esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A simulation study of NH listeners measured syllable recognition in a noisy environment, using both Fixed-Channel and Channel-Picking coding schemes. The results were also compared with CI users' performances. CI users were divided into two groups corresponding to the coding schemes available. Twenty NH subjects and 45 CI users participated in this experiment. The acoustic material was the Fournier French dissyllabic lists mixed with a cocktail-party noise.  
The results obtained in the simulation with the NH subjects indicated an advantage of the fixed-channel strategy over the channel-picking coding in a middle SNR range (from 0 to +6 dB); parameters (patients, technology and protocol) were well controlled in this approach. This trend was confirmed using the sigmoid curve regression. The results seemed similar with the CI users.  
Nevertheless, results were less reliable with CI users, probably due to the wide dispersion in the patients' results. Additionally, an inversion of the coding strategy was seen with high SNRs, with CI users. This aspect should be examined in the future, considering its practical application and we need to consider the physiological and electrical phenomena involved in a multichannel stimulation such as channel interaction. Simulation and tests with CI users are useful as they give two complementary insights into the difficult task of determining an \"optimal\" sound coding strategy to enhance the auditory performance of CI users.  
Conceptualization, C.B.-V., E.T. and S.G.; formal analysis, P.A.C. and S.G.; investigation, P.A.C.; methodology, P.A.C., C.B.-V. and S.G.; resources, E.T.; software, P.A.C. and F.M.; supervision, C.B.-V, E.T. and S.G.; visualization, P.A.C. and C.B.-V; writing--original draft preparation, P.A.C. and C.B.-V; writing--review and editing, P.A.C., C.B.-V, F.M., R.H., E.T. and S.G.  
This research received no external funding.  
The authors would like to thank the people and institutions who participated in this study: [PERSON] who initiated the work, [PERSON] and [PERSON] for their scientific contribution, [PERSON] for contacts with the ethic-committee, the members of the CRIC team of the Edouard Herriot University hospital of Lyon for their collaboration, the normal-hearing subjects and the cochlear implant users who entered the study and the Hospitals of Lyon and the Polytechnic School of Lyon for their administrative support.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Adequate Size and Reliability of the Sample  
To assess the internal consistency and reliability of the collected data, [PERSON]'s alpha coefficient was computed using IBM SPSS software version 26. As shown in Table 11, the [PERSON]'s alpha values for the groups of benefits of using DTs and advantages of integrating AI-based DTs are 0.909 and 0.883, respectively. In addition, values for the group of challenges of using DTs and the group of challenges of integrating AI-based DTs are 0.883 and 0.872, respectively, as shown in Table 11. The value was 0.853 for the group for the resource implementation of AI-based DTs. These values exceed the acceptable level (0.70). This result indicates that the internal consistency of data for the five groups was satisfied.  
Out of the 142 responses received, 106 were completed and analyzed. This sample size is sufficient and reliable [76]. Several attempts were made to expand the sample size and increase participant diversity by sending the survey link to local institutions, including the Saudi Engineering Council, the Contractor Commission, and ACE firms. However, the total number of responses received was 142, of which only 106 were fully completed and analyzed. Figure 2 shows the demographics of the research participants.  
The study involves a complex dataset with multiple variables (17 benefits and 16 challenges) related to DT adoption, 3 benefits and 7 challenges for integrating AI-based digital twins (DTs) in the construction industry, and five resources for enablers of the AI-based DT integration.
## 1 Introduction  
Kedah is one of the northern states in Malaysia that is very well-known for its main and largest river, known as the Muda River. The Muda River flows from the East, channeling through Baling, Sik, and Sungai Petani districts down to lower land until the Strait of Malacca in the west. The water resources in the Muda River basin have been established over the year and consumed for irrigation and as potaband the supplies have been extended to other states like Penang [2] with almost 80% dependency and at the Southern area of Perlis. Freshwater in this basin is very crucial for agricultural activities in Kedah and the industrial sector in Penang, as well as for domestic usage [3].  
A decade ago, Malaysia had sufficient water supplies, however, the country has recently suffered from a shortage of clean water for domestic usage. This is due to increasing consumption and high-water demand exceeding the capacity of the river basin, especially in the escalation of development sectors and the growth trend of agricultural industries. [PERSON] et al. [4] reported that the Muda area experienced a moderately increasing rate of water usage from three major activities from 1984 to 2001, these are land clearing (1.2%), agriculture (1.35%) and urbanization (4%). In addition to the expansion of population density, the issue of water pollution also threatens the supply of clean water to certain areas and is affecting the ecology, especially in Kedah. In 2017, numerous complaints were reported regarding the failure of clean water and water shortage supply to rural areas such as Baling and Sik, Kedah. To overcome the issues, the Malaysian Government embarked on groundwater exploration, exploitation, and sustainability plans to optimally utilize the groundwater reserve.  
