esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Experimental Results and Comparison
#### 4.3.3 Results Comparison on the PV Module Model  
The RMSE values of the third case listed in Table 8 indicate that SOS, together with IJAYA, can provide the smallest RMSE value (2.4251 \(\times\) 10\({}^{-3}\)) among all methods. Based on the optimal extracted parameters in Table 9, the corresponding characteristic curves are rebuilt and illustrated in Figure 7. It is clear that the output current and power calculated by SOS are highly in coincidence with the measured values. The SIAE results presented in Table 10 repeatedly manifest that SOS can achieve the most accurate values for the unknown parameters, followed by ANS, BLPSO, LWOA, CTLA, and CSO. The curves presented in Figure 8 state clearly that SOS is consistently faster than its competitors from beginning to end.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Min** & **Max** & **Mean** & **Std. dev.** \\ \hline CARO & \(2.427\times 10^{-3}\) & NA & NA & NA \\ IJAYA & \(2.4251\times 10^{-3}\) & \(2.4393\times 10^{-3}\) & \(2.4289\times 10^{-3}\) & \(3.7755\times 10^{-6}\) \\ PS & \(1.18\times 10^{-2}\) & NA & NA & NA \\ SA & \(2.70\times 10^{-3}\) & NA & NA & NA \\ ANS & \(2.4310\times 10^{-3}\) & \(2.5658\times 10^{-3}\) & \(2.4702\times 10^{-3}\) & \(2.9121\times 10^{-5}\) \\ BLPSO & \(2.4296\times 10^{-3}\) & \(2.5616\times 10^{-3}\) & \(2.4884\times 10^{-3}\) & \(3.3055\times 10^{-5}\) \\ CSO & \(2.4537\times 10^{-3}\) & \(3.0650\times 10^{-3}\) & \(2.5804\times 10^{-3}\) & \(7.7274\times 10^{-5}\) \\ CTLA & \(2.4782\times 10^{-3}\) & \(3.5579\times 10^{-3}\) & \(2.7760\times 10^{-3}\) & \(2.4714\times 10^{-4}\) \\ LWOA & \(2.6352\times 10^{-3}\) & \(6.7023\times 10^{-2}\) & \(1.0936\times 10^{-2}\) & \(1.3115\times 10^{-2}\) \\ SOS & \(2.4251\times 10^{-3}\) & \(2.5103\times 10^{-3}\) & \(2.4361\times 10^{-3}\) & \(1.7503\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: RMSE results for the photovoltaic (PV) module model.  
Figure 6: Convergence curves for the double diode model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**V\({}_{\text{L}}\) (V)**} & \multirow{2}{*}{**\(I_{\text{L}}\) Measured (A)**} & \multicolumn{6}{c}{**\(I_{\text{L}}\) Calculated (A)**} \\ \cline{3-10}  & & & **ANS** & **BL
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge
### LoRa PHY Layer
#### 2.2.1 Packet Format  
Packet format in LoRa includes the following fields: preamble, payload, and payload cyclic redundancy check (CRC). Additionally, packets can take one of the two following forms: explicit and implicit. As depicted in Figure 3, the difference lies in the fact that first form packet format contains a header and a CRC for the header, in order to verify the integrity of the packet.  
The preamble synchronizes the receiver with the transmitter. Payload is a variable field, which contains the data from end-devices. The header field has 2 bytes and gives information related to the payload length, the CR as well as the presence or not of CRC payload. Of note, the payload error detection is performed only in uplink traffic. On the other hand, in implicit format header is not necessary, as both sides have set CR and the presence of CRC before initiation of messaging. Following this procedure, the transmission time can be reduced in comparison to the explicit mode [29].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SF** & **DR** & **Physical Bit Rate (bps)** & **Sensitivity (dBm)** & **SNR (dB)** & **ToA for 11 Bytes Payload (ms)** \\ \hline
7 & 5 & 5470 & \(-123.0\) & \(-7.5\) & 61 \\
8 & 4 & 3125 & \(-126.0\) & \(-10.0\) & 113 \\
9 & 3 & 1760 & \(-129.0\) & \(-12.5\) & 205 \\
10 & 2 & 980 & \(-132.0\) & \(-15.0\) & 371 \\
11 & 1 & 440 & \(-134.5\) & \(-17.5\) & 823 \\
12 & 0 & 250 & \(-137.0\) & \(-20.0\) & 1482 \\ \hline \hline \end{tabular}
\end{table}
Table 2: LoRa values at 125 kHz [5, 26, 27, 28].  
