esa-sceva/satcom-chunk-collection huggingface dataset:
# ORC Optimal Design through Clusterization for Waste Heat Recovery in Anaerobic Digestion Plants
###### Abstract  
Waste heat recovery (WHR) systems through organic ranking cycles (ORCs) in anaerobic digestion plants may improve degeneration efficiency. Cogeneration unit power output, flue gas temperature, and mass flow rate are not constant during the day, and the thermal load requested by digesters shows seasonal variations. For this reason, a proper design of the ORC is required. In this study, a design methodology is proposed, based on the clustering of the boundary conditions expected during one year of operation and the anaerobic digestion plant operation. The design has to be a compromise between part-load operation and nominal power rating. In this study, the ORC design boundary conditions were partitioned into four representative clusters with a different population, and the centroid of each cluster was assumed as a potential representative boundary condition for the cycle design. Four different ORC designs, one for each cluster, were defined through an optimization problem that maximized the cycle net power output. ORC designs were compared to those resulting from the seasonal average boundary conditions. The comparison was made based on the ORC off-design performance. Part-load behavior was estimated by implementing a sliding-pressure control strategy and the annual production was therefore calculated. ORC off-design was studied through a detailed Aspen HYSYS simulation. Simulations showed that the power output of each design was directly connected to the cluster population. The design obtained from the most populated cluster generated 10% more energy than that from a system designed by taking into account only the year average conditions.  
organic ranking cycle; waste heat recovery; operating condition clustering; design optimization; off-design; Aspen HYSYS
###### Abstract  
Great improvement recently appeared in terms of efficient service delivery in wireless sensor networks (WSNs) for Internet of things (IoT). The IoT is mainly dependent on optimal routing of energy-aware WSNs for gathering data. In addition, as the wireless charging technology develops in leaps and bounds, the performance of rechargeable wireless sensor networks (RWSNs) is greatly ameliorated. Many researches integrated wireless energy transfer into data gathering to prolong network lifetime. However, the mobile collector cannot visit all nodes under the constraints of charging efficiency and gathering delay. Thus, energy consumption differences caused by different upload distances to collectors impose a great challenge in balancing energy. In this paper, we propose an adaptive dual-mode routing-based mobile data gathering algorithm (ADRMDGA) in RVSNs for IoT. The energy replenishment capability is reasonably allocated to low-energy nodes according to our objective function. Furthermore, the innovative adaptive dual-mode routing allows nodes to choose direct or multi-hop upload modes according to their relative upload distances. The empirical study confirms that ADRMDGA has excellent energy equilibrium and effectively extends the network lifetime.  
adaptive dual-mode routing; mobile data gathering; wireless energy transfer; rechargeable wireless sensor networks; Internet of things +
Footnote †: journal: Applied Sciences
# 5G Mobile Phone Network Introduction in Colombia
## 4 MIMO Systems
### Massive MIMO and 5G  
The 5G network helps increase the number of applications that make use of artificial intelligence and mobile computing because they require higher wireless data speeds. In 5G NR, this is achieved by adopting massive MIMO and carrier aggregation (CA) with a wide channel bandwidth (BW) of up to 200 MHz [32]. The concept of traditional MIMO is taken to another level, moving from tens to hundreds or thousands of antennas at the base station (BS) by serving a large number of autonomous terminals [33] with the goal of increasing antenna gains through appropriate fairly narrow beam-forming techniques, such as through accurate channel status information (CSI) [5]. Massive MIMO is a wireless physical layer technology [33]. Gives interference robustness, low latency, and security [3] by improving power output and system spectral efficiency [5] as a result of spatial multiplexing and providing uniform quality of service in different environments, especially in urban and suburban areas, while complementing or even replacing the process of densification of the ultra-network [25] able to support a large number of users [34]. Massive MIMO has good affinity with mm-W communications because the antenna gains compensate for the propagation loss inherent in the VHF (Very High Frequency) band [6]. The massive antenna at base stations can be deployed in co-located or distributed configurations. In co-located massive MIMO, all antennas are located in a compact area and have the advantage of low backhaul requirements. In contrast to distributed massive MIMO systems, the antennas are distributed over a large area, offering a much higher coverage probability than co-located massive MIMO (mMIMO), at the expense of higher backhaul requirements [35]. Although the ideal bands for mMIMO are the millimetre bands (frequencies from 30 GHz upwards) [36], the MinTIC only plans to auction up to the 3.5 GHz band for the time being [37].
