esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle
### LMS and RLS Techniques  
Both LMS and RLS algorithms are intended to iteratively minimalize the squared error at all discrete frequencies. The LMS algorithm stands as a stochastic gradient descent minimization using instantaneous error estimates, with the corresponding weight update as [27]  
\[W(k+1)=W(k)+\mu[U(k)-W(k)V(k)], \tag{5}\]  
where W\((k)\) signifies the MIMO channel matrix vector at frequency \(k\), U\((k)\) indicates fast Fourier transform (FFT) of detected data blocks, \(\mu\) denotes the step size, and V\((k)\) represents the FFT of sampled received signals.  
In conventional LMS, the convergence rate and equalization performance depend on the same value of the scalar step size \(\mu\). The convergence speed can be enlarged by choosing an adaptive step size based on the power spectral density (PSD) of input signal, which can be conveyed as [28]  
\[\mu(k)=\frac{\alpha}{\text{S}(k)}, \tag{6}\]  
where S(k) signifies the PSD of a posterior error block, and \(\alpha\) symbolizes the adaptation rate, which determines both the convergence speed and equalization performance of the noise PSD directed (NPD) LMS algorithm. The basic schematic of an MDM transmission system using the LMS algorithm is shown in Figure 6.  
In contrast, the RLS algorithm depends on the iterative minimization of an exponentially weighted cost function, whose convergence speed is not strongly dependent on the input statistics, with the corresponding weight update as  
\[\text{W}(k+1)=\text{W}(k)+[\text{U}(k)-\text{W}(k)\text{V}(k)]\cdot\text{V}(k )^{H}\cdot\Big{[}\text{R}(k)\cdot\beta^{-1}\Big{]}, \tag{7}\]  
Figure 5: Eye diagrams of a typical mode-division multiplexing (MDM) transmission system for (**a**) back-to-back; (**b**) over-fill launch without coupler; (**c**) LP\({}_{01}\) channel; (**d**) LP\({}_{12}\) channel [26].  
where R(k) symbolizes a tracked inverse time-averaged weighted correlation matrix, the superscript H signifies the Hermitian conjugate, and \(\beta\) denotes a forgetting factor, which gives an exponentially lower weight to older error samples. Consequently, the main difference between the conventional LMS and RLS algorithms is that RLS has a growing memory due to R(k) and \(\beta\), and, thus, might achieve an even lower OSNR and faster adaptation. Specifically, the tracked inverse time-averaged weighted correlation matrix can be denoted as [29]  
\[R(k+1)=\left[R(k)\cdot\beta^{-1}\right]-\Bigg{\{}\frac{\left[R(k)\cdot\beta^{ -1}\right]\cdot\mathrm{V}(k)\cdot\mathrm{V}(k)^{H}\cdot\left[R(k)\cdot\beta^{ -1}\right]}{1+\mathrm{V}(k)^{H}\cdot\left[R(k)\cdot\beta^{-1}\right]\cdot \mathrm{V}(k)}\Bigg{\}}, \tag{8}\]  
which assigns a different step size to each adjustable RLS equalizer's coefficient at their updates, rendering all frequency bins with a uniform convergence speed, thus cultivating the total algorithmic performance.
# High Performance GAA SNWT with a Triangular Cross Section: Simulation and Experiments
## 3 Experiments
### Key Process of Triangular Nanowire Formation  
Owing to different atomic densities of crystal planes, the etching rate of TMAH on (1 1 1) is usually much smaller than that on (1 0 0) and (1 1 0), so the etching of TMAH can be self-stopped [13; 14; 15]. A schematic diagram of suspended triangular nanowires by TMAH wet etching is shown in Figure 5. This process can accurately control the size and shape of the silicon nanowires, which are formed from different initial line widths of the silicon Fin. The specific process flow is listed as follows:  
1. Thermal oxidation to produce a silicon oxide buffer layer;
2. Chemical vapor deposition to produce silicon nitride as a hard mask layer;
3. Electron beam lithography and anisotropic dry etching to form a silicon nitride hard mask and silicon Fins;
4. Remove the native oxide on the silicon surface with a dilute hydrofluoric acid solution at room temperature;
5. Anisotropic wet etching silicon Fins at 35 \({}^{\circ}\)C and in 25% TMAH solution, so triangular silicon nanowires are formed under the silicon nitride hard mask, while the silicon substrate will also be etched to form a triangle;
6. Remove silicon nitride hard mask by 170 \({}^{\circ}\)C phosphoric acid;
7. Remove the silicon oxide buffer layer by diluted hydrofluoric acid at room temperature, and the triangular suspended silicon nanowires are achieved.  
