esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description
### Adaptive NNs Backstepping Controller Design with PPF and ESO
Then, the time derivative of \(V\) is obtained  
\[\dot{V}=\dot{V}_{3}+\frac{1}{2}\dot{\varepsilon}^{T}P\varepsilon+\frac{1}{2} \varepsilon^{T}P\dot{\varepsilon}-\widetilde{W}_{h}^{T}\Lambda_{h}^{-1}\dot{ \Psi}_{h} \tag{69}\]  
Substituting (64) and (66) into (39), we can rewrite the time derivative of \(V_{2}\) as follows  
\[f(X)=\frac{1}{m_{s}}[k_{s}(x_{1}-x_{3})+c_{a}(x_{2}-x_{4})] \tag{63}\]0  
Based on (47) and (70), we can rewrite  
\[ \begin{array}{c}\dot{V}=-k_{1}z_{1}^{2}+z_{2}\widetilde{W}_{h}^{T}S(X)+z_{ 2}\eta(X)+z_{2}(d_{0}+d(t))+z_{3}\widetilde{f}_{2}+z_{3}\omega_{0}^{3} \varepsilon_{6}\\ \qquad\qquad+z_{3}(k_{1}k_{2}+1)\omega_{0}\varepsilon_{2}+k_{3}z_{3}\omega_{0 }^{2}\varepsilon_{5}+\frac{1}{2}\dot{\varepsilon}^{T}P\varepsilon+\frac{1}{2} \varepsilon^{T}P\dot{\varepsilon}-\widetilde{W}_{h}^{T}\Lambda_{h}^{-1}\dot{ W}_{h}-k_{2}z_{2}^{2}-k_{3}z_{3}^{2}\end{array}  \tag{71}\]  
Substituting (26) and (67) into (71), we have:  
\[ \begin{array}{c}\dot{V}=-k_{1}z_{1}^{2}-k_{2}z_{2}^{2}+z_{2}(d_{0}+d(t))+z_{ 2}\widetilde{W}_{h}^{T}S(X)-k_{3}z_{3}^{2}+z_{2}\eta(X)-\widetilde{W}_{h}^{T} \big{[}S(X)z_{2}-\delta_{h}\hat{W}_{h}\big{]}\\ \qquad\qquad+\varepsilon^{T}PM\frac{d_{0}+d(t)}{\omega_{0}}+z_{3}\widetilde{ f}_{2}+z_{3}\omega_{0}^{3}\varepsilon_{6}+z_{3}(k_{1}k_{2}+1)\omega_{0} \varepsilon_{2}+k_{3}z_{3}\omega_{0}^{2}\varepsilon_{5}-\omega_{0}\| \varepsilon\|^{2}\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \qquad+\varepsilon^{T}PM\frac{\hat{T}_{1}}{\omega_{0}}+\varepsilon^{T}PN\frac{ \hat{T}_{2}}{\omega_{0}^{3}}+\varepsilon^{T}PN\frac{\hat{S}\omega}{\omega_{0}^ {3}}+\varepsilon^{T}PK\frac{d(t)}{\omega_{0}^{3}}\end{array}  \tag{72}\]  
According to [PERSON]'s inequality, it yields  
\[ \begin{array}{c}z_{2}\eta(X)\leq\frac{z_{2}^{2}}{2}+\frac{\eta^{2}(X)}{2}\\ \widetilde{W}_{h}^{T}W\leq\frac{\widetilde{W}_{h}^{T}\widetilde{W}_{h}}{2}+ \frac{W_{h}^{T}W_{h}}{2}\end{array}  \tag{73}\]  
Applying (51), (52) and (73) into (72), we can write  
\[ \begin{array}{c}\dot{V}\leq-k_{1}z_{1}^{2}-k_{2}z_{2}^{2}-k_{3}z_{3}^{2}+ \frac{1}{2}z_{2}^{2}+\frac{1}{2}(\left|d_{0}\right|_{\max}+\left|d(t)\right|_{ \max})^{2}+\frac{1}{2}z_{2}^{2}-\frac{\delta_{h}\left\|\widetilde{W}_{h} \right\|^{2}}{2}+\frac{\delta_{h}\left\|W_{h}\right\|^{2}}{2}\\ \qquad\qquad+\frac{\eta^{2}(X)}{2}+\omega_{0}^{3}\varepsilon_{6}z_{3}+\frac{1 }{2}\|\varepsilon\|^{2}+(c_{3}|\varepsilon_{1}|+c_{4}|\varepsilon_{2}|+c_{5}| \varepsilon_{5}|)z_{3}+(k_{1}k_{2}+1)\omega_{0}\varepsilon_{2}z_{3}\\ \qquad\qquad+k_{3}\omega_{0}^{2}\varepsilon_{5}z_{3}+\frac{\phi_{5}^{2}}{2 \omega_{0}^{3}}|h(t)|_{\max}^{2}+\frac{1}{2}\|\varepsilon\|^{2}+\frac{\phi_{5} ^{2}}{2\omega_{0}^{2}}\left(\left|d_{0}\right|_{\max}+\left|d(t)\right|_{\max }\right)^{2}\\ \qquad\qquad+\left(-\omega_{0}+\frac{\phi_{1}(c_{1}+c_{2})}{\omega_{0}}+\frac{ \phi_{2}(c_{3}+c_{4}+c_{5})}{\omega_{0}^{2}}+\frac{\phi_{2}(c_{5}+c_{4})}{ \omega_{0}^{2}}|u|_{\max}\right)\|\varepsilon\|^{2}\end{array}
