esa-sceva/satcom-chunk-collection huggingface dataset:
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 1 Introduction
In order to maximize the sum rate for a NOMA network with ipSIC, the authors in [23] optimized the circularity coefficient of the improper Gaussian signalling (IGS) of the downlink NOMA system. Furthermore, [PERSON] et al. [24] discussed a massive NOMA system in the presence of ipSIC and proposed several schemes to improve the overall system performance. With the development of RF digital-to-analogue converters directly operating at the channel frequency, digital-intensive polar and quadrature TX architectures have received much attention [25]. The authors in [26] presented a novel digital-intensive hybrid transmitter (TX) architecture, combining conventional in-phase and quadrature (I/Q) with constrained phase modulation. However, it is well known that the RF transceivers of the hardware may not be perfect due to limitations of the technology in the actual communication scenario [27; 28; 29]. In practice, the hardware is damaged by the following-phase noise I/Q imbalance (IQI) and high power amplifier non-linearities [30; 31; 32]. In particular, some existing papers have studied the influence of hardware impairments of IQI on the system performance. In [27], a method of a non-iterative blind compensator to mitigate IQI was introduced and investigated by using inactive subcarriers in orthogonal frequency division multiplexing with index modulation (OFDM-IM) data transmission. To investigate the effect of different IQI on the channel estimation and sum rate, [PERSON] et al. [33] derived the linear minimum mean square error estimator on a MIMO system with large antenna arrays. Furthermore, the expressions of the outage probability were derived by assuming that all the destinations and relay nodes were IQI impaired in [34]. Looking at the open up-to-date technical literature, most works used Rayleigh fading channel, which simplifies all mathematical operations widely. However, when a mirror or line-of-sight (LoS) component exists between the transmitter and the receiver, it cannot capture the change of fading. In addition, the future 5G wireless network will tend to a super-dense deployment, which will lead to more LoS transmissions due to the short propagation distance [35]. Therefore, it is very necessary to study NOMA systems with Rician fading channels [36].
# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 4 Conclusions  
In this study, we introduced ECE-TTS, a zero-shot emotion-controllable TTS system designed to simplify model architecture while achieving precise and flexible emotional control. ECE-TTS reduces model complexity by eliminating the reliance on phoneme alignment, duration predictors, and text encoders. Additionally, it leverages ConvNeXt V2 modules to refine text representations, improving text-speech alignment and enhancing the naturalness of synthesized speech. By integrating Emotion-Adaptive Spherical Vectors, we achieve a structured representation of continuous emotional dimensions, enabling more fine-grained and flexible emotional style control. For emotion intensity control, we adopted the emotion arithmetic vector method, which allows direct and efficient modulation of intensity without requiring additional ranking-based processes or complex embedding manipulations. Furthermore, to enhance model performance and data availability, we constructed a 200 h emotional speech dataset through an automatic annotation pipeline, eliminating the need for manual labeling while scaling the dataset size. Experimental results demonstrate that ECE-TTS outperforms existing models in both subjective (MOS) and objective (WER, Aro-Val-Domin SIM) evaluations, achieving superior speech naturalness, expressive accuracy, and robust emotion intensity control. These results validate ECE-TTS as a practical and efficient solution for zero-shot emotional speech synthesis.  
While ECE-TTS demonstrates strong performance in emotional speech synthesis, its evaluation has been limited to an English dataset, restricting its validated effectiveness to monolingual settings. Future work will focus on cross-lingual adaptation, enabling ECE-TTS to generalize across diverse linguistic characteristics and broadening its applicability to various speech synthesis tasks.  
Conceptualization, S.L., R.Z. and Q.Y.; methodology, S.L.; software, S.L. and R.Z.; formal analysis, S.L.; investigation, S.L.; resources, R.Z.; data curation, R.Z.; writing--original draft preparation, S.L.; writing--review and editing, S.L., R.Z. and Q.Y.; visualization, Q.Y.; supervision, R.Z. and Q.Y.; project administration, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 6: Subjective evaluation of happiness intensity. Higher emotion intensity scaling factor \(\alpha\) values correspond to increased perceived happiness ratings.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 3 5G New Radio Hybrid MIMO-Based Fibre-Wireless System  
The proposed RoF system is integrated into the wireless system to obtain a hybrid fibre-wireless system. The proposed system has been explored utilising an eMBB scenario in this article. A supercell with a frequency of 3 GHz and an indoor femtocell with a frequency of 20 GHz are tested in this study.  
The following are the four primary components of this experimental system:  
* Generation of signal and its utilization in the system.
* Digital pre-distortion.
* Analog RoF-based OFH for the transmission of radio frequency (RF) signals.
* A fibre-wireless system with a total of 2 \(\times\) 2 MIMO channels.
