esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 2 Water Supply System Architecture
### Water Distribution Networks (WDNs)  
A WDN consists of a network of links interconnected by nodes. The links constitute the pipes, pumps, and valves, while the nodes have the junctions, tanks, and reservoirs. The junctions are points where several pipes are joined with one another. They also serve as water supply or demand points to and from the network. The valves (such as pressure reducing valves, flow control valves, throttle valves, pressure breaker valves, etc.) are utilized to limit the pressure at a point in the network.  
The end users are the consumer's tap where potable water is consumed. Besides the impact of climate change which disrupts the water cycle and accessibility [1], water demand at the consuming end also varies with time. These, among other factors such as water leakage, make the management of modern WSSs operations challenging. Nevertheless, WSS operation is generally managed and regulated  
Figure 1: A water supply system architecture.  
using a set of programmable logic controllers and supervisory control and data acquisition systems. Unfortunately, previous ICT solutions could not provide the necessary support for applications to deal with the dynamics of the changing water demand. Moreover, tremendous growth in technology offers better possibilities to enhance WSSs operations. The incorporation of innovative technologies such as a cyber-physical system into WSSs will, therefore, enhance the operational management of water supply from sustainable perspectives. In the next section of this article, we investigate the architecture of a conventional CPS and its applications to engineering field. In the architecture, reference is made to a water system perspective.
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
### Challenges of WSNs  
Although WSNs have emerged as a promising future technology to fulfill a purpose of an ultra-connected reality, a number of challenges have emerged alongside [9]. One such challenge is increasing the fault tolerance of sensor nodes for deployment in harsh environments. Another is the ability to scale the network, as the number of sensors is typically in the order of hundreds or thousands and these need to integrated into the network without overloading it. Perhaps the most constraining challenge is the reliance of WSNs on low-cost and resource-deprived equipment, which incurs several hardware limitations, including limited processor speed, small memory size, and low battery life. Limited power supply at the sensor due to low-cost consideration clashes with the surveillance-oriented nature of many sensor network applications. Indeed, the surveillance nature of sensor nodes automatically necessitates a long operational time; coupled with hazardous sensing scenarios negating battery replacement as a feasible response, there is a desperate need for an innovative solution.
# Microalgal Growth in Aquaculture Effluent: Coupling Biomass Valorisation with Nutrients Removal
## 3 Results and Discussion  
### Microalgal Growth  
Figure 2 describes the evolution of _C. vulgaris_ growth over time. Overall, the results show that _C. vulgaris_ grew successfully in aquaculture wastewater. Comparing the growth curves of AW and C+ assays, it is possible to observe that in the beginning they are similar; however, from day 4, the microalgal growth in AW decreases, possibly due to microalgae having already consumed practically all the nutrients in the wastewater. Additionally, as predicted, there was no microalgal growth in C\(-\), proving that there was no presence of microalgae in the effluent.  
