esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Effect of DLC and WCC Coatings on the Efficiency of Manual Transmission Gear Pairs
## 4 Conclusions  
In this paper, a tribo-dynamic model has been developed to investigate the effect of DLC and WCC-type coatings on the frictional losses of gear pairs in manual automotive transmissions. RDE driving cycle conditions were used as input so that the results are representative of realistic OEM transmission testing. The contact mechanics of the coated gear teeth were considered using an analytical and computationally efficient frictional model, facilitating the coupling of tribology and dynamics models.  
In the tribology model, AFM experimental measurements were conducted to obtain the coating characteristics at asperity level and then numerically expanded to the conjunction level. The accuracy  
Figure 16: Power losses for uncoated and coated gears, RDE cycle part.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Power Loss Decrease [\%]** & \\ \hline
**Gear** & **DLC** & **WCC** \\ \hline
1 & 16\% & 21\% \\
2 & 12\% & 16\% \\
3 & 24\% & 28\% \\
4 & 26\% & 22\% \\
5 & 31\% & 31\% \\
6 & 54\% & 60\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of coating on gear power losses.  
of the developed model from the asperity level to the conjunction level was validated using a pin-on-disc experiment.  
The numerical simulations using a tribo-dynamic model for two types of coatings, as well as various operating conditions, showcase that the application of coatings on manual transmission gears can be beneficial with respect to frictional losses. All the transmission gear pairs demonstrated improvements on power losses under all the operating conditions examined. It was noted that larger gears tend to achieve higher power loss reduction (around 40%) compared to smaller gears whose losses were reduced around 10-20%. Finally, even though the plain DLC and WCC coatings that were examined do present some surface properties differences and some changes on the percentage of power consumption of the gears, from an efficiency point of view, only the coatings can be considered equally beneficial for the efficiency improvement of automotive manual transmissions.  
Conceptualization, all authors; methodology, A.L., M.M. and S.T.; code development, A.L.; validation, A.L. and M.M.; analysis, A.L., M.M. and S.T.; writing--original draft preparation, A.L., M.M. and S.T; writing--review and editing, all authors; funding acquisition, R.D. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ford Motor Company.  
The authors would like to express their gratitude to Ford Motor Company for its financial support under its University Research Programme.  
The authors declare no conflict of interest.
## 3 Results
### Pain Processing  
Due to technical issues during the data collection from one person with CNSLBP and two HCs, the cuff algometry outcomes were based, respectively, on n = 19 and n = 18 participants. The details are presented in Table 2. At PRE, persons with CLBP showed significantly lower cPPT (\(\Delta=-5.5;p=0.040\)), TS (\(\Delta=-0.79;p=0.042\)), and CPM (\(\Delta=-10.0;p=0.023\)) in comparison to HCs. After performing a HIIT protocol, no significant PRE-POST differences were found in cPPT (\(\Delta=0.8;p=0.717\)), TS (\(\Delta=-0.41;p=0.121\)), or CPM (\(\Delta=-0.5;p=0.831\)) in the persons