## 1 Introduction
The major contributions of this paper are summarized as follows:  
* This paper proposes an innovative hybrid approach combining Hilbert envelope demodulation with EEMD to address the limitations of conventional vibration analysis in detecting rolling bearing faults. The method effectively separates fault-related features from non-linear, non-stationary signals while overcoming mode-mixing issues inherent in traditional EMD. * A novel normalized energy distribution feature vector (ED) is introduced as a quantitative diagnostic indicator. By analyzing the energy distribution patterns of EEMD-derived IMFs, the method distinguishes faulty bearings (non-monotonic ED) from healthy ones (monotonic ED) with high interpretability and computational efficiency. * The proposed method is rigorously validated on the Case Western Reserve University bearing dataset, achieving 100% fault identification accuracy under varying operational conditions, including different sampling rates (12 kHz/48 kHz) and fault locations (drive end/fan end). This exceptional performance highlights the algorithm's strong noise immunity and practical applicability. The remaining sections of this article are organized as follows: Section 2 is the related work. Section 3 presents a theoretical foundation of bearing faults. In Section 4, the paper proposes the materials and methods about bearing faults. The experimental results are presented and analyzed in Section 5. We summarize this article in Section 6.
# Defense and Security Mechanisms in the Internet of Things: A Review
## 3 Materials and Methods
### Malicious Software  
A critical group of cyber threats consists of malicious software. Malware denotes software designed to harm or damage users, computer systems, or networks. Cybercriminals may use malware to steal data, hijack devices, eavesdrop on users, disrupt systems, and demand ransoms [92; 93; 94]. We can highlight the following malware types:  
* A virus is a computer program that replicates itself by infecting other computer programs or files. A virus typically gets triggered when a user executes an infected file.
* A worm, which is similar to a virus but can propagate without any user intervention (typically across a network).
* A trojan, which is malicious software that masquerades as legitimate applications or files to possess the user's trust and net possess access to the system.
* Ransomware, which is software that encrypts data on the victim's device and demands a ransom to unlock it.
* Spyware, which is a program that collects data about the user without their knowledge, such as login details, browsing history, or keystrokes.
* Adware, which is software that displays unwanted advertisements, often in an aggressive and difficult-to-remove manner.
* Rootkit, which is a tool that allows to hide the presence of other malware or an attacker, often allowing to gain administrator privileges.
* Keylogger, which is a tool that registers all keystrokes, which allows to steal passwords, credit card numbers, etc.
* Botnets are networks of devices (so-called zombies) taken over by malware, used to carry out DoS attacks, send spam, or mine cryptocurrencies.  
Malware gets onto devices via email attachments (for example, fake invoices, photos, documents), infected websites (drive-by downloads), software downloaded from unreliable sources, exploiting system or application security holes or fake software updates. Protection against such software involves eliminating known security holes, using antivirus programs, preventing unauthorised access to the network, avoiding suspicious sites, links, and attachments, and, above all, educating users, as awareness of threats and the ability to recognise fraud attempts may prove to be an essential security and defense mechanism [92; 93; 94].
# Spectrum Demand Forecasting for IoT Services
## 3 A Market Study
### Number of Devices  
Once the general trend for IoT penetration and market size for each product/service is determined in your country, it is necessary to quantify the number of devices that will be IoT-connected. We propose to gather the largest amount of historical data regarding the sales of any of those products in your country in at least 10 years, and project this information using econometric models such as _ARIMA(p,d,q)_[44].  
These projections allow us to identify the number of connected and non-connected objects of any kind, year after year, to evaluate the use of spectrum. For instance: imagine a central business district, where the number of vehicles in the peak hour is known. According to the projections, it will be possible to estimate for each year how many of those vehicles are going to be connected to entertainment on board services, or connected to infrastructure for traffic-control services, or will have vehicle-to-vehicle connectivity for semi-autonomous driving.  
The main purpose at this stage is to create a future scenario for spectrum contention. We call this a _peak case_. It represents a case where the spectrum will be highly used. To know the exact number of millions of IoT-connected cars in a country is relatively useless for spectrum forecasting, what is really needed is the density of IoT-connected cars per unit area. A central business district may be a good example for a peak case, providing several connections in the range of coverage of an LTE cell or a traffic control system. This number of devices per hectare (ha = 100 m \(\times\) 100 m) or square km (1 ha = 1/100 km\({}^{2}\)), is the number that will dispute a certain band of frequency and will determine its efficiency of use. Due to the typical order of magnitude, it is easier to express device densities in devices per hectares (\(dev/ha\)).
## 4 Protocol Constraints
### Dimensioning Buffer Sizes  
The adequate dimensioning of MAC buffers for the cluster-head nodes is a critical issue, because it has a clear impact upon message discards in cluster-tree networks [27]. Thus, it is important to define a boundary equation for the buffer usage, that should be imposed to the overall scheduling of the network. This boundary equation is relevant for avoiding message discards due to buffer overflows.  
