esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Practical Application of a Self-Adapting IoT Network
**Built-in Resiliency of the System** Each layer of the system provides a different type of resiliency:  
* Sensors connected to the management unit use separate, redundant connectors to mitigate physical failures. For example, connectors can failover between wired and wireless connections to ensure continuous data transmission. * Management units are equipped with local storage, enabling them to temporarily store data in case of communication failures. This ensures that data are not lost and can be transmitted to the cloud service once the connection is restored. **Feedback Loop and Self-Learning** The capabilities used to describe various aspects of the system can be adjusted through self-learning methods. Self-learning relies on the collection of historical data, such as readings from the light sensor, as well as user feedback that evaluates the current state of the system on a scale from 1 to 5. A mobile application prompts users to provide feedback regularly, typically once per day. As part of the self-learning process, the system detects the system state, such as the intensity of light, that generates the highest feedback. The collected feedback, along with other historical data, is then used to redefine the capabilities of the system, aiming to represent its optimal state.
## 6 A Case Study  
In this section, the results obtained in this study in terms of trained ANN-based models, are adopted for a practical engineering problem to assess the seismic bearing capacity of a strip footing placed over an unsupported void. It has been assumed that the strip footing, featuring \(B=8\) m, is located over homogeneous soil characterized by \(c_{0}=300\) kPa, \(\gamma=18\) kN/m\({}^{3}\), and \(\varphi=20^{\circ}\). As shown in Figure 17, the footing is placed near a square void, which is 4 m wide and 4 m high, and the center of which is placed at a depth of 12 m and at a distance of 16 m from the footing centerline. To predict the undrained seismic bearing capacity in the specific case \(k_{h}=0.2\), the ML method introduced in this research can be adopted as follows:  
First, the nondimensional input parameters of the problem need to be computed to obtain:  
\[\frac{c_{0}}{\gamma B}=\frac{300\ [\text{kPa}]}{18\ [\text{kN/m}^{3}]\cdot 8 \ [\text{m}]}=2.08\]  
Figure 16: Performance of the ML algorithms: comparison between the FELA results and the forecasts provided by the three algorithms for 1% randomly selected data, in terms of the seismic bearing capacity \(Q=q_{\mu}/\gamma B\) of the shallow strip footing.  
Figure 17: Geometry of the case study (measures in m).  
\[\frac{kB}{c_{0}}=0\] \[\alpha=\frac{W}{B}=\frac{4\left[\mathrm{m}\right]}{8\left[\mathrm{m} \right]}=0.5\] \[\beta=\frac{H}{B}=\frac{4\left[\mathrm{m}\right]}{8\left[\mathrm{m }\right]}=0.5\]  
According to Figure 17, where the center of the void is shown to be placed at a depth of 12 m and at a distance of 16 m from the footing centerline, the additional nondimensional depth and eccentricity parameters amount to:  
\[D=\frac{Z}{B}=\frac{12-4/2\left[\mathrm{m}\right]}{8\left[\mathrm{m}\right]}= 1.25\]  
\[S=\frac{X}{B}=\frac{16\left[\mathrm{m}\right]}{8\left[\mathrm{m}\right]}=2\]  
For this case study, the Optum G2 software has provided the lower and upper bounds on the ultimate bearing capacity as 3193.65 kPa and 3301.85 kPa, respectively, so the mean value has been given as 3247.75 kPa. The ML methods here investigated have instead provided the estimations reported in Table 6, along with the relevant errors with respect to the aforementioned ground-truth value. As already pointed out in Section 5, MLP is shown to perform better than the two alternative tools, giving rise to a representative error smaller than 2%. Very similar results can be obtained for any other geometry falling within the interval of characteristic parameters allowed for while assembling the dataset of Table 4.