What is still not clear, and has yet to be quantified, is the depth estimation of the Muda River basin. Even though there are many methods that can be applied to investigate the subsurface parameters particularly used as input in groundwater basin modeling, geophysical techniques are considered as among the best. However, an appropriate geophysical method to be carried out for an oriented investigation depends solely on the purpose of the study. Therefore, the aim of this study is to utilize the gravity method to investigate the subsurface structure and depths to sources of anomalies through extended gravity analyses, primarily by radially power spectrum and 3D Euler deconvolution. It is used to determine the subsurface properties according to anomalies that indicate the rocks' density variations [5]. This potential method was employed because of its quick-moving survey, extensive coverage area and it is less expensive compared to other geophysical methods [6].
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 4 Numerical Evaluations
### Throughput  
We first show the normalized throughput of different schemes, defined as the average total rank received at the node divided by \(M\). This scales the throughput to the range \([0,1]\). Recall that the throughput in BNC literature only measures the amount of information carried by the packets and does not reflect the decoding time.  
Figures 6 and 7 show the cases when \(M=4\) and \(8\), respectively. The blue plots \"RAP3\" and \"SAP3\" correspond to three-phase RAP and three-phase SAP, respectively. The red plots \"RAP2\", \"SAP2\", and \"heur.\" correspond to two-phase RAP, two-phase SAP, and the heuristic Algorithm 2, respectively. The brown plot \"TAP\" is the fire-and-forget adaptive recoding in [64,73]. The cyan plot \"base\" is the (fire-and-forget) baseline recoding where every batch sends \(M\) recoded packets regardless of the rank. At last, the black dashed plot \"cap.\" is the capacity of the network, which is achievable by hop-by-hop retransmission until successful reception (which omits the resources constraint), i.e., the theoretical upper bound on the throughput of the network.  
Figure 6: The throughput of BNC when \(M=t_{\text{avg}}=4\) with various \(p\). (**a**) \(p=10\%\); (**b**) \(p=20\%\); (**c**) \(p=30\%\).  
From the plots, we can see that when the loss rate \(p\) is small, SAP and RAP achieve nearly the same throughput. The difference between RAP and SAP can be observed when \(p\) becomes larger. The reason is that for SAP, there are too many remaining resources for the last phase. That is, we send too many \"useless\" packets in the last phase, so it is beneficial to allocate more resources to the previous phases. Note that it is not common to have a large independent packet loss rate because we can change the modulation to reduce the loss rate. On the other hand, we observe that the throughput of two-phase RAP and that given by Algorithm 2 are nearly the same, which implicates the accuracy of our heuristic.  
To show the throughput gain, we take \(M=4\) and \(p=10\%\) as an example. At the 9-th hop, the throughput gain of two-phase RAP (and SAP) from fire-and-forget adaptive recoding and baseline recoding are 21.67% and 32.03%, respectively. The throughput of three-phase RAP (and SAP) is very close to the capacity. This also suggests that instead of sending more batches, the inner code can help in combating the loss efficiently when feedback is available.
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 3 Descriptive Analysis
### Distribution by Year  
Figure 2 illustrates the chronological order of the reviewed publications. Most publications appeared recently, from 2017 onward. Notably, 2021 and 2022 accounted for 27% and 25% of the publications, respectively. In 2019 and 2018, there were eight and four publications, respectively. There was only one relevant publication per year from 2010 to 2016. This pattern suggests that the combined use of RS and ML in railway condition monitoring is at the forefront of current research. Traditional ML algorithms such as support vector machines (SVMs), logistic regression, naive Bayes, random forest, and gradient boosting gave way to deep learning architectures that utilize large multi-layer artificial neural networks (ANNs), including variants like convolution neural networks (CNNs) that researchers optimized for machine vision [15].  
Figure 1: Citation mapping.  
