esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geological Setting  
### Regional Geology  
The Krzemianka and Udryn Fe-Ti-V ore deposits are located in the Mesoproterozoic Suwalki Anorthosite Massif (SAM) in the NE part of Poland [9; 10; 11; 12; 13], within the Mesoproterozoic-zoic beltiform magmatic AMCG (Anorthosite-Mangerite-Charnockite-Granite rapakivi) suite known as the Mazury Complex (Figure 1) [9]. This is a belt of granitoids and associated mafic and intermediate igneous rocks following an E-W-trending lineament extending from the Baltic Sea through northern Poland and southern Lithuania to western Belarus [14]. Anorthosite occurs at three autonomous massifs: Sejny, Suwalki, and Ketrzyn. The SAM has an oval shape and occupies an area of 250 km\({}^{2}\). Most of the crystalline basement of NE Poland is represented by late Svecofennian (1.84-1.80 Ga) oncogenic granitoids and supracrustal succession [15]. All the abovementioned units were intruded by plutons of the Mesoproterozoic AMCG suite around 1.5 Ga [15]. This suite is dominated by A-type granitoids with a rapakivi-like texture. The subsequent intrusions are gabbro-norite, anorthosite, and locally mangerite and charnockite rocks. The central part of the SAM consists of anorthosites surrounded by rings of norites, gabbronorites, diorites, and granites (Figure 1). The formation of the AMCG suite was a complex process with multiple magma batches sequentially differentiating and probably undergoing mixing and crustal assimilation [16]. A network of fractures and fault zones cutting the SAM rock sequences was filled by low-temperature S-type granite and pegmatic veins, emplaced between 1495 \(\pm\) 15 Ma (Udryn, Jeleniewo) and 1488 \(\pm\) 5 Ma (Udryn) [15]. These younger tectonic structures, which are widespread within the SAM, postdate the last episode within the SAM. New insights also allowed for the description of the occurrence of even later jotunite and nelsonite dikes within the massif [17,18]. Nelsonites are ore-bearing apatite rocks formed through a liquid immiscibility mechanism [19,20]. They were encountered as small dikes or veins in the SAM, as the final stage of ore mineralization. All these crystalline basement rocks are covered by 750-1200 m of sedimentary Phanerozoic rock complexes that dip towards the SW border of the East European Craton [21]. The sedimentary cover is dominated by sandstones, shales, and mudstones deposited in the Ediacaran to early Cambrian and in the early Triassic. The Cretaceous unit is built up mostly of chalk-type facies carbonates. The Cenozoic rock s are composed of unconsolidated, clastic sediments [12].
# Uplink NOMA-MIMO Systems with Rayleigh Power Distribution
###### Abstract  
The article is devoted to multiple-input multiple-output antenna systems, also called MIMO systems, which are widely used in wireless communication systems. In this article we consider a case when the MIMO system works in overloaded mode. In this mode MIMO systems can be considered as a system with non-orthogonal multiple access NOMA. The main goal of this article is to analyze this interesting case using statistical computer simulation. Based on the analysis of the capacity of a discrete-continuous multiuser MIMO uplink channel, the possibility of such systems functioning in overload mode is proved.  
MIMO; PD-NOMA; load factor; channel capacity +
Footnote †: journal: Sensors  
1
Footnote 1: institutetext: Correspondence: [EMAIL_ADDRESS]; Tel.: +7-495-957-79-27
## 3 Mobile Communications Background
### Evolution of Mobile Technology
#### 3.1.2 Second Generation (2G)  
Unlike the earlier technology, 2G was the first digital system offering mobile voice communication superior to 1G. It was first made available in 1991. In 2G, it was possible to go as fast as 1 Mbps. It has a speed of 64 kbps and transmits voice via digital signals. The 2G has a bandwidth of 30-200 kHz. Short Message Services (SMS), photo messaging, and Multimedia Message (MMS) features are just a few of the services that 2G offers. It makes use of both Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA) digital techniques. The most popular 2G mobile standard is the GSM (Global System for Mobile Communications) [41; 43].
Sensors_**2013**, _13_, 8352-8376; doi:10.3390/s130708352  
_Sensors_**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Data Communications Using Guided Elastic Waves by Time Reversal Pulse Position Modulation: Experimental Study**  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 3**  
\({}^{1}\) Department of Engineering and Aviation Sciences, University of Maryland Eastern Shore,  
Princess Anne, MD 21853, USA  
\({}^{2}\) Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh,  
PA 15213, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) School of Physical Electronics, University of Electronic Science and Technology of China,  
Chengdu 610054, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +410-621-3410; Fax: +410-651-3686.  
