esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PCS Framework for Designing RL Algorithms for Digital Intervention Development
### Stability (S)  
Stability concerns how an RL algorithm's results change with minor perturbations and the documentation and reproducibility of results. In online RL, stability plays two roles. First, the RL algorithm must run stably and automatically without the need for constant human monitoring and adjustment. This is particularly critical as users abandon digital interventions that have inconsistent functionality (unstable RL algorithm) [20; 21]. Second, the RL algorithm should perform well across a variety of potential real-world environments. A critical tool in assessing stability to perturbations of the environment is the use of simulation test beds. Test beds include a variety of plausible environmental variants, each of which encodes different concerns of the investigative team. The following are challenging attributes of probable environments in digital intervention problems that one could design test beds for:  
* **User Heterogeneity:** There is likely some amount of user heterogeneity in response to actions, even when users are in the same context. User heterogeneity can be partially due to unobserved user traits (e.g., factors that are stable or change slowly over time, like family composition or personality type). The amount of between-user heterogeneity impacts whether an RL algorithm that pools data (partially or using clusters) across users to select actions will lead to improved rewards.
* **Non-Stationarity:** Unobserved factors common to all users such as societal changes (e.g., a new wave of the pandemic), and time-varying unobserved treatment burden (e.g., a user's response to a digital intervention may depend on how many days the user has experienced the intervention) may make the distribution of the reward appear to vary with time, i.e., non-stationary.  
* **High-Noise Environments:** Digital interventions typically deliver treatments to users in highly noisy environments. This is in part because digital interventions deliver treatments to users in daily life, where many unobserved factors (e.g., social context, mood, or stress) can affect a user's responsiveness to an intervention. If unobserved, these factors produce noise. Moreover, the effect of digital prompts on a near-term reward tends to be small due to the nature of the intervention. Therefore, it is important to evaluate the algorithm's ability to personalize even in highly noisy, low signal-to-noise ratio environments.
## 5 Numerical Studies
### Sensitivity Analysis
#### 5.4.2 Impact of the Depot Capacity  
This subsection is intended to explore the influence of the depot capacity by using instances Ins-19-4 and Ins-45-1 as examples. For this purpose, the value of \(Q\) is incrementally increased, starting from 20 for Ins-19-4 and 22 for Ins-45-1, with a step size of 2. To demonstrate the impact of depot capacity, we will focus on three key metrics: (i) the total cost; (ii) the travel cost; (iii) the number of dispatched trucks. The experimental results are depicted in Figure 8.  
We can observe from Figure 8 that there is a notable decrease in the number of dispatched trucks as the depot capacity \(Q\) increases. Specifically, for Ins-45-1, the fleet size of dispatched trucks will decrease from 18 to 13 when \(Q\) increases from 20 to 30. This trend is in line with our expectation, as meeting the demand for trucks to make an intermediate stop at the depot to deposit empty containers becomes increasingly easier with an expanded depot capacity. As a result, trucks can be free from effortless waiting at the depot or OF customers' locations and thus can be fully utilized to fulfill requests. Besides, we can also find that the total cost exhibits a decreasing trend with the increase of \(Q\), which is undoubtedly attributed to the reduction in the number of trucks. Similar to \(e\), the reduction in the total cost and the number of trucks employed for service will ultimately level out when the depot capacity exceeds a certain threshold. Interestingly, the travel cost shows a completely opposite trend. Results in Figure 8 reveal that the travel cost increases from 65.6175 to 68.4807 when \(Q\) increases from 20 to 30. One plausible explanation for this occurrence is as follows: with an increase in depot capacity, trucks are permitted to make intermediate stops at the depot for depositing empty containers, resulting in slightly longer travel times compared to direct travel to subsequent customers' locations. Consequently, the travel cost tends to increase with the growth of depot capacity. However, the decline in the number of trucks compensates for the increase in travel cost, contributing to overall cost savings. This finding is also exemplified by Ins-19-4 and the illustrative example in Section 3.4.  