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 5 Performance Analysis
### Wireless Issue  
The wireless network performance considering several system bandwidths is illustrated in Figures 23 and 24 respectively. The performance indicators were assessed in light of the two-tier LTE hexagonally shaped mobile network with 19 base stations. The throughput indicates the number of bits per second transferred. Instead, the word \"energy efficiency\" quantifies the number of bits sent per watt, equal to the ratio of overall throughput to the total output essential for the base station. A system that delivers higher throughput and energy efficiency performance is always desirable. Figure 23 demonstrates that the given system has enough throughput performance and is similar to the traffic intensity profile. In addition, the larger system bandwidth gives a better value to the output performance because of the higher energy consumption. As referred to in (10), the energy  
Figure 21: Net present cost of the HetNet under different system BW.  
Figure 22: Cost of energy of the HetNet under different system BW.  
efficiency is in a straight line related to the network's performance. This results in increased energy efficiency curves with an increase in bandwidth, as seen in Figure 24.
## 2 Materials and Methods
### Data Acquisition
#### 2.1.1 Experimental Procedure
The calibration data were always acquired at the beginning of the session and no data from previous sessions were used. The feedback consisted of a textual message that informed the participant if the decoded movement matched the requested movement. If there was a mismatch, the predicted movement was also shown. Feedback runs always used imagined movements. At the end of each feedback run, participants were informed of the proportion of correctly predicted movements to provide them with a target to improve upon. Figure 2 shows the procedure of a single trial during a feedback run. Feedback runs consisted of 8 repetitions per movement, resulting in 32 trials per run. The procedure for feedback runs was the same as calibration, with the addition of feedback after the MI phase. Including the time to decode, the feedback phase lasted 3 s. In addition to textual feedback, the fixation cross turned green on a match and red on a mismatch. The feedback decoding model used a sliding window of 2.5 s, starting 1 s before the GO signal and ending after the first 3 s of movement imagination, with strides of 0.25 s. This resulted in 7 overlapping windows for which a prediction was made. To obtain a match, the majority of windows should predict the requested movement. Figure 1: One trial in a calibration run of the data acquisition procedure. Figure 2: One trial in a feedback run of the data acquisition procedure.
## 3 Results and Discussion
### VOCs of Green Coffee Beans
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**R.T. (Min)**} & \multirow{2}{*}{**CAS Number**} & \multirow{2}{*}{**Volatile Compounds**} & \multicolumn{2}{c}{**Quantities**} \\  & & & **Medium** & **Low** \\ \hline \multicolumn{5}{c}{**Furans**} \\
9.0 & 108-29-2 & Dihydro-5-methyl-2(3H)-furanone & 0.70 a  & 0.76 a  & 0.28 b  \\
12.4 & 3777-69-3 & 2-Methyl-furan & 1.04 b  & 2.07 a  \\
22.0 & 51080-20-7 & Furan & 0.81 b  & 2.33 a  & 2.27 a  \\
38.1 & 104-61-0 & Dihydro-2-methyl-2(3H)-furanone & 1.00 ns  & 0.00 & 0.00 \\
**Pyrazines and pyridines** & & & & \\
13.7 & 110-86-1 & Pyridine & 1.52 c  & 1.94 b  & 6.97 a  \\
15.9 & 75354-36-8 & (E)-7-Methyl-1,6-dioxaspiro (Pay, 2016)decane & 0.08 c  & 0.26 b  & 0.90 a  \\
24.1 & 24683-00-9 & 2,5-Dimethyl-pyridine & 2.46 c  & 3.44 b  & 4.88 a  \\
24.9 & 3146-84-7 & 3,4-Dihydro-2,2,3-trimethyl-2H-pyrrole 1-oxide & 0.00 ns  & 0.00 & 1.28 \\
28.0 & 20189-42-8 & 1H-Pyrrole-2,5-dione, 3-ethyl-4-methyl- & 1.31 a  & 1.17 a  & 0.54 b  \\
30.5 & 11-Pyrrole & 1.30 ns  & 1.40 & 0.00 \\
32.4 & 120-72-9 & 1H-Indole & 0.00 ns  & 0.00 & 0.32 \\  & **Aldehydes** & & & \\
2.0 & 123-72-8 & Butanal & 1.49 ns  & 0.00 & 0.00 \\
2.8 & 96-17-3 & 2-Methyl-butanal & 2.27 a  & 1.27 c [FONOTE:c]Footnote a: footnotemark: [ENDFOOTNOTE] & 1.99 b  \\
5.3 & 66-25-1 & Hexanal & 2.03 c  & 2.99 b  & 4.29 a  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Content of volatile compounds (mean %, \(n=3\)) obtained from green coffee beans of different qualities. R.T. = retention time. Different lowercase letters indicate significant differences (one-way ANOVA followed by Tukey’s test. \(p<0.05\)) between coffee bean samples qualities.
