esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction and Comparison of In-Vehicle CO\({}_{2}\) Concentration Based on ARIMA and LSTM Models
## 3 Results
### Fitting Models and Prediction Result Analysis
#### 3.2.3 Results of Model Predictions and Comparative Analysis  
The duration of each field test is 1200 s (20 min), with a data collection interval of 5 s. A total of 240 sets of data were collected in each test, which are then divided into training and testing sets at a seven-to-three ratio to evaluate the predictive performance of two models--ARIMA and LSTM. Figure 8 illustrates the comparison between predicted values and actual values for both models. The two models effectively learned the regular patterns in the historical data and are capable of accurately predicting variations in CO\({}_{2}\) concentration under different fan speeds, occupancy levels, driving speeds, and vehicle volumes. Furthermore, their prediction curves closely align with the actual value curves, obtaining an accurate prediction result. The prediction results demonstrate the accuracy and good generalization ability of both models in predicting changes in CO\({}_{2}\) concentration within a vehicle, effectively capturing fluctuations during driving. The predicted curve closely aligns with the actual curve and exhibits excellent fitting effects. The two models are capable of forecasting variations in CO\({}_{2}\) concentration across diverse vehicle models and driving scenarios. However, in terms of the prediction results for the T2, T3, and T5 datasets, the ARIMA prediction curves show a closer alignment with the actual values compared to LSTM.  
The RMSE and MAE values for the prediction results of both models are presented in Table 9. The ARIMA model exhibits superior performance, with an average MAPE of 0.46% and RMSE of 19.62 ppm, respectively, compared to the LSTM model's values of 0.56% and 22.76 ppm. The MAPE metric reflects the accuracy of the model's predictions, and the RMSE metric measures the average deviation between predicted and true values. Lower values indicate better performance. The ARIMA model exhibits lower MAPE and RMSE values compared to the LSTM model, indicating superior prediction accuracy in forecasting CO\({}_{2}\) concentration changes within a vehicle. And the degree of deviation between the predicted value of ARIMA and the true value is also lower than that of LSTM.  
Figure 8: ARIMA and LSTM prediction results: (**a**) prediction result of the T1–S1 dataset, (**b**) prediction result of the T2–S4 dataset, (**c**) prediction result of the T3–1 person dataset, (**d**) prediction result of the T4–suburban road dataset, (**e**) prediction result of the T5–expressway dataset, (**f**) prediction result of the T6–Honda Fit dataset.
## 3 Methodology
### Preprocessing and Filtration of Sensor Data  
Initially, signal enhancement is applied to the inertial sensor data to eliminate redundancy, irrelevancy, and inconsistency in the framed data. The band stop notch filter [26] is used for signal representation to improve the quality of data. A second-order band stop (notch) filter has two cut-off frequencies, lower cut-off and upper cut-off, which are applied to the framed data. Such a filter passes all frequencies from zero to both cutoff frequencies. All the frequencies between the lower cutoff and the upper cutoff are rejected. The bandwidth of the notch-filter is calculated by subtracting the lower cut-off frequency from the higher cut-off frequency, as shown in Figure 2. The filtered data and unfiltered data of the accelerometer signal are plotted on the same axis. The red signal represents the filtered data and the blue signal represents the unfiltered data. Moreover, an on-figure magnifier is added on the plotted accelerometer data showing the zoom-in area of the graph.