Regardless of any other issue, each cluster-head must be able to store, in the worst case, all messages generated by its descendant nodes (child nodes). That is:  
\[\eta\leq\sum_{i\in S_{below}^{j}}S_{i}\leq B_{j}, \tag{11}\]  
where \(B_{j}\) is the size of the MAC buffer of cluster-head \(j\) (expressed in terms of number of messages), and \(\eta\) is the number of messages generated by the \(S_{below}^{j}\) message streams located in the descendant nodes (child nodes) of \(CH_{j}\) during one beacon interval.  
Therefore, we have the following Buffer Constraint that must also be satisfied by a cluster-tree WSN:  
\[B_{j}\geq\sum_{n\in S_{below}^{j}}\mathbb{S}_{n},\quad\text{for }1\leq n\leq N _{nodes} \tag{12}\]  
We assume that messages in cluster-head buffers are priority ordered using a Rate-Monotonic priority scheme [43], i.e., message streams with shorter periods will have higher priorities.
# Plasmonic Filter and Demultiplexer Based on Square Ring Resonator
## 3 Applications in Sensing and Switching  
We choose the structure corresponding to Figure 3b to discuss the following applications. Since the ring resonator is sensitive to ambient RI, such a channel drop filter can be applied to on-chip sensing. The sensing performance can be assessed by two factors [14]:  
\[S =\frac{\Delta\lambda}{\Delta n} \tag{9}\] \[FOM =\frac{S}{FWHM}\]  
where \(S\) is the sensitivity defined as the peak wavelength shift per unit ambient RI, and the figure of merit (FOM) represents the resolution of such a shift. Figure 4a shows the drop wavelength shift with different ambient RI values. This structure has a high sensitivity up to 1303 nm/RIU, as indicated in Figure 4b, which can meet the requirement of chemical-sensing [31]. Besides, the sensitivity can be easily enhanced by increasing the side length of the SRR [14]. Since the FWHM is 67 nm, the FOM is approximately 20/RIU. This value is better than those reported in some recent works based on MIM structures [15; 16; 17; 18; 19].  
A dynamically tunable device can also be realized based on such a filter filled with nonlinear optical materials, as proposed in Figure 4c. Here we apply nonlinear polymer Kerr material, the RI of which depends on the pump light: \(n=n_{0}+n_{2}I\), where \(I\) is the pump intensity, \(n_{0}\) is the linear RI, and \(n_{2}\) is the nonlinear RI. In this case, \(n_{0}=1.3\) and \(n_{2}=2.87\times 10^{-8}\) cm\({}^{2}\)/W [24]. The pump beam can be input from the top (Z axis direction) of this device [12]. Figure 4d shows that optical switching can be achieved at 1.97 \(\upmu\)m. With no pump light, the drop efficiency at 1.97 \(\upmu\)m is 0.7%, corresponding to the OFF state. After applying the pump light with 6.62 MW/cm\({}^{2}\), the drop efficiency can be enhanced to 53%, which is the ON state. Such pump intensity is easy to reach in practical experiments [32]. The modulation depth \(M\) is defined as [12]:  
\[M=10\lg\frac{T(I_{on})}{T(I_{off})} \tag{10}\]  
where \(T(I_{on})\) and \(T(I_{off})\) are the transmissions corresponding to the ON and OFF states at the modulated wavelength, respectively. In this work, \(M=18.8\) dB, which is comparable with some previous similar works [10; 11; 12; 13].  
Figure 3: (**a**) The drop spectrum corresponding to different values of \(l\). The inset is the reflector structure. (**b**) The drop spectrum with a reflector from FDTD and CMT, and the reflection spectrum when \(l=34\) nm.
###### Abstract  
This paper solved the problem of transmitting quantum bits (qubits) in a multi-hop and bidirectional way. Considering that the Greenberger-Horne-Zeilinger (GHZ) states are less prone to the decoherence effects caused by the surrounding environment, we proposed a bidirectional quantum communication scheme based on quantum teleportation and the composite GHZ-GHZ states. On a multi-hop quantum path, different types of GHZ states are previously shared between the adjacent intermediate nodes. To implement qubit transmission, the sender and intermediate nodes perform quantum measurements in parallel, and then send their measurement results and the types of previously shared GHZ states to the receiver independently. Based on the received information, the receiver performs unitary operations on the local particle, thus retrieving the original qubit. Our scheme can avoid information leakage at the intermediate nodes and can reduce the end-to-end communication delay, in contrast to the hop-by-hop qubit transmission scheme.  
