esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Conclusions
The investigation highlights that the ORC integration provides noteworthy energy and environmental performance improvements for data centers. For the investigated DC, the exploitation of the heat waste through the proposed ORC guarantees an electric production corresponding to 4.1% of the DC total electric request and 22.7 kg\({}_{\text{CC02,eq}}\) GHG emission savings per MWh of electric consumption. This research concluded by offering guidelines for analyzing ORC systems, including exergy analysis, and detailing the process of sizing the evaporator and condenser. A forthcoming investigation will focus on energy process optimization by exploring various system layouts, enhancing heat exchange mechanisms, and considering alternative operating conditions and working fluids. Conceptualization, O.C., A.A., and P.F. ; methodology, O.C., A.A., and P.F. ; software, O.C. ; validation, O.C., A.A., and P.F. ; formal analysis, O.C., A.A., and P.F. ; investigation, O.C. and A.A.; resources, P.F. ; data curation, O.C. ; writing--original draft preparation, O.C., A.A., and P.F. ; writing--review and editing, O.C., A.A., and P.F. ; visualization, O.C., A.A., and P.F. ; supervision, A.A. and P.F. ; project administration, P.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. A. Artificial intelligence  
CCHP. Combined cooling, heating, and power  
CFC. Chlorofluocarbons  
CHP. Combined heating and power  
COND. Condenser  
COP. Coefficient of performance  
CRAC. Computer room air conditioning  
DC. Data center  
DE-SH. Desuperheater  
DSC. Differential scanning calorimetry  
ECO. Economizer  
EED. Energy Efficiency Directive  
ERE. Energy reuse effectiveness  
ERF. Energy reuse factor  
EVA. Evaporator  
GHG. Greenhouse gas  
GWP. Global warming potential  
HFC. Hydrofluorocarbons  
HT-FLOW. High-temperature flow  
HVAC. Heating, ventilation, and air conditioning  
ICT. Information and communication technology  
IEA. International Energy Agency  
IT. Information technology  
IUS. Industry and urban symbiosis  
L-COOL. Cooler in the liquid phase for ORC  
LT-AIR. Low-temperature air  
KPI. Key performance indicators  
NPV. Net present value  
ODP. Ozone depletion potential  
ORC. Organic Rankine cycle  
PRE-HEAT. Pre-heater  
PUE. Power usage effectiveness  
PUMP-ORC. ORC pump  
PUMP-WA. Water pump  
SUP-HEAT. Superheater  
TURBO-EXP. Turbo-expander  
TLC. Trilateral cycle  
WHR. Waste heat recovery  
Symbols  
A. Area (m\({}^{2}\))  
a. Coefficient (-)  
b. Coefficient (-)  
c. Coefficient (-)  
cp. Specific heat (kJ/kgK)  
d. Diameter (m)  
E. Exergy (W)  
h. Specific enthalpy (kJ/kg)  
h. Convective heat transfer coefficient (W/m\({}^{2}\)K)  
Hm. Pump head (m)  
L. Width (m)
# Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews
## 4 Experimental Evaluation
### Performance Evaluation (Rqi)  
The prediction results of the MPCAR model proposed in this paper and other comparison methods for 10 Amazon subdatasets are shown in Table 4.  
Table 4 shows that our proposed MPCAR is the highest performing model on the 10 benchmark datasets. This establishes the validity of our proposed model. The following conclusions can be obtained by analyzing the experimental results.  
First, models that consider review information (D-ATT, DeepCoNN, NARRE, MPCN, and MPCAR) perform better than traditional collaborative filtering models (MF) that consider only rating data. Review information can solve the effect of data sparsity caused by using only rating data and improve the expression quality of hidden factors. Thus, the method using review text information (DeepCoNN D-ATT, NARRE, MPCN, and MPCAR) has better recommendation quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **ID Embedding** & **Document** & **Review** & **Deep Learning** \\ \hline MF & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ DeepCoNN & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ D-ATT & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ NARRE & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ MPCN & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ MPCAR & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison methods.  
Second, among the recommendation models that also consider review information, review-based recommendation models (NARRE, MPCN, and MPCAR) have better recommendation performance than document-based recommendation models (DeepCoNN and D-ATT). This is because the former models use an approach that considers the usefulness of individual reviews. These methods capture information about users' preferences for a particular product by modeling each review. Therefore, considering the usefulness of individual reviews can further improve the performance of the model and achieve better recommendation results.  
