esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Boolean Masking for Addition Circuits
### Kogge-Stone Adder (KSA)  
KSAs [14] are parallel-prefix adders that are similar to greedy algorithms in the sense that the maximal number of PG terms are combined in each stage. An \(n\)-bit KSA requires \(\log n\) stages to compute all \(\text{g}(\{i,0\})\) terms and one additional stage to compute the final sum bits. In order to better illustrate the architecture, an 8-bit KSA is depicted in Figure 1. When excluding the preprocessing step and the final sum calculation, it requires 3 stages and 14 PG blocks.  
In the first stage after the preprocessing step, every PG term is combined with its neighbor, i.e., \(PG(\{i,i\}\,\ \{i-1,i-1\})\ \forall 0<i<n-1\) is computed. Therefore, \(n-1\) PG function blocks need to be instantiated in that stage. In the second stage, \(PG(\{i,i-1\}\,\ \{i-2,i-4\})\ \forall 1<i<n-1\) is computed, requiring \(n-2\) PG function blocks. Note that \(p(\{1,0\})\) is not needed in this computation and the hardware for its generation can therefore be omitted in the first stage. In general, stage \(k\) computes \(n-2^{k}\) PG terms as (when numbering the stages, the stage number \(k\) of the first stage after preprocessing is zero):  
\[PG\Big{(}\Big{\{}i,i-2^{k}+1\Big{\}}\,\ \Big{\{}i-2^{k},i-2^{k+1}+1\Big{\}} \Big{)}\ \forall k<i<n-1.\]As we do not consider the output carry generation, the calculation of \(pg(\{n-1,i\})\)\(\forall i\
eq n-1\) can be skipped, saving \(\log n\) PG blocks. This reasoning does not only apply to Kogge-Stone adders but also to the other structures discussed below. Over all stages excluding the preprocessing, \((n-1)\cdot\log n-n+1\) PG blocks are required. Of these blocks, \(n-2\) blocks do not need to compute the propagate term. In the preprocessing step, which is the same for all parallel-prefix adders, \(n\) generate functions and \(n-1\) propagate functions are needed. A summary of the required numbers of all the basic blocks for all adder types is provided in Table 2. By combining Tables 1 and 2, we can summarize that an \(n\)-bit Kogge-Stone adder requires \((n-1)\cdot\log n-3\cdot n+1\) XOR gates and \(2\cdot(n-1)\cdot\log n-2\cdot n+3\) AND gates. The number of elementary operations per adder is depicted in Table 3.  
Masking KSAs  
When compared to the other designs discussed in this work, an implementation of a KSA requires the highest number of PG blocks, resulting in the largest area requirement when implemented using masking. As \(d\)th-order secure HPC2 AND-Gadgets require \(d\cdot(d+1)/2\) bits of fresh randomness, the total randomness requirement of a HPC2-masked \(n\)-bit KSA is the highest of the discussed variants at \(d\cdot(d+1)\cdot((n-1)\cdot(\log n-1)+1)\) bits. However, the required randomness can be drastically reduced if only univariate security is considered, as shown in [2]. Here, a first-order secure TI of a KSA using three shares that only needs \(n\) bits of fresh randomness is proposed. As noted by the authors, their second-order variant does not provide protection against multivariate attacks.
# Preparation of Carbon Nitride Dots with Sizes Larger than 20 nm
## 3 Results and Discussion  
As shown in Figure 1, the synthesis of CNDs was carried out by thermal treatment of carbon nitride polymer formed via the reaction of EDA and CCl\({}_{4}\). After heat treatment, the polymer underwent cross-linking and ring-formation [16]. The yellow solid at the top of the inner wall of the quartz tube was collected as our CNDs. These have solubility in water and polar organic solvents. The residual solid with a black color at the bottom of the inner wall of the quartz tube was discarded. This does not dissolve in water, however can disperse in polar organic solvents such as \(N\), \(N\)-dimethylformamide and ethanol.  
SEM observation reveals that the yellow product is composed of micro-grade particles (Figure 1). On higher magnification, these particles are seen to be formed via high packing of smaller particles with sizes of several tens of nanometers. However, the residual black solid has structure of a similar scale (Figure 2a), which shows a glabrous surface after magnification (Figure 2b). This structure may result from excessive cross-linking and ring-formation of carbon nitride polymer.  
