esa-sceva/satcom-chunk-collection huggingface dataset:
# Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation
## 3 Problem Specification  
The IBL Problem (IBLP) can be formally defined as follows. A set of \(n\) students \(S=\{s_{1},\ldots,s_{n}\}\) are to be allocated to industry placements offered by \(m\) companies \(C=\{c_{1},\ldots,c_{m}\}\). Each company \(c\in C\) has a limited number, \(r_{c}\), of placements available. The total number of placements available is defined as \(R=\sum\limits_{c\in C}r_{c}\). Each company \(c\in C\) is associated with one or more of \(b\) business sectors. Let \(B_{i}\subseteq C\) denote the companies associated with business sector \(i\) and \(Q=\{B_{1},B_{2},\ldots,B_{b}\}\).  
Each company interviews a subset of the students and gives each interviewed student a _preference rating_. The preference rating assigned to student \(s\) by company \(c\) is denoted by \(p_{sc}\in\{1,2,3\}\). The following is the meaning of the preference values:  
**Rating 1:**: The company has a strong preference for the student;
**Rating 2:**: The company has a preference for the student but would prefer a student with rank 1;
**Rating 3:**: The company does not have a placement that is suitable for the student.  
Students who are not interviewed by a company are automatically given a rating of 3 by that company. The following data are used in the allocation:  
1. **Gender**. the students are partitioned into two subsets of \(S\) which are denoted female \(F\subseteq S\) and male \(M\subseteq S\). Some companies explicitly require an equitable gender mix;
2. **Degree type**: there are \(d\) different degree types. For example, students may be enrolled in a computer science, software engineering, or an information technology degree. Companies may have a student preference partly based on degree type;
3. **Previous placement sector**: For each student \(s\), the list of the sectors of any previous placements undertaken is denoted by \(Q_{s}\), which is defined as follows: \[Q_{s}=\bigcup\limits_{i\in E_{s}\subset\{1,\ldots,b\}}B_{i}\] (1) where \(E_{s}\) is the set of business sectors for the student.  
There are three sets of constraints to be satisfied when creating a feasible allocation: (a) if there are more students than placements available, then all placements should be filled (preferences permitting), (b) if not all placements can be filled, companies requiring fewer placements should be given preference, and (c) students taking more than one placement cannot undertake more than one placement in a given sector.
## 4 Discussion
As the required EO concentration against foodborne and spoilage bacteria and yeast is affected by the interactions of the EO chemical constituents with the food medium and ingredients, higher quantities are necessary to achieve appropriate food preservative action. This undesirably influences the organoleptic and sensorial properties of the foodstuff [7; 52]. To resolve this problem, an interesting alternative is the application of an association of moderate heat treatment with EOs, which improves the antibacterial and antifungal effects of the EO influencing the vapor pressure of the compounds [55; 56; 57]. Consequently, the association of EOs with medium heat processing can be investigated for emerging food conservation technologies. Few studies have focused on the association of EOs (_Eucalyptus and Mentha_) with thermal processing at 55 \({}^{\circ}\)C [7; 52]. This approach considerably decreases the EOs concentration necessity, offers a very valuable interaction, as the rise of heating increases the quantity of EO in the vapor phase, thus it improves its anti-yeast effect. [PERSON] et al. [58] detected that neither the presence of the EO at their greater doses alone nor the heat treatment alone, was able to maintain or decrease the yeast and fungal stability of the drinks against _Saccharomyces cerevisiae_. In opposition, when applied in association, moderate heat processing (55 \({}^{\circ}\)C, 15 min) improved the activity of EO compounds (pinene, linalool and citral) and made an increase in their vapor pressure, which in turn amplified their chance to solubilize in the fungal cell membrane. The current research also revealed the improvement in the antifungal effect of EGEO on association with moderate heat action. The association of medium heat treatment with EGEO application has not been formerly published for avoiding Orangina juice contamination. Currentfindings demonstrated that EGEO can be used with moderate heat action for the food protections of Orangina juices.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 4 Modeling Cascading Failures in Cyber-Physical Supply Networks
### Cascading Failure Model
#### 4.1.3 Cascading Failure in the Cyber-Physical Supply Networks  
The cascading failure process in the cyber-physical supply networks can generally be described as follows [3; 27; 38]:  
1. One or several nodes in cyber-physical supply networks will be selected as the initial failure. When a node fails, all connectivity links connected to it will become dysfunctional and are viewed as failed.