Table 2 contains the calculated kinetic parameters for each assay. The microalgal specific growth rates in AW (0.260 \(\pm\) 0.014 d\({}^{-1}\)) and C+ (0.276 \(\pm\) 0.004 d\({}^{-1}\)) assays were not statistically different (\(p\) > 0.05), which may be an indication that the microalgae in the aquaculture wastewater had the necessary conditions for growth, and that no harmful contaminants were present. [PERSON] et al. (2013) used aquaculture wastewater to produce microalgal biomass and obtained a growth rate for the _C. vulgaris_ of 0.081 \(\pm\) 0.008 d\({}^{-1}\), a value lower than the one obtained in this study. The C+ assay reached a statistically higher (\(p\) < 0.05) maximum biomass value (555.8 \(\pm\) 18.9 mgdw L\({}^{-1}\)), compared to the AW assay (455.9 \(\pm\) 10.9 mgdw L\({}^{-1}\)). The maximum productivity was not statistically different (\(p\) > 0.05) between the AW (60.0 \(\pm\) 11.8 mgdw L\({}^{-1}\) d\({}^{-1}\)) and C+ (58.8 \(\pm\) 1.3 mgdw L\({}^{-1}\) d\({}^{-1}\)) assays. The average productivity was statistically higher (\(p\) < 0.05) in C+ (41.6 \(\pm\) 0.1 mgdw L\({}^{-1}\) d\({}^{-1}\)) than in AW assay (32.9 \(\pm\) 1.6 mgdw L\({}^{-1}\) d\({}^{-1}\)). These parameters provide a better understanding of the optimal microalgae conditions, leading to more efficient growth. Therefore, although the microalgae in the AW assay were not cultivated in an optimal medium, they were able to grow as much as the positive control. This reveals that the use of aquaculture wastewater in microalgae cultivation is appropriate and viable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \(\mu\) & \(\mathbf{X_{max}}\) & \(\mathbf{P_{x,max}}\) & \(\mathbf{P_{x,avg}}\) \\  & **(d\({}^{-1}\))** & **(mgdw L\({}^{-1}\))** & **(mgdw L\({}^{-1}\) d\({}^{-1}\))** & **(mgdw L\({}^{-1}\) d\({}^{-1}\))** \\ \hline AW & 0.260 \(\pm\) 0.014 ** & 455.9 \(\pm\) 10.9 ** & 60.0 \(\pm\) 11.8 ** & 32.9 \(\pm\) 1.6 ** \\ C+ & 0.276 \(\pm\) 0.004 ** & 555.8 \(\pm\) 18.9 ** & 58.8 \(\pm\) 1.3 ** & 41.6 \(\pm\) 0.1 ** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic growth parameters calculated for the AW and C+ assays.  
Figure 2: Growth curves of AW, C\(-\) and C+ assays.
## 3 XAI Approaches for Chest X-ray Image Analysis and Report Generation
### Layer-Wise Relevance Propagation (LRP)  
Layer-wise relevance propagation unravels the prediction of a deep neural network by propagating the prediction backwards through the layers of the network to compute relevance scores for the pixels of the input image. This backward propagation is performed as follows. For each neuron \(i\) in the last layer, the neural network computes an output \(X_{i}\) through its activation function. This output \(X_{i}\) is the relevance score for neuron \(i\). Consider now a neuron \(j\) in network layer \(L_{l}\) with relevance score \(R_{j}^{l}\). This relevance score is backpropagated to the neurons \(k\) in the previous layer \(L_{l-1}\) that provide input to \(j\) so that  
\[R_{j}^{l}=\sum_{k\in L_{l-1}}r_{jk} \tag{4}\]  
where \(r_{jk}\) is the fraction of the relevance score of neuron \(j\) transferred to neuron \(k\). Equation (4) defines a conservation property, so that the total relevance score of the neurons in each layer is the same, and it is equal to the value of the prediction \(p(X)\) computed by the neural network for image \(X\).  
The relevance score for a neuron \(k\) in layer \(L_{l-1}\) is computed as follows:  
\[R_{k}^{l-1}=\sum_{j\in L_{l}}r_{jk} \tag{5}\]  
The relevance scores of pixels are obtained from the relevance scores of the neurons in the first layer. The relevance scores are visualized as a heat map. The functions \(r_{jk}\) in Equation (4) are called _propagation rules_. Different propagation rules have been proposed, including LRP-0 [68], the epsilon rule [68], LPR-\(\alpha\beta\)[68], the \(Z^{+}\) rule [69] and the gamma rule [70].  
Deep Covid-Explainer, a neural network ensemble for automatic detection of COVID-19 symptoms from chest X-rays is described in [47]. Class discriminating regions are highlighted using Grad-CAM++ and LRP to provide explanations and to identify critical regions on patients' chests.  
In [65], two ConvNets, VGG16 and ResNet60, are used to detect pneumonia caused by the COVID-19 virus. LRP, LIME and Grad-CAM are used to generate explanations for the predictions made by the two models.  
In [66], a model for detecting pneumonia and COVID-19 is presented that uses deep neural networks trained with transfer learning. LRP was used to discover that the words and letters printed in X-rays can influence the predictions of the model. In [67], a COVID-19 detection model was designed that consists of a segmentation module and a 201-layer ConvNet. LRP was used to generate heat maps which were correlated with the Brixia scoring system used by radiologists to measure the severity of COVID-19 in different lung regions.  
