esa-sceva/satcom-chunk-collection huggingface dataset:
# Structure and Principles of Operation of a Quaternion VLSI Multiplier
## 5 Hardware Implementation of Quaternion Multiplication
### ASIC Implementation  
Both quaternion multiplication operations have also been implemented as ASICs. The OpenLane environment was used for this purpose. OpenLane is an environment composed of tools for the synthesis of digital circuits, the result of which is an integrated circuit in the form of a GDSII file. It should be mentioned that the tools creating OpenLane are integrated with the publicly available SkyWater 130 nm technological process. This makes it possible to produce a physical layout. The entire synthesis process is automated; the environment synthesizes the system into interconnected logic cells. In the next step, it arranges and connects previously generated cells, each time analyzing the system using DRC and LVS until the synthesis assumptions are met. The starting point in this project is the description of the system in a high-level hardware language.  
The two versions of the multiplier considered are described in VHDL. The direct algorithm resulting from Formula (5) in this description is available as a free software on GitHub repository [[http://github.com/[PERSON]/IIR3D](http://github.com/[PERSON]/IIR3D)]([http://github.com/[PERSON]/IIR3D](http://github.com/[PERSON]/IIR3D)) (accessed on 23 July 2024) in the quaternion VHD directory. The part of this description resulting from the Formula (5) is presented in Listing 1.  
```
libraryIEEE; useIEEE.STD_LOGIC_1164.ALL; useIEEE.NUMERIC_STD.ALL; architectureFlow ofmulis begin process(clk,a0,a1,a2,a3,b0,b1,b2,b3) begin ifrising_edge(clk)then y0<=a0*b0-a1*b1-a2*b2-a3*b3; y1<=a0*b1+a1*b0+a2*b3-a3*b2; y2<=a0*b2+a2*b0+a3*b1-a1*b3; y3<=a0*b3+a3*b0+a1*b2-a2*b1; endif; endprocess; endFlow;
```  
The automatically generated testbanch for designing a fast quaternion multiplication system is also available as a free software on GitHub repository.[[http://github.com/[PERSON]/IIR3D](http://github.com/[PERSON]/IIR3D)]([http://github.com/[PERSON]/IIR3D](http://github.com/[PERSON]/IIR3D)) (accessed on 23 July 2024).  
The resulting layout of this system is shown in Figure 6.  
For comparison, the ASIC generated for direct multiplication is shown in Figure 7. In the first case, the system was generated in 43 min, and in the second one, the system was generated in 69 min.  
The most important parameters related to the ASIC implementation of both quaternion multiplication methods are summarized in Table 3.  
As you can easily see, the fast algorithm gives over 25% savings in chip area and resource use. Identical synthesis settings were used when designing the systems, so the results obtained are more objective and fully comparable.  
Figure 6: Implementation of fast quaternion multiplication in an ASIC.  
Figure 7: Implementation of direct quaternion multiplication in an ASIC.
## 8 Application of GPC and Geopolymer Concrete Mortar (GCM)
The fly ash-based GPC exhibits stability when exposed to elevated temperatures [213]. Likewise, it was reported that GPC increased its strength when exposed to elevated temperatures and hence has a superior fire resistance than conventional concrete that loses approximately all of its strength when exposed to the temperature of 800 \({}^{\circ}\)C [214, 215]. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Properties** & **OPC Concrete** & **GPC** & **Remarks** \\ \hline Setting time & Slower & Faster & Generally GPC has faster rate of setting than ordinary concrete but the setting time also depends on factors like aluminosilicate source and actuators. \\ \hline Compressive Strength & Lower & Higher & Compressive strength of GPC depends on number on factors such as aluminosilicate source, precursor reactivity, activator type and curing conditions. Further, early age strength of GPC is greater than normal concrete \\ \hline Tensile Strength & Lower & Higher & Tensile strength usually higher than OPC concrete. Moreover, tensile strength of GPC exhibits similar behavior to compressive strength and depends on the same factors on which compressive strength depends \\ \hline Water Absorption & Slightly lower & Moderate & Water absorption depends on internal pore structure of GPC \\ \hline Shrinkage & Lower & Moderate & Shrinkage cracks in GPC mainly depends on the curing condition. \\ \hline Porosity & Lower & Moderate & Porosity depends on internal geopolymeric structure of GPC. \\ \hline Acid Attack & Lower resistance & Higher resistance & GPC have higher durability in terms of acid attacks than OPC concrete due to the presence of alumino-silicate products \\ \hline Freeze and thaw cycling & More susceptible & Less susceptible & GPC have higher physical and chemical resistance against aggressive environment \\ \hline Durability & Lower & Higher & GPC has higher durability than OPC concrete because of the chemical alumina and silicate products \\ \hline Fire Resistance & Limited & Typically higher & GPC can withstand heat without age degradation when compared to OPC concrete \\ \hline Insulating property & Limited & Higher & Usually have higher insulation but depends on type of precursor and activator along with curing conditions \\ \hline CO2 Emission & High & Low & GPC has lower global warming potential than ordinary concrete \\ \hline Age Degradation & High & Low & During heating OPC concrete has extreme age degradation and carbonation when compared to GPC \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between OPC concrete and GPC. Figure 34: Application of GPC. (**a**) Prefabricated bridge, (**b**) deck bridge [192].
