esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Number of Learning Iterations  
To confirm the correctness of STUN according to the number of learning iterations, we changed the values of the episode count and the step function execution count on a 120-core machine and analyzed the results. To improve the accuracy of the analysis, the evaluation used the recorded file from the result of the test workload under all parameters to avoid the fluctuation of the workload results.  
First, to confirm the effect of the number of episodes, the number of the step was fixed to 500, and the number of episodes was set to 25, 50, or 100. We then analyzed the parameter state changes and the performance of Sysbench. Figure 4 shows the graphical representation of each evaluation process. For Figure 4A,B, the number of episodes was 25; for Figure 4C,D, 50; and for Figure 4E,F, 100. The x-axis of each graph is the number of learning iterations, and the y-axis of Figure 4A,C,E indicates the performance of Sysbench (the total number of events). The y-axis of Figure 4B,D,F represents the state values of each parameter.  
The results show that if the number of episodes is not enough, the learning cannot find the optimal value. In Figure 4A,B, when the number of iterations is about 8000, the parameters converge, but it does not show the best performance. On the other hand, in Figure 4E, if the number of episodes is too large, the learning can find the optimal performance when the number of training iterations is about 45,000. However, Figure 4F shows that the parameter values do not converge because of the over-fitting problem. When the number of episodes is too large, the rewards are accumulated around the optimal value, so the result becomes unstable. Therefore, the number of episodes has to be set to an appropriate value to find the optimal and stable parameter value, as shown in Figure 4C,D.  
Figure 5 shows the analysis of changes in the parameters obtained by a fixed number of episodes (50) while changing the number of steps to 200, 500, or 1000 for Sysbench. The x-axis and y-axis are the same as those in Figure 4. For Figure 5A,B, the number of the steps is 200; for Figure 5C,D, it is 500; and for Figure 5E,F, it is 1000. The analysis results are similar to those for changing the number of episodes. We confirm that if the number of steps is less than 200, the learning is insufficient, and if the number of the steps is over 1000, the model is excessively over-trained. Based on these evaluation results, we used the number of episodes as 50 and the number of steps as 500 for all subsequent evaluations.  
Figure 4: STUN performance according to the number of episodes. The x-axis of each graph is the number of learning iterations. The y-axes of (**A**,**C**,**E**) show the performance of Sysbench (the total number of events), and the y-axes of (**B**,**D**,**F**) show the state values of each parameter.
## 2 Problem Description and Model Analysis
### Model Analysis
#### 2.2.1 Model Establishment
Since the ejection time of the unloader in this paper occurs after the valve plate is opened, only the coupling between the unloader and valve plate during the withdrawal and maintenance of the unloader is considered. The equations and meanings of the three coefficients are as follows. The relative position coefficient, \(\phi= \begin{cases}1&x_{v}\geq x_{mu}\\ 0&x_{v}<x_{mu}\end{cases} \), determines whether the unloader and valve plate are in contact with each other, by establishing the position of the unloader and valve plate. Figure 4: Co-simulation block diagram of the reciprocating compressor flow control system. The state coefficient, \(\varphi= \begin{cases}1&\frac{|F_{p},r_{m}-F_{p}|}{m_{p}}\geq\frac{|F_{m_{p},r_{m}+F_{ m}}|}{m_{m}}\\ 0&\frac{|F_{p},r_{m}-F_{p}|}{m_{p}}<\frac{|F_{m_{p},r_{m}+F_{m}}|}{m_{m}}\end{cases} \), under the assumption that the interaction force is not considered, determines whether the speed change of the unloader is less than that of the valve plate. Regarding the interaction coefficient, \(\varsigma=\phi\times\varphi\), when the relative position and state coefficients are both 1 and they are in contact with each other, the movement of the valve plate is controlled by the movement of the unloader. Therefore, an interaction force between the two is considered to occur at this point in the process.