## 6 Recent Trends in Ammonia Storage Technologies
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Storage Tank** & **Comment** & **Storage Facility** \\ \hline
**Double-wall steel storage tanks** & Double-wall tanks or double-containment tanks are steel tanks with two walls and perlite insulation between them. & Several precautions need to be taken to ensure the secure operation of liquid ammonia storage tanks: (1) Auto-closing valves must be installed on the principal input and output flow lines. (2) The cooling system must be constructed with robust and time-tested industrial compressors. (3) Auto-mode compressors can assist with tank pressure regulation during loading and unloading. Critical service should be inspected, and critical instruments and controls should have a backup. (4) To reduce fugitive ammonia emissions, a closed ventilation system for ammonia should be included in the construction. Thermal relief valves must be installed on ammonia systems to prevent blockage or heat penetration. (5) An ammonia gas leakage detection system is required inside the storage container. (6) Lightning and earthing protection must be provided for the tank. One of the refrigerated holding compressors requires emergency power to maintain tank pressure during power outages. In emergency situations, a flare is essential, and it is advisable to indicate the direction of the wind. \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Ammonia storage tank and storage facility types [113]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **Unit** & **Compressed Hydrogen** & **Liquid Hydrogen** & **Liquid Ammonia** \\ \hline Storage method & - & Compression & Liquefaction & Liquefaction \\ \hline Temperature & \({}^{\circ}\)C & 25 & \(-\)252.9 & 25 \\ \hline Storage pressure & bar & 690 & 1 & 9.9 \\ \hline Density & kg/m3 & 39 & 70.8 & 600 \\ \hline Volumetric hydrogen content & kg-H2/m\({}^{3}\) & 42.2 & 70.8 & 121 \\ \hline Energy to extract hydrogen & kJ/mol-H\({}_{2}\) & - & 0.907 & 30.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Compressed hydrogen, liquid hydrogen, and liquid ammonia comparisons [115]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amine** & **Density (g/cm\({}^{3}\))** & **Gravimetric Hydrogen Wt\%** & **Ammonia Vapor Pressure at 25 \({}^{\circ}\)C (bar)** & **Decomposition Temperature** \\ \hline Sr(NH3)\({}_{8}\)Cl\({}_{2}\) & 1.3 & 8.20 & 0.554 & 308 \\ \hline Mg(NH3)\({}_{6}\)Cl\({}_{2}\) & 1.16 & 9.19 & 0.002 & 415 \\ \hline Ca(NH3)\({}_{8}\)Cl\({}_{2}\) & 1.09 & 9.78 & 0.627 & 305 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The characteristics of various metal amine complexes [116]. Much research in this field is required to improve this way of storage. As a result, future development should concentrate on solid-state storage.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### 3D waveguide.  
In addition, [PERSON] et al. [27] and [PERSON] et al. [28] construct a feedforward neural network through a 3D waveguide written directly by a laser, as shown in Figure 4. At present, there are only two layers of network, so the signal is a one-way transmission. The multiple IO channels are finally combined into one output port. Three-dimensional waveguide achieves the goal of dimension expansion, but the signal is still a one-way transmission. The input signal is summed by the N \(\times\) 1 beam combiner. This completes the interconnection between different layers and the waveguides works as a summator.
## 4 Results and Discussion
### Effects of Harmonic Response on Compressor Operation for Ideal and Real Gas Models  
In this section the effects of harmonic response on compressor mass flow rate, indicated power and specific work are examined for both ideal and real gas models, as shown in Figure 13. The first four order harmonic responses are carefully selected for analysis since most of the energy in the compressor is usually associated with the first few harmonics. These key compressor harmonics that have strong response are properly tuned by altering the length of the pipe in this work. Also, the acoustic responses of suction system are separated from those belonging to the discharge system. The base compressor is the one without pressure pulsations in the suction and discharge systems.  
As can be seen, with the first harmonic response in the suction system, the compressor mass flow rate is reduced, but the indicated power and specific work required to compress the working fluid increased considerably. As an example, the specific work is augmented by 10.7% for real gas. With transference to the second harmonic with a weaker response compared to the first harmonic, however, the mass flow rate is raised significantly with a so-called supercharging effect but the specific work is increased slightly. For instance, the mass flow rate and specific work increased by 10.2% and 1.6%, respectively, for the real gas. As the response is shifted to harmonics higher than second order, the pressure pulsations will have not much effect on the compressor operation. The mass flow rate and specific work gradually stabilized at a certain value somewhat higher than those for the base compressor. As for the harmonic responses in discharge system, it is generally deduced that both the increase in mass flow rate and reduction of indicated power and specific work versus harmonic order behave in a monotonous pattern. Moreover, although the mass flow rate is changed little by pressure pulsations in harmonic response, the indicated power and specific work increased significantly compared to the base compressor. For instance, the specific work increased by 13% in the first harmonic response for the real gas.  
