esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FSS Analysis
### Polarization Stability  
When the FSS works, the effect of the polarization mode and incident angle of the electromagnetic wave incident on the FSS is unknown. If FSS shows different transmission characteristics and reflection characteristics for TE-polarized wave and TM-polarized wave, FSS may not work unstably. To eliminate the influence of the polarization of the incident wave on the performance of the FSS, the designed FSS must have similar frequency response characteristics for the TM wave and the TE wave.  
Figure 9 shows the reflection coefficient curves for various oblique angles of incidence under TE- and TM-polarized waves. It can be seen from the figure that the proposed FSS has multiple resonance points within 30 GHz, and has multiband characteristics. Table 3 shows the resonant frequency and the deviation rate of the proposed FSS with different polarized obliquely incident waves at different angles. Here, the parameter of average polarization shift is defined to analyze the polarization stability of the proposed FSS and calculated as follows:  
\[\overline{\Delta f_{a}}=\frac{\frac{n}{1}\frac{\int_{d}^{\text{TM}}-f_{a}^{ \text{TE}}|}{f_{a}^{\text{TM}}}}{n}\times 100\% \tag{4}\]  
where \(f_{a}^{\text{TE}}\) is the resonant frequency of the TE wave incidents at \(\theta\) angle, \(f_{a}^{\text{TM}}\) is the resonant frequency of the TM wave incidents at \(\theta\) angle, \(n\) is the total number of \(\theta\) angle. The lower the value, the better the polarization stability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**3 dB Bandwidth (GHz)**} \\ \cline{2-5}  & **1 st Band** & **2 nd Band** & **3 rd Band** & **4 th Band** \\ \hline \(W_{1}=0.10\) & 1.00–6.65 & 8.35–16.90 & 18.04–24.00 & 24.50–27.84 \\ \(W_{1}=0.12\) & 1.00–6.10 & 7.62–15.56 & 16.55–22.21 & 22.69–25.96 \\ \(W_{1}=0.14\) & 1.00–5.65 & 7.05–14.43 & 15.28–20.60 & 21.08–24.26 \\ \hline \(G=0.10\) & 1.00–6.19 & 7.42–15.93 & 16.67–23.10 & 23.51–27.94 \\ \(G=0.12\) & 1.00–5.94 & 7.20–15.14 & 15.91–21.76 & 22.25–26.01 \\ \(G=0.14\) & 1.00–5.52 & 6.81–14.02 & 14.75–20.31 & 20.82–24.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The 3 dB bandwidths of stopbands with varying \(W_{1}\) and \(G\).  
Figure 8: Reflection coefficients of the proposed FSS with different values for (**a**) parameter \(W_{1}\); (**b**) parameter \(G\).  
From Table 3, it can be seen that the FSS has an offset of 2.60% at the first resonance frequency (\(f_{1}\)), no frequency offset at the second resonance frequency (\(f_{2}\)), and only 0.21% at the third resonance frequency (\(f_{3}\)). This shows that the polarization stability of the FSS is very good.
## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Biological Profile Estimation
#### Age Estimation from Skeletal Structures  
In legal medicine, when identification documents of children or adolescents are missing, as may be the case in asylum seeking procedures or in criminal investigations, estimation of physical maturation is used as an approximation to assess unknown chronological age. Some of the established radiological methods estimating unknown age in children and adolescents are based on visual examinations of bone ossification in X-ray images of the hand [26; 79], even if some proposals to automatically analyze the sternal end of the fourth rib in CTs have also been presented [80]. Ossification is best followed in the hand due to the large number of assessable bones that are visible in X-ray images from this anatomical region, together with the fact that aging progress is not simultaneous for all hand bones. From the level of ossification assessed by the radiologist, the most common methods for the estimation of physical maturation of an individual are the GP [81] method and the TW2 [82; 83] method. The GP method is the approach used by the majority of radiologists due to its simplicity and speed. This method is based on the comparison between the X-ray image of the hand and an atlas at various chronological ages. The patient's radiograph is compared to the suitable image in the atlas. The TW2 method analyzes specific bones, instead of the whole hand as in the GP method. In particular, this method takes into account a set of specific ROIs divided into epiphysis/metapysis ROIs and carpal ROIs. Very recently, ConvNets have shown to be successful for bone age estimation and there are some published applications based on the GP method [84; 85; 86; 87; 88] and on the TW2 method [59; 90; 91; 92]. Table 2 includes an overview of the main AI-based approaches employed in the literature for age estimation. [PERSON] et al. [85] used