# Beyond Kanban: POLCA-Constrained Scheduling for Job Shops
## 3 POLCA and the Enhanced Contraint Programming: A Basic Case  
### Problem Description  
In this section, we introduce the _Basic Case_, a simplified job shop environment designed to evaluate the integration of POLCA into a FCS framework. The objective is to compare a standard reactive POLCA system with a proactive scheduling approach that incorporates POLCA as a finite-capacity constraint. The job shop consists of \(m\) workcenters, each with a single processing resource, and \(n\) production orders. Job routings are randomly generated to introduce variability and accurately reflect the operational complexity inherent in HMLV production environments. Processing times are stochastic and follow a predefined distribution.  
1. **Baseline POLCA (Reactive Control)**: Jobs are released based on a high-level MRP, and job flow is controlled only by POLCA cards. Jobs queue in work centers and POLCA loops, with earliest due date (EDD) prioritization. The number of POLCA cards per loop is varied to assess its impact on WIP and lead time.
2. **POLCA and FCS (Proactive Control)**: POLCA is embedded as a constraint in a constraint programming (CP) model, which pre-determines the start times of operations while respecting POLCA-imposed WIP limits. The scheduler optimizes job sequencing based on multiple performance objectives.  
In both approaches, the number of POLCA cards is fixed at \(C\) for all workcenters. The experiment aims to determine whether a scheduling approach that integrates POLCA constraints improves performance in terms of lead time, tardiness, and WIP reduction compared to a purely reactive POLCA implementation. In the following subsections, we present the Constraint Programming model that enforces POLCA's workload limitswhile scheduling, describe the Simulation model used to replicate the Basic Case dynamics and measure system performance, and finally outline the computational experiments conducted to compare the two approaches, highlighting key metrics such as lead time, tardiness, and WIP.
## Results
### Literature Search  
Based on the keywords, no systematic reviews in secondary literature sources were found, while 131 articles were returned from the primary literature databases. Carefully applying the inclusion criteria we selected 38 papers. Among these papers we identified three reviews, three studies only presenting robotics devices and 32 studies on clinical applications (Figure 1 shows the flow diagram that summarizes the process and results of the primary literature search).  
The 3 review papers [41; 42; 43] investigated robotic devices for upper limb rehabilitation in children with neurological disorders but with substantial differences from the purposes of the present work:  
* [PERSON] et al. [41] based their work on a detailed review of the application of one specific robot (_InMotion2_, later explained) for children with hemiplegia due to CP;
* [PERSON] et al. [42] examined the effectiveness of several upper limb robots applied only to children with CP;
* the third review [43] is focused on clinical protocols performed on children with different neurological diseases but with the use of two specific robots (_InMotion2_ and _NJIT-RAVR_).  
In the present work we have a more general view and, as described in the introduction section, we aim to analyze and present all the devices and the clinical protocols for upper limb robot-assisted rehabilitation designed for children with neurological disorders (CP, Stroke, ABI).
## 2 Related Work
[PERSON] et al. [20] investigated the mandatory lane-changing behavior of vehicles at ramp entrances and exits. The study used driving simulation experiments to collect data and analyzed the variation of various microscopic parameters, including lane change merging gap, duration, and maximum longitudinal deceleration. However, the effect of microscopic variables on the length of the weaving section was not investigated. AVs have excellent application prospects in improving the capacity and safety of traffic flow. At present, there has been considerable research on the operation mechanism of AVs, such as car-following and lane-changing driving behaviors, and the different penetration rate effects in mixed traffic flow and interaction with human-driven vehicles. For driving behavior, [PERSON] et al. [21] proposed a latent Dirichlet allocation (LDA) model to study the behavior of different drivers, which could help AVs understand personalized driving behavior. [PERSON] et al. [22] proposed an unsupervised Bayesian algorithm to extract the behavioral features of drivers. [PERSON] et al. [23] designed a hierarchical deep reinforcement learning (DRL) algorithm to learn lane-changing behavior in dense traffic, where the design of lane-changing behavior is based on two characteristic parameters: lane-changing angle and acceleration-braking rate. In terms of lane change trajectory planning, [PERSON] et al. [24] proposed a dynamic lane change trajectory planning (DLTP) model, which builds a lane-change decision algorithm using the trajectory parameters. [PERSON] et al. [25] combined a deep auto encoder (DAE) network with the XGBoost algorithm, and proposed a lane-changing decision model based on parameters such as lateral and longitudinal velocity and acceleration during the lane-changing procedure, so that AVs can execute human-like decision making. [PERSON] et al. [26] developed a game-theory-based forced lane-changing model (AZHW model) for traditional environments, taking lane-changing behavior as an alternative decision parallel to acceleration and deceleration and extended it to connected environments, proving that the developed AZHW model is more accurate and results in better performance than existing models. In general, most studies focus on the interaction effects between vehicles and trajectory simulation of lane-changing behavior and model the overall decision making and process of lane-changing behavior. However, few studies focus on possible influencing factors of lane-changing behavior, as well as various other influencing factors, such as the correlation between influencing factors and lane-changing distance. Based on the actual data, NGSIM vehicle trajectory, this research will explore the operational indicators that have a conspicuous impact on vehicle lane changing, and then propose a model for the longitudinal driving distance of the lane-changing process, providing a reference for the buffer zone length design.
# Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques
## 5 Tests and Results
### Results Considering Four Clusters
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Method** & **Clusters** & **OS Distribution** & **Operation Time [s]** & **Violations** \\ \hline \multirow{4}{*}{K-means} & 1 & 4 8 12 16 & 109.1561 & 0 \\ \cline{2-5}  & 2 & 1 2 3 13 15 & 82.3264 & 0 \\ \cline{2-5}  & 3 & 5 6 7 9 10 11 14 & 314.8834 & 1 \\ \cline{2-5}  & total & & 506.3659 & 1 \\ \hline \multirow{4}{*}{SOM} & 1 & 4 8 12 16 & 109.1561 & 0 \\ \cline{2-5}  & 2 & 1 3 9 13 15 & 113.5926 & 1 \\ \cline{2-5}  & 3 & 2 5 6 7 10 11 14 & 383.7345 & 1 \\ \cline{2-5}  & total & & 606,4832 & 2 \\ \hline \multirow{4}{*}{Hierarchical Clustering} & 1 & 4 8 12 16 & 109.1561 & 0 \\ \cline{2-5}  & 2 & 1 2 3 13 15 & 58.0175 & 0 \\ \cline{2-5}  & 3 & 5 7 9 11 & 145.1465 & 1 \\ \cline{2-5}  & total & & 63.7431 & 0 \\ \hline \multirow{4}{*}{Hierarchical Clustering} & 1 & 4 8 12 16 & 109.1561 & 0 \\ \cline{2-5}  & 2 & 1 3 13 15 & 58.0175 & 0 \\ \cline{2-5}  & 3 & 5 7 9 11 & 145.1465 & 1 \\ \cline{2-5}  & 4 & 2 6 10 14 & 161.9506 & 0 \\ \cline{2-5}  & total & & 474.2702 & 1 \\ \hline \end{tabular}
\end{table} 
Table 5: Results considering three clusters. An adjustment of the clustering was made while using the heuristic that was proposed in Section 4.4. The main idea with this heuristic is to improve the results of the clustering methods. Table 7 and Figure 9 present the adjustment results. It can be seen that the coordination is guaranteed, since constraint (2) does not present any violation in any of the clusters. In this case, the optimal coordination is finally achieved and, therefore, no further clustering is required. Table 8 presents the protection coordination settings for the test system. For each relay, \(ipickup\) and \(TMS\) in each group are presented; furthermore, backup relay information is also presented. \begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Method** & **Clusters** & **OS Distribution** & **Operation Time [s]** & **Violation** \\ \hline \multirow{4}{*}{Heuristic Adjustment} & 1 & 4 8 12 16 & 109.1561 & 0 \\ \cline{2-5}  & 2 & 1 2 3 13 15 & 82.3264 & 0 \\ \cline{2-5}  & 3 & 5 6 7 10 11 & 266.9944 & 0 \\ \cline{2-5}  & 4 & 9 14 & 38.93 & 0 \\ \cline{2-5}  & total & & 497.4069 & 0 \\ \hline \end{tabular}
\end{table} 
Table 7: The final results considering four clusters. Figure 9: Illustration of final clusters. For illustrative purposes, Figure 10 depicts the coordination between a main and a backup relay in OS12 for the IEC microgrid when fault in line DL-5 takes place. In this case, the main and back up relays are RP2 and RB4, respectively. Note that the coordination between these two relays is evident.