When the TMAH solution etches the side of the silicon Fin, the etching window is determined by the top silicon nitride hard mask and the bottom corners of silicon Fin. If the silicon Fins have a sufficient height and long enough etching time, both sides of the silicon Fins will be completely etched and lead to punch-through, an inverted triangular silicon nanowire will be formed under the silicon nitride hard mask, and a triangle bump on the silicon substrate will be simultaneously formed, as shown in Figure 5. At this time, the side surfaces formed by the wet etching are all (1 1 1) crystal planes, presenting an angle of 54.7\({}^{\circ}\) with the (1 0 0) crystal plane of the surface.  
Figure 5: The diagram of triangular suspended nanowire by TMAH.  
Figure 6 illustrates the SEM images of different TMAH etching times of the triangular device, and it is noted that even if the over-etching time has exceeded 10 s (over-etching time exceeds 50%) in Figure 6c, the width and cross section of the silicon nanowires remain basically the same, and only the sharp corners are slightly etched, indicating that the TMAH etching technology has the strong capability to suppress cross section fluctuations.
## 2 Materials and Methods
### Collection of Dust and Soil Samples  
The collection of dust sampling was conducted at 20 sites across the Al-Baha region (Figure 3), during the summer season in 2022, with nine sites in the Tihama plains, eight in the Sarawat highlands, and three in the eastern mountains. Dust samples were collected using three different methods (Figure 4): Big Spring Number Eight (BSNE) Dust Collectors, Surface Dust Collectors (SDCs), and Marble Dust Collectors (MDCOs).  
Figure 3: Locations of sample collection from all 20 sites where the dust-sampling methods were employed. Colored lines represent contours in meters, illustrating the topographical variations across the study area.  
Big Spring Number Eight (BSNE) Dust Collectors are designed to monitor wind erosion by capturing dust at various heights (25, 50, 75, and 100 cm) above the soil surface, providing a comprehensive understanding of vertical dust distribution in the atmosphere. Studies such as those by [PERSON] and [PERSON] [36] have demonstrated the BSNE's effectiveness in quantifying airborne dust and monitoring wind erosion patterns in arid environments. Surface Dust Collectors (SDCs) focus on capturing surface dust directly affected by wind erosion. Positioned close to the ground, they assess the immediate impact of wind-driven dust on the soil surface. Their reliability in estimating surface-level dust deposition has been validated in studies like those by [PERSON] and [PERSON] [36]. Marble Dust Collectors (MDCOs) utilize a methodology based on [PERSON] et al. [37]. These collectors target deposited dust (falling dust) using rectangular trays fitted with a marble filter, providing precise measurements of dust accumulation on the soil surface. Prior research highlights their utility in quantifying settled dust in areas with variable deposition rates [14; 36; 37].  
In this study, 20 BSNE devices were installed at different locations across the study area to monitor wind erosion, and the SDC and MDCO devices were co-located at the same 20 sites to complement the data collection. The proximity of the different devices at each location was within 5-10 m, ensuring consistent comparative data across methodologies.  
In addition, soil samples were collected from 40 locations, at a depth of 0-30 cm around the locations of the dust collectors (2 soil samples per site). The sampling locations were selected using a systematic sampling approach to ensure a representative distribution across the study area, with consideration for accessibility and potential environmental variations. The samples were packed in airtight bags for laboratory analysis. The study area was classified according to its topographic distributions, water flow, height from sea, and soil types. Soil loss was further quantified in relation to dust mass collected in dust collectors, and soil samples were analyzed for their physiochemical characteristics to determine the possible impact of wind erosion and dust deposition.