a ConvNet over a total of 14,036 clinical hand radiographs and their corresponding reports, obtained from two children's hospitals, to train and validate the model. The RMS of a second test set composed of 1377 examinations from the publicly available GP Digital Hand Atlas [95; 98] was compared with an existing automatic model, termed BoneXpert (BoneXpert automatically reconstructs the borders of 15 bones (including metacarpal and phalangeal bones, the distal radius, and the ulna) from radiographs of hands using a generative model (active appearance model), and then estimates the age from the shape, intensity, and texture scores derived from principal component analysis for each bone. However, it is important to remark that this software is used
## 2 Polarization State and Observation Analysis  
### Polarization State  
The solar flux and polarization reflected by the Earth can be described by the vector \(S\):  
\[S=\begin{bmatrix}I&Q&U&V\end{bmatrix} \tag{1}\]  
where \(I\) is the total reflection flux and \(Q\) and \(U\) describe the linear polarization flux. The linear polarization imaging obtains the measurement target from the three components of \(I\), \(Q\) and \(U\). \(V\) describes the circular polarization flux. In nature, the circular polarization information is basically 0, so the first three vectors are enough to express the polarization characteristics [12]. That is, through different \(\theta\) azimuths, different measurements are combined, and \(I\), \(Q\) and \(U\) in the Stokes parameters can be obtained simultaneously. There are two common combinations of three polarization angles: \(0^{\circ}\), \(60^{\circ}\), \(120^{\circ}\) and \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\). In the case of the same positioning error, different positioning methods have different measurement errors. In comparison, the polarization detection azimuth setting (\(0^{\circ}\), \(60^{\circ}\), \(120^{\circ}\)) has less measurement error for the polarization detection error, which is an ideal polarization analytical azimuth parameter.  
\[I=\frac{2}{3}(I_{0}+I_{60}+I_{120}) \tag{2}\]  
\[Q=\frac{2}{3}(2I_{0}-I_{60}-I_{120}) \tag{3}\]  
\[U=\frac{2\sqrt{3}}{3}(I_{60}-I_{120}) \tag{4}\]  
Partially polarized light can be regarded as a mixture of completely polarized light and non-polarized light. The degree of linear polarization (DOLP) is used to represent the ratio of linearly polarized light intensity to total light intensity, which is the ratio of completely linearly polarized light intensity to total light intensity:  
\[DOP=\frac{\sqrt{Q^{2}+U^{2}}}{I} \tag{5}\]  
The polarization angle (AOP) describes the angle of rotation of the most obvious direction of the electric vector vibration relative to the x-axis:  
\[\varepsilon=\frac{1}{2}\mathrm{arctan}\frac{U}{Q},-\frac{\pi}{4}\leq \varepsilon\leq\frac{\pi}{4} \tag{6}\]  
When the beam is linearly polarized light, the epsilon angle satisfies \(\tan 2\varepsilon=0\); when the beam is non-polarized light or circularly polarized light, \(\tan 2\varepsilon=\pm 1\). When \(\tan 2\varepsilon\) is another value, it means that the beam is elliptically polarized light or partially polarized light [13].
## 2 Materials and Methods
### Physicochemical Characterization  
#### 2.3.1 Chemical and Structural Characterization of HA  
The HA powder was characterized by X-ray diffraction using HZG4 (Carl Zeiss, Oberkochen, Germany) with CuK\(\alpha\) radiation (\(\lambda\) = 1.5418 A), \(\theta\)--2\(\theta\) varying between 10 and 100\({}^{\circ}\), and a step of 0.05 \({}^{\circ}\)/s. For the HA discs, the grazing-incidence X-ray diffraction technique (GXRD) was used, with a fixed incident angle \(\theta\) = 0.5\({}^{\circ}\), 2\(\theta\) detector in the range of 10-60\({}^{\circ}\), and a step of 0.04 \({}^{\circ}\)/s.  
The Fourier-transform infrared spectroscopy (FTIR) spectrum of the HA powder was collected by a IR-Prestige-21/ AIM-880 spectrophotometer (Shimadzu Scientific Instruments, Riverwood Drive Columbia, MD, USA), over a range of 4000-400 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\) and 500 scans using KBr pellets (Shimadzu Scientific Instruments, Riverwood Drive Columbia, MD, USA). The HA discs were analyzed via Attenuated Total Reflectance (ATR) using an AIM-880 Microscope (FTIR/ATR, Shimadzu Scientific Instruments, Riverwood Drive Columbia, MD, USA), coupled to an IR-Prestige-21, using a range of 4000-750 cm\({}^{-1}\), with a resolution of 4.0 cm\({}^{-1}\). Three areas in different sample regions were analyzed using 200 scans/region. The spectra were analyzed by IR-solution software version 1.60 when using an atmosphere correction.  