Figure 3: LoRa packet format.  
The preamble synchronizes the receiver with the transmitter. Payload is a variable field, which contains the data from end-devices. The header field has 2 bytes and gives information related to the payload length, the CR, as well as the presence or not of CRC payload. Of note, the payload error detection is performed only in uplink traffic. On the other hand, in implicit format header is not necessary, as both sides have set CR and the presence of CRC before initiation of messaging. Following this procedure, the transmission time can be reduced in comparison to the explicit mode [29].
# Marble and Glass Waste Powder in Cement Mortar
## 2 Materials and Methods
### Mix Design  
The mortar mix design for this study was tailored to meet the requirements of ASTM C109 [27] with the mix proportions determined based on the number of cubes to be produced. Mortar batches, sufficient to create six cubes each, were prepared in accordance with ASTM C305, incorporating GWP and MWP as partial replacements for cement. The mixes were prepared using a mechanical mixer. Table 3 presents the detailed mix proportions utilized in the experimental study.  
To optimize the replacement levels, three proportions of waste powders (1:1, 1:2, and 2:1 MWP: GWP) were tested for the selected percentage replacements of cement. A pilot study was conducted using a trial with a constant water-to-binder ratio (w/b) of 0.53 and a cement-to-sand ratio of 1:2.75 for the production of 2-inch (50 mm) mortar cubes. The compressive strength results from the trial mixes indicated that the 1:2 (MWP: GWP) ratio consistently delivered the highest strength for the selected replacement percentages. As a result, the 1:2 ratio was adopted for all subsequent experimental studies at various cement replacement levels (0%, 5%, 10%, 15%, 20%, 25%, and 30%).  
For the comprehensive experimental investigation, 420 mortar cubes were produced in compliance with ASTM C109 [27], each measuring 2 inches (50 mm). The study encompassed testing for compressive strength, ultrasonic pulse velocity, sulfate attack resistance, water absorption, porosity, and microstructural properties. A minimum number of three cube specimens were tested for each percentage replacement at various curing ages (3, 7, 28, 56, and 91 days).
## 3 Results and Discussion
### Extracellular Polysaccharides Production  
Data concerning the EPS production are given in Table 2a-c. EPS were synthesized in various amounts, with the maximum values ranging between 0.3-1.2 g/L (sucrose substrates; S-20, S-25) and 0.95-3.94 g/L (molasses substrates; M-20, M-25). Comparing EPS biosynthesis performed in the different media, it seems that the most suitable substrates for all _Morchella_ species were those with molasses addition. It is worth mentioning that EPS produced by _Morchella_ species in molasses substrate with the highest C/N ratio (M-25) reached the greatest concentration of 3.0-4.0 g/L. There are reports stating that a high C/N ratio enhances EPS production by means of supplying to the culture higher concentrations of carbon source. Lower EPS values were recorded by [PERSON] et al. [13] when _M. vulgaris_ and _M. elata_ were cultivated on molasses (1.17, 1.61 g/L), rice cereal hydrolysate (1.67, 2.17 g/L) and wheat cereal hydrolysate (0.59, 1.34 g/L) with ratio C/N = 20 and when they were examined on the same substrates with C/N ratio = 50, EPS accumulation by _M. elata_ was two-fold higher suggesting that its raise enhanced this process, as in this research. On the contrary, in sucrose substrates, the C/N ratio raise led to a slight drop. As about agitation, it had no significant effect upon EPS production as also [PERSON] et al. [28; 29] reported for _M. elata_ (0.80 g/L; static, 1.06 g/L; agitated) and _M. esculent_ (0.70 g/L; static, 0.65 g/L; agitated) EPS production on glucose substrate (C/N -20; Gl\({}_{\text{c}}\)0 = 30 g/L).  