with CNSLBP. Conversely, HCs showed significant PRE-POST improvements in cPPT (\(\Delta=5.2;p=0.019\)) after a HIIT protocol. However, no between-group PRE-POST differences were found in either cPPT (\(\Delta=4.4;p=0.119\)), TS (\(\Delta=0.40;p=0.412\)), or CPM (\(\Delta=0.1;p=0.977\)) after a HIIT protocol. Furthermore, no between-group PRE-POST differences in cPPT (\(\Delta=0.4;p=0.601\)), TSP (\(\Delta=0.2;p=0.371\)), or CPM (\(\Delta=3.1;p=0.289\)) were found in persons with CNSLBP performing a HIIT or a MIT protocol. \begin{table}
\begin{tabular}{c c c c} \hline  & **HIIT** & **MICT** \\ \hline
**QST Variables** & **CNSLBP (n = 19)** & **HC (n = 18)** & **Between-Group** & **CNSLBP** & **Between-Group** \\  & & **CLBP-HC\(\Delta\) [CI] (\(p\))** & **(n = 19)** & **HIIT-MICT \(\Delta\) [CI] (\(p\))** \\ \hline PRE cPPT & 28.2 \(\pm\) 7.1 & 33.7 \(\pm\) 8.1 & 5.5 [0.3;10.8] (0.040) & 29.3 \(\pm\) 9.1 & 1.0 [ –2.2;4.2] (0.512) \\ \hline POST cPPT & 28.9 \(\pm\) 11.8 & 38.9 \(\pm\) 12.6 & 9.9 [1.5;18.3] (0.022) & 30.5 \(\pm\) 10.9 & 1.4 [ –4.3;6.3] (0.690) \\ \hline Delta & 0.8 \(\pm\) 8.4 & 5.2 \(\pm\) 7.8 & 4.4 [ –1.2;10.0] (0.119) & 1.3 \(\pm\) 9.6 & 0.4 [ –5.9;6.3] (0.601) \\ \hline Within-group PRE-POST (\(p\)) & 0.717 & 0.019 & & 0.609 \\ \hline PRE TS & 1.11 \(\pm\) 0.89 & 0.32 \(\pm\) 1.23 & 0.79 [0.03;1.55] (0.042) & 0.75 \(\pm\) 1.26 & 0.3 [ –0.8;0.3] (0.322) \\ \hline POST TS & 0.70 \(\pm\) 1.04 & 0.31 \(\pm\) 1.62 & \
\begin{tabular}{c} 0.39 [ –1.36;5.08] \\ (0.417) \\ \end{tabular} & 0.28 \(\pm\) 0.97 & 0.0 [ –0.6;0.3] (0.941) \\ \hline Delta & 0.41 \(\pm\) 0.99 & 0.01 \(\pm\) 1.67 &
\begin{tabular}{c} 0.40 [ –0.58;1.38] \\ (0.412) \\ \end{tabular} & 0.47 \(\pm\) 0.74 & 0.2 [ –0.8;0.3] (0.371) \\ \hline Within-group PRE-POST (\(p\)) & 0.121 & 0.980 & & 0.023 \\ \hline PRE CPM cPPT & 36.2 \(\pm\) 11.6 & 46.3 \(\pm\) 13.3 & 10.0 [1.5;18.7] (0.023) & 36.4 \(\pm\) 14.6 & 0.6 [ –7.3;6.1] (0.844) \\ \hline POST CPM cPPT & 35.7 \(\pm\) 13.9 & 45.6 \(\pm\) 16.4 & 9.9 [0.3;21.6] (0.044) & 38.2 \(\pm\) 15.5 & 2.4 [ –4.6;3.3] (0.469) \\ \hline Delta & \(-0.5\) \(\pm\) 10.3 & \(-0.7\) \(\pm\) 14.8 & 0.1 [ –9.0;8.7] (0.977) & 1.8 \(\pm\) 7.1 & 3.1 [ –2.9;3.1] (0.289) \\ \hline Within-group PRE-POST (\(p\)) & 0.831 & 0.850 & & 0.330 \\ \hline \end{tabular}
\end{table} 
Table 2: Pain sensitivity outcomes before and after a HIIT and MIT protocol in persons with CNSLBP and HCs.
## 3 Results
#### 3.1.1 Preparation and Application Behavior  
A multi-parameter evaluation was conducted, taking into consideration four criteria: preparation difficulty, complexity in application and modeling, textural adaptation, and workability. Each parameter was scored on a scale from 0 to 100. The resulting data were visualized using a Kiviat diagram with a radar chart, which allowed for a quantitative representation of multiple process variables and a comparative assessment of the materials' performance, enabling the analysis of complex data through graphical representation [35; 36].  
**Results**. In terms of preparation difficulty, the highest scores were recorded for M6 and M9, indicating greater difficulty in their preparation, while M3 was the easiest to prepare. Regarding application and modeling complexity, M9 showed the highest complexity, followed by M8 and M1, whereas M3 and M4 had the lowest values, suggesting simpler application (Figure 5a).  