Figure 12: Comparison of suction and discharge pressure pulsation influence on specific work for ideal and real gas models.
## 3 Wireless Sensor Networks
### SPWS Validation  
In a first step, the SPWS devices were tested in terms of packet loss. It was verified that within the expected range of communication, the reception was almost perfect, making a more detailed analysis unnecessary for the purpose being considered. Additionally, the door and windows state detection as well as the PIR motion detection were checked for correct functionality, having shown correct operation. Another validation was made in terms of the temperature and relative humidity data acquired by various SPWSs, by relating it to data acquired by the Tmote Sky-based sensors that were employed in previous work.  
The Tmote Sky-based sensors, previously used, were enclosed in a large box with plenty of air circulation. The size of the box is clearly too large, therefore it is not acceptable for an MPC-based HVAC and thermal comfort product. Figure 8 shows SPWS devices and Tmote sensors, under different enclosures. The numbers within red squares are the identifiers of the following sensors and configurations: 46--Tmote inside an open box; 43--Tmote inside a closed box; 1--SPWS outside the box; 3--SPWS inside the designed enclosure; 45--Tmote outside the box; 44--Tmote inside an open box, 2--SPWS device placed in the same box. The devices measured the air temperature (Figure 9) and relative humidity (Figure 10) inside a room in a period of 24 h.  
Analysing Figure 9, there is clearly an offset of around 1.5 \({}^{\circ}\)C between the two different devices, although the trend is the same among the two types of devices. A possible explanation lies in the fact that the Tmote temperature conversion equation depends on the power supply employed and, as the factor for 3.3 V power supply was not available in the SHT11 datasheet, the 3 V factor was employed. It should also be noted that, in another experiment, the SI7021 sensor employed in the SPWS was compared to the new Sensirion SHT25 sensor. Very similar measurements were obtained, in accordance with the accuracy specified in the data sheets.  
Among the SPWS devices good measurement agreement was obtained. It should also be noticed that no significant difference is obtained between the measurements made by sensors inside (device 3) or outside (device 1) the box, validating the air circulation in the sensor area of the PCB.  
Figure 8: Tmote Sky-based sensors and SPWS-based nodes.  
For relative humidity (Figure 10), only slight differences for the sensors inside the boxes are obtained, perhaps due to the boxes isolation, a similar trend being achieved among all sensors. The maximum offset among the SPWS devices is around 4%, perfectly within the accuracy margin specified for the sensor. This means that also for relative humidity, the sensor developed may be used inside the box to provide reliable relative humidity measurements.  
Figure 10: Relative humidity recorded by Tmote Sky devices and the SPWSs developed.  
Figure 9: Air temperature recorded by the Tmotes and the SPWS.
## 4 Evaluation
### Baseline Algorithms  
This section presents the details of the baseline algorithms used for NIDS; namely, DT, XGBoost, and RF.  
* Decision Tree (DT)  
The DT algorithm is a supervised tree-based method. It can be used for regression or classification tasks. DT creates and trains the model to predict the target's class value by learning simple tree rules. It starts from the first or the root node in the tree graph, compares the nodes' values with the given input, and searches in depth until it finds the required answer. DT can be constructed from the root node, which represents the entire record. The splitting process, which is the technique that makes the DT, works with it. The third decision node represents the node that has been chosen by the model after one iteration selection -- the fourth terminal node, which is the last level in the tree or the sub-tree. The ID3 metric is used to build the tree. This algorithm uses the entropy function and information gain as a metric, as shown in the following equation.  
\[H(S)=\sum_{c\in C}(-P(c)\log_{2}P(c)) \tag{2}\]  
Here, \(S\) is the current dataset for which the entropy is being calculated. \(C\) is the set of classes in \(S\), \(C=Yes\), \(No\). \(P(c)\) is the proportion of the number of elements in class c to the number of elements in set \(S\). Using the ID3 and entropy metrics, the attributes are chosen to split the decision node. The smallest entropy is the first candidate to be chosen and split.  