ISNet [64] is a ConvNet-based system that is able to perform segmentation and classification as a single process. ISNet introduced the concept of relevance segmentation in LRP maps to minimize background relevance.
# Effect of Fermentation Humidity on Quality of Congou Black Tea
## Materials and Methods
### Sensory Evaluation  
Based on the Chinese National Standard (GB) GB/T 23776-2018, a tea sensory evaluation was performed. Specifically, the congou black tea samples were mixed and placed in different sample trays for appearance evaluation. Next, the tea samples were blind coded by random numbers. After weighing 3 g of dry tea into the evaluation cup, 150 mL of boiling water was added, followed by covering the cup, steeping the tea for 5 min, and then pouring the tea infusion into separate evaluation bowls. At 30 s intervals, five professional tea tasters scored the tea independently on a 100-point scale in terms of dried tea appearance (25%), liquid color (10%), aroma (25%), taste (30%) and infused leaves (10%). The specific sensory features of the black tea were also scored by quantitative descriptive analysis (QDA), including moistness, tightness, tippy hair content, evenness, blending and integrity in appearance; redness and brightness in liquid color; floral and fruity odor; sweetness, grassy and miscellaneous odors in aroma; acidity, bitterness, greenness, astringency, umami, sweetness and concentration in taste; redness, brightness and blending in the infused leaf. The intensity was characterized by a 5-point scale, with 0 for none and 5 for strong.
## 1 Introduction
The hydrogen concentration was found to decrease with the increase of the distance between the leak and the vent [29]. [PERSON] et al. [30] found the average concentration of hydrogen increased with the increase of the height. A relatively higher concentration appeared in a short time near the corners of a cabin for different leakage positions [31]. [PERSON] et al. [32] found that the flammable zone was larger in the space with ventilations than in a space without ventilations. The volume of a flammable gas cloud increased with the decrease of the distance between the leakage position and an obstacle [33]. Ventilation systems were required to ensure a maximum hydrogen mole fraction at 1% due to the lower flammability limit (LFL) of 4% vol (ISO 19880) [34]. [PERSON] et al. [35] investigated a hydrogen leakage in a naturally ventilated car park, and found the mole fraction of the hydrogen reached 1% after 20 s of the leakage. However, for a cuboid space with two vents, at both the top and the bottom walls, the diffusion of iso-surface of 1% mole fraction of hydrogen needs to be further explored [36]. The differences of hydrogen diffusion velocity for the specified space with different leakage positions were important for the utilization and layout of hydrogen sensors [37]. Yet the changes of hydrogen concentration and the flow state were often investigated in terms of the distribution of velocity and concentration. Hydrogen sensors have often been used to monitor hydrogen concentration. [PERSON] et al. [38] developed a hydrogen-sensing material to optimize sensors' stability in monitoring hydrogen concentration rapidly in a wide low-temperature range. Hydrogen concentration was monitored at the corners of a cuboid space with two vents, with the leakage taking place at the floor. The concentration in the space, with the bottom vent near the ceiling, was found to be higher than other bottom vent positions [8]. Sensors were also set up at different heights in a cuboid space, with the leakage taking place at the center of the floor, and the hydrogen concentration at the top of the space was higher than that at other heights [39]. With helium sensors set up at different heights, the helium concentration at the top was greater than that at other heights [40]. Since the hydrogen diffusion was not isotropic and spreading evenly, the concentration in the space might not be accurately reflected by the monitoring points [41]. Therefore, the concentration of iso-surfaces could be used to study the diffusion of hydrogen comprehensively. In the present study, a simplified model was developed to investigate the effect on hydrogen dispersion of leakage positions in a cuboid space with two vents. The influence of the leakage direction on the stratifications and diffusion velocity was also studied. In addition, the diffusion of the flammable area was analyzed with iso-surfaces of 4% mole fraction to advance the understanding of monitoring mechanisms.
