esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Trading Strategy for Heat and Electricity-Coupled Microgrid Based on Cooperative Game
## 3 Model of Energy Consumer
### Utility Model of Consumer  
In this paper, Users can profit through electrical energy consumption rather than just pay for it. Especially for industrial and commercial prosumers, their profits increase with the growth of energy consumption [20]. Therefore, the user's utility in the paper mainly considers the utility from energy consumption, the expenditure of buying electric/thermal energy, and the cost of comfortable dissatisfaction caused by reduced heat supply. And the objective function of user \(i\) can be expressed as:  
\[\max\,U_{i}=\sum_{h=1}^{H}\left[k^{h}_{i}\ln(1+el^{h}_{i})-p^{h}_{i}el^{h}_{i}- \lambda^{h}_{i}tl^{h}_{i}-\delta_{i}\Delta hl^{i}_{i}2\right] \tag{6}\]  
where, \(k^{h}_{i}\ln(1+el^{h}_{i})\) is the utility that user \(i\) achieves from electrical energy consumption \(el^{h}_{i}\) in time slot \(h\). \(k^{h}_{i}\) is the preference parameter, which reflects the energy consumption characteristics of user \(i\). The larger \(k^{h}_{i}\) means more willing users maximize profits through energy consumption. The logarithmic function \(\ln(\cdot)\) has been widely used in the economics for modeling the behavior preferences of users, which is closely related to utility function and leads to proportionally fair DR [21]. \(p^{h}_{i}\) and \(el^{h}_{i}\) are the electrical bargaining price and electrical energy consumption between user \(i\) and MGO, respectively. \(\lambda^{h}_{i}\) and \(tl^{h}_{i}\) are the thermal bargaining price and thermal energy consumption of user \(i\) to MGO, respectively. \(\delta_{i}\) is the cost factor of a comfortable temperature. According to the energy policies of some countries and cost constraints, \(p^{h}_{i}\) and \(\lambda^{h}_{i}\) should be within a limited range  
\[p^{h}_{gb}\leq p^{h}_{i}\leq p^{h}_{gs} \tag{7}\]  
\[\lambda^{\text{min}}_{s}\leq\lambda^{h}_{iM}\leq\lambda^{\text{max}}_{s} \tag{8}\]  
where \(p^{h}_{gb}\) and \(p^{h}_{gs}\) are the buying price and selling price of the power grid in time slot \(h\), respectively. \(\lambda^{\text{min}}_{s}\) and \(\lambda^{\text{max}}_{s}\) are the lower and upper limits of thermal price, respectively.
## 2 Materials and Methods
### Methods and Analysis
Based on the changes in seasonal distributions of GPP and NEE, six different months (Apr, May, Jun, Aug, Sep, and Dec) of 2015 and 2018 were used to develop spatial distribution maps of GPP and NEE. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline
**NEE.** & **Apr** & **May** & **Jun** & **Aug** & **Sep** & **Dec** & **GFP (g C m\({}^{-2}\) m\({}^{-1}\))** & **Apr** & **May** & **Jun** & **Aug** & **Sep** & **Dec** \\ \hline \(-175\) to \(-150\) & & 0.01 & \(-0.05\) & \(-0.06\) & & & 0 to 50 & 25.70 & 17.67 & 19.02 & \(-8.15\) & \(-4.82\) & 16.48 \\ \(-149\) to \(-125\) & & 0.05 & \(-0.04\) & \(-0.15\) & & 51 to 100 & \(-11.43\) & 6.21 & 23.75 & \(-21.55\) & \(-15.59\) & \(-15.57\) \\ \(-124\) to \(-100\) & & 0.15 & \(-0.02\) & \(-0.28\) & & 101 to 150 & \(-6.22\) & \(-8.28\) & 2.24 & \(-10.99\) & 21.05 & \(-0.72\) \\ \(-99\) to \(-75\) & & 0.73 & \(-7.94\) & \(-0.57\) & & 151 to 200 & \(-14.80\) & \(-14.04\) & \(-13.65\) & 4.06 & \(-2.48\) & \\ \(-74\) to \(-50\) & & 0.04 & 0.92 & \(-24.17\) & \(-2.49\) & \(-2.55\) & 201 to 250 & 6.40 & \(-8.39\) & \(-19.04\) & 8.46 & \(-1.92\) & \\ \(-99\) to \(-25\) & & 8.42 & 3.38 & \(-13.18\) & \(-13.97\) & \(-17.15\) & & 251 to 300 & 0.34 & 5.75 & \(-11.82\) & 6.28 & \(-1.18\) & \\ \(-24\) to \(-15.92\) & & \(-30.90\) & 32.30 & \(-7.74\) & \(-18.44\) & \(-4.41\) & 301 to 350 & 0.77 & \(-0.66\) & 7.58 & \(-0.20\) & \\ \(11\) to \(25\) & & 10.77 & 24.71 & 30.99 & 25.25 & 36.18 & 5.14 & 351 to 400 & 0.27 & 0.18 & 5.71 & \(-0.01\) \\ \(26\) to \(50\) & \(-327\) & 0.85 & 0.11 & & 2.05 & 0.20 & \(>\)400 & 0.04 & 8.49 & \\ \(51\) to \(75\) & \(-0.04\) & 0.06 & & & & \(-1.10\) & & & & & \\ \(76\) to \(100\) & & & & & 0.17 & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Change in spatial coverage of specified (particular) NEE and GPP ranges between selected months of 2018 and 2015 across Texas. All of the values for respective months are in percentage.
