esa-sceva/satcom-chunk-collection huggingface dataset:
# A Household Energy Efficiency Index Assessment Method Based on Non-Intrusive Load Monitoring Data
## 2 Household Energy Efficiency System Architecture and Data  
### Energy Efficiency Assessment Framework  
By assessing the energy efficiency of residential users, energy-saving suggestions and power value-added services can be provided to users, which can help power companies exploit power package services according to users' features, assist governments in developing energy-saving and emission reduction plans, and serve as a reference for the dispatching work of electric power companies. Hence, power companies can motivate users to actively participate in grid demand-side management to minimize operating and maintenance costs.  
As shown in Figure 1, the energy efficiency assessment framework for residential users is divided into two modules: data acquisition and energy efficiency service application. Energy service enterprises need to conduct surveys to collect basic household information, including information on permanent household residents, household income, and housing area. Smart meters with NILM technology are used to obtain energy consumption information, and this information is bidirectionally uploaded to a data center for data storage and management through internet, optical fiber, 4G or 5G, power line communication (PLC), or wireless broadband. The energy consumption and user information are evaluated through an energy efficiency service application so as to generate energy recommendationsfor the user. The system is mainly intended for power companies or power selling companies. Power companies can adjust the power sales strategy according to the results.
## 4 Results and Discussion
### Optimization Results
Additionally, the smallest total readout displacement noise was 4.12 \(\text{pm}/\sqrt{\text{Hz}}\), which corresponds to (\(f_{\text{lower}},\)\(f_{\text{upper}},f_{\text{ADC}},f_{\text{PT}},f_{\text{USO}},\)\(m\)) = (3 MHz, 23 MHz, 96 MHz, 94 MHz, 4512 MHz, 0.44). From Equation (4), different \(f_{\text{upper}}\) values correspond to different optimal \(P_{\text{local}}\) values. Therefore, when \(f_{\text{upper}}\) = 23 MHz, \(P_{\text{local}}\) = 1.92 mW, and \(f_{\text{upper}}\) = 24 MHz, we obtain \(P_{\text{local}}\) = 1.99 mW, \(f_{\text{upper}}\) = 25 MHz, and \(P_{\text{local}}\) = 2.06 mW. According to Taiji mission budget, the position noise is 8 \(\text{pm}/\sqrt{\text{Hz}}\)[6], which includes laser frequency noise, readout displacement noise, laser pointing noise, tilt-to-length noise, and so on. Among them, the frequency stability is 30 \(\text{Hz}/\sqrt{\text{Hz}}\), the limit of is laser-pointing noise and tile-to-length noise is 1 \(\text{pm}/\sqrt{\text{Hz}}\)[25], and the readout displacement noise is about 7.5 \(\text{pm}/\sqrt{\text{Hz}}\)[26]. After parameter optimization, the total readout displacement noise is reduced to 4.12 \(\text{pm}/\sqrt{\text{Hz}}\). The sensitivity curve of Taiji program detection limit and after optimization with other noise budgets the same, is shown Figure 4. It can be seen from the figure that the optimized parameters have improved the sensitivity in the range of 10 mHz-1 Hz. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\(f_{\text{lower}}\)} & \multirow{2}{*}{\(f_{\text{upper}}\)} & **Feasible** & \multirow{2}{*}{\(f_{\text{ADC}}\)} & \multirow{2}{*}{\(f_{\text{PT}}\)} & \multirow{2}{*}{\(f_{\text{USO}}\)} & \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(\delta x^{\text{total}}\)} & \multirow{2}{*}{\(P_{\text{local}}\)} \\  & & **(MHz)** & **(MHz)** & **Range** & **(MHz)** & **(MHz)** & **(MHz)** & **(pm/\(\sqrt{\text{Hz}}\))** & **(pm/\(\sqrt{\text{Hz}}\))** \\ \hline
1 & 3 & 25 & 23 & 92 & 90 & 41
## 4 Discussion and Conclusions
### Visual Analysis of Results  
A visual analysis of the examples (Figure 3) showed that the proposed approach allowed for more accurate identification of fine and thin cracks, which traditional three-channel models often ignore or segment with less confidence. This was particularly noticeable for DeepLabV3+, ENet, and FastFCN, which produced sharper crack contours with fewer false-positive regions.  
