esa-sceva/satcom-chunk-collection huggingface dataset:
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 5 Estimation of Tilted Global Irradiation (GTI) from Horizontal Global Irradiation (GHI)
### Choice of the Number of Input Data  
The purpose of Section 6 is to predict the future hourly solar irradiation (at different time horizons) based on the past observed data, i.e., mathematically:  
\[\hat{X}_{t+h}=f(X_{t},X_{t-1},X_{t-2},X_{t-3},\ldots,X_{t-n}) \tag{10}\]  
A variable, X, with the symbol,' represents a forecasted data; without this symbol, X is a measured data. The solar data at future time step (t+h), \(\hat{X}_{t+h}\) is forecasted from the observed data X measured at the times (\(t\), \(t-1\)..., \(t-n\)); thus, the first objective consists of determining the value of \(n\), i.e., the dimension of the input matrix; to do it, an auto mutual information method [56; 57; 58] was used. The auto mutual information is a property of the time series, it depends on each dataset and is characteristic of the degree of statistical dependence between \(X_{t+h}\) and \(X_{t-i}\) with \(0\leq i\leq n\). It is a dimensionless quantity with units of bits. High mutual information indicates a large reduction in uncertainty about one random variable, \(X_{t+h}\), given knowledge of another \((X_{t},X_{t-1},X_{t-2},X_{t-3},\ldots)\). The auto-mutual information method showed that the number of inputs (value of n in Equation (9)) for predicting GHI is six and for DNI, it is seven.
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 4 Designing an Appropriate Inoculum for Phytoremediation or Phytostabilization
[170] evaluated the microbial diversity in a bare soil and in four rhizocpartments (from the most distant part of the rhizosphere to the endopshere) of three plant species: _Gossypium hirsutum_, _Tamarix chinensis_, and _Lycium chinense_. At the end of the experiment, 12 years after, the three plant species had recruited significantly more bacterial and fungal species, whereas archaeal communities were the most diversified in the bare soil. Their results also indicated that the phytoremediated soil and bare soil differed regarding the electrical conductivity and soil moisture, thus affecting the composition of the microbial community. In addition, plant species differed in regard to the content oftotal nitrogen, total carbon, and available potassium in the soil, which affected, in turn, the structure of microbial communities. According to [PERSON] et al. [162], if we consider the plant microbiome as a superorganism, the microbiome is the part that would increase plants' adaptability to their environments. Furthermore, we have to deal with a potential incompatibility between microbial candidates and associate the good partners, displaying complementary beneficial effects for plants [162]. The combination should increase the potential of the inoculum, and the selected strains should be cultivated separately in optimal growth conditions and be mixed at a second time in optimal proportions that enable both to be maintained in the rhizosphere [162]. Moreover, the selected microbes must be produced rapidly and in high quantity to be used at a large scale. [PERSON] et al. [137] distinguish four main types of inoculum, namely liquid, slurry, granular, or powder, that can be formulated in organic (for example, with peat, optionally associated with other organism(s) or biological matter(s), to improve the growth of some bacteria, such as dead mycelium, or lignite, charcoal, coil dust, or composts), inorganic (inorganic materials, natural polymers, or synthetic material), polymeric (alginate, agar, \(\lambda\)- and \(\kappa\) carrageenan, pectin, chitosan, and bean gum), and encapsulated (active microbes trapped in polymer matrix) formulations. Technical aspects include inoculation techniques that can be made by seed imbibition, watering the soil with a microbial suspension, applying inoculum powder on root system when preparing plants in greenhouse, or, more rarely, shoot spraying or through hydroponic plant culture [162]. Thus, selected strains must be compatible with such techniques.
