esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wideband Power Amplifier in 65 nm CMOS Covering 25.8 GHz-36.9 GHz by Staggering Tuned MCRs
## 2 Analysis and Design  
Figure 1a is the schematic of the designed broadband power amplifier. To increase the output power, two-way current-combining is used with stronger symmetry than voltage-combining [22]. Figure 1b is the cell stage of the power amplifier. In this work, we aim to design the output matching network for realizing a wideband \(Z_{opt}\) for the power stage. The interstage matching network between power stage and driver stage 2 is used to achieve wideband impedance matching. We also design the interstage matching between driver stage 2 and driver stage 1 to realize a high-pass form filter for the staggering. The input-matching network is used to tune the gain shape of its stage by adopting a \(\omega/Q\)-compensated asymmetrical MCR to enhance the gain at the band edge (\(f_{L}\) and \(f_{H}\)), so that a flattened wideband performance of the whole circuit is achieved.
## 3 Experimental and Analysis
### Model Robustness Analysis  
To evaluate the robustness of the proposed model, test images containing different scenes were selected for validation analysis, with the detection results shown in Figure 15. From left to right, the images include the original image, the detection results for the YOLOv5s model, and the detection results for the CER-YOLOv5s model. Observing Figure 15a,b, it can be seen that the YOLOv5s model failed to accurately detect the excavator target in environments with either ample daylight or insufficient night-time illumination, leading to false negatives and false positives. In contrast, the improved model demonstrated stronger robustness to changes in lighting conditions, accurately predicting target locations and category information, thus providing reliable technical support for night-time monitoring. Observing Figure 15c, it is evident that CER-YOLOv5s performed better than the YOLOv5s model in detecting small targets at a distance, effectively increasing the confidence in small target detection. From Figure 15d, it can be seen that CER-YOLOv5s was able to effectively recognize different targets in dense multi-scale scenes with higher detection confidence compared with the previous model, providing a beneficial solution for detection tasks in complex scenes. Figure 15e shows that, in complex occlusion scenarios, CER-YOLOv5s demonstrated better target association and background reasoning abilities, accurately detecting partially occluded excavator targets and reducing the false positive rate. Overall, CER-YOLOv5s showed better generalization and stronger robustness across different scenarios.  
Figure 15: Detection performance in different scenarios: (**a**) well-lit scene; (**b**) low-light scene; (**c**) scene with a small target; (**d**) scene with multi-scale targets; (**e**) occluded scene.
## 2 CATS Aerosol-Typing Algorithm Description
The most recent CALIOP Version 4 (V4) aerosol-typing algorithm distinguishes seven tropospheric aerosol subtypes: marine, dusty marine (new in V4), dust, polluted dust, clean continental, polluted continental/smoke, and elevated smoke. Due to the challenges in differentiating polluted continental and smoke aerosol types in the CALIOP data, the V4 algorithm combines these types together, unless the aerosol layer is elevated and is assigned as an elevated smoke layer. Additional details on the CALIOP aerosol-typing algorithm can be found in [PERSON] et al. (2018) [31] and heritage from the Version-3 algorithm can be found in [PERSON] et al. (2009) [24]. Figure 2: An example of how CATS observables and MERRA-2 aerosols were used to inform aerosol type for a subset of the CATS track (red box) on 6 August 2016. CATS 1064 nm total attenuated backscatter **(a)** and 1064 nm depolarization ratio **(b)** are used to identify features and first classify as cloud or aerosol **(c)** along with ancillary information, as described in [PERSON] et al., 2021 [23]. CATS observables (e.g., depolarization ratio to identify desert dust) are further used with additional ancillary information and MERRA-2 aerosols to identify aerosol type **(c)**. In Figure 5, we show the day and night 2015-2017 July, August, and September most frequently classified aerosol type between 0 and 4 km for the CATS V3-00, MERRA-2 simulated aerosols mapped to the CATS aerosol types following the methodology of [PERSON] et al. (2015) [28], and CALIOP V4.20. Comparing the CATS and CALIOP aerosol type maps, we find good agreement for dust over the Sahara and Arabian Peninsula and smoke over South American and central Africa at all altitudes. Over the western U.S. and eastern Europe, however, the CATS aerosol layers are predominately dust, while CALIOP classifies those layers as polluted dust. This difference can be attributed to differences in the thresholds and wavelengths used in each algorithm to discriminate dust from dust mixed with other aerosol types. Additionally, owing to the incorporation of the MERRA-2 aerosol information content, the V3-00 CATS algorithm depicts the downwind transport of pollution originating from Asia over the north Pacific Ocean [55; 56] and smoke associated with biomass burning in the US Pacific Northwest between the surface and 4 km [49; 50]. Figure 4: Seasonality of the CATS V3-00 most frequently classified aerosol type below 4 km for 2015–2017. Figure 5: Comparison of the most frequently classified aerosol type below 4 km for CATS, MERRA-2, and CALIOP for JAS 2015–2017. Figure 3: Effect of spectral depolarization ratios on the discernment between polluted marine and clean marine aerosol layers observed by CATS on 8 March 2015.
