esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
(1) Background: The visual system homeobox 1 (VSX1) may contribute to the incidence of keratoconus (KC) in different populations. The present study investigated the role of VSX1 in autosomal recessive Pakistani families and sporadic KC patients using in silico analysis of the rare variants for the identification of the cis-acting elements in _VSX1_; (2) Methods: Mutation analysis of _VSX1_ was undertaken using Sanger sequencing of samples from seven KC families and 100 sporadic patients. In silico analysis of the rare variants and identification of cis-acting elements was determined using Human Splicing Finder (HSF), ESE finder, RESCUE-ESE and through Exon-Identity Element (EIEs) prediction software suits, combined with various algorithms to identify the effect of variations in splicing motifs; (3) Results: Screening of _VSX1_ did not reveal any novel mutation in the KC panel, but a synonymous polymorphism rs12480307 (c.546A\(\lambda\)>G; p.Ala182 Ala) in exon three and 3'UTR rs76499395 (c.*496A\(\lambda\)>G) were observed in two separate grounds. These polymorphisms were not found in any of the sporadic KC cases or 100 ethnically matched control samples. The analysis of these rare variants revealed a plausible role for these two single nucleotide polymorphisms (SNPs) in KC development through the identification of novel cis-acting elements, an exonic splicing enhancer element (ESE) and binding motifs for two micro RNAs, miRNA-203 binding and hsa-miR-3938, in the VSX1 gene structure; (4) Conclusions: Rare genetic variations in the _VSX1_ were found to have a potential contribution to KC development.  
Keywords:k keratoconus; VSX1; rare variants; cis-acting elements; miRNA +
Footnote †: journal: Appl. Sci. **2023**, 13, 4888. [[https://doi.org/10.3390/app13084888](https://doi.org/10.3390/app13084888)]([https://doi.org/10.3390/app13084888](https://doi.org/10.3390/app13084888))
## 5 Conclusions  
The results of the evaluation of some physiological parameters under normal and water deficit stress during the flowering and seed-filling stages in safflower genotypes showed that drought significantly decreased relative water content, stomatal conductance, osmotic adjustment, water potential and agronomic water use efficiency and increased the water stress index of the plant. The values of the mentioned traits among safflower genotypes were significantly different. The correlations between the physiological traits were positive (negative for plant water stress index) and significant. They also showed positive (negative for water stress index) and significant correlations with seed yield (except for osmotic adjustment). The equations for the regression analysis showed that the water stress index, water potential or agronomic water use efficiency can independently explain more than 80 percent of the total variation in seed yield under all water conditions. These parameters can be used more reliably to predict seed yield under drought stress and non-stress conditions. It is concluded that water stress index, water potential and agronomic water use efficiency are able to select high-yielding and drought-tolerant safflower genotypes for the late season. The presence of positive and significant correlations between seed yield and the number of capitula per plant (0.86) and the number of seeds per capitula (0.92) indicates that the two yield components mentioned play a more important role in the formation of seed yield than others. Of the genotypes analysed, Golemehr and Mec. 295 have higher seed yields under both normal and water deficit conditions and can be recommended for cultivation in marginal, dry and saline areas.  
Conceptualisation, B.P.E., T.O. and L.B.; methodology, B.P.E.; software, B.P.E., validation, B.P.E., T.O. and L.B.; formal analysis, B.P.E., investigation, B.P.E., T.O., A.C.B. and L.B.; resources, T.O., A.C.B. and L.B.; data curation, B.P.E.; writing--original draft preparation, B.P.E., T.O. and L.B.; writing--review and editing, B.P.E., T.O., A.C.B. and L.B.; visualisation, B.P.E., T.O., A.C.B. and L.B.; supervision, B.P.E.; project administration, B.P.E.; funding acquisition, T.O. and L.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the corresponding author. The authors would like to thank the director and staff of Khosrow-Shah Agronomic Research Station for their support during the field work and [PERSON] for his technical assistance in data analysis. The authors would also like to thank the University of Tabriz for providing facilities during this study. The authors declare no conflicts of interest.