Additionally, higher EPS quantities were synthesized at the relatively early growth phase (5 th-12 th day after inoculation), suggesting that the concentration of EPS was remarkably reduced as the fermentation proceeded, with the notable exception of _M. rotunda_ and _M. vulgaris_ in molasses substrates which EPS production was higher at the last day of fermentation. As at the early growth stages the biomass production is limited and sugars concentration rather high, this EPS reduction probably happened due to bio-degradation of previously produced EPS compounds. This phenomenon on liquid cultures has been also detected in other studies for many mushroom fungi including _Morchella_ spp. EPS of the basidiomycetes _Flammulina veluipes_, _Ganderma applanatum_, _Pleurots pulomarius_ and ascomycota _M. esculent_ were produced at an early growth phase (8 th-12 th day after inoculation) and they were notably reduced as the fermentation proceeded [29]. _M. esculent_ EPS production was the fastest between the 2 nd and 3 rd day of incubation, after the third day of incubation their increase showed a reduction in total EPS concentrations [56]. Also, _M. vulgaris_ and _M. elata_ presented higher EPS synthesis on the 8 th day after inoculation, but the amount of EPS was reduced at the 14 th day after inoculation, on all used substrate (glucose, molasses and waste flour-rich hydrolysates) [13].
## 3 Results
### Seasonal Performance Optimization and Analyses
The values in parenthesis of the seasonal strategies represent their percentage performance with respect to the case when the best annual-strategy for each service is used. The results are summarized in Table 7, where we can observe that, for the grid-commitment, the seasonal strategy presents only a marginal improvement of 0.1% over the annual strategy. Similarly, for the grid-peak-power there is no difference between both approaches. On the other hand, we have a marginal decrease of 0.5% in the CO\({}_{2}\) content for the seasonal strategy respect to the annual strategy, whereas the energy-cost experience a more significant reduction of 9.1% if the seasonal strategy is used. We can conclude then that the seasonal strategy presents an overall better performance with respect to the annual strategy. At the view of the results that are presented in Table 5, where it is clearly stated that, for the different seasons of the year, different forecasting methods should be used in order to obtain the optimal performance. Therefore, an annual strategy, that makes use of a single forecast throughout the test period, is expected to under-perform a seasonal strategy. We can then conclude that seasonal effects, such as: prices of electricity, carbon footprint, accuracy of forecasts, consumption, and production patterns, do affect the performance in the services under study. There are seasons that present conditions that allow better performance (summer, spring) than others (winter, autumn). Best seasonal strategies are different than annual strategies and we proved that using seasonal strategies yield better performance than using annual strategies in some services such as the energy-cost, while, for other services, it is indifferent (e.g., GPP). A flow diagram that depicts the logic followed in this section in order to tackle the different research questions that wanted to be explored along with references to the figures or tables where the answers are found, is presented in Figure 8. \begin{table}
\begin{tabular}{l c c} \hline  & \multicolumn{2}{c}{**EMS Strategy**} \\
**Performance Indicator** & **Seasonal** & **Annual** \\ \hline
**EC** (\(\in\)/kWh) & 0.150 (\(-\)9.1\%) & 0.165 \\
**CO\({}_{2}\)** (gCO\({}_{2}\)/kWh) & 62.8 (\(-\)0.5\%) & 63.1 \\
**GPP** (kW) & 15 (0\%) & 15 \\
**GC** (\%) & 100 (+0.1\%) & 99.9 \\ \hline \end{tabular}
\end{table} 
Table 7: Performance obtained while using a seasonal and an annual energy management strategy.
## 4 Experiments
### Results on MNIST Dataset
#### 4.2.2 Robustness to Other Hyperparameters  
We conducted more experiments to compare the robustness of the eight iterative methods to batch sizes and initial model parameters. Figure 2(a) shows the impacts when batch sizes were set as 16, 32, 64, and 128, and Figure 2(b) shows the impacts when we repeated the experiment four individual times in which the network was initialized using the same random seed (\(1,10,30,50\), respectively) for different optimizers. For fairness, we set learning rate to 0.01. Under the level, all eight methods have a roughly equivalent accuracy (refer to Figure 1). It is observed from Figure 2(a) that the robustness to batch size, from strong to weak, is in the order \(Momentum\approx Adagrad\approx Adamax\approx Adacomp\succ Adadelta\succ SGD \succ RMSprop\succ Adam\) (note that Adam was divergent when the batch size was set to 16). It is observed from Figure 2(b) that the robustness to initialized seeds is in the order \(Adagrad\approx Momentum\approx Adacomp\approx Adamax\succ Adadelta\succ SGD \succ Adam\succ RMSprop\) (note that RMSprop was divergent when the seed was set to \(30,50\)). In summary, for a certain setting of learning rate, Momentum, Adagrad, Adamax, and Adacomp have the most robustness to batch sizes and initialized seeds, while RMSprop and Adam have the weakest robustness.  