## 6 Algorithms and Frameworks in Federated Learning
### Model Aggregation Methods
#### 6.3.3 Geometric Median Aggregation  
Instead of averaging, some aggregation techniques aim to find the geometric median of the model updates. The geometric median offers robustness against adversarial or Byzantine attacks, as it is less susceptible to outliers.  
Advantages:  
* Robustness against adversarial updates.
* Suitable for scenarios where security and integrity are paramount.
* Challenges:
* Computationally more intensive than averaging methods.
* Determining the geometric median in high-dimensional spaces can be challenging.
## 2 Study Area and Method
### Model Parameters and Input Data
#### 2.3.3 Meteorological Data  
Observational data of 410 weather stations in Zhejiang Province and its surrounding provinces from 1985 to 2015 was provided by the National Meteorological Center of China Meteorological Administration [43; 44], including the monthly maximum temperature, monthly minimum temperature, average relative humidity, total precipitation, and monthly average radiation. The inverse distance weighting method was used to interpolate site meteorological data into 1 km resolution spatial data.
## 2 Explanation of the Working Process and Perturbation Theory of the Seeder
### Perturbation Theory Analysis of Seed-Filling Process  
When the pneumatic seeder works, the linear serrated structure on the seed discharge disk will pass through the surface of the seed population close to the seed discharge disk. This process enables the separation and perturbation of a small number of tiny cabbage seeds that close to the seed discharge disk. In the process, the cabbage seeds start to accelerate under the effect of perturbation and then arrive at the holes to be adsorbed by the holes and rotate with the seed discharge disk at a uniform speed. The motion analysis of cabbage seeds in the seed-filling process is shown in Figure 2. Kinematic modeling of the seed-filling process is based on particle dynamics [17; 18].  
\[\left\{\begin{array}{l}Kx=0.5 mv_{1}^{2}-0.5 mv_{0}^{2}\\ v_{1}=nR_{0}\\ P=\frac{4K}{nd^{2}}\end{array}\right. \tag{1}\]  
where:  
\(K\)**-pressure provided by the negative pressure gas chamber, N \(x\)**-effective distance of negative pressure adsorption of seeds in the air chamber, mm \(m\)**-mass of cabbage seeds used, kg \(R_{0}\)**-radius of the seed discharge disk where the typed hole is located, mm \(d\)**-diameter of the seed discharge disk type hole, mm \(v_{0}\)**-initial seed velocity, m/s \(v_{1}\)**-speed of seed after being adsorbed by the typed hole, m/s \(P\)**-gas chamber vacuum size, Pa  
Figure 1: Structure of pneumatic seeder. 1. Seed box housing, 2. seed box cover, 3. seed guide tube 4. drive bearing, 5. drive shaft, 6. linear serrated disk, 7. air chamber sealing ring, 8. air chamber cavity.  
Figure 2: Kinematic analysis of the seed-filling process. Where \(\omega\) is the angular velocity, \(v_{1}\) is the forward speed of the planter, \(v_{2}\) is the tangential velocity at disengagement, and \(a_{k}\) is the centripetal acceleration.  
