esa-sceva/satcom-chunk-collection huggingface dataset:
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Single Carrier Amplitude Only  
Referring to single carrier waveforms that solely modulate the signal by amplitude, this type of waveform is characterized by its simplicity and robustness to phase noise. As weak points, they shows very low spectral efficiency and high OOB emissions. As a representative implementation of single carrier amplitude only, widely employed in optical systems, we can mention on-off keying (OOK). Pulse with modulation (PWM) and pulse position modulation (PPM) are other types of single carrier amplitude modulation that encode each symbol with different pulse width and pulse position, respectively. OOK, PWM, and PPM provide very low spectral efficiency. To achieve a higher value, several techniques were proposed. One of these consists of adding several levels of amplitude to the resulting modulated symbols. In this way, pulse amplitude modulation (PAM), multilevel-PWM (M-PWM) and vector weight multilevel PPM (vw-MPPM) [32] arise.  
Finally, an effort to reduce the huge OOB emissions inherent to pure single carrier waveforms is to generate Gaussian pulses instead of rectangular pulses. Furthermore, the average power is reduced with this mechanism and so, the PAPR magnitude.
## 3 Results
### Cervical Pain  
3.4.1. Prevalence of Cervical Pain  
According to data analysis, 43.3% of the total sample presented short-term cervical pain in the last 2 months (no significant difference in the prevalence of pain was observed between males and females), while 56.7% of the sample had no cervical pain.  
In those who experienced cervical pain, the mean duration of pain (total days of pain in 2 months) was 2.54 \(\pm\) 0.99 days and the mean intensity of pain (from 1 to 10) was 3.65 \(\pm\) 1.41, while the days off from daily activities due to cervical pain were 0.46 \(\pm\) 1.96 days and the days with difficulties in daily activities due to cervical pain were 3.81 \(\pm\) 7.14 days. It should be mentioned that independent samples \(t\)-tests showed that there were no significant differences between males and females in the duration and intensity of cervical pain, as well as in the days off from daily activities due to cervical pain (\(p>0.05\)).  
3.4.2. Relationship of Cervical Strength and ROM with Cervical Pain  
Bivariate correlation analysis showed a statistically significant small negative correlation of the duration of cervical pain (total days of pain) with cervical forward flexion strength (\(r=-0.288\), \(p<0.05\)), extension strength (\(r=-0.255\), \(p<0.05\)), right lateral flexion strength (\(r=-0.287\), \(p<0.05\)), and left lateral flexion strength (\(r=-0.345\), \(p<0.05\)). On the contrary, bivariate correlation analysis showed that cervical ROM did not significantly associate with cervical pain (\(p>0.05\)).
## 6 Various Criteria for Assessing the Power Quality and Efficiency of the Studied System
### Power Quality
#### 6.1.4 DCB Overcharging
The first approach involves adjusting modulation schemes [156; 116; 67; 118; 129; 131], while the second involves the use of additional hardware [113; 117; 119; 130]. In the first approach, switching states are selected by analyzing the current associated with these states to identify those that are likely to cause overcharging. These problematic states are then avoided during the modulation process. This method of voltage regulation, which assumes the direction of the load current, is referred to as the passive regulation method [157]. However, the disadvantage of this approach is that it reduces the number of vectors available for modulation, which can negatively impact the quality of the output  
Figure 19: DTLI structure (the overcharging effect). Figure 20: Examples of currents affecting the DCB-C as a function of the switching vectors applied: (a) \(V_{11}V_{1}\), (b) \(V_{2}V_{7}\), (c) \(V_{2}V_{3}\), (d) \(V_{i1}V_{6}\). voltage. A strategy to switch the master inverter while keeping the slave inverter engaged is presented in [118], but this approach involves a larger modulation area, which may result in higher harmonic distortion in the inverter output. To improve power quality, a coupled discontinuous carrier modulation technique is proposed in [129], but this can increase switching losses. A significant limitation of passive control is that it assumes the direction of current, which can be affected by the power factor of the load [157]. An enhanced version of passive control, called active control, was proposed in [156]. This approach actively selects the appropriate states based on control requirements and measured phase current values. The second approach, which utilizes additional hardware, is based on the nested rectifier-inverter structure proposed in [117] (see Figure 21) and further refined in [158]. In this structure, the slave inverter with a lower DCB voltage is nested inside a higher-voltage DCB feeding the master inverter. This configuration enables the implementation of advanced modulation schemes to optimize voltage utilization for motors [119] or to suppress ZSCs in an averaged manner [130]. It should be noted that, while the nested rectifier-inverter configuration can address the overcharging problem, it requires an additional isolated DC power supply. This addition can lead to increased complexity, size, and cost of the motor drive system [118; 129].