_Received: 19 March 2013; in revised form: 21 June 2013 / Accepted: 26 June 2013 / Published: 1 July 2013_  
**Abstract:** In this paper, we present and demonstrate a low complexity elastic wave signaling and reception method to achieve high data rate communication on dispersive solid elastic media, such as metal pipes, using piezoelectric transducers of PZT (lead zirconate titanate). Data communication is realized using pulse position modulation (PPM) as the signaling method and the elastic medium as the communication channel. The communication system first transmits a small number of training pulses to probe the dispersive medium. The time-reversed probe signals are then utilized as the information carrying waveforms. Rapid timing acquisition of transmitted waveforms for demodulation over elastic medium is made possible by exploring the reciprocity property of guided elastic waves. The experimental tests were conducted using a National Instrument PXI system for waveform excitation and data acquisition. Data telemetry bit rates of \(10\) kbps, \(20\) kbps, \(50\) kbps and \(100\) kbps with the average bit error rates of \(0\), \(5.75\times 10^{-4}\), \(1.09\times 10^{-2}\) and \(5.01\times 10^{-2}\), respectively, out of a total of \(40,000\) transmitted bits were obtained when transmitting at the center frequency of \(250\) kHz and a \(500\) kHz bandwidth on steel pipe specimens. To emphasize the influence of time reversal, no complex processing techniques, such as adaptive channel equalization or error correction coding, were employed.  
**Keywords:** piezoelectric sensors; structural health monitoring; communication; telemetry; time reversal; guided elastic waves; timing acquisition; steel pipes
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 2 Theoretical Background
### Gradient Descent Method  
In on-board processing, the design of an FPGA system is faced with various strict nonlinear constraints, such as resources, processing capacity, power consumption, volume, weight, etc. Therefore, it was essential to research an FPGA system's design under multiple constraints.  
Mathematical optimization theory can shed light on solving nonlinear multi-constrained problems. These can be expressed by the objective function and constraints, as shown in Formula (4):  
\[\begin{array}{ll}\text{Objective functions}:&MIN\ F(X_{1},X_{2},\ldots \ldots,X_{n})\\ \text{Constraints}&g_{i}(X_{1},X_{2},\ldots\cdots X_{n})\geq 0\ \ i=1,2, \ldots\ldots m\\ &X_{j}\geq 0\ \ j=1,2,\ldots\ldots n\end{array} \tag{4}\]  
If the objective function \(F\) can be expressed as a formula, then the nonlinear optimization problem could be solved mathematically by obtaining the optimum value \(X_{j}\). Since there are many types of FPGA logic resources and the resource values are discrete, neither the objective function nor constraints can be expressed as a function, thus making it very difficult to apply the optimization theory to solve the problem directly.  
That said, we introduced the gradient descent method in optimization theory into the design of the FPGA system to solve the dual-constraint problem between resources and processing capacity. The basic principles of the gradient descent method are described below.  
The gradient descent method was proposed by [PERSON] in 1847 and has become the basis of many constrained and unconstrained optimization methods [54]. The gradient descent method sets the negative gradient direction of the objective function as the searching direction of each iteration so that the value of the objective function decreases the fastest in the first few steps. For a nonlinear function \(f\) with a first-order continuous partial derivative, \(X\) contains \(n\) variables \(x_{n}\), and the gradient is defined by Formula (5):  
\[\
abla f(X)=\frac{\partial f}{\partial X}=[\frac{\partial f}{\partial x_{1}}, \frac{\partial f}{\partial x_{2}}\ldots,\frac{\partial f}{\partial x_{n}}] \tag{5}\]  
Figure 1: FPGA calculation flow of the algorithm. “FFT” refers to “Fast Fourier Transform”. “IFFT” represents “Inverse Fast Fourier Transform”.  
We used the gradient descent method to map each FPGA scheme's resources and processing capacities in two-dimensional coordinates. Then we analyzed the relationship between resources and processing capacity through the gradient between the schemes. After that, we identified the balanced scheme. The detailed steps are described in Section 5.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix A Literature Review
### Statistical Approach to Anomaly Detection  
The statistical techniques for anomaly detection can be split into two families: statistical tests and predictive models. Both suffer from the curse of dimensionality and model/data mismatch. When anomaly detection relies on hypothesis tests, it usually tests whether the observations are drawn from a known distribution [18], supposing that the user knows the probability distribution of the normal observations. Such a parametric framework narrows down the scope of applicability of hypothesis tests, as the data do not always coincide with the assumed distribution. Moreover, the tests provided in the literature are not suitable in multivariate settings [19]. The statistical techniques that rely on fitting a predictive model to each time series also require strong assumptions on the data. Predictive models are usually autoregressive (AR), moving average (MA), or ARMA models. Anomalies are then detected relative to the forecasts suggested by the model [17]. In these parametric approaches, some parameters have to be specified again, starting with the order of the models. Selecting the optimal model parameters with respect to an information criterion is not always possible. Additionally, these models assume that the time series are homogeneous, i.e., drawn from the same distribution [20]. This is not always satisfied in the financial risk management case where several types of market risk factors are treated simultaneously.
## 2 Materials and Methods  
### Study Area  
The Qinghai-Tibet Plateau (Figure 1), covering 26.8% of China's total land area, is renowned as the Third Pole of the World [10]. The Qinghai-Tibet Plateau boasts an average altitude exceeding 4000 m above sea level, with terrain sloping from northwest to southeast [11]. The Qinghai-Tibet Plateau has heterogeneous land cover types, with forests, shrubs, grasslands, and deserts punctuated by water bodies and wetlands from southeast to northwest. Grassland is the dominant land cover type with roughly (44%) of China's grassland, and 6% of the global total [12]. The particular geography makes the vegetation extremely sensitive to human influence and climatic variations. The Qinghai-Tibet Plateau exhibits distinctive climatic characteristics, including high solar radiation intensity, extended duration of sunshine, lower air temperature and pressure, increased cloud cover, and noticeable seasonal and spatial variability in precipitation [13]. Research shows that over the past 50 years, the Qinghai-Tibet Plateau has been one of the areas experiencing the strongest global climate warming [14]. From 1961 to 2020, the annual average temperature here has an increasing trend of 0.35 \({}^{\circ}\)C/10 years, more than twice the global warming rate (0.16 \({}^{\circ}\)C/10 years) during the same period [15]. In addition to the increase in carbon emissions caused by human activities, carbon emissions caused by the melting of permafrost on the Qinghai-Tibet Plateau also account for an important proportion. Research shows that from 1961 to 2020, the area of permafrost on the Qinghai-Tibet Plateau decreased by 16% [16]. Therefore, significant changes in climatic conditions, annual increases in CO\({}_{2}\) and fragile environmental conditions make the generation of accurate GPP data crucial for ecological research on the Qinghai-Tibet Plateau.
## 6 Discussion  
For broader comparison and higher credibility, the NMSE performance of our work is compared with the best performance of all models concluded by [39], not only those introducing the attention mechanism. MRFNet [40] achieves the best NMSE = \(-15.95\) dB in outdoor scenarios at \(\eta=1/4\). The gap between our model and MRFNet is only \(0.15\) dB. The NMSE of ENet [41] is \(-11.20\) dB in indoor scenarios when \(\eta\) equals \(1/32\), which has been surpassed by our model. In outdoor scenarios, a neural network named CsiNet+DNN proposed in [42] gets remarkable results at the expense of extremely high computational overhead. Compared to TransNet+ and other attention models, the deployment resource overhead of CsiNet+DNN may be significantly larger. In the remaining cases, TransNet+ outperforms all the other networks.  
Although TransNet+ achieves higher recovery accuracy, which reaches SOTA for almost all scenarios, practical deployment issues are not within the scope of the study. The majority of research in this field relies on simulations where CSI samples are readily generated using channel modeling software such as COST 2100, facilitating the acquisition of extensive CSI data for model training purposes. However, the implementation of DL-based CSI feedback in real wireless systems necessitates consideration of data gathering and online training. This necessity arises primarily for two reasons. Firstly, during wireless communication, the UE may not have the opportunity to collect a sufficient quantity of CSI samples. Secondly, the actual propagation environment can undergo changes, particularly in mobile wireless settings, rendering the network parameters trained on static data inadequate for adapting to dynamic real-time channels. This is one of the significant directions for our future research.
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 4 Interface Design of the Portable Device
### Interface Design of the Application  
The interface of the proposed APP displays the information of the specific transformer that to be measured. With this system, users can search for specific transformers by their property numbers and locate the capacity and original phasor of all transformers labeled under that code. After a transformer is selected as a target of measurement, phasor identification begins. Characteristics including time, phasor, frequency, and voltage are measured, and the data are saved in the mobile device to enable subsequent comparison. Figure 7 presents the interface page of about transformers information and phasor identification results.
# Network Attack Classification in IoT Using Support Vector Machines +
## 1 Introduction
Two support vector machine (SVM) models are evaluated to be used as IDS agents: the classification SVM (C-SVM) and the one-class SVM (OC-SVM). The two SVM classification techniques were used to emphasize the importance of using abnormal activity when creating detection models. Previous work on SVM evaluated the accuracy results of the C-SVM detection models when trained and evaluated in the same network topology. The accuracy metric alone offers partial information on classification efficacy [2,3]. In this work, the results are presented using multiple binary classification metrics and explore precision and sensitivity, along with accuracy and the quality of the binary classification of two SVM classification techniques and in two network topologies. The SVM detection models were created using local node network packet activity. The OC-SVM is created with local node activity when executing the intended application with no malicious intervention; the benign application. For creating the C-SVM detection model, node activity was used from the benign application and activity when the node is compromised; the malicious application. The SVM models were evaluated in an unfamiliar network topology against three types of attacks (and their variations). A general routing layer was used to implement the attacks, so that to eliminate artifacts stemming from control messages of more complex protocols, like Routing Protocol for Low Power and Lossy Networks (RPL) over 6 LowPAN (Low power Wireless Personal Area Networks [4]. C-SVM, when evaluated with unknown activity, achieved up to 100% classification accuracy, approximately 49% more than OC-SVM, in which the highest accuracy achieved was 60.8%. The results corroborate with the findings of previous work [1], that using both benign and malicious activity provides better understanding of the node status and better detection rates. In summary, the contributions of this paper are as follows: (i) use of actual network traffic, (ii) use of supervised learning for network-layer attack classification in WSN and IoT networks, (iii) use of two SVM techniques, namely classification and one-class, (iv) evaluation and contrast of the two SVM techniques on two topologies and five attacks, (v) combination of data and models to provide a generalized detection for all attacks, (vi) establishment of the suitability of C-SVM in detecting the attacks in unknown topologies and (vii) verification of the superiority of C-SVM vs. OC-SVM in detecting network-layer attacks in WSN and IoT Networks. The rest of the paper is structured as follows: Section 2 presents background information on SVM. Section 3 presents the methodology used to create IDS using SVM and Section 4 presents the training and evaluations results of the proposed solutions. Section 5 discusses related work on machine learning detection techniques and IDS. Section 6 concludes the current work.
## 3 Results
### Collected Data  
In the analysis of the collected data, a total of 5432 measurements were recorded during the active phase of the monitoring system. Figure 13 presents the initial findings, utilizing histograms to depict the distribution of certain parameters. It is evident that some parameters, such as temperature, pH, and humidity, exhibit variations within a specific range over time. On the other hand, the levels of NPK macronutrients show relatively minimal fluctuations.  
The assessment of SQ in the stevia crop where the monitoring system was implemented reveals a low-quality status. The pH values indicate strong acidity rather than being close to neutral. While the temperature remains within acceptable limits, the humidity  
Figure 12: Monitored variables’ evolution over one day. Plot generated using the web platform.  
Figure 13: Histograms of data collected using the monitoring system.  
levels predominantly remained low, only reaching optimal conditions temporarily during irrigation. The electrical conductivity of the soil remained consistently low, indicating favorable conditions. However, the concentration of macronutrients, specifically nitrogen, phosphorus, and potassium, was consistently low throughout the monitoring period. Consequently, the overall SQ is estimated to be low at 2.5 out of 10.  
These findings have been shared with the owners and farmers of the stevia crop, along with recommended actions to improve SQ based on the identified deficiencies.
## 4 Discussion
### Biomechanical Impact
It achieved higher anterior and central height restoration (up to 96.20% and 101.13%, respectively) and maintained better results after cyclic loading, surpassing kyophoplasty in both initial and long-term outcomes [27]. One of the potential advantages of the SpineJack device is its ability to direct the force required for fracture reduction along the craniocaudal axis. In contrast, during balloon kyphoplasty, the direction of the force is determined by the patient's specific anatomy and the balloon itself. In this regard, a pre-clinical study by [PERSON] et al. investigated the biomechanical performance and height restoration achieved with the SpineJack device in comparison to balloon kyophoplasty using cadaveric models. The study found that the SpineJack device achieved greater restoration of anterior, central, and posterior height compared to balloon kyophoplasty (\(p<0.05\)) [11]. In another study conducted by [PERSON] et al., comparing balloon kyophoplasty with the SpineJack, the latter demonstrated a shorter procedure time, similar improvements in pain relief and function, and a substantial increase in vertebral height and vertebral body angle [28]. So far, biomechanical and clinical comparative studies versus percutaneous kyrophoplasty or vertebplasty have reported non-inferiority of spine implants with a reduced volume of injected cement [29]. Instead, concerning preclinical studies on the biomechanical impact of these devices in _traumatic_ vertebral compression fractures, a study conducted on 28 cadaveric spine segments with induced burst fractures demonstrated that the combination of an intravertebral reduction device, with or without cement augmentation, was more effective in restoring central endplates and reducing spinal canal narrowing compared to dorsal instrumentation alone [30]. Additionally, [PERSON] et al. demonstrated that in a study involving 36 fresh cadaveric vertebrae, the SpineJack system was more effective than balloon kyrophoplasty in maintaining vertebral height after reducing traumatic wedge fractures. The study showed that with the SpineJack, it is possible to reduce the cement volume to 10% of the vertebral body volume without compromising height restoration, unlike kyrophoplasty, which requires a 30% cement volume to achieve similar results. Moreover, the SpineJack better preserved intraoperative height gain, with an average loss of 1% compared to 16% with kyrophoplasty [31].