Elemental analyses of Ca and P were carried out using an X-ray Fluorescence (XRF) Spectrometer (Axios mAX, Malvern Panalytical, Malvern, UK) operating at 40 kV and 50 mA with a Ge (111) crystal, a collimator of 550 um, and a flow detector for the K\(\alpha\) lines of Ca and P.
# SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems
## 3 Background
### Vector Commitment  
For a given element e and a sequence number \(i\), VC [19] can be used to identify whether \(e\) is equal to the \(i\)th element of an ordered set **e** (\(e_{1}\),...\(e_{n}\)). In blockchain systems, the transactions in a block form an ordered set of elements, thus VC can be used to identify whether a given transaction is in a block and its position is also correctly provided. VC demonstrates the features of hiding, position binding, conciseness and an ability to be updated. The following six algorithms are used to define VC:  
1. Given security parameter \(k\) and the size \(n\) of ordered set **e**, the \"_KeyGen_\" algorithm outputs the public parameters \(pp\). \[pp\gets KeyGen(1^{k},n)\] (1)
2. Given the public parameters pp and the ordered set **e**, the \"Commitment\" algorithm outputs the commitment **C** of **e**. \[\textbf{C}\leftarrow Commitment_{pp}(e_{1},...,e_{n})\] (2)
3. The \"Open\" algorithm is used to generate the proof \(\mathbf{\pi}_{i}\) for the \(i\)th element. \[\mathbf{\pi}_{i}\gets Open_{pp}(i,e_{1},...,e_{n})\] (3)
4. The \"Verify\" algorithm is employed to check whether the given element e is equal to \(e_{i}\) through the proof \(\mathbf{\pi}_{i}\). If the proof is accepted, the algorithm outputs 1. \[\{0,1\}\gets Verify_{pp}(\textbf{C},i,e,\mathbf{\pi}_{i})\] (4)When the \(i\)th element of \(\mathbf{e}\) needs to be updated with \(e^{{}^{\prime}}\), the \"Update\" algorithm can be run to output a new commitment \(\mathbf{C}^{{}^{\prime}}\) and the update information U. \[\mathbf{C}^{{}^{\prime}},U\gets Update_{pp}(\mathbf{C},e,e^{{}^{\prime}},i)\] (5)
6. When the \(i\)th element of \(\mathbf{e}\) is updated, the proofs of other elements (e.g., \(e_{j}\)) also need to be updated. The algorithm \"ProofUpdate\" outputs the updated proof \(\pi^{\prime}_{j}\) of element \(e_{j}\). \[\pi^{\prime}_{j}\gets ProofUpdate_{pp}(\pi_{j},e^{{}^{\prime}},i,U)\] (6)
## 2 Materials and Methods
### Split Hopkinson Pressure Bar Test  
Concrete cylinders with a diameter of 150 mm and a height of 305 mm were cast with ordinary Portland cement, river sand, and crushed granitic rocks with a particle size ranging from 5 to 10 mm. The average 28-day compressive strength of the concrete was 18.5 MPa.  
A total of 9 concrete columns with a 70 mm diameter and a 38 mm height were prepared. Among them, 3 concrete cylinders were wrapped with one layer of the PET FRP, of which the nominal thickness, the tensile strength, and the elastic modulus were 0.841 mm, 751 MPa, and 17.9 GPa, respectively, 3 concrete cylinders were wrapped with two layers of the PET FRP, and 3 specimens were wrapped with one layer of the CFRP, of which the nominal thickness, the tensile strength, and the elastic modulus were 0.165 mm, 4423 MPa, and 240 GPa, respectively, for the purpose of the comparison (Table 1), according to the manual wet layup way [30]. A continuous fiber sheet saturated with the epoxy resin, mixed at a ratio of the main resin component L-500 AS to the hardener L-500 BS of 2:1 (provided by SANYU Shanghai Company), was wrapped around the concrete cylinder with the fibers orientated in the hoop direction of the cylinder. To prevent the debonding failure, a fiber sheet with a length of 350 mm (i.e., one circumference + half circumference of the overlapping zone) was used for the fabrication of one-layer FRP-confined concrete specimens. To reduce the test scatter, three identical specimens were tested at each impact velocity using a 75 mm-diameter SHPB device to investigate the dynamic compressive behavior of the FRP-confined concrete (Figure 3). The apparatus comprised an energy source, a 600 mm-long projectile, a 5000 mm-long incident bar, a 3000 mm-long transmission bar, a digital oscilloscope, and a velocity testing system that was used to record the initial velocity of the projectile.  