Overall, the above results clearly highlight that the depot capacity has great impacts on the considered metrics. Scaling up the depot capacity proves advantageous in reducing the overall cost. Nevertheless, the operator should determine the optimal depot capacity based on several factors, including space availability, cost effectiveness, turnover rate of empty containers, and so on.  
Figure 8: Variations of the total cost, travel cost, and the number of dispatched trucks with the increase of \(Q\).
# A Review of Blockchain-Based Secure Sharing of Healthcare Data
## 2 Blockchain
### Smart Contracts  
Smart contracts are computer protocols that disseminate, validate or enforce contracts in an informational manner [19]. Smart contracts do not require third-party authentication and successful transactions are traceable and irreversible. A computer program is used to write a legally valid contract, and this contract can be executed automatically. A smart contract is a code deployed on the blockchain that ensures that transactions are safe and secure without third-party oversight [20]. The smart contract process is shown in Figure 2.  
1. Decentralized The execution of smart contracts does not need to rely on the participation or intervention of third-party organizations, and the supervision and arbitration of contracts are performed by computers;
2. Untamperable: Once a smart contract is deployed, all contents cannot be modified. This is somewhat like a contract in the traditional world, which cannot be modified once it is signed;
3. Low cost: Since smart contracts do not require supervision by a third-party intermediary, once a breach of contract occurs, the code is enforced and has a much lower cost compared to traditional contracts;
4. Open and transparent: Once deployed successfully, a smart contract will run according to the design code and can be viewed by anyone, with a high degree of transparency [21].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Decentralization** & **Throughput** & **Cost** & **Scalability** \\ \hline Public chain & high & low & high & poor \\ Consortium chain & medium & medium & medium & great \\ Private chain & low & high & low & great \\ Hybrid chain & - & - & low & great \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different types of blockchains.
# Optimization of the Control Algorithm for an Active Bearing Support
## 2 Natural Frequencies of the Spindle System
Based on the results, it was found that the lowest natural vibration frequencies were 265 Hz for shaft A and 278 Hz for shaft B, which corresponded to the form of vibrations associated with the spindle rocking, which is accompanied by the largest relative displacements of the bearing rings. This theory was confirmed by the research whose results the author included in the article [3]. Figure 2: Harmonic forcing, where \(A\)—vibration amplitude; \(\omega\)—circular frequency of vibrations, \(\varphi_{0}\)—phase shift angle; \(t\)—time [22]. The natural frequencies determined in this way were used to estimate the range of harmonic excitations on a real object. The range of 150-450 Hz was adopted for experimental research. As part of the preliminary research presented in article [23], the author checked the correct functioning of the piezoelectric actuators (PSt 150/10/40 VS15 from Piezomechanik GmbH) mounted in the active support, the displacement sensors, and also showed that as the preload of the bearings increases, the stiffness of the system increases at the spindle and the amplitude of vibrations of its front tip decreases. These tests consisted of a manual (step) change of the bearing preload value in their full range (60-430 N) and  
Figure 3: Relative vibration amplitudes of both shaft lengths: (**a**) the first form, shaft type A, frequency 265 Hz; (**b**) the first form, shaft type B, frequency 278 Hz; (**c**) the second form, shaft type A, frequency 1147 Hz; (**d**) the second form bastard shaft type B, frequency 979 Hz; (**e**) the third form bastard shaft type A, frequency 2191 Hz; (**f**) the third form bastard shaft type B, frequency 1878 Hz. the measurement of the average frequency value for the first form of the spindle's natural vibrations. In this way, it was demonstrated that the system is sensitive to changes in the bearing preload value. These results gave the impetus for further work on the fully active control of the preload value and active change of spindle stiffness during its operation.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 4 Experimental Results
### Results and Discussion
#### 4.3.2 Time Classification

Table 3: Result of classifying “night” and “day (dawn)” time periods using intersection with the weighting approach. that the proposed scheme is superior to the existing deep learning models for time period classification even though three classes (day, dawn, and night) exist. The existing models have high complexity because they train a whole area of images and find optimal feature. The usual dataset includes various place, season, objects, and backgrounds. Therefore, it needs to focus on the discriminative feature of time information. We simplify this problem through three approaches: sky detection, weighted histogram comparison, and hierarchical classification structure. As a result, we have better performance than other models. However, there are a few things to that need to be improved. If various data can be collected, we can generate a more representative reference histogram for robust classification. In addition, future studies will be needed for more detailed time classification such as dawn, morning, noon, afternoon, evening, and deep night.