Third, in the review-based recommendation model, our MPCAR model has better results than the MPCN and NARRE models. This fully proves that it is meaningful to consider the deep word-level interaction between users and items and the introduction of additional auxiliary information (ID embedding) to enhance the representation of users and items.
# Antioxidative and Photop Protective In Vitro Potential of Lavandula Angustifolium
## 4 Discussion
It can be assumed that linalyl acetate acts here in synergy with linalool, which has an -OH group and can directly scavenge radicals. Both compounds can jointly stabilize lipid free radicals and protect lipids from oxidative damage, thus affecting the stabilization of cell membranes. A similar synergy of these two compounds has been observed earlier in essential oils [66]. The extracts obtained from flowers of _Lavandula angustifolia_ using MeOH, EtOH, n-ProOH, and isoProOH also contain coumarin and 2H-1-benzopyran-2-one,7-methoxy- (7-methoxy-cournarin). The content of the first compound is a maximum of 4.9%, and the second compound is 5.9%. These compounds also show antioxidant activity, previously described in the scientific literature--with the methoxy derivative of coumarin being greater. Studies have shown that 7-methoxycournarin has the ability to neutralize free radicals and has a hepatocyte effect [67]. However, in the studies presented in this work, attention is drawn to the very high content of these two compounds in the extracts obtained from the herb of _Lavandula angustifolia_ using MeOH, EtOH, n-ProOH, and isoProOH and at the same time the very low content of linalool and linalyl acetate in them. It can therefore be assumed that coumarin and its methoxy derivative are responsible for the antioxidant activity of extracts from the herb of _Lavandula angustifolia_ obtained in this work. It is also worth noting that for these extracts, the content of these two compounds decreases with the increase in the concentration of the solvent used for extraction. It is therefore beneficial to use the solvents tested as extractants while maintaining their lowest concentration of 40% (\(v/v\)).
## 5 Simulation Results
### Case 4: Variable Tariff Considered  
In this case, a peak-valley tariff is considered for the purchase and sale of electricity as given in Table 1. The hourly allocation of the HES is similar to what was observed in Case 3. However, total cost in this case is $2.30 which is a drop of 2% in comparison to the cost in case 3. This difference is comparatively small although a substantial cost saving was anticipated because of peak-valley tariff. This is due to the fact that price for selling the excessive electricity has reduced too in intermediate and valley periods of the tariff to incentivize the buyer.  
Case 5 and 6 evaluate the impacts of EV charging on the utility loading and the SH's daily energy cost. The study of EV charging is divided into two cases: Case 5 considers the EV charging without any scheduling, and Case 6 has three charging strategies which consider the schedulable EV charging. Table 4 summarizes the characteristics of four electric vehicle supply equipment (EVSE) for EV charging. The impact of these charging techniques on charging powers of EV and utility loading are presented in Figure 11 and 12.  
Figure 9: Optimized generation schedule with BESS included.
