esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 2 S-Band and Adjacent Bands Systems
### Globalstar  
Globalstar is a mobile satellite service (MSS) system that provides mobile voice and data communication to global users. The system consists of 48 low Earth orbit (LEO) satellites at 1,414 km altitude and terrestrial gateways. This system divides the bandwidth of 16.5 MHz into 13 frequency division multiplexing (FDM) channels with a bandwidth of 1.23 MHz [29; 30]. A code division multiple access (CDMA) technique is used for each FDM channel, and the spreading sequence consists of inner and outer pseudorandom noise (PN) sequences. The chip rate and length are 1.2288 Mcps and 1024 chips for the inner PN sequence and 1.2 kcps and 288 chips for the outer PN sequence [30].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency** & **Allocated Services** \\ \hline \multirow{2}{*}{2450–2483.5} & Fixed \\  & Mobile \\  & Radiological * \\ \hline \multirow{4}{*}{2483.5–2500} & Fixed \\  & Mobile \\  & Mobile \\  & Mobile satellite (space-to-Earth) \\ \cline{1-1}  & Radiological * \\ \cline{1-1}  & RDSS (space-to-Earth) \\ \hline \multirow{4}{*}{2500–2520} & Fixed \\  & Fixed satellite (space-to-Earth) ** \\ \cline{1-1}  & Mobile (except aeronautical mobile) \\ \cline{1-1}  & Mobile satellite (space-to-Earth) *** \\ \hline \hline \end{tabular}  
* Secondary service in region 1. ** Not allocated in region 1. *** Only allocated in region 3.  
\end{table}
Table 1: Frequency allocations in the S-band and adjacent bands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Service** & **Center** & & **Code Length** & **Message** \\
**Name** & **Frequency** & **(MHz)** & **(Family)** & **Type** & **Data Rate** \\ \hline \multirow{2}{*}{SPS-S} & \multirow{2}{*}{2492.028} & BPSK(1) & 1023 chips & \multirow{2}{*}{IRNSS} & 25 bps \\  & & (Gold) & & (50 sps) \\ \cline{1-1}  & & BOC\({}_{s}\)(5,2) & N/A & N/A & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Signal design parameters of the S-band Navigation with Indian Constellation (NavIC) signals.  
The baseband signal is filtered by the Nyquist square-root-raised-cosine (SRRC) filter with a roll-off factor of 0.2, and the power spectral density (PSD) of the SRRC filter applied to the \(k\)th FDM channel can be expressed as follows [21]:  
\[G_{SRRC}^{k}(f)=\left\{\begin{array}{ccc}1&if&|f-kB|\leq\frac{\beta}{2}(1- \rho)\\ 0&if&|f-kB|\geq\frac{\beta}{2}(1+\rho)\\ g(f)&if&\frac{\beta}{2}(1-\rho)\leq|f-kB|\leq\frac{\beta}{2}(1+\rho)\end{array}\right. \tag{1}\]  
with  
\[g(f)=\frac{1}{2}+\frac{1}{2}\cos\biggl{(}\frac{\pi}{2\rho f_{c}}(f-(1-\rho)f_{ c}-kB)\biggr{)} \tag{2}\]  
where \(k\) is an FDM channel index (\(k=-6,-5,\dots,\,6\)), \(\rho\) is a roll-off factor, \(B\) is a bandwidth of a single FDM channel, and \(f_{c}\) is a cutoff frequency (same as \(B/2\)).
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
###### Abstract  
This paper presents a new SiGe HBT-based high dynamic range double-balanced down-conversion differential mixer. Operating within the 0.5 GHz to 1.8 GHz range, the suggested mixer is appropriate for a variety of applications, such as cellular base stations, satellite communication (SATCOM), and military radar. The down-conversion mixer is made up of a single-ended to a differential-balanced radiofrequency (RF) stage, a dual feedback linearization for the RF stage, a local oscillator (LO) balun, LO mixing cores, and a fixed intermediate frequency (IF)-tuned circuit connected between two outputs to serve as a load at 145 MHz. Compared to earlier research in the literature, the measured SSB noise figure is approximately 7 dB \(\pm\) 0.4 dB, and the measured conversion gain is approximately 12 dB \(\pm\) 1 dB for a full band of operation. The mixer achieves a good return loss of over 8 dB for an RF and LO port in the desired band and a measured return loss of over 18 dB at 145 MHz and IF frequency. Furthermore, the design achieved an RF-to-IF isolation of greater than 35 dB, LO feedthrough, and an LO leakage isolation of better than 50 dB. Lastly, the measured third-order intercept point was found to be +4.7 dBm, and the 1 dB compression point was approximately \(-\)8 dBm. These results demonstrate good linearity performance.  
