esa-sceva/satcom-chunk-collection huggingface dataset:
# Gene Expression Space Shapes the Bioprocess Trade-Offs among Tier, Yield and Productivity
## 4 Conclusions  
In this work, we demonstrate the need for metabolic burden models as well as their utility. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature [5]. Our model can be used to predict cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. Multiscale models, like the one presented here, incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and the expression of enzymes of interest, are useful to understand the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. With our approach it will be easier to fulfill industry-level overproduction demands optimally, as measured by the key performance metrics: titer, productivity rate and yield (TRY).  
Conceptualization, F.N.S.-N., Y.B., A.V. and J.P.; methodology, F.N.S.-N. and Y.B.; software, F.N.S.-N.; validation, F.N.S.-N., Y.B. and A.V.; formal analysis, Y.B., A.V. and J.P.; writing--original draft preparation, F.N.S.-N. and J.P.; writing--review and editing, Y.B., A.V. and J.P.; visualization, F.N.S.-N. and Y.B.; supervision, A.V. and J.P.; project administration, A.V. and J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by grants MINECO/AEI, EU DPI2017-82896-C2-1-R 662 and MICINN/AEI, EU PID2020-117271 RB-C21. F.N.S.-N. thanks the UPV grant number PAID-01-2017.  
Not applicable.  
The open source implementation of the multiscale model and the scripts we used to generate the figures of this work are available at [[https://github.com/sb2](https://github.com/sb2) cl/MDPI2021-MultiscaleModel]([https://github.com/sb2](https://github.com/sb2) cl/MDPI2021-MultiscaleModel) (accessed on 31 May 2021).  
The authors would like to thank [PERSON] and [PERSON] for their ideas and suggestions during the developing of this work.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TRY & Titer, Rate, Yield \\ RBS & Ribosome binding site \\ \end{tabular}
## 3 Methodology
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & & **Efficiency** & **Input01** & **Input02** & **Input03** & **Input04** & **Output01** & **Output02** \\ \hline
**II** & Input01 & \(-\)0.174 & & & & & \\  & Input02 & \(-\)0.131 & \(-\)0.002 & & & & \\  & Input03 & 0.117 & 0.188 & \(-\)0.083 & & & \\  & Input04 & 0.006 & 0.021 & 0.54 & 0.005 & & \\  & Output01 & 0.295 & 0.017 & \(-\)0.126 & 0.176 & 0.004 & & \\  & Output02 & **0.394** & 0.171 & \(-\)0.183 & 0.302 & 0.135 & \(-\)0.076 & \\  & Output03 & **0.346** & 0.272 & 0.098 & 0.029 & \(-\)0.044 & 0.116 & \(-\)0.01 \\ \hline
**III** & Input01 & \(-\)0.088 & & & & & \\  & Input02 & 0.014 & \(-\)0.045 & & & & \\  & Input03 & \(-\)0.151 & 0.105 & \(-\)0.213 & & & \\  & Input04 & \(-\)0.029 & \(-\)0.111 & 0.736 & \(-\)0.176 & & & \\  & Output01 & 0.314 & 0.339 & \(-\)0.053 & 0.26 & \(-\)0.074 & & \\  & Output02 & 0.208 & 0.126 & \(-\)0.221 & 0.078 & \(-\)0.264 & 0.017 & \\  & Output03 & **0.620** & \(-\)0.075 & 0.236 & \(-\)0.224 & 0.252 & 0.079 & \(-\)0.397 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Correlation analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**SHE Projects**} & \multicolumn{2}{c}{**PI Projects**} & \multicolumn{2}{c}{**RM Projects**} \\ \cline{2-6}  & **LQ** & **UQ** & **LQ** & **UQ** & **LQ** & **UQ** \\ \hline I & 3 & 4 & 0 & 4 & 4 & 9 \\ II & 4 & 2 & 3 & 7 & 6 & 4 \\ II & 4 & 5 & 5 & 1 & 2 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Number of projects in LQ and UQ of efficiency according to the type of project. Correlation and benchmark analysis complement each other. The second one highlights the key variables that have the greatest impact on efficiency, while the first identifies the DMUs that should serve as a reference for guiding future initiatives. The two reference variables for the three classes are Output02 and Output03, respectively, the disparity between the project's actual and initially planned completion date and the planned period required for the project. Synthesizing, the variables with the most significant positive influence on efficiency are Output2 and Output3. Hence, forthcoming initiatives should concurrently emphasize greater accuracy in meeting deadlines and reducing the estimated time to completion.