esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing Safe General LED Lighting that Provides the UVB Benefits of Sunlight
###### Abstract  
The ultraviolet (UV) rays emitted from sunlight greatly influence human health. Excessive exposure to UV rays can be harmful to eyes and skin; however, limited UVB exposure is essential for the synthesis of vitamin D. Nowadays, owing to insufficient exposure to natural light, there is increasing concerns about low vitamin D amongst individuals. To address this issue, many lighting devices that provide UVB doses have been released; however, such devices are only used for treatments or for special purposes. This study proposes a general indoor lighting system with a UVB LED light source to provide safe UVB doses to users who spend large amounts of time indorors. The optical characteristics of two UVB LEDs with output of 20 and 100 mW were analyzed based on their distances and applied currents. The light source combination of UVB LEDs that meets the UV hazard standard of IEC-62471 was derived; this is a photobiological safety evaluation standard of LED lighting devices. We then produced a lighting module in which the UVB LED light source was applied to general LED lighting and measured and analyzed the spectral irradiance of the proposed lighting according to the measurement standard for the general lighting of IEC 62471. The actinic UV hazard (AUV) and near-UV hazard (NUV) were calculated to be 0.001 and 10 W/m\({}^{2}\), respectively. Thus, the provision of UVB dose did not pose any risks. In addition, the total EUV (Errythermal weighted UV) dose when the proposed lighting was implemented for 16 h was 187.66 J/m\({}^{2}\), confirming that this dose did not cause erythema for the general skin types (Skin Types 1-6). Further, the design plan of general indoor lighting with a UVB LED light source is presented.  
UVB; UVB LED general lighting; UV safety evaluation; general lighting design; UVB characteristics of sunlight +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this paper, the deformation and failure characteristics of overburden in deep buried thick Jurassic coal seam mining in western China have been investigated by using different approaches.  
1. The overburden structure of the Jurassic deep coal seam in western China is quite different from the overburden structure of the Carboniferous Permian in north China. At the same time, the key layer of overburden is located mostly in the middle and upper parts, resulting in large fracture height characteristics. The empirical formula based on the conventional \"three regulations\" is not suitable for the calculation of the \"two zones\" height, so a different approach is needed.
2. The average thickness of the coal seam in the test area is 4.35 m, the maximum height of the measured water-flowing fractured zone is 106 m, and the development height of the caving zone is 22 m, so the R\({}_{\text{f}}\) is 24.4 times, slightly larger than 21 times the three-dimensional numerical simulation, which is basically consistent with the comprehensive test results of the adjacent mining area (with an average of 26 times). At the same time, the overburden failure mode is a saddle-shaped failure section with a high middle and a low middle at both ends. In the range of 20-70 m of overburden vertical height, the influence range of advance abutment pressure of the working face can reach approximately 60 m, and the separation fracture development zone is in a range of 120-127 m.
3. The field resistivity measurement period is complete, and the effective electric field data are obtained from 393 m in front of the orifice to 120 m behind the orifice. This effectively captures information about the overburden process from initial stability to deformation, then during fracture development, fracture and secondary stability after mining, which has a certain guiding role in the analysis of overburden deformation and fracture development rules.  
Conceptualization, R.W. and B.S.; methodology, B.S.; software, L.Z.; validation, R.W., Y.W. and B.S.; formal analysis, R.W.; investigation, L.Z.; resources, G.Z.; data curation, Y.W.; writing--original draft preparation, R.W.; writing--review and editing, B.S.; supervision, B.S.; project administration, R.W.; funding acquisition, B.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Foundation of the Key Laboratory of Mine Geological Disaster Prevention of Anhui Province (grant no. 2023-MGDP-02); the Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining (grant EC2022010).  
This study involves field observation data, numerical simulation data, and the theoretical calculation data. The data used to support the findings of this study are available from the corresponding author upon request.  