m  
military radar; satellite communication (SATCOM); cellular base stations; mixer; lo balun; impedance matching +
Footnote †: journal: Physics Letters A
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 3 Results and Discussion
### Computational Scheduling Kernel Implementation and Validation
Finally, the computational scheduling kernel stores the multiplication and bias parameters for the convolution and fully connected layers in an internal BRAM, with 8 channels, 2048 depth, 128 bit width memory for the multiplication parameters and a 256 depth, 128 bit width memory for the bias parameters, which can support the computation of a maximum 2048 output channel tensor. The parameters are stored in BRAM36k, a 36 bit width, 1024 depth memory cell, consuming 69 BRAM36k and 1 BRAM18k.
# On the Need for a General REST-Security Framework
## 5 Related Work Analysis
### OAuth  
OAuth [29; 51] is an authorization framework for granting access to end users' resources for third party applications. Currently, two versions of OAuth have been published.  
The OAuth v1 specification of the IETF [51] has an inherent support for protecting a request by a signature. The signature string is the concatenation of the HTTP method, the resource path including the query, the host header and a set of OAuth v1 specific parameters. The latter parameters consist of a realm, a key identifier that is called consumer key, an OAuth token, a timestamp and a nonce. OAuth v1 does not enable to add any other parameters or headers in the signature. The authentication information is stored in the authorization header. Like the other approaches discussed so far, the authenticity of the request is considered solely. No means for signing a response have been defined.  
In contrast to the first version, OAuth v2 does not include any security means on its own [29]. Instead, the security is merely based on TLS. If a message-oriented protection is yet required, OAuth v2 can be augmented by either the OAuth MAC tokens [52] specification or by the extension a method for signing an HTTP requests for OAuth [53]. The OAuth MAC token draft demands to sign the HTTP method, the resource path including the query and at least the host header. Further meta-data can be considered by defining a list similar to some of the previously discussed approaches. The resulting signature value has to be included into the authorization header.  
The second OAuth v2 extension a method for signing an HTTP requests for OAuth uses a JSON web signature (JWS) [54] to guarantee the authenticity of HTTP messages. The JWS object used in this specification owns a set of members, which contains the method, the host including the port, the resource path, the query, the headers, an HMAC authenticator of the body and a timestamp. Using JWS as the pillar can be a stable groundwork, since it is a well advanced IETF draft for signing JSON objects [55] that is already used in many applications. However, the main drawback of this specification lies in the fact that all mentioned JSON members are optional. Even though most of these elements are vital to guarantee the authenticity and integrity of an HTTP message, none of them is set as mandatory for the signature. Also, this draft does not state any information whether the JWS object is stored in a header or in the body.  
The common problem of both OAuth versions is the tight coupling to the actual application domain of these authorization frameworks. As a result, adopting these standards to other contexts is not feasible in a straightforward manner. As with the other approaches, the major disadvantage of the OAuth protocols is that they do not specify a protection of the response.
## 5 Datasets and Experimentation Setup  
### Datasets  
We conducted a set of experiments using two datasets of plant, namely plant seedlings dataset (V2) and Flavia database. The plant seedlings database [12] is a public image database for benchmark of plant seedling classification. The dataset contains 5537 images of 5184 \(\times\) 3456 pixels of crop plants and weeds all in the seedling stage. The database includes 12 species of Danish agricultural plants and weeds with 253-762 images per species. Images of the different plants were collected with a DSLR camera at different stages separated by 2-3 days. Here we randomly selected 80% of images for training and 20% of images for evaluation. The second dataset (Flavia dataset) consists of 1907 leaf images of 1600*1200 pixels of 32 different plant species with 50-77 images per species [13]. Each image contains a single trimmed and isolated leaf. The images were collected on the campus of the Nanjing University and the Sun Yat-Sen arboretum using scanners or digital cameras on a plain background. The provided database contains RGB color images with a white background. We also randomly selected 80% of images for training and 20% of images for evaluation.