## 3 Results and Discussion
### Identification and Quantification of the Volatile Compounds in XYM] Green Tea
#### 3.2.1 Alcohols  
The alcoholic compounds varied during the XYMJ green tea manufacturing, and the changes in the presentative alcoholic compounds were shown in Figure 3. Spreading significantly increased the concentrations of trans-furan linalool oxide, trans-nerolidol, phytol, isophytol, dehydrodiisopropyltol, and cis-cubenol. The highest concentrations of trans-furan linalool oxide and trans-nerolidol were 6638.6 \(\upmu\)g/L and 4422.4 \(\upmu\)g/L at the spreading stage, respectively. Spreading is an indispensable process in the aroma formation of premium green tea. Volatile metabolomics and transcriptomics revealed that the concentration of trans-furan linalool oxide significantly increased after spreading [28]. Except for phytol, isophytol and dehydrodiisopropyltol, the fixing significantly decreased the concentrations of other alcoholic compounds. The lowest concentration of linalool was 4265.1 \(\upmu\)g/L at the fixing stage. After fixing, remarkable losses were observed for linalool, trans-furan linalool oxide, and trans-nerolidol during green tea manufacturing [10; 11; 20]. From fixing to drying, linalool and trans-furan linalool oxide, trans-nerolidol, and cis-cubenol, presented the same trends, respectively. The concentrations of phytol, isophytol, and dehydrodiisopropyltol gradually increased from spreading to shaping and then decreased at drying. The aroma profiles of green tea made with fresh tea leaves plucked in summer were studied by the analysis of volatile compounds during the manufacturing, which showed that the concentration of phytol in tea shoots was significantly higher than the summer green tea [29], which is inconsistent to the present study. We speculate that the reason might be due to the different manufacturing techniques and the harvested season of tea shoots.  
Figure 2: Category and concentrations of volatile compounds in XYMJ green tea during manufacturing.
## 3 Material and Methods
### Data Collection and Analysis
The Relaxation Index, also ranging between 0 and 100, demonstrated the participant's mental calmness or relaxation. Several studies have confirmed that unstable brainwaves affect attention, thereby affecting the development of learning capacity. Therefore, the BCI in this study retrieved MindWave data through an EEG biofeedback mechanism and uploaded the user's EEG Attention Index to a cloud server for statistical analysis of subsequent EEG attention training. According to the official data published by NeuroSky, the data accuracy of the MindWave headset is up to 90-96% [48]. Other studies that have employed the MindWave headset have also confirmed that the headset has an accuracy up to 80% that facilitates a cost-effective, noninvasive brain machine interface for mind-controlling robotic arms by retrieving EEG signals [49]. Another study reported that EEG-based e-learning had a 96% accuracy in detecting user's emotions [50].
# Can Biochar Improve the Sustainability of Animal Production?
## 2 Summary of Biochar-Production Techniques
### Production Processes
#### 2.1.3 Torrefaction  
Torrefaction is one of the mildest processes for the synthesis of biochar. The basic procedure includes heating biomass between 200 and 300 \({}^{\circ}\)C without oxygen at atmospheric pressure [40]. As the overall process shares similarities with pyrolysis, torrefaction is sometimes termed mild pyrolysis [41].  