* XGBoost  
The XGBoost [38] algorithm is a tree-based method. It uses a gradient-boosting framework. It can be used in regression and classification-ranking tasks on a tabular or structured dataset, which fits our research. Using the depth-first search approach, XGBoost builds the trees and optimizes the gradient, using parallel processing criteria. It uses a regularization term penalty to avoid overfitting/bias phenomena during the training process. For a given dataset with n examples and m features (x,y), a tree ensemble model uses K additive functions to predict the output, as follows.  
\[Y=\sum_{k=1}^{k}f_{k}(x_{i}),f_{k}\in F \tag{3}\]Here, \(F\) is the space assigned to regression trees. XGBoost tries to optimize and minimize the loss function using the following regularized objective.  
\[I()=\sum_{i}^{k}I(Y_{true},Y_{predicted})+\sum_{i}^{k}\Omega(f_{k}) \tag{4}\]  
\[\Omega(f_{k})=\gamma*T+\frac{1}{2}\lambda|W|^{2} \tag{5}\]  
where \(\gamma*T\), is the complexity penalized term of the model and \(\frac{1}{2}\lambda|W|^{2}\) is the regularization term penalty.  
* Random Forest (RF)  
RF is a supervised ensemble machine learning algorithm. The ensemble approach is used when multiple machine learning algorithms are used and combined. RF is constructed from multiple decision trees, which may achieve more accurate and stable results. RF is trained using bagging methods; it can be used in classification and regression tasks.
## 3 Results and Discussions  
### Temperature Evolution in the Simulation  
Figure 3 shows the temperature evolution of the system during the simulation at different target temperatures (\(T=4000\) K, 8000 K, 16,000 K, 32,000 K). The modeling was conducted in two stages: (1) a thermalization stage in the NVT ensemble using Langevin dynamics, and (2) ion dynamics in the NVE ensemble, during which physical observables were computed.  
At the initial moment, the system, as a rule, is not in an equilibrium state, i.e., the temperatures of individual particles do not correspond to a target temperature. To bring the system to thermodynamic equilibrium, Langevin equations of motion are applied:  
\[m\frac{d\mathbf{v}}{dt}=-\gamma m\mathbf{v}+\mathbf{R}(t), \tag{11}\]  
where \(m\) is the mass of the particle, \(\gamma\) is the coefficient of friction, which determines the rate of thermal exchange with the virtual heat bath, \(\mathbf{R}(t)\)--random force modeling thermal fluctuations in the system. The MD simulations were performed using ASE, where the value of \(\gamma=0.5\) was used. This corresponds to a balanced regime where the particles do not lose too much energy (as in the case of high friction) and do not remain too 'heated' (as in the case of low friction). In 200 simulation steps, the temperature is stabilized at a level corresponding to the target value.  
After reaching the equilibrium state, the thermostat is switched off, and the system switches to the NVE ensemble. In this mode, the system temperature is maintained naturally by the conservation of the total energy of the system:  
\[E_{\text{total}}=K+U=\text{const}, \tag{12}\]  
where \(K\) is the kinetic energy, \(U\) is the potential energy of the interatomic interaction. Smooth temperature fluctuations around the stationary level confirm the correctness of  
Figure 3: Temperature evolution during the QMD simulations for different target temperatures (\(T=4000\), 8000, 16,000, 32,000 K). In the first 200 steps, the Langevin thermostat with \(\gamma=0.5\) is used to thermalize the system (denoted as the NVT ensemble). After that, the system evolved in the NVE ensemble without an additional temperature control mechanism.  
the chosen modeling time step and the adequacy of the used method of integration of the equations of motion (Velocity Verlet).