bidirectional quantum communication; GHZ; quantum teleportation +
Footnote †: journal: Applied Physics Letters  
_Article_
###### Abstract  
In human hearing, frequency resolution is a term used to determine how well the ear can separate and distinguish two sounds that are close in frequency. This capability of breaking speech sounds into various frequency components plays a key role in processing and understanding speech information. In this paper, a piecewise-linear frequency shifting algorithm for digital hearing aids is proposed. The algorithm specifically aims at improving the frequency resolution capability. In the first step, frequency discrimination thresholds are processed by a computer testing software. Then, the input signal is parsed through the proposed piecewise-linear frequency shifting algorithm, which comprises of linearly stretching and compressing the frequency content at different frequency ranges. Experimental results showed that by using the proposed frequency shifting algorithm, the separation of formant tracks was increased in the stretching region and slightly squeezed in the adjacent compression region. Subjective assessment on six hearing-impaired persons with V-shaped audiograms demonstrated that nearly a 10% improvement of speech discrimination score was achieved for monosyllabic word lists tested in a quiet acoustic setting. In addition, the speech reception threshold was also improved by 2-8 dB when disyllabic word listswere tested in a noisy acoustic scenario.  
Keywords:hearing aids; frequency discrimination threshold; piecewise-linear frequency shifting algorithm; speech discrimination score; speech reception threshold +
Footnote †: journal: Applied Sciences
## 4 Characterization Framework
### Observability  
The large and complex nature of modern systems, dynamic infrastructure, and monitoring the health of these systems and taking the necessary actions as a result of this monitoring reveal the importance of observability.  
As shown in Figure 8, monitoring collects information about a system by communicating with services. In order for this need to continue uninterruptedly, the system must have a scalable infrastructure and it must be easy to query the collected information. Monitoring focuses on runtime metrics created by the applications themselves and related measurements, such as CPU, memory, I/O, etc., which are the infrastructural metrics of the system. Distributed tracing in a system, on the other hand, is where requests are spread over multiple services and each service responds to this request by communicating with different layers. It follows the behavior of the application while responding to this request and whether it is experiencing any problems by assigning an external request ID to each request and recording it. In log aggregation, it is ensured that the logs coming from all these services are collected in a central service and can be queried and analyzed from there. In addition, by creating alerts for specific logs to be examined, developers are notified when such logs occur. Exception tracking, on the other hand, concentrates on exceptions and records the exceptions that occur in the system. With the help of the recorded data, various inquiries and informing the developers using alerts are provided when necessary. In this way, with a central exception tracking infrastructure, developers are prevented from working continuously with the same error because historic data are provided for the relevant error type and the user knows that the error has been solved before. Audit logging, on the other hand, records the information that the system users performed on the system and the stages they went through.
# Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices
## 6 Conclusions  
An image classification system for identifying VDDs was deployed on a constrained device. This study demonstrated that these kinds of systems can be used in remote areas, avoiding the connectivity issues associated with cloud computing in IoT scenarios. We reviewed the most appropriate hardware to use and selected Raspberry Pi 3 for our study as it is inexpensive and runs at only 1.5-4 Watts, making it ideal for use in remote areas. Moreover, it can be powered even by a solar cell if no mains or battery power is available. The identified software components necessary for developing the image classification system were presented, and the system was evaluated against the results of existing models.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline [PERSON] et al. [27] & 99 \\ [PERSON] et al. [26] & 78 \\ [PERSON] et al. [28] & 93 \\ Proposed Model (HAM10000) & 78 \\ Proposed Model (ISIC 2020) & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of image classification model metrics.  
Figure 8: Model accuracy and loss vs. epoch graph from [PERSON] et al. [27].  
Future work will include comparing a wider range of algorithms and investigating the tuning of the most promising candidates, as well as using model compression techniques such as quantization and pruning. Additionally, more robust evaluation methods and additional evaluation metrics will be considered to validate the results. As we found that increasing the amount of training data increased our accuracy, future work will include sourcing and training on even larger datasets. More powerful boards, such as the UDOO BOLT and Orange Pi 5, could also play a significant role in expanding and improving this work. Edge computing approaches could also be integrated to facilitate federated learning. With these improvements in place, this system could pave the way for low-spec devices to be used for real-time detection of VDDs in remote and low-connectivity areas.  
Conceptualization, T.W. and C.C.; methodology, T.W.; software, T.W.; validation, T.W.; formal analysis, T.W. and P.J.B.; investigation, T.W.; resources, T.W. and C.C.; data curation, T.W.; writing--original draft preparation, T.W.; writing--review and editing, C.C., P.J.B., B.E.B. and G.K.; visualization, T.W.; supervision, C.C.; project administration, C.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Edinburgh Napier University Starter Grant 100031685.  
Not applicable.  
Data availability statementData available in a publicly available repository.  
The authors declare no conflicts of interest.