrow\)F2 & 3.90 \(\pm\) 0.07 & 4.16 \(\pm\) 0.03 \\  & F2\(\rightarrow\)F1 & 3.26 \(\pm\) 0.06 & 3.19 \(\pm\) 0.03 \\  & M1\(\rightarrow\)M2 & 3.75 \(\pm\) 0.07 & 4.22 \(\pm\) 0.03 \\  & M2\(\rightarrow\)M1 & 3.28 \(\pm\) 0.05 & 3.41 \(\pm\) 0.07 \\  & Average & 3.55 \(\pm\) 0.06 & 3.74 \(\pm\) 0.04 \\ \hline \multirow{4}{*}{Inter-gender} & F1\(\rightarrow\)M1 & 3.13 \(\pm\) 0.03 & 3.22 \(\pm\) 0.04 \\  & F1\(\rightarrow\)M2 & 3.76 \(\pm\) 0.07 & 4.25 \(\pm\) 0.03 \\  & F2\(\rightarrow\)M1 & 3.09 \(\pm\) 0.03 & 3.21 \(\pm\) 0.04 \\  & F2\(\rightarrow\)M2 & 3.71 \(\pm\) 0.08 & 4.18 \(\pm\) 0.04 \\  & M1\(\rightarrow\)F1 & 3.18 \(\pm\) 0.04 & 3.13 \(\pm\) 0.03 \\  & M1\(\rightarrow\)F2 & 3.91 \(\pm\) 0.07 & 4.10 \(\pm\) 0.04 \\  & M2\(\rightarrow\)F1 & 3.13 \(\pm\) 0.04 & 3.12 \(\pm\) 0.04 \\  & M2\(\rightarrow\)F2 & 3.93 \(\pm\) 0.08 & 4.20 \(\pm\) 0.04 \\  & Average & 3.48 \(\pm\) 0.06 & 3.68 \(\pm\) 0.04 \\ \hline \multicolumn{2}{c}{Average} & \multicolumn{1}{c}{3.50 \(\pm\) 0.06} & 3.70 \(\pm\) 0.04 \\ \hline \end{tabular}
\end{table}
Table 4: Average MOS values, along with the 95% confidence intervals, produced by MOSNet for the speech samples converted by DiffVC (baseline) and CycleDiffusion (proposed).
## 2 Approach to the Concept and Production of Biochar  
Biochar is a substance formed via the reductive pyrolysis of high-carbon-containing dead plant and/or animal biomass (heated oxygen-free at high temperatures). Its structural properties strongly depend on the organic materials used for the reductive \"burn\", formation temperature, and duration of action, i.e., the production conditions. The literature distinguishes two major groups of production processes in terms of the initial raw materials:  
1. Biochar produced a relatively low (450-550 \({}^{\circ}\)C) pyrolyzing temperature, most often produced with high carbon-content substances according to the literature. It is mainly capable of long-term binding of groundwater and dissolved ions, and usually originating from plant residues, byproducts, and/or animal manures.
2. Biochar produced from animal bones at high temperatures (600-650 \({}^{\circ}\)C or higher) with a high calcium phosphate content, along with apatite minerals with significantly lower carbon contents.  
In addition to biochar, oils and various synthesis gases are also generated during pyrolysis (Table 2). However, their exact proportions in the final products may vary as a function of the raw materials and production methods.  
\"Terra Preta\" soil is well supplied with both organic matter and minerals, the most important of which are nitrogen, phosphorus, and potassium (NPP macronutrients) (Table 1). There are both direct and indirect reasons for this. In biochar production, plant waste  
Figure 1: Main types of biochar and their agricultural uses.  
and animal manure are mainly used, and the final product's composition is proportional to the initial concentrations of raw materials. Its morphology is closely related to pore development, as well as to the shapes of pores. The smaller the pores, the larger the biochar surface area in general [22]. Biochar can show some variability in morphological structures when different initial feedstocks are used (Figure 2).  