Figure 1: Schematic illustration of the procedure for preparing carbon nitride dots (CNDs).  
Figure 2: Scanning electron microscope (SEM) images with different magnifications of the residual black solid at the bottom of the inner wall of the quartz tube. Low magnification (**a**), and high magnification (**b**).  
TEM image of CNDs and their size distribution are shown in Figure 3a,b, respectively. The size of CNDs is mostly distributed in the range 23-70 nm and the average calculated diameter is ca. 44 nm, which is consistent with SEM observation. The height of CNDs was measured by AFM, as shown in Figure 3c-f. The synthesized CNDs have heights between 0.6 and 2.5 nm, indicating that they are composed of 1-4 layers of CN.  
Figure 4 shows the optical properties of CNDs aqueous dispersion. It is seen that there are two typical absorption peaks at 275 and 325 nm, attributed to \(\pi\)-\(\pi^{\bullet}\) transition of s-triazine and n-\(\pi^{\bullet}\) transition of defect state on the surface of the CNDs, respectively (Figure 4a) [17]. CNDs emit blue fluorescence at ca. 420 nm under the excitation of 360 nm irradiation (Figure 4b). Similar to most of carbon dots, the PL of the obtained CNDs is also excitation wavelength-dependent. The emission peak of CNDs is red-shifted from 375 to 450 nm with the excitation wavelength changing from 300 to 400 nm.  
Figure 4: Optical properties of CNDs aqueous dispersion. UV-vis absorption spectrum (**a**), and PL emission spectra (**b**).  
Figure 3: TEM image (**a**), and the corresponding size distribution (**b**), of CNDs; AFM images of CNDs with different magnifications (**c**,**d**), and their height distribution (**e**) and height profile along the line CD (**f**).
## 5 Validation of the Simulation Model
### Validation of the Emissivity Sensitivity of the Model of Radiation Thermometry  
Simulation model results have been compared to experimental measurements at 250 \({}^{\circ}\)C according to case 2--measurement of the flat-plate calibrator FLUKE 4181 using Heitronics TRT II radiation thermometer (Figure 11).  
The uncertainty due to emissivity was evaluated by comparing the reference measurements, obtained with nominal instrumental emissivity setting of 0.95, with subsequent deviated measurements, obtained with instrumental emissivity, which was deviated according to uniform distribution with boundary range of \(\pm\)0.02 (emissivity uniformly distributed in range [0.93; 0.97]), to correspond to simulation settings. The effect of emissivity uncertainty was measured in a total of 200 cycles of reference and deviation measurements, presented in Figure 12. Upon every change of instrumental emissivity, a pause of 1 s was initiated before obtaining temperature measurements, in accordance with the response time of the radiation thermometer.  
Figure 11: Experimental setup of emissivity uncertainty analysis. The Heitronics TRT II (on the left), connected to the computer via serial interface, and FLUKE 4181 (on the right).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measured** & **Input** & **Simulated** & **Experimental** & **Experimental Sensitivity** \\
**Temperature** & **Deviation** & **Deviation** & **Deviation** & **tivity Mismatch** \\ \hline
1000 \({}^{\circ}\)C & 14 hPa & 5.84 \({}^{\circ}\)C & 7.57 \({}^{\circ}\)C & \(-\)22.9\% \\
1200 \({}^{\circ}\)C & 14 hPa & 7.16 \({}^{\circ}\)C & 9.72 \({}^{\circ}\)C & \(-\)26.3\% \\
1300 \({}^{\circ}\)C & 14 hPa & 7.88 \({}^{\circ}\)C & 10.79 \({}^{\circ}\)C & \(-\)26.9\% \\
1500 \({}^{\circ}\)C & 14 hPa & 9.43 \({}^{\circ}\)C & 12.93 \({}^{\circ}\)C & \(-\)27.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Uncertainty and sensitivity coefficient results for the influence of atmospheric humidity (in water vapor partial pressure) on temperature measurement using KT19 in 2–2.7 \(\upmu\)m spectral range.  
