esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 1 Introduction  
Non-orthogonal multiple access (NOMA) is a promising technology to increase the spectral efficiency of next-generation communication systems [1, 2]. Unlike orthogonal multiple access (OMA), which transmits only one data point in one resource block (RB), NOMA can transmit multiple data in one resource block at once. Therefore, NOMA is a promising technology that can enhance the overall system data rate [3]. Among the various NOMA techniques, power-domain NOMA (PD-NOMA), which utilizes power as a resource to transmit multiple data streams simultaneously, is currently receiving significant research attention [4]. The key techniques of PD-NOMA are superposition coding (SC) at the transmitter and successive interference cancellation (SIC) at the receiver [5]. SC is a technique that combines two or more types of data with power allocation, and SIC is a technique that decodes the stronger signal first and removes the decoded signal from the combined signal to decode the weaker signal. In a NOMA system with multiple users,users with better channel conditions perform more SIC to decode their signals [6].  
Since NOMA, which has been studied so far, transmits data to multiple users in one cluster by dividing power resources, there is a problem that it is easily modified by noise power during the SIC process at the receiver [7]. Unlike OMA, in which multiple users' data are sent by dividing the power resource, NOMA compresses multiple data into one resource, leading to enhanced data rates but potentially decreased error performance. In a system with two users, when combining the data of both users through SC, if the bits of the two data are the same, they would be indistinguishable from sending data of one user in an OMA system. The decrease in error performance arises from differences in the bits of the two data. Taking this into account, we have researched a direction to reduce the data length combined through SC while modifying the bits similar to OMA, aiming to improve bit error rate (BER) performance. In this paper, we propose a coding method that transforms the data of two users through exclusive OR (XOR) bitwise operations and adds redundancy data. The advantage of this approach is that it can improve the error performance of both users. Furthermore, since power allocation in SC does not apply to the original signal, this method leads to an enhancement in sum rate and better user fairness.  
Notations: \((\cdot)^{\text{OMA}}\) and \((\cdot)^{\text{NOMA}}\) represent information in OMA and NOMA systems, respectively; \((\cdot)\) denotes information in the proposed design; \(\oplus\) is the XOR bitwise operator; \((\cdot)_{\text{I}}\) and \((\cdot)_{\text{Q}}\) represent the in-phase and quadrature components, respectively; \(\text{P}(\cdot)\) denotes the probability of an event; and \(\text{P}_{k}(e)\) denotes the bit error probability of user \(k\).
## 2 Multi-Body Constellation Design
### Candidate Orbits: Halo Southern Families  
The dynamical properties around the Lagrange points coupled with the fact that they have a large line of sight access area to the Moon's surface make them a good candidate for deploying constellations with a minimal number of spacecraft [19]. Consequently, the southern families of halo orbits around collinear liberation points L1 and L2 could fit perfectly for this type of operation thanks to their prolonged permanence in the southern hemisphere of the Moon; for example, orbital mechanics analysis for the Lunar Gateway has selected L2 southern halo orbits, thereby demonstrating strong interest in this type of solution [20].  
The most interesting subgroup of this family comprises Near-Rectilinear Halo Orbits (NRHOs). Within low-fidelity models, such as the three-body problem, NRHOs show favourable stability properties that suggest the potential to maintain their motion over a long duration while consuming few propellant resources; this can provide a good starting point for high-fidelity models. NRHOs offer advantages like relatively inexpensive transfer options from the Earth and feasible transfer options to the lunar surface and other orbits in cislunar space and beyond [21]. From a geometry point of view, they are distinguished by their elongated shape, a feature required for local coverage applications, and their constant visibility with selected ground stations on Earth's surface. A spacecraft orbiting in an NRHO may be subject to the eclipsing of different durations and frequencies; it is therefore essential to control and predict this phenomenon. Orbital resonance is an important consideration for meeting eclipse constraints. Resonance in terms of the sidereal period is focused on the time required for a celestial body to complete a revolution in its orbit relative to another body (the Moon's sidereal period is around 27.322 days): it corresponds to the period to see the Moon return to the same location in the sky from the centre of the Earth. On the other side, the synodic period is based on the time between the successive conjunctions of a celestial body with the Sun, and, in this case, it corresponds to the time to see the Moon return to the same location with respect to the Sun (the Moon's synodic period is about 29.5306 days). Lastly, ref. [21] presented preliminary transfer studies demonstrating that NRHOs are accessible from Low Earth Orbits (LEOs) for a relatively low cost and short time of flight, which are crucial features for a crewed mission.