## 2 Materials and Methods
### Ensemble Empirical Mode Decomposition  
Empirical mode decomposition (EMD) was introduced by [PERSON] et al. in ref. [57]. A variation of EMD is the ensemble EMD (EEMD), which decomposes the original signal over an ensemble of noisy copies and obtains the final results by averaging. White noise can provide a uniformly distributed scale in the time-frequency domain. The EEMD algorithm can be described as follows [58]:  
1. Generate \(x(n)^{(m)}=x(n)+\beta\cdot w^{(m)}\), where \(w^{(m)},\ m=1,\ldots,K\) are different realizations of white noise and \(x(n)\) is the atrial activity signal (P wave);
2. Decompose each \(x(n)^{(m)},\ m=1,\ldots,K\) completely by EMD, obtaining its modes \(c_{i}^{(m)}\), where \(i=1,\ldots,M\) indicates modes and \(K\) is the so-called ensemble number;
3. Assign \(\bar{c}_{i}\) as the \(i\)th mode (IMF) of \(x(n)\), obtained by averaging the corresponding modes. It should be mentioned that in EEMD, each \(x(n)^{(m)}\) is decomposed independently of the different noise realizations, and for each of them, a residual \(r(n)\) is obtained at each stage. Having obtained the intrinsic mode function components from the EEMD, the Hilbert transform is applied to each IMF component [59]. This transform allows one to obtain one of the instantaneous amplitudes (InAs--the absolute values of the analytic signals) [8]. Taking into account the \(i\)th IMF and applying the Hilbert transform, the respective analytic signal, \(x_{i}^{a}(n)\), is calculated as \[x_{i}^{a}(n)=\text{IMF}_{i}+\text{j}\cdot\mathcal{H}(\text{IMF}_{i}),\] (10) where \(\text{j}^{2}=-1\) and \(\mathcal{H}(\cdot)\) is the Hilbert transform operator. The \(i\)th InA (InA\({}_{i}\)) is defined as the absolute value of the analytic function \(x_{i}^{a}(n)\). The trend of the InA can be used to evaluate atrial activity. The linear regression operator results in the calculation of the trend/slope. The slope is positive for normal sinus rhythm because a P-wave exists. However, for AF, the slope is less steep or even negative due to the lack of a P-wave [8]. In this work, two slope values (denoted \(\beta_{2}\) and \(\beta_{3}\)) are estimated on the basis of InA\({}_{2}\) and InA\({}_{3}\), respectively [8]. The waveforms of InA\({}_{2}\) and InA\({}_{3}\) in the case of non-AF and AF episodes, as well as the corresponding regression lines, are presented in Figure 5.
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
## 1 Introduction
Most previous models are optimized with a sole phoneme recognition objective directly or implicitly constrained by extra error states towards the correct diagnosis. Such a single recognition loss tries to predict each phoneme equally. To some extent, we hope the system can detect more mispronunciations with little/no sacrifice of performance on correct pronunciations. To tackle the inconsistency problem of optimization goals, some researchers attempt to incorporate the ASR task with auxiliary tasks, e.g., by classifying phone states (i.e., whether the phone is correctly pronounced) [18], classifying accents of learners' L1 speech [25], and estimating word-level mispronunciation probabilities [26]. Due to the difference of learning objectives between phoneme recognition and MDD tasks, previous methods fail to focus on mispronunciations explicitly and, thus, are less effective in detection and diagnosis. Based on the above introduction and analysis, we can find that training MDD systems faces the problem of data scarcity. This study is motivated by that fact that some previous studies tackled this problem to some extent by using SSP models and incorporating prior text and that it is still worth investigating how to make full use of them. Aiming to further improve the performance of MDD, we propose an E2E mispronunciation MDD method using transfer learning that leverages the pretrained acoustic model and the linguistic knowledge from text. Specifically, the main contributions of this study are as follows:  
1. We explore the use of the pretrained model wav2 vec2.0 for MDD tasks, especially under different configurations for low resource MDD;
2. We propose an effective text-audio gate control module to effectively leverage the linguistic information from text modality. It can enforce the model to align textual information to the most related acoustic regions while ignoring irrelevant parts automatically;
3. To further unleash the power of prior text, we refine the loss to bridge the learning objective gap between phoneme recognition and MDD by explicitly discriminating the probability of reference and annotation sequences. Conducting experiments on the L2-Arctic dataset confirm the effectiveness of our proposals. The remainder of this paper is structured as follows. Section 2 introduces the related work wav2 vec2.0. Section 3 introduces the proposed methodology for MDD, including the pretrained model based MDD framework, the textual modulation gate, and the contrastive learning strategy. Section 4 shows the experiments and results. Section 5 concludes this paper and gives outlook for future work.
# Ensemble Learning Traffic Model for Sofia: A Case Study
## 2 Preliminaries: Ensemble Learning Algorithms
### Optimization and Evaluation Metrics  
Complex algorithms like Random Forest and XGBoost that we use here, have additional intrinsic parameters (hyperparameters), such as the learning rate or the maximal depth of trees, whose optimal values differ according to the specific context. To make it even worse, some of these parameters are categorical, e.g., the type of activation function in classification problems. Hence, hyperparameter tuning is a highly non-trivial problem in of itself and requires lots of computational power to determine how the default values need to be altered in each particular case in order to benefit the overall performance of the model, so instead of brute-forcing with GridSearch, we use RandomizedSearch which runs much faster at the expense of allowing some uncertainty. The evaluation is being done via k-fold cross-validation, which means the training-test data split is performed k times and the corresponding metric values are then being averaged for the overall outcome. This way we avoid false evaluation due to biased data in a specific sample. As for the evaluation metrics, for regression problems it is customary to use the so-called coefficient of determination \(r^{2}\), equal to the square of the Pearson correlation coefficient. Another commonly used predictor of accuracy is the mean absolute percentage error MAPE, i.e., the average of the absolute values of all relative errors in the predictions. What we refer to as 'accuracy' here is simply \(1-\text{MAPE}\) [%], evaluated using cross-validation (just like \(r^{2}\)).