# Scattering Reduction and Resonant Trapping of Flexural Waves: Two Rings to Rule Them
## 2 Analytical Methods  
### Scattering Reduction of Flexural Waves in a Thin Plate by a Finite Cluster of Masses  
We begin with an elementary example, where a point scatterer, represented by a single mass \(M\) and incorporated in a thin plate, is replaced by a finite cluster of smaller masses \(m_{j}\) (\(j=1,\dots,N\)) such that \(\sum_{j=1}^{N}m_{j}=M\). Here, the masses \(m_{j}\) are placed at points \(A_{j}\) (\(j=1,...,N\)) along a circle of radius \(R\) centred at the origin (see Figure 2).
# Optimizing Latent Factors and Collaborative Filtering for Students' Performance Prediction
## 4 Proposal for Improving The Prediction
### Three Approaches for The Optimization  
In light of a DS approach, it is necessary to keep in mind two considerations. First, there are many different possibilities when selecting the values of the pair (\(\beta\), \(\lambda\)), since they are real numbers chosen from different ranges, where the precision of the floating point values can be important. On the contrary, there are not many possible values for \(K\) since it is an integer number chosen from a relativelyrestricted range, for example between two and 256 [6] in the PSP context. Therefore, we propose three approaches for solving the optimization problem of finding the optimal values of \(K\), \(\beta\), and \(\lambda\) for which \(RMSE\) is minimum:  
* _GAK_: This method implements a loop of predictions where for each \(K\) value, the GA algorithm is applied in order to obtain the optimal pair (\(\beta\), \(\lambda\)), as outlined in Figure 5. This way, the minimum \(RMSE\) found would correspond to the optimal values of \(K\), \(\beta\), and \(\lambda\).
* _GA3_: This method applies the same GA algorithm than in GAK, but including \(K\) as the third optimization parameter. This way, there is no need for a \(K\) loop, since GA optimizes in each run the three parameters \(K\), \(\beta\), and \(\lambda\) together.
* _PS3_: This method follows the same idea as GA3, although replacing GA by PS as the optimization algorithm.
## 5 Proposed Framework
### ZTA Design
#### 5.1.2 Continuous Authentication  
Our continuous authentication system implements a multi-modal behavioral analysis framework for RIC entities, as illustrated in Figure 3. Each xApp/rApp undergoes real-time verification through three parallel detection channels:Authentication tokens are refreshed every 50 ms using ECDSA-P384 with the following security parameters:  
* Key size: 384-bit (NIST P-384 curve);
* Signature overhead: 104 bytes;
* Verification time: 1.2 ms (\(\times\)86) /0.8 ms (ARMv8).  
Table 7 presents the performance metrics of continuous authentication systems, including false accept rate, false reject rate, detection latency, and throughput [55].
## 2 Materials and Methods
### Identification and Semi-Quantification of Minor Volatile Compounds  
HS-SPME was performed followed by GC/MS analysis. The filtered sample (5 mL) was placed in a microextraction flask, and 1.5 g of NaCl was added. 1-Pentanol was used as an internal standard (300 mg/L) to semi-quantify the minor volatile compounds. The vial was incubated in a water bath at 35 \({}^{\circ}\)C for 5 min, and then the SPME fiber (DVB/CAR/PDMS 50/30 \(\upmu\)m, Supelco, St. Louis, MO, USA) was exposed in the headspace for one hour. After that time, the fiber was exposed to the injection port of an Agilent 7890A gas chromatograph for 10 min at 250 \({}^{\circ}\)C for the thermal desorption of volatile compounds. The injector worked in splitless mode using a liner for SPME (0.75 mm id, Supelco). The components were separated on an FFAP column (30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m). Ultra-high-purity helium was used as the carrier gas at a 1 mL/min flow rate. The oven temperature was set to 40 \({}^{\circ}\)C for 3 min and then increased to 52 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min. After 1 min at 52 \({}^{\circ}\)C, oven temperature increased to 10 \({}^{\circ}\)C/min until it reached 200 \({}^{\circ}\)C. Finally, the temperature was maintained at 200 \({}^{\circ}\)C for 15 min. The mass spectrometry detector (Agilent 5975C) was operated at 230 \({}^{\circ}\)C, an ionization voltage of \(-\)70 eV, and SCAN mode (1.6 scans per second). The compounds were identified using the NIST 2011 mass spectral library with a \(\geq\)80% match and the retention indices were calculated using an alkane series (C7-C30).  