## 2 Materials and Methods  
### Experimental Site and Design  
The vineyard was located in Manas County of Xinjiang Uygur Autonomous Region, one of the famous wine regions at the north foot of the Tianshan Mountains (44\({}^{\circ}\)14\({}^{\prime}\)0\({}^{\prime}\)N-86\({}^{\circ}\)14\({}^{\prime}\)39\({}^{\prime}\) E, elevation 498 m), in Northwest China. The soil type was \"silt loam\", with 1.2% organic matter content and the pH value was about 8.3. The cultivar used in the experiment was own-rooted 'Cabernet Sauvignon' (_V. vinifera_ L. cv.) grapevines planted in the year 2002. Row and vine spacing were 2.8 \(\times\) 1.0 m, respectively, with the direction of the plant being 66\({}^{\circ}\) north by east. All the experimental vines were trained to the modified vertical shoot-positioned spur-pruned cordon system (M-VSP) [21], with a canopy of 1.2 m in height and 0.8 m in width. The cordon was 0.6 m above the ground. The experiment was carried out in the two successive years of 2018 and 2019, which were the normal vintages in Xinjiang. And the irrigation method for grapes was furrow irrigation.  
In order to monitor the grape development process, the samples were collected at five developing stages: pea size, veraison 5%, veraison 100%, 10 days after veraison 100%,and the commercial harvest stage. Three adjacent experimental rows were treated as replicates in each block. There were three statistical replications for this experiment. And for obtaining the representative grape berries, there were 20 vines with similar growth vigor for each replicate. Five hundred berries of each replicate were randomly collected from different positions of the clusters at harvest. After sampling, 100 berries were randomly selected to measure the physicochemical parameters, and the remaining berries were frozen immediately in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C for the subsequent analysis of the flavonoids and aroma compounds.  
The treatments were:  
Control: clean tillage between rows;  
Treatment: purslane (_P. oleraca_ L.) was grown between rows in the vineyards.  
The covering crop used in this study was purslane (_P. oleraca_ L.), which belonged to the dicotyledonous plants. In the management of the vineyard ground, purslanes were planted manually in the spring and pruned regularly to keep their height at about 15 cm above the ground, and maintained this state until the grapes were harvested. For the control, it adopted the management of the clean tillage, and the weeds between rows were removed regularly, according to that of the purslane covering. There was no irrigation between rows regardless of the treatment and the control during the whole grape berry development.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 4 Results and Discussion
### Comparative Results
#### 4.3.1 One-Step Approach and Work-Cycles  
This phase of the experiment is carried out to demonstrate the importance of work-cycle identification as a prerequisite for communication state/mode modeling. Among the algorithms in the comparison set, K-Means and Agglomerative clustering can be deployed on the dataset without any assumptions on the expected number of clusters, i.e., the number of work-cycles. Preliminary, the data were fitted to this subset of unsupervised learning algorithms with the addition of MeanShift clustering. The identified number of clusters by all the algorithms on the original dataset was 2.  
The results of fitting the data, without the work-cycle identification step, to the set of algorithms with the order of 2 and 5 are illustrated in Figure 5. It is clear that none of the learning algorithms succeeds in capturing the work-cycles or the operational mods. With a higher-order model, all the algorithms tend to set a higher number of spatial thresholds and miss the temporal dynamics of the system altogether, Figure 5d-f. The performance differences are limited to the threshold values, with a variance of 0.05 on the normalized filtered throughput values for the second-order model, and 0.04 for the fifth-order model.  