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Models and Methods for Sound Transmission Loss Calculation
#### 4.1.2 Transfer Matrix Method  
This method (TMM) can be used to model acoustic waves passing through a double wall structure such as the cylindrical wall as described by [PERSON] et al. [99]. The calculation is based on the relations between displacements and stresses at the interface of each layer. The method can be used to model multi-layered noise control treatment for example, in functionally graded material and composite laminates. [PERSON] et al. [33] employed this method to estimate the STL of a functionally graded cylinder under subsonic external flow. Effects of certain parameters such as the Mach number, incident angle, panel thickness and radius of the cylindrical structure on the STL were investigated. Their study showed that the model can predict the STL of multi-layered structures in the low, medium and high frequency regions. [PERSON] and [PERSON] [100] adopted the TMM to solve the vibro-acoustic problem of composite cylindrical shells with poroelastic core under external mean flow. The [PERSON]'s theory was used to model the shells while the [PERSON]'s theory as will be explained in the subsequent subsection, was used to model the poroelastic core. By employing the transfer matrix formulation with suitable boundary conditions, the solution of the system was obtained. Three different configurations were tested using this method. The configuration whose wave amplitude and the shell displacement were obtained from the derived six shell equations and four boundary conditions, gave the least optimal solutions. However, the STL was enhanced for the configurations with additional boundary conditions due to the presence of air cavity gap in the core. Moreover, the STL of the three configuration lined with poroelastic material was higher than those without poroelastic ones. Two forms of TMM are the impedance matrix model and superposition model. A comparison between these two models on the transmission loss was performed by [PERSON] et al. [101]. It was observed by the authors that the superposition method was better than the impedance matrix method because for the superposition method, matrix assembling was not required for obtaining the transmission loss.
## 3 Results and Discussion
### Frequency Response Curve
It should be noted that the first resonance has a wider frequency bandwidth. Interestingly, close to the second resonance, for a frequency of \(\omega\approx 120\) rad/s, a strong dynamic absorption of the top magnet occurs. For a frequency of \(\omega\approx 110\) rad/s, the oscillation of the bottom magnet disappears. In Figure 7d, the multistability regions with two stable and one unstable solution in the first and second resonances are plotted (orange region). An analysis of both regions reveals that the multistability region in the second resonance is larger. When the excitation amplitude increases, the multistability tongues expand. However, in the first resonance, an unstable region appears (pink region in Figure 7d), caused by the Neimark-Sacker (\(NS\)) bifurcation. This bifurcation is interesting because it causes the equilibrium to lose stability. The time histories of the bottom and top magnets (black lines) at \(\omega=60\) rad/s and \(A=0.6\)\(g\) are shown and compared in Figure 8a. Both time responses are similar and have two harmonic components. For the top and bottom magnets, the peak-to-peak values are comparable and equal to approximately 0.008 m. The signal of the induced current (red line) is clearly periodic. The peak-to-peak induced voltage equals 0.024 A. Figure 8b shows the time histories of the induced current in the first and second resonances for an amplitude of \(A=1\,g\). It can be observed that the peak-to-peak induced current decreased from 0.027 A to 0.015 A (comparing both resonances).