The experimentally obtained standard uncertainty contribution due to the uncertainty of emissivity was assessed at 1.78 \({}^{\circ}\)C for the low temperature range and 0.87 \({}^{\circ}\)C for the high temperature range. The simulation model calculated these standard uncertainty contributions at 1.91 \({}^{\circ}\)C and 0.89 \({}^{\circ}\)C, which is only slightly higher. Simulation mismatch in comparison with experimental results is calculated in Table 6. The difference can originate from spectral mismatch between simulation data and real experimental parameters (such as spectral sensitivity data, obtained by digitalization of the manufacturer-supplied graph, which appears to have been smoothened) as well as rounding of the instrumental emissivity setting in Heitronics TRT II, with a finite resolution of 0.001.  
Table 6 presents a comparison of simulated and measured results of uncertainty contributions due to the influence of emissivity uncertainty.
## 2 Materials and Methods  
### Assessment of Electromagnetic Hazards  
Following international regulations, the relevant parameters characterizing electromagnetic hazards need to be compliant with the requirements regarding basic restrictions (BRs, provided by the International Commission on Non-Ionizing Radiation Protection--ICNIRP) or dosimetric reference limits (DRLs, provided by the Institute of Electrical and Electronics Engineers--IEEE) [17; 28]. The limits set for BRs and DRLs were provided in order to protect against the harmful direct thermal effects of EMR absorption in the body of an exposed human, assessed using numerical simulations. The assessment relevant for the immediate impact from EMR emitted by the analyzed WIT device concerns the SAR coefficient [17; 28].  
The SAR BRs and DRL limits are defined as spatially averaged values--whole-body average SAR and local 10g-SAR averaged over 30 and 6 min, respectively. The SAR limits provided for occupational exposure evaluation (in restricted environments) are as follows:  
* Whole-body average = 0.4 W/kg;
* Local 10g-SAR in head and torso = 10 W/kg;
* Local 10g-SAR in limbs = 20 W/kg.  
The SAR limits provided for general public exposure (in unrestricted environments) are one-fifth of the limits for occupational exposure.  
Due to the fact that the exposure scenarios considered small WIT devices located near the body, only local SAR values were considered, based on simulations applying only models of body parts adjacent to the ARCM.
# Predictive Model for the Optimized Mixed-Air Temperature of a Single-Duct VAV System
## 6 Conclusions  
In this study, a predictive model-based economizer control for optimized mixed-air temperature was established to reduce the cooling energy, and the detailed results are as follows:  
1. When controlling the economizer of the HVAC system, a control using the optimal mixed-air temperature set point considering both indoor and outdoor conditions was proposed.
2. A co-simulation was established using EnergyPlus, Matlab, and BCVTB to configure a simulation-based real-time economizer optimal control system. The building and systems were modeled using EnergyPlus; the control logic was simulated using Matlab. Different programs were combined through BCVTB.
3. A building load and cooling energy prediction model was developed using an ANN. The load prediction model consisted of one input layer, four hidden layers, and one output layer. In addition, the input layer consisted of two nodes, the hidden layer consisted of five nodes, and the output layer consisted of one node. Moreover, the energy prediction model consisted of one input layer, four nodes; one hidden layer, four nodes; and one output layer, one node. The developed prediction models were verified using CV(RMSE) and R2. The CV(RMSE) of the building load and cooling energy prediction model was 21.8% and 20.6% and the R\({}^{2}\) was 0.96 and 0.86, indicating a high prediction rate.
4. A predictive model-based economizer control was evaluated using a simulation. The results confirmed that the set point value of the mixed-air temperature continuously changed. Moreover, the total energy consumption of the building was reduced compared to the existing economizer control by 28.9%.  
Figure 8: Total energy consumption.  
An economizer control using predictive model-based mixed-air temperature optimization was proposed and its evaluation was conducted through a simulation. In the future, it will be necessary to evaluate the model's performance through field application of the technology, and to verify the technology through real-time interlocking of the automatic control system and the data analysis program.  
All authors contributed to this work. Conceptualization, J.-H.L. and Y.-H.C.; Supervision, Y.-H.C.; Writing--original draft, J.-H.L.; Writing--review and editing, Y.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2019 Yeungnam University Research Grant (219A380177).  