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 6 Conclusions  
This paper puts forward a module to prevent attackers from performing distributed denial-of-service (DDoS) attacks and a transport layer security (TLS) protocol with an encrypted token identity authentication module to ensure the internet security of SCADA in the IIoT energy management system. This two-way identity authentication process was available through the tokens, devices, and remote control of the server. In addition, as tokens are highly private and the identity information of IoT devices is confidential, this feature could help protect sensitive materials from disclosure. As it requires every entity to run authentication procedures before accessing the network information, the mechanism proposed in this paper could protect industrial networks from external threats. Moreover, in addition to this mechanism defending against DoS attacks, it can also defend against man-in-the-middle, replay, and impersonation attacks. After applying and verifying the mechanism in the energy management system of a Smart Green Energy Science City in southern Taiwan, the experimental results also showed that this mechanism could effectively improve security and was compatible with the real field system. Furthermore, the hardware used in this mechanism was of low cost, which indicates that the mechanism could be applied in reality on a large scale.  
Conceptualization and methodology, Y.-S.Y., S.-H.L. and W.-C.C.; Formal analysis, S.-H.L. and W.-C.C.; Supervision, C.-S.Y., Y.-M.H. and T.-W.H.; Project administration, S.-H.L. and C.-S.Y.; Writing--original draft preparation, S.-H.L.; Writing--review and editing, S.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by the Ministry of Science and Technology of Taiwan (under grants No. 110-2221-E-006-038). The authors would like to thank Editor-in-Chief, Editor and anonymous reviewers for their valuable reviews.  
The authors declare no conflict of interest.
## Appendix A Cellular Network Technologies
### Architecture  
In GSM cellular networks, devices are usually called mobile stations (MSs), and they allow people to communicate with the GSM/GPRS/EDGE radio access network (GERAN or 2G RAN), as shown in Figure 11.  
The GERAN is organized into base station subsystems (BSSs). Each subsystem was originally composed by two architectural elements: the base transceiver system, also referred to as BTS (BTS is equivalent to a BS, or base station, in 3G and 4G. For this reason, a BS is often used also in 2G for BTS), which includes several components, such as transmitters, receivers, and other equipment for wireless air-interface operations; and the base station controller (BSC), which is basically a switch in charge of frequency management among the BTSs belonging to the same BSS. Each BSC can control from one to hundreds of BTSs. Later, to support GPRS packet switching, an additional component called the packet control unit (PCU) was added [28]. It controls the packet traffic, and its most common position is right after the BSC, as shown in Figure 11.  
At the highest level, the network and switching subsystem (NSS) supports the core network services for both circuit-switching (CS) and packet-switching (PS) connections.
## 3 Results
### Evaporation and Transpiration Partitioning
Another notable difference between DisALEXI-PT and DisALEXI-PM is the use of \(\rm r_{c}\) within the DisALEXI-PM framework. To be consistent with the original PM version of TSEB implemented by [38], \(\rm r_{c}\) is held constant at 50 s m\({}^{-1}\) (see Section 2.3.2). However, this value represents a reference short crop (i.e., well-watered and full canopy), which may cause issues in taller and/or more heterogeneous canopies where the sensitivity of \(\rm r_{c}\) to VPD will be more significant. To evaluate the model's sensitivity to \(\rm r_{c}\), we ran DisALEXI-PM using incrementally increasing \(\rm r_{c}\) values (10 to 95 s m\({}^{-1}\)) and calculated the error in T, E, and ET (Figure 8). Figure 7 shows the mean bias error (MBE; mm day\({}^{-1}\)) between the DisALEXI-PM-derived T, E, or ET and the CEC-derived T or E (green and brown bar, respectively) for each model run (defined by the value of \(\rm r_{c}\); x-axis). Additionally included is the combined ET modeled bias (Figure 7; black line with yellow highlight). The results suggest that an increase in \(\rm r_{c}\) causes modeled E values to increase (negative to positive bias) and T values to decrease (bias becoming less positive). This is expected as an increase in \(\rm r_{c}\) while holding VPD constant will result in decreasing T. Accordingly, the largest improvements in MBE achieved by increasing \(\rm r_{c}\) are found at RIP and VAC, where advection is more common, and at OLA, where a more substantial overestimation of T was originally derived (i.e., a decrease in modeled T inherently improves error predictions). Figure 8 suggests that DisALEXI-PM E and T estimates would benefit from site-specific \(\rm r_{c}\) values better representing surface conditions as biases in T decrease to minimums at  