## 2 Theoretical Analysis of Energy Spectrum Study
### Energy Balance Analysis
#### 2.3.3 Energy Balance Analysis  
Widely accepted for energy balance analysis, the relative energy balance equation [25; 42; 43; 44] was used in this study to explore the earthquake input energy spectrum. Considering the damping effect of a single dashpot, by multiplying Equation (9) by \(x\) and integrating over the entire duration of an earthquake, i.e., from \(t=0\) to \(t_{f}\) (here, the subscript f refers to the instant when the earthquake fades away), it can obtained for the original SDOF element:  
\[\int_{0}^{t_{f}}\vec{mxxdt}+\int_{0}^{t_{f}}(c+c_{d})xxdt+\int_{0}^{t_{f}}kxxdt =-\int_{0}^{t_{f}}\vec{mx_{g}}xdt \tag{12}\]  
The right-side term of Equation (12) is conventionally defined as the input energy (\(E_{10}\)), whereas the dissipation energy contributed by the single dashpot and primary SDOF element is defined as  
Figure 4: Mechanical models of the analyzed structures: (**a**) original single-degree-of-freedom (SDOF) element; (**b**) SDOF element connected to a single dashpot; (**c**) SDOF element connected to the tuned viscous mass damper (TVMD).  
and \(E_{s0}\), respectively. To eliminate the influence of mass on the seismic-induced energy spectra, \(E_{10}\), \(E_{d0}\), and \(E_{s0}\) are given as follows:  
\[E_{10}=-\int_{0}^{t_{f}}\overset{\sim}{x}_{g}xdt,\ E_{d0}=\int_{0}^{t_{f}} \frac{c_{d}}{m}xxdt,\ E_{s0}=\int_{0}^{t_{f}}\frac{c}{m}xxdt \tag{13}\]  
In addition, the left-side terms of Equation (12), kinetic energy (\(E_{k0}\)) and elastic strain energy (\(E_{e0}\)) are defined as \(E_{k0}=\int_{0}^{t_{f}}m\overset{\sim}{xxdt}\) and \(E_{e0}=\int_{0}^{t_{f}}kxxtt\), respectively. However, for a damped elastic SDOF system, \(E_{k0}\) and \(E_{e0}\) are almost zero at the time when the earthquake vanishes (or when the vibration of the structure stops).  
In terms of the SDOF element connected to the TVMD, the energy balance analysis was conducted by multiplying Equation (10) by \(\left[x,x_{d}\right]^{T}\) and integrating over the entire duration of the earthquake:  
\[\begin{array}{l}\int_{0}^{t_{f}}m\overset{\sim}{xxdt}+\int_{0}^{t_{f}}c \overset{\sim}{xxdt}+\int_{0}^{t_{f}}kxxtt+\int_{0}^{t_{f}}c_{d}x_{d}xt_{d}dt+ \int_{0}^{t_{f}}m\overset{\sim}{in}x_{d}dt\\ +\int_{0}^{t_{f}}k_{d}(x-x_{d})(x-x_{d})dt=-\int_{0}^{t_{f}}mx_{g}xdt\end{array}\, \tag{14}\]  
where the expression of input energy \(E_{I}\) is consistent with the case in Equation (13). The dissipation energy contributed by the dashpot of the TVMD, \(E_{d}\), and the primary SDOF element, \(E_{s}\), are changed as follows:  
\[E_{d}=\int_{0}^{t_{f}}\frac{c_{d}}{m}x_{d}x_{d}dt,\ E_{s}=\int_{0}^{t_{f}} \frac{c}{m}xxdt \tag{15}\]
_Applied_  
_sciences_  
_Article_  
**Performance of _Pistia stratiotes_, _Salvinia molesta_,  
**and _Eichhornia crassipes_ Aquatic Plants in the Tertiary Treatment of Domestic Wastewater with Varying  
**Retention Times**  
**[PERSON] 1,2,*(c) and [PERSON] 3,4,*(c)**  
1 College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), Kajang 43000,  
Selangor Darul Ehsan, Malaysia  
2 Department of Chemistry, Kaduna State University (KASU), Tafawa Balewa Way, Kaduna,  
PMB 2339, Nigeria  
3 Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Kajang 43000,  
Selangor Darul Ehsan, Malaysia  
4 Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN),  
Kajang 43000, Selangor Darul Ehsan, Malaysia  
*Correspondence: [EMAIL_ADDRESS] (H.M.M.); [EMAIL_ADDRESS] (G.H.) check for  
updates  
**Abstract:** Organic matter, nutrients, and chemicals are some of the waste materials found in most domestic wastewaters. The purpose of this research was to evaluate the potentials of _Pistia stratiotes_, _Salvinia molesta_, and _Eichhornia crassipes_ aquatic weed plants in the tertiary treatment of secondary domestic wastewater using hydroponic systems. Physicochemical analysis that considered turbidity, phosphate, ammoniacal nitrogen, and nitrate was conducted on the wastewater samples at different retention times of 6, 12, and 24 h for 14 days. The best performance was observed for a 24 h retention treatment system, with up to 91% (_P. stratiotes_), 94% (_S. molesta_), and 89.3% (_E. crassipes_) reduction for turbidity; 81.2% (_P. stratiotes_), 82.7% (_S. molesta_), and 88.5% (_E. crassipes_) for phosphate reduction; 88.66% (_P. stratiotes_), 90.47% (_S. molesta_), and 89.11% (_E. crassipes_) for ammoniacal nitrogen reduction; 83.6% (_P. stratiotes_), 92.1% (_S. molesta_) and 93% (_E. crassipes_) for nitrate reduction. Hence, the application of hydroponic systems in cultivation of the macrophytes proved to be effective in the phytoremediation process by removing the residual nutrients present in the treated secondary domestic wastewater. Additionally, this method can be used for the cultivation of plant-based biomass and other non-edible useful crops using the free nutrients content of the wastewater before its discharge into the natural environment.  