The authors declare no conflict of interest.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant
### Perceptual Layer
#### 4.1.4 Proposed Strategy at the End  
For in-depth solar plant monitoring, it is imperative to sense performance parameters at each level (such as module, arrays, inverters, and batteries) for predictive fault diagnosis. However, cost (monetary and power consumption) is a major design consideration in the selection of end devices. Keeping in view these two parameters, a low-cost and power-efficient end device is being proposed, as presented in Figure 4. Arduino Nano BLE [27] has a 32-bit ARM Cortex M4 CPU operating at 64 MHz. This is enough computation power for sensing current, voltage, and panel temperature, as listed in Figure 4. Furthermore, it has an ultra-low power consumption mode with integrated BLE. This integrated BLE module with Arduino Nano BLE also helps in reducing the design complexity of the proposed end device. With 19 mA current consumption, this is the optimum choice with respect to cost and current consumption. To keep the overall cost low, it is proposed to integrate only current, voltage, and panel temperature sensing sensors with Arduino Nano BLE. For the current sensor, ACS712 is proposed, as this is the most popular choice in the existing literature [2; 3; 4; 9; 14; 21; 23; 29; 31; 33; 36; 38; 39; 45; 47; 52]. Arduino voltage sensor [51] is proposed for voltage sensing. LM35 [2; 7; 9; 21; 23; 29; 30; 32; 36; 52] is proposed for integration for the solar panel's temperature sensing. As can be observed in Table 6, the proposed power-efficient end devices can be fabricated at a cost of approximately USD 25.  
Sensed parameters from end devices at different levels (panel, array, inverters, and battery) can be sent to edge nodes at the edge layer. To keep the overall cost of end devices low, meteorological sensors (such as dust, irradiance, temperature, and humidity) should be integrated with edge nodes only. This is also instrumental in reducing data transmission requirements from the perception layer to the edge layer, thus resulting in reduced current consumption requirement at end nodes. As presented in Table 5, the range of BLE is low as compared to other wireless technologies. However, in our proposed architecture, the edge devices will be within the 100 m range of end devices.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Technology** & **Operating Frequency** & **Range** & **Data Rate** & **Power Usage** \\ \hline BLE & 2.4 GHZ & 1–100 m & 1–24 Mbps & 1–100 mW (TX power of 4 dBm) \\ \hline Zigbee & 2.4 GHZ, 900 MHz and 868 MHz & 75–291 m & 20–250 kbps & 1–100 mW (TX power of 18 dBm) \\ \hline Wi-Fi & 2.4, 3.6, 5, 6.5 and 60 GHZ & 1–150 m & 2 Mbps–6.7 Gbps & 100–200 mW \\ \hline RF & 3 Hz–300 GHz & 1 cm–105 km & Varies & Varies (typically low power) \\ \hline
6 LoWPAN & 2.4–2.48 GHZ & 10–100 m & 250 kbps & low power (similar to BLE) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different wireless communication technologies.
## 3 Method
### YOLOv5 with Transformer Detection  
Figure 2 shows the overall structure of transYOLOv5, which contains three modules: Backbone, Neck, and Head, and describe how to introduce Swin Transformer to the YOLOv5 network.  
The image is preprocessed through the input terminal, including data enhancement, adaptive anchor calculation, adaptive zooming, and other operations. The preprocessing effectively improves the model's feature extraction ability by employing the CSP Market53 residual backbone structure. The Neck section uses a combined FPN and PAN structure to improve performance while maintaining efficiency. The FPN part uses a Gaussian weighted feature pyramid to enable multiscale target detection. The Head section outputs the predictions via bounding box loss and NMS suppression. However, YOLOv5 convolutional networks lack long-distance modeling and global information. Therefore, we introduce Transformer to address this limitation.  
Backbone: Two CSP structures are designed in the backbone. CSP1_X is applied to the backbone and CSP2_X to the Neck. Then, 3 \(\times\) 3 convolutions before each CSP module serve as a down-sampling function. With five CSP modules, the 640 \(\times\) 640 input image is reduced to a 20 \(\times\) 20 feature map. We mainly introduce the Swin Transformer module in the Backbone network to improve the feature extraction performance in this paper.  
Neck: The CSP2 structure, SPPF module, and FPN+PAN method based on CSPnet are fused, which strengthens the network feature fusion ability.  
Head: The CIoU loss is adopted for bounding boxes and NMS suppression to eliminate redundant detections. NMS finds local maxima and suppresses non-maximal elements. It eliminates overlapping detections by selecting the optimal box and removing redundant ones based on confidence scores and IOU overlaps.  