Figure 8: Bar plot of mean bias error (MBE; mm day\({}^{-1}\)) between the DisALEXI-PM-derived T or E and the CEC-derived T or E, green bar and brown bar, respectively, for each site. MBE is calculated for individual DisALEXI-PM model run when \(\rm r_{c}\) is varied at 5 s m\({}^{-1}\) increments from 10 to 95 s m\({}^{-1}\). different \(\mathrm{r_{c}}\) values. Note that minimal biases in T do not explicitly align with minimal biases in E. Although magnitudes are different, decreases in T bias align with an increase in E bias. When combined as ET, we find little difference in the modeled bias (Figure 8; black line with yellow highlight), suggesting changes in \(\mathrm{r_{c}}\) have an effect on partitioned fluxes but offer little improvement in the modeled total ET. Declining T biases corroborate results presented in [43], where a partitioning algorithm that included an \(\mathrm{r_{c}}\) formula that relates maximum stomata conductance to VPD produced the most reasonably partitioned estimates of E and T.
# Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
## 2 Materials and Methods
### Statistical Analysis and Feature Selection
#### Time-Dependent XAI  
Unlike other ad hoc explanation methods, such as SurvLIME [27] and survNAM [43], SurvSHAP [18] also takes into account time in the explanation process. This method generalizes Shapley additive explanations (SHAP) [25] to survival models, giving a global explanation of the overall behavior of the model over time. SurvSHAP can reveal the importance of comorbidities affecting the prognosis over the follow-up period, offering valuable insights into the progression of OSA and strategies for improving patient prognosis. Similarly, SurvLIME explains individual predictions, so it is helpful for analyzing single-case predictions and planning personalized treatment over the follow-up period.  
Specifically, SurvSHAP describes variable contributions across the entire considered time period to detect variables with a time-dependent effect. Its aggregation approach determines the variables' importance for a prediction better than the others. \(\mathbb{D}=\{(X_{i},y_{i},t_{i})\}\) denotes the dataset used for training, where \(\mathbb{D}\) contains \(m\) unique time instants \(t_{m
## 3 Annual Rainfall Prediction
### Random Forest Regression Model  
The number of estimators obtained using hyperparameter tuning was 150, the number of random states obtained was 10, the maximum depth was 110 and the size of the rolling window 'L' was 5. After fixing the hyperparameter values, the RF regression model was imported from Scikit-Learn and then the model was instantiated and fitted on the training data, with the size of the window fixed as 5. Then, the prediction was made on the testing dataset, which ranges between 2001 and 2020. The predicted rainfall was compared with the actual rainfall and the performance of the RF model was evaluated based on the metrics mentioned. Figure 6 shows the bar chart of the predicted values versus the actual rainfall for the RF model. The RF model shows an improved performance in predicting the annual rainfall value; however, the error for the years 2016, 2018 to 2020 still remains high.  
Figure 5: A bar plot of the predicted and observed annual rainfall for ARMA model (0,0,2).  
Figure 6: Bar plot of the predicted and observed annual rainfall for random forest regression model.
## 4 Discussion  
The major finding here was that the two experimental models we developed provide values that enable valid monitoring of the aerodynamic drag on elite alpine skiers performing the technical disciplines. Both models involve functions of the normalized shoulder height h\({}_{n}\), with the first model estimating the product of the coefficient of aerodynamic drag and cross-sectional area (C\({}_{d}\)-S) and the second estimating the coefficient of aerodynamic drag C\({}_{d}\) and the normalized cross-sectional area of the skier S\({}_{n}\). In addition, normative values for C\({}_{d}\), S\({}_{n}\), h\({}_{n}\), and C\({}_{d}\)-S in the three positions (high, middle, and tuck), typically  
Figure 4: Bland–Altman plots demonstrating the mean differences between the calculated and measured values for the three parameters of interest. (**a,b**): product of the coefficient of aerodynamic drag and cross-sectional area (C\({}_{d}\)-S), (**c,d**): coefficient of aerodynamic drag (C\({}_{d}\)), and (**e,f**): the normalized cross-sectional area (S\({}_{n}\)). The diagrams on the left (**a,c,e**) depict these differences in the high and middle positions, while those on the right (**b,d,f**) are for the downhill (tuck) position. The dashed lines represent the limits of agreement. CV = coefficient of variation.  