Author [PERSON] was employed by the company Anhui Huizhou Geology Security Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 6 Conclusions  
This paper discusses the problem of signal receivers based on deep learning in an MIMO-OFDM scenario. Compared with a single antenna, the output signal length in the multi-antenna scenario is longer and the receiver design is more complex. The proposed receiver based on deep learning, DMCNet, combines complex convolution and a complex full connection structure, which has a better performance and fewer parameters than previous fully connected networks.  
Compared with traditional receiver algorithms, the receiver scheme based on deep learning is more robust, and the accuracy is less affected when nonlinear noise (such as cyclic prefix and peak clipping) is introduced, which also indicates that the deep learning receiver has a certain ability to fit the channel changes. In addition, the influence of some key modules on the accuracy of the neural network is compared. Based on the data-driven model DMCNet, this work expands the model-driven scheme to DMCNET-Pro. Combined with the relevant algorithms in traditional communication, the model-driven scheme has a higher accuracy and fewer parameters in some scenarios.  
In summary, the MIMO-OFDM receiver based on deep learning has more advantages than traditional methods in some aspects, and has certain research and application prospects.  
Conceptualization, T.Z.; Methodology, P.L. and Y.X.; Formal analysis, X.Y. and Z.L. ([PERSON]): Investigation, Q.Y.; Writing--original draft, G.Y.; Writing--review & editing, Z.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Key Technologies R&D Program (2022 YFB4300400), China Scholarship Council (202106475002), Belt and Road Initiative Innovative Talent Exchange Foreign Expert Project (DL2023107003L), and in partly by National Natural Science Foundation of China (61801035).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 12: Comparison of data-driven and model-driven results (**left**) and model-driven Res-Block channel numbers (**right**).
## 4 Materials and Methods
### Hidden Markov Models  
Hidden Markov model (HMM) is a model consisting of two parts (elements, \"layers\"): hidden and observed (inner and outer) [11; 40; 41; 42]. The hidden (inner) part is subordinated to the Markov process, of which the states are not observed (that is why they are called hidden), while we observe the external part of the model where the observed values depend on the realization of the hidden part. The identification of hidden states and the relationship between the hidden and the observed part consists in the analysis of a sequence of events (sequence of states) that has been recorded (observed).  
Below, we consider a hidden Markov model with discrete outputs and assume that the hidden part is described by a homogeneous Markov chain:  
* \(S=\{S_{1},\ldots,S_{m}\}\)-a set of hidden states;
* \(V=\{V_{1},\ldots,V_{k}\}\)-a set of outputs (set of observed states);
* \(\pi=(\pi_{1},\pi_{2},\ldots,\pi_{m})\)'initial distribution, where \(\pi_{i}\stackrel{{ def}}{{=}}\pi_{S_{i}}=P(X_{0}=S_{i})\geq 0\);
* \(P=\left[p_{ij}\right]_{1\leq i\leq m}\)-transition probability matrix between the hidden states, where for any \(n\in N\) the value \(p_{i}\stackrel{{ def}}{{=}}p_{S_{1}S_{j}}=P\big{(}X_{n+}=S_{j} \big{|}X_{n}=S_{i}\big{)}\) denotes the probability of transition from state \(S_{i}\) at time \(n\in N\) to state \(S_{j}\) at time \(n+1\);
* \(G=\left[g_{ij}\right]_{1\leq i\leq m,1\leq j\leq h}\)-output probability matrix, where for any \(n\in N\) quantity \(g_{ij}\stackrel{{ def}}{{=}}g_{S_{1}V_{j}}=P\big{(}O_{n}=V_{j} \big{|}X_{n}=S_{i}\big{)}\) is the probability of observing the state \(V_{j}\) at time \(n\), provided that the hidden part of the system is in the state \(S_{j}\).  