# Optimizing Foreign Language Learning in Virtual Reality:
## 5 Case Study
### Case Study Two: Rosetta Stone VR
#### 5.2.4 Conclusions  
In comparing the participant groups of the two case studies, distinct characteristics emerged, influencing the perceived applicability and efficacy of Duolingo VR. The first case study's participant group, comprising 56 individuals, included speakers of English, Korean, and Mandarin, offering insights into Duolingo VR's effectiveness among learners with these language backgrounds. The second case, with a larger group of 75 participants, expanded this diversity to encompass speakers of English, Korean, Mandarin, and Thai. This inclusion of Thai speakers provided an additional dimension to understanding Duolingo VR's adaptability in different linguistic contexts. The broader age range and varied professional backgrounds in the second case study also contributed to a deeper comprehension of how different demographic segments engage with and benefit from virtual reality in  
Figure 7: Rosetta Stone VR: VR-CCL framework application and outcomes.  
language learning. These contrasting participant profiles across the two studies highlight the significance of considering a wide range of language learners to gauge a language education tool's universal applicability and effectiveness, reinforcing the necessity of diverse and adaptable language learning solutions in our increasingly global society.  
Rosetta Stone VR is a shining example of how virtual reality is paving the way for transformative experiences in language education. The platform, while deeply rooted in its established pedigree of language instruction, leverages the immersive nature of VR to offer a learning journey beyond mere vocabulary and grammar lessons.  
Integrating the VR-CCL framework into Rosetta Stone VR's design is a testament to the platform's dedication to pedagogical soundness. The emphasis on fostering interactive engagements between learners and content creates a dynamic learning environment. Moreover, its efforts in ensuring the cognitive load are effectively managed, combined with a robust personalization strategy, ensures that the platform caters to individual learner needs, optimizing retention and application.  
While Rosetta Stone VR has made significant inroads, there is always room for refinement. Expanding on cultural scenarios would allow learners to better understand the languages they are studying. Additionally, fostering more meaningful social interactions within the platform can further simulate real-world language usage, and refining feed-back mechanisms can ensure more precise learner corrections and understanding.  
Rosetta Stone VR epitomizes the evolution of language learning in the age of digital technology. Its pioneering approach and potential growth areas set the stage for a future where VR plays an integral role in education. It offers insights and lessons for those keen on pushing the boundaries of learning in the digital age.
## 4 Proposed Routing Algorithm for Wireless Sensor Networks  
Location Routing Algorithm with Cluster-Based Flooding (LORA_CBF) [32] was developed by the principal author of this work, and was modified to meet the requirements of precision agriculture applications.  
LORA-CBF is formed with one cluster-head, zero or more members in every cluster and one or more gateways to communicate with other cluster-heads. Each cluster-head maintains a \"Cluster Table.\" A \"Cluster Table\" is defined as a table that contains the addresses and status of neighbor nodes. A node in LORA-CBF can be in any of the following four states:  
* Undecided: A node is in this transitional state when it is in search of a cluster-head. Nodes are initially undecided when they enter the network or when they wake up.  
* Member: A node that is a member of any cluster assigned to a cluster-head. A member in LORA-CBF cannot retransmit a search packet.
* Cluster-head: A node that is responsible for all the nodes in its cluster. The cluster-head is responsible for periodically transmitting Hello messages. The cluster-head also maintains the cluster table of the member and gateway nodes in its cluster.
* Gateway: A node that is member of at least two cluster-heads that can be used for communication between clusters.
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 2 Related Works  
Utilizing AI approaches to improve positioning accuracy is boosting these years. Some of them use AI-based schemes such as neural networks with a single hidden layer [7; 8; 9], deep belief networks [10; 11], deep neural networks [12; 13], Gaussian regression [14], fuzzy neural networks [15], artificial synaptic networks [16], support vector machines (SVM) [17], random decision forest [18], or combinations of them [19]. [PERSON] et al. [20] correlate measurement in a clinical environment by using a convolutional neural network (CNN). [PERSON] et al. [21] use CNNs and [PERSON] et al. [22] use long short-term memory (LSTM) recurrent neural networks (RNN) to process a time series of radio signaling measurements. [PERSON] et al. [23] use LSTMs on ToAs in a TDoA system setup. Some recent work even estimates positions in reinforcement learning settings [24]. These works mainly target to improve positioning accuracy or improve processing efficiency.  
However, data-driven localization easily suffers from environmental changes. To make the AI model more robust against environmental change, there are works on utilizing transfer learning or optimized data set construction. Firstly, optimizing the data set construction is one well-known approach. In this approach, the data set is collected from more diverse scenarios [25]. Secondly, another approach is using transfer learning, in which an AI model is pre-trained and then the model is tuned by the newly collected data in the target scenario. In [26], the CNN model is pre-trained and then adapts the trained model to a new configuration in LOS conditions by using simulated data rather than realistic field data to reduce the site survey overhead. In addition, [27] considers a similar manner for the adaptation to a new environment. The difference is that it considers the NLOS condition. To reduce the effort of data collection in a realistic network, [28; 29] consider a solution in another way around. In these works, the AI model is firstly pre-trained by using a simulated data set and then fine-tuned by using a small amount of data from a realistic network. However, all these works just consider how to apply transfer learning in an effective way and focused scenarios are not IIoT scenarios that encounter significant gaps in the accuracy requirement. In addition, these works lack analysis of individual factors affecting the generalization performance. To the best of our knowledge, our paper is the first one to investigate the impact on generalization capability from individual typical factors in IIoT scenarios.  
