esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
The complete implementation of the system required a total period of four months. The activities are distributed over time as illustrated in the Scheme 1 below.  
The key points of improvement of this system are four. The first point concerns reliability, as the reading errors committed are practically negligible, thus speaking of a reliability factor that hovers around 99%. Before the advent of this technology, due to various human factors such as reading or typing errors, reliability hovered around 90%. This was mainly due to the fact that suppliers are numerous and each presents a transport document with a different structure. The second fundamental point is related to loading times: from the moment of scanning, in a maximum time that varies between 10 and 15 min the document is loaded into the system for an entire block of shipping notes. Before the advent of this technology, however, it took from 2 to 5 min to load a single transport document, depending on the number of lines it presented. The third fundamental point concerns the moment of loading the transport document, as today the accounting loading is carried out in parallel with the physical entry of the items. Previously, this activity was not possible to follow due to the excessive workload for the operator, who carried out a massive load at the end of the day. The fourth and final fundamental point is related to the reduction of the workload of the operators. This activity, in fact, has not only drastically streamlined the flow but has also allowed to eliminate a non-value-added action such as the loading of documents into the system. Overall, it can be stated that thanks to the text recognition algorithm it was possible to save 1 FTE (full-time equivalent). In the early stages of go-live, of course, there were no such results: the document loading time was around one hour and thirty minutes, with much lower reliability, and many of these documents were going into \"Need Attention\", which is the area where the action of the \"human in the middle\" is needed. Over time, the situation considerably improved: firstly because of the training that the artificial intelligence performed with the newly sent documents, and secondly because of the directives that were defined to suppliers and subcontractors. Thus, their documents presented the following information as accurately as possible: transportation document number, document date, order number (by entering \"Order Ref.\" + internal purchase order), item code (mandatory with the company coding), item description, quantity, pallet number, gross weight in kilograms, and net weight in kilograms. To date, the varied structures of suppliers' shipping notes are all traceable to such a structure as in Figure 6 below:
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
#### 7.1.2 Limitations of Centralized Cloud Computing in Healthcare
RQ3: What Are the Differences between Centralized and Decentralized Cloud Healthcare Solutions Regarding the System's Performance (Response Times, Latency), the Privacy of Patient Data, and Cost Efficiency? This survey examined two different cloud computing models: centralized and decentralized. The analysis of these models demonstrated that each has unique advantages and disadvantages. The healthcare industry faces challenges in deciding which model to choose, as both models impact significantly how healthcare data are stored, secured, accessed, and shared. The choice between centralized and decentralized architectures depends on various factors, including the specific requirements, scalability needs, data locality, and security considerations of the applications and services deployed. The comparison below highlights their differences in architecture, data management, system performance and latency, security and privacy of patient data, and cost efficiency. 7.3.1. Architecture  
The centralized model in healthcare means that all the related information is hosted at the central server or data center. This model belongs to the client-server type of model, in which the clients are the healthcare workers and the patients who require the services of the server [52]. This model has been implemented to offer the best performance in processing healthcare information, especially within major healthcare institutions [54]. As for decentralized cloud computing, such a system can be described as a set of nodes. This type of cloud computing is configured throughout the world. This model is a peer to-peer model where every node is both a client and a server sending and receiving data. This means that the network does not deploy a single point of control, but the entirety of nodes in the network work harmoniously in handling data. This system does not require a network control server to control and oversee the network and assets in the network [83].