## 3 Materials and Methods
### GeoSpark System Overview
#### 3.6.3 Spatio-Temporal Partitioning  
The proposed approach considers a naive temporal partitioning from which a collection of timestamps is considered. These timestamps are related to the sampling period where the system recorded the spatio-temporal data with its usage. As a result, the size of the loaded data in BikesRDD may be different for different timestamps since some mobile objects may not have a spatio-temporal  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **PointRDDs** \\ \hline
**Features** & **Type of PointRDD** \\ \hline \((x,y)\) & Spatial Points RDD \\ \hline \((u,\theta)\) & Polar Points \\ \hline \((a_{x},Ux)\) & Hough-\(X\) of \(x\) PointRDD \\ \hline \((a_{y},U_{y})\) & Hough-\(X\) of \(y\) PointRDD \\ \hline \((b_{x},w_{x})\) & Hough-\(Y\) of \(x\) PointRDD \\ \hline \((b_{y},w_{y})\) & Hough-\(Y\) of \(y\) PointRDD \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Different Types of Point Resilient Distributed Dataset (RDDs) According to Selected Features.  
footprint for all recorded timestamps. Here, in our simulations, the sampling period is supposed to be similar for all bike riders trajectory data. From the trajectory (i.e., spatio-temporal) points of a number of bike riders objects at different timestamps, a collection of BikesRDD is constructed.  
Following the previous aspects, GeoSpark can easily utilize spatial partitioning over the temporal partition of the data of these bikes. In each temporal partition, a bike rider may have not necessarily moved because random delays may be employed. Hence, in a specific time partition, the spatial data of all bikes may not necessarily participate.  
BikesRDD constitutes a specialized GeoSpark PointRDD, which consists of individual bike riders' records. The spatial partitioning on the BikesRDD concerns bike riders selected features, such as the location data. In several temporal partitions, the BikesRDD are simulated one by one.  
More to the point, GeoSpark offers low overhead spatial partitioning approaches that take into consideration the spatial distribution of the data as well as repartitions a loaded Spatial RDD in an automatic way. In each Spatial RDD, several spatially proximal objects are grouped into the same partition and, as a result, the partitioning layer in GeoSpark partitions the workload in a spatial way and periodically repartitions this workload in order to keep balanced partitions. Also, it supports a variety of grid-type partitioning methods, such as equal-grids, \(R\)-tree, Quad-tree and \(KDB\)-tree, to name a few [33].
## 3 Results
### Expression Analysis of Genes Related to ABA Hormone Metabolism and Signaling  
A total of 8 unigenes were identified as encoding ABA metabolic enzymes, one for zeaxanthin epoxidase (ZEP), one for 9-cis-epoxycarotenoid dioxygenase (NCED), 4 for ABA-aldehyde oxidase (AAO), and two for ABA 8\({}^{\prime}\)-hydroxylases (CYP707A) (Figure 2A). The genes encoding ZEP were up-regulated differentially (RPKM ratio > 1) in PHS tolerant cultivar 'Woori' under both PHS and ABA treatment (WP and WA, respectively) and not differentially (RPKM ratio < 1) in PHS susceptible cultivar 'Keumgang' under both PHS and ABA treatment (KP and KA, respectively). Along with ZEP, NCED is involved in the early phase of ABA hormone metabolism (conversion of violaxanthin to xanthoxin). The gene encoding NCED was up-regulated only in 'Woori (WP and WA)'. Most \(AAO\) genes involved in ABA metabolic pathway were differentially up-regulated in all samples, and especially highly up-regulated in WA. Two ABA catabolic genes \(CYP707A\) were identified in our dataset and showed contrasting expression patterns.  