2. Load redistribution of failed nodes. In the physical supply network, failed nodes will reduce the loads of upstream and downstream nodes. In the cyber supply network, loads of failed nodes will redistribute to upstream and downstream nodes.
3. Calculate the new loads in the cyber-physical supply network. In the physical supply network, if the load of a node is less than its minimum capacity and the node fails, remove the underload nodes. In the cyber supply network, if the load of the nodes is more than its maximum capacity and the node fails, remove the overload nodes. For removed nodes, check whether the counterpart nodes fail or the neighbors fail.
4. A node that has no connections with a node from its couple network is regarded as failed. In the physical supply network, a node fails if its load is less than its minimum capacity. If the failed node is coupled to a counterpart node in the cyber supply network, then the counterpart node in the cyber supply network is also removed. In the cyber supply network, a node fails if its load is larger than its maximum capacity. If the failed node is coupled to a counterpart node in the physical supply network, then the counterpart node in the physical supply network is also removed.
5. Cascading failures will continue on the cyber-physical supply network until no further failed node and link occur.  
As shown in Figure 3, a cascading failure is triggered by the failure of node \(v_{i}^{p}\). First, \(v_{i}^{p}\) is removed from the system along with its connectivity and dependency links. As a result, \(v_{s}^{p}\) fails due to the load redistribution from \(v_{i}^{p}\). The failure of \(v_{i}^{p}\) means that \(v_{i}^{s}\) has no support and consequently fails. Second, \(v_{f}^{c}\) fails due to the load redistribution from \(v_{i}^{c}\), and the failure of \(v_{s}^{p}\) makes \(v_{s}^{c}\) has no support and consequently fails. Third, \(v_{f}^{p}\) fails from the lack of support due to the failure of node \(v_{f}^{c}\). Finally, \(v_{f}^{p}\) is removed, and the cascading failure stops. The remaining functioning nodes are \(\left\{v_{e}^{p},v_{m}^{p},v_{h}^{p},v_{o}^{p},v_{h}^{p}\right\}\) in network \(G^{p}\) and \(\left\{v_{e}^{c},v_{m}^{c},v_{h}^{c},v_{o}^{c},v_{u}^{c}\right\}\) in network \(G^{c}\).
# New Compact Wearable Metamaterials Circular Patch Antennas for IoT, Medical and 5G Applications
## 7 Wearable Metamaterial Antennas for 5G, IoT and Medical Applications  
The antennas presented in this paper may be used in 5G, IoT and medical applications. The metamaterial antennas S11 variation near the patient body were computed by using the structure presented in Figure 24a. Several dielectric constant and conductivity of human body tissues are given in Table 5[16]. The antenna location on the human body is considered by computing S11 for different dielectric constant and conductivity of the body tissues. The dielectric constant of the body tissues varies from 5 at fat tissues to 63 at the colon area, and to 117 at the kidney tissues. The dielectric constant of the body tissue shifts the wearable antenna resonant frequency by 2% to 5%. Wearable antennas may be inserted inside a belt as shown in Figure 24b. The belt thickness and dielectric constant shift the antenna resonant frequency. The belt thickness and dielectric constant was optimized to get the  
Figure 23: (a) Radiation pattern of the Double Layer Circular microstrip antenna with CSRR at 2.7 GHz. (b) 3D radiation pattern of the Double Layer Circular patch antenna with CSRR. Figure 22: S11 parameter on a human body of the Double Layer Circular Patch Antenna with CSRRs. best electrical performance. The antennas impedance and radiation characteristics were computed and measured for air spacing, between the antennas and patient body, of 0 to 10 mm. A comparison between computed and measured results of antennas with and without CSRR is given in Table 6. The measured results agree with the computed results. Results listed in Tables 6 and 7 show that the gain of printed antennas with CSRR is 2 dB to 3 dB higher than the antennas without CSRR. Wideband passive and active slot antennas, loop and patch antennas were presented in [1]. Printed dipoles with and without CSRR were presented in [2]. A comparison of computed and measured results of compact wearable antennas for medical, 5G and IoT systems is listed in Table 7.  