## 5 Experimental Results
Apart from the K-MeansH clustering algorithm, the PMRSE also uses the CF based recommendation system for providing personalized music to recommend to the user. In order to implement the CSMRS, we downloaded the python code available at the github repository [27], whereas, in case of the PMRSE, we modified the python code available at the GitHub repository [28] with respect to the K-MeansH clustering algorithm. For training the EPMRS, we use the data presented in the MSD (Section 4.1). As the MSD is a huge dataset, we selected the songs of nine genres: breakbeat, dancehall, downtempo, drum and bass, funky house, hip hop, minimal house, rock, and trance. From each genre, we selected the top 3000 songs to train the DCNN approach. To train the WFE approach, we used the user data available in the 3000 songs of each genre. To test the music genre classification, we crawled 1000 songs for each of nine genres from the JunoRecords website [29]. We call this dataset the Junorecords dataset. The Junorecords dataset contains the music data but does not contain user data. We randomly selected 100 users data (e.g., user rating on songs, user listening behavior, etc.) from the MSD and associated that users data with music data (e.g., audio signals of songs, the metadata of songs, etc.) of the Junorecords dataset. Once the user data are selected, all methods--the EPMRS, the CSMRS, and the PMRSE--use the selected user data for performance comparison. Table 1 shows the mean accuracy of song recommendations in the nine genres for the 100 users. The experimental results show that the EPMRS gets significant improvement in the song recommendation accuracy except the genres: the funky house and the minimal house as compared with the CSMRS. Although the EPMRS does not outperform the CSMRS in funky house and minimal house genres, the EPMRS still obtains overall improvement than the CSMRS. The performance of the PMRSE approach is lower as compared with the EPMRS and the CSMRS approaches for the nine genres. Unlike the CSMRS and the PMRSE, the EPMRS uses the audio content and the metadata presented in the songs' data to extract latent features for 100 users. Even though the PMRSE approach uses the CF recommendation system, the performance of the EPMRS is better as it uses the WFE approach. The WFE approach of the EPMRS identifies the important latent features from the data and provides weights to those features. Therefore, for each user, the EPMRS maintains a separate weighted users-to-features relationship data. The appropriate songs containing the user's weighted features are recommended to the user. The CSMRS and the PMRSE do not have any mechanism for extracting weighted features with respect to each user. This is also the main reason why the EPMRS shows better accuracy of song prediction than the CSMRS and the PMRSE. In the EPMRS, the user's latent features are updated continuously from the user's music listening behavior. Therefore, the MusicAI can provide better recommendations to the users by continuously learning their music listening behavior.
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 5 Results and Discussion
### The Impact of IRSs on Connection Time  
The performance of C-MAB with and without IRS is also shown in Table 2. We assume that an IRS can reflect one of the beams from a BS in a 180 degree perimeter based on its orientation, which is fixed, providing coverage to one vehicle. Even though the IRS is passive and thus incurs in more path loss, when strategically placed they achieve more than a doubling in average connection time with the traditional greedy approach. This is because it is difficult for a BS to be in LoS with more than one optimum connection area, such as the waiting zone of a traffic light or a blind spot. Therefore, by using the IRS as a \"mirror\", these spots become \"visible\" to the BS. When paired with a C-MAB algorithm, the new connection options can be more intelligently exploited, resulting in more than a quadrupling of connection time. The proposed algorithm achieves this result by \"memorizing\" the conditions that lead to the longest and shortest connection times. Therefore, we try to avoid connections to vehicles that find themselves in conditions that might lead to short connections and choose instead those vehicles with conditions prone to long connections.