# Adaptive Cruise Control with Look-Ahead Anticipation for Driving on Freeways
## 2 Look-Ahead Anticipatory Scheme of Adaptive Cruise Control
### Anticipatory Feature with ACC
Instead of capturing any momentary speed fluctuations, we consider a consistently accelerating condition over the last \(2\tau\) s and propose estimating \(\bar{a}_{\text{p}}(t)\) as follows:  
\[\bar{a}_{\text{p}}(t)=\bar{a}_{\text{p}}(t)\theta_{1}(v_{\text{p}}(t))\text{ e}^{-a(\tau+h_{\text{la}}/2)}. \tag{11}\]  
Figure 1: The decision-making process of the proposed La-ACC, where the driving decision is taken using the look-ahead or predicted states and the basic model of ACC. where \(\theta_{1}(\cdot)\) is a limiting function; using the natural exponent e, constant \(\alpha\) defines the decaying factor at the mid of look-ahead horizon \(h_{\text{Ia}}/2\), and the unconditional estimated acceleration \(\hat{a}_{\text{p}}(t)\) at \(t\) with the limiting function \(\theta_{2}(\cdot)\) is provided by the following. \[\hat{a}_{\text{p}}(t)=a_{\text{p}}(t-\tau)+\theta_{2}\hat{a}_{\text{p}}(t-\tau). \tag{12}\]  
In (12), the recent acceleration \(a_{\text{p}}(\cdot)\) and its rate \(\hat{a}_{\text{p}}(\cdot)\) at \(t-\tau\) is obtained as follows. \[a_{\text{p}}(t-\tau) = (v_{\text{p}}(t)-v_{\text{p}}(t-\tau))/\tau, \tag{13}\] \[\hat{a}_{\text{p}}(t-\tau) = (v_{\text{p}}(t)-2v_{\text{p}}(t-\tau)+v_{\text{p}}(t-2\tau))/2\tau. \tag{14}\]  
The limiting functions \(\theta_{1}(\cdot),\theta_{2}(\cdot)\) that impose the condition on persistence prediction are defined as follows. \[\theta_{1}(\phi)=\left\{ \begin{array}{ll}1,&\text{for $0<\phi<V_{\text{max}}$,}\\ 0,&\text{otherwise,}\end{array} \right. \tag{15}\]  
\[\theta_{2}(\phi)=\left\{ \begin{array}{ll}-\delta_{\text{max}},&\text{for $\phi<-\delta_{\text{max}}$,}\\ \delta_{\text{max}},&\text{for $\phi>\delta_{\text{max}}$,}\\ \phi,&\text{otherwise.}\end{array} \right. \tag{16}\]  
where constant \(V_{\text{max}}\) denotes the limit of speed, and \(\delta_{\text{max}}\) denotes the limit of the acceleration rate. The above-described conditional persistence prediction technique uses only a few parameters with very specific meaning and, in typical driving conditions, can closely predict a vehicle's state for a short look-ahead horizon as verified on experimental driving data [30]. The proposed La-ACC does not guarantee collision avoidance in transient conditions since most parameters are the same as the original ACC. Therefore, sometimes it needs to be deactivated in some critical situations, e.g., at low speed, lane change, and shock-wave. It is assumed that the driver can take control of the vehicle immediately in such a case. Once the vehicle returns to a steady condition, La-ACC can be reactivated again.
###### Abstract  
Granite in underground water-sealed storage caverns has usually been immersed for a long time. The immersion affects the mechanical properties and failure mechanism of granite with maximum free water absorption; therefore, it is crucial to study the behavior of granite under different confining pressures for engineering construction. A triaxial compression test with maximum free water absorption was conducted on granite and its mechanical properties were analyzed. A fracture scanning electron microscope test was carried out to analyze the microstructural characteristics and reveal the failure mechanism. The test results showed that the differential stress-axial strain curve can be divided into the initial compaction stage, the elastic deformation stage, the plastic deformation stage, and the post-peak strain-softening stage. With an increase in confining pressure, the duration of the initial compaction stage decreased, while the plastic deformation stage and the peak strength and peak strain stages increased. For the confining pressure range of 0-20 MPa, the peak stress difference of granite with maximum free water absorption was between 146.0 and 307.6 MPa. The elastic modulus was between 31.36 and 44.18 GPa. The cohesion (c) of the rock sample studied was 26.84 MPa and the internal friction angle (\(\varphi\)) was 51\({}^{\circ}\). The failure mechanism of granite is tensile-shear composite failure, predominantly with tensile failure under low confining pressure regimes, and the inclined fracture surface is mainly due to shear failure under high confining pressure conditions. These research results provide updated reference data for rock engineering involving granular mechanical properties and failure mechanisms in submerged caverns.  
Keywords:granite; confining pressure; water absorption; mechanical properties; failure mechanism +
Footnote †: journal: Applied Sciences