However, for some models, such as U-Net, adding the probability map did not yield significant improvements and, in some cases, even worsened the segmentation quality. This may be due to the specific structure of U-Net, which may require adaptation of the training process to better utilize the additional information from the fourth channel.
## 2 Literature Review
### Quality of Experience  
[PERSON] defined quality of experience (QoE) as the user's point of view of the whole performance of a system. According to the \"Qualinet White Paper on Definitions of Quality of Experience\" [50], QoE is the degree to which users of an application or service are happy or annoyed. It is based on the user's personality, current state, and expectations for the utility and/or enjoyment of the application service. Moreover, perceived QoE is related to the user's evaluation of the environmental and the personal capabilities in facing them [51]. In addition, [PERSON]'s self-efficacy theory could explain the quality of experience as the users enjoy when using IT may affect users' beliefs about their ability [44]. In fact, the term QoE has been used to describe all these aspects, which ultimately leads to the acceptability of the system, application, or service. In short, QoE can describe well the acceptability level of students using the Internet as media to learn English from all aspects.  
QoE has been applied in learning research in recent years. For example, different levels of academic self-efficacy beliefs in students has been investigated in regards to their learning activities and associated QoE. The high self-efficacy students had proved enjoyment experience in learning with higher academic aspirations [52]. Then, [PERSON], [PERSON], [PERSON] and [PERSON] verified the impact of system quality, course quality, and service quality students' continuance intention to learn significantly in MOOCs classes [53]. Moreover, [PERSON] et al.'s research identified that QoE of using videos before classes had a positive impact on perceived helpfulness in the flipped classroom method [46].  
In short, while students learning English through internet-based resources, their CQ level, English self-efficacy, and perceived hedonic motivation would influence their QoE of Internet-based resources English learning. Since the English learning content is highly related to western culture, students' CQ levels will impact their Internet QoE, obviously. Then, with better English self-efficacy, students will be able to have higher QoE while learning English through the internet-based resources. After that, hedonic motivation always is related to students' internet QoE. Therefore, three hypotheses can be proposed in the following list.  
**Hypothesis 1 (H1).**_EFL students' cultural intelligence has a significant and positive influence on the quality of experience on the internet_.  
**Hypothesis 2 (H2).**_EFL students' English self-efficacy has a significant and positive influence on the quality of experience on the internet_.  
**Hypothesis 3 (H3).**_EFL students' hedonic motivation has a significant and positive influence on the quality of experience on the internet_.
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 4 Proactive Service Caching Method
### Collective Service Popularity Representation  
When a sequence of video frames is given, the ConvLSTM model can predict the next frame. Specifically, the ConvLSTM model predicts the value of each pixel in the next frame by considering the spatio-temporal correlation in a set of past video frames. To take advantage of this feature of the ConvLSTM model, we construct a heatmap representing the service popularity distribution over a MEC system during a time slot and use it as an input to the ConvLSTM model. Specifically, each MECS \(m\) maintains a set \(Z_{m}(t)=\{b_{m,1}(t),\ldots,b_{m,K}(t)\}\) during a time slot, where \(b_{m,k}(t)=\sum_{u\in U_{m}(t)}b_{m,u,k}(t)\). At the end of each time slot, a controller collects \(Z_{m}(t)\) from each MECS \(m\in\mathcal{M}\). Then, a controller calculates \(f_{m,k}(t)\)s for all \(m\in\mathcal{M}\) and \(k\in\mathcal{K}\) at the end of each time slot as follows.  
\[f_{m,k}(t)=\frac{b_{m,k}(t)}{\sum_{k\in\mathcal{K}}b_{m,k}(t)}. \tag{8}\]  
By considering each \(f_{m,k}(t)\) as the \((m,k)\)-th pixel in the heatmap image, a controller constructs a \(M\times K\) grayscale heatmap image with the set of \(f_{m,k}(t)\)s.  