Figure 2: The number of publications by year.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
###### Abstract  
Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.  
aquaculture water quality; dissolved oxygen (DO); forecasting; EEMD; LSTM +
Footnote †: journal: Applied Physics Letters B
###### Abstract  
Ginsenosides are bioactive secondary metabolites in ginseng, which have gained popularity for their usage in traditional Oriental medicine. Many studies have reported that ginsenosides exert their effects through multiple pathways, such as GPCR-related pathways. However, focusing on their specific interactions with ADGRG3 (GPR97) can provide possible insights to inform targeted intervention strategies in oncology and immunotherapy through the tumor-immune microenvironment interactions. Thus, this study employed an integrative in silico computational strategy to investigate ginsenosides as possible targets of ADGRG3. First, gene expression was analyzed using multiple databases such as TCGA, cBioPortal, and TIMER, revealing the differential expression of ADGRG3 across cancers, with notable overexpression in leukemia. Then, the virtual screening of 128 ginsenosides identified five top candidates (Rg3, Rk3, F5, Rg7, and F1) that showed strong binding energy (\(-10.7\)\(-10.6\), \(-10.5\), \(-10.4\), and \(-10.3\) kcal/mol, respectively) with ADGRG3, as determined through in silico molecular docking (MD). Computational approaches such as molecular dynamics simulations (MDSs), free binding energy calculations (MM-PBSA), and ADMET profiling confirmed the stability of these complexes' favorable ADMET predictions, respectively, which warrants further experimental validation through in vitro and in vivo pharmacokinetic studies. Finally, the computational protein-protein interaction and pathway enrichment analyses of ADGRG3 demonstrated immune-related pathways, such as neutrophil degranulation and GPCR signaling, emphasizing its role in cancer progression and immune modulation. These computational findings predict ADGRG3 as a viable target for cancer and immune pathways and ginsenosides as natural ligands. Further in vitro and in vivo preclinical and clinical studies are warranted to validate the interactions of ADGRG3 with ginsenosides.  
natural compounds; ginsenosides; in silico screening; gene expression analysis; molecular dynamics; cancer; immune pathways; ADGRG3
## 4 Proposed System Model
### Smart Contracts  
In the proposed system, multiple smart contracts are used to manage data and service sharing among network users. The smart contracts are: ACC, RC, and JC. Where ACC manages the overall access control of the system, the RC is used to register users (subjects and objects) in the system. It also generates a registration table, which stores the information of users. Users' authentication and authorization are also maintained by the registration table. Furthermore, behavior of subjects is determined by the JC. It checks if any misbehavior is conducted by the subject or not. When a subject sends too many requests or cancels the generated request, it is considered to be misbehavior. After the misbehavior conduction, the penalty is imposed on subject by the JC. Hence, trustfulness of the subject is determined by its behavior. If no misbehavior is done by the subject, then permission levels are checked. After that, the request of subject is passed by ACC to the corresponding object, to get the required service. Moreover, a detailed description of ACC, RC, and JC is given below.
## 3 The Proposed Solution
The consumer may turn on any appliance at any moment of the day without taking into account that, at that moment, it can be also in peak hours. The proposed fuzzy-based algorithm allows the switching on of the appliance or suggests to the consumer which is the more appropriate start time. In fact, when an appliance is turned on, it sends a request to the EMU that checks the available stored energy in the storage system. Moreover, the EMU communicates also with the smart meter in order to know about the updated prices in that time slot. When the EMU receives the information about the amount of stored energy, it schedules a convenient start time for the appliance according to the proposed fuzzy-based algorithm and notifies the consumer. At this stage, the consumer can decide whether to accept the schedule proposed by the EMU, for instance the time period falls during peak hours or the level of stored energy is not enough, or to ignore it, switching on the appliance. The proposed approach is not limited to the appliances that operate mostly autonomously and on a periodic basis, such as refrigerators, washers, _etc_. In fact, the proposed approach is valid and enforceable on all household appliances. For instance, considering a specific case, the system could suggest, through the smart plug, to wait after the consumer is about to start drying his or her hair after taking a shower when connecting and turning on the hair dryer, whereby the consumer can reject any scheduling proposal since he or she has the need to use the hair dryer at that precise moment. On the contrary, the consumer could prefer to remain with wet hair (or dry it with a towel) in order to shift the appliance cycle to hours where electricity prices are comparatively low. A different conclusion can be deducted if the appliance taken into account is not a hair dryer, but a vacuum cleaner, an iron, _etc._, for which, for example, if the consumer agrees, it is possible to postpone its use following the timing suggested by the proposed algorithm. This run-time mechanism of acceptance or rejection of the schedule can be managed through smart plugs. In this paper, smart plugs have not been analyzed in detail, because the main aim is to present the entire energy management approach. In any case, it is obvious that on the market, there are several devices that allow one to implement this mechanism of interaction between the consumer and household appliances that is introduced in this paper.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 5 Discussion  
### Considering Different Time Windows  
The experiments in the previous chapter demonstrated that the dataset performs well in predicting when the time window is set to 10 years. However, we recognize that different time windows may impact the model's predictive performance. For example, shorter time windows (such as 2 years and 5 years) may capture different patterns of seismic activity. Therefore, based on our preliminary exploration, we selected 2-year, 5-year, and 10-year time windows for comparison and conducted a detailed analysis using two evaluation criteria: MAE, MSE, and RMSE, as well as the accuracy of the predicted magnitude within \(\pm\)0.5 magnitude from the actual magnitude (Table 6). We hope this choice will provide a preliminary perspective for earthquake prediction across different time scales.  