## Nomenclature \begin{tabular}{l l} ARMA & Auto-regressive moving average \\ CC-SSI & Crystal clear stochastic subspace identification \\ CSI & Combined deterministic-stochastic subspace identification \\ CSMI & Continuous structural modal identification \\ DOFs & Degrees of freedom \\ DSI & Deterministic-stochastic subspace identification \\ EFDD & Enhanced frequency domain decomposition \\ ERA & Eigensystem realization algorithm \\ FD & Frequency-domain \\ GRA & General realization algorithm \\ ITD & Ibrahim Time-domain \\ MIMO & Multiple-input multiple-output \\ MNExt-ERA & Multiple-reference natural excitation technique combined with ERA \\ MOESP & Multivariable output error state-space \\ MSD & Mass-spring-dashpot \\ OKID & Observer/Kalman filter identification \\ PPM & Polynomial power spectrum method \\ PRISMA & Preferred reporting items for systematic reviews and meta-analyses \\ PSO & Particle swarm optimization \\ \end{tabular}   
PZM  
Power spectrum z-transform method  
RD  
The random decrement  
RSSI-COV  
Covariance-driven recursive stochastic subspace identification  
SHM  
Structural health monitoring  
SIMO  
Single-input multiple-output  
SMI  
Structural modal identification  
SMIT  
Structural modal identification toolsuite  
SSI  
Stochastic subspace identification  
SSI-COV  
Covariance-driven stochastic subspace system identification  
SSI-DATA  
Data-driven stochastic subspace system identification  
ST-SSI  
Short-time stochastic subspace identification  
TARMA  
Time-varying analysis method using time-dependent auto-regressive moving average  
TD  
Time-domain  
TFD  
Time/frequency domain  
VDD  
Vibration-based damage detection  
VSS  
Viscoelastic sandwich structure  
WSNs  
Wireless sensor networks  
WT  
Wavelet transform
###### Abstract  
A solid phase microextraction (SPME) method coupled with liquid chromatography (LC) and fluorescence/ultraviolet-diode array detection was developed for the simultaneous determination of quercetin and trans-resveratrol. The chromatographic, detection, and SPME extraction/desorption conditions were systematically optimized. The performance of four commercial SPME fibers--polyacrylate (PA), polyethylene glycol (PEG), polydimethylsiloxane (PDMS), and polydimethylsiloxane-divinylbenzene (PDMS-DVB)--was evaluated and compared with a homemade polydopamine (PDA)-coated fiber. While all of the fibers successfully extracted the target analytes, their efficiencies varied significantly. The PA, PEG, and PDA fibers demonstrated superior performance, exhibiting wide linearity ranges (0.03-1 \(\mu\)g/mL (PA and PEG) and 0.06-1 \(\mu\)g/mL (PDA) for quercetin, 0.01-1 \(\mu\)g/mL for trans-resveratrol); high sensitivity (LODs of 0.01 \(\mu\)g/mL (PA and PEG) and 0.02 \(\mu\)g/mL (PDA) for quercetin, 0.003 \(\mu\)g/mL for trans-resveratrol); and excellent precision. Among these, the polyacrylate coating delivered the best analytical performance and was selected for further application. The optimized method was applied to analyze winemaking by-products (seeds, skins, and stalks) using SPME on ethanol-macroated extracts subjected to brief ultrasonication. Quercetin and trans-resveratrol were quantified in pomace extracts at concentrations of 104.3 \(\pm\) 8.2 \(\mu\)g/g and 38.5 \(\pm\) 4.1 \(\mu\)g/g, respectively. Recovery experiments confirmed the method's accuracy, with recoveries of 99.1 \(\pm\) 7.4% for quercetin and 98.5 \(\pm\) 9.8% for trans-resveratrol. This study establishes a reliable, sensitive, and efficient approach for the determination of these bioactive compounds in complex matrices, with potential applications in the food and pharmaceutical industries.  
Keywords:solid phase microextraction; liquid chromatography; quercetin; trans-resveratrol; winemaking waste +
Footnote †: journal:
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 2 An Overview of MSE Growth of GaN
### Basic Principles of Reactive Sputtering  
Reactive sputtering and the details of its underlying mechanisms have been extensively described [19; 20]. Therefore, this part will only focus on the sputter mechanism to help understand the sputter epitaxy process of GaN films.  
