esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The term TMD (temporomandibular disorders) encompasses a spectrum of disorders that are associated with pain in the masticatory muscles, temporomandibular joint, and associated structures [1]. TMD has multifactorial etiology, with anatomical, physiological, social, and psychological factors playing a role in its development [2]. Dental conditions that alter occlusion and masticatory patterns, such as trauma or chronic periodontitis, have been shown to contribute to temporomandibular joint pain [3; 4; 5; 6; 7; 8]. Because of the effect that it has on occlusion, orthodontic treatment has emerged over the years as a significant factor that might contribute to the development of TMD. Several authorshave attempted to explore the relationship between orthodontic treatment and TMD. Theoretically, orthodontic treatment is believed to upset the delicate balance of the stomatognathic system, because it has been postulated that the alteration of the occlusal morphology and the application of orthodontic forces can cause displacement of the condyle [9]. Some studies have shown that undergoing orthodontic treatment significantly increased the odds of developing signs and symptoms of TMD [10]. However, this association has not been verified by systematic reviews [11; 12; 13]. In their systematic review, [PERSON] et al. [12] could not identify any clinically significant association between orthodontic treatment and TMD. More recently, [PERSON] et al. [13] performed a systematic review that explored the association between orthodontic treatment and TMD. They found that the included trials reported widely discrepant results. They concluded that there was no evidence of a cause-effect relationship between orthodontic treatment and TMD. The main drawback in several previous studies was the age group of the patients. Traditionally, orthodontic multibracket treatment is carried out in the adolescent period or in young adults. In general, the prevalence of TMD is high in this group, and the addition of orthodontic treatment could merely be a chance finding [14]. However, the demand for orthodontic treatment in adult patients is increasing and so, through a cohort of adult patients, the weight of age as a confounding factor can be resized. Another drawback was that the specific kind of TMD had not been characterized in previous studies. Myofascali pain and disc displacement are two distinct entities of TMD and may require different forms of diagnosis and management.  
To this end, the aim of this medium-term retrospective study was to analyze the effect of orthodontic treatment on signs and symptoms of temporomandibular disorders in a cohort of young adult patients.
## 1 Introduction  
The smart city consists of a number of key enablers, which are smart education, smart healthcare, a smart economy, smart factories, smart warehouses, smart transportation, etc. [1, 2]. The key enablers of smart cities require uninterrupted and efficient power supplies to fulfill their requirements [1, 2, 3]. Moreover, to effectively pursue all the functionalities of a smart grid system, a suitable wireless communication is required that can provide a high data rate, reliability, flexibility, low latency, massive connectivity, security, and the necessary adaptive functionalities [1, 4]. Among the various applications of the smart grid, massive connectivity and monitoring, secured communication for resource allocations and operations, and time-critical operations are the vital factors in the case of an effective, uninterrupted, and reliable power supply for smart cities [5, 6]. Future 6G wireless communication is a suitable solution for the communication of smart grid systems by providing all the required key performance indicators (KPIs) in terms of communication to successfully perform the abovementioned smart grid applications [7, 8, 9].  
The smart city consists of several key enablers, which are the smart grid, smart factories, smart warehouses, smart healthcare, smart transportation, smart education, a smart economy, smart agriculture, a smart environment, etc., and these are illustrated in Figure 1. The aim of providing a smart, uninterrupted power supply and management is the prime concern for successfully operating all of the smart city applications [1; 10; 11; 12].  
Each of the smart city key enablers require their own respective power requirements [13; 14; 15; 16; 17; 18]. Thus, an uninterrupted smart power supply is the basic requirement for the key enablers to conduct all of the smart applications of smart cities. The smart grid is a viable power supply and distribution remedy for the smart city key enablers. Moreover, the advancement of the smart grid is a trendy research area. The basics of the smart city and the smart city key enablers are briefly introduced in Section 1.1. Moreover, the overview of the smart grid and 6G wireless communication are briefly introduced in Sections 1.2 and 1.3. The relevant literature overview of the previous works and the motivation of this paper are described in Section 1.4. In addition, the key contributions of this article are briefly described in Section 1.5.