For textural adaptation, the highest score was obtained for M3, indicating that it had the best fit for the required texture, while M9 and M7 had the lowest scores, reflecting a lower adaptation capacity. Finally, in terms of workability, M8 received the lowest score, indicating poor ease of handling, while M9 and M3 stood out for their high workability.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 5 Discussion
### Uncertainties and Future Needs  
Uncertainties are inherent for most satellite data, including the HJ-1A/B satellite. Errors may exist when translating satellite data into sowing and maturity dates. In this study, a 30 \(\times\) 30 m pixel was considered as 100% irrigated or rainfed, and thus, fragmented small fields may not have been well resolved. The above two aspects may have induced uncertainty in our estimated irrigated and rainfed surface areas. Typically, satellite data with higher spatial resolution perform better in mapping irrigated and rainfed areas for regions with small and scattered crop fields, but such data may be limited by a long satellite revisit time. Satellite data, such as MODIS, have a high temporal resolution and provide broad coverage area. Therefore, data assimilation is required to take advantage of different satellites and obtain high spatiotemporal resolution datasets for irrigated area mapping. Heterogeneous and small patchy crop areas are another challenge for classification. Some methods (e.g., mixed pixel decomposition) are needed to estimate the fraction of irrigated and rainfed areas in one grid cell, especially for regions with scattered farmland and a variable topographic surface.
## 3 An Adaptive Attitude Estimation Method Based on Multi-Sensor Fusion
### Attitude Estimation Based on Kalman Filter
#### 3.3.2 Data Fusion Method of Gyro and Accelerometer  
For the angular attitude measurement of roll angle \(\gamma\) and pitch angle \(\theta\) when the influence of maneuvering acceleration can be ignored, the data of gyro and accelerometer are fused to obtain accurate attitude estimation value by Kalman filter.  
The roll angle \(\gamma\) and pitch angle \(\theta\) are taken as the state variables of Kalman filter, that is the state variables of the state equation in Equation (10) is  
\[x_{k}=\big{[}\begin{array}{cc}\gamma^{\prime}_{k}&\theta^{\prime}_{k}\\ \end{array}\big{]}^{T}. \tag{16}\]  
The angular attitude calculated by the accelerometer and the angular velocity calculated by the gyro in the geographic coordinate system are taken as the measurement variables of Kalman filter. The measurement variables of the measurement equation in Equation (10) is  
\[z_{k}=\big{[}\begin{array}{ccc}\gamma_{k}&\dot{\gamma}_{k}&\theta_{k}&\dot{ \theta}_{k}\end{array}\big{]}^{T}, \tag{17}\]  
where \(\gamma^{\prime}_{k}\) and \(\theta^{\prime}_{k}\) are the actual roll and pitch angular attitude, \(\gamma_{k}\) and \(\theta_{k}\) are the roll and pitch angular attitude calculated by the accelerometer and \(\dot{\gamma}_{k}\) and \(\dot{\theta}_{k}\) are the roll and pitch angular velocity calculated by the gyro at \(k\)-time. In this fusion method, control input \(\mu_{k}=0\), random process noise sequence \(\omega_{k}\) and measurement noise sequence \(v_{k}\) can be ignored.
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### Other Applications
#### 2.10.1 Utilization and Capture of Toxic Gases
Paired mesoporous poly(ionic liquids), based on 3-aminoethyl-1-vinylimidazolium cations copolymerized with divinylbenzene and Glu, Asp, Ala, or Gly anions, exhibited high catalytic efficiency in the cycloaddition of CO\({}_{2}\) to aziridines, resulting in high yields (>95%) and regioselectivity (>99%) for 5-substituted oxazolidinones [269]. High yields (96-99%) of cyclic carbonates obtained under solvent-free and halogen-free conditions have also been reported for [Bmim][Asp], [Bmim][Glu], [Mor\({}_{1.8}\)][Glu], [HiQuin][Asp], [N\({}_{4444}\)][His], [N\({}_{4444}\)][Asp], [N\({}_{4444}\)][Glu], methoxylbutyl-3-methylimidazolium (MOBIm) [Arg], [MOBIm][Gly], and others [270-272]. On the other hand, [Ch][Pro] and [Ch][Gly] have been shown to support the fixation and formation of ammonium carbamate and ammonium carbonate [273]. Additionally, [Ch][Arg], [Ch][His], [Ch][Tyr], [Ch][Glu], [Ch][Gln], and [Ch][Pro] demonstrated the ability to undergo at least five cycles of CO\({}_{2}\) chemical sorption without significant loss of capacity [274].