## 3 Results and Discussion  
### Monitoring Culture Medium Selection  
Cultures of two or more microorganisms require specific methodologies to differentiate one species from the other. For this, techniques such as flow cytometry [30; 31; 32] and procedures based on molecular biology [33] have been used. However, an alternative is plate counting using differential and selective media. This work used Wallerstein agar and two media based on MGYP agar.  
The antimicrobial properties of crystal violet have been known for a long time [34], but nowadays, it is mainly used in the textile industry [35]. Its use has been suggested to avoid cross-contamination of beers [36] and control yeasts associated with food production [37]. This work tested the usefulness of crystal violet for the recovery of _S. cerevisiae_ in co-culture with non-Saccharomyces yeasts. The concentrations of 0.001% and 0.0005% completely inhibited the growth of _T. delbrueckii_ and _Z. bailii_. In comparison, a concentration of 0.0001% caused an inhibition of close to 96% in both yeasts. Otherwise, all three concentrations allow the development of _S. cerevisiae_. At a concentration of 0.0001%, 100% of _S. cerevisiae_ grows, the concentration of 0.0005% causes inhibition of 2.5%, and, finally, a concentration of 0.001% inhibits the growth of _S. cerevisiae_ by 38%.  
On the other hand, the selective medium MGYP+CuSO\({}_{4}\) completely inhibits the development of _S. cerevisiae_, and the recovery of non-Saccharomyces yeasts varies depending on the species. For _Z. bailii_, an inhibition of 6.7% was observed, and _T. delbrueckii_ showed an inhibition of 44.8%. Moreover, in addition to inhibiting _S. cerevisiae_, this medium allows the differentiation of the other two yeasts. The _T. delbrueckii_ colonies presented a phase coloration in the colony's center, while _Z. bailii_ grows as white colonies. Figure 1 shows the growth of _S. cerevisiae_ and _T. delbrueckii_ in co-culture in the M2 medium, differentiating both yeasts using the MGYP+CuSO\({}_{4}\), MGYP+CV, and WLN media.  
Figure 1 shows that the MGYP+CuSO\({}_{4}\) medium exhibited slightly lower viable cell counts of _T. delbrueckii_ compared to the WLN medium. Nonetheless, it is evident that MGYP+CV and MGYP+CuSO\({}_{4}\) are two low-cost media compared with the WLN medium for the appropriate differentiation of the yeasts tested here. Consequently, they allow the individual monitoring of each population's development during the co-culture of these three yeasts. Regarding the interaction between the yeasts, _T. delbrueckii_ coexists without problems with _S. cerevisiae_ during the first 12 h of culture. However, co-culture likely triggers a response from _S. cerevisiae_ that cannot tolerate _T. delbrueckii_ since, in the following 12 h, the latter's viability decreases significantly. This agrees with what [PERSON] et al. [38] reported. These results led to the selection of MGYP+CV and MGYP+CuSO\({}_{4}\) media to survey the binary and ternary mixed cultures.
## 6 Results
### ML-TZSA Pre-Processing Results  
Figure 7 (left) shows the pre-processing times required to complete the tasks of Sections 4.1 and 4.2 for different transfer-zone sample sizes. For transfer-zone sample sizes of 50 and above, pre-processing times increase linearly with transfer-zone sample size. This is because the number of calls of [PERSON]'s algorithm required for calculating open transport network inter-transfer-zone objective criteria contributions is equal to the number of transfer zones. The task of generating the transfer zones is only dependent on the size of the pedestrian transport network and not the number of transfer zones. For low transfer-zone sample sizes, the task of calculating minimum connection times for consecutive public transport legs takes a longer amount of time because there are more possible public transport connections in larger transfer zones. The calculation of inter-transfer-zone objective criteria contributions in the public transport network takes the least amount of the total pre-processing time because public transport routes are pre-defined and the public transport network is generally a fraction of the size of any of the open transport networks.  