The elements of the transition matrix and output probability matrix satisfy the condition  
\[\sum_{j=1}^{m}p_{ij}=\sum_{j=1}^{h}g_{ih}=1\ \ \text{for any}\ i\in\{1,2, \ldots,m\} \tag{8}\]  
and the initial distribution \(\pi\) satisfies the condition  
\[\sum_{j=1}^{m}\pi_{j}=1. \tag{9}\]  
The hidden Markov model with discrete outputs is identified by the set \(\lambda=(\pi,P,G)\), where \(\pi\in[0,1]^{m}\), \(P\in[0,1]^{m\times m}\) and \(G\in[0,1]^{m\times h}\), satisfying the conditions (8) and (9) [6; 40; 41].
## 5 Conclusions  
This paper verified the effectiveness of 1 st eigenmode transmission in multiuser massive MIMO assuming LoS dominant channels in the millimeter wave band. Because of the 1 st eigenmode's robustness and high-gain property, quite a large number of UEs can be spatially multiplexed; it is effective in enhancing the system throughput rather than UE throughput. Additionally, we proposed simplified beamforming with highly efficient CSI estimation in order to ease heavy computation complexity imposed by 1 st eigenmode transmission. It can also suppress the additive noise effect and thus improve CSI estimation accuracy. Our approximate approach achieved performance comparable to that of rigorously applied 1 st eigenmode transmission.  
**Acknowledgments:** The authors are sincerely grateful to [PERSON] and [PERSON] of NTT Access Network Service Systems Laboratories for their constant encouragement.  
**Author Contributions:** [PERSON] conducted computer simulations and wrote the paper; [PERSON] contributed simulation tools; [PERSON] designed the system model and the proposed scheme; [PERSON] verified the proposed scheme; Y.S. and S.K. contributed to the analysis of the results; and [PERSON] coordinated and supervised the work.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Average beamforming gain versus signal-to-noise power ratio (SNR).
## 1 Introduction
To summarize, the main objective in this work is to navigate the relay node to a position (between two nodes) where the RSS is at a local maximum or at least above the minimum threshold needed to maintain a strong connectivity. Figure 1: Server-relay-client (SRC) architectural view of this study. Figure 2: An example architectural view of the SRC networked wireless robot node system. We first review the available literature in relay robot position optimization methods for improving communication quality in Section 2 and highlight how the proposed method differs from previous approaches. Secondly, we present an overview of the radio signal propagation and wireless network capacity followed by formally stating the problems considered in this paper in Section 3. We then continue by proposing a robust stochastic optimization algorithm and discuss its implementation in detail in Section 4. The proposed solution is evaluated with simulation experiments presented in Section 5, after which we validate the results with field trials in Section 6. Finally, we conclude the paper and summarize the key values and advantages of the proposed method.
## 4 Theoretical Analysis
### Computational Complexity
#### 4.1.1 Complexity of the Hole Determination Algorithm  
The hole determination algorithm consists of two steps: determining the stuck nodes (the nodes where local minimum phenomenon may happen and locating the hole boundary. The stuck node determination algorithm is conducted locally at every node. Specifically, every node first needs to sort its neighbor list by the clockwise order, and then it searches all its two adjacent neighbors to find stuck angles. As the number of neighbor is \(b\), the computational complexity for sorting the neighbor list using famous sorting algorithms such as Heap sort, Quick sort, etc. is \(O(b\log b)\); The computational complexity for searching all of the adjacent neighbor pairs is \(O(b)\). Consequently, the total computational complexity for determining the stuck nodes is \(O(b\log b)\).  
To locate the hole boundary, nodes on the hole boundary must forward HBA messages. To determine the next hop of an HBA message, the node performs RIGHT-HAND rule which investigates all neighbors of the current node to find the most-right neighbor. The computational complexity for determining the next hop by the RIGHT-HAND rule is \(O(b)\).  
In consequence, the computational complexity of hole determination algorithm is \(O(b\log b)\).