The sputtering mechanism takes place through the ejection of atoms from a sputter target under the bombardment of ions. A working gas, usually inert Ar and N\({}_{2}\) gas in the case of III-nitride growth, is introduced into a vacuum sputtering chamber. Negative voltage bias is applied to the sputtering target to start the gas ionization process. As the gas is ionized, plasma consisting of electrons and positively charged ions is formed. Due to the target's negative bias, the ions accelerate and hit the target with kinetic energy proportional to the target bias. Above an energy threshold, the target's atoms will be ejected, travel across the plasma, and eventually condense on the surface of the substrate. In addition to ejecting the target materials, this ion bombardment process also generates secondary electrons which are required for maintaining the plasma. For magnetron sputtering system, permanent magnets are installed behind the target in order to elongate the trajectory of electrons in a helical loop, helping to increase the plasma density with increasing scattering probability to gases in the vicinity of the target.  
Under reactive sputtering, reactions take place between the metal adatoms and ions sputtered from a target and the reactive gas on the substrate surface, forming a compound thin film. A schematic of the reactive sputtering process for GaN deposition is shown in Figure 1. The Ga target is connected to the cathode and is under a negative bias. The target is bombarded with positive nitrogen and argon ions, and sputtered Ga atoms and ions travel through the plasma onto the substrate surface. Reaction takes place between nitrogen and Ga reactive species, including atoms and ions, on the substrate surface, forming a nitride film.
## 2 Principle of Stress Wave-Based Pulse Position Modulation (PPM) Communication
### Refocusing Principle  
The multi-modal and dispersive characteristics of stress wave propagating along pipes make it difficult to interpret the received waveform correctly. For the purpose of overcoming the ambiguity, correlation analysis is used in an interpreting procedure to refocus the dispersed waveform. If we assume \(p(t)\) to be one pulse sequence and \(h(t)\) to be the impulse response of channel, then the received signal \(y(t)\) can be described as  
\[y(t)=p(t)*h(t) \tag{3}\]  
where \(*\) denotes the convolution operator.  
In stress wave based communication, an impulse is first sent to obtain the impulse response before a second signal, which contains the actual data is sent. Therefore: _1_) An initial, single pulse is sent out, and we assume the received waveform as the impulse response \(h(t)\) of the channel. _2_) Afterwards, the modulated signal containing the desired information is transmitted and the corresponding received signal is denoted by \(y(t)\). Then, the impulse response \(h(t)\) is inverted and convolved with \(y(t)\) to refocus the received waveform. The following two equations can represent these two processes:  
\[y(t)*h(-t)=p(t)*[h(t)*h(-t)] \tag{4}\]  
Figure 1: Coding format.  
\[G(t)=h(t)*h(-t)=\int_{-\infty}^{\infty}h(\tau)h(\tau+t)d\tau \tag{5}\]  
In these two processes, the maximum value is reached at \(t=0\).
The use of recycled aggregates in concrete and mortar can be undermined by limits imposed by CDW composition; for example, recycled materials usually have higher water absorption, lower specific mass, and less mechanical resistance when compared with natural aggregates [17]. On the other hand, [PERSON] et al. [18] state that using coarse recycled aggregates could be a successful substitute for natural aggregates in the production of concrete; however, these materials can have problems related to high water absorption and particle distribution. Also, fine recycled aggregates present poorer mechanical properties than coarse recycled aggregates due to the greater angularity of the coarse fraction [19] and the difficulty of separating the contaminants (wood, tiles, styrofoam, glass, and others) [20] contained in the fine recycled aggregates. For example, the addition of wood chips significantly reduced the splitting tensile strength [20]. However, the use of recycled aggregates is more appropriate in cases where there is no need for high structural performance, low water absorption, and exposure to aggressive environments [21; 22]. A construction material that does not require structural function is lightweight concrete, which is defined as hardened concrete with a specific mass between 800-2000 kg/m\({}^{3}\)[23]. Aerated concretes are a variation within lightweight concretes that have a specific mass between 320-1920 kg/m\({}^{3}\) and a compressive strength above 1.5 MPa [24]. Lightweight concretes can be obtained by the use of lightweight aggregates in the mixture or by the incorporation/production of air in the form of bubbles distributed into the mortar [25; 26]. The lightweight concretes known as autoclaved aerated concrete (AAC) are those that are produced with bubbles of air inside the mortar via a chemical reaction process activated by curing in an autoclave [27]. Another variation of cellular concrete is known as foamed cellular concrete (FCC), where bubbles of air are incorporated into the mortar by the addition of preformed foam [28]. Using CDW for the production of foamed cellular concrete has been pointed out as one of the possible uses for such residue [29]. However, the chemical composition and physical properties of residues can vary drastically according to the geographic region and the construction processes of the industry where they are generated. Thus, the main aim of this work was to characterize the CDW from the Passo Fundo region of Rio Grande do Sul (RS), Brazil, and to evaluate its use for the production of foamed cellular concrete blocks. This study consisted in determining the influence of CDW granulometry (CDWG) and foam amount (FA) upon foamed cellular concrete properties.