The gas chamber vacuum \(P\) required for stable adsorption of cabbage seeds at the type pore is obtained from Equation (1) as:  
\[P=\frac{2m\big{(}n^{2}R_{0}^{2}-v_{0}^{2}\big{)}}{\pi xd^{2}} \tag{2}\]  
As can be seen from Equation (2), under the condition that the rotational speed of the seed-discharging disk remains unchanged, the gas chamber vacuum P required for the stable adsorption of the cabbage seeds at the typed hole is inversely proportional to the initial speed v\({}_{0}\) of the cabbage seeds. When the initial speed v\({}_{0}\) of the cabbage seed is more incredible, the air chamber vacuum P required for the cabbage seed to be stably adsorbed at the hole in the seed-filling stage is more minor. Therefore, the addition of a serrated seed-disturbing structure on the surface of the seed discharging disk can first separate a small number of seeds from the population. It could facilitate accurate adsorption of the seeds, so the cabbage seeds can obtain a large initial velocity in the filling stage, thus reducing the value of the negative pressure that needs to be supplied by the air chamber, and reducing the power consumption of the fan.
## 1 Introduction
### Safety Standards and HRC Modes
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Description** & \multicolumn{1}{c}{**Standard**} \\ \hline Type A Standard & Basic safety standards for general requirements & ISO 12100: 2010 “Machine safety, general design principles, risk assessment, and risk reduction” IEC 61508: terminology and methodology [17] \\ Type B Standard & Generic safety standards & – & B1 standards (ISO 13849-1, IEC 62061): specific safety aspects [18] \\  & & – & B2 standards (ISO 13850, ISO 13851): safeguard [19; 20] \\  & & – & ISO 10218: safety of industrial robots \\  & & – & ISO 10218-1:2011 “Robots and equipment for robots, Safety requirements for industrial robots, Part 1: Robots”, safety requirements for robot manufacturers (robot and controller) [21] \\ Type C Standard & Safety countermeasures for specific machineryPrioritized over Type A and & – & ISO 10218-2: 2011 “Robots and equipment for robots, Safety requirements for industrial robots, Part 2: Systems and integration of robots”, safety requirements for system integrators (robot and ancillary devices), [22] \\  & & – & ISO TS 15066; 2016 “Robots and robotic devices, Collaborative Robots”, guidance on collaborative robot Operations [23; 24] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Safety standards. has moved away from the system. If the robot system operates at a reduced speed, the protective distance is reduced accordingly. The workspace may be divided into \"zones\", whereby if the human is in the green zone, the robot may operate at full speed, if in the yellow zone, the robot operates with reduced speed, and if the human enters the red zone, the robot's operation is stopped. Vision systems are used to monitor these zones. * Power and force limiting (PFL)  
PFL [10; 13] is a collaborative approach in which limits are set for motor power and force such that the human operator and robot may work side-by-side. These limits are set as a risk reduction method, defined by a risk assessment. To implement this approach, specific equipment and control modes are required in order to handle collisions between the robot and human and prevent any injuries to the human. These four collaborative modes can be applied to both traditional industrial robots and collaborative robots. For traditional industrial robots, additional safety devices such as laser sensors or light curtains are required. On the other hand, for collaborative additional features such as force and torque sensors, force limits, vision systems, laser systems, and anti-collision systems are required [12].
## 2 Problem Statement
### Comparison of the Average Throughput  
When analyzing the average throughput of TCP and SCTP, the PERs changed from 0% to 50%. Figure 2 shows the comparison between the average throughput of SCTP and TCP at different PERs.  
When PER increased, the corresponding average throughput of SCTP and TCP decreased rapidly, but the average throughput of SCTP was always greater than that of TCP. When the packet error rate reached 30%, the average throughput of both SCTP and TCP dropped below 10 KBps. Due to the poor link condition, neither SCTP nor TCP were ideal in data transmission rate. At this time, the average throughputs of both were relatively low. By comparing the real-time throughput with the average throughput, SCTP could make better use of network bandwidth and transmit data better with the same PER.