Figure 7 (right) shows the portions of all inter-transfer-zone journeys that are actually possible in each transport network. These values provide a measure of the connectivity of each transport network. For the case of pedestrian, bike and car networks, approximately 0.8 of all inter-transfer-zone trips are possible, independent of transfer-zone sample size. This reflects the fact that the Isle of Wight is not connected to mainland Britain via these networks. The e-scooter network, which is a subset of the bike network, has the lowest level of connectivity, which is because of the city-based geofence constraints on the use of hireable e-scooters. For the public transport network, the connectivity appears to decrease with increasing transfer-zone sample size, which is because smaller transfer-zones each contain fewer public transport stops and therefore fewer ways to reach other transfer zones. The connectivity of the public transport network is least accurately judged on this criterion because the real connectivity of the public transport network relies on transfers, where that of the open transport networks does not. Multi-modal journeys facilitate a connectivity level greater than that of any of the individual constituent transport networks.  
Figure 7: (**Left**) Pre-processing times and their components. (**Right**) The connectivity of different transport networks measured as the portion of all inter-transfer-zone trips that are possible.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 4 Application to GPS and Galileo Signals
### Comparison of GPS L1 C/A and L5 Signals Acquisition
#### 4.2.2 Determination of Ratio of Complexity for One Frequency Bin
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Implementation** & **T\({}_{\text{TT}}\)** & \begin{tabular}{c} **Input Memory** \\ **(bit)** \\ \end{tabular} & \
\begin{tabular}{c} **Total Memory** \\ **(bit)** \\ \end{tabular} & **Memory Ratio** &
\begin{tabular}{c} **Product of** \\ **Ratios** \\ \end{tabular} \\ \hline L1 C/A Parallel NC & 460 & 942,080 & 3,727,360 & & \\ L5 Parallel NC & 320 & 6,553,600 & 33,996,800 & 9.1 & 97.6 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 20,275,200 & 5.4 & 116.2 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 18,903,040 & 5.1 & 162.3 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 17,520,380 & 4.7 & 150.6 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 16,158,720 & 4.3 & 138.8 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 14,786,560 & 4.0 & 169.3 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 13,414,400 & 3.6 & 153.6 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 12,042,240 & 3.2 & 172.3 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 10,670,080 & 2.9 & 213.6 \\ L5 Semi-parallel NC & 320 & 6,553,600 & 9,297,920 & 2.5 & 265.9 \\ L5 Serial NC & 320 & 6,553,600 & 7,925,760 & 2.1 & 453.2 \\ L5 Parallel C & 146 & 9,990,080 & 22,650,880 & 6.1 & 29.8 \\ L5 Semi-parallel C & 146 & 2,990,080 & 12,820,480 & 3.4 & 33.6 \\ L5 Semi-parallel C & 146 & 2,990,080 & 11,837,440 & 3.2 & 46.5 \\ L5 Semi-parallel C & 146 & 2,990,080 & 10,854,400 & 2.9 & 42.6 \\ L5 Semi-parallel C & 146 & 2,990,080 & 9,871,360 & 2.6 & 38.7 \\ L5 Semi-parallel C & 146 & 2,990,080 & 8,888,320 & 2.4 & 46.5 \\ L5 Semi-parallel C & 146 & 2,990,080 & 7,905,280 & 2.1 & 41.3 \\ L5 Semi-parallel C & 146 & 2,990,080 & 6,922,240 & 1.9 & 45.2 \\ L5 Semi-parallel C & 146 & 2,990,080 & 5,939,200 & 1.6 & 54.3 \\ L5 Semi-parallel C & 146 & 2,990,080 & 4,956,160 & 1.3 & 64.7 \\ L5 Serial C & 146 & 2,990,080 & 3,973,120 & 1.1 & 103.7 \\ L1 C/A Parallel + assist. & 400 & 819,200 & 3,604,480 & & \\ L5 Serial NC + assist. & 260 & 5,324,800 & 6,696,960 & 1.9 & 22.7 \\ L5 Serial C + assist. & 120 & 2,457,600 & 3,399,680 & 0.9 & 4.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Memory requirements (bit) of the different implementations for a sensitivity of \(-\)150 dBm with average losses, considering the input memory. Ratios are computed with the values of the L1 C/A signal. Figure 12: Ratio of complexity (product of processing time ratio and memory ratio) between the L5 and L1 C/A signals. (**Left**) without assistance, (**right**) with assistance. Solid curves do not consider the input memory, while dashed curves consider the input memory.
# Slipping Trend Prediction Based on Improved Informer
## 4 Experiment  
In order to prove the feasibility and effectiveness of the slipping classification prediction method based on Informer, a large number of experimental analyses of locomotive operation data were carried out. The HXD2 locomotive (Figure 6) was used as the test locomotive. Its parameters are shown in Table 1. The test line section was Xinfengzhen Station-Ankang East Station, within the jurisdiction of Xi'an Railway Administration. The gradient of the selected experimental line ranges from 6 to 12% and features continuous uphill and downhill sections; that is, we selected sections where the locomotive would be prone to slipping. The experimental data were actual operation data collected by the TCU. There is a data acquisition unit in the TCU of a locomotive, which can continuously collect data over several months, and these data can be applied for control, analysis, and maintenance.  