Figure 2: Instron drop-weight impact system.  
The specimen was sandwiched between the incident and transmission bars. The stress wave was reshaped with a rubber shaper (1 mm in thickness and 20 mm in diameter) installed on the impact end of the incident bar. The time histories of the strain \(\varepsilon(t)\), stress \(\sigma(t)\), and strain rate \(\dot{\varepsilon}\) (\(t\)) of the specimen can be obtained by Equations (1)-(3), according to the two-wave theory [27]:  
\[\varepsilon(t)=\frac{2C}{L_{\mathrm{s}}}\int_{0}^{t}\left[\varepsilon_{i}(t)- \varepsilon_{t}(t)\right]\mathrm{d}t, \tag{1}\]  
\[\sigma(t)=E\frac{A}{A_{\mathrm{s}}}\varepsilon_{t}(t), \tag{2}\]  
\[\dot{\varepsilon}(t)=\frac{2C}{L_{\mathrm{s}}}[\varepsilon_{i}(t)-\varepsilon _{t}(t)], \tag{3}\]  
where \(\varepsilon_{i}(t)\) and \(\varepsilon_{t}(t)\) are the strain of the incident and transmission bars, respectively; \(A\) is the cross-sectional area of bars; \(A_{\mathrm{s}}\) and \(L_{\mathrm{s}}\) are the initial cross-sectional area and the height of the specimen, respectively.
# SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives
## 4 Feature Selection Algorithms
### Jaguar Selection Algorithm  
Jaguar works identical to Falcon, but with an extra step at the beginning. Instead of using the list of candidate features sorted by added value, it re-sorts the list based on an Added Value Per Cost (AVPC)  
Figure 2: The Falcon features Selection Algorithm.  
metric. The algorithm takes into account the product/category level objectives that require the total property value for the product or category to be \"more than\" or \"less than\" a certain value. Every Property Value (_PV_) of every feature associated with such constraints will be quantified using Equation (5).  
\[PV=\left(\frac{Val-Min}{Max-Min}\right)\star 100 \tag{5}\]  
The variable \"_Val_\" is the property value before quantification. The min and max property values from Equation (5) are calculated based on the minimum and maximum property values of the candidate features. The average value of all the quantified properties is then calculated and defined as \"feature cost\", FC shown in Equation (6).  
\[FC=\left(\frac{\sum_{i=0}^{n}PVi}{n}\right) \tag{6}\]  
The _AVPC_ metric for each feature, is calculated by dividing the feature added value by the feature cost. Using Equation (6), _AVPC_ is calculated as in Equation (7).  
\[AVPC=\left(\frac{FAV\star n}{\sum_{i=0}^{n}PVi}\right) \tag{7}\]  
The _AVPC_ metric for each feature, is calculated by dividing the feature added value by the feature cost. Once _AVPC_ is calculated for all the candidate features, Jaguar will sort the list of candidate features based on _AVPC_ and pass it to Falcon for further processing. Figure 3 shows the re-sorting process of Jaguar.  
Jaguar is expected to be slightly slower than Falcon, as it performs some minor additional calculations and a sorting of the list of features. However, in terms of added value and depending on the dataset, it should be able to give better results. It is worth to be noted that the algorithm can only operate when product/category property level objectives are present.
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks
### Consensus Layer
#### 5.2.3 Multi-Leader  
[PERSON] et al. [89] analyzed raft vs. multi-raft for network latency and leader's traffic. It was observed that the traffic generated in raft was greater than multi-raft. This improvement is only visible in high-throughput networks with balanced nodes across multiple leaders. About a 30% reduction in total network traffic was due to the balancing. The overhead on the leader is due to the total number of simultaneous connections and log synchronization. Multi-raft also has a lower cpu at a higher scale and, therefore, is scalable.  
The authors of [28] proposed, and enhanced the Practical Byzantine Fault Tolerant (PBFT) [92] algorithm called \"score-voting based byzantine fault tolerance\". In this scheme, the authors used a reward and punishment scheme to select nodes which can participate in the consensus process. The reduced bandwidth is due to the minimization of broadcasts during the consensus process. Consensus nodes do not broadcast but unicast to the primary node, thus, reducing communication complexity. Their technique proved superior to (PBFT), Window-based BFT (WBFT) [93] and Reputation-based BFT (RBFT) [94] in terms of the number of communications required, delay and transactional throughput. They realized an increase in transactional throughput from 160 tps to 270 tps and a reduced latency of 100 ms when compared to PBFT, which had a latency of 325 ms.  