The concentration of the minor volatile compounds was determined as described by [PERSON] et al. [28] using the equation:  
\[C_{x}=C_{i}*\frac{A_{x}}{A_{i}}\]  
\(C_{x}\) is the concentration (mg/L) of the unknown compound, \(C_{i}\) is the concentration of the internal standard (mg/L), \(A_{x}\) is the peak area of the unknown compound, and \(A_{i}\) is the peak area of the internal standard. The results obtained from this equation are semi-quantifications based on the mass of the internal standard and are not absolute quantities. Then, each compound is expressed as a mass equivalent to 1 \(\mathrm{\SIUnitSymbolMicro g}\)/L of the internal standard (1-Pentanol). However, this allows us to establish statistical differences between compounds under different experimental conditions.
## 6 Simulation Illustrations
### Simulation of the Water Flow Process and the Movement of Particles in a Karst Pipeline Flow
It should be noted that the flow in karst pipelines follows [PERSON]'s law, while the physical and mechanical parameters of fluid and solid particles are detailed in Table 1. The \(x\) and \(y\) directions include 255 and 147 lattice grids, respectively. A pressure gradient is imposed between its inlet and the outlet as follows:  
\[\Delta P=p_{\text{in}}-p_{\text{out}}=c_{s}^{2}\rho_{0}(\rho_{\text{in}}-\rho _{\text{out}}) \tag{37}\]  
where \(\rho_{\text{out}}=1\) and \(\rho_{0}\) is the physical density of the fluid. The \(y\)-direction component, \(v_{y}\), is equal to 0 at the inlet and outlet, respectively. The \(x\)-direction component, \(v_{x}\), can be obtained using Equation (15). The velocity at both the upper and lower boundaries is zero. The \(\rho_{\text{in}}\) value is fixed according to Equation (15). Here, corner nodes belong to fixed walls, and they are initialized in the same way as wall nodes (\(v_{x}=0\) and \(v_{y}=0\)). A series of snapshots is presented in Figure 9, illustrating the detachment of particles under hydraulic loading and their subsequent transport towards the outlet of the karst pipeline by the flow. Upon removal of a particle, no specific treatment is required for the resulting fluid nodes, as they were already treated as such when part of the solid particle. With an increase in hydraulic gradient, there is a more pronounced initial oscillation in Darcy velocity of particles, with a maximum value of 0.022 cm/s and a minimum value of \(-0.015\) cm/s, where '-' indicates the opposite direction of flow from the inlet to the outlet. When the hydraulic gradient exceeds 6, Darcy velocity gradually tends to become constant, as shown in Figure 10a. As the hydraulic gradient increases further, the particles at the upper and lower boundaries move away from the inlet of the karst pipeline and towards the outlet on the right-hand side. At a hydraulic gradient greater than 6, upper boundary particles exhibit sharp back-and-forth movement. In contrast, lower boundary particles remain at a constant position of 7 cm from the inlet, as depicted in Figure 10b. The return movement of upper boundary particles results from setting this boundary as bounce-back during numerical simulation calculations. \begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Property (Units)** & **Value** \\ \hline LBM & Water density (kg m\({}^{-3}\)) & \(\rho_{0}=1000\) \\  & Fluid kinematic viscosity (m\({}^{2}\)/s) & \(v=1\times 10^{-6}\) \\  & Relaxation parameter & \(\tau=1.1\) \\  & Lattice size (m) & \(h=2.77241\times 10^{-5}\) \\  & Interval time (s) & \(\Delta t=1.53726\times 10^{-4}\) \\ DEM & Solid grain density (kg m\({}^{-3}\)) & \(\rho_{\text{in}}=2650\) \\  & Normal stiffness (MPa) & \(h_{\text{in}}=3\) \\  & Tangential stiffness (MPa) & \(k_{s}=1.6\) \\  & Interval time (s) & \(\Delta t_{D}=5.12419\times 10^{-5}\) \\  & Internal friction angle (\({}^{\circ}\)) & 21 \\ \hline \end{tabular}
\end{table} 
Table 1: Physical and mechanical parameters of fluid and solid particles using coupled LBM-DEM.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 4 Measurements and Results
### Rf Power Harvester Measurements  
Figure 29 shows the fully assembled self-powered temperature sensor where the prototype chip was attached to the antenna by means of an epoxy resin. The final prototype presents an overall dimensions of 8 cm \(\times\) 2.5 cm.  