The algorithms do not detect the repeated temporal pattern since the increased amount of data provides more support for the distance and/or similarity between the values. While the data is clustered and the learning process shows acceptable results for algorithms without any parameter tuning, the prediction accuracy of the algorithms on the unseen data is very low; even though the unseen data bears high similarity with the training dataset, as illustrated in Figure 3b. One reason for this poor performance is that the algorithms do not take the order of the observed data into account. The commonly used unsupervised and clustering algorithms work based on maximizing the intracluster while minimizing the intercluster similarities. Therefore, two points with similar values close to a cluster center are more likely to be assigned to the same cluster despite their time of occurrence or order.  
As discussed previously, throughput varies in different time intervals due to the dynamic behavior of the devices. Hence, it is accessible that sole spatial profiling does not provide sufficient information for estimating the required resource and planning for an ongoing process that expands in time.
# CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization
## 4 Methodology
### Dual Neural Matrix Factorization  
In this section, we introduce the dual neural matrix factorization module, which not only fits the historical loan records but also estimates the default risk of loan. The dual neural matrix factorization module consists of three parts, i.e., feature encoding, loan recommendation, and default risk estimation.  
**Feature encoding:** In this part, we convert the features of applicants and loan projects into vectors. Note that the loan project \(v=(id,\mathbf{a})\) and applicant \(u=(uid,\mathbf{a}^{\prime})\). \(\mathbf{a}^{\prime}\) represents the features of applicants, including the gender, age, and income grades. These features can be classified into two groups, i.e., value features \(\mathbf{a}_{f}\) and category features \(\mathbf{a}_{c}\). For value features, we first normalize them into \((0,1)\) and feed them into the MLP layer to obtain the input. For category features, we employ embedding techniques to convert them into vectors:  
\[\mathbf{f}_{f} =MLP(Normalize([a_{1},a_{2},\dots,a_{k_{f}}]) \tag{10}\] \[\mathbf{f}_{c} =MLP(concat(Embedding([a_{1},a_{2},\dots,a_{k_{c}}])))\]  
where \(k_{v}\) and \(k_{c}\) are the number of features. We assume that the dimensions of \(\mathbf{f}_{f}\) and \(\mathbf{f}_{c}\) are equal to \(d\). For \(u\) and \(v\), we use the same method to generate their value features and category features. Then, we concatenate them and feed them to a \(MLP\) to obtain their features.  
\[\mathbf{f}^{u} =MLP(concat(\mathbf{f}_{f}^{u},\mathbf{f}_{c}^{u})) \tag{11}\] \[\mathbf{f}^{v} =MLP(concat(\mathbf{f}_{f}^{v},\mathbf{f}_{c}^{v}))\]  
For the applicant, we also involve the statement embedding to the features and obtain the final representation of the applicant.  
\[\mathbf{f}^{u}=MLP(concat(\mathbf{s},\mathbf{f}^{u})) \tag{12}\]  
**Loan recommendation.** Taking the representations of \(u\) and \(v\) as inputs, CSRLoan employs a neural matrix factorization method to fit historical loan records. In this part, we first construct an applicant-loan matrix \(\mathbf{R}\). Each element \(\mathbf{R}_{i,j}\) in the matrix indicates the number of user \(u_{i}\) that successfully completed loan project \(v_{j}\). Then, we fit these numbers with the representation vectors.  
\[o_{i,j}^{r}=MLP_{r}(concat(\mathbf{f}^{u},\mathbf{f}^{v})) \tag{13}\]  
**Default Risk Estimation.** Similarly to loan recommendations, CSRLoan employs another neural matrix factorization model to fit the historical default loans. We also construct an applicant-loan matrix \(\mathbf{D}\). Each element \(\mathbf{D}_{i,j}\) in the matrix indicates the number of user \(u_{i}\) defaulting the loan project \(v_{j}\).  