Figure 1: Network analysis of (**A**) DEGs group-I; (**B**) II; (**C**) III; and (**D**) IV. Analysis shows specific gene ontology (GO) categories enrichment of DEGs that showed higher expression in ‘Woori’ compared to ‘Keumgang’.  
Based on our RNA-seq results, 28 unigenes (7 _PYR/PYL/RCAR_, 4 _PP2C_, 7 _SnRK_, 6 _ABIs_, and 4 _ABF/AREBs_) were annotated as ABA signaling related genes (Figure 2B). Among 7 genes encoding ABA receptors PYR/PYL/RCAR, 5 genes showed same expression patterns (differentially up-regulated in KP, WP, and WA, and down-regulated in KA). Most signaling pathways downstream of PYR/PYL/RCAR were differentially up-regulated, but ABIs and one AREB/ABF were differentially down-regulated in KP. The detailed information and comparative expression value of ABA metabolic and signaling genes are shown in Table S9.
## 1 Introduction
These may include fixation of atmospheric nitrogen, phosphate solubilization, iron acquisition, balancing the levels of plant hormones, and prevention of pathogen attack [19; 20; 21; 22]. In agricultural practice, the application of PGPR consortia, instead of a single strain, is increasingly more common to achieve or elicit all the desired responses [23; 24]. Additionally, several variables, such as temperature, water availability, soil characteristics, plant species, and so on, limit the effectiveness of PGPR use [25]. For example, it was found that while an increase in soil salinity could not affect rhizobacteria growth, it could induce a loss in the production of their plant-growth promoting traits [26]. Nonetheless, several studies have not only identified salt tolerate PGPR but also associated PGPR consortia specific for salt-stressed plants [18; 24]. Halophytes like _H. portulacoides_ can inhabit high salinity areas, but it is well-known that even salt-tolerant species have their NaCl limitations. Exceeding the tolerance limit provokes physiological and biochemical alterations, affecting growth and potentially causing death [8]. Despite this, most studies on PGPR-inducing salt-stress resistance have been undertaken in salt-sensitive plants, while information regarding PGPR application to improve salinity tolerance in halophytes is still scarce. For this study, PGPRs species most abundant in coastal salt marshes, specifically _Bacillus aryabhattai_, _Stentroroplomonas rhizophila_, _Pseudomonas oryzaehibitans_, and _Salinicola endophyticicus_ have been selected in order to build a potentially effective consortium. Our choice was supported by recent promising findings on salt stress amelioration in halophyte plants [27; 28; 29; 24]. The consortium was assembled using high salinity resistant rhizobacteria with different plant-growth promotion activities and traits that complement and reinforce each other as seen in Table 1. This work aims at evaluating the physiological fitness and PGPR-mediated improvement of stress tolerance of _H. portulacoides_ under mild and severe salt stress. Therefore we have inoculated experimental plants with a PGPB consortium composed of rhizobacteria with different plant-growth promoting traits and have exposed them to mild and severe salinity levels. We have analysed the photochemical performance and biochemical traits. We will discuss the potential PGPR have to ameliorate adverse effects of salinity on \(H\). _portulacoides_. The question is at what degree the biotechnological potential for re-vegetation of salt-affected soils can be improved or actually be completely restored.
## 4 Summary and Conclusions
### Conclusions Related to the Energy Efficiency of the Building  
The results of energy simulations indicated that the annual energy consumption per square meter of conditioned area of the reference building located in Bialystok was 41.08 kWh/m\({}^{2}\), 31.92 kWh/m\({}^{2}\) and 26.26 kWh/m\({}^{2}\) in the case of the installation of double-, triple- and quadruple-glazed windows, respectively. In the case of the Kiruna location, the energy demand was higher by about 25%; therefore, the corresponding indicators had higher values, i.e., 56.87 kWh/m\({}^{2}\), 42.84 kWh/m\({}^{2}\), and 34.60 kWh/m\({}^{2}\), compared to the previous ones.  