## 4 Discussion
### Soil Temperature, Moisture and \(\mathrm{R_{s}}\)
It is important to note that the spatial variation in R\({}_{\rm s}\) is usually driven by soil characteristics as well as biological processes, while temporal variation is usually driven by climatic variables [26]. We found few differences in the soil's chemical properties between the vegetation types. Perhaps the soil environment was similar enough across the vegetation types that the vegetation's effect on annual mean R\({}_{\rm s}\) was minimal. A study by [PERSON] and [PERSON] examined R\({}_{\rm s}\) and its influences, such as moisture and site characteristics for five different forest types. They noted an apparent lack of effect of dominant vegetation type on R\({}_{\rm s}\)[27]. Similar to our study, the site conditions were relatively homogenous. Our topography did not significantly differ, nor did site characteristics or soil chemical properties. In our case, vegetation type may have minimal utility in predicting R\({}_{\rm s}\) on a spatial scale. [PERSON] compared R\({}_{\rm s}\) across cover types in a southern Appalachian hardwood forest. Their model included soil temperature and soil moisture as the two drivers of variation. Our R\({}_{\rm s}\) rates were similar to those reported by [PERSON] [28] at the same soil temperature. Their sites were located on either foot or shoulder slopes and at lower elevation than ours and they found slight but significant differences due to changes in overstory species composition. Differences in site quality correlated with the differences they saw in R\({}_{\rm s}\) between the cover types [26; 28]. Perhaps similar to the study by [PERSON] et al. [29], they experienced topographically induced soil moisture regimes which may have contributed to changes in R\({}_{\rm s}\) between cover types. Our sites were much wetter overall. The average soil moisture values at our sites were higher than highest soil moisture value found by [PERSON] [28] suggesting soil moisture is not driving differences in R\({}_{\rm s}\) between our vegetation types. There has been controversy in the past with some studies saying the influence of vegetation on soil microclimate is sufficient enough to explain differences in R\({}_{\rm s}\) among vegetation types [30]. Others say the correlation between climate characteristics, net primary productivity, and R\({}_{\rm s}\) has caused scientists to speculate which factors are truly driving the differences in R\({}_{\rm s}\) between different vegetation types [31]. In the future, we should focus on developing models that try to isolate vegetation effects and partition the R\({}_{\rm s}\) into its autotrophic and heterotrophic components in order to get a better picture of species' influence on R\({}_{\rm s}\).
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 4 Channel Coding Schemes for Error Correction in VLC in E-Healthcare Scenarios
### Turbo Codes and Polar Codes  
Another powerful coding scheme employed in VLC systems is the turbo code, which is designed to approach the Shannon limit, in terms of error correction performance. Turbo codes are iterative codes that combine two or more convolutional codes in parallel, separated by an interleaver. In the context of VLC, turbo codes have been implemented to improve indoor data communication. For example, a study using MATLAB simulations version R2022a showed that turbo codes significantly reduced BER compared to uncoded systems, demonstrating their potential to improve the reliability of VLC systems even in high-interference environments [72]. This improvement was further validated by hardware implementations, which confirmed that reliable data transmission over distances up to 5 m indoors is achievable with turbo codes, making them suitable for applications in e-health, such as real-time patient monitoring.  
Polar codes, introduced as the first class of codes to achieve channel capacity, have also gained traction in VLC systems. Polar codes use channel polarization techniques to divide the channel into \"good\" and \"bad\" sub-channels, transmitting data over the more reliable sub-channels, to optimize error correction. A novel application of polar codes in RLL coding has been proposed, to further improve BER performance and mitigate flicker in VLC systems [73; 74]. In RLL decoding, frozen bit indices are used to generate RLL codes, improving the resilience of VLC systems against flicker--a significant concern when LEDs are used for both illumination and data transmission in healthcare environments. The advantage of polar codes lies in their scalability; as the code length increases, so does their ability to approach the Shannon limit, making them highly efficient for both short-range and long-range VLC communication.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reinforcement Learning Energy-Efficient Routing Protocols (RL-Based E2 RPs)
#### 5.4.2. Q-Learning-Based Energy-Efficient Lifetime Aware Routing Protocol (QE2 LARP)  
In [156], the author suggested a routing protocol based on Q-learning that is cognizant of network lifetime and is energy-efficient. In this protocol, the Q-values are determined on the basis of successful data packet transmission. In the Q-learning technique, an assistant's performance is determined by the reward value that is given based on their actions. The node distribution energy and the remaining energy of a node are used to formulate the proposed routing protocol reward function. The proposed protocol will select a route, in which a route has high remaining energy with shortest path. Therefore, if the protocol selects a route with low remaining energy, rather than negative reward will be offered. Although, when compared with vector-based forwarding (VBF) routing protocol, the proposed routing protocol achieves great performance in terms of energy efficiency and data packet delivery.
# Internet Traffic Prediction with Distributed Multi-Agent Learning
## 2 Related Work
### Machine Learning-Based Prediction Models  
Machine learning models are applied for network traffic prediction because of their strong learning ability for mapping historical network traffic data to future situations in a supervised learning approach. With the accumulation of network traffic datasets and the growth of computation capacities, machine learning models are becoming popular in the computer network domain, not only for traffic prediction but also for a wider range of applications, e.g., traffic classification, routing and intrusion detection [21].  
Compared with deep learning models represented by deep neural networks, the machine learning models discussed in this subsection are those with shallow structures, e.g., decision trees and random forests. These tree-based machine learning models represent better interpretability and less overfitting severity compared with neural networks and are still used in network traffic prediction tasks, e.g., Gaussian process regression has been adopted for network traffic prediction in different network scenarios [22; 23; 24]  
Besides, different machine learning models can be used jointly, e.g., random forest (RF) and LightGBM are used together for mobile network traffic prediction when RF is used for feature filtering and LightGBM is used to make predictions [25]. Machine learning models can also be used together with statistical models for further performance improvement, e.g., the combination of soft clustering and traditional time series models [26].