## 2 Architecture of Dima System
### Hardware Layer
#### 2.2.4 Network  
Processing components and I/O handling components exchange signals and communicate through Avionics Data Communication Network (ADCN) [28]. ADCN is based on Avionics Full-Duplex Switched Ethernet (AFDX) with a switched star topology [29], which is a standard protocol in DIMA architecture. The central concept of AFDX networks is Virtual Link (VL), which designates the flow of information between functions. A VL is an unidirectional logic path connecting the source to all of the destination end-systems with only switches as intermediate nodes in the path. From a graphical point of view, it consists of paths from the source node to each of the terminal nodes.  
Switches and cables are the backbone of AFDX network hosting the communications between CPMs and other devices connected to the network. AFDX messages between functions are realized by connection of cables between devices located in different cabinets.
## 4 Simulation Results  
### Control Parameters  
The first aspect of user control for this model lies in the physical parameters of the system. Provided that algorithmic stability is ensured, the end user has total and independent control over all of the physical parameters defined in this work. The measurements mentioned in Section 2.3 can be used as a sort of safeguard, if one is concerned with synthesising realistic bowed string instrument sounds; the more adventurous composer can experiment with different parameters, not limited by physical constraints. It is important to note that because of the nature of bowed string playing, unusual combinations of physical parameters may drastically limit playability.  
The second aspect is gesture control, defined by two sets of dynamically varying parameters (three for the bow, two for the finger):  
* Bow parameters:
* bow position along the string, closer to or further away from the bridge
* bow downwards force (denoted by \"bowing pressure\" in most papers)
* bow tangential force, which will determine the bowing velocity (and, in turn, the amplitude of bowed string motion)
* Finger parameters:
* finger position along the string, determining the (possibly varying) pitch of the resulting note
* finger downwards force, which can be used to capture the string against the fingerboard, or to lightly push the finger against the string for playing natural harmonics  
These five gestural parameters are fed into the algorithm as time series. Their value at a certain time is determined with the help of a score file, defining breakpoint functions for every one of them. The resulting piecewise linear time series can be modified at will; a representative example is the addition of an oscillatory term in the finger position function, so as to simulate a vibrato sound (see Section 4.3).  
All the simulations presented here are run at audio sample rate (\(F_{s}=44.1\) kHz, with the exception of those for which spectrograms are computed, where the sampling rate has been increased in order to enhance the spectrogram resolution), as close as possible to the stability limit. The output waveform is read as the displacement of the last mobile point of the string before the bridge termination. Accompanying simulation videos and synthetic sounds are available online [41].
## 3 SSMA Broadcast Protocol
### Broadcast Approaches
#### 3.3.2 Slotted Contention-Based Transmission  
A _slotted CBT_ (SCBT) approach aims at alleviating the possibility of collision during message broadcasting by employing the slotted Aloha [17]. Thus, a BSS is further divided into N BSs as depicted in Figure 3, and can be expressed as a sequence of BSs:  
\[BSS=(BS_{1},\;BS_{2},\;\ldots,\;BS_{N})\]  
where \(BS_{i}\) indicates the \(i\)th broadcast slot within \(BSS\) and \(N=len(BSS)/len(BS)\). To alleviate competition, it is desirable to have nodes take different BSs if possible. Thus, every node, say \(x\), chooses \(BS\) by using \(startBS(x)\) as follows:  
\[startBS(x)=random(1,\;N) \tag{9}\]  
If either the number of nodes is much greater than the number of \(BS\)s, or the \(BS\) assignment by the random function is biased, multiple nodes can be assigned the same \(BS\). Thus, nodes using the same \(BS\) contend for a channel using _rdelay_ in Equation (8). If the current \(BS\) is the last one, a node that fails to acquire a channel has to rebroadcast a message. Otherwise, it is allowed to delay channel competition to the next \(BS\) only once.  
This approach has a couple of shortcomings. First, some siblings can be assigned the same \(BS\). Since they are located closely to each other, and thus are more likely to have connection to some nodes in common at one upper level, their simultaneous rebroadcasts can increase the possibility of collision. Second, if some hidden nodes connect to some nodes at one upper level in common, and are assigned the same \(BS\), collision is inevitable regardless of the use of _rdelay_.