## 5 Case Study
### Equilibrium Solution  
According to the Algorithm 1, each community operator will optimize its own bidding strategy by estimating its opponents' types with the probability distribution. Consequently, the bidding strategy of 3 community operators is presented in Figure 3. It shows that community operator 2 has obtained the highest market share during 18:00-21:00, while community operator 3 has the least market share in the DR bidding. From the aspect of DR resource's grade, without the assistance of gas boiler and energy storage, community operator 3 can only participate in the bidding for the 3 rd grade of DR resource. And in most of time, community operator 1 and 2 can participate in the bidding for the 2 nd grade of DR resource. Since the limitation of percentage of heat demand and discharging power of energy storage, community operator 1 and 2 can participate in the bidding for the 1 st grade of DR resource in only a few time slot (i.e., \(t=7\)-8 for operator 1 and \(t=1\) for operator 2). In addition, in time slot \(t=5\)-8, the bidding amount of community operator 3 only achieves 50%-60% of its maximal DR resource, while community operator 2 has achieved above 90% of its maximal DR resource. The main reason is that, since community operator 1 and 2 have equipped gas boiler or energy storage, they can compete more DR amount by promoting the grade of DR resource to reduce the punishment of breach amount. However, community operator 3 only has the original DR resource with high breach percentage, if operator 3 compete more DR amount, then it has to face the high punishment, especially in time slot 5-8.  
Figure 2: DR resources of 3 community operators.  
Table 2 shows the optimal profit of 3 community operators. Obviously, community operator 2 has the highest total profit, while operator 3 has the lowest total profit. It shows that community operators with auxiliary equipment will have a higher profit than the profit of operators without auxiliary equipment. Furthermore, due to the high investment and maintenance cost of energy storage, community operator 2 has the negative profit from energy storage. But, in fact, community operator 2 can have a positive profit from energy storage by increasing the electricity price. As long as the electricity price is lower than the price set by public grid, users will be willing to purchase energy from energy storage. Moreover, comparing the initial profit of 3 community operators (i.e., operator 1 is \(28.39\times 10^{3}\) RMB, operator 2 is \(30.72\times 10^{3}\) RMB, and operator 3 is \(7.55\times 10^{3}\) RMB), one can see that after optimization with the proposed approach, each community operator will have a better performance on the daily profit. Therefore, for community operators in the DR market, they are willing to participate in the optimization of bidding strategy.
## 3 Feature Extraction of PQDs  
The definition of eigenvalue selection is often closely related to the results of signal processing and analysis. For the different amplitude curves of the Kalman filter and unscented Kalman filter results, the maximum, minimum, variance, and standard deviation of the curve waveform features can be extracted as the distinguishing indicators [22]. To ensure the reliability of feature distribution, 150 sets of random feature data were generated using Matlab 2020 based on the mathematical model provided in Table 2, which corresponds to the maximum, minimum, coefficient of variation, waveform index, peak index, margin index and root-mean-square amplitudes of voltage swell, sag, interruption, fluctuations, pulse, oscillation, and harmonics are extracted. In total, 150 sets of feature data corresponding to each of the seven feature indexes of the seven disturbances are drawn to the boxplot. The red line in the box represents the median value, and the red plus sign represents the outlier. The median value is not affected by the extreme value in the feature data, and the distribution of the sequence is more representative, which can determine the optimal feature vector set. The minimum and peak index feature distributions of the five single disturbances are shown in Figure 3. It can be seen from Figure 3a that the minimum value distribution of sag and pulse is not very different; however, the minimum value distribution of voltage swell, interruption, and fluctuation waveform amplitude is obviously different, and the degree of differentiation is obvious. It can be seen from Figure 3b that the peak index can clearly distinguish sag, interruption, and pulse. The median peak index distribution of voltage swell and fluctuations is relatively close, and the degree of discrimination is general.  
The coverage of the above seven feature indexes with obvious discrimination for seven single disturbances is summarized, as shown in Table 3. It can be seen from the table that only one feature index cannot distinguish all types of disturbances, and the feature index combining the minimum and the peak index can cover seven single disturbances. Therefore, the amplitudes of fundamental wave, third harmonic, fifth harmonic, seventh harmonic, and oscillatory wave can be tracked, and their respective minimum and peak index can be used to distinguish PQDs type. The optimal feature vector is formed, which is defined as \([F_{1},F_{2},\ldots,F_{10}]^{T}\).  
Figure 3: The index distribution of different feature quantities with single disturbance. (**a**) Minimum feature distribution; (**b**) peak index characteristic distribution.