# Durability of Subsea Tunnels under the Coupled Action of Stress and Chloride Ions
## 3 Effect of Stress on Chloride Ions Transfer  
Under the pressure of the surrounding rock, each section of a subsea tunnel bears the bending moment, axial force, and shear force. Accordingly, the stress on the section includes compressive stress, tensile stress, and shear stress. Under normal circumstances, the service load is not sufficient to cause the degradation of the concrete. However, as time passes, the load changes the distribution of pores and micro-cracks in the concrete, and can even produce new macroscopic cracks or micro-cracks, which will affect the diffusion and permeation of chloride ions in the concrete [24; 25; 26; 27]. The components under tension will experience more severe corrosion than the those experiencing compression [28; 29].
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 3 Taxonomy of Input Data Types  
The first taxonomy organizes the analyzed works by the input data type, which is related to the equipment used to obtain the data and further influences the processing algorithms used. It allows readers to familiarize themselves with the most common representations of data structures and the wide range of their kinds.
# Generalized Pustular Soriasis: Divergence of Innate and Adaptive Immunity
## 2 Gene Mutations in GPP
### MPO Mutation  
The _MPO_ gene encodes myeloperoxidase (MPO), an essential component of neutrophil azurophil granules [81]. Although the relationship between MPO deficiency and pustular psoriasis was first described in 1996 in an individual case report, it was only recently that a mutation in _MPO_ gene was recognized as a background for GPP [82; 83]. [PERSON] et al. performed a whole-exome sequencing of 19 unrelated individuals with GPP and identified a subject harboring a homozygous splice-site mutation in _MPO_. _MPO_ screening in diseases phenotypically related to GPP uncovered further disease alleles in one patient with acral pustular psoriasis and in two subjects with AGEP [83]. Importantly, all three _MPO_ gene variants that were observed in that study have a well-established impact on protein function, as they have been repeatedly observed in individuals with MPO deficiency [84; 85]. Moreover, the phenotypic effects of _MPO_ mutations were explored using a phenome-wide association study (PheWAS), which allowed identification of important relationships between genetic variants and a wide range of phenotypes. In vitro functional analysis revealed that mutations in the _MPO_ gene cause an increase in neutrophil accumulation and activity, as well as a reduction in the number of apoptotic neutrophils. This observation further supported the role of this gene in neutrophil hemostasis and indicated its role in GPP pathogenesis [83]. These important findings regarding the significance of _MPO_ gene variants in GPP were further confirmed by [PERSON] et al., who discovered that 15 out of 74 patients affected by GPP carried eight variants in _MPO_ gene that were all validated as loss-of-function mutations. They also performed a downstream analysis, which subsequently found that the activity of neutrophil elastase (NE), CTSG, and proteinase 3 (PR3), serine proteasesthat cleave IL-36 precursors into very active pro-inflammatory IL-36 cytokines, inversely correlated with MPO activity. This observation demonstrated that MPO deficiency was strongly linked to IL-36 pathway activation. Moreover, MPO deficiency caused defective formation of neutrophil extracellular traps (NETs) in the phorbol myristate acetate-induced pathway and reduced phagocytosis of neutrophils by monocytes (efferocytosis), thereby contributing to the prolonged persistence of harmful neutrophils and the reduced ability to resolve skin inflammation in GPP. Notably, a genotype-phenotype relationship similar to that of _IL36 RN_ gene was found in the abovementioned study, as the dosage of abnormal alleles of _MPO_ gene negatively correlated with the age of disease onset [86]. Considering that the results of these studies implicated MPO as an important modulating enzyme of inflammation, MPO itself or MPO-related pathways represent attractive targets for anti-inflammatory therapies in GPP.  
The above described mutations underlying GPP and their significance are depicted in Table 1.