## 3 Results
In the most sensitive method (GB), the average errors are more than 15 days apart for both SD and HD (Figure 7c,d). Figure 4: Violin plot comparing field data (FD) with sowing (SD) and harvest (HD) dates estimates for the different packages, CP (CropPhenology), DT (DEA tools), GB (greenbrown), PF (phenofit), PX (phenex), and TM (TIMESAT), using different data sources, MODIS (MCD13, MOD09, MOD13) and Sentinel-2 (S2). Figure 5: Dispersion between the data observed in the field and sowing (**a**–**f**) and harvest (**g**–**l**) estimates for the different datasets and packages tested, CropPhenology (**a**,**g**), DEA tools (**b**,**h**), greenbrown (**c**,**i**), phenofit (**d**,**j**), phenex (**e**,**k**), and TIMESAT (**f**,**l**). The black line represents the 1:1 line. There are inconsistencies in some estimates made by the packages. The major source of errors is double cropping (lower R and more outliers). Packages assume only one crop in the mentioned season, often picking the wrong sowing or harvest date. In this case, even though TIMESAT has features to determine the number of cycles, it often fails to identify multiple cycles, particularly for harvest estimation (Figure 8). Figure 6: Spatial pattern of the difference between observed and estimated sowing date for TIMESAT (**a**) and observed and estimated harvest date for phenex (**b**) in days. The figures consider estimates for the MCD13Q1 EVI2 data source. Root Mean Square Error (RMSE) is in days. Figure 7: Distribution of the difference between the field data and sowing (**a**,**c**) and harvest estimates (**b**,**d**) using the different data sources, EVI index, and TM (**a**,**b**) and GB (**c**,**d**) packages. FD_MC13: difference between field data and MCD13; FD_MD09: difference between field data and MOD09; FD_MD13: difference between field data and MOD13; FD_S2: difference between field data and Sentinel-2. Dotted lines represent the peak of each distribution. The main goal of these algorithms is to quickly produce a sowing and harvest date map over a large area. Even though the algorithms are based on point scale, some of them provide features that enable the input of rasters, which facilitates the spatialization process. Thus, as an example, spatialization was performed using the CP and PF packages (Figures S10 and S11) with the MCD13 product. The quickest algorithm was the GB, and the slowest was the TM (Table 2).
## 6 Epigenetic Regulation of CMT
### Studies Using Human Cell and Mouse Models
#### 6.1.1 Noncoding RNAs  
Although CMT type 1A (CMT1A) is known to be caused by increased gene dose of _PMP22_, the age of onset and severity of symptoms vary considerably among individual patients [25]. A common single nucleotide polymorphism was found in the miRNA miR-149, which was predicted to target several CMT-inducing genes, including _PMP22_[140]. The polymorphism was located around the 3\({}^{\prime}\) end of the precursor RNA of miR-149. Association studies between the polymorphism and clinical phenotypes of CMT1A patients revealed that the polymorphism in miR-149 was closely related to the onset age and severity of symptoms, suggesting that the variant in miR-149 is a genetic modifier that can cause the phenotypic heterogeneity of CMT1A (Table 2) [140].