In order to measure the capability of the tag to harvest RF power and convert it into a DC power, the setup depicted in Figure 30a was implemented. The E4425B ESG-AP Series 3 GHz RF generator is used to provide an 910 MHz RF signal that is divided in two RF paths by an RF splitter. One path is used to irradiate the RF power through three RF adapters, a RG142B/U coaxial cable, and a 2000-1035-R Anritsu antenna. Likewise, the second path is employed to visualize the RF signal by means of two RF adapters, a RG142B/U coaxial cable, and a 54833B Infiniium oscilloscope.  
Figure 28: Temperature measurements of the LF section. (**a**) \(I_{TC}\) vs. temperature. (**b**) \(V_{REF}\) vs. temperature. (**c**) Frequency of oscillation vs. temperature.  
Figure 29: Fully assembled proposed self-powered temperature sensor.  
Figure 30 reports the transient measurement response where the yellow and green traces correspond to the RF input signal and the DC harvested power, respectively. It can be noted that the harvested start-up time is about 300 us because the RF generator start-up time affects this measurement. A \(V_{REC}=0.9\) V DC output voltage is also generated when a \(v_{ask}=0.4\) V RF input voltage is applied. The \(V_{REC}=0.9\) V DC output voltage represents an output power of 5.5 uW, where 4.5 uW are transferred into the low-frequency section of the proposed sensor, while 1 uW is drained into the oscilloscope test probe.
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 4 Discussion  
### Discussion of Vehicle Trajectory Characteristic Results  
In Scenario 1, analyzing novice drivers' left-turn trajectories, we observe similar lateral fluctuation ranges across no guide line, one guide line, and full guide lines. This suggests minimal impact on novice drivers in a small intersection with a single left-turn lane. The straightforward route and simple layout of small intersections with one left-turn lane enable drivers to execute turns smoothly without explicit guide lines. Thus, the impact of guide line settings on left-turn behavior is relatively small.  
The analysis of trajectory characteristics reveals a significantly larger lateral fluctuation range in Scenario 2-A compared to Scenarios 2-B and 2-C, indicating a notable impact of guide line settings on novice drivers. In Scenario 3, the absence of guide lines leads to inner left-turn lane vehicles occupying the outer lane, resulting in a larger lateral fluctuation range. The presence of guide lines, especially full guide lines, reduces this range significantly, highlighting the impactful role of guide line settings. Medium intersections, with their multiple lanes and turning options, pose complexities. Lack of guide lines may height confusion for novice drivers, while guide lines provide clear direction, aiding smooth navigation and promoting compliance with traffic rules. This is particularly crucial for inexperienced drivers facing unfamiliar traffic rules and intersection layouts.  
In Scenarios 4, 5, and 6, trajectory analysis reveals significant insights. In Scenario 4, the lateral fluctuation range in 4-A and 4-B is notably larger than in 4-C, showcasing a substantial impact of full guide lines on novice drivers. Scenario 5 displays a decreasing lateral fluctuation range, indicating the impact of guide line settings on novice drivers. Moreover, this impact intensifies with more guide lines. In Scenario 6-A, the evident increase in lateral fluctuation range surpasses 6-B and 6-C, with 6-B being slightly larger than 6-C. This signifies a notable impact of guide line settings on novice drivers, increasing with the number of guide lines. Large intersections with larger radii and complex structures diminish the reference effect of roadside features during left turns, leading to noticeable lateral fluctuations for novice drivers. Guide lines offer precise guidance, reducing confusion and indicating specific lanes. Additionally, large intersections with more traffic and lane choices may cause misunderstandings without clear guide lines, leading to unnecessary lateral movements. Guide lines contribute to standardized driving behavior, providing crucial visual cues for correct and safe left turns, enhancing overall traffic flow stability and safety. The results align with the findings of the research work of [27; 32].