As is commonly known, an increase in the number of panes results in a decrease in the heat transfer coefficient but at the same time limits heat gains from solar radiation. Thus, a triple-glazed window reduced heat gains by 32% compared to a double-glazed window. An even greater reduction of over 50% occurred in the case of quadruple-glazed windows.  
A separate issue is the decrease in the amount of daylight associated with the increase in the number of panes in the window. Light transmission affects both occupant comfort and the additional consumption of electricity for artificial lighting. The simulation results showed that the use of double-glazed windows reduces the energy demand by about 2%,  
Figure 11: Dependence of IRR values in the considered time period after taking into account the 2% increase in energy prices: (**left**)—Bialystok, (**right**)—Kiruna.  
with about 1% for triple-glazed windows, compared to windows with four panes. Slightly higher differences appeared in the case of the building location in Kiruna and amounted to about 2.5% and 1.2%. Therefore, it can be concluded that an increase in the number of glass panes affects the increase in electricity consumption for artificial lighting, but this effect is insignificant.
# Estimation of Gross Primary Productivity (GPP)
## Results  
### GPP Temporal Variability  
The SRC plantation showed very high plant photosynthesis, as indicated in Figure 2a by cumulative daily GPP values. The plant photosynthesis varied largely depending on environmental conditions and management activity (Figure 2). The seasonal and interannual variability of photosynthesis was associated mainly with soil water content (SWC, Figure 2b). The 2016 growing season, which corresponds to the third and the last year of the third rotation, showed the highest values of daily GPP. The constant and high values of SWC, for most of the year, did not limit the carbon photosynthesis uptake for that year. In contrast, the photosynthesis in the 2018 growing season, which had the hottest and driest summer, was reduced by a sharp decrease of SWC at the end of the summer and a low SWC until September. Compared to 2016 and 2018, an earlier recharge of SWC was observed after the summer in 2017 (Figure 2b). This increase of SWC arose GPP activity at the end of the 2017 growing season. Air temperature and solar radiation were similar between the three growing seasons (Figure 2a).  
The coppicing strongly reduced the photosynthetic activity of the SRC plantation in spring 2017 (Figure 2a). The maximum GPP was only half that in other years (15-30 g C m\({}^{-2}\) d\({}^{-1}\)).
# A Next-Generation Core Network Architecture for Mobile Networks
## 1 Introduction
In particular, it is now possible, for example, to host an UE's core network (i.e., its VCN) on: public cloud infrastructure (e.g., Microsoft Azure, Amazon AWS); private cloud infrastructure (e.g., enterprise, Government); cellular operator infrastructure. Furthermore, due to its single IP-layer architecture (see Section 3.3) and VCN's live migration capability (see Section 7), the new core network improves on overall latency and delay. Besides classical eNodeBs, we also consider a second scenario with an enhanced eNodeB, thus breaking the assumption of backwards compatibility with existing access networks. Furthermore, we discuss additional aspects of the new architecture such as IP addressing and non-security-related novel services enabled by the new architecture. Finally, we add additional cellular traffic measurements in order to better study the feasibility of the new architecture and fine-tune some of its most critical parameters. The rest of the paper is organized as follows. In Section 2, we briefly discuss current efforts in cellular core network technology. Section 3 introduces our new architecture and its main elements, while in Section 4 we discuss IP addressing in the new architecture. In Section 5, we present message flows for basic operations of the cellular network (e.g., registering, making a call), while Section 6 discusses in detail the concept of \"UE redirection\". Section 7 introduces a new type of mobility management and Section 8 presents some high level considerations on scalability. Relevant advantages of the new architecture are discussed in Sections 9 and 10, with a particular focus on security. In Section 11, we present experimental measurements and related architectural considerations. Finally, Section 12 concludes the paper.