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 3 Preliminary
### Relative Density of a Cluster  
Cluster connectivity primarily depends on two fundamental components: internal connectivity and external connectivity. This study categorizes these concepts as the 'internal degree' and 'external degree' of a cluster, respectively. To formulate more independent metrics, several approaches incorporate density and cut features by leveraging the concepts of the internal and external degrees of a cluster. The internal degree of a cluster, denoted as \(int_{deg}(c_{i})\), is determined by counting the total number of edges within the cluster. This is expressed as \(\sum_{v\in c_{i}}int_{deg}(v)\), where \(c_{i}\) represents the cluster and \(v\) signifies individual vertex within that cluster. Conversely, the external degree of a cluster, represented as \(ext_{deg}(c_{i})\), is  
Figure 2: Five possible definitions of an edge degree. (**a**) Uniformly assigning a value of 1 to each edge. (**b**) Considering the degree at one endpoint of an edge. (**c**) Multiplying the degrees of endpoints in an edge. (**d**) Summative combination of endpoint degrees in an edge. (**e**) Power function product of endpoint degrees.  
computed by tallying the total number of edges that link the vertices of the cluster with those outside the cluster. This is articulated as \(\sum_{v\in c_{i}}\mathit{ext}_{\mathit{deg}}(v)\).  
\[\mathit{int}_{\mathit{deg}}(c_{i})=\sum_{v\in c_{i}}\mathit{int}_{\mathit{deg} }(v) \tag{3}\]  
\[\mathit{ext}_{\mathit{deg}}(c_{i})=\sum_{v\in c_{i}}\mathit{ext}_{\mathit{deg} }(v) \tag{4}\]  
Relative density [2, 64], denoted as \(\delta_{r}(c_{i})\) for cluster (\(c_{i}\)), is a metric that quantifies the ratio of cluster internal edges to its total degree. The total degree is the sum of the cluster's internal and external degrees. Therefore, the mathematical definitions of the relative density of a cluster \(\delta_{r}(c_{i})\) and the average relative density \((\mathrm{Avg}_{(\delta_{r})}(\mathcal{C}))\) for a set of clusters are provided in Equations (5) and (6), respectively.  
\[\delta_{r}(c_{i})=\frac{\sum_{v\in c_{i}}\mathit{int}_{\mathit{deg}}(v)}{\sum _{v\in c_{i}}\mathit{int}_{\mathit{deg}}(v)+\sum_{v\in c_{i}}\mathit{ext}_{ \mathit{deg}}(v)} \tag{5}\]  
\[\mathit{Avg}_{(\delta_{r})}(\mathcal{C})=\sum_{i=1}^{k}\delta_{r}(c_{i}) \tag{6}\]
# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 2 Related Works
### Simulation Software--From the Cloud to the Edge  
The majority of real-world systems are too complex to be modelled using _analytical_ approaches, hence such systems are studied using _simulation_[19]. Cloud computing is such a complex system comprising different interacting components, the behaviour of which can be modelled using a simulation approach. The core elements of the cloud _system_ model describe data center hardware resources such as CPUs, memory, storage and network capabilities, the virtual environment where services are deployed, and user interaction with the services in the form of requests, jobs or tasks.  
In cloud computing, simulation is typically used to evaluate system performance by modelling the relationships between hardware resource consumption and user request execution. Performance is typically measured by resource utilisation, task completion time, and/or service costs.  
The most common simulation method for cloud systems is _Discrete Event Simulation_ (DES) [20; 21] where every system change is modelled as an event. Each event occurs in a sequential order and is capable of changing the state of the entire system, hence a strict, orderly queue of events must be observed. Maintaining such a queue of events can become a limiting factor for modelling large scale systems, particularly warehouse scale and beyond. Such models require a lot of memory and are hard to parallelise. New approaches, such as _Discrete Time Simulation_ (DTS) [22], are emerging to overcome these limitations. DTS eliminates the need for a queue and calculates the system state in parallel at each time step.  