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 4 Zeroize Method Performance Analysis
### Aportioned Bandwidth Reduction  
The apportioned bandwidth ratio of the proposed Zeroize method is discussed in this section. The apportioned bandwidth ratio was analyzed with 10-100 calls. The Zeroize, SVF, and ZSP methods were analyzed in comparison to the S-RTP method. Figures 7 and 8 show the bandwidth reduction of the Zeroize, SVF, and ZSP methods in comparison to the S-RTP method. The apportioned bandwidth saving when using the Zeroize method was around 20% compared with that of the S-RTP method with the G.723.1 codec. However, the bandwidth saving when using the Zeroize method underperformed the SVF and ZSP methods. This condition was because the header of the IPv6 protocol header was twice the IPv4 protocol header and consumed more bandwidth for each call. The size of the superfluous fields of the SVF and ZSP methods was more than that of the Zeroize method.  
The SVF and ZSP methods outperformed the Zeroize method in terms of Ch-Capacity and apportioned bandwidth reduction. However, the Zeroize method works over IPv6 networks, unlike the SVF and ZSP methods, which work over IPv4 networks. The SVF and ZSP methods utilize many fields as superfluous fields to carry the voice data. This condition makes them workable only in specific scenarios, which are the scenarios that they are designed for. However, the Zeroize method utilizes only the source IPv6 address field as a superfluous field to carry the voice data. This condition makes the Zeroize method a more general method that works in all scenarios. The SVF and ZSP methods burden  
Figure 6: Ch-Capacity (G.726 codec).  
the network compared with the Zeroize method for several reasons. The SVF and ZSP methods are designed to work at the gateway routers. Therefore, the resources of these routers are consumed, especially with the high number of calls. The Zeroize method is designed to work at the client-side without burdening the routers. The Zeroize method uses only one field (source IPv6 address) to carry the voice data without the need to set back the original value of this field. However, the SVF and ZSP methods use several fields to carry the voice data, thereby imposing more operations and calculations, such as setting back the original value of the superfluous fields at the receiver gateway, calculating the new length field value of the UDP protocol, and assigning a new value to the Internet header length field of the IP protocol. Performing these operations for every call packet consumes the routers' resources, especially with a high number of calls. The SVF and ZSP methods burden the network compared with the Zeroize method.
# A Scalable Deep Network for Graph Clustering
## 3 The Proposed Method
### Dual Self-Supervised Module
```
0: Initial features: \(\mathbf{X}\), Graph: \(\mathcal{G}\), Numbers of clusters: \(K\), Adjacency matrix: \(\mathbf{A}\), Iteration number: \(MaxIter\), Layer number of linear encoder: \(L_{E}\), Layer number of linear propagation module: \(L_{P}\);
0: Clustering results \(\mathbf{R}\);
1: Initialize \(\mathbf{W}_{e}^{(l)}\), \(\mathbf{W}_{d}^{(l)}\), \(\mathbf{b}_{e}^{(l)}\), \(\mathbf{b}_{d}^{(l)}\) with pre-train auto-encoder;
2: Initialize \(\mu\) with K-means on the representations learned by pre-train auto-encoder;
3: Initialize \(\mathbf{W}_{m}^{(l)}\) randomly;
4:for\(ite=1\) to \(MaxIter\)do
5: Generate DNN embeddings \(\mathbf{E}^{(0)}\), \(\mathbf{E}^{(1)}\), \(\mathbf{E}^{(2)}\), \(\cdots\), \(\mathbf{E}^{(L)}\);
6: Use \(\mathbf{E}^{(L)}\) to calculate the distribution \(Q\) by Equation (17);
7: Calculate target distribution \(P\) by Equation (18);
8:for\(l=1\) to \(L_{E}\)do
9: Set the balance coefficient \(\sigma=0.5\) to calculate \(\tilde{\mathbf{H}}^{(l)}\) by Equation (5);
10: Calculate the embeddings of the next layer of MLP by Equation (6);
11:endfor
12: Set \(\tilde{\mathbf{H}}=\mathbf{H}^{(L)}\)
13:for\(l=1\) to \(L_{P}\)do
14: Calculate the embeddings by Equation (15);
15:endfor
16: Set the transmission probability \(\alpha=0.3\) to calculate the distribution \(Z\) by Equation (16);
17: Feed \(\mathbf{H}^{(L)}\) into the decoder to obtain the refactored feature \(\tilde{\mathbf{X}}\);
18: Calculate \(\mathcal{L}_{res}\), \(\mathcal{L}_{clu}\), \(\mathcal{L}_{mlp}\), respectively;
19: Calculate the whole loss function \(\mathcal{L}\) by Equation (21);
20: Back propagation and update parameters in SDN;
21:endfor
22: Calculate the clustering results based on distribution \(Z\);
23: return \(\mathbf{R}\);