[PERSON] et al. [90] voted a group of nodes, called a committee, to undergo the consensus process. Members of the committee were randomly selected to ensure fairness. The bandwidth saving and the scalability of this approach depends on the size of the committee. For a network consisting of 130 nodes, their approach has a latency of 5 s when compared to PBFT, which achieved a latency of 15 s.
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 4 Proposed Methods  
### Noise-Floor-Based Feature for ASC  
The input features for the baseline approach are the MBFE features, directly computed on the input signal. Based on this feature, we propose to introduce noise floor estimate as an extra feature into the ASC system to extract information _only_ relevant to the background noise. For noise floor, we first calculate an estimate of the power spectral density of the background or ambient signal. It may be obtained by any of the well-known statistical methods (e.g., minimum statistics (MS) methods [19], Minimum Mean-Square Error (MMSE) [21]). Essentially, this consists of recursively smoothing an estimate of the signal periodogram, thereby removing the short variations attributable to speech and preserving  
Figure 1: ResNet Architecture: C1 = 20 and C2 = 40 in small-footprint model; C1 = 80 and C2 = 160 in large-footprint model.  
Figure 2: Double Conv Block.  
the long-term characteristics of the background. In this work, we adopt Speech Presence Probability Minimum Mean-Square Error (SPP-MMSE) [21] as our noise floor estimation method. Finally, the acoustic features are derived in the same manner as MFBE from the noise floor estimate. We term these the noise-floor-based MFBE features (NF-MFBE).  
With this feature, we expect that the background noise information is well preserved while the speech information irrelevant to the acoustic scene is reduced, which should then lead to better classification performance when speech is present. But also as observed in the noise floor application in the iVector framework [15], it unavoidably removes some background noise information and brings in distortion when no speech is present or at very low SBRs. It requires further effort to balance the trade-off so that a consistent improvement is obtained across different SBRs.
# Satellite-Derived Bathymetry for Selected Shallow Maltee Coastal Zones
## 3 Methods
The models were then applied to all cells over the regions of interest to render bathymetry charts. The outputs were interpolated using the nearest neighbour technique to obtain complete maps.
## 4 Discussion  
### Uncertainty in the MODIS CI Retrievals  
The quality of MODIS-derived CI data relies heavily on the quality of input data (i.e., the MCD43A1/A2 and MCD12Q1 products) and the configuration of the retrieval algorithm. By using the improved MODIS C6 surface reflectance and cloud mask products and the enhanced daily retrieval algorithm, the C6 MCD43 products present improved data quality compared with the C5 products [35]. Correspondingly, the C6 CI data exhibit a higher proportion of main algorithm inversion and a lower proportion of missing values than the C5 CI data globally. The C6 CI data are also more consistent with the field-measured CI data than the C5 CI data are. Moreover, the input MCD12Q1 IGBP land cover data determine the crown shape in the retrieval algorithm and thus influence the accuracy of the CI data [25]. The land cover map provides the basic distribution pattern of the global CI data, as the needleleaf forest comprises shoot level clumping rather than the leaf element level clumping in broadleaf vegetation types. As the accuracy of the BRDF product has been implied by the CI QA data, caution should be exercised when determining the uncertainty of land cover data in the utilization of the CI product.  
In addition to the uncertainties of the input data, the configuration of the retrieval algorithm is also crucial to the accuracy of the MODIS CI data. As the RTLSR BRDF model used in the operational MODIS BRDF inversion algorithm reportedly underestimates the reflectance at the hotspot [25; 89], the RTCLSR model [74] is used in this study. The RTCLSRBRDF model rebuilds the hotspot magnitude and width with two parameters based on an exponential approximation of a physical hotspot kernel, which corrects the hotspot signatures with high accuracy and without distinct scale dependence [26]. The linear CI-NDHD equations have been widely used to retrieve global CI maps [25; 28; 29; 30; 111] and are considered to be the best guess for the surface CI [24; 26]. As the CI-NDHD relationships are affected by sparse vegetation coverage and high background reflectance, uncertainties in the CI data in these circumstances should be treated with some caution [24; 25; 47]. Furthermore, terrain-induced shadows can also increase the uncertainty of CI retrievals, which deserves further investigation [24; 49; 111].