adopted by elite skiers when performing the technical disciplines at three different wind speeds (40, 60, and 80 km/h), were obtained.  
These models were based on monitoring 10 elite skiers in three different standard positions in a wind tunnel, assuming that each skier adopted these positions appropriately. However, even if some skiers, despite their high level of skill, took somewhat different positions, this variation actually provided more continuous data for calculating our models. Although it would have been possible to require our skiers to take strictly defined optimal positions, as in certain other studies [12; 14; 15], in reality, the positions of elite skiers in the technical disciplines often deviate from optimal as they adapt their turns to a variety of gate setups, as well as varying terrain and snow conditions [17].
## 5 Conclusions and Future Enhancements  
In our case study, Random Forest with Extra Trees Regressor Model is the best-performing model across all evaluated criteria and is recommended for integration into the web-based application for RUL prediction. Linear Regression is a suitable alternative for scenarios requiring simpler implementation with high interpretability. Both LSTM and ANN can perform exceptionally well, especially when there's enough computational power  
Figure 19: User interface of the web-based simulator app for forecasting the remaining useful life (RUL) of lithium-ion 18650 batteries using machine learning models.  
to support their complexity. Future research could incorporate Transformer-based models or hybrid physics-informed frameworks to evaluate their comparative strengths on the same dataset. Although the original HNEI database was generated under regulated room temperature, our models were also trained and tested using data obtained at 25 \({}^{\circ}\)C to guarantee consistency. However, temperature was not included as a modeling input variable. Future researchers could find it beneficial to take temperature as a major consideration when developing predictive models since maintaining batteries at roughly 25 \({}^{\circ}\)C helps them perform better and reduces the possibility of rapid capacity loss, which can influence dependability and safety. Furthermore, there should be more focus on extending the application's capabilities to integrate this machine learning models into EV's for Real time Management. Features like automated alerts for critical battery conditions, multilingual support, and seamless integration with other battery management systems are planned for subsequent iterations. Additionally, efforts will be made to optimize the computational efficiency of the backend to reduce latency further.  
Conceptualization; all authors; methodology, all authors; formal analysis, all authors; writing--original draft preparation, all authors.; writing--review and editing, all authors; visualization, all authors; supervision, all authors. All authors have read and agreed to the published version of the manuscript.  
The authors sincerely thank Apple and IBM for their generous support, which has significantly contributed to the advancement of this research project. Additionally, we extend our gratitude to Southern University and A&M College for their invaluable support in the successful completion of this work.  
The link to the data presented in this study is available in the article.  
The authors declare no conflict of interest.
## 3 Experiments and Results
### Experimental Results
#### 3.3.3 Transfer Learning Experiment  
To verify the cross-domain adaptability and generalization ability of the proposed model in complex farmland scenarios, this study designed a two-stage transfer learning framework. In the first stage, based on the lightweight model LKF-DCANet after knowledge distillation, the pretrained weights obtained on the public dataset GLH-water were transferred to the self-constructed UAV-acquired farmland water body dataset. In the second stage, only 10% of the labeled samples in the UAV dataset were used to fine-tune the weights of the LKF-DCANet, enabling it to quickly adapt to the brand-new farmland water body environment. Finally, predictions were made on the remaining 90% of the unlabeled data based on the fine-tuned model. The experimental results, as shown in Table 4, indicate  
Figure 10: Qualitative segmentation results of different model variants on GLH-water.  
that a segmentation accuracy of 96.28% is achieved on the self-constructed UAV dataset, enabling efficient knowledge transfer at a limited annotation cost.  