## 2 High Dimensional \(\ell_{1}\) Major Component Detection and Analysis (MCDA)
### Calculation of the Central Point  
Our \(\ell_{1}\) MCDA assumes that the data is symmetrically distributed around a central point. This assumption will be eliminated in the future but is needed for now so that the calculation of the central point is accurate. Let the data be \(\{\bar{\mathbf{x}}_{m}\}_{m=0}^{M-1}\). The central point of the data in Step 1 is to find the \(\hat{\mathbf{x}}\) that minimizes  
\[\sum_{m=0}^{M-1}d(\hat{\mathbf{x}},\bar{\mathbf{x}}_{m}) \tag{1}\]  
where \(d(\hat{\mathbf{x}},\bar{\mathbf{x}}_{m})\) is the distance function for data points \(\hat{\mathbf{x}}\) and \(\bar{\mathbf{x}}_{m}\) in the data space. The distance function \(d\) can be any \(\ell_{p}\) norm or \(p\)-th power of the \(\ell_{p}\) norm or other norm function that the user wishes to choose. After subtracting the central point out of the data, the point cloud is centered at the origin of the space. For simplicity, we still denote the dataset after this change by \(\{\bar{\mathbf{x}}_{m}\}_{m=0}^{M-1}\).  
In our experiment, we chose the \(\ell_{1}\) norm as the distance function, _i.e._, the multidimensional median is the central point of the dataset. The multidimensional median is not guaranteed to be an appropriate central point unless the data comes from a distribution that is symmetric with respect to the origin. For example, a point cloud in 2D representing a corner is a distribution in which the data are densest along the sides of a \"V\". In this case, the central point should be the vertex of the V rather than the multidimensional median of the data. We do not consider this issue in this present paper but will handle it in future research.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
###### Abstract  
This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users' (PUs') (or licensed users') activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance; or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs' activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry P3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner; and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs' activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.  
D2D communication; 5G; sensor network; sensor; end-to-end delay; USRP; Raspberry P3 primary users 2021 10 387[[https://doi.org/10.3390/electronics10040387](https://doi.org/10.3390/electronics10040387)]([https://doi.org/10.3390/electronics10040387](https://doi.org/10.3390/electronics10040387))
# Performance Comparisons of Broadband Power Line Communication Technologies
## 2 Structures of PLC
### IoT PLC
#### 2.1.1 CTC Encoder  
Figure 2 shows block diagram of the CTC encoder [9; 29]. The CTC consists of two constituent encoders and a CTC interleaver as shown in Figure 2a. A double binary circular recursive systematic convolutional (RSC) encoder with a constraint length of 4 is used for the constituent encoder. Figure 2b shows the constituent encoder. When blocks \(A\) and \(B\) are encoded, a parity bit block \(Y_{1}\) is generated. The inputs are then interleaved by the CTC interleaver, and the interleaved bits are encoded again by the constituent encoder, generating a parity block \(Y_{2}\). \(Y_{1}\) and \(Y_{2}\) each have \(N\) bits. The CTC interleaver shuffles the bit order in such a way that the bits are spread as evenly as possible. The outputs of the CTC encoder are \(A,B,Y_{1}\), and \(Y_{2}\), resulting in \(4N\) output bits. For the initial state of the encoder, the start and end states are set to be the same, which is known as tail biting encoding and can improve the decoding performance without adding trailing bits at the encoder [30]. The CTC encoder generates \(4N\) bits with a \(2N\) bit input, resulting in a code rate of \(1/2\).  
Figure 1: Block diagram of IoT PLC.  
Figure 2: Block diagram of CTC encoder: (**a**) CTC encoder; and (**b**) constituent encoder.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 3 State of the Art, Regulation, Opportunities, and Challenges
### State of the Art  
mmWave radar technology found its initial application in automobiles during the early 1970s. Several companies and research institutes explored the potential of distance radar to prevent collisions, leading to the development of initial applications and prototypes. However, the integration challenges, substantial size, and high costs hindered the market entry of any product until the late 1990s. It was only in 1998 that the first generation of automotive radar sensors operating in the 77 GHz band became commercially available, overcoming the technological and economic barriers that had previously impeded widespread adoption. The volume and weight constraints for an automotive radar necessitated integrated solutions. To address this challenge, it was only reasonable for stakeholders to explore the mmWave frequencies to keep the hardware footprint small. Nevertheless, the manufacturing of a monolithic microwave integrated chip (MMIC) to cater to such high frequencies without compromising the RF performance was a challenge. To achieve a high RF performance, yesterday's automotive radars were based on gallium arsenide (GaAs). However, integrating digital logic for radar signal processing could not be harnessed using MMICs. In the interest of time, owing to the compatibility with standard silicon processes and the integration of digital logic circuitry, bipolar complementary metal oxide semiconductor (BiCMOS) SiGe technology emerged on the horizon [40]. To realize a fully integrated radar sensor in a very small footprint, the latest evolution involves the adoption of a 45 nm RFCMOS MMIC [7], integrating an exceptional blend of superior digital logic performance and tailored modifications for a high RF performance. This has been a massive breakthrough, since the use of pure CMOSs in the realization of efficient power amplifiers at high temperatures was a hurdle as recent as the last decade [41]. The high cost, another challenge to be overcome, was addressed through the economy of scale offered by mass production in the wake of ADAS ubiquity.