During torrefaction, water is removed from the biomass followed by the depolymerization of the hemicellulose, cellulose, and lignin [42]. As a result, the physical structure of the biomass is altered resulting in the formation of porous biochar with hydrophobic properties [43] Depending on the feedstock, torrefaction can be under dry conditions (inert), wet conditions (with water or steam), or oxidative conditions (oxygen or steam) [41]. Although wet torrefaction shares similarities with HTC, the purpose of wet torrefaction is predominantly to upgrade the biomass for subsequent processing [44; 45].  
[PERSON] et al. [46] tested the torrefaction of elephant manure to produce bio-coal. The manure was torrefied between 200 and 300 \({}^{\circ}\)C for 20-60 min and systematically analyzed for physical, chemical, and thermodynamic properties. Torrefaction at 200 \({}^{\circ}\)C for 20 min provided optimum benefits from an energy balance perspective. In a different study, the torrefaction of poultry litter and sludge was investigated between 250 and 280 \({}^{\circ}\)C for 15-60 min by [PERSON] et al. [47].  
Their analyses suggested that litter and sludge retained up to 73.6-99.5% of the energy of the original biomass and was consistent with the other lignocellulosic biomasses tested under similar conditions while the effects of temperature were more pronounced than processing times. The torrefaction of cattle manure was investigated by [PERSON] [48] at 250 \({}^{\circ}\)C (60 min) to assess its feasibility as biomass pretreatment. They reported that torrefaction enhanced the energy content by 27% and the high heating value by 14%.
## 4 Discussion  
### Effects of Segment Size and Joint Positions  
In PSCB, the segment size and joint positions affect the location of the failure cross-section. In practice, the joints are generally avoided to arrange at the mid-span, and the failure may occur at the joint cross-sections adjacent to the mid-span. The effects of segmental size and joint positions are discussed here.  
Figure 15 shows three PSCB-IUCFRPs with the same span length of 8.4 m but different segment sizes. The cross-section details are shown in Figure 7 with the parameter values of \(A_{\text{p}}\) = 253.4 mm\({}^{2}\), \(\sigma_{\text{pe}}\) = 980 MPa, and \(f_{\text{c}}\) = 40 MPa. In Figure 15a,b, the segmental joint is not at the mid-span. Based on the numerical analysis results, the curvature distributions and failure modes at the ultimate states are shown in Figure 15. For beams Seg-7 and Seg-9, the tendon elongations occur at two joint cross-sections adjacent to the mid-span. However, the total elongations are close to the results of Seg-12, in which the failure occurs at one joint  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experimental Results**} & \multicolumn{3}{c}{**ACI 318-14 [40]**} & \multicolumn{3}{c}{**ACI 440.4R [48]**} & \multicolumn{3}{c}{**AASHTO LRFD [50]**} & \multicolumn{3}{c}{**Proposed Equation**} \\ \cline{2-10} \multirow{2}{*}{**Beams**} & \(\Delta\sigma_{\text{p}}\), & \(M_{\text{exp}}\) & \(\Delta\sigma_{\text{p}}\) & \(M_{\text{exp}}/M_{\  
cross-section. When the segment size is small relative to the span length, the failure mode of Seg-7 and Seg-9 can be equivalent to the simple style shown in Figures 13 and 15c. The proposed equations can also provide a simplified prediction for the tendon stress increment and flexural capacity. Figure 16 shows the load vs. tendon stress increment curves of three numerical beams and the predictions by the proposed equations. The results show that the tendon stress increments of three beams are close, and the prediction is on the safe side.
# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## 3 Machine Learning Algorithms Implementation on Low-Cost FPGAs
### Artificial Neural Networks (ANN)  
#### 3.1.1 Overall Structure of ANN  
The implementation of ANN, for example, with eight inputs and two hidden layers (eight neurons within each layer) is shown in Figure 2a. This ANN model includes a Memory Unit (Mem), a Finite State Machine (FSM), eight Multiplying Accumulator (MAC) computing units, Multiplexers (MUX), an Activation Function Unit (AF), and a Buffer Unit (BUFFER). The Memory Unit (Mem) stores the weights and biases after training. The FSM manages the computation order and the data stream. The MAC units are designed with multipliers, adders, and buffers for multiplying and adding operations within each neuron. Here, we implement eight MAC blocks to process in parallel the multiplication of the eight neurons. Initially, features are serially entered for registration. Then, the MAC is used to sequentially process the ANN from the first input feature to the eighth feature on the first  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**ANN** & **DT** & **k-NN** & **SVM** \\ \hline \hline Data width (max = 13); & & & \\ Number of inputs & Data width (max = 13); & Data width (max = 13); & Data width (max = 13); \\ (max = 16); & Number of depth & Number of inputs & Number of inputs \\ layers (max = 4); & (max = 6); & (max = 16); & (max = 16); \\ Number of neurons in each hidden layer & Number of leaf nodes & Number of neighbors & Number of targets \\ (max = 8); & (max = 64); & (max = 16); & (max = 16) \\ Number of targets (max = 16); & Number of targets & Number of targets & \\ Activation functions & (max = 16) & (max = 16) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurable parameters.  