Previous research [20; 21] suggests that CloudSim is the most cited simulation framework used for cloud computing. In our review, this is also the case. CloudSim [6] uses DES and is open source. As such, it is widely used in scholarly research in its original form and has been extended by different researchers with additional features [23; 24; 25], to support more complex models and use cases [26; 27; 28; 29], as well as enabling the evaluation of a wider set of KPIs [30; 31]. Other DES simulation frameworks used for cloud research include OMNeT++ [32], CloudSched [13], DCSim [33], GreenCloud [34; 35] and SimIC [36]. A more comprehensive overview of simulation software can be found in [PERSON] et al. [20; 21].  
Given the scale and complexity of modern cloud computing, let alone fog and edge computing, recent articles [37; 38] have noted the need for simulator software to be designed (i) to simulate parallel-scheduling models, (ii) to support the scale of modern hyperscale infrastructure, (iii) for easy replication and extension, and (iv) for validation against a trustworthy source. It is questionable whether this is possible at hyperscale with a pure DES approach. While the use of DTS simulation software is in its nascency, their advantages in terms of scalability and speed are promising for fog and edge computing research [22].
# Acoustic Emission for Evaluating the Reinforcement Effectiveness in Steel Fiber Reinforced Concrete
## 2 Experimental Details
### Four-Point Bending  
Five concrete mixtures were produced at the laboratory for the manufacture of the three different fiber reinforced concrete specimens per category. The specimens shape was a prismatic beam of size 100 \(\times\) 100 \(\times\) 400 mm for conducting the four-point bending test. As far as the concrete mixture is concerned, the aggregates consisted of 56% crushed sand, 13.95% fine gravel and 30.05% coarse gravel with a maximum aggregate size of 31.5 mm, while the water/cement ratio was 0.69 by mass. The crushed sand's density and water absorption were 2611 kg/m\({}^{3}\) and 0.97%, of fine gravel 2631 kg/m\({}^{3}\) and 0.74% and of coarse gravel 2685 kg/m\({}^{3}\) and 0.62%, respectively. The cement type was CEMI/A-M(P-LL). The precise mix proportions were as follows: cement (type II 42.5 N) 80 kg/m\({}^{3}\), cement (type II 32.5 N) 200 kg/m\({}^{3}\), water 195 kg/m\({}^{3}\), crushed sand 1055 kg/m\({}^{3}\), fine gravel 265 kg/m\({}^{3}\), coarse gravel 570 kg/m\({}^{3}\), retarder--plasticizer (CHEM I) 1.54 kg/m\({}^{3}\), retarder--plasticizer (CHEM II) 1.96 kg/m\({}^{3}\). The concrete's actual bulk density was 2365 kg/m\({}^{3}\) while the ambient temperature was 21 \({}^{\circ}\)C. For the fiber-reinforced concrete with 0.5% vol. of fibers, the addition of 39.3 kg/m\({}^{3}\) of hooked and waved steel fibers was made, while 2% was also applied. Their diameter was 0.6 mm, their length was 25 mm, their density was 7850 kg/m\({}^{3}\) and they were provided by Chircu Prod-Impex Company Srl, Romania. The addition of the steel fibers was applied during the mixture of the wet concrete with the maximum uniformity. The workability as measured by the slump test was 11 cm. The specimen was cured in water saturated with calcium hydroxide at 23 \(\pm\) 2 \({}^{\circ}\)C.  
The four-point bending set up is in accordance with ASTM C1609M-05 [25] standard and can be seen at (Figure 4) with the AE sensors mounted by tape. The sensors were of R15a type by Mistras Group. The experiments were conducted in the MSS-NDE laboratory of the University of Ioannina, Ioannina, Greece.
## 2 Materials and Methods
### Data Acquisition  
To ensure that the welding electrode holder and the light sensor move simultaneously during the data acquisition process, the system shown in Figure 1a-d was designed. This system made it possible to acquire arc light data precisely.  
The system design shown in Figure 1a,b configures the electrode holder and the light sensor to move simultaneously. This design was developed to ensure that the light sensor remained at a constant distance from the arc light emitted during the welding process and to optimize its movement in the three-dimensional plane. In addition, welding glass with number 9 was integrated into the system to filter the arc light data. The applied version of the design is shown in Figure 1c,d.  