Figure 2: Surface morphology of some biochar, based on the different feedstocks [22; 23; 24; 25; 26]. (**A**) pinewood-derived, (**B**) sawdust-derived, (**C**) bovine-bone-derived, (**D**) fish-scale-derived, (**E**) food-waste-derived, (**F**) poultry-litter-derived biochar.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 3 Related Work and State-of-the-Art
### Outlier Ensemble Models
#### 3.1.1 Model Normalization  
The simplest approach to model normalization is not to normalize at all. This is possible for some data-centered ensembles where the models do not differ substantially in their output characteristics. In our point of view, this is certainly a viable approach since our ensemble is data-centered and it is unclear how, if at all, the constituent model outputs statistically differ. Their differences may also be meaningful and normalization may in fact decrease the ensemble performance. Normalization has been shown to be beneficial in the case of data-centered ensembles multiple times [28; 29]. A good summary of available normalization methods can be found in [34]. Two key properties of outlier scores for the purposes of model normalization are regularity and normality. An outlier score \(S\) is called regular if for any scored object \(o\), \(S(o)\geq 0\), and \(S(o)\approx 0\) for an inlier and \(S(o)\gg 0\) for an outlier. \(S\) is called normal if \(S\) is regular and \(S(o)\in[0,1]\) for any \(o\). A process of making \(S\) regular, respectively normal while preserving the rankings of different data points is called regularization, respectively normalization. Normally, an outlier score is first regularized, then normalized. Normalization can be useful even if \(S\) is already normal: the requirements for normality are too lose to guarantee the possibility of meaningful outlier score combination in an ensemble. Perhaps the simplest regularization method is simple linear scaling. A baseline value \(\mathit{baseS}\) is chosen, equal to the expected inlier value. Then, the function \(S\) can be regularized as follows:  
\[S_{\mathit{reg}}(o)=\mathit{max}\ \{0,\pm s\ (S(o)-\mathit{baseS})\}\]  
The sign (\(\pm s\)) depends on whether low outlier score \(S(o)\) indicates that \(o\) is an outlier (plus sign is used) or otherwise (minus sign is used). This regularization method is often used as a preprocessing step to make it easier to apply one of the normalization functions below.
## 5 Simulation and Experiment
### Comparison Experiment  
#### 5.2.1 Comparison with Basic Algorithms  
To verify the effectiveness and superiority of the DABAA for large-scale scheduling in an agricultural machinery rental service, the performance of the DABAA is compared with the performances of the GA, ABC and MMAS, among them, the GA algorithm has been applied to many fields because of its strong global search ability and fast convergence speed. Tested on nine large-scale problems for all four algorithms, the parameter settings of each algorithm are shown in Table 4. Table 5 shows the size of the test, T(\(n\),\(m\)) is the size of the task, where \(n\) represents the total number of tasks in the scheduling task pool, and \(m\) represents the number of tasks in a scheduling scheme. According to the evaluation index of the task, the optimal scheduling scheme is generated, and all rental tasks are completed through multiple scheduling schemes. In this paper, \(m\in 10\), \(20\), \(30\) to verify the performance of the DABAA through different scales. In actual tasks, \(m\) is determined according to the actual maintenance time. When the number of machines is insufficient, the number of tasks (i.e., \(m\)) of a machine in one scheduling sequence can be reduced to increase the coverage and real-time performance of task scheduling.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{284.5 pt}} \hline \hline
**Algorithm** & **Parameters** \\ \hline GA & Cross probability is 0.7, mutation probability is 0.15, population size (\(P\)) = 40, and maximum number of iterations (\(M\)) = 300. \\ ABC & \(P\) = 40, \(M\) = 300, and the limit of mining operations is 40. \\ MMAS & Heuristic factor \(\alpha\)=1, expectation factor \(\beta\) = 4, pheromone range \([10,100]\), volatilization rate \(\rho\) = 0.002, \(P\) = 40, and \(M\) = 300. \\ DABAA & \(NB_{min}\) = 60, \(NB_{max}\) = 150, and adjustment coefficient \(\varphi\) = 0.85. The other parameters and settings are the same as those of the MMAS and ABC. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm parameter setting.  
Figure 3: Process of optimization.  
All algorithms run 20 times for the 9 tasks in Table 5. Table 6 shows the data statistics of experimental results, which are statistics of QoS fitness values, including the optimal and average QoS fitness value obtained by the experiment in this paper. It can be seen that DABAA has better convergence accuracy compared with other algorithms. Figure 5 is a comparison of the accuracy of the four algorithms. Figure 5 shows that DABAA performs better in solving problems than other algorithms. As the amount of data increases, the advantage of the DABAA is more obvious, and the precision of convergence increase. Therefore, the performance of the DABAA is superior to that of the other three algorithms, and the DABAA is more conducive to solving large-scale problems in agricultural machinery rental.