**Keywords:** retention time; turbidity; phosphate; nitrate; ammoniacal nitrogen; hydroponic system; aquatic plants
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 3 Sensor Design  
Monitoring an acoustic environment requires a reasonable quantity of reliable acoustic sensors to gather samples of the acoustic landscape of the place. For these applications, the sensors should meet the following requirements:  
1. **Continuous measuring:** each sensing node must be capable of measuring continuously and without being interrupted, to avoid sample loss.
2. **Maximum semblance between devices:** To obtain comparable metrics (in terms of measured noise level but also in terms of temperature response, speed and reliability), all the sensing nodes of the network should be as similar as possible among them. Additionally, they should be calibrated prior to their deployment, to ensure that the results that they are supplying are comparable.
3. **Low cost:** The price of each node of the network must be moderate to ensure the deployment of many sensors. This way, a wider area can be covered. For this work, a node is considered as low-cost if its price per unit is around EUR 100 or less, which is considerably low compared to Class-I commercial measuring sensors (which typically have a unitary price higher than EUR 1000 [25]).
4. **Scalability:** It is not only the price that limits the number of sensors that can be deployed over a certain area; other factors must be considered as well. For instance, adding nodes to the network should be as fast as possible, as human resources are usually needed for this task.
5. **Remote connectivity to access each node:** for monitoring purposes, addressing software failure or checking the status of the nodes, each node of the network should be accessible remotely. Occasionally, the physical location in which the sensors are deployed is not easily accessible to the technicians in charge of managing the sensing nodes. For this reason, remote access to the nodes becomes crucial when deploying a wireless network in a real-operation environment. However, it must be taken into account that despite having remote access to the nodes, there are problems that still must be solved physically, such as hardware failure (e.g., the microphone or the computing unit breaks and must be replaced).  
The remainder of this section explains the different elements that compose the nodes of the proposed WASN and how have they been assembled.
# The Smart Buildings Revolution: A Comprehensive Review of the Smart Readiness Indicator Literature
###### Abstract  
The construction industry is embracing advanced digital technologies, such as the Internet of Things and automation systems, to enhance energy management and occupant comfort in smart buildings. Recognizing the need to assess the readiness of buildings to support energy-efficient and adaptive functionalities, the European Commission introduced the smart readiness indicator (SRI) in 2018. While the SRI provides a standardized framework, its adoption, limitations, and potential to drive the evolution of smart buildings remain underexplored. This study addresses these gaps through a systematic literature review, incorporating bibliometric and qualitative analyses to evaluate the state of research on the SRI. The bibliometric analysis reveals that research on smart readiness is growing rapidly, with a strong focus on energy efficiency and smart buildings. This literature primarily evaluates and promotes the adoption of the SRI within buildings, aligning with the need to explore the paths for the evolution of smart buildings. The qualitative review summarizes six understudied research topic required to drive the evolution of smart buildings in the literature: _The applicability of the SRI to different contexts, including various building types and climatic conditions; the subjectivity in the framework; the alignment with other certificates and standards; the SRI as a tool for smart retrofit; expansion to the neighborhood and district levels_; and _the score correlation with energy performance_. The findings show that, although the SRI was originally introduced for buildings, it has much wider applicability, at the more detailed building component level as well as at the broader neighborhood and district levels. Future research could focus on the role of the SRI in evaluating smart readiness at the neighborhood scale and determining the minimum acceptable SRI score.  