The data acquisition system and sequence are shown in Figure 2. The welding process was carried out using a Magmaweld Monotig 200i (Turkey) manual metal arc welding (MMAW) machine (1, 2). Arc light data were acquired using a CEM DT 1309 (China) model luxmeter (3) and CEM DT 1309 interface software (4, 5). Current data were acquired using a Lem LA 305-S (Swiss) current transducer (6), an NI USB-6001 (USA) DAQ card (7), and LabVIEW 2021 software (8, 5). Necessary calibration procedures were performed for Lem transducer. All calculations, analyses, and artificial intelligence applications were performed using MATLAB software (R2024a). LabVIEW software (Version 21.0) was used for acquiring current data and performing calibration calculations. DT1309 interface program was used to acquire arc light data. The current and arc light signals were acquired at a sampling frequency of 9 Hz for synchronization between the CEM DT 1309 and NI USB-6001.  
Examples of experimental applications of the study are presented in Figure 3.  
Figure 1: This figure illustrates the designs of the arc light acquisition and filtering system used during the welding process: (**a**) 3D design incorporating the electrode holder and the light sensor; (**b**) 3D design combining the light sensor with the number 9 welding filter glass; (**c**) the practical implementation of the arc light sensor design; (**d**) a configuration enables the arc light sensor and electrode holder to move along three-dimensional axes.  
Figure 2: The data acquisition system.
# Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
###### Abstract  
The vibrations, due to their abundance in most industrial processes, constitute an attractive solution for the power supply of Industrial Wireless Sensor (IWS). However, the amount of energy that can be harvested presents numerous fluctuations due to the engines' different operating modes (overload, full load, or even operation without charge). Most designs do not incorporate this fluctuation in the definition of the specifications of the autonomous IWS. This paper then presents a design method to ensure the node's energy autonomy while maximizing its Quality of Service (QoS). To precisely define the specifications of the IWS, vibration measurements were carried out at its location for one month. The recorded data was used to propose a new Predictor of the Harvestable Energy from Vibrations (PHEV). A comparative evaluation of the proposed PHEV performances with a state-of-the-art predictor is carried out. The results obtained show that the PHEV makes it possible to minimize the Root Mean Square Error (RMSE) from 28.63 mW to 19.52 mW. A model of energy dissipation in IWS, considering the Internet of Things' requirements, was established. The model is based on Long-Range (LoRa)/Long-Range Communication Wide Area Network (LoRaWan). The amount of data transmitted is then maximized according to the expected energy harvest rate by setting up a Maximization Data Size Protocol (MDSP). The proposed method makes it possible to ensure an acceptable QoS without resorting to reconfigurable circuits, which are sometimes bulky for miniature devices such as the IWS.  
IWS; vibrations; energy prediction; PHEV; energy dissipation; RMSE; LoRa/LoRaWan; MDSP; QoS Article
## 3 Results and Discussion  
### Experimental Verification  
#### 3.1.1 Sensing Properties  
To verify sensing properties in the fabric, the main approach is to measure the resistance value of the fabric's conductive part when the sensing area on the fabric surface is pressed. The VC890D multimeter (Shenzhen Yisheng Victory Technology Co., Ltd., Shenzhen, China) was set to the resistance measurement mode (range: 20 k\(\Omega\)), with the red and black probes connected to the ends of the conductive yarn shown in Figure 2b. At this point, the resistance value of the conductive part of the fabric was measured to be 10.48 k\(\Omega\). When the sensing area-1 shown in Figure 2a was pressed with a finger, the resistance value was measured to be 8.51 k\(\Omega\). After removing the finger from the fabric, the resistance value returned to 10.46 k\(\Omega\). This process was repeated 1000 times, and the resistance value measurement results of the fabric's conductive part are shown in Figure 9.  
The experimental results indicate that through the fabricating process mentioned above, pressing the sensing area on the fabric surface significantly reduces the resistance value of the conductive part of the fabric. Moreover, this electrical signal change demonstrates stability and repeatability, providing a foundation for the subsequent design of circuit modules.