# In Vivo Efficacy of Contact Lens Drug-Delivery Systems in Glaucoma Management. A Systematic Review
## 4 Discussion
### Future Perspectives  
To date, only the nanoparticle-loaded CL or the CL with implants can release drug for weeks. However, the initial burst is an issue that should be overcome. An exception is the CL loaded with micelles, where initial burst release is reduced compared with eyedrops, but with extended wearing, this particular CL cause ocular irritation [49]. Also, in CL with implants, the initial burst is limited by entrapping the drugs in the implant's matrix structure. However, it is still present [41].  
Another possible solution could be loading the nanoparticles in a thin, collagen membrane, embeddable into the CL. A recent article compared in vitro CL with or without collagen membrane, both loaded with nanoparticles containing lidocaine [84]. The CL with this membrane released 23.4% of the drug loading after seven days compared to the CL without, which released 64% of it. The CL with membrane also showed a reduced initial burst release (16.2% of the initial drug loading) compared to the CL without (41.8% of the initial loading). However, in vivo studies are warranted to confirm these results [84].  
Furthermore, [PERSON] et al. developed a CL with nanopores (100-250 nm) interconnected via multiple nano-channels to limit the initial burst release [85]. This CL released the drug according to the initial drug load and the loading method. In particular, a deep drug embedding with soaking and centrifuging avoided the initial burst release. It also showed a sustained drug release for 30 days. In vivo studies are recommended to confirm these results.  
The same authors loaded this lens with thermosensitive nanogel without altering optic CL physical properties opening the possibility of temperature-triggered drug release [85]. The following study of [PERSON] and co-workers showed this possibility in glaucomatous rabbits [86]. IOP decreased significantly by 33% in the group wearing a CL compared to the untreated control group (\(p<0.01\)).  
It is essential to mention two techniques recently developed for a drug-delivery system based on the CL--the supercritical solvent impregnation and ion-exchange reaction [83; 87; 88]. The first method uses a supercritical solvent as a carrier for the drug that also swells the polymeric matrix, increasing its free volumes and facilitating the drug diffusion [83]. This method allows tuning of the loaded drug, changing the operational settings, such as temperature and pressure. Also, it applies to a commercially available CL without altering its physical properties.  
Instead, the ion-exchange reaction is based on adding a cationic functional group inside the CL polymeric matrix [87; 88]. Thanks to this method, the CL can store an anionic drug and release it once applied. However, further studies are required to investigate the in vivo behaviour of both techniques.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 2 IberSpeech-RTVE 2022 Evaluation Databases  
### RTVE Database  
The RTVE database is a collection of TV shows that belong to diverse genres and were broadcast by the public Spanish Television (RTVE). The database has been built incrementally for the 2018, 2020 and 2022 Albayzin challenges resulting in RTVE2018 DB, RTVE2020 DB and RTVE2022 DB datasets. The RTVE database contains nearly 1000 h of broadcast content and subtitles comprising 54 programs of diverse genres and topics, produced and broadcast by RTVE. These programs present various challenges for speech technologies, including the diversity of Spanish accents and slang, overlapping speech, spontaneous speech, acoustic variability, background noise, and specific vocabulary. Table 1 shows a summary of the content of the RTVE database in terms of genres, TV show names, dataset, speech and transcribed hours. The primary aim of the RTVE database is to offer challenging data for the Albayzin evaluations, allowing us to observe the progress of speech technologies when applied to audiovisual content. For the IberSpeech-RTVE 2022 evaluation, the RTVE2018 DB and RTVE2020 DB datasets were provided for training and development purposes, while the RTVE2022 DB dataset contains additional training, development and test partitions. The RTVE2018 DB dataset comprises complete TV shows spanning a variety of genres that were broadcast on Spain's public National Television from 2015 to 2018. The audio collection amounts to 570 h, of which roughly 460 h come with subtitles, approximately 109 h have undergone human-revised transcription, and 38 h have been labeled with the speaker turns. Additionally, the database features a set of text files that were extracted from all the subtitles broadcast on the RTVE 24H Channel throughout 2017. The RTVE2018 dataset includes partitions with all the files needed to evaluate systems for speech-to-text and speaker and multimodal diarization. A full account of the RTVE2018 dataset content and formats can be found in the dataset description report [13], and its use in the IberSpeech 2018 challenge is described in [11]. The RTVE2020 DB dataset is a compilation of complete TV shows from various genres that were broadcast on Spain's public National Television between 2018 and 2019. It contains a total of 56 h of audio, which have been transcribed and reviewed by humans. Furthermore, a subset of 33 h has been categorized according to speaker, face, and scene descriptors. The RTVE2020 dataset includes partitions with all the files needed to evaluate systems for speech-to-text, speaker diarization and identity assignment and multimodal diarization and scene description. Further details about the RTVE2020 dataset content and formats can be found in the dataset description report [14], and information about its use in the IberSpeech 2020 challenge can be found in [12].