Figure 24: (a) Wearable Antenna environment; (b) Wearable Medical System. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Antenna**} & \begin{tabular}{c} **Frequency** \\ **(MHz)** \\ \end{tabular} & \multirow{2}{*}{**BW \%**} & \begin{tabular}{c} **Computed** \\ **Gain dBi** \\ \end{tabular} & \begin{tabular}{c} **Measured** \\ **Gain dBi** \\ \end{tabular} & \
\begin{tabular}{c} **Length.** \\ **(cm)** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency** \\ **\%** \\ \end{tabular} \\ \hline Circular patch with CSRR & 2630 & 8 & 7.5 & 7.8 & 3.6 & 85 \\ \hline Circular patch without CSRR & 2630 & 1.5 & 4.5 & 4.3 & 4.8 & 85 \\ \hline Printed dipole with CSRR & 350 & 10 & 5.5 & 5.7 & 19.8 & 95 \\ \hline Dipole without CSRR & 400 & 10 & 2.5 & 2.5 & 21 & 90 \\ \hline Stacked circular patch with CSRR & 2700 & 8 & 8.5 & 8.4 & 4 & 95 \\ \hline Stacked circular patch without CSRR & 2700 & 8 & 5.5 & 5.3 & 4.8 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between printed antennas with and without CSRR.
## 3 Review of ANN Models for Prediction in the Renewal Energy Sector
### ANN in Wind Energy  
Wind energy is the kinetic energy generated from wind and is transformed into another type of energy, as electric energy for its consumption in industry and homes. The application of ANN models to replicate production behavior patterns of wind energy is extensive, using as input environmental variables wind speed or humidity. These models look for relevant links among such variables and the detection of anomalies in energy production. In the same line as before, the reader can find in Table 3 a comparison of ANN application on wind farms. As in the table for PV, there is a column about the study objective (A = IMEP; B = MFM; C = ARAM), a second column with the employed methodology (D = Neural Network; E = Comparisons Models; F = Others), and another with the used parameters for prediction (WS = Wind Speed; H = Humidity; T = Temperature; SR = Solar Radiation; WH = Work Hour; O = Others). Here again, authors compare techniques in order to describe the advantages of ANNs and new techniques of data mining with respect to the physical and classic models. Previous reviews in this field remark on the increasing use of ANNs to predict, due to good and fast adaptation when faced with a peculiar quality of unknown environmental situations. ANNs have been used by authors to predict the energy production of wind farms in different parts of the world, obtaining good results with very low mean square errors. The developed models have been effective in order to plan production of energy and especially for their importance to business models. For the entire system, short- and medium-term wind speed prediction statistics and data mining algorithms have been more frequently utilized. The common input variable for all referenced authors has been wind speed. References [80; 82] have been oriented towards failure diagnosis/prediction through ARAM testing the effectiveness against other AI-ML (Artificial Intelligence-Machine Learning) models, such as LR (Logistic Regression), Support Vector Machines (SVM) or Random Forest (RF) algorithms in order to identify faults and reasoning about root causes. Recent research on wind turbine condition monitoring focused more on individual components than on the entire system. It is crucial to determine properly the criticality of each component avoiding numerous alarms than can outgrow the more important alarm.
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 6 Structure, Function and Regulation of Pkc1  
Like _YPK2_, the _PKC1_ gene was first identified and isolated via its ability to hybridize to cDNA probes encoding isozymes of rat PKCs [38]. However, the _PKC1_ locus was also identified genetically in several ways: (a) as the _stt1_ mutation [208], one of 14 distinct complementation groups that conferred both temperature-sensitive growth and elevated sensitivity to the killing action of staurosporine, a bis-indole anti-fungal agent that acts as an ATP mimic and thus is able to block the active site of protein kinases [209]; (b) as several allelic temperature-sensitive mutations (_cly5_, _cly7_, _cly15_) that caused spontaneous cell lysis when shifted to the restrictive temperature [210; 211]; and, (c) as the _hpo2_ mutation, which displayed enhanced cell killing by the cell swelling caused by shifting cells to hypotonic medium [212]. Moreover, although a \(pkc1\Delta\) mutation is lethal [38], and cells depleted of Pkc1 arrest cell division with small buds and at a point subsequent to DNA replication [38; 210], \(pkc1\Delta\) cells are able to grow on medium containing an osmotic support (1 M sorbitol), but with grossly abnormal morphology and rapidly lyse if then shifted to normal medium [210; 213]. In marked contrast, although the temperature-sensitive growth defect of \(ypk1^{ls}\)\(ypk2\Delta\) cells is ameliorated on sorbitol plates [46], the inviability of \(ypk1\Delta\)\(ypk2\Delta\) cells is not [214]. Examination of Pkc1-deficient cells by electron microscopy (EM) revealed that they have thinner cell walls (with less \(\beta\)-glucan and phosphomannoprotein) and develop large holes in the wall mainly at the bud tip [215], the primary site of polarized growth [216]. Collectively, these observations strongly indicated that loss of Pkc1 function causes a major defect in cell wall integrity and suggested that an important function of Pkc1 is to control the process of cell wall remodeling during growth, which is indeed the case, as described further below.