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 4 Functions of the Fog Server in Details
### Synchronization with Cloud  
As shown in Figure 9, the fog server selects all the data that need to be synchronized and transfers them to the cloud periodically. The time period of synchronization is a user-defined parameter, and the default is 5 min.  
Figure 7: Data uploading workflow.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Antenna Simulation in Anatomical Models
#### Female Phantom  
As we see in Figure 23, the implanted antenna showed at 402.5 MHz a reflection coefficient of \(-10.65\) dB, while at 2.45 GHz it had a reflection coefficient of \(-12.4\) dB. These values were below the \(-10\) dB limit. They were considered acceptable for the signal transmission power outside the body.  
Figure 21: Anatomical model (**a**) and simulation of the implanted antenna on the pancreas surface (**b**).  
Figure 20: Selected anatomical models from the ViP3.1 virtual population.
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Operating Room Scheduling  
Operating room scheduling is a combination of type one and type two schedule, and can have a focus on one or the other. This can also involve scheduling the patients and medical staff to each operating room in addition to scheduling which operating rooms are available and what they will be used for. It is a more specialized example of scheduling that involves scheduling a resource, in this case, the operating room, to be used by the hospital. In many cases, this involves trying to minimize downtime while maximizing the effectiveness of the resource. As this is a more specific case of appointment rostering, many papers regarding this focus largely on ideas and algorithms that can be used to improve and evaluate the schedules rather than on algorithms that design and production schedules. The latter is covered more thoroughly by outpatient scheduling. Operating room scheduling must deal with many of the same considerations as outpatient rostering, such as rescheduling, cancellations, and maximizing the efficiency of a resource that deals with stochastic and online variables. Additionally, they are subject to unique pressures. The Canadian government has put a focus on reducing wait times and this has led to increased surgical volumes and bed utilization, which in turn lead to cancellations and wait times [33]. Operating rooms are especially valuable as they are one of the most profitable areas. About 42% of a hospital's profit originates from the operating rooms while only accounting for 9% to 10% of the budget [2]. Other typical constraints are listed in Section 4.3.1.  
The contributions of each paper are summarized in Table 4.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 3 RLQR Approach
### 1-Neighbor-Selection Algorithms
#### 3.2.2 UCBNS: UBC-Based 1-NS Algorithm  
Upper confidence bounds (UCB) is a family of MAB algorithms. Unlike the epsilon greedy algorithm, which pays attention only to the average reward of the actions, UCB algorithms rely on the average reward and on the confidence in the estimated reward's values of the selected actions [26]. It is worth mentioning that the UCB family of algorithms always chooses the best available actions and they do not randomly explore unlike epsilon greedy does. In this paper, we focus on the UCB1 algorithm and adapt it to define our 1-NS algorithm UCBNS. As illustrated in Algorithm 2, UCBNS starts by exploring all the \(m\) neighbors for the first \(m\) queries (see lines 1-9 Algorithm 2). Thereafter, it selects, for each query \(q_{t}\), the neighbor \(n_{i}(q_{t})\) with the highest estimated reward and confidence value. This estimation is calculated with respect to the number of times the neighbor \(n_{i}\) was selected, denoted \(c_{n_{i}}\), and its average reward \(r_{n_{i}}\) for previous queries (see line 11 Algorithm 2). Worth noting that \(r_{n_{i}}\) and \(c_{n_{i}}\) will be updated after receiving the reward of the selected neighbor \(n_{i}(q_{t})\) (see lines 13-15 Algorithm 2).  