## 2 Related Work
### Sentiment Analysis Using Social Context  
This section provides a brief summary of works that have leveraged social context for sentiment analysis, following the taxonomy of approaches by [PERSON] and [PERSON] [1].  
Figure 1: Model of social context, including: content (\(C\)), users (\(U\)), relations (\(R^{c}\), \(R^{u}\), and \(R^{uc}\)), and interactions (\(I^{u}\) and \(I^{uc}\)).  
[PERSON] et al. [32] was one of the first works to incorporate social context information, which the authors called heterogeneous graph on topic, to infer (user) sentiment. The underlying ideas behind that work were user consistency and homophily. A function to measure each of those attributes was provided, and the model tried to maximize the overall value. The authors compared alternative ways to construct the user network, using variations of follower-followee relations and direct replies (interactions). However, the approach could be categorized as _meso\({}_{r}\)_, for two reasons. Firstly, in their work, relations and interactions yielded similar results. Secondly, in the original, formulation edges (relations or interactions) were not weighted, so users were influenced equally by all their neighbors. Interactions were bound to be noisy, and aggregating them in this fashion was likely to provide little or no advantage over a simple relation. The SANTmodel [33] follows similar ideas, but for content classification. It is also a _meso\({}_{r}\)_ approach that combines sentiment consistency, emotion contagion, and a unigram model in a classifier.  
[PERSON] et al. [2] extended the model by [PERSON] et al. [32]. Their model used what they called an approval network, which effectively added weights for edges between users. The rationale for that change was that friendship did not imply approval and that a weighted network of interactions should better capture emotion contagion. This addition invalidated the two reasons for not considering it a _meso\({}_{i}\)_ approach.  
Other models have exploited community detection, which included them into the _meso\({}_{e}\)_ category. An example is [PERSON] et al. [34], which incorporated weak dependencies between microblogs, using community detection (different algorithms) on a network of microblogs. In their work, microblogs were connected if their authors were (i.e., there was a follower-followee relation).
## 8 Technical Improvements
### Off-Clinic Point-of-Care Implant Monitoring  
Point-of-care (POC) diagnostics incorporating the microfluidic system was recommended to accelerate diagnostic processes that are time-consuming [47]. Microfluidic technology is easily scalable (integrated system), fast, sensitive, and inexpensive; requiring only a small volume of sample obtained, either invasively or non-invasively. In a POC system, instead of having the patient do a post-check up at the hospital, they can opt to independently monitor their health condition by utilizing a simple diagnostic tool. An example would be a portable device such as those for monitoring blood glucose. The patient's blood samples can be collected directly using a lancet and then the equipment/meter will show the glucose level [2].  
However, a non-invasive option would be to analyze saliva. Saliva has been noted as a diagnostic fluid that can be easily obtained. One design such as the lab-on-a-chip technology is a device that measures putative biomarker of periodontal disease present in saliva. Its function is to measure the concentration of MMP-8 (_Matrix metalloproteinase-8_) in saliva to determine early diagnosis of periodontitis. The instrument is a microchip electrophoretic immunoassay (\(\mu\)CEI) allowing direct, automatic measurement of total MMP-8 content in saliva. It was originally designed for chair-side use which relies on a compact reader instrument and a disposable fluidic cartridge. It is also acknowledged that \(\mu\)CEI assays have the capability to measure the said biomarkers rapidly from just a small sample volume thus, making it a diagnostic tool with characteristics applicable to a numerous range of diseases [47]. While the examples mentioned are not applied for monitoring the released ions from metal implants, it is a suggested concept whereby a similar colorimetric-based principle can be adopted to monitor the corroded by-products released from an biodegradable metal implant [2].
## 4 Experimental Results
### Evaluation of the Defect Image Classification and Defect Detection  
The classification of the dataset was distinguished. The accuracy score for defect classification was 98.6%. Figure 5 presents the CM of the six defects classified. Crazing, rolled-in scale and scratches were classified at 100%. For the inclusion defect, one image was classified as pitted surface and two images as scratches. In the case of patches and pitted surface, only one image was incorrectly classified, respectively. The patches image is classified as pitted surface, and the pitted surface image is classified as patches. The proposed classification model performed quite well and was effective.  