Since each \(f_{m,k}(t)<<1\) in general, it is not suitable to directly use \(f_{m,k}(t)\)s for training a ConvLSTM model. To enhance the model training performance, we normalize \(f_{m,k}(t)\)s in each MECS \(m\) as follows. At the end of each time slot, a controller derives the popularity of the most popular service in each MECS \(m\), \(f_{m}^{M}(t)=\max_{k\in\mathcal{K}}\{f_{m,k}(t)\}\). Then, the controller normalizes \(f_{m,k}(t)\) with \(f_{m}^{M}(t)\) as  
\[\
u_{m,k}(t)=\frac{f_{m,k}(t)}{f_{m}^{M}(t)}. \tag{9}\]  
Then, a controller obtains a normalized service popularity vector for a MECS \(m\) during a time slot \(t\) as follows.  
\[\
u_{m}(t)=\{\
u_{m,1}(t),\ldots,\
u_{m,K}(t)\}. \tag{10}\]  
Therefore, at the end of a time slot \(t\), a controller constructs a heatmap image for a MEC system as follows.  
Figure 3: Service cache decision procedure.  
\[\Omega(t)=\begin{bmatrix}v_{1}(t)\\ \dots\\ v_{m}(t)\\ \dots\\ v_{M}(t)\end{bmatrix}=\begin{bmatrix}v_{1,1}(t)&\dots&v_{1,K}(t)\\ \dots&\dots&\dots\\ v_{m,1}(t)&\dots&v_{m,K}(t)\\ \dots&\dots&\dots\\ v_{M,1}(t)&\dots&v_{M,K}(t)\end{bmatrix} \tag{11}\]
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 3 Microbial H\({}_{2}\)-Bio-H\({}_{2}\) Generation in DORs
### Reservoir Engineering Aspects
#### 3.2.1 Mode of Application  
A solution of carbohydrate (which may be supplemented with other feeding chemicals) is injected into the selected DOR with various degree of depletion. A bioreactor is expected to form in the reservoir at a distance from the well, which depends on the incubation time, injection rate, reservoir geometry, and petrophysics. Based on laboratory studies, which have defined incubation times of less than 10 days, and the injection rates that are mostly applied, the distance of the bioreactor from the injector cannot exceed 10-20 m. During the process, H\({}_{2}\) is generated, along with other reaction products such as organic acids, CO\({}_{2}\), and H\({}_{2}\)S. The H\({}_{2}\) can be separated from the gas stream downhole or at the surface. The CO\({}_{2}\), which is more soluble in water and oil compared to H\({}_{2}\), as shown in Figure 2, tends to remain in the reservoir if not produced alongside oil and water [44; 45]. The process is a derivative of MEOR, which means that a portion of the remaining oil after the primary and secondary recovery phases can be mobilized. This can increase economic figures, but it raises questions about separating produced gases from the oil at the surface. Additionally, there are potential negative environmental impacts of co-producing a carbon-intensive fluid like oil, especially if it is utilized at a later stage. From a technical standpoint, the process is similar to EOR analogies used in the oil industry, which means that it has a TRL of 7-8.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Carbon Source** & **Experiment** & **Max H\({}_{2}\)** & **Microorganisms** & **Ref.** \\
**Used** & & **Yield 1** & \\ \hline Glucose & Bottle, atm & 4.08 & _Escherichia coli_ & [43] \\ Glucose & Bioreactor, atm, 35 \({}^{\circ}\)C & 1.65 & _Clostridium_ species & [25] \\ Sucrose & Bioreactor, atm, 35 \({}^{\circ}\)C & 4.52 & _Clostridium_ species & [25] \\ Sucrose & Bottle, atm & 2.98 & _Clostridium butylcium_ & [28] \\ Glucose & Bioreactor, atm, 32 \({}^{\circ}\)C & 3.80 & _Clostridium pasteuranium_ & [31] \\ Glucose & Bottle, atm, 70 \({}^{\circ}\)C & 1.10 & _Thermotoga_ strains & [11] \\ Crude oil & Bottle, atm, 70 \({}^{\circ}\)C & 0.10 & _Thermotoga_ strains & [11] \\ Glucose + crude oil & Bottle, atm & 1.65 & _Thermotoga_ strains & [11] \\ Glucose & Bottle, atm, 37 \({}^{\circ}\)C & 1.12 & Reservoir community 2 & [12] \\ Glucose & Bottle, 16 bar, 37 \({}^{\circ}\)C & 0.70 & Reservoir community 2 & [12] \\ \hline \hline \end{tabular}  
* In mol/mol carbohydrate. 2 Community containing _Halanerobium_.  