# MEMS Vaporizing Liquid Microthruster: A Comprehensive Review
## 4 Instability Control into VLMs
### Active Control of Flow Instability
Experiments were carried out by varying the mass and heat. Results point out that nucleate boiling gives way to film boiling as the microheater's heat flux increases. In particular, the film boiling begins to appear on the microheater above a threshold level of the heat flux which strictly depends on the mass flux. Figure 10: Microchannel geometry modification for boiling heat transfer enhancement: (**a**) triangular configuration [34]; (**b**) divergent configuration [29]; (**c**) saw-tooth configuration [33]; (**d**) inlet restricts [37]. Another interesting experimental study was conducted to investigate heat transfer characteristics of a micro-gap heat sink under transient heat loads for flow boiling of HFE-7000 [50], characterized by a low surface tension and high wettability. The authors found that the higher the mass flux, the more the boiling regime moves from a discrete bubble regime to a more intense boiling regime with an increasing surface temperature after boiling initiation. Instead, when increasing the heat-flux, the surface temperature rises before and after boiling onset. Furthermore, the dependency between the mass flux and the surface temperature response weakened as the pulse duration was reduced to 2 ms.  
More recently, the dynamic response of a heated microchannel experiencing flow boiling of HFE-7100 in a single heat flux pulse mode was experimentally investigated in [51]. Three heat flux levels were analyzed, corresponding to a single-phase flow, continuous two-phase flow, and above critical heat flux, respectively. Results have shown that by applying a high heat flux pulse to the microchannel after the establishment of an oscillating boiling regime, the characteristic frequency of the pressure drop instability significantly increases. Furthermore, compared to the one with a single-phase flow, the microchannel experiencing time-periodic boiling can withstand a longer high-heat flux pulse, even with the higher temperature at walls prior to the pulse. Based on these findings, a second experimental campaign was performed by applying a time-periodic series of heat flux pulses with frequencies ranging from 0.1 Hz to 100 Hz [52]. Results show that at heating frequencies below 1 Hz (see Figure 12a), the dynamic response consists of multiple subsequent heating steps, during each of which a new steady condition is reached. At heating frequencies ranging between 1 Hz and 10 Hz (see Figure 12b,c), the flow boiling instabilities and performance heavily couple to the temporal profile of the heat flux, causing the pressure drop oscillation frequency to match the heating pulse frequency. Finally, at frequencies above 10 Hz (see Figure 12c), the transient heat flux profile is attenuated, approaching continuous heating when _f_s approximately exceeds 25 Hz. Figure 11: Heat transfer coefficient enhancement in the presence of synthetic jets at different heat fluxes as measured in [46].
## 4 System Design
### Database Storage  
The parameters entered into the DQN neural network model were learned from data collected over a period of time in the database. We collected general data from the network and data from the erasure coding network topology. The main data collected from the networks are the link bandwidth, link-available bandwidth, and hop count, and the main data collected from erasure coding are the failed nodes and switches, block sizes to be  
Figure 6: Structure of the DQN-based EC network routing architecture.  
transmitted, source nodes, and destination nodes. The list of data stored in the database and the parameters entered into the DQN neural network model are shown in Table 1.  
The parameters entered into the DQN neural network model correspond to symbols \(x_{1}\) through \(x_{18}\), which corresponds to the period of time in which \(x_{1}\) is collected. When transferring data and parity blocks, elements that can be collected and stored in a typical network topology correspond to \(x_{2}\) through \(x_{9}\), and elements that can be collected and stored in an erasure coding network topology correspond to \(x_{10}\) through \(x_{18}\). In particular, among the data that can be collected through the SDN controller, dynamic data are stored through a calculation process conducted once before the data collection. The RTT sends the immediately returned packets to the connected switch and measures them divided by two, when considering the bidirection received through the response. Here, AB\({}_{sw}\) is measured by calculating the difference between the maximum bandwidth of the link and the data rate of the next connected switch. The data rate is the difference between the number of bytes transmitted at time t and the number of bytes transmitted at time t+1.  