## 6 Applications and Simulations
### Data Storage and Retrieval
#### 6.2.1 Where to Store the Data  
Given a datum, finding the location to store it is the key issue in data-centric networking. To this end, we exploit the result of volumetric harmonic mapping, which maps the original network with an irregular shape to a ball. We adopt the polar coordinate system, where a point in a unit sphere can be represented by \((\rho,\alpha,\beta)\), where \(\rho\) is the distance from this point to the origin, \(\alpha\) is the angle formed by the line connecting this point and the origin with the \(X\)-axis and \(\beta\) is the angle between the line and \(Y\)-axis. \(\rho\) is bounded to \(0\leq\rho\leq 1\) for a network without a hole or \(r^{\prime}\leq\rho\leq 1\) for a network with a hole, where \(r^{\prime}\) is the radius of the inner sphere in volumetric harmonic mapping.  
First, we map a datum (possibly with multiple attributes) to a series of bits by using the method introduced in [39, 40, 41]. We select the \((3k+1)\)-th bits (where \(k\) ranges from zero to the largest value that depends on the length of the bit series) and concatenate them to a binary string, which is further  
Figure 11: Load distribution in routing.  
normalized to yield \(\rho\). Similarly, \((3k+2)\)-th and \((3k+3)\)-th bits are used to determine \(\alpha\) and \(\beta\), respectively. Since the unit ball (or hollow ball) is the volumetric harmonic mapping of the tetrahedron mesh of the original network, a point \((\rho,\alpha,\beta)\) must be within a tetrahedron. Thus, we simply select one of the four vertex of this tetrahedron to store the datum.
# Dynamic Range Compression and the Semantic Descriptor Aggressive
###### Abstract  
In popular music productions, the lead vocal is often the main focus of the mix and engineers will work to impart creative colouration onto this source. This paper conducts listening experiments to test if there is a correlation between perceived distortion and the descriptor \"aggressive\", which is often used to describe the sonic signature of Universal Audio 1176, a much-used dynamic range compressor in professional music production. The results from this study show compression settings that impart audible distortion are perceived as aggressive by the listener, and there is a strong correlation between the subjective listener scores for distorted and aggressive. Additionally, it was shown there is a strong correlation between compression settings rated with high aggressive scores and the audio feature roughness.  
dynamic range compression; music production; semantic audio; audio mixing; 1176 compressor; FET compression; listening experiment +
Footnote †: journal: Applied Sciences
## 2 Advances and Achievements in Seed Biotechnologies in the Decade 2014-2024
### Seed Quality Improvement Through Biotechnological Innovations
#### 2.2.5 Synthetic Biology Approaches in Seed Quality Improvement
A third-generation genome-editing tool, CRISPR from _Prevotella_ and _Francisella_ 1 (CRISPRR/Cpf1), has been employed for gene editing in plant genomes rich in thymine [169] and used in soybean and rice for the improvement of seed quality. Studies into seed vigor improvement have suggested that L-isoasparatly methyltransferase (PIMT) protein accumulation in seeds (an enzyme that functions in the recognition and catalysis of damaged L-iso aspartyl and D-aspartyl groups in proteins) [170]. Further studies into the genetic mechanisms contributing to higher seed vigor are required [116]. Soybean is a key source of edible protein and comprises \(\beta\)-conglycinins (7S) and glycinins (11S), seed-storage proteins. Studies have suggested rebalancing seed proteome and increased seed proteins in soybean line BSH-3 [171]. [PERSON] and coworkers [30] documented genes encoding soybean lipases: _sugar dependent1-1_ (_Gmsdp1-1_), _Gmsdp1-2_, _Gmsdp1-3_, and _Gmsdp1-4_. The overexpression of these genes in RNAi-silenced lines of soybean showed a considerable increase in seed oil content and changes in fatty acid profiles. The quality and yield of seed oil in soybeans can be improved by adopting these knockdown strategies. Genetic engineering approaches have also focused on developing crops with improved biotic and abiotic stress tolerance. Studies into improving drought tolerance in _Z. mays_[172] and _T. aestivum_[173; 174] have successfully provided feasible solutions to address climate changes. However, genetically modified crops and their usage are under regulatory procedures and need approval, which varies among geographical regions [175; 176] and must be monitored for environmental impact.