## 3 Results
### HFM Average Method
The minimum test duration for the experimental mock-up and the detached house was 72 h, in view of the fact that the second condition consisting of limiting the deviation between the U-value obtained at the end of the test from the value obtained 24 h earlier to 5% was fulfilled (1.4% and 0.4% respectively, as shown in Table 5), and that the third condition consisting of limiting the deviation between the U-value obtained by analyzing data from an initial period from the value obtained from data for the last period of the same duration to 5% was also fulfilled in both cases (2.3% and 1.5%, respectively, as shown in Table 6). Then, the variability of the results was verified, in both cases, through the coefficient of variation of the resulting U-values (Equations (3) and (4)). It was confirmed that the test could be ended at 72 h in both cases, since the coefficients of variation were lower than expected from 72 h of testing (Figure 7). \begin{table}
\begin{tabular}{c c c c} \hline \hline \
\begin{tabular}{c} **Number of Cycle** \\ **Evaluated** \\ \end{tabular} &
\begin{tabular}{c} **Related Cycles in the** \\ **Evaluation** \\ \end{tabular} & \multicolumn{2}{c}{**Deviation in \(\textbf{U}_{\textit{m-HFM}}\) (\%)**} \\ \cline{2-3}  & **Experimental Mock-Up** & **Detached House** \\ \hline
**3 (72 h)** & **3 vs. 2** & 1.4 & 0.4 \\ \hline
**4 (96 h)** & **4 vs. 3** & \(-\)0.6 & 0.7 \\ \hline
**5 (120 h)** & **5 vs. 4** & 1.1 & 0.0 \\ \hline
**6 (144 h)** & **6 vs. 5** & 0.4 & 1.2 \\ \hline
**7 (168 h)** & **7 vs. 6** & 0.4 & \(-\)2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Deviation of the U-value from the value obtained 24 h earlier, for both the experimental mock-up and the detached house, using the _HFM_ average method. Figure 6: Measured U-values and their associated combined uncertainties in the experimental mock-up and the detached house for the seven cycles of test duration, using the HFM average method. The measured thermal transmittance in both the experimental mock-up and the detached house was closely adjusted to the theoretical transmittance value, with absolute values of relative differences of less than 5%. However, it can be observed that the measured U-value of the experimental mock-up fit better with the theoretical U-value (1.05%) than the one of the detached house (3.06%) (Table 4). This difference in fitting could be due to two situations. Firstly, the theoretical thermal transmittance of the experimental mock-up was greater than that of the detached house. Secondly, conditions for conducting in situ tests were more optimal during the monitoring process of the experimental mock-up than those of the detached house. Consequently, the average temperature difference between indoor and outdoor environments was almost 9 \({}^{\circ}\)C greater during the testing process of the experimental mock-up than that of the detached house.
## 3 ROR: Reliable Opportunistic Routing for CRSN
### Receiver Contention Prioritization  
The receiver contention prioritization is based upon a priority scheme, which gives more priority to nodes that make more progress towards the sink to forward the packet. Nodes with longer progress have higher priority over other nodes. Based on the location information, the region is divided into Q priority regions, _i.e._, \(A_{i},i=1,2,Q\). All nodes that pass the \(I_{DIFD}\) operation determine its priority region based upon the location information in the header of the data packet. Next, each node delays sending its ACKpacket, as explained next.  
Each priority region, \(A_{i}\), corresponds to a delay window size of short inter-frame space (SIFS), \(SIFS_{i}\). Thus, a node will delay \(\sum_{j=1}^{i-1}SIFS_{j}+sifs_{i}\), where \(sisf_{i}\) is randomly chosen, such that \(sifs_{i}\in[0,EIFS]\), where the extended inter-frame space \((EIFS)=SIFS_{i}-SIFS_{i-1},\forall i\). Based on this prioritization, only nodes in the same priority region will compete in sending the ACK. Once a node wins and sends the ACK packet, when other nodes that are undergoing the delay overhear this packet, it means an appropriate forwarder has been selected; as a result, they refrain from contesting and drop the packet. This is as illustrated in Figure 6, wherein a case for three priority regions is presented.  
Assuming Node \(i\), has the data packet to forward, it broadcasts the same on the data channel; all eligible nodes that passed the \(I_{DIFD}\) operation will initialize their SIFS according to their respective regions, as illustrated in Figure 5. According to the scheme, the nodes in the region, \(A_{1}\), will compete in sending their ACK packet first after the expiration of time \(SIFS_{1}\). If one node wins, all other nodes refrain from sending the acknowledgment and drop the data packet. However, if no ACK is heard by the nodes at the expiration of the time, \(SIFS_{2}\), the nodes in \(A_{2}\) will then compete in sending their ACK packet, and so on. It should be noted that the possibility of two nodes sending an ACK packet without hearing each other cannot arise, because all contending nodes have already been organized in VCGs. The other case that can arise in such a scheme will be the possibility of the collision of ACK packets arising from the same priority region. This can lead to the sending of ACK packets from nodes in lower priority regions, thus leading to the selection of such a node as the next forwarding node. For the fact that the cost of trying to resolve this outweighs the gains, ROR does not seek to resolve this problem. Moreover, the segregation of the nodes into contention regions makes the probability of such an occurrence very low.  