# A General Framework for Visualization of Sound Collections in Musical Interfaces
## 3 Proposed Framework
### Dimensionality Reduction  
The two-dimensional visualization of sound collections is typically accomplished through the dimensionality reduction of the position features. Many algorithms have been used in the literature for audio collections (see [33] for a review). Here, we focus on three algorithms: Principal Components Analysis (PCA), Multi-Dimensional Scaling (MDS) and Uniform Manifold Approximation and Projection (UMAP).
## 3 System Model
### Model of Multi-Beam Satellite Communication System
The user \(u_{q}\) has several types of packets, which are defined as \(A=\left\{a_{q1},a_{q2},\ldots,a_{qq}\right\}\), \(I\) is the number of packets.The segmentation packet is defined as \(a_{qi}^{c}\), where the label \(c\) represents the segmentation number of the packet \(a_{qi}\). The satellite \(s_{p}\) has a set of beams \(BM=\left\{b_{p1},b_{p2},\ldots,b_{p1}\right\}\), \(I\) is the number of beams, in which each beam may have the same or different coverage. In this paper, different satellites use spectrum resources with the same bandwidth of \(B\), and the beam uses OFDM to transmit. The number of subchannels of the satellite \(s_{p}\) is \(N\), where the total number of subchannels occupied by the beam \(b_{pj}\) is \(N_{b_{pj}}=\frac{N}{\sum\limits_{b_{pj}\in\mathbb{M}}b_{pj}}\), and the bandwidth occupied by each sub-channel is \(B_{n_{pq}}=\frac{B}{N}\). Make \(V_{a_{qi}}^{b_{pj}}=\left(f_{a_{qi}^{pj}}^{b_{pj}},t_{a_{qi}^{pj}}^{b_{pj}},a_{ qi}^{b_{pj}}\right)\) denoted the allocation radio resources set, which is allocated to the packets \(a_{qi}\) of the user \(u_{q}\). \(f_{a_{qi}^{pj}}^{b_{pj}}\) represents the number of subchannels occupied by the packets \(a_{qi}\) in the frequency domain. \(t_{a_{qi}^{pj}}^{b_{pj}}\) represents the slot occupied by the packets \(a_{qi}\) in the time domain, and \(d_{a_{qi}^{pj}}^{b_{pj}}\) represents the demand for the packets \(a_{qi}\). The packet \(a_{qi}\) is assigned a set of radio resources which are represented as \(f_{a_{qi}^{pj}}^{b_{pj}}\times t_{a_{qi}^{pj}}^{b_{pj}}\). Figure 2 describes the transmission process of multi-beam satellite communication systems from resources requests to packet transmitting. At first, after the connection between satellites and users is established, resources requests are transmitted to the gateway. Then, the gateway optimizes the radio resources of the beam of the satellite according to the request. Next, the optimal resources are assigned to users' packets. Then, the packets are segmented into smaller packets. In the end, the smaller packets are mapped to the optimal resources, and users can transmit the packets by the optimal resources.
## 3 Background
### Hyperledger Fabric-Chainlink  
#### 3.10.1 Fabric Network Architecture  
Hyperledger Fabric [67; 68], an open-source blockchain endeavour sponsored by the Linux Foundation, is an implementation of a permissioned blockchain. A security architecture for authentication and permission is built into Hyperledger Fabric (membership service (MS), employing a certificate authority (CA), which is an entity that can generate certificates for key pairs for signing and encryption for the peer nodes and solution users (SUs)). An MS's objective is to enable peer and user enrolment and transaction authorization using public-key certificates. This is one of the fundamental distinctions between permissionless blockchain architecture and the permissioned blockchain framework. Hyperledger Fabric also supports anonymous credentials with multiple CAs and the usage of threshold signatures. Besides the MS, peers and an ordering-service node, or orderer, are the other primary architectural components of Hyperledger Fabric. The orderer is a node (or a group of nodes) that runs the communication service that ensures delivery, such as atomic or complete order broadcast. Transaction verification and order are used to accomplish this. The digital signature of the transaction issuer and the so-called endorsement policy are validated during the authentication process. The endorsement policy is a specification for a chain code that tells a peer how to determine whether a transaction is valid. For example, all network peers must validate (and so sign) a transaction. The orderer must verify that all peers have signed the transaction and that the signatures are legitimate [18].
# Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures
## 5 Performance Comparison
### Authentic Key Exchange-Phase 1  
First, every previously described DLT and, if applicable, their different setups are compared in terms of execution times. Using the Boxplot representation shown in Figure 4 of the collected data, the mean values and standard deviations of the datasets can be extracted directly.  
When compared with the other technologies, the _IPFS private_ setup is executing the NFC-KE process the fastest. On average, the public key exchange happens within 286 to 334 ms, thereby not being influenced by the number of peers within the network. Additionally, the distribution of data points and the number of significant outliers is also lower when compared to the other DLT setups. IPFS Filebase is also executing the NFC-KE Phase 1 faster than the previously proposed HLF extension, although this setup experiences more outliers due to increased network jitter and API restrictions. On average, the authentic public key exchange has been completed within 992 and 1654 ms. The public IPFS setup based on Web3.Storage is significantly slower in comparison to the Filebase setup. With a mean completion time within 1921 and 2353 ms, the Web3.Storage setup is the slowest IPFS-based storage solution and also experiences the most outliers in direct comparison to the Filebase and private IPFS setups. The Via Skynet setups share the last places among all tested setups in terms of execution times. Since the proposed setup uses the integrated SkyDB for public key query, two different API endpoints need to be contacted. To get a deeper understanding of the different execution times, we also included a setup without the SkyDB and thus without the public key query functionality. Using this setup, only one public key can be stored and queried which is only useful for testing. The results show that even without the SkyDB functionality, the Skynet-based approach is significantly more unstable and slower in comparison to all other DLT setups. With a mean execution interval of 4377 to 5746 ms, the Skynet solution does not offer any in-time storage solution. By adding the SkyDB backup functionality, and thus enabling the main NFC-KE Phase 1, the execution times range between 12.8 to 14.8 s. When compared to other setups, the outliers are less scattered in the latter setup case.  
The stark performance degradation between the public and private setups is to be expected since the external APIs are highly reliant on the network performance. Furthermore, another main result of this particular comparison is the comparably fast execution times of the private IPFS solution in comparison to the HLF execution times. Even the  
Figure 4: NFC-KE Phase 1 execution time comparison.  
public Filebase approach is, on average, faster than the proposed HLF NFC-KE extension, although being more prone to outliers in the execution times and thus less reliable.
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 2 Industrial Distributed Antenna System Assisting 5G and 6G Services  
The industrial distributed antenna system, also known as IDAS, includes a remote antenna unit (RAU), main hub unit (MHU) and over-the-air (OTA) interface for wireless links to provide a remote unit (RU) via mmWave and/or a THz-based bridge for mobile fronthaul [8]. The RoF-DAS network is depicted in Figure 2. First, the intermediate frequencies (IF), which include \(f_{IF1}\) for mmWave and \(f_{IF2}\) for THz processing, are injected into the Mach Zehnder modulator for optical transmission through a fibre. The optical coupler separates to the respective RAUs for mmWave and THz processing. At the 5G RAU, the 5G signal is photo-detected by the PIN photodiode and is then amplified by an in-built trans-impedance amplifier. The 5G-RAU signal detected by the optical transceiver is reshaped and retimed in the electrical-driven mixer-based phase-locked loop clock receiver equipment, and is further used to convert the IF signal to a 5G signal in the desired frequency band.  
Both signals undergo filtering to be sure that the 5G RAU and 6G RAU signals go to their respective sections for post-processing. An electrical mixer driven by an RF local oscillator (LO) can be used for frequency up-conversion from the IF-to-mmWave band. At 6G-RAU, the optical signal is passed through an optical band-pass filter (OBPF), which is then combined with the output of the laser operating at \(\lambda_{2}\) nm, and then the combined signal is detected by the uni-travelling carrier photodiode (UTC-PD). The 6G-RAU includes an optical LO operating at \(\lambda_{2}\) and a UTC-PD for optical heterodyne mixing. The photo-mixing generates various optical beat components, among which \(\lambda_{2}\) and \(\lambda_{RSB}\) are used, as it maximizes the throughput of the received signal.
## 2 Materials and Methods
### Determination of Groups  
The selected specimens were randomly divided into four groups (n = 15/each group) regarding the type of instrumentation file.  