The \(x_{2}\) element, \(G(V,E)\), represents the location and orientation in a two-dimensional planar form, where nodes and switches can be located in a network topology. The \(x_{3}\) and \(x_{4}\) elements, RTT and MSS, are the elements of network packets needed to transmit parity blocks, \(x_{5}\) represents the current available bandwidth in the link, and \(x_{6}\), \(x_{7}\), and \(x_{8}\) are the bandwidths of nodes and switches present before and after the present time. In addition, \(x_{9}\) and \(x_{10}\) represent the hop count of the nodes and switches that exist before and after the current location, and \(x_{11}\), \(x_{12}\), and \(x_{13}\) represent the total number of nodes with data blocks and parity blocks in the erasure coding network topology, and the number of data nodes and parity nodes for each block. Moreover, \(x_{14}\) and \(x_{15}\) are currently active and inactive nodes used to determine the number of failed nodes, and \(x_{16}\), \(x_{17}\), and \(x_{18}\) are the data blocks, the source nodes to which the parity blocks are to be transmitted, and the size of the destination nodes and blocks.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Absorbed Radiation  
Energy exchange and photosynthesis are proportional to the amount of radiation absorbed by plant canopies, which is determined by the direct beam fraction of incident radiation, the canopy structure, and the optical properties of the plant elements [34]. Incident PPF\({}_{\mathrm{0}}\) and shortwave radiation within the growth chamber were measured at canopy height. Shortwave radiation between 0.285 and 2.8 \(\mu\)m was measured with a precision spectral pyranometer (The Eppley Laboratory, Model PSP, Newport, RI, USA). Incident non-photosynthetic, shortwave radiation (NPSW\({}_{\mathrm{o}}\)) was determined by subtracting PPF\({}_{\mathrm{0}}\) (converted to energy units assuming 5 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) per W m\({}^{-2}\) for HPS lamps) from the total shortwave radiation. The fraction of PPF absorbed by the canopy (PPF\({}_{\mathrm{abs}}\)) was calculated from the product of radiation capture and PPF\({}_{\mathrm{o}}\), as described by [PERSON] and [PERSON] [30]. A diffuse light fraction of 0.7 was measured in the chamber using a shadow band to shield the quantum sensor from direct radiation. The fraction of non-photosynthetic, shortwave radiation absorbed by the canopy (NPSW\({}_{\mathrm{abs}}\) = (1 - \(\varrho_{\mathrm{c}}\)) NPSW\({}_{\mathrm{o}}\)) depends on the canopy reflection coefficient (or surface albedo), \(\varrho_{\mathrm{c}}\), in the near-infrared (NIR). \(\varrho_{\mathrm{c}}\) was estimated using Equation (2) from the single leaf scattering coefficient (\(\sigma_{\mathrm{S}}\)) [35]:  
\[\varrho_{\mathrm{c}}=[1-(1-\sigma_{\mathrm{S}})^{1/2}]/[1+(1-\sigma_{\mathrm{S }})^{1/2}]. \tag{2}\]  
\(\sigma_{\mathrm{S}}\) varies with the wavelength of the radiation and equals the sum of the fractions of reflected and transmitted light. In the visible spectrum, the \(\varrho_{\mathrm{c}}\) of the high planting density wheat canopies was 0.055 during vegetative growth [36], which corresponds to a \(\sigma_{\mathrm{S}}\) of 0.2. Since the NIR \(\varrho_{\mathrm{c}}\) was not measured directly, it was derived from Equation (2) assuming an NIR \(\sigma_{\mathrm{S}}\) of 0.8. For comparison, the single leaf reflectance (0.43) and transmittance of winter wheat (0.33) in the NIR combine to give an NIR \(\sigma_{\mathrm{S}}\) of 0.76 [37]. Thus, NPSW\({}_{\mathrm{abs}}\) was 0.62 \(\times\) NPSW\({}_{\mathrm{o}}\) for a \(\varrho_{\mathrm{c}}\) of 0.38 in the NIR. Although this approximation overestimates \(\varrho_{\mathrm{c}}\) in sunny conditions (e.g., high direct beam radiation), it predicts it accurately under overcast conditions [38], similar to the highly diffuse radiation found in these controlled-environment chambers.