# Parallel Makespan Calculation for Flow Shop Scheduling Problem with Minimal and Maximal Idle Time
## 7 Solving Method
### SA Variants and CUDA Implementation
The above algorithm is then extended to a number of jobs \(n\) larger than double the size of a block. This is performed as follows. Multiple blocks are run at once (as many as necessary, given \(n\)) and the prefix sum is computed independently. This will not affect the total complexity, which will remain at \(O(\log n)\)), as long as the GPU has enough parallel resources to run all such blocks in parallel. The problem with independent calculation is that each block \(i\) starts summing from 0 instead of the sum of all previous elements in previous blocks. To fix this, one needs to know the (inclusive) sum of all elements prior to that block, which is obtained as follows. First, Algorithm 2 is slightly modified, such that at the end of the up-sweep phase and before the zeroing of the last element, the value of that last element (which is the total sum of the given input sequence) is stored in another array \(sums\) at index \(i\), which is the number of the block. After executing this modified algorithm for \(k\) blocks, \(sums[i]\) will contain the total sum of elements in block \(i\). A prefix sum is performed again, this time on \(sums\) alone (which is a single block) and the result is stored in yet another array \(incr\). This way, \(incr[i]\) holds the total sum of all elements in the blocks before \(i\). A simple CUDA kernel can now be used to add the value of \(incr[i]\) to every element of block \(i\) for all blocks in time \(O(1)\). It should also be noted that additional arrays \(sums\) and \(incr\) are in global memory, but when the prefix sum kernel is launched, those arrays are only used as the starting input and final output. All other kernel operations were performed using fast shared block memory and local variables. For additional information on efficient prefix sum computation on CUDA, please consult [67] (accessible via PDF). One disadvantage of this approach is that \(sums\) is the size of one block, which limits this method to 512 blocks. This approach thus cannot currently handle problems with more than \(512\times 512=262,144\) jobs. An extension that would enable larger problem sizes is possible, but would likely affect the performance. The job shift scan is similarly implemented to the prefix sum, except the operator \(\oplus\) is employed instead of \(+\). This also means that the additional arrays \(z\), \(z_{sums}\) and \(z_{incr}\) are required. The same global-shared memory and bank conflicts considerations as with the prefix sum apply. Moreover, CUDA is used to update the best-known solution \(\pi^{*}\). Namely, for SAC and SAG variants, one must copy it with a single thread in time \(O(n)\). For the SAP variant, it can be copied in time \(O(1)\) using \(n\) processors via a simple CUDA kernel. Due to this, for SAG, all solutions are kept on the GPU throughout the algorithm and the final best solution is copied back to CPU memory only once at the end of the algorithm.
## 4 Radiation Analysis
### Radiation Simulations Results
#### 4.2.3 Rover on the Lunar Surface  
Two shields were simulated for a representative rover for astronaut transportation on the Moon surface. Initially it was supposed that three-cm-thick rover shielding would suffice for temporary protection, but a one-day GCR simulation together with elevated doses from the SPE of August 1972 pointed towards the need for a supplementary polyethylene layer. The addition of a reasonable layer of polyethylene allows the SPE-induced dose to be brought down by 63% (see Table 5), given the fact that astronauts are only meant to use the rover for immediate transfer back to shelter once a high solar activity alert is received. The literature indicates that constant Sun surveillance allows the prediction of SPEs hours or days in advance due to the difference in the speed at which the electromagnetic information and solar protons propagate [48]. However, some outpost plans are likely to include large field exercises and far sample collection and, with the relatively low speed of rovers on extra-terrestrial terrains, it is advisable to take precautions for radiation protection of the surface transport vehicles and therefore the use of polyethylene is advised. three cm of aluminium shielding will contribute to a large mass of the machine thus limiting further its mobility, on top of low speeds generally available to rovers. Due to reduced mobility and potential SPE alerts, it is strongly advised to enhance the protection with a polyethylene layer, and future work will look into alternative materials to help reduce the rover mass.