Compared with existing works, the contribution of our paper is summarized as follows:  
* Identify typical factors affecting the generalization performance and then evaluate the exact impact;
* Verify the feasibility of existing generalization solutions to reduce the side effect of variation of the typical factors;
* For the cases where existing generalization solutions do not help much, we further propose directions to improve the performance.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 4 Experimental Results
### Results
#### 4.4.2 Satisfaction of Derived Quantities  
Figure 3 shows that AETM works better than MGARD uniform, but worse than MGARD non-uniform in QoI preservation. After we obtain the reconstructed output via AETM, we perform the reduction of QoI errors. We call our entire framework, including QoI satisfaction, AETMC (AETM followed by constraint satisfaction). To analyze particle simulation properties, it is crucial for XGC scientists to maintain low errors of QoI (less than 0.001 NRMSE) after decompression. As we discussed in Section 3.2.1, the QoI satisfaction process produces four Lagrange multipliers per node, and therefore we need to store four additional floating-point numbers per node. To improve storage efficiency, we use PQ to  
Figure 3: Reconstruction error comparison of PD (**a**) and the average of six QoI (**b**) between AETM, MGARDU, and MGARDN. The abbreviations here and in all other figures are expanded in Table 1. The horizontal line here and in Figures 4, 4(b) and 6 indicate the acceptable QoI error threshold. The graphs show that AETM outperforms the two versions of MGARD in terms of PD NRMSE, but QoI errors of AETM need to be improved.  
quantize the Lagrange multipliers. Hence, there is a trade-off between the amount of the reduction of the QoI errors and compression ratios as shown in Figure 4. Additionally, we need to consider the increase in PD errors after the constraint satisfaction step, as discussed in Section 3.2.1. Table 3 and Figure 4(a) show that the increase in PD errors is tolerable and the AETMC outperforms MGARD in PD NRMSE. Figures 5 and 6 show the overall results and the performance on each QoI.
## 2 Background
### Research Gaps and Contributions of This Study  
Several research gaps exist in the body of prior publications. First, although a large body of literature has explored the TCO of HDTs with diverse geographical scopes and methods [13], few studies have attempted to explore the operation cost estimation of introducing ATs due to their uncertain nature. Second, previous literature has observed the potential to offer widespread gains in safety as well as time savings; nonetheless, few studies have explored the economic side effects of ATs. Third, previous literature has not compiled and integrated a wide range of factors of operation costs of freight transport operators into the analysis framework. Fourth, only two prior studies, to our best knowledge, have employed the cost-benefit analysis framework using actual cost data from a freight transport operator. Fifth, previous studies have not analyzed TCO savings by focusing on a middle-mile route; specifically, they have not attempted to separate middle-mile travel from first- and last-mile travel as part of the analysis. We aim to fill these gaps in this study.
# Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell's Curl Equations
## 2 Theory and Method  
Figure 1 shows the frameworks of different EPT methods. Conventional differential-based methods (Figure 1a,b) rely on inverse processes involving second-order differential kernels in the EP reconstruction. In the proposed approach (Figure 1d), a forward process is used, which investigates the relationship between the EPs and the amplitude of \(B_{1}^{+}\) data, and only involves first-order derivative; thus, it is Laplacian-operator free. Details of differential equations for the description of radiofrequency problems in MRI can be found in the literature [18]. When the other conditions are pre-known (such as the structures of the coil and subject), the amplitude of the \(B_{1}^{+}\) field is mainly determined by the EPs of the imaging subject. Therefore, it is possible to optimize the EPs during the forward process so that  
the calculated amplitude of the \(B_{1}^{+}\) field can match the measured one. The efficiency and accuracy of this approach rely on the forward solver and the optimizer. Because the EPs are iteratively optimized during each forward process, an efficient forward EM solver is required to achieve a practical solution. As opposed to conventional methods, the proposed method calculates the EP values using the full set of magnetic field components available from the EM solver, without neglecting the z-component of the magnetic field (\(B_{2}\)) [18]. The details of the forward solver and optimization process are given below.