```
1:\(q_{t}\): search query at trial \(t\). \(N=\{n_{1},\ldots,n_{m}\}\): set of neighbors \(R=(r_{n_{1}},\ldots,r_{n_{m}})\): average rewards of the \(K\) neighbors \(C=(c_{n_{1}},\ldots,c_{n_{m}})\): vector storing the number of times we have selected each of the \(K\) neighbors \(n_{i}(q_{t})\): the neighbor to be selected for \(q_{t}\)
2:for all\(n_{i}\in N\)do
3:if\((r_{n_{i}}==0)\)then
4:\(n_{i}(q_{t})=n_{i}\)
5:endif
6:endfor
7:\(n_{i}(q_{t})=\operatorname*{argmax}_{n_{i}\in N}\{r_{n_{i}}+\frac{log(\sum_ {j=1}^{m}c_{n_{j}})}{c_{n_{i}}}\}\)
8: update(\(r_{n_{i}},r_{n_{i}}(q_{t})\))
9:\(c_{n_{i}}++\)
```  
**Algorithm 2** UCBNS: UBC-based 1-NS algorithm
###### Abstract  
The aim of this study was to produce epoxidized monoalkyl esters (EMAE), a valuable class of olechemicals used in a wide range of products and industries, from used soybean cooking oil (USCO) and fused oil via a three-step chemoenzymatic process. This process consists of a first enzymatic hydrolysis of USCO to produce free fatty acids (FFA). Here, five microbial lipases with different specificities were tested as biocatalysts. Full hydrolysis of USCO was obtained after a 180 min reaction time under vigorous stirring (1500 rpm) using a non-specific lipase from _Candidia rugosa_ (CRL). Then, monoalkyl esters (MAE) were produced via the esterification of FFA and fused oil in a solvent-free system using the lipase Eversa(r) Transform 2.0 (ET2.0) immobilized via physical adsorption on poly(styrene-divinylbenzene) (PSty-DVB) beads as a biocatalyst. Different water removal strategies (closed and open reactors in the presence or absence of molecular sieves at 5% m.m.\({}^{-1}\)) on the reaction were evaluated. Maximum FFA conversions of 64.3 \(\pm\) 2.3% (open reactor after a 30 min reaction time) and 73.5 \(\pm\) 0.4% (closed reactor after a 45 min reaction time) were observed at 40 \({}^{\circ}\)C, using a stoichiometric FFA:fused oil molar ratio (1:1), without molecular sieves, and 5 mg of immobilized protein per gram of reaction mixture. Under these conditions, maximum FFA conversion was only 30.2 \(\pm\) 2.7% after a 210 min reaction time in a closed reactor using soluble lipase. Reusability tests showed better retention of the original activity of immobilized ET2.0 (around 82%) after eight successive batches of esterification reactions conducted in an open reactor. Finally, the produced MAE was epoxidized via the Prizehaev reaction, a classical chemical epoxidation process, using hydrogen peroxide and formic acid as a homogeneous catalyst. The products were characterized by standard methods and identified using proton nuclear magnetic resonance (\({}^{4}\)H NMR). Maximum unsaturated bond conversions into epoxy groups were at approximately 33%, with the experimental epoxy oxygen content (OOC\({}_{\text{cap}}\)) at 1.75-1.78%, and selectivity (S) at 0.81, using both MAEs produced (open or closed reactors). These results show that this new process is a promising approach for value-added olecochemical production from low-cost and renewable raw materials.  
epoxidized monoalkyl esters; chemoenzymatic process; waste oil; fused oil 2023
## 3 Results and Discussion
### TPCB's Effect on the Performance of Asphalt
#### 3.2.2 TPCB's Effect on the Rheological Properties of Asphalt
(3) Stiffness and m-Value  
The low-temperature creep properties of GF-70 and TPCB/GF-70 were investigated by BBR testing, and the creep stiffness and m-values were recorded at 60 s. In order to ensure that asphalt binders have good low-temperature performance to anti-cracking, AASHTO M 320 requires that the stiffness not be higher than 300 MPa, and the m-value should not be lower than 0.300. The stiffness and m-value results of GF-70 and TPCB/GF-70 are shown in Figure 7; meanwhile, the temperatures correspond to the stiffness of 300 MPa and the m-value of 0.300 are shown in Table 5.  