SRI; smart readiness indicator; smart buildings; energy efficiency; demand response +
Footnote †: journal: Applied sciences
## 3 Results
### Thermal Performance Evaluation of the ENMH
(**c**) Temperature stability under a constant voltage of 0.6 V for 2 h. (**d**) Short-term cyclic heating behavior under repeated ON/OFF switching of 0.6 V at 10 s intervals. (**e**) Long-term thermal durability test under repeated ON/OFF switching of 0.6 V over 100 cycles. (**f**) Comparison of heating performance with recently reported textile-based wearable heaters [40; 41; 42; 43; 44; 45; 46]. To evaluate the long-term operational safety of the heater, over 100 ON/OFF voltage cycling tests were performed (Figure 4e). The heater demonstrated stable and repeatable thermal responses throughout the cycling period, exhibiting excellent durability and reliability with no significant degradation in heating rate or responsiveness. Compared to textile-based heaters, the present device achieved a high temperature of 120 \({}^{\circ}\)C at a low driving voltage of 0.6 V, outperforming other devices requiring higher voltages, thereby confirming its superior heating efficiency and low-power operation (Figure 4f and Table S1). Furthermore, the heater maintained stable heating performance under oxidative (85 \({}^{\circ}\)C for 48 h) and humid (85% relative humidity at 25 \({}^{\circ}\)C) conditions, with no notable changes in heating rate, peak temperature, or electrical resistance during ON/OFF cycling at 0.6 V (Figure S6). To simulate actual washing conditions, the samples were immersed in a 4 g/L SDBS solution at 40 \({}^{\circ}\)C under stirring at 800 rpm for 30 min. While samples washed with DI water maintained stable performance without significant changes, those subjected to SDBS washing showed Cu layer dissolution, leading to severe degradation of electrical properties. Surface damage and a decrease in Cu distribution were also observed. In contrast, samples coated with PDMS prior to washing retained their electrical and structural stability under the same conditions, demonstrating that a polymer protective layer can effectively enhance the durability of the heater in washing environments (Figure S7).
## 1 Introduction
### Regulation of Cholesterol Synthesis  
There are many ways cholesterol synthesis can be regulated, including coordinate transcriptional control [9] of enzymes and targeted miRNA degradation of the enzymes. Review articles on microRNAs regulating cholesterol metabolism homeostasis can be found in [10; 11]. In this study, we have focused on the main control point of cholesterol biosynthesis: the reaction catalyzed by HMGCR.  
Cholesterol levels are regulated dynamically. This regulation depends on the following factors:  
1. Expression of the sterol-dependent gene, such as sterol-regulatory element-binding protein ( SREBPP-2). When there is a low cholesterol level, the reductase gene is upregulated by a mechanism involving the transcription factor SREBPP-2 protein [12]. The combination of SREBPP-2 in response to the DNA's sterol regulatory element (SRE) leads to an increase in enzyme levels and, thus, to enhanced cholesterol synthesis;
2. Degradation of the enzyme intensified by sterols (if the concentration of sterols in the SER is high--the reductase is transferred to the cytosol and undergoes ubiquitination and degradation in proteasomes);
3. Covalent modification catalyzed by AMP-activated protein kinase (AMPK), an enzyme sensitive to the AMP: ATP ratio, and protein phosphatase; a phosphorylated form of AMPK, converts HMGCR to its phosphorylated and inactive form, whereas HMGCR phosphatase reverses this process and produces a dephosphorylated and an active form of HMGCR;
4. HMGCR activity hormonal control via phosphorylation-dephosphorylation mechanisms --on the one hand, an increase in insulin concentration or in thyroid hormones promotes reductase dephosphorylation (activation). On the other hand, an increase in glucagon, epinephrine, or glucocorticoid concentration causes the opposite effect. Moreover, HMGCR can be ubiquitylated by the intermediates of mevalonate and cholesterol derivatives, such as lanosterol, 24,25-dihydro-lanosterol, and 25- and 27-hydroxycholesterol;
5. Drugs inhibiting selected pathways (statins) are structural HMG-CoA analogs; they are reversible and competitive inhibitors of HMGCR.