Through the evaluation process, we combined the two disparate models. The evaluation metric used in this paper is AP and mAP. The AP of individual defects is evaluated and the mAP of all six defects is calculated. Table 2 exhibits the AP of individual defects during the evaluation process.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Average Precision**} \\ \hline Pitted Surface & 0.8504 \\ Inclusion & 0.7117 \\ Patches & 0.8987 \\ Rolled-in Scale & 0.6794 \\ Crazing & 0.6928 \\ Scratches & 0.7942 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average precision of individual defects.  
Figure 5: Confusion matrix of classification of six defects.
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 2 Materials and Methods
### Experimental Setting  
Since the noise databases, for example the NOISEX-92, the NOIZEUS, the CHiME background noise and the AURORA-2, are processed and machined to be applicable to most audio signal processing, it is easy to compare the results with those of other studies. However, the databases do not provide the variety of mixed sound found in real life.  
Ten types of noises were recorded in the actual environments in which hearing aids are used: white noise (white, N0), cafe noise around Inha University, Korea (cafe, N1), interior noise in a moving car (car interior, N2), fan noise in a laboratory (fan, N3), laundry noise in a laundry room (laundry, N4), noise in the library at Inha University (library, N5), normal noise in a university laboratory (office, N6), various noises in a restaurant (restaurant, N7), noise in subway car (subway, N8), and traffic noise around an intersection (traffic, N9). Each noise was recorded three times at different times on different days, and the noise data for each noise type were generated for 30 min. The noises were recorded at 44.1 kHz, which is the highest sampling frequency of microphone recording to minimize loss of audio data as possible, and the noise data was need to down-sampled to 16 kHz, because 44.1 kHz was too high to use in hearing aids signal processing. The process of down-sampling was that low-pass filter with 8 kHz of cut-off frequency was used to prevent the effect of aliasing. Unlike the noise database, the speech database used TIMIT because speech needs to be provided with a constant speed and a uniform tone to mix with the noise. Noisy speech used a mixture of TIMIT sentences with recorded noises at 0, 5, 10, and 15 SNR.  
The DNNs used three hidden layers, and each layer had 1024 nodes and rectified linear unit (ReLU). The standard backpropagation algorithm with dropout regularization was used to train the DNNs, the dropout rate of which was 0.2. The stochastic gradient descent algorithm with Adagrad was used for training the weights in the DNNs. A momentum rate of 0.5 was used in the first five epochs, after which the rate was increased to 0.9. The cost function in the last layer was the mean square error (MSE). The batch was set to 1024, the learning rate was 0.001, and the network was trained for 20 epochs.  
The noise dataset was generated and the DNNs architecture designed with MATLAB R2019a (MathWorks Inc, Natick, MA, USA) and Parallel Computing Toolbox in MATLAB was use for deep learning.
## 8 Conclusions  
The paper shows the development of an architecture that creates clusters and establishes connections between sensor of the same type by building different sensor networks. Cluster heads manage the network since they have connections with other cluster heads and these connections allow connecting cluster members from different clusters. Cluster members of the same type form a specialized network. Although there are several proposals of cluster-based systems in existence, the novelty of our proposal is that it could be used to build different networks with different routing protocols, while other cluster-based networks can run just one routing protocol and can build only one type of network. One of the main goals is that if all cluster heads switch off at the same time, the system is able to continue working, although there will not be new connections between clusters through CHs.  
In this paper, the description of the protocol developed and the flow of the messages have been presented. The performance of the network and how nodes perform in different execution cases have been shown. It has been demonstrated that the architecture requires low bandwidth to run and work properly. We are currently implementing the protocol in an embedded sensor.  
Comparing our proposal with others, it can be seen that the detailed description of our protocol allows its easy implementation. Other protocols compared in this paper do not take into account the discovery algorithm, and/or the node starting procedure, and/or the fault tolerance and/or even some parts of their proposal are not described in detail, so more messages that are not included in their description will be needed.  
This paper also starts a new research line because, in many cases, not all sensors can interact with the others in the same networks, so, in these cases, different sensor networks are needed and there is no other architecture in existence to support it.  
One of the main contributions is that the proposed system allows the creation of virtual wireless sensor networks and the wireless sensor networks virtualization. As far as we know, it has not been introduced before in the scientific field.
# Acoustic Simulation for Performance Evaluation of Ultrasonic Ranging Systems
## 4 Discussion  
Figures 3 and 4 show that the proposed simulative approach allows us to state that the ranging technique based on the cross-correlation achieves a ranging error less than 0.35 mm in every point of the volume in presence of a sufficient SNR level. In detail, in Figure 3a-d, there is a point error pattern, where each point error is limited to one spatial quantum, here equal to \(T_{S}\,c_{air}=0.343\) mm for SNR above 0 dB. However, when the SNR drops under 0 dB (Figure 3e,f), the used ranging technique is no longer able to cope with such a high noise level and the error increases up to over 500 mm. In fact, in the presence of high noise levels, the error is mainly due to an incorrect recognition of the peaks, i.e., recognition of a peak as a maximum different from the true one. This error is typically a multiple of the distance between two consecutive peaks in the cross-correlation vector. In the present case, the distance between two consecutive peaks of the cross-correlation is equal to 24 space samples, i.e., about 8.2 mm.  
As a significant result, the proposed approach allows us to identify the SNR level that is capable of giving the desired level of ranging error over the entire volume of the room for the transducer and signal shape adopted. Furthermore, the simulation (just an  
Figure 4: Numerical results at different SNR levels using a linear chirp 30–50 kHz. A linearized air absorption around 40 kHz has been assumed: (**a**) cumulative distribution function (percent of readings with error less than the value of a given abscissa) of the ranging error along the vertical section A of Figure 1, for decreasing SNR levels from 30 dB down to \(-\)20 dB (\(y\)-axis zoomed), (**b**) \(x\)-axis zoomed portion of the cumulative distribution function in the range 0–5 mm.  
example) shows that it is possible to check whether a particular technique achieves the required performance in the desired space already at the initial design phase.  
However, the proposed approach still has significant limitations. In fact, the software tool used for the simulation of acoustic propagation does not consider some effects that are important in the field of indoor ranging. First, the reflection phenomenon is not modeled, and therefore it is not possible at the moment to simulate in a simple way the reflection of the signal, for example, by walls. In other words, phenomena caused by multipath propagation such as self-interference, typical of even a partially reflective environment, are not considered. Secondly, the simulator calculates the propagation as if it occurs in any case in free space and does not consider any obstacles and near-line-of-sight situations. It is therefore not possible to evaluate effectively many situations typical of real applications.
# Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked Generalization Method
## 2 Related Work  
Previously, different practices were used to identify LGA fetus. The most common methods were using estimated fetal weight (EFW), abdominal circumferences (AC), ultrasound surveillance of an obese woman, maternal BMI, gestational weight, gestational diabetes mellitus (GDM), etc. Most of them were observational or retrospective studies that used simple logistic regression to extract discriminant features subset for the establishment of an LGA prognosis process. For example, [PERSON] used sonographic estimated fetal weight (EFW) of Chinese women to classify a fetus as LGA or non-LGA and achieved specificity and sensitivity of 48.1% and 97.3% respectively [13]. [PERSON] used AC with EFW for LGA classification [14]. [PERSON] proposed to use ultrasound surveillance of obese women before 32 weeks of the gestational period to classify a fetus as LGA or non-LGA [15]. [PERSON] used maternal BMI with gestational weight for LGA classification [16]. [PERSON] established a cohort analysis and demonstrated that LGA fetus exhibits dichotomous risks at term [17]. [PERSON] used linear modelling to observe the risk of parental complications in pregnant women with an LGA fetus [18]. In addition, some research was proposed to monitor variations in fetus bio-chemical indicators during different physical checkups to control its consequences [19; 20; 21]. Based on this, it can easily be foreseen that most of them were observational or retrospective studies that used simple logistic regression to extract discriminant features subset for the establishment of an LGA prognosis process.  
Perhaps, in our previous work, we were the first who exploited machine learning (ML) techniques for the establishment of an efficient LGA prognosis process. In [22], we used information gain (IG) feature selection scheme for the LGA prognosis and achieved precision and Area Under the Curve (AUC) score of 0.71 and 0.70, respectively. In [23], we used IG with an ensemble scheme to improve classification performance with the extraction of useful features to establish an efficient LGA prognosis process where we achieved precision and AUC scores of 0.84 and 0.72 respectively. Furthermore, in [24], using experts' expertise, we reached to obtain prediction precision and AUC scores of 0.95 and 0.86, respectively. In this research, we identified ranked twenty in practice features for the establishment of an efficient LGA prognosis process. However, still, there is still room to improve the prediction performance of an LGA classification system. Therefore, a master feature vector is created and GridSearch-based Recursive Feature Elimination with Cross-validation (RFECV) scheme followed by stacked generalization is introduced to select, rank, extract suitable feature subset with higher classification prediction performance with reduced generalization errors. RFECV and stacked generalization have previously been proven best in various related application domains [25; 26].