## 6 Blade Loads  
Multiple aerofoil sections and chord lengths, 22 specified stochastic load cases and an angle of twist with numerous blade pitching angles results in a complex engineering scenario. Therefore, the use of computer analysis software such as fluid dynamics (CFD) and finite element (FEA) is now commonplace within the wind turbine industry [35]. Dedicated commercially available software such as LOADS, YawDyn, MOSTAB, GH Bladed, SEACC and AERODYN are utilised to perform calculations based upon blade geometry, tip speed and site conditions [15].  
To simplify calculations, it has been suggested that a worst case loading condition be identified for consideration, on which all other loads may be tolerated [4]. The worst case loading scenario is dependent on blade size and method of control. For small turbines without blade pitching, a 50 year storm condition would be considered the limiting case. For larger turbines (\(D>70\) m), loads resulting from the mass of the blade become critical and should be considered [4]. In practice several load cases are considered with published methods detailing mathematical analysis for each of the IEC load cases [6].  
For modern large scale turbine blades the analysis of a single governing load case is not sufficient for certification. Therefore multiple load cases are analysed. The most important load cases are dependent on individual designs. Typically priority is given to the following loading conditions:  
* emergency stop scenario [36]
* extreme loading during operation [6]
* parked 50 year storm conditions [34]  
Under these operational scenarios the main sources of blade loading are listed below [6]:  
1. Aerodynamic
2. Gravitational
3. Centrifugal
4. Gyroscopic
5. Operational  
The load magnitude will depend on the operational scenario under analysis. If the optimum rotor shape is maintained, then aerodynamic loads are unavoidable and vital to the function of the turbine, considered in greater detail (Section 6.1). As turbines increase in size, the mass of the blade is said to increase proportionately at a cubic rate. The gravitational and centrifugal forces become critical due to blade mass and are also elaborated (Section 6.2). Gyroscopic loads result from yawing during operation. They are system dependant and generally less intensive than gravitational loads. Operational loads are also system dependant, resulting from pitching, yawing, breaking and generator connection and can be intensive during emergency stop or grid loss scenarios. Gyroscopic and operational loads can be reduced by adjusting system parameters. Blades which can withstand aerodynamic, gravitational and centrifugal loads are generally capable of withstanding these reduced loads. Therefore, gyroscopic and operational loads are not considered within this work.
## 4 Results and Discussion
### Thermal Shock Resistance of Cladding Coating  
The thermal shock test was used to characterise the adhesion between the layer and substrate in the experiment, using the following process: the size of the sample was 10 mm \(\times\) 10 mm \(\times\) 8 mm before the experiment; the workpiece was polished with SiC paper (grade 800); the inside temperature of the muffle furnace was heated to 650\({}^{\circ}\)C; the sample was placed in the muffle furnace for 10 min; and the sample was quenched and cooled in water with a temperature of 25\({}^{\circ}\)C and dried in the air. The top and the side of the initial cladding layer is shown in Figure 12a,d. After 30 cycles, the result of the layer is shown in Figure 12b,c; the result of the substrate is shown in Figure 12e,f. It was found that both the coating and substrate were oxidized at a high temperature, and some parts had peeled off, but the cladding layer and substrate were still well bonded. The reason for this was mainly that the thermal expansion coefficient of the coating had little difference to that of the substrate, and a good metallurgical bonding between the cladding layer and substrate was formed, creating a layer with a strong thermal shock resistance.  
Figure 11: Microhardness curves of the layer.