## 2 Principles and Methods
### The Design of SDN-Based ISTNs
#### 2.3.1 The Functional Block Diagram of SDN-Based ISTNs
An asking-acknowledge re-transmission mechanism is designed to ensure the transmission of managing and controlling messages between nodes, taking into account link turbulence caused by cosmic radiation, particle flow, and other factors. A managing and controlling message from the source node will be re-transmitted if the acknowledgment message from the destination node is not received in the source node. Despite the fact that the routing strategies for managing and controlling messages and service data are diametrically opposed, managing and controlling messages and service data can be transmitted in the same AOS frame if the out-port to destination is the same, i.e., managing and controlling messages using the **data communication network** field while not taking up the **transfer frame data field** for the service data.
# A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation
## 3 Mobility-Aware Adaptive Duty Cycling Mechanism in Tunnel Excavation
### Network Model  
In clustering based protocols, for example LEACH [25], nodes make groups with their neighbors and direct the data of member nodes to a leader node called the cluster head, to reduce the amount of data that must be transmitted to the base station. This is because it is impossible and inefficient for all sensor nodes to communicate with the base station directly. Cluster heads near the base station consume more energy than other cluster heads due to a bottleneck in the data traffic. This problem can be solved by using advanced routing protocols [26; 27]. Each cluster head (\(CH_{i}\)) is connected with two neighbor cluster heads (\(CH_{i-1}\), \(CH_{i+1}\)) and gathers data of the mobile sensors (member nodes) within its communication range \(r\), as shown in Figure 5. If the mobile sensor changes its location as marked by the movement vector \(v\) and cannot communicate with the old cluster head (\(CH_{i}\)), a new cluster head (\(CH_{i+1}\)) that can communicate with the mobile sensor enrolls and manages the mobile sensor. In terms of mobile sensors, their movement determines their sleep time. Each node is defined as in a non-mobility state if and only if its change of RSSI value (\(\Delta RSSI\)) is less than the threshold \(\lambda\); whereas the mobility state implies the opposite. A sensor in a mobility state (\(\Delta RSSI>~{}\lambda\)) decreases its sleep time to frequently send its state information to the cluster head; whereas, a mobile sensor in a non-mobility state (\(\Delta RSSI<~{}\lambda\)), increases its sleep time to rarely send its state information to the cluster head. For example, as shown in Figure 4, the dump truck stopped to load mucus corresponds to the latter, and the pay loader that travels back and forth between muck and the dump truck corresponds to the former.  
The distance between adjacent cluster heads \(l_{c}\) must be less than or equal to \(2r\) to localize and track from at least one cluster head. In order to find a more appropriate range of \(l_{c}\), the minimum number of wake-up times within a certain cluster head \(n\) and the maximum velocity of the mobile sensors \(v_{max}\) are required in sensor field [28]. Note that the user can choose a number \(n\) (\(\geq 1\)) depending on the tracking reliability; a higher \(n\) gives higher delivery performance for tracking. Thus