## 5 Feature Parameter Selection
### Correlation Coefficients between Geometric Information and Feature Parameters
Overall, transmitted waves are mainly related to crack depths, and reflected waves are mainly related to crack angles. The correlation coefficient standard is necessary to choose feature parameters for the SVM model to improve the prediction accuracy for crack depths and angles.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
### Energy-Efficiency Focus for Limestone Grinding  
A review of the global literature on energy efficiency in the extraction and processing of mineral resources covers a broad range of scientific studies, focusing on minimising energy consumption while maintaining high process efficiency. Energy use in the mineral industry--particularly in grinding and crushing processes--is one of the main cost drivers and has significant importance from both sustainability and production optimisation perspectives [62; 63]. Achieving energy efficiency in mineral grinding is particularly challenging due to the variability of the geomechanical properties of raw materials, the complexity of the process itself, and the multi-objective nature of grinding goals. Efforts are made to reduce energy consumption while improving grain-size distribution and increasing grinding throughput [14; 64].  
Literature reports indicate that the energy efficiency of mineral grinding processes, including limestone, can be improved by the following [13; 65]:  
* Selecting appropriate grinding technology--Using roller or high-pressure roller mills instead of traditional ball mills can lead to significant energy savings;* Optimising operational parameters--Adjusting parameters such as mill rotational speed, grinding time, and feed particle size can significantly influence the energy efficiency of the process;
* Implementing advanced process-control systems--The real-time monitoring and control of grinding parameters allow for ongoing adjustments of operating conditions, which reduce energy consumption.  
The implementation of these actions can result in substantial energy savings and improved grinding efficiency in the mineral-processing industry.
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 1 Introduction
Our research goal is to systematically analyze the state of the art in this domain. To this end, we identify the privacy threats posed by today's smart services as well as the strengths and weaknesses of the available privacy mechanisms. By comparatively reviewing these two dimensions, we elaborate which issues are already effectively covered by today's privacy mechanisms and which open research questions still need to be addressed as part of future work. With this in mind, we make the following three contributions in our paper:  
1. We present modern-day smart services from seven application domains. For each of them, we analyze which data they capture, which types of processing are used to extract information from them, and which knowledge can be derived. The selected application scenarios serve to illustrate the general data processing requirements and the privacy concerns inherently associated with such smart services. 2. We discuss state-of-the-art privacy measures for the identified data types and forms of processing. Hereby, we provide an overview of the current state in terms of the protection of sensitive data when dealing with smart services. 3. We identify, based on our findings, open privacy issues in the context of smart services that need to be overcome in order to comply with the data protection by design principle. The remainder of this paper is structured as follows: In Section 2, we analyze seven real-world application scenarios of smart services with respect to the involved data processing and highlight the opportunities offered by these smart services as well as the inherent privacy threats. Based on our findings concerning data quality requirements and potential privacy risks, we discuss appropriate state-of-the-art privacy measures and work out their strengths and weaknesses in Section 3. In Section 4, we map the opportunities and threats against the strengths and weaknesses to reflect the current state of privacy mechanisms for smart services. Based on this contrasting juxtaposition, we identify future prospects with regard to privacy issues in smart services and how to overcome them in Section 5 before concluding the paper in Section 6.
## 2 Data Processing and Network Description
### Temporal Singular Spectrum Graph
Low-frequency signals exhibit smoother patterns, whereas high-frequency signals display more pronounced variations,  
Figure 2: Converted four different signals to TSSG. In the waveform plot, the red line represents the I channel of the signal, and the blue line represents the Q channel. In the TSSG, the left side corresponds to the I channel, and the right side corresponds to the Q channel. (**a**) 32 PSK; (**b**) QPSK; (**c**) OOK; (**d**) 64 QAM. facilitating the differentiation of different modulation types. The I and Q components of the 32 PSK signal exhibit almost identical variations, but the amplitude of the Q component is smaller than that of the I component, resulting in nearly consistent patterns in the transformed images, albeit with lighter colors. The QPSK signal demonstrates intense variations, thus appearing as a dense grid of points in the image, with the Q component gradually decreasing in amplitude, leading to a transparent effect in the bottom right corner of the image. The OOK signal exhibits rapid and chaotic changes, which are reflected in its transformed image. The 64 QAM signal, both in the I and Q components, undergoes smooth variations, resulting in large areas of solid color in the transformed image. Moreover, the I and Q components exhibit opposite trends, resulting in identical shapes but contrasting colors.