However, it is still possible that Node \(i\) does not receive an ACK, because the eligible node suddenly finds itself in a local minimum area, due to the failure of its neighboring nodes. If the node still receives no response after \(k\) retries, it determines that a local minimum is reached and switches to its route management module to resolve the issue, as explained next.
## 2 Materials and Methods
### Serial Dilutions of Aluminium Exposures  
A stock solution of 10 ppm Al was prepared by dissolving 1 mg aluminium nitrate nonhydrate 99.997% (Sigma-Aldrich, St. Louis, MO, USA) in 100 mL reverse osmosis (RO) water. The stock solution was aerated 24 h before the serial dilutions were prepared for the exposures: 120, 60, 30, 5 ug/L. The control solution contained RO water with no added aluminium. All exposures were prepared in triplicate in acid-washed 200 mL PYREX(r) glass beakers. Mean in situ water quality parameters (pH, DO, temperature, conductivity, TDS) measured at the beginning of each 24 h exposure interval, and again after, indicated that laboratory conditions remained constant throughout the experiment, with low standard deviations recorded within each parameter.  
Eggs in exposure beakers were monitored at 24 h intervals by inspecting them with a dissection microscope (Carl Zeiss Stemi 305, Carl-Zeiss-Promenade 10, 07745 Jena, Germany). Hatching, juvenile life stages, swimming pattern, mortalities, and abnormalities were noted during each inspection. Hatching in any concentration (control, 5, 30, 60, 120 \(\upmu\)g/L) was recorded as day 1. Micrographs of initial hatching and larval stages were taken with a dissection microscope (Carl Zeiss Stemi 305, Carl-Zeiss-Promenade 10, 07745 Jena, Germany) during exposures and additional micrographs to study temporarily mounted specimens were taken with a stereomicroscope (Carl Zeiss, Carl-Zeiss-Promenade 10, 07745 Jena, Germany).  
In situ physiochemical water quality parameters were measured every 24 h using a YSI 556 Multi-Probe meter (YSI Incorporated, Yellow Springs, OH, USA) and a 10 mL water sample from each beaker taken for metal analysis using ICP-MS (NexION(r) 300 Series Inductively Coupled Plasma-Mass Spectrometer, Perkin Elmer, Boston, MA, USA).  
Exposure concentrations were prepared in aerated RO water and replaced every 24 h. This process was repeated until all juvenile stages perished.  
Water samples were pipetted from each concentration (10 mL) from each beaker, daily. The samples were acidified to 1% with Suprapur(r) Nitric acid (HNO3) in preparation for ICP-MS analysis for Al. Water samples were stored at room temperature and analysed twice per sample, and Al recovery rates of 80% to 133% were recorded (level of detection for Al (LOD) = 0.24 \(\upmu\)g/L). The instrument's calibration was verified using quality proven Standard Reference Material 1643e, obtained from the National Institute of Standards and Technology in Gaithersburg, Maryland, United States.
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 5 Error Analysis and Discussion  
Affected by processing technology, installation technology, and other factors, the LDV values may have a specific difference from the expected value, leading to errors in the angle dynamic measuring results. The following analyzes and discusses four key error sources and proposes corresponding improvement and optimization methods.