Figure 2: Comparison of test accuracy between Adadelta and Adacomp (**top** figure) and effective intervals (accuracy \(\geq\) 95%) of eight iterative methods (**bottom** figure). Dataset is MNIST.
## 5 Discussion
The corresponding mismatch rate of CHD+AW and the algorithm in this paper is about 15%, but the difference between the mismatch rate of CHD+AW when the brightness of the left and right images is different and the brightness is the same, small, and when the brightness of the left and right images of this algorithm is different, its matching effect is basically the same as that of CHD+AW, because when there is a brightness difference between the left and right images, due to the corresponding truncation absolute error value set for CAD, CAD is basically replaced by its truncation absolute error value in parallax calculation; that is, at this time, it is similar to only using CHD as its similarity measure, so when the image brightness is different, the corresponding error-matching rate of this algorithm is only slightly higher than that of CHD+AW. At the same time, when there is no difference in brightness, its matching effect is better than CAD+AW and CHD+AW. In general, this algorithm inherits the robustness of census transform to illumination distortion. At the same time, when there is no difference in brightness, its matching effect is better than that when using a single similarity measure. It can be seen from Figure 10 that the first list is the left image of three-dimensional images of heart soft tissue after correction, the second is the right image of the three-dimensional image of the heart soft tissue after correction, and the third column is the selected interest area corresponds to the result parallax image, which is in the left side obtained from the algorithm, and the fourth is the image that expands the difference of the result parallax image. It can be seen from the diagram after the expansion of the differences and the resulting parallax image. The three-dimensional matching algorithm of this article can obtain a dense visual image corresponding to the heart soft tissue interest block, and it can basically reflect the general outline of the corresponding cardiac tissue, and there are few obvious errors. That is, the three-dimensional matching algorithm in this article can also obtain a better parallax figure in the actual scenario.
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
## 2 Free Space Optic (FSO) Receiver Design  
### Receiver and Transmitter Parameters  
The described FSO receiver was designed and assembled with the aim of providing the highest signal sensitivity and largest communication radius. It was planned mainly for interior use with multipath signal propagation. However, such optical channel properties might lead to a lower transfer frequency. Multipath light propagation loads the optical receiver input with unwanted jitter. Its size depends on the maximum path length difference between straight and mirrored rays. In contrast, the presence of many reflections enhances the SNR, as shown in Equation (7), and justifies the use of parallel amplifier topology. Another assumption is that the receiver has full mobility and responds to the whole hemisphere. As interior areas have humans present, the selected optical source spectrum was short wave infrared (SWIR), and the background light level was considered high.  
To generate many useful light signal reflections, the optical source, particularly light-emitting diode (LED), must be sufficiently powerful. Powerful LEDs are not a problem; the problem is rather high-speed and high-powered LEDs. The solution is either an array of many LEDs with lower emitted power and excellent transfer frequency or a single luminophore free power LED with high emitted power and high transfer frequency. To record the measurements, an optical transmitter with one power LED VSMY99445 (Vishay, Malvern, PA, USA, 2015) by Vishay was used. Its peak wavelength is between 920 and 960 nm. Its radiant intensity reaches the level of 500 mW\(\cdot\)sr\({}^{-1}\) and a total radiant power of 935 mW. Both these values are achievable with a forward current of 1 A. Its beam allows switching between 0 A and 1 A currents with 15 ns and 18 ns rise and fall times, respectively. The LED driver was built as a high current, high frequency source. The tandem driver and the power LED can deliver a pulse train with a data speed as high as 10 Mbit\(\cdot\)s\({}^{-1}\).
## 4 Simulation Results
### System Parameters  
The transmitted frequency is selected to get a higher frame rate as dictated by Equation (4). When considering commercial availability of RF components, 94 GHz is an acceptable frequency for small UAVs. A one-millisecond sweep duration corresponds to a PRF of 1 kHz. According to the Nyquist theorem, the PRF of conventional FMCW SAR systems should be greater than the Doppler bandwidth. However, the PRF of the proposed system can be chosen to be 4000/4 Hz, using four virtual array channels consisting of two Tx and two Rx channels (\(M=2\), \(N=2\)). To ensure a slant range resolution of 0.15 m, a chirp bandwidth of 1 GHz is necessary. The beat frequency offset is selected to be 2 MHz for an 80-m range swath width or scene size, based on calculations using Equation (11). The sampling frequency for analog-to-digital conversion (ADC) of the proposed system and a reference single channel ViSAR system are, 4 MHz, and 2 MHz, respectively.  