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
## 3 Preliminaries
### Public Key Encryption  
Public key encryption has advantages in key management compared with symmetry key encryption. Users only need to keep their secret keys safe instead of memorizing many passwords. RSA and ElGamal (including ECC) are the most commonly used public key encryption schemes. In RSA, we can either encrypt with a public key or secret key and decrypt with the other, respectively. In ElGamal, the public key is for encryption via Equation (2), and the secret key is for decryption via Equation (3). Public key encryption allows anyone to create an encrypted message and send it to the secret key owner to establish secure communication. We mainly focus on ElGamal here:  
\[a,r\in\mathbb{Z}_{p}, \tag{1}\]  
\[pk_{A}=g^{a}, \tag{2}\]  
\[sk_{A}=a. \tag{3}\]  
where \(pk_{A}\) is the public key, and \(sk_{A}\) is the secret key. Ciphertext \(c\) is encrypted with the public key \(pk_{A}\) and the clear message \(m\) via Equation (4):  
\[c=\langle c_{0},c_{1}\rangle=\langle g^{r},m\cdot pk_{A}^{r}\rangle=\langle g^{ r},m\cdot g^{ar}\rangle. \tag{4}\]  
The secret key is required for decryption via Equation (5):  
\[m=\frac{c_{1}}{(c_{0})^{sk_{A}}}=\frac{m\cdot g^{ar}}{(g^{r})^{a}}. \tag{5}\]  
The ElGamal scheme satisfies the CPA security. Given the same input \(m\), the output \(c\) is different each time according to the random value \(r\). To achieve CCA security, validation is required before the decryption. It detects if the adversary had modified the ciphertext.
## The NB-IoT Channels and Coverage Enhancements
### NB-IoT Channels  
Besides basic dedicated signals, such as the narrowband reference signal (NRS), narrowband demodulation reference signal (NDMRS), narrowband primary and secondary synchronization signals (NB-PSS and NB-SSS), narrowband positioning reference signal (NPRS), or narrowband wake up signal (NWUS), the NB-IoT introduces five new physical channels. In the downlink direction, there are one broadcast and two configurable downlink channels:  
* The narrowband physical broadcast channel (NPBCH) is transmitted in the first subframe and contains the NB-IoT master information block (MIB-NB);
* The narrowband physical downlink control channel (NPDCCH) carries mainly paging indications, system information updates, and scheduling assignments with granted modulation schemes;
* The narrowband physical downlink shared channel (NPDSCH) contains data, paging messages, system information messages, and the random access response (RAR).  
In the uplink direction, there are two defined configurable channels:  
* The narrowband physical random access channel (NPRACH) enables the random access procedure;
* The narrowband physical uplink shared channel (NPUSCH) carries actual user data transmissions (Format 1) or acknowledge/non-acknowledge (ACK/NAK) transmissions (Format 2).  
The downlink transmission scheme for the NB-IoT adopts conventional orthogonal frequency-division multiplexing (OFDM), using a cyclic prefix and a sub-carrier spacing of 15 kHz. A minimal dispatching unit for NPDSCH transmission is a resource block (RB). The modulation scheme for all downlink channels is quadrature phase shift keying (QPSK).  
For NB-IoT uplink transmission-based single-carrier frequency-division multiple access (SC-FDMA), both single-tone transmissions and multi-tone transmissions are defined. A single-tone transmission employs 3.75 kHz or 15 kHz subcarrier spacing, yielding 12 or 48 consecutive sub-carriers, respectively. Multi-tone transmissions are based on 12 sub-carriers with 15 kHz of spacing, OKwhere the sub-carriers can be further grouped into sets of 3, 6, or 12 consecutive subcarriers. Modulations used in uplink comprise versions of binary phase shift keying (BPSK) and QPSK in single-tone transmissions, whereas multi-tone transmissions use only QPSK. Additionally, the duration of the resource unit (RU), which is a minimal dispatching unit used in NPUSCH scheduling, depends on the number of assigned sub-carriers and equals to 32 ms or 8 ms in cases were single-tone is used, with 3.75 kHz or 15 kHz spacing, respectively, for NPUSCH Format 1. In the multi-tone version, RU is defined as 3 sub-carriers for 4 ms, 6 sub-carriers for 2 ms, or 12 sub-carriers for 1 ms. Moreover, ACK/NAK is transmitted via single-tone BPSK transmission on NPUSCH Format 2 and an RU, schedulable for NPUSCH with ACK/NAK transmission, and is defined as a single 3.75 kHz sub-carrier for 8 ms, or a single 15 kHz sub-carrier for 2 ms.  
Since the default NPRACH is based on a single-tone transmission with a spacing of 3.75 kHz, the minimum equipment support for NPUSCH is also considered to be the same as for NPRACH.