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 5 Conclusions  
The main goal of this work is to study the feasibility of fuzzy approaches to estimate the energy performance of buildings. The characteristics of a building are an important factor in determining the necessities of heating and cooling loads. Therefore, it becomes very useful to determine the most relevant building characteristics, related to the heating and cooling, needed to maintain comfortable indoor conditions in order to design and build energy-efficient buildings. This research starts from a previous study [17], which designed a set of buildings with the aim to predict the heating and cooling load of buildings taking into account variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution.  
Two fuzzy methodologies have been studied in this research, i.e., the fuzzy inductive reasoning (FIR) and the adaptive neuro-fuzzy inference system (ANFIS). In order to test the generalization performance of FIR and ANFIS fuzzy models we use 10-fold cross validation. The training and testing processes were repeated 100 times to the whole dataset, which was randomly permuted in each run prior to splitting it into training and testing subsets. The results achieved by ANFIS and FIR methodologies were compared to those obtained by thirteen machine learning methodologies presented in previous works, i.e., IRLS, RF, SVR, GLR, CHAID, CART, SVM, RBFNN, BPNN, MARS, GSGP, GSGP-LS, GSGP-LS-LIN [17; 19; 20; 21]. From the results, it can be concluded that SVR, together with the two fuzzy approaches (ANFIS and FIR), works much better than the rest of the methodologies. An interesting result is that the feature selection process of the FIR methodology finds that only two input variables, i.e., relative compactness and glazing area, contain the relevant information needed to accurately predict heating and cooling loads. Obtaining ANFIS and FIR models that use only these two variables, and are just as accurate as the models that use the whole data set of eight input variables, proves this finding. Moreover, these quick and accurate prediction models can be very useful for buildings design and decision making.  
A.N. proposed the research and its conceptualization. A.N. together with F.M. performed the formal analysis, the investigation, the methodology and the software. A.N. performed the validation and wrote the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by the MINECO project CTQ2015-69802-C2-1-R MINECO/FEDER, UE. The authors declare no conflict of interest.
## 2 Materials and Methods  
### Study Area  
Ben Tre and Tra Vinh provinces (Figure 1) are located on the coast of the South China Sea in Vietnam. The area of interest is 8500 km\({}^{2}\) and centered in 106.4\({}^{\circ}\)E, 9.9\({}^{\circ}\)N (UTM 48 WGS84). Terrain height is only few meters above sea level. The land use mainly consists of rice, orchard, coconut, sugar cane, aquaculture and urban and rural settlements. The rainy season lasts from May to October/November and the dry season from November/December to April. The distribution of rainfall is unequal increasing from south-west to north-east [31].  
In the Mekong Delta region, Vietnam, salinity intrusion occurs in ocean coastal areas, where saline water moves into freshwater aquifers. This can lead to contamination of drinking water sources and problems in agriculture. The salinity intrusion happens naturally to some extent, however, climate change and changes in rainfall are causing more severe salinity intrusion. In addition to the salinity intrusion, drought and rising temperatures cause problems to agriculture and aquaculture. The salinity intrusion may be affected by the climate change, e.g., changes in rainfall, sea level rise and storms.  
During the dry season salinity becomes a problem in the coastal areas. Even though surface water is very abundant, over three quarters of the Ben Tre surface water areas (water in rivers, canals, lowland areas) are affected by salinity from two or three months to the whole year [32]. The salinity intrusion is constrained to some extent using dykes, shuices and sand dunes along the coast.  
In general, when farmers rely on wet season rainfall, the salinity intrusion results _in situ_ation where there is sufficient time to grow only one rice crop per year. Farmers may also use fresh water from network of canals to provide supplementary irrigation where required, although in years when saline water intrusion occurs early, this is not possible. Over the past 30-40 years, many rice farmers in the saline affected areas have adapted to the natural conditions by growing rice in the wet season, then using the rice fields for growing shrimp (aquaculture) in the dry season [33].  
In the recent years in Ben Tre, the ratio of fruit trees output value has grown sharply. In 2002, the cultivation scheme was moving toward the reduction of low-yield rice fields, switching to aquaculture, sugar cane crops and fruit trees [34]. In Tra Vinh, rice is still the most important crop.