## 3 Results and Discussion
### Calibration of CR-39 detectors
The percentage errors on the proton range calculated with TRACK-P with respect to SRIM lie within a 3% range. The proton track diameter evolution as a function of the incident energy for a CR-39 detector, which results from TRACK-P calculations in the 1-9 MeV proton energy range is presented in Figure 4b (blue triangles). It can be observed that the proton track diameter magnitude decreases from 9.38 \(\upmu\)m (for 1 MeV proton energy) to 0.5 \(\upmu\)m (for 9 MeV proton energy) as the proton energy increases, presenting a similar behavior with the one reported in [8], for 1-6 MeV proton energy range. Figure 4b (red points) illustrates the proton track diameters measured on the post-etched CR-39 detectors after irradiation with monoenergetic proton beams. A simple analytical function of type y = Aexp(\(-\)BE) was used to fit the proton track diameter dependence on energy (see Figure 4b), where E is the proton energy, and the fitting parameters A, B are indicated in the figure legend. The A and B fitting parameters were iteratively determined using the least-squares fitting procedure in order to assured a minimum percentage error (within \(\pm\)15%) of the measured proton track values with respect to those calculated with the TRACK-P software. By calculating the proton track diameter with the analytical function from Figure 4b, a value of -9 \(\upmu\)m at 1 MeV proton energy results, together with a 0.88 \(\upmu\)m track diameter for a proton energy value of 9 MeV. The proton track diameter dependence on the proton energy shown in Figure 4b (calculated both experimentally and theoretically) is in good agreement with previous results [3; 8] for the used etching parameters.
# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
## 3 Model Established
### Model Evaluation  
How can the quality of a model be assessed? When building and evaluating predictive models, selecting appropriate evaluation metrics is crucial. The accuracy of model predictions and the model's applicability should be assessed in the context of the data characteristics. In order to test the predictive performance of the LSTM model and the LSTM model optimized by SSA algorithms, this paper uses common metrics such as root-mean-square error [33; 34; 35] (\(RMSE\)), mean absolute error [33; 35] (\(MAE\)), and the correlation coefficient [36] (\(R^{2}\)) to evaluate the model's performance. The specific calculation formulas are stated in Equations (15)-(17):  
\(MAE\) can visualize the degree of average absolute deviation of the predicted value from the true value and is easy to understand.  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\left|(y_{i}-\hat{y}_{i})\right| \tag{15}\]  
A smaller \(RMSE\) indicates lower prediction errors and higher model accuracy. \(RMSE\) quantifies the difference between predicted and true values. It is intuitive and easy to interpret. Furthermore, \(RMSE\) is highly sensitive to outliers (i.e., extreme values), making it effective at reflecting the model's performance in the presence of large errors. This enables \(RMSE\) to not only evaluate overall prediction accuracy but also uncover the model's bias in extreme cases.  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{16}\]  
It has the following advantages: it is easy to calculate, intuitive to interpret, and unit-independent, and also allows cross-model comparison. \(R^{2}\) is particularly suitable for preliminary evaluation and the selection of regression models, providing a quick performance assessment basis for practical applications. This simple scoring mechanism is intuitive and easy to understand, making it convenient for quickly comparing model performance. The value of \(R^{2}\) ranges from [0, 1], with values closer to 1 indicating better model fit.  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)}{\sum\limits _{i=1}^{n}\left(y_{i}-\overline{y}_{i}\right)} \tag{17}\]  
\[\overline{y}=\frac{1}{n}\sum\limits_{i=1}^{n}y_{i} \tag{18}\]  
where \(y_{i}\) is the true value, \(\hat{y}_{i}\) is the predicted value, \(n\) is the number of test samples, and the formula for \(\overline{y}\) is given in Equation (18).
## 6 Conclusions  
This paper develops an FTIGC for flight vehicles without LOS angle rate measurement information, considering the potential actuator fault. To begin with, the transformation relationship between the LOS angles and BLOS angles is established and an ESO is designed by observing BLOS angles, providing the estimation of the LOS angular rate for feedback and object acceleration information for feedforward. An FTIGC, based on the adaptive backstepping and DSC techniques, subject to the external disturbance and actuator fault, is proposed, such that LOS angular rates will converge to zero. With the application of the parallel approaching principle, it can be concluded that the flight vehicle will accurately arrive at the object position. The effectiveness of the proposed FTIGC and the robustness of the system are verified theoretically and numerically. In our future work, we will pay more attention to analyzing the influence of the control parameters and application in the hardware environment.  
Conceptualization, X.Y. and S.L.; methodology, X.Y.; software, X.Y. and F.Z.; validation, S.L.; formal analysis, X.Y.; investigation, X.Y.; resources, S.L.; data curation, X.Y. and F.Z.; writing--original draft preparation, X.Y. and F.Z.; writing--review and editing, X.Y. and S.L.; visualization, X.Y.; supervision, S.L.; project administration, F.Z.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Innovation Program of Hunan Province (2023 RC3265).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors [PERSON] and [PERSON] were employed by the company Hunan Vanguard Group Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.