## 5 Experiments
### Evaluation Metrics  
To measure the matching performance, we compute the matching Precision, Accuracy, Recall and F1-score from the ground-truth correspondences. According to registration ability, we use the indicators proposed in [58; 59]: relative rotation error (RRE), relative translation error (RTE), and success rate.  
_RTE_ and _RRE_ are defined as:  
\[RTE=\left|\hat{\mathcal{T}}-T^{*}\right| \tag{9}\] \[RRE=arccos\frac{\mathcal{T}(\hat{R}^{T}R^{*})-1}{2}\]  
where \(T^{*}\) and \(R^{*}\) denote the ground-truth translation and rotation, respectively. Meanwhile, \(\hat{T}\) is the estimated translation and \(\hat{R}\) is the estimated rotation matrix after registration.
## 5 LoW Mob Mobity Support Scheme
### Proposed Message Formats
#### 5.3.3 Message Format of a Data Packet Sent from a CN to the MN  
When a CN, which is located either outside or inside the PAN, wants to send a data packet to a MN; it first sends the packet to the GW. This is because the GW has a binding cache which stores the mapping of MN's ID with its current location in terms of SN's short address. The GW then fragments and compresses the received packet, if needed. Thereafter, it attaches GW-MN dispatch and the packet's header. The dispatch indicates that this packet is for the MN and sent by the GW. The packet's header contains 16 short bits address of the SN, with which the MN is currently associated, the source CN's 16 bits address, and the destination MN's 16 bits ID. The message format is shown in Figure 9.  
The packet, sent by the GW, is then relayed over a number of hops as it reaches the destination SN. By looking at the dispatch value, the intermediate relaying nodes know that the packet is sent from the GW. When the destination SN receives the packet, it takes off GW-MN dispatch and the destination SN's address from the packet; and forwards it to the MN. Thus in this mobility support scheme we propose to identify mobility-related packets with the use of specific dispatch values. The dispatch values of some of the messages are shown in Table 1 below.  
Figure 8: Message format of a data packet sent from the MN to the CN via the tunnel from MN’s associated SN to the GW.  
Figure 9: Message format of a data packet sent from the CN the MN via the tunnel from GW to SN.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 5 Discussion  
As an extension of the existing ground application system, HY-2A CAPF enables users to efficiently and effectively work with HY-2A satellite data. With the capability of solving user's on-demand processing requirements, it has the potential to make a large ocean remote sensing dataset available to a wider user community.  
A beta version of the framework was released to an intranet environment in September 2016. The framework helps users accomplish their daily work and greatly increases the work efficiency. According to user feedback, further iterations are being planned to expand the capabilities of the system. The first iteration will focus on developing more pre-defined algorithms to expand case studies and facilitate users to design their own processing workflows. For example, in order to help the Xue Long icebreaking research vessel explore the Polar Area, we need to develop the thematic mapping algorithms for the Xue Long vessel.  
Furthermore, with the progress of cloud computing technology, it is necessary to follow the trend and adopt novel techniques of data analysis and data storage. As future work, we will take advantage of cloud storage to store processing results from researchers. Additionally, the integrated algorithmic environment should support more programming languages for users' customized algorithms.  
It is expected that the framework can be applied to other domains. In other words, it is necessary to standardize the interfaces to access other remote sensing data from different agencies and platforms. For example, the meteorological remote sensing data from the China meteorological administration can be integrated into the framework to enrich the content and widen the user community.  
Figure 8: A Cloud Liquid Water thematic map of a single HY-2A swath for 25 March 2015 derived from the Customized Processing System. The sample points of cloud liquid water are mapped to a 0.25-degree grid. The colors show the cloud liquid water.
## 2 Materials and Methods
### Description of the Precipitation Products
#### 2.1.3 Satellite Precipitation Products  
Low Earth Orbit (LEO) and geostationary (GEO) satellites can carry instruments providing precipitation amounts at the surface. [PERSON] et al. [12] listed the three categories of sensors that measure the precipitation: the infrared or visible sensors aboard LEO and GEO satellites, the passive and active microwave (PMW/AMW) sensors aboard LEO sensors, and the blended products from the combination of both techniques. Another way to estimate the precipitation accumulation is based on the attenuation of Ka-band signals of broadband communication satellites [22; 23].