# Seismic Behavior of Steel Plate-Concrete Shear Walls with Holes
## 2 Experiment
### Design of Specimens and Instrumentation  
To accurately reflect shielding plants and internal SPSW structures in a nuclear power station, specimens in this study were designed with reference to Japan's _Technical Regulations on Earthquake-resistant Design of Steel Plate Concrete Structure_ (JEAC4618-2009) [24] and design documents of AP1000 demonstration projects. According to _Technical Regulations on Earthquake-resistant Design of Steel Plate Concrete Structure_, the ratio between the thickness of SPSW for nuclear safety and thickness of the surface steel plate should be controlled within 30:200. On this basis, thicknesses of the shear wall and surface steel plate were set. Considering the actual welding ability in the laboratory, the thickness of the surface steel plate in the test specimens was determined to be 2.5 mm.  
In this study, low-cycle reciprocal tests of nine SPSW specimens were conducted. Dimensions of SPSW specimens were 800 mm (height) \(\times\) 800 mm (width) \(\times\) 125 mm (thickness). SPSW specimens were divided into three types: (1) without holes, (2) with small holes, and (3) with large holes. Considering the loading capacity of the laboratory, the SPSW specimens without holes and with small holes were flat (Figure 2), whereas the SPSW specimens with large holes were I-shaped (Figure 3). The stiffening plates were spread around the hole, with a width is 90 mm, and the length determined by the perimeter of the hole. The hole size, thickness of the stiffening plate, hole position, and axial pressure ratio are listed in Table 1. In the SPSW specimens, the steel plate was made of Q235B, and the grade of the concrete strength was C35. Material properties are shown in Tables 2 and 3. Fine aggregates with a diameter smaller than 10 mm were used.  
Figure 3: Typical I-shaped specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Number** & \begin{tabular}{c} **Axial Compressive** \\ **Strength (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Axial Tension Strength** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Elasticity Modulus** \\ **(MPa)** \\ \end{tabular} \\ \hline C35 & 25.98 & 2.33 & \(3.25\times 10^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of the concrete.  
Figure 2: Typical flat specimens.  
In the test, five displacement sensors were set (Figure 1), three of which were set vertically on the wall. The first displacement sensor on the side of the loading beam of the shear wall tested the top wall displacement under reciprocal loads. The displacement sensor in the center of the shear wall examined the bottom slippage during the loading process. The displacement sensor on the side of the bottom beam tested the overall slippage of specimens to assure test accuracy. In addition, two dial indicators were installed at diagonal positions of the wall to test for outward deflection.
# Applicability of Honey on Silkworms (_Bombyx mori_) and Quality Improvement of Its Biomaterials
## 3 The Impact of Honey on Silk Fibroin-Based Biomaterials
### Silk Fibroin as a Biomaterial  
As an emerging field, tissue regeneration represents one of the top priorities for the scientific community. Even though there have been advances in this direction, there are numerous drawbacks, such as the biocompatibility or biodegradability of the materials used for treating or replacing damaged tissues [93].  
Silk represents one of the most promising natural biopolymers that exhibit a great potential for biomedical applications and beyond. The main source for this biopolymer is _Bombyx mori_, which produces silk that displays numerous and unique features. The silk produced by _Bombyx mori_ is composed of two fibers of fibroin and sericin that holds them together by acting like a glue. The fibroin fibers are responsible for the extraordinary features of _Bombyx mori_ silk [94]. As for sericin, it is removed by using a degumming process due the fact that is has been shown that the presence of sericin deteriorates the outstanding mechanical strength and the superior level of biocompatibility exhibited by fibroin [93]. In addition, silk fibroin possesses extraordinary features, such as biodegradability, stiffness and thermal stability [10; 95]. As for the structure of the fibroin fibers, it includes a complex consisting of a light chain (L-chain) and a heavy chain (H-chain) linked through disulfide bonds [96] and a glycoprotein P25 that is linked to the complex [93]. In the light of this, silk fibroin has received great attention from the scientific community [97]. Its features make silk fibroin the most important candidate for developing hydrogels, sponges, nanofibers, biofilms or nanoparticles in order to make important advances in biomedicine and regenerative medicine [89]. The most important directions of using silk fibroin are wound healing, drug delivery and bone tissue engineering [93; 98]. There have been performed numerous studies that successfully used silk fibroin-based biomaterials for biomedical applications (Table 6).