The procedure is as follows: (1) All the prediction boxes are sorted by confidence in descending order. (2) With the highest confidence, we select the prediction box, confirm the prediction is correct, and calculate its IOU with other prediction boxes. (3) If the CIoU  
Figure 2: The Framework of our transYL network.  
removal overlap calculated in Step 2 is high, the IOU > threshold value will be directly deleted. (4) Return to step 1 for the remaining prediction boxes until there are no more left.  
TransYL: Our TransYL introduces Swin Transformer modules into Backbone layers 7 and 9, reducing computation via convolutions. The backbone extracts features, fusing local and global information through the SPPF. The Neck further fuses features before final prediction in the Head.
# Passive Sampling and Analysis of Naphthalene
## Theory  
### Passive TWA Naphthalene Gas Sampling with Retracted SPME Fiber  
Gas-phase naphthalene TWA sampling with retracted SPME fibers is controlled by diffusion from the SPME needle opening to the retracted SPME fiber. TWA concentration (\(C_{g}\)) of naphthalene in air flow was estimated by Fick's Law. SPME gas sampling rate with retracted SPME fiber was controlled by gas-phase molecular diffusion coefficient for naphthalene (\(D_{g}\)) and retraction depth (\(Z\)).  
\[C_{g}=\frac{m\times Z}{D_{g}\times t\times A} \tag{1}\]  
where:  
\(m=\) naphthalene mass extracted by fiber coating (mg);  
\(Z=\) SPME fiber retraction depth (diffusion path length) (cm);  
\(D_{g}=\) gas-phase naphthalene diffusion coefficient (cm\({}^{2}\cdot\)s\({}^{-1}\));  
\(t=\) extraction (sampling) time (s);  
\(A=\) cross-sectional area of SPME needle opening (cm\({}^{2}\)).
## 3 Results and Discussions
### Degradation of Amoxicillin by Solar Photolysis  
The control experiments (in shade), both for the photolysis and photocatalysis processes on 0.1 and 1 m\({}^{2}\) surface FPR, did not show any degradation under any pH (3.5, 4.15, 7 and 9), with (at 4 mM) and without the addition of H\({}_{2}\)O\({}_{2}\), analyzed by UV-Vis spectrophotometry and COD.  
In the treatments by solar photolysis on 0.1 m\({}^{2}\) and 1 m\({}^{2}\) surface FPRs, the results of the experiments carried out at different pH (3.5, 7, and 9), showed no decrease in drug concentration as determined by UV-vis spectrophotometry. No change was noticed by the COD analysis, except in the experiment with pH 4.15 and without the addition of H\({}_{2}\)O\({}_{2}\) for both reactors, reflecting a COD reduction of 3-8% at minute 5, to later show no degradation trend.  
[PERSON] et al. in 2015 [48], confirmed that although direct photolysis can slightly help in the degradation of AMX, the effect of pH in the solution plays a very important role, due to increased or decreased formation of hydroxyl radicals depending on solution conditions. In a study carried out by [PERSON] et al. in 2011 [49] the degradation behavior of amoxicillin in solutions of organic matter isolated in a process of solar photolysis, reached 6 to 21% removal for simulated natural waters; however, when interacting with a solution of organic matter in an excited state with the addition of sorbic acid and under deoxygenated conditions, degradation of 48 to 74% was achieved. It is presumed that the degradation obtained by photolysis is due to the energy transferred by the excited state of the organic matter solution, due to the proximity of the formation site of the \(\bullet\)OH radicals, as well as the decrease in the pH level of the solution, coinciding with what was previously described by [PERSON] et al. in 2015 [48].
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 3 Method
### Foreground Segmentation  
To determine foreground regions, a Mask R-CNN [49] is utilized to partition the images into image segments (i.e., image regions or objects), which can be used as clues to improve foreground depth prediction. Mask R-CNNs are an extension of Faster R-CNNs [67], which use region proposal networks (RPNs) to propose candidate object bounding boxes and then classify and resize the bounding boxes. To segment the shapes of objects, Mask R-CNNs add mask branches to reshape the bounding boxes. In our model, RGB images are segmented from multi-scale images to extract image segments of different scales. After partitioning the images into image segments, the areas of the segments are then used to classify the segments into the foreground and background categories using the K-means cluster method. Finally, the foreground segments \(f_{s_{i}}\) are used for foreground depth prediction (where \(i=1\) to 4). The details of our foreground and background segment classification strategy are shown in Section 4.  
Figure 3: The foreground depth prediction model architecture.