The visual segmentation results are shown in Figure 11, which illustrates qualitative predictions across representative categories of agricultural surface water bodies. Specifically, the model demonstrates stable recognition performance for narrow linear structures such as drainage ditches and irrigation channels (Figure 10(a)), as well as spatially fragmented or irregular water bodies, including farm ponds and shallow seasonal reservoirs (Figure 10(b),c). Furthermore, Figure 10(d) presents challenging shallow water bodies such as flooded fields and vegetated wetland patches, which often exhibit low spectral contrast and mixed contamination from surrounding terrain. These examples highlight the model's capacity for fine-grained segmentation, even in visually complex environments, and its adaptability to diverse water body types commonly encountered in agricultural watersheds. Overall, the proposed LKF-DCANet achieves accurate boundary delineation and strong robustness against noise and appearance ambiguity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **IoU (\%)** & **F1-Score** \\ \hline GLH-Water [44] & 85.95 & 92.10 \\ Self-Constructed Dataset & 96.28 & 97.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transfer performance of the distilled LKF-DCANet on GLH-water and UAV datasets.  
Figure 11: Qualitative prediction results on unlabeled UAV data after fine-tuning.
## 3 Results and Discussion
### Fatigue Characteristics
In addition, the fatigue limit was determined as the highest stress level at which three test specimens withstand 2 million cycles, and it is equal to 1.48, 2.24, and 2.71 MPa for data standardized using relative compressive strength values related to the specimen's age of 28, 90, and 180 days (see Figure 9b). The fatigue parameters of quasi-brittle materials are not commonly determined, although many structures made of these materials are often subjected not only to static but also to repetitive cyclic loads. If only the measured data without the effect of the particular age of specimens during the cyclic test are compared, then it might seem from Figure 9a that the investigated AAS composite has slightly better fatigue behavior than the OPC material with the same s/b and w/b ratios stored in the water [44]. In fact, the OPC material has slightly better fatigue behavior when the age of specimens is taken into the account and standardized data to the age of 28 days are compared with Figure 9b. The fatigue limit is about 0.5 MPa higher in the case of the OPC material [44].
## 2 The Theoretical Basis of the Algorithms Used in This Research  
### Failure Mode and Effects Analysis (FMEA)  
FMEA is a systematic and proactive approach used in various industries to identify and reduce possible failures or errors in processes, products, or systems. This powerful tool increases quality, reliability, and safety by evaluating the impact of different failure modes and prioritizing them based on their severity, probability, and detectability [23]. FMEA is structured on the principle of predicting and preventing failures before they occur, thereby reducing the likelihood of defects, accidents, or inefficiencies. The effectiveness of this method is ensured by focusing on key components within the system, including failure mode identification, severity determination, probability assessment, detectability assessment, risk priority number calculation, risk mitigation and action plans, and continuous monitoring and improvement. The standard method for conducting FMEA includes calculating the risk assessment as follows [24]:  
\[Risk=Occurrence\times Severity\times Detectability \tag{1}\]  
in which:  
1. Occurrence is the possibility of error or failure. It is usually graded on a scale of 1 to 10, where 1 represents a very low probability and 10 represents a very high probability.
2. Severity is the degree of impact of the error or failure on the end user or system. This factor is evaluated on a scale of 1 to 10, where 1 and 10 represent minimum and maximum effects, respectively.
3. Detectability is the ability to detect errors before they reach the end user. This assessment is also carried out on a scale of 1 to 10, where 1 is easy to detect and 10 is difficult to detect.  
The higher the risk value, the more critical the problem status and the failure situation. FMEA aims to identify high-risk areas and take action to reduce the risk [25]. In addition to the above formula, additional parameters and various weighting factors may be included in FMEA analysis depending on the specific method or industry.