## 3 Results and Discussion
### Compaction Characteristics
As expected, the addition of waste rubber results in a decrease in both the OMC and MDD values compared to the natural aggregate mixture with the same binder content. This is a result of rubber's hydrophobic nature, rough surface (Figure 5), and low density, but also its high elasticity that absorbs some of the compaction energy. As can be seen from Figure 5, rubber particles have more irregular and indented surface which captures air molecules thus taking up more space in the structure of the mixture which reduce the volume of the mixture that hydrates with cement. Furthermore, for irregularly shaped particles it is more difficult to adhere to the particles with flat surfaces, such as sand particles, and thus create larger gaps in the mixture. The results are in accordance with some previous research dealing with waste rubber applications in the pavement system [26; 43; 44]. From Table 2, it can be noted that there is a larger difference in OMC than MDD between waste rubber and non-waste rubber mixtures for the Proctor compaction method. This greater drop in the compaction characteristics for the Proctor compaction method means that the choice of laboratory compaction method should be made carefully if alternative materials are used. As presented in Table 2, a lower density (\(\rho\)) is observed for the vibrating hammer method compared to the modified Proctor compaction method, for which an increasing trend is observed with an increase of the cement content. However, for the vibrating hammer method, with a high cement content a decrease in density occurred. During compaction, a cement paste layer formed on the sample surface at the end of each compaction layer (Figure 6), resulting in a decrease of the apparent density. Under high vibrations and a high cement paste content within the mixture, segregation occurred, influencing the apparent density decrease. The same phenomenon can explain the high compressive strength difference for higher values of f\({}_{\text{c,28}}\). Namely, cement paste segregation on the sample surface caused uneven density of the sample and a higher f\({}_{\text{c,28}}\) difference when comparing the two compaction methods, as presented in Figure 7b. Figure 5: Optical microscopy image of sand grain (**a**) and waste rubber grain (**b**). The addition of waste rubber also resulted in a density decrease due to the lower rubber density compared to the replaced sand. The rubber's high elasticity and compaction energy absorption resulted in a greater difference in the measured density. For the vibrating  
Figure 6: Vibrating hammer compaction of mixture with high cement content. Figure 7: Correlation between compressive strength and MDD (**a**) and density (**b**). hammer method a 1.63%, 6.3% and 11.31% decrease in density occurred for 20%, 30% and 60% sand replacement by rubber respectively, while for the Proctor compaction method the density reduction was 2.62%, 4.8% and 10.53% compared to the reference mixture (5%C).
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## 3 Scatter Phy
### Benefits of SCATTER PHY  
In this section, we describe the two main advantages of SCATTER PHY. The first advantage is that the COT based filtering mitigates OOBE, making SCATTER PHY more spectrally efficient. OOBE might interfere with radios using adjacent channels, decreasing the quality of the signals received by them. Therefore, the interference caused by OOBE impacts directly on the throughput achieved by those radios. The reduced OOBE allows SCATTER PHY to operate closer, in the frequency domain, to radios transmitting at adjacent channels. This, in turn, reduces spectrum band wastage by increasing the spectral efficiency, as depicted in Figure 4. The second advantage offered by SCATTER PHY is that it is possible to configure all PHY parameters in real time through the control messages. This makes SCATTER PHY a very flexible and agile SDR based PHY, allowing higher layers to configure the available parameters based on spectrum band availability, for instance.  
Figure 4: Closer coexistence to other radios due to reduced Out-Of-Band Emissions (OOBE).
## 2 Methodology
### Mathematical Modeling
#### 2.2.3 Calculations of Savings  
In this section, power savings, energy savings, diesel savings, reductions in carbon emissions, and carbon tax savings are quantified. Here, rated AC power of the refrigerator is used as a reference to perform an analysis. Energy savings per year in kWh is given by Equation (19),  
\[E_{\text{savings}}\ =\ Power_{\text{savings}}\times\ 24\times 365 \tag{19}\]  
The equivalent amount of diesel savings is calculated by converting energy savings to joules and dividing by the calorific value of diesel. The calorific value of diesel used in this analysis is 35.9 MJ/liter [48]. The efficiency of a diesel generator is accounted for in calculating the total amount of energy used. In this study, the efficiency of the diesel generator is used as 33% and the amount of diesel savings is given by Equation (20).  
\[\mathit{Diesel}_{\text{savings}}\ =\ \frac{E_{\text{savings}}}{\mathit{calorie }\ \mathit{value}\ \times\ \eta_{\mathit{generator}}} \tag{20}\]  
The quantification of diesel emission is made based on the amount of diesel saved over a year. This quantity is given by relation (21). For this calculation, an emission rate of 2.63 kg of CO\({}_{2}\) per liter of diesel is used while a carbon tax rate of USD 35 per tonne of CO\({}_{2}\) is used in the analysis [16].  
\[\mathit{CO}_{2}\ \mathit{savings}\ =\ \text{quantity of diesel savings}\times\text{emission per liter} \tag{21}\]  
Finally, total cost savings is the summation of energy cost savings from diesel generators and carbon savings. This is calculated by using formula (22).  
\[\text{Cost savings}\ =\ \left(\text{Energy}_{\text{savings}}\times\text{Diesel }\cos\text{t}\right)\ +\ \left(\text{CO}_{2}\ \text{ emissions}\times\text{carbon tax}\right) \tag{22}\]