## 4 Efficient Data Distribution and Storage Method
### I/O Combining
As presented in Table 2, when 16 128 KB-sized I/Os are processed one at a time in 16 + 2 EC, a total of 288 8 KB-sized I/Os are needed to be processed, whereas only 18 128 KB-sized I/Os are needed to be processed if 288 I/Os can be combined into multiple sets, each consisting of 16 I/Os.
## 2 Materials and Methods  
### Study Area and Sampling  
The City of Johannesburg, South Africa, has over 180 informal settlements that house 16% of its population [25]. According to the City of Johannesburg, the term informal settlements refers to \"an impoverished group of households who have illegally or without authority taken occupation of a parcel of land (with the land owned by the Council in the majority of cases) and who have created a shanty town of impoverished illegal residential structures built mostly from scrap material without provision made for essential services and which may or may not have a layout that is more or less formal in nature.\" Due to their illegal and unplanned nature, most of the informal settlements are not serviced by municipal waste collection infrastructure. Therefore, these informal settlements are characterized by extensive littering and illegal dumping of wastes in the streets, public spaces, and vacant land [26]. In this study, soils from five illegal dump sites within the Princess informal settlement (S 26.135, E 27.850), located in the Roodepoort area, Gauteng Province, South Africa were analyzed.  
The sampling sites included litter free soil (ID1), motor vehicle garage/repair shop dump (ID2), train station dump (ID3), sports ground dump (ID4), roadside dump 1 (ID5), and roadside dump 2 (ID6). The main wastes in roadside dump 2 included plastics, which were incinerated to reduce volume. Soil samples were collected from a depth of 5-15 cm using a dredge sampler (Kajak, KC-Denmark) based on a multi-point mixed sampling method. At every sampling site, 5\(-\)6 replicate samples were collected within an area of 2 \(\times\) 2 m and then mixed into a composite sample. The collected soil samples were placed in clean polyethylene bags and transported immediately to the laboratory under cold storage for analysis.
## 7 Potential Applications and Broader Impact
### Domain-Specific Applications  
Healthcare and Surgical Training: In healthcare, MHMMs offer significant promises for revolutionizing surgical training and intraoperative support. By continuously monitoring metrics such as HRV, TCT, and subjective workload indices from trainees, the system can provide adaptive feedback and personalized training curricula [72]. Wearable devices enable real-time physiological data collection, supporting the development of data-driven training systems [72]. For instance, the real-time detection of cognitive overload or excessive fatigue can trigger automated assistance from a robotic surgical system, thereby reducing error rates and enhancing patient safety [41]. Recent work has demonstrated the value of using physiological and kinematic data to assess surgical skills, underscoring the relevance of multimodal models for creating data-driven, objective, and personalized medical education [73].  
Aviation and Aerospace: In aviation, MHMMs can be embedded within next-generation flight simulators and operational cockpit systems to personalize pilot training and augment in-flight safety. By classifying mental states, such as high workload, distraction, or fatigue, the system can dynamically adjust scenarios' complexity or modify cockpit interfaces to mitigate the risks associated with cognitive overload, especially during critical flight phases or long-haul operations [74]. The aerospace industry is increasingly focused on leveraging AI-driven workload assessment to address persistent challenges in pilot performance and safety, with MHMMs offering scalable solutions for both adaptive training and real-time risk mitigation [75].  
Smart Manufacturing and Industry 5.0: Within smart factories, the MHMM framework empowers cobots to adapt their behaviors based on a human operator's real-time proficiency and cognitive state. This aligns directly with the Operator 5.0 paradigm, where technology acts as a supportive partner [71]. For example, a novice operator exhibiting signs of high cognitive load might receive more detailed, stepwise guidance and proactive physical assistance from a cobot. Conversely, an expert would be entrusted with more strategic, supervisory tasks, optimizing workflow efficiency and team resilience [76]. This dynamic, human-aware adaptation is a cornerstone of the Industry 5.0 vision, enhancing productivity, safety, and the overall well-being of the workforce [70; 71].  
Education and Adaptive Learning: Beyond industrial settings, MHMMs can enhance educational platforms by monitoring student engagement, proficiency, and affective states (e.g., frustration, and confusion) using wearable sensors and behavioral analytics. The real-time classification of mental workloads enables personalized learning pathways, where content difficulty, resource recommendations, and task assignments are dynamically adjusted to the individual learner's needs. This fosters more inclusive, engaging, and effective learning environments, particularly in complex domains such as STEM education [77].