Owing to the introduction of macroporous ion exchangers with a sponge-like structure into the industry, it was possible to improve the kinetic sorption parameters of these resins [47]. The use of a given ion exchanger depends on the type of its functional groups which are responsible for ion exchange and determine the optimum efficiency of a given resin at various pH levels. In a large group of ion exchangers, anion exchangers with different basicity of functional groups found the largest practical application in the sorption process of acid, direct and reactive dyes [48; 49; 50; 51]. Other important parameters affecting the efficiency of dye retention are matrix composition (polyacrylic, polystyrene, phenol formaldehyde) and porosity. The purpose of the paper was to assess the efficiency of C.I. Direct Yellow 50 uptake from aqueous solutions by the weakly basic anion exchangers differing in matrix composition. C.I. Direct Yellow 50 is commonly used to dye cellulosic fabric, paper, leather etc. In addition, direct dyes are superior to others in terms of cost, better lightfastness, ease of application or shorter dye cycle. There is no complete information in the literature so far on the use of weakly basic anion exchange resins with different matrix compositions for DY50 removal. Kinetic and equilibrium parameters of the sorption process were determined and the influence of such factors as time, concentration or pH on the efficiency of dye adsorption from solutions containing additional salts and surfactants was evaluated. Anion exchange resins regeneration was investigated, too.
## 3 Energy Efficiency Analysis
### Total Power Consumption of Two-Tier HCN  
When referring to BS power consumption, it is generally agreed that BSs have two types of power consumption: circuit power consumption and transmit power consumption [38; 40]. The circuit power consumption is caused by signal processing, site cooling, and battery backup. In this paper, we use the linear approximation model given by [41]:  
\[P_{k}=N_{TRk}\cdot P_{Ck}+\theta_{k}P_{Tk} \tag{11}\]  
where \(N_{TRk}\) is the number of transceivers of a BS in the \(k\)-th tier; each transceiver serves one transmit antenna element. Here, \(P_{Ck}\) is the static power expenditure and \(\theta_{k}\) is the slope of load-dependent power consumption. Thus, the total power consumption of all BSs in a two-tier HCN can be written as  
\[P_{sum}=A\lambda_{1}(N_{TR1}\cdot P_{C1}+\theta_{1}P_{T1})+A\lambda_{2}(N_{TR2 }\cdot P_{C2}+\theta_{2}P_{T2}) \tag{12}\]
# A New AI-Based Semantic Cyber Intelligence Agent
## 3 Materials and Methods
### Term Frequency Generation Process  
The text analysis method known as term frequency-inverse document frequency (TF-IDF) is used to determine the relative value of words in a document [17; 19; 20; 21; 22]. A word's frequency in a document is multiplied by the word's inverse frequency over the entire corpus to arrive at this number (inverse document frequency). The resulting score gives terms that are significant in a specific document but uncommon across the entire corpus greater weight. As seen from line 12 of Algorithm 1, the TF-IDF process generates a vector of words along with frequencies of each word as \(\{\textit{tw}_{j},\textit{tw}_{j}\},\ldots\ \textit{\}\). Here, \(w_{j}\) is a word, and \(\textit{f}_{w_{j}}\) is the corresponding frequency.  
Porter stemming is a method for condensing words to their root or fundamental form [17]. It normalizes words by deleting frequent suffixes from them. For instance, the roots of the words \"running\", \"runs\", and \"run\" would all be the same. By lowering the number of unique terms and combining similar words, this method can increase the accuracy of text analysis. Line 13 of Algorithm 1 demonstrates the creation of vector \(\{\textit{tw}_{k},\textit{f}_{w_{k}}\},\ldots\ \textit{\}\), where \(w_{k}\) is the root word and \(\textit{f}_{w_{k}}\) is the corresponding frequency.  
In a text, n-grams are continuous groups of n words [17; 19; 20]. They are frequently employed in text analysis to record linguistic context and structure. For instance, trigrams (\(n=3\)) can capture more complicated word sequences, such as \"deep learning algorithms\", while bigrams (\(n=2\)) can capture pairs of words that frequently occur together, such as \"machine learning\". For text classification, clustering, and other text analysis tasks, n-grams can be utilized to produce features. Line 14 of Algorithm 1 shows unions of words (i.e., \(\bigcup_{1}^{l}w_{l}\)) and their corresponding frequency, \(f_{0}\). For bigrams, it is \(w_{1}\cup w_{2}\), and for trigrams, it is \(w_{1}\cup w_{2}\cup w_{3}\). TF-IDF, Porter stemming, and n-grams can all be used in conjunction to preprocess and analyse text data, which can enhance the precision and efficiency of text analysis operations.