```  
**Algorithm 1** Training process of SDN.
# Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel
## 3 Results
### Gasification

Table 7: Energy balance results for gasification trials. In summary, the results suggest that both air gasification (gasifying totally dried material) and combustion (up to 10 wt% moisture) allow recovering net energy from wet SRF. On the other hand, the entire process including air/steam gasification of SRF as the main step has resulted in an autothermal process, which does not seem useful for energetic purposes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Number** & **1** & **2** \\ \hline Experiment code & T800\_S/C0\_ER29.6 & T800\_S/C1\_ER37.7 \\ \hline Moisture of the input SRF stream to gasification (\%) & 0 & 33.7 \\ \hline \multicolumn{3}{c}{Gasification gas composition (kg/kg of dry matter in SRF)} \\ \hline \(\mathrm{H_{2}}\) & 0.017 & 0.018 \\ CO & 0.263 & 0.151 \\ CO\({}_{2}\) & 0.612 & 1.03 \\ CH\({}_{4}\) & 0.051 & 0.041 \\ C\({}_{2}\)H\({}_{6}\) & 0.005 & 0.003 \\ C\({}_{2}\)H\({}_{4}\) & 0.057 & 0.056 \\ H\({}_{2}\)S & 0.0007 & 0.0049 \\ H\({}_{2}\)O & 0.216 & 0.75 \\ N\({}_{2}\) & 1.85 & 2.49 \\ \hline \multicolumn{3}{c}{Air flow fed to the furnace} \\ \hline Excess above stoichiometric (\%) & 25 & \\ O\({}_{2}\) (kg/kg of dry matter in SRF) & 0.883 & 0.761 \\ N\({}_{2}\) (kg/kg of dry matter in SRF) & 2.91 & 2.51 \\ \hline \multicolumn{3}{c}{Calculation of generated output gases (kg/kg of dry matter in SRF)} \\ \hline CO\({}_{2}\) & 1.36 & 1.56 \\ H\({}_{2}\)O & 0.566 & 1.08 \\ O\({}_{2}\) & 0.177 & 0.152 \\ N\({}_{2}\) & 4.76 & 4.99 \\ Total mass & 6.86 & 7.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Mass balance results of gasification gas combustion. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Number** & **1** & **2** \\ \hline Experiment code & T800\_S/C0\_ER29.6 & T800\_S/C1\_ER37.7 \\ \hline Moisture of the input SRF stream to gasification (\%) & 0 & 33.7 \\ \hline \multicolumn{3}{c}{Energetic terms in the gasification energy balance} \\ \hline \(\Delta h_{input}\) (MJ/kg of dry matter in SRF) & \(-\)4.38 & \(-\)13.5 \\ Heat losses (MJ/kg of dry matter in SRF) & 0.656 & 2.03 \\ \(\sum m_{i}\cdot\Delta h_{f,i}^{0}\) (MJ/kg of dry matter in SRF) & \(-\)19.79 & \(-\)28.62 \\ \(\sum m_{i}\cdot Cp_{i}\) (MJ/kg of dry matter in SRF) & 0.0081 & 0.0097 \\ Output T (K) & 2112 & 1647 \\ \hline \multicolumn{3}{c}{Heat exchanger: amount of steam produced} \\ \hline Mass of steam (kg/kg of dry matter in SRF) & 4.12 & 3.53 \\ \hline \multicolumn{3}{c}{Summary} \\ \hline Energy recovered in the heat exchanger to produce & 11.58 & 9.92 \\ high-pressure steam (MJ/kg of dry matter in SRF) & 15.01 & 7.69 \\ Total energy recovered after gasification and heat & 8.85 & 7.73 \\ \multicolumn{3}{c}{exchanger (MJ/kg of dry matter in SRF)} & 6.16 & \(-\)0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Energy balance results for gasification gas combustion.