The dataset comprised approximately 240,000 samples, with around 30,000 samples corresponding to slipping states. The data were partitioned into a training set and a testing set, constituting 80% and 20% of the total dataset, respectively. During this period, due to interference from the environment, it was expected that there would be missing or distorted sampling of the data, and the data used in the model training and testing were cleaned. Even if there are some disturbances, since the proposed model contains many dropout layers, it is robust and can effectively prevent the overfitting problem caused by these disturbances. Therefore, the impact of these disturbances on training and testing is small, and can be ignored.
## 3 Results and Discussion
### SEM, TEM, and EDX Analysis  
The morphological characteristics of ZnO-NPs biosynthesized using _C. chinense_ fruit extract is shown in Figure 2. In the SEM micrograph, the lower magnification of the NPs showed that they are composed of a mixture of rice-shaped and spherical morphology (Figure 2a). However, a higher magnification micrograph (Figure 2b) presents a clearer image, showing that the spherical particles were agglomerates of the smaller sized rice shaped particles. This agglomeration might be ascribed to the effect of the high temperature of calcination, which increased the surface reactivity [52]. The TEM images of ZnO-NPs (Figure 2c) approve that the products were indeed spherical in shape and the particle length were observed to be 12.7 nm (Figure 2d). The EDX spectrograph of ZnO-NPs (Figure 2e) revealed zinc (Zn) and oxygen (O) peaks only, which confirmed the presence of zinc in the oxide form. Apart from Zn and O, there were no other elements found in the NPs sample. The EDX results, presented in Table 1, also showed the composition of each element. The weight percentage of Zn and O was found to be peaked at 77.51% and 22.49% with atomic weight percentage of 45.93% and 54.07%, respectively. The elemental mapping (Figure 3) showed that all component elements were uniformly distributed across the surface of the NPs. Furthermore, the ZnO-NPs were also synthesized conventionally without the use of fruit extract in order to evaluate the influence of _C. chinense_ fruit extract on the particle size and morphology. The SEM micrograph (Figure 4a) showed that the ZnO-NPs were also spherical but at a higher degree of agglomeration. This was also confirmed by the TEM image of Figure 4b, which presents highly agglomerated pseudo spherical nanoparticles with a mean particle size of about 24.0 nm (Figure 4c). The increase in size and higher agglomeration in the sample prepared without plant extract, indicated that the extracts also play the role of size modulation.  
Figure 1: XRD analysis of ZnO-NPs biosynthesized using _C. chinense_ fruit extract.  
Figure 2: SEM images at (**a**) low, (**b**) high magnification, (**c**) EDX spectrum, (**d**) TEM image, and (**e**) particles distribution histogram of ZnO-NPs biosynthesized using extract of _C. chitines_ fruits.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Elements** & **Weight \%** & **Atomic \%** \\ \hline Zinc & 77.51 & 45.93 \\ Oxygen & 22.49 & 54.07 \\ Total & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental composition of Zn and O.
# System for Indoor Comfort and Health Monitoring Tested in Office Building Environment
## 3 Methodology of the Proposed IoT System Development
### Solution of the Smart IoT System for Monitoring of Indoor Comfort and Health
The MiCS-VZ-89 TE module is supplied with 3.3 V and has a maximum current of 40 mA, being provided with I2C communication and PWM output. As mentioned above, the Sensirion SCD30, Sensirion SFA30 and MiCS-VZ-89 TE modules were configured to use I2C communication, which requires two wires, with each module having a distinct address. The PMS5003 particle sensor needs a DC supply voltage in the [4.5 V; 5.5 V] range and requires 100 mA. It is also provided with UART communication, with logic on 3.3 V.  
An example of indoor condition information gathered by all of the sensors connected to the Arduino board (temperature, relative humidity, TVOC, HCHO, CO\({}_{2}\)) can be seen on the developed dashboard shown in Figure 3. At this preliminary stage, the other parameters provided by the RadonEye Plus 2 and the Netatmo smart home weather station are independently monitored by the user. In the following stages, there is a more user-friendly IoT system envisaged in which all the data are integrated through a Node-RED.