\end{table}
Table 2: Results of H\({}_{2}\) generation through DF at laboratory scale from selected references.  
Figure 2: Comparison of the solubilities of CO\({}_{2}\) and H\({}_{2}\) in brine and oil at T = 100 \({}^{\circ}\)C. Data were compiled from [44; 45; 46; 47; 48].
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 6 Validation of the MC-NOMA Simulator  
Validation of the simulator and proposed algorithms is an important issue. Different methods have been adopted in the literature to validate the correctness of the proposed software suit. As with [10], the authors compared their findings with the 3 GPP minimum performance requirements. However, this technique cannot be applied to our scenario. This is because research on NOMA-based systems is still at an early stage and has not yet been standardized. Thus, we cannot compare the performance of our simulator with that of the standard. Therefore, we have adopted a strategy of comparing our results with the corresponding published papers from other authors and from our laboratory to check the authenticity of our proposed simulator. The research in [26] verifies our NOMA simulator that the code-word-level SIC is better than symbol-level SIC. The enhanced performance of code-word-level SIC is caused by channel decoding being performed during signal detection. Consequently, the impact of the error propagation can be greatly minimized. However, detection will take longer, and channel decoding and re-encoding add computing complexity.
# A Decentralized Multi-Agent-Based Approach for Low Voltage Microgrid Restoration
## 3 Modeling of the Microgrid Components
### Microsource Modeling  
There are several dynamic models for microsources in the literature. To model the PV cell, this paper uses the single-diode model or five-parameter model [13]. This model provides an adequate trade-off between simplicity and accuracy. A PV system is commercially available in the form of modules in which there is a number of series cells. The modules are connected in series to make a string with an appropriate voltage level. While, to increase the current rating, the strings are connected in parallel and form an array. In [37], the model of the PV array based on the five parameter model is found. In this paper, it is assumed that the PV arrays work at the maximum power point.  
The WESS model used in this study is based on the constant speed wind turbine that is available in [38]. Likewise, regarding the period of study, only the average value of the wind speed is considered (i.e., the wind speed is constant).  
To model the dynamic behavior of the microturbine, the gas turbine (GAST) model [39] is used. There are two types of the microturbine including single-shaft microturbine (high-speed) and split-shaft microturbine (low-speed). In the single-shaft microturbine, the turbine speed range is from 50,000 to 120,000 rpm. So, this type of microturbine requires an AC/DC/AC converter for connecting to the grid. The split-shaft microturbine uses a power turbine that is rotated at 3600 rpm and can be connected to a conventional induction generator using a gearbox [39].  
Reference [40] provides a basic dynamic model for a solid-oxide fuel cell (SOFC) that is used in this paper. This model has some assumption to achieve an integrated dynamic model for using in the power systems simulations.
# Study on the Effect of Rock Mass Structure on CO2 Transient Fissure Excavation
## 2 Theoretical Analysis
### Control of Rock Mass Structure  
In general conditions, the rock structure effect existing in the field of explosive rock breaking is also applicable to the transient cracking process of carbon dioxide. The rock of massive structure is classified as the rock with difficulty in blasting. With the increase of the crushing degree of rock structure, the cracking difficulty of rock decreases accordingly. The dynamic load impact effect at the first stage would decrease to some extent, and the shock wave generated in the transient cracking role of carbon dioxide would unavoidably speed up the attenuation velocity due to the increase of the quantity of structural surface. However, with the increase in the quantity of structural surfaces, the subsequent high-pressure gas swelling role at the second stage would generate a better effect at the expansion-break-through stage of the fissure. Of course, a larger quantity of structural surfaces certainly would not generate a better effect. When the rock is crushed to a certain degree, the transient cracking role of carbon dioxide will cause the inside of the rock to rapidly generate the fissure connecting the free surface. It is too late for the high-pressure swelling gas at the second stage of transient cracking to act on the rock, and then the high-pressure swelling gas would release in the connecting fissure, so the cracking effect would decrease accordingly.  