In Group I (Manual Instrumentation), a total of 15 root canals were manually instrumented using hand nickel-titanium (Ni-Ti) K-files (VDW, Munich, Germany) in accordance with the step-back technique. Each consecutive file was used with the same trend until the apical preparation size reached a 30 Ni-Ti K-file. Repetition with the 30 Ni-Ti K-file was performed to prevent ledge formation.  
In Group II (ProTaper Gold), a total of 15 root canals were shaped with the ProTaper Gold Rotary System until they reached F3 Size (Maillefer, Dentsply, Switzerland). The parameters of the X-smart Plus motor (Maillefer, Dentsply, Switzerland) were set as 300 rpm and a torque of 5 Ncm. First, Sx was used with a brushing action, until it encountered resistance. Following Sx, S1 was used again with the brushing action mode, until 1 mm short of the working length, to ascertain radicular access. Afterward, S2 was used in the same manner until it reached the working length. The finishing preparation of root canals was instrumented with F1, F2, and F3 finishing files in a non-brushing action, with each insertion deeper than the previous insertion until the working length was reached.  
In Group III (RaceEvo), a total of 15 root canals were prepared using the RaceEvo Rotary System (FKG, Switzerland), consisting of a size 15/RE1, size 25/RE2 and a size  
Figure 1: Speed images of perforated root canals that were excluded from the study.  
30/RE3 with 0.04 tapers. The parameters of the X-smart Plus motor ([PERSON], Dentsply, Switzerland) were adjusted to the recommended speed range at 800 rpm with continuous rotation and a torque of 1.5 Ncm. Initially, the RE1 file was used until the working length was reached to perform a glide path. Then, the RE2 file was used, and the final instrumentation was performed using the RE3 file until the definitive working length. Once each instrument reached the working length, it was immediately removed to prevent over-enlargement of the apical foramen.  
In Group IV(R-Motion), a total of 15 root canals were shaped with the R-Motion Rotary System (FKG, Switzerland), including a size 15/R-Motion Glider (0.03 taper), a size 25/R-Motion 25 (0.06 taper) and a size 30/R-Motion 30 (0.04 taper). The parameters of the X-smart Plus motor (Maillefer, Dentsply, Switzerland) were arranged according to recommendations which used only the standard reciprocating mode (170\({}^{\circ}\) counterclockwise and 50\({}^{\circ}\) clockwise rotation modes). First, the mechanized R-Motion Glider instrument was used to establish a glide path until it reached working length. Afterward, the shaping procedure of root canals was performed with R-Motion 25 and R-Motion 30 files. Again, once each instrument reached the working length, it was immediately removed to prevent over-enlargement of the apical foramen.  
Throughout the preparation, the root canals were regularly and frequently irrigated with 1.5% NaOCl and saline solution after each instrument had been used.
## 2 Principle Analysis  
The principle analysis of TSOM is separated into three parts--illuminating, scattering, and imaging. Firstly, Kohler illumination in the TSOM system is explored. Secondly, the scattered electromagnetic field is analyzed after the illumination wave is reflected from a complex target. Thirdly, the image-scanning procedure along the optic-axis direction that collects an in-focus image and a series of defocused images of the target is investigated.
## 2 Data and Methods
### Bernaola-Galvan Segmentation Algorithm  
The Bernaola-Galvan Segmentation Algorithm (BGSA) was adopted to detect non-linear, nonstationary sequence mutations [48]. Its principle is to divide a non-stationary time series by making the time series consist of many segments with different means, such that the difference in means between neighboring segments is maximized. Compared to traditional mutation detection methods, the BGSA showed better performance in estimating climate evolution [49; 50]. In this study, we used the BGSA algorithm to detect the mutations of R\({}^{2}\) series derived from the two machine-learning algorithms (Section 2.3.3).