Figure 1: Block diagram of the MLoF system architecture.  
hidden layer (Figure 2b). Next, the second hidden layer is sequentially processed from the first hidden neuron to the eighth neuron. Finally, we process the output by the activation function. As demonstrated in Figure 3, the multiplexers (MUX) allocate the data stream. The AF computes the activation functions, which will be discussed in Section 3.1.2. The buffer stores data computed from each neuron.  
The entire procedure for performing a hardware-based ANN architecture is described below. First, eight features are serially inputted to an ANN model. Each feature is entered into eight MAC units simultaneously and multiplications for eight neurons in the first layer are then completed. An inner buffer is used to store the multiplication results. Next, all eight results are added to a user-specified activation function. The output is further stored in the buffer unit as the input of the next hidden layer. Finally, the results are exported from the output layer following the second hidden layer.
## 3 Photodetector Electrical Structures
### Metal-Semiconductor-Metal (MSM) Detectors  
PIN photodiodes produce a voltage drop across the diode terminals in response to an external optical input. Such devices are categorized as photovoltaic devices. On the other hand, MSM photodetectors are photoconductive devices whose conductivity is altered when an optical illumination is imposed. Therefore, MSM photodetectors are only functional under non-zero external bias. MSM photodetectors possess the advantage of low capacitance and relative ease of fabrication. The intrinsically low capacitance resulting from its configuration has always been utilized to fabricate high-speed large area detectors. One issue in early Ge MSM photodetectors was their high dark current density, which gives rise to high stand-by power consumption, thus making Ge MSM photodetectors unfavorable and impractical. Due to the narrow bandgap and strong Fermi-level pinning of the metal/Ge interface at valence band, hole injection over Schottky Barrier Height (SBH) is the major component of the dark current in Ge MSM detectors. Regarding this issue, application of dopant segregation (DS) to Ge MSM photodetectors for dark current suppression was experimentally demonstrated by [PERSON] _et al._[34-36]. Metal-Ge Schottky barrier height modification by an intermediate layer of large bandgap material such as amorphous Ge and SiC is also proposed [37]. While the demonstrated Ge MSM detectors are able to achieve dark current suppression of two to four orders of magnitude, it is still an open question whether these MSM Ge photodetectors are competitive with PIN devices.
## 3 Results and Discussion
### Batter Analysis
#### 3.1.3 Impedance Study  
Electrical impedance spectroscopy is a quantitative tool for evaluating food quality and safety. Impedance obtained as a complex of conductance and capacitance opposes the electric current. The impedance of fat included resistive and capacitive components. Figure 3a represents the impedance values of the cake batter in the frequency range of 1-100 Hz. At a lower frequency, fats act similar to an insulator [30]. Irrespective of the batter type, the impedance of the batter samples was observed to be higher in the lower frequency range. However, these values were lowered in the higher frequency region. In the lower frequency (<10 Hz) region, the impedance values had a trend Sp-C > SpB-C > Control  
Figure 2: Bright-field and polarized microscope images of cake batter. The orange arrow shows the appearance of wax crystals at the gas-cell interface in the micrographs.  
> Sa-C > SaB-C. The probable reason behind this can be the availability of ions. Hydrophilic emulsifier SP binds efficiently with the starch molecules and may lead to less release of ions in the batter emulsion, resulting in reduced conductivity followed by increased impedance in the case of Sp-C and SpB-C. In the higher frequency region, the trend in the impedance values somehow remained similar except for the highest impedance value in Sa-C.