\[o_{i,j}^{d}=MLP_{d}(concat(\mathbf{f}^{u},\mathbf{f}^{v})) \tag{14}\]
# Nonlinear Fatigue Damage of Cracked Cement Paste after Grouting Enhancement
## 2 Materials and Methods  
The cement paste sample was prepared with white Portland cement (P.W 32.5 based on GB/T 2015-2005) with designed water to cement (\(w/b\)) ratio of 0.5. The mixture was poured into a mold and placed on an electromagnetic vibration shaker roughly 20 min. The basic mechanical properties of the samples were tested after 28 days curing at room temperature. The grouting reinforcement material (bisphenol A epoxy resins and amine hardener) was provided by Shanghai Jiagu Building Materials Co., Ltd. (Shanghai, China). The epoxy resin matrix (bisphenol A epoxy) and curing agents (amine hardener) were mixed with a volume ratio of 2:1. The epoxy resin was fully stirred and poured into the polytetrafluoroethylene (PTFE) mold. The epoxy resin was cured at room temperature for 72 h before testing. The mechanical properties, such as tensile strength \(s_{\text{t}}\), elastic modulus \(E\), Poisson's ratio \(m\), and compressive strength \(s_{\text{c}}\), were measured experimentally through a splitting test and a uniaxial compression test, as listed in Table 1.  
The regular single crack and parallel double crack cement paste specimens with a size of 50 mm \(\times\) 25 mm \(\times\) 100 mm were prepared by using the embedded strip method [19] and the crack angle \(a\) = 60\({}^{\circ}\), crack length \(2a\) = 20 mm, and crack spacing \(H\) = 10 mm. Epoxy resin was injected into the crack and cured for 72 h. The flatness of the specimen surface and penetration of cracks were checked after demolding to meet the test requirements. The sample and grouting preparation are shown in Figure 2. Fatigue tests were conducted on a Hualong WDW-20C test apparatus, manufactured by the Shanghai Hualong Co., Ltd. (Shanghai, China). The test parameters included the greatest stress \(\sigma_{\text{max}}\), the smallest stress \(\sigma_{\text{min}}\), the static compressive strength \(\sigma_{c}\), the stress amplitude \(\sigma_{a}\), stress ratio \(R\), and stress level \(S\). The relationship among the parameters is shown in Figure 3. The greatest stress and the smallest stress were entered as control parameters. Then a triangle wave loading based on the frequency was automatically loaded. The fatigue loading in this test was loaded at a frequency of 0.1 Hz. Three different stress ratios and four different stress levels were chosen for comparison. They were \(R\) = 0.1, 0.2, 0.3 and \(S\) = 0.41, 0.51, 0.61, 0.76, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material Type**} & **Tensile Strength** & **Compressive** & **Elastic Modulus** & **Poisson’s Ratio** \\  & **(MPa)** & **Strength (MPa)** & **(GPa)** & **Poisson’s Ratio** \\ \hline Portland cement & 2.04 & 24.261 & 4.68 & 0.22 \\ Epoxy resin & 28.149 & 107.5926 & 4.07 & 0.357 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mixture components’ mechanical properties.
## 3 Results and Discussion
### Usefulness of the Applied Substrate Modifications in Plant Production  
The relative chlorophyll content in leaves, measured using the SPAD method, is a sensitive and non-invasive indicator of the physiological status of plants and the efficiency of photosynthesis. In crop cultivation on coconut-based substrates, this parameter can serve as a reliable indicator of substrate quality, reflecting its ability to provide adequate water-air and nutrient conditions for plant growth [30].  
To determine the effect of the applied substrate modifications, SPAD measurements of the relative chlorophyll content were conducted on raspberry plant leaves. The developed substrate modifications did not significantly affect the chlorophyll content in raspberry leaves (Figure 16), indicating that all tested substrates provided a comparable growth environment to that of the traditional substrate composed solely of coconut fibres. Moreover, the use of the substrate, including the coating process involving the wood-derived isolate, had a positive effect on the quality of the produced fruit, as confirmed by [PERSON] et al. [12]. The harvested raspberry fruit exhibited increased levels of phenolic compounds and vitamin C, as well as enhanced antioxidant potential.  
Figure 16: Relative SPAD chlorophyll content in raspberry leaves depending on the applied substrate. NOTE: Different lowercase letters indicate significant differences between modifications of the same type of fibre, whereas uppercase letters denote significant differences between the same modifications applied to different fibres, with a significance level of \(\alpha=0.05\).