The results show that with a 2-year time window, the LSTM model exhibits higher MAE, MSE, and RMSE, indicating greater prediction errors. The overall accurate prediction rate is only 39%, and the prediction accuracy for earthquakes with \(M_{\rm L}>6\) is merely 5%. These findings indicate that a short time window does not provide sufficient seismic activity information, which limits the model's predictive capability. When the time window is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time** & **MAE** & **MSE** & **RMSE** & **Accuracy Rate** & **Accuracy Rate** & **Accuracy Rate** \\
**Window** & **MAE** & **MSE** & **RMSE** & **Rate** & **of 5 \(<M_{\rm L}\leq 6\)** & **of \(M_{\rm L}>6\)** \\ \hline
2Y & 0.57 & 0.41 & 0.64 & 39\% & 54\% & 5\% \\
5Y & 0.55 & 0.44 & 0.66 & 54\% & 68\% & 26\% \\
10Y & 0.48 & 0.30 & 0.55 & 68\% & 85\% & 37\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of LSTM model on different time window datasets.  
Figure 8: LSTM model prediction results for the maximum magnitude of the earthquake in the coming period of time.  
five years, although the MAE decreases, the MSE and RMSE are slightly higher than the 2-year window, which may be due to the inclusion of more seismic activity information and complexity, resulting in increased errors. Despite these issues, the accurate prediction rate rises to 54%, with a prediction accuracy of 68% for \(5<M_{\rm L}\leq 6\) earthquakes and 26% for \(M_{\rm L}>6\) earthquakes. The 5-year window demonstrates a notable advantage in capturing seismic activity trends and improving predictive performance, although some errors remain. With a 10-year time window, the model's MAE, MSE, and RMSE significantly decrease, resulting in the smallest prediction errors. The overall accurate prediction rate reaches 68%, with an accuracy of 85% for \(5<M_{\rm L}\leq 6\) earthquakes and 37% for \(M_{\rm L}>6\) earthquakes. Therefore, the choice of time window size affects the performance of the LSTM model. A longer time window provides more seismic activity information, enhances the model's learning ability, and better captures long-term trends in seismic activity, thus improving prediction accuracy and reliability.
## 2 Application of DT to Sustainable Cities under Climate Change
### Emergency Response across Hazard-Stricken Urban Communities  
The emergency response, which aims at minimizing of functionality losses of CISs and the mortality of those hazard-stricken urban communities in the immediate aftermath of hazard events, has been proposed and deemed a promising approach to resilient and sustainable cities [23; 24]. Nevertheless, it should be highlighted that, due to the dimensions of the state space of real-world CISs, any exhaustive search-based optimization routine will be computationally unaffordable and even unviable regarding the sequential decision-making on post-hazard emergency responses, which are inherently supposed to be planned and completed in a rapid pattern. To address this challenge, DT has also been used to inform the planning of the emergency response across urban communities under natural hazards.  
[PERSON] et al. developed a DT based on the integrated textual-visual-geo information of urban communities in the immediate aftermath of hazard events [25]. It is noteworthy that, in the case of disruptive events like natural hazards, traditional data sensing tools (e.g., remote sensing and satellite images) often struggle to generate real-time situational awareness of the urban community due to their fast-evolving socio-physical status. Nevertheless, driven by the massive amount of user-generated data from social media (Twitter, in particular), the developed DT in this research would enable the estimation of the geographical scope and severity of the damage inflicted on the built environment, and it is thereby complementary to those traditional tools. Based on a case-study on the water release from flood-control reservoirs in Houston during Hurricane Harvey in 2017, the DT model was demonstrated to be helpful regarding the rapid damage assessment, laying the foundation for the emergency response thereafter.  
More recently, [PERSON] et al. established an autonomous planning system of the post-hazard emergency response of modern road networks (_RNs_) using the latest breakthrough in artificial intelligence [26], i.e., the development of _AlphaGo_[27; 28]. In this research, the partial yet rapid repair of hazard-decimated bridges was adopted as a viable approach to the emergency response of _RNs_ under hazard events. Such a planning system was applied to a real-world, regional-scale _RN_ in _Luchon_, _France_, under catastrophic earthquake scenarios. The simulation outcome revealed that the emergency response campaign guided by the established planning system outperformed any of the expert opinion-based heuristics, without domain knowledge instilled.