## 3 Generation-Load Uncertainty Model
### Load Uncertainty Model Considering Demand-Side Management
#### 3.2.2 Uncertainty Model of Intraday IDR Virtual Unit  
The users of IDR have a short response time and a large elastic margin. The users participating in IDR in a certain area can be gathered into a virtual unit, and the power company adopts the incentive mode of ladder compensation price to dispatch in the intraday stage, so as to realize the rapid increase or decrease of load in the power system operation. According to the IDR curve shown in Figure 2,it can be known that users' cutting load rate \(\lambda\) fluctuates within a range \([\lambda_{2}(\rho_{\text{IDR}}),\lambda_{1}(\rho_{\text{IDR}})]\) at a certain compensation price \(\rho_{\text{IDR}}\). The uncertainty of users' response at a certain incentive level can be simplified into uniform distribution:  
\[\lambda(\rho_{\text{IDR}})=\frac{1}{\lambda_{1}(\rho_{\text{IDR}})-\lambda_{2} (\rho_{\text{IDR}})} \tag{7}\]  
where \(\lambda(\rho_{\text{IDR}})\) is the cutting load rate of the incentive response virtual unit under the incentive price \(\rho_{\text{IDR}}\)\(\lambda_{1}(\rho_{\text{IDR}})\) and \(\lambda_{2}(\rho_{\text{IDR}})\) are the upper and lower limits of the cutting load rate respectively, and the calculation formula is referred to in the [22].  
The actual response quantity \(P_{\text{IDR},\,t,\text{act}}\) of IDR users in the intraday stage is:  
\[P_{\text{IDR},t,\text{act}}=\lambda_{\text{IDR},t}P_{\text{IDR},t} \tag{8}\]  
where \(\lambda_{\text{IDR},t}\) is the cutting load rate of IDR virtual unit at time \(t\), and \(P_{\text{IDR},t}\) is the power when users' do not participate in the incentive demand response at time \(t\).
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 6 Evaluation and Experiments
### Factory Experiments  
As the testbed experiments only evaluate a part of the design in terms of environment and corner cases, we also conduct experiments in a factory. The factory environment is harsher and more multivariate than the testbed. It can have a great effect on the results of experiments, which may be below the current results in the testbed experiments. This makes the practical implementation  
Figure 14: Energy consumption comparison (\(E_{MN}\)).  
Figure 13: Comparison of RLP-\(V\), REP-\(V\), RLP-\(MN\) and REP-\(MN\) with a 6% packet error rate. (**a**) RLP-\(V\); (**b**) REP-\(V\); (**c**) RLP-\(MN\); (**d**) REP-\(MN\).  
necessary in order to prove the performance of the proposed mechanism. As shown in Figure 15, several WMs are marked by red circles on the pushcart and they are all portable. Figure 15 is the welding machine and the W-CAN node.  
Both upstream and downstream flows are supported in the WirelessCAN system to monitor and control all WMs. The practical implementation consists of 12 WMs with W-CAN nodes, all of which could be turned into _MN_s. Thus the metrics of RLP-_MN_, REP-_MN_ and \(E_{MN}\) are difficult to obtain. However, we obtain the number of handoffs (\(N_{h}\)) in each solution by counting the number of parent changes in the gateway. On each weekday, five experiments are implemented to amply demonstrate the performance of our mechanism. Table 2 lists the results of the experiments, and Figure 16 show the difference of RLP-_V_ and REP-_V_ between the three solutions by selecting the worst and best results of the five experiments. A varied number of handoffs greatly influence the performance of WirelessHART and RSSI-based solutions. Moreover, a lower number of handoffs not only make the network more stable but save management slots. The results also roughly indicate that the expired and lost packets are reduced when using the WirelessCAN system. On the contrary, other two solutions cannot solve the mobility problem well as the environments become harsher and multivariate. In general, WirelessCAN system can offer reliable packet end-to-end delivery to an average of 98.5% in an industrial factory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **WirelessHART** & **RSSI-Based** & **WirelessCAN** \\ \hline
1 & (73, 2.63\%, 4.63\%) & (102, 2.14\%, 4.04\%) & (48, 1.26\%, 2.18\%) \\
2 & (64, 1.57\%, 3.82\%) & (93, 1.29\%, 3.28\%) & