esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Modern-day applications of fifth-generation (5G) and sixth-generation (6G) systems require fast, efficient, and robust transmission of multimedia information over wireless communication medium for both mobile and fixed users. The hybrid amalgamation of massive multiple input multiple output (mMIMO) and orthogonal frequency division multiplexing (OFDM) proves to be an impressive methodology for fulfilling the needs of 5G and 6G users. In this paper, the performance of the hybrid combination of massive MIMO and OFDM schemes augmented with fast Fourier transform (FFT), fractional Fourier transform (FrFT) or discrete wavelet transform (DWT) is evaluated to study their potential for reliable image communication. The analysis is carried over the Rayleigh fading channels and M-ary phase-shift keying (M-PSK) modulation schemes. The parameters used in our analysis to assess the outcome of proposed versions of OFDM-mMIMO include signal-to-noise ratio (SNR) vs. peak signal-to-noise ratio (PSNR) and SNR vs. structural similarity index measure (SSIM) at the receiver. Our results indicate that massive MIMO systems incorporating FrFT and DWT can lead to higher PSNR and SSIM values for a given SNR and number of users, when compared with in contrast to FFT-based massive MIMO-OFDM systems under the same conditions.  
m 2022 11 621. [[https://doi.org/10.3390/electronics11040621](https://doi.org/10.3390/electronics11040621)]([https://doi.org/10.3390/electronics11040621](https://doi.org/10.3390/electronics11040621))  
17 January 2022  
Accepted: 14 February 2022  
Published: 17 February 2022
# Recycled Carbon Fibers with Improved Physical Properties Recovered from CFRP by Nitric Acid
## 4 Discussion
### The Mechanism of the Improvement of Tensile Strength of Carbon Fiber  
The mechanism that increased the tensile strength in the rCF compared to the virgin carbon fibers was considered. Two possible factors that led to an increase in tensile strength were confirmed.  
The first is that the voids were observed near the surface inside the carbon fibers and that the abundance of the voids decreased in vCF 8 h and rCF 12 h to 120 h, compared to vCF. Since carbon fibers are brittle materials, their strength is largely affected by defects on the surface and inside the fibers [21]. As indicated in a previous report, it has been confirmed that smaller diameters of voids inside the fiber resulted in higher fiber strength [29]. As a result, in fibers with higher tensile strength, it was considered that the abundance of voids was smaller, and there was a smaller number of points where stress concentrates when a tensile load is applied, leading to higher tensile strength. The second possible factor is that the voids were found to contain functional groups with O and N. This was considered that the contained functional groups formed a compression layer to the carbon fiber surface that may have produced higher tensile strength. Here, a decrease in tensile strength was observed in rCF 120 h. Since this material showed a large \(I_{\rm{DI}}\)/\(I_{\rm{GI}}\) ratio, it was considered that prolonged immersion of carbon fibers in nitric acid resulted in damage such as disruption and defects of the graphite structure [19]. However, vCF 24 h which was the maximum immersion time to nitric acid in this research, showed a decline in tensile strength compared to vCF 8 h, which was the minimum immersion time to nitric acid in this research, however, did not show a significant decline to the tensile strength when compared to the decline in rCF 120 h. We considered that the immersion time of vCF to nitric acid was not sufficient in 24 h to induce damage to the graphite structure and longer immersion might have induced damage to the graphite structure of vCF and led to a decrease in tensile strength. We would like to confirm this point in further research.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
## 4 Result Interpretation
### Bending Strain at the Tunnel Lining  
The bending strains computed at the tunnel lining in the transverse direction were compared with the measured values, as shown in Figure 8a. The positive value represents the tensile strain, and the negative value represents the compressive strain. In comparison with the measured results, the computed results for both basement types exhibited similar trends. Moreover, the symmetrical bending strain distribution with regard to the tunnel centerline could be determined. The compressive strain occurred at the tunnel springlines, while the tensile strain occurred at the other tunnel elements. In the transverse direction, the maximum tensile bending strain was 91 \(\upmu\)\(\upepsilon\) and 108 \(\upmu\)\(\upepsilon\) for the square and circular basement excavation, respectively. There was only a small change in the transverse direction for these two excavations types, not only in the magnitude, but also in the distribution. Thus it was concluded that, in the transverse direction, the basement shape had little influence on the tunnel's bending strain. Based on the American Concrete Institute [35], the maximum tensile strain was 150 \(\upmu\)\(\upepsilon\). The computed bending strains did not exceed the given allowable limit nor the measured value, assuming that zero bending strain occurred before the basement construction.  
Figure 8b shows the bending strains computed after the excavation in the longitudinal direction in comparison with the measured values. The positive sign denotes the tensile strain, while the negative sign denotes the compressive strain occurring at the tunnel lining crown. Similar trends were observed in the measured and computed results. With regard to the basement center, it was found that the distribution of the bending strain was symmetrical. For the square and circular basement, the maximum tensile strain computed by the numerical method was 86 \(\upmu\)\(\upepsilon\) and 65 \(\upmu\)\(\upepsilon\), respectively. The computed maximum tunnel lining bending strain in the longitudinal direction, which was caused by the circular excavation, was 32% smaller than that caused by the square excavation. Based on ACI [47], cracks may occur in the tunnel lining assuming that the tensile strain exceeds 85 \(\upmu\)\(\upepsilon\) before the circular basement excavation.  
Figure 7: Earth pressure change around the tunnel lining (absolute value).
# Quantifying Total Phosphorus and Heavy Metals in Residential Septage
## 3 Results and Discussion
### Mass and Watershed Mass of Pollutants from Domestic Septage
Improper disposal of paints (e.g., flushing down sinks or toilets) can increase copper, nickel, and zinc loads to wastewater systems [87]. [PERSON] and [PERSON] [81] found that flushing artistic paints can be a source of cadmium. These studies suggest that sources of heavy metals are less common and more variable in TP. Therefore, the similarities in the median concentrations and masses of heavy metals could indicate that most households generated trace amounts of heavy metals. Pollutant concentrations are generally not considered when planning septic tank maintenance schedules. The frequency of septic tank pumping is generally planned based on the household occupancy and tank size [4, 47], which are used to estimate wastewater generation and solids accumulation rate. In the current study, information regarding the time since last pumping was not available for every septic tank. However, there were 4 septic tanks that had not been pumped for at least 8 years, which exceeded the typical 3-5-year pumping frequency [46]. The concentrations of TP and metals in the scum and sludge layers in these tanks were elevated relative to the liquid layer (Table 5). Furthermore, these data were similar to TP and heavy metal ranges in sludge and septage reported by past studies (Table 3). The median concentrations of contaminants in the liquid layers of these 4 septic tanks were elevated relative to the median concentrations of TP (Figure 3) and metals (Table 2) for the liquid layers of all septic tanks. Based on the layer thickness, Tank 2 was predominantly comprised of solids and typically had the greatest pollutant concentrations (Table 4). Tanks 3 and 4 were near the recommended maximum solid volume percent [3], whereas Tank 1 contained the lowest solid content (16%) of the 4 tanks. However, TP and metal concentrations in Tank 1 tended to be greater than those observed in Tanks 3 and 4. Therefore, the accumulation of solids alone may not be a reliable predictor of the pollutant concentrations in residential septage. Tanks 1 and 2 tended to contain elevated metal concentrations relative to Tanks 3 and 4 across all layers, which may have occurred from differences in the wastewater characteristics. Future work should incorporate a homeowner survey to ascertain the household wastewater practices, time since last pumping, water use, and other factors that affect wastewater characteristics. These data could then be used in conjunction with water quality indicators from domestic septage quality for further understanding between the septage pollutant concentrations and homeowner practices.
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 4 Experimental Evaluation
### Efficiency of Real Dataset  
In this subsection, we conduct the effect of processing time under the different number of grids, facility types, and objects in the real dataset.  
**Effect of grids:** For US_A dataset, we set facility types \(m=2\). Both the preferable facility types and unpreferable facility types are 1. We vary the number of grids \(n^{2}\) with \(200\times 200\), \(300\times 300\), \(400\times 400\), \(500\times 500\) and \(600\times 600\) respectively.  
Figure 17 shows the results. We observe that the processing time of our MRGASKY algorithm is particularly stable. In contrast, for GASKY algorithm, the processing time increases very fast with the increase of the number of grids.  
**Effect of facility types:** In US_B, we fix the number of objects \(obj=1000\) for each facility type and the number of grids \(200\times 200\). We set the number of types \(m\) as 2, 4, 6 and 8, respectively.  
In Figure 18, we can observe that the number of facility types has a significant effect on the processing time. Moreover, similar to previous experiments, the processing time of our MRGASKY smoothly changes below 50 while GASKY increases from 50.  
Figure 16: Processing Time of SYN_C.  
Figure 17: Processing Time of US_A.  
**Effect of objects:** In US_C, we vary the number of objects with 2000, 4000, 6000, 8000 and 10,000. The number of grids and the number of facility types are fixed to \(m=2\) and \(n^{2}=200\times 200\).  
The results are demonstrated in Figure 19. We observe that when the number of objects increases, the processing time also increases. Besides, for the MRGASKY algorithm, the processing time is much smaller than GASKY algorithm.
## 2 Numerical Modeling of Ropes and Large Floating Structures Based on the ANCF
### Element Assembly  
To clearly delineate the relationship between the structure and its elements, consider the assembly of the stiffness matrix as an illustrative example. The stiffness matrix of each element is subdivided into four quadrants, each a 4 \(\times\) 4 matrix, denoted as \(\mathbf{K}^{n}=\begin{bmatrix}K_{11}^{n}&K_{12}^{n}\\ K_{21}^{n}&K_{22}^{n}\end{bmatrix}\). Consequently, the global stiffness matrix can be articulated as follows.  
\[\mathbf{K}=\begin{bmatrix}K_{11}^{1}&K_{12}^{1}&0&0&0&0&0\\ K_{21}^{1}&K_{22}^{1}+K_{11}^{2}&K_{12}^{2}&0&\vdots&\vdots&\vdots&\vdots\\ 0&K_{21}^{2}&K_{22}^{2}+K_{11}^{3}&K_{12}^{3}&\vdots&\vdots&\vdots&\vdots\\ \vdots&0&K_{21}^{3}&\ddots&0&0&\vdots&\vdots\\ \vdots&\vdots&0&0&\ddots&K_{12}^{n-2}&0&\vdots\\ \vdots&\vdots&\vdots&\vdots&K_{21}^{n-2}&K_{22}^{n-2}+K_{11}^{n-1}&K_{12}^{n- 1}&0\\ \vdots&\vdots&\vdots&\vdots&0&K_{21}^{n-1}&K_{22}^{n-1}+K_{11}^{n}&K_{12}^{n} \\ 0&0&0&0&0&0&K_{21}^{n}&K_{22}^{n}\end{bmatrix} \tag{45}\]  
Since there are \(n+1\) nodes, the assembled total stiffness matrix becomes a square matrix denoted by \((4n+4)\times(4n+4)\). The assembly of other matrices is conducted following a similar procedure. Through the further derivation and finite element assembly of the element stiffness matrix, mass matrix, and external load matrix, the assembled matrices are substituted into the motion equation to form a complete equilibrium equation.  
Due to the considerably smaller dimensions of the floating structure relative to the slender cable structure during deep-sea operations, the model is simplified, computational efficiency is enhanced, and the dynamic variations of the cables are highlighted by considering the connection method as hinged, which restricts only translational movements and not torsional rotations. With the floating structure being constrained at the end of the cable, it is sufficient to add the mass of the floating structure to the mass matrix and apply algebraic constraint equations to finalize the modeling of dynamics.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 3 Preliminaries of D2D Communications
### Resources in LTE-V2V Mode 4
#### 3.3.2 Resource Selection  
LTE-enabled devices/vehicles require wireless resources to broadcast messages. Two mechanisms of resource (RBs) scheduling defined in Release 14 of the 3 GPP are: (a) Scheduled Resource Allocation (SRA), referred to as Mode 3, and (b) ARS, referred to as Mode 4 [12].  
SRA is a centralized mechanism wherein a UE initially sends a Radio Access Control (RAC) request to the associated eNodeB, that in return allots some resources for it to send the status of its buffer via a Buffer Status Report (BSR). After this report, eNodeB schedules resources to the LTE UE for D2D communication [31], as illustrated in Figure 4.  