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 5 Proposed Energy Efficient and Reliable Routing Approach
### Multi-Queuing Scheme for Efficient Data Delivery  
Aiming to ensure efficient data delivery for IEMS, in our proposal, we adopt the queuing model presented in [53] to handle the critical and non-critical traffic types. To achieve high reliability and lower delay when routing critical data traffic, reliability sensitive packets should be given high priority as compared to regular packets. Hence, this priority can be specified through a multi-queuing system, which is controlled by each node. Consequently, the implementation of a multi-queuing strategy allows the prioritization of data traffic to ensure high reliability to critical packets. In our priority queuing approach, we use two different queues, one for critical traffic, and the other abide non-critical traffic (i.e., regular traffic). The queuing model sends packets from the highest priority queue first,followed by the lowest priority queue. The high priority queue is referred to as a critical traffic queue (CTQ), which is used by critical packets, the low priority queue is denoted as regular traffic queue (RTQ), which is used by the non-critical packets or regular packets. We undertake that each source node is aware of the importance of each data packet it is transmitting that can be translated into a predetermined priority level. For the IEMS use case of IIoT, the application layer defines the priority level of each data packet generated by adding extra information to differentiate critical and non-critical packets. Subsequently, the classifier routes the packets into the appropriate queue based on the packet type. In the emergency monitoring network, when an unusual situation occurs, the node generates data for the appropriate action; therefore, the number of critical packets in the queue would be relatively low compared to regular packets, and for some time extent, the queue could be empty. Meanwhile, lower priority traffic may be obstructed by high priority traffic indefinitely. To overcome this situation, we use the timeout policy for each packet, so that contention priorities can be specified explicitly between packets from the same node. Algorithm 1 describes the implementation of the multi-queuing strategy in this paper.  
```
Predefined: Queue types, priority levels Output: packets in respective queue Begin at \(v_{i}\in NB(i)\) for each received packets do ifpkt_type = CRITICAL then insert packet \(\rightarrow\) CTQ; end if else insert packet \(\rightarrow\) RTQ; packet timeout initialization break; end if end if for each timeout expiry of packet do  move packet \(\rightarrow\) CTQ; end for each transmission of packet do  revoke timeout; end
```  
**Algorithm 1**Pseudocode for multi-queuing operation
## 4 Discussion  
### Degradation of the Telescope Sensitivity Due to Atmospheric Opacity  
Atmospheric opacity leads to the deterioration of the telescope sensitivity in any case. However, it is worth obtaining numerical estimates of this deterioration. In particular, this can help better understand the acceptable value of opacity in various conditions. For this purpose, we estimate the quantity, which can be called a \"degradation factor\":  
\[R_{\rm D}=\frac{T_{\rm SYS}^{*}}{T_{\rm RX}}\,, \tag{7}\]  
where \(T_{\rm RX}\) is the receiver noise temperature and \(T_{\rm SYS}^{*}\) is the system temperature \"above the atmosphere\", i.e., calculated from the system temperature at the receiver front-end taking into account the attenuation in the atmosphere. Neglecting antenna losses and background emission,  
\[T_{\rm SYS}^{*}=T_{\rm RX}e^{\frac{\tau}{\tau\cos\theta}}+T_{\rm a}\Big{(}e^{ \frac{\tau}{\tau\cos\theta}}-1\Big{)}\,. \tag{8}\]  
In the case of no opacity, \(R_{\rm D}=1\). The plot of the degradation factor \(R_{\rm D}\) in dependence on the receiver noise temperature \(T_{\rm RX}\) and zenith opacity \(\tau\) for the zenith angle \(\theta=45^{\circ}\), assuming \(T_{\rm a}=250\) K, is presented in Figure 5. For example, if the degradation factor of 1.5 is considered to be acceptable and the receiver noise temperature is \(T_{\rm RX}\sim 100\) K, then the required zenith opacity is \(\tau\lesssim 0.1\). It is worth noting that the integration time required to achieve the same sensitivity varies as \(R_{\rm D}^{2}\). In the case of \(T_{\rm RX}\gg T_{\rm a}\), which happens at very high frequencies, the degradation factor approaches \(e^{\tau/\cos\theta}\). Then, \(R_{\rm D}=1.5\) implies \(\tau\approx 0.29\) at \(\theta=45^{\circ}\).  
Figure 4: Probability density and cumulative distributions of 230 GHz zenith opacity at the Terskol peak (blue and green, respectively) in comparison with the Suffa plateau (black and red) calculated from the output of NASA global numerical weather model GEOS-FPIT for 12 years (1 January 2008–31 December 2019). The plots are based on the calculations described in [9].