To sum up, the transient cracking of the carbon dioxide effect would tend to increase at first and then decrease with the development of the rock's structural surface and the increase of the crushing degree of rock structure. The crushing degree of rock controls the cracking effect at the critical point with the optimal cracking effect.
## 5 Further Analyses and Discussion: Towards the Unwanted Chain of Errors Avoidance
This, unwanted chain of errors leading to potential hazards has been found, especially in this instant of time, to pose a serious threat to the navigation venture. Figure 11: The model of ENC cause-and-effect chain of adverse events. Source: Authors. Subsequent findings can be elaborated as follows:  
* the trend of digital means' acceptance has increased, as expected,
* even though the system has been generally accepted among the end-users, there have still been specific issues which the system users have been pointing out, with similar key points confirmed by the respective organisations,
* like any other technical instrument, the system is prone to failures, what has been expectedly recognised by the end-users,
* the system users have identified the handling-independent problem of the ENC coverage,
* the ENC updates are dependent on satellite communication systems and internet availability,
* the quality of the displayed ENC information differs, depending on their depth accuracy level,
* in certain instances, the PNCs can offer a better overview of some navigable waters (coastal navigation, port approaches) due to their size,
* besides handling the system and familiarizing with, it is necessary to obtain, gain, and maintain a certain level of knowledge and skills to operate with the system properly,
* a problem of overreliance can emerge in terms of exaggerated confidence in the system,
* there are still OOWs without system-approved equipment specific training. * opinions regarding PNC removal from the service widely differs between research participants,
* a recognized and justified need to incorporate traditional navigational means with modern navigational means to contribute to the safety of navigation has appeared. Modern technology has speeded up the process of navigation planning, and facilitated easier monitoring of the vessel's movement and navigation. Even though most of the respondents agreed about the PNC removal, it would be prudent not to disregard suggestions from traditional navigation advocates. According to the presented analyses, a minimum set of PNCs should be kept on board as a back-up arrangement in a case of emergency as a worst-case scenario, especially when taking into consideration the _ECDIS EHO_ participants' legitimate comments and concerns about issues on the system. Those have been addressed not only as system crash downs, screen freeze, and issues related to system and ENC updates but also as the announcements of certain hydrographic offices regarding the PNCs removal from the service [26]. There has been more than one-fourth of the respondents--_after_ the transitional period completion--who have still been favouring the PNCs. As for navigational charts, the potential problem is standing in front of the considerable number of modern seafarers who have still been stranded in the traditional navigation era.
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 3 A Cooperative Multi-Agent RL for Single-Satellite Beam Hopping  
Because dynamic beam hopping satisfies the characteristics of MDP, this chapter will introduce the construction process of the MDP model for power allocation and beam hopping. Aiming to achieve multi-beam hopping in LEO satellites, we apply a MARL-VDN algorithm architecture and introduce the algorithm.
## 2 Processes of Constructing Extended SAC Labels
From (2), any two labels with different indexes \(j\) are orthogonal and have zero correlation value. For any two labels with the same index \(j\), the correlation properties are similar to the ones of MLS codes. The cross-correlation of the extended SAC labels is defined as follows:  
\[\lambda\ =\ \frac{1}{MN}\sum_{l\ =\ 1}^{M}\sum_{k\ =\ 2}^{N}\mathbf{E}(1,1) \odot\mathbf{E}(k,\ l) \tag{3}\]  
Figure 1 shows the average correlation of the extended SAC labels for different code lengths. For similar \(L\), \(\lambda\) is decreased with \(M\). The larger \(M\) is, the more zeros are included in the code vectors, so the overlapping chips between two labels are reduced. For cross-correlation expressions, extended SAC and MLS labels are similar, where \(\lambda=(N+1)/4\) and \(\lambda=(L+1)/4\), respectively. As \(N\) is always smaller than \(L\) when \(M\geq 2\), the extended labels (\(M=2\), \(4\)) have a smaller \(\lambda\) than that of MLS labels (\(M=1\)). For example, \(\mathbf{E}(1,1)\) and \(\mathbf{E}(1,2)\) in Table 1 have \(L=6\) and \(\lambda=2\), respectively, while the cross-correlation of MLS codes with \(L=7\) is double (\(\lambda=4\)).