Due to the signaling overhead involved for each UE, Mode 3 is not a good choice for delay-critical applications.  
ARS uses a distributed approach. A static resource pool comprising many RBs is presented after every Transmission Time Interval (TTI). The UE can autonomously select RBs from it with no prior signaling [31]. Figure 5 presents this idea. The dotted line in Figure 5 represents RB selection for data transmission. Step 2 involves sending feedback regarding RB selection.  
Mode 4 does not involve signaling for the request of resources from LTE, and hence, it is more suitable for delay-constrained vehicle safety applications. However, the lack of signaling also means contention for the RB selection and the probability of resource collisions [32]. The probability of resource collisions is bound to increase for densely populated networks [16]. This scenario of resource collisions among multiple vehicles and the broadcast nature of vehicle safety messages brings Release 14 of the 3 GPP away from its design objectives of LTE-V2X for vehicular broadcast communications.
# Dual-Band UWB Monopole Antenna for IoT Applications +
## 6 Conclusions  
A dual-band UWB coplanar waveguide antenna composed of two ground planes and a rectangle patch has been presented in this paper. The ground plane is enhanced by inserting a \(5.7\) mm \(\times\)\(0.5\) mm slit. The proposed antenna resonates at a frequency of \(4.9\) GHz and has a return loss of \(-31\) dB. The radiation pattern is both omni- and bidirectional, with a maximum and minimum gain of \(4.68\) dB and \(2.81\), respectively. The antenna's impedance bandwidths are improved by correctly cutting a horizontal slit. The measured findings reveal that the suggested antenna can attain a large impedance bandwidth.  
Conceptualization, S.M. and M.Z.; methodology, S.M. and M.Z.; software, S.M. and D.T.; validation, S.M., M.Z. and Y.A.; formal analysis, M.Z.; investigation, M.Z.; writing--original draft preparation, D.T. and S.M.; writing--review and editing, Y.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data can be obtained from the corresponding author on request.  
The authors declare no conflict of interest.
# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 4 Evaluation
### Filebench Benchmark  
We further evaluate the benefits of ReZNS under the Filebench benchmark [53], which is one of the well-known macro benchmarks. Before performing an evaluation, we pre-filled dummy data up to 50% of total capacity on ZNS SSD; it helps to trigger zone-reset command, as mentioned above. Then, we ran the varmail and fileserver workloads for 1 min, respectively. The varmail workload emulates the synchronous I/O activities of a mail server that adopts the parameter of 1000 files, 16 threads, 1 MB I/O size, 1,000,000 directory width, and 16 KB append size. The fileserver workload emulates a series of asynchronous file operations, such as create, delete, read, and write, that adopt the parameters of 10,000 files, 50 threads, 1 MB I/O size, 20 directory width, and 16 KB append size. In summary, the varmail workload frequently issues synchronous commands (i.e., fsync operation), whereas the fileserver rarely does.  
Figure 5 shows the overall performance of two different workloads. As shown in Figure 5, ReZNS outperforms the traditional ZNS SSD by up to 60% and 13% for the varmail and fileserver, respectively. This is extremely meaningful in that both workloads are write-intensive workloads. Note that the performance gap of the varmail is much larger than that of the fileserver workload. This is because the varmail issues synchronous I/O operations, and it quickly consumes available free space inside ZNS SSDs. In other words, the traditional ZNS consecutively writes all data from the applications, and it performs the zone-reset commands to reclaim the zones even if the zones include available free space if the number of zones reaches the maximum zone value. In contrast, ReZNS reuses the available free space inside the zones to be reclaimed, not performing the zone-reset commands.  
To confirm our intuition, we plotted the number of zone-reset commands on NVMeVirt [28]. Figure 6 clearly shows that the varmail workload issues the commands much more frequently than the fileserver. Also, Figure 6 compares the number of zone-reset commands obtained from all experiments on NVMeVirt [28]. As expected, ReZNS reduces the number of zone-reset commands by up to 29% and 20% for the varmail and fileserver workload, respectively. This reduction comes from sharing and reusing the flash blocks with free space instead of creating a new zone. Also, this advantage of ReZNS has positive impacts on the consecutive I/O operations as the likelihood of delay caused by the zone-reset command decreases.  