To achieve the required resolution in the azimuth, the integration angle of the synthetic aperture should be greater than 1.14\({}^{\circ}\). The integration angle in this simulation is 1.17\({}^{\circ}\). To observe the required scene size, the transmitting and receiving antenna beamwidth can be chosen to 4\({}^{\circ}\). The distance between Tx antennas is 0.04 m. The distance between Rx antennas is 0.02 m. System parameters are selected such that uniform spatial sampling is applied, as defined by Equation (8). The distance between the phase centers in the virtual array is 0.02 m. In the single channel ViSAR system, the receiving antenna is identical to the transmit antenna. In the MIMO ViSAR system, the transmitting and receiving antennas are placed in the azimuth. Table 1 lists technical and geometric parameters of both systems investigated. The operation velocities of 20 m/s and 40 m/s are selected considering those of commercial or military UAVs such as NEO S-300 by Swiss UAV [37] and RQ-7 Shadow 200 by AAI [38]. As mentioned in Section 2.2, two times higher operation velocity of the proposed system is applied to increase the frame rate due to its proportionality to the platform velocity.  
Figure 10 shows an illustration of the block diagram for the proposed MIMO video SAR system, with two Tx and two Rx channels. As described in Section 3, there are four channels in the equivalent virtual array in this case.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **MIMO ViSAR** & **Single Channel ViSAR** \\ \hline Transmitted frequency & 94 GHz & 94 GHz \\ Bandwidth & 1 GHz & 1 GHz \\ Slant range to scene center & 1000 m & 1000 m \\ Operation velocity & 40 m/s & 20 m/s \\ Scene size & 80 m & 40 m \\ Resolution & 0.15 \(\times\) 0.08 m & 0.15 \(\times\) 0.08 m \\ Radar losses & 10 dB & 10 dB \\ Number of transmit apertures & 2 & 1 \\ Number of receive apertures & 2 & 1 \\ Transmit antenna gain & 15 dB & 15 dB \\ Receive antenna gain & 15 dB & 15 dB \\ Sweep duration & 1 msec & 1 msec \\ Sampling frequency & 4 MHz & 2 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters. ViSAR: video synthetic aperture radar.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 4 Diamond-Based Electrodes
### Detection of NADH, a Dinucleotide of Adenine and Nicotinamide  
Nicotinamide adenine dinucleotide (NADH) is composed of two nucleotides linked through their phosphate groups. This essential biomolecule for cell metabolism is involved in a variety of cellular redox reactions, including the citric acid cycle and the oxidative phosphorylation chain, two essential processes for energy production in living cells. Detection of NADH was achieved with a BDD thin film electrode, with well-defined voltammograms even at low NADH concentrations (0.2 mM), with a good amperometric performance and a detection limit of 10 nM (S/N ratio of 7). The electrode response was found to be stable for a storage period of up to 3 months [43].
## 2 Materials and Methods
### Surface Roughness  
In order to characterize the surface roughness of the different materials under investigation, several roughness parameters were evaluated using a Mitutoyo Surftest SJ 500P profilometer (Mitutoyo Co., Kawasaki, Japan). This device has an extendable arm and a stylus tip at its end that consists of a conical diamond with a tip radius of 5 mm and 60 degrees. The specimens are placed on the profilometer platform so that the diamond tip travels across the surface of the sample during its movement. This instrument is connected to a computer and controlled via the Formtracepak software (Mitutoyo, Aurora, IL, USA). As a means of removing shape deviation and waviness, the roughness profile (R-profile) was isolated using a cut-off wavelength \(\lambda\)c filter. Surface roughness measurements--R\({}_{\text{a}}\) (arithmetical mean roughness), R\({}_{\text{z}}\) (mean roughness depth), R\({}_{\text{q}}\) (root mean square) and R\({}_{\text{sk}}\) (roughness skewness)--were in accordance with DIN/ISO standards, with 2D roughness parameters being calculated according to EN ISO 4287 [14].