From Figure 7, the stiffness and m-values of GF-70 and TPCB/GF-70 satisfy the requirement of anti-cracking performance at a temperature of \(-12\)\({}^{\circ}\)C, but do not satisfy it at a temperature of \(-18\)\({}^{\circ}\)C, which shows that the low-temperature PG grade of both GF-70 and TPCB/GF-70 is \(-22\)\({}^{\circ}\)C. In detail, from Table 5, the critical low temperature of GF-70 and TPCB/GF-70 based on stiffness is \(-24.4\)\({}^{\circ}\)C and \(-22.6\)\({}^{\circ}\)C, respectively, and the critical low temperature of GF-70 and TPCB/GF-70 based on m-value is \(-24.2\)\({}^{\circ}\)C and \(-22.6\)\({}^{\circ}\)C, respectively, which shows that the critical low temperature of TPCB/GF-70 is \(1.6\)\({}^{\circ}\)C higher than that of GF-70. Therefore, TPCB can decrease the anti-cracking performance of GF-70, and a 15% dosage of TPCB has an effect on the low-temperature performance of GF-70 within one PG grade.
## 2 Design Aspects of the Tactile Internet  
The Tactile Internet (TI) has some features that are not apparent on the traditional Internet. The traditional Internet facilitates audio and visual communication, whereas the TI facilitates transmitting a sense of touch and actuation along with haptic feedback.  
The TI depends on haptic and non-haptic control to have communication between end-to-end operations. Figure 3 is the pictorial representation of the architectural design of the TI, which consists of three sections, i.e., master, network, and slave sections. With haptic command and haptic feedback signals, the master and slave sections communicate via the network section. There is a two-way exchange of information between the master and slave sections.
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 4 Results and Discussion
### Case 4  
The focus of case 4 is to develop a system that schedules the loads in context of users' comfort. Burst loads can only be operated at a scheduled time, otherwise the scheduler becomes ineffective,  
Figure 14: Total cost (case 3).  
Figure 13: Lighting cost (case 2).  
and the net cost will not be reduced as expected by the use of the scheduler. The parameter of User Comfort Index (UCI) is introduced to measure and ensure the user comfort. In UCI, the user has been provided with the flexibility to switch ON the desired burst load at any time according to his requirement; however, UCI asks users to provide some input about comfort level. The UCI is defined as the following:  
\[UCI=\begin{cases}T_{min}=23^{\circ}C\ and\ T_{max}=27^{\circ}C\ \ for\ HVAC\\ T_{min}=5^{\circ}C\ and\ T_{max}=10^{\circ}C\ \ for\ Refrigrator\\ T_{min}=45^{\circ}C\ and\ T_{max}=55^{\circ}C\ \ for\ Water\ Heater.\end{cases} \tag{12}\]  
By default, the settings for appliances are: HVAC 27 \({}^{\circ}\)C, refrigerator 5 \({}^{\circ}\)C and water heater is 55 \({}^{\circ}\)C. When the scheduler is used to switch ON burst load, no change in these thresholds is required. When using the option of UCI, it is necessary to provide the required change in threshold levels to ensure the operation of burst load, which has now changed its priority and has become an essential load. There are some fixed slots in which the thresholds are relaxed automatically in order to implement the concept of flexible load categorization defined in Section 3. Now, these appliances go to standby mode and new settings are updated which are: HVAC 23 \({}^{\circ}\)C, refrigerator 10 \({}^{\circ}\)C and water heater 45 \({}^{\circ}\)C. These appliances will now be ON if the desired comfort is breached; otherwise, they will stay in standby mode till the burst load is switched OFF by the user. Figure 15 shows the results of UCI.  
When the burst load is activated under UCI, the threshold of every appliance changes and it goes to standby mode. In Figure 15, \"1\" stands for active and \"0\" stands for standby mode. The burst load is switched ON between 8:00 to 12:00 a.m. When burst load is activated, the threshold changes as depicted in Figure 15. It is clearly seen that before 08:00 a.m., the threshold of the refrigerator was 5 \({}^{\circ}\)C, but, after the activation of the burst load, the threshold has been changed. Switching the device to standby mode will provide the user an advantage, as in this mode, these appliances consume power almost equal to zero Watts. For instance, in Figure 16, power consumed by the refrigerator during this period is almost negligible. It is the same case for the water heater and HVAC.  
Figure 15: User Comfort Index (UCI) results (case 4).  
Table 2 shows total cost for load consumed for one day while carbon footprint results are tabulated in Table 3.