Figure 4: The number of zone-reset commands on the FIO benchmark.
## 2 Materials and Methods
### Mineral Analysis  
The methodology employed for micronutrient assessment entailed post-incineration of the samples following digestion with hydrochloric acid. Quantification of the elements calcium (Ca), magnesium (Mg), potassium (K), manganese (Mn), copper (Cu), lead (Pb), cadmium (Cd), zinc (Zn), iron (Fe), chromium (Cr), nickel (Ni), and sodium (Na) was conducted using atomic absorption spectrophotometry (Thermo Scientific Series iCE 3000, Thermo Scientific, Waltham, MA, USA). Phosphorus (P) was quantified through molecular absorption spectrophotometry (Thermo Electron Corporation evolution 300 LC, Waltham, MA, USA).
# Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model
###### Abstract  
DDoS (Distributed Denial of Service) attacks have become a pressing threat to the security and integrity of computer networks and information systems, which are indispensable infrastructures of modern times. The detection of DDoS attacks is a challenging issue before any mitigation measures can be taken. ML/DL (Machine Learning/Deep Learning) has been applied to the detection of DDoS attacks with satisfactory achievement. However, full-scale success is still beyond reach due to an inherent problem with ML/DL-based systems--the so-called Open Set Recognition (OSR) problem. This is a problem where an ML/DL-based system fails to deal with new instances not drawn from the distribution model of the training data. This problem is particularly profound in detecting DDoS attacks since DDoS attacks' technology keeps evolving and has changing traffic characteristics. This study investigates the impact of the OSR problem on the detection of DDoS attacks. In response to this problem, we propose a new DDoS detection framework featuring Bi-Directional Long Short-Term Memory (BI-LSTM), a Gaussian Mixture Model (GMM), and incremental learning. Unknown traffic captured by the GMM are subject to discrimination and labeling by traffic engineers, and then fed back to the framework as additional training samples. Using the data sets CIC-IDS2017 and CIC-DDoS2019 for training, testing, and evaluation, experiment results show that the proposed BI-LSTM-GMM can achieve recall, precision, and accuracy up to 94%. Experiments reveal that the proposed framework can be a promising solution to the detection of unknown DDoS attacks.  
distributed denial of service (DDoS); machine learning; long short-term memory (LSTM); gaussian mixture model; incremental learning 2021
# Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia
###### Abstract  
Water has become more important in agricultural implementations over the years, as has the need for water management. Thus, Water Use Efficiency (WUE) has been used as an alternative form of detecting the variability of water management based on the carbon-water cycle. The study aimed to map and quantify the spatio-temporal distribution of WUE based on its interactions with environmental changes. It focused on an agricultural area in the westernmost region of Bahia, Northeast Brazil (NEB). For WUE estimation, data from Collection 6 MODIS Gross Primary Productivity (GPP) and Evapotranspiration (ET) products with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) were obtained from the Earth Explorer website. Subsequently, annual WUE anomalies were calculated based on the 2001-2019 period. The results obtained indicated that the highest values of GPP (580 gC/m\({}^{2}\)), ET (3000 mm), and WUE (3.5 gC/mm\(\cdot\)m\({}^{2}\)) occurred in agricultural areas, associated with cultural treatments and insertion of irrigation, which helped in the higher WUE values and consequently increased agricultural productivity in the study region. In addition, there was a marked influence of the phases of the climate variability mode--El Nino-Southern Oscillation (ENSO)--on the annual variability of the WUE, with a reduction of 96% during the La Nina of 2016 (an increase of 89% during El Nino of 2005). During El Ninos, vegetation had greater efficiency resulting from the adaptation of vegetation in maintaining the carbon-water balance, using water more efficiently. However, unlike Las Ninaes, with excessive precipitation there is an interference in the WUE, which interferes with the absorption of radiation and nutrients for the biophysical processes of vegetation and agriculture and, consequently, agricultural production. The use of WUE for agriculture is extremely important, especially for Brazil and countries with an economy based on primary production. This information on the way vegetation (native or agricultural) responds to interactions with the environment aids in decision-making about water management, possibly lowering losses or agricultural damage caused by a lack of water.  
environmental factors; water use efficiency; west Bahia; agricultural areas +
Footnote †: journal: _substainability_  
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