## 3 Proposed Algorithm
### Properties of ISOD
#### 3.4.3 Sensitivity Analysis  
As can be seen from the description of the algorithmic process in Section 3.3 above, the ISOD algorithm independently calculates the skewness coefficients for each dimension as weights to be combined with the quantiles in each dimension. Therefore, there are no special requirements on the distribution of data, slight data noise, or the percentage of outliers. Therefore, we can confidently say that the ISOD algorithm is a robust anomaly detection algorithm that is insensitive to data noise, and this property will have a positive impact on its practical application.
# A Comparison of Ensemble and Dimensionality Reduction DEA Models Based on Entropy Criterion
## 4 Summary, Conclusions and Directions for Future Work  
A significant amount of research in the DEA literature has focused on dimensionality reduction/variable selection techniques for small datasets. These techniques are often criticized and have their limitations, with no clear way of selecting which technique is the best. A better approach would be to use an ensemble DEA score that does not make any additional assumptions and provides models that have high entropy values and normally distributed scores under restrictive assumptions. [PERSON] (2014), in his study, has already provided a theoretical foundation for the reliability of ensemble DEA scores. The added benefit of ensemble DEA scores is that they provide unique DMU rankings.  
The normality of ensemble DEA scores is not guaranteed unless the ensemble DEA scores are normalized sums generated from independent identically distributed MDE pdfs. This assumption  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON]–[PERSON]**} & \multicolumn{4}{c}{**[PERSON]–[PERSON]**} \\ \cline{2-7}  & **Statistic** & _df_ & **Sig.** & **Statistic** & _df_ & **Sig.** \\ \hline Ensemble BCC & 0.196 & 14 & 0.149 & 0.876 & 14 & 0.051 \\ Ensemble CCR & 0.182 & 14 & 0.200 & 0.944 & 14 & 0.477 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of normality tests.  
Figure 5: The maximum decisional efficiency (MDE) probability density function (pdf) for \(\alpha=5\) and \(\alpha=10\), respectively.  
may not be strictly satisfied in most real-world datasets, but the current study shows that minor deviation from this assumption may be tolerated because the entropy of all MDE pdfs is maximized when normalized sums have a value greater than 0.5. This means that, typically, the differences in means between the underlying pdfs (Equation (2l)) for ensemble entropy scores will be less than 0.5, and, while these pdfs may not be identically distributed, the means of these distributions will be close, resulting in the likely normal distribution of ensemble scores in most real-world cases. The normality of ensemble DEA scores allows for the application of traditional statistical tests for return-of-scales hypothesis tests. Traditional DEA hypothesis-testing methods are not perfect and are known to be slightly biased [28]. Future research may focus on comparing ensemble DEA-based hypothesis testing with traditional DEA hypothesis testing to identify which method provides reliable results.  
This research received no external funding.  
The authors declare no conflict of interest.
# Incorporating Clinical Imaging into the Delivery of Microbeam Radiation Therapy
## 2 Materials and Methods
### Imaging the Treatment Stage  
High accuracy alignment on the DynMRT stage requires an X-ray source and imaging detector. The alignment of small animals in this work requires low dose imaging with a minimum field size of \(45\,\mathrm{mm}\times 30\,\mathrm{mm}\) (vertical \(\times\) horizontal) and, ideally, a pixel size no larger than that of the CT dataset (0.3 mm).  
There are several imaging options available on IMBL. Typically, the primary beam is diffracted to produce a monochromatic beam. This allows for high-resolution images with greatly reduced intensity; however, given the DynMRT setup, the field of view is limited to \(2\,\mathrm{mm}\times 30\,\mathrm{mm}\).  
[PERSON] et al. developed a dynamic scanning method to overcome the field of view limitation [32], although this still requires horizontal tiling of dynamically scanned images to achieve a horizontal field of view greater than 30 mm. Additionally, this method requires swapping between pink and monochromatic beams as well as the manual insertion and removal of an imaging detector, which are time-consuming processes that extend the length of time patients must be sedated.  
[PERSON] [29] and [PERSON] et al. [33] describe the use of a Toshiba X-ray tube and Hamamatsu C10900D flat panel detector which offers a field of view encompassing the entire DynMRT stage with a single exposure at an effective pixel size of \(0.16\,\mathrm{mm}\times 0.16\,\mathrm{mm}\). Operating at 40 kVp and 10 mA s, this configuration also provides the lowest doses with good contrast for image guidance on the treatment stage [29,33].  
This imaging option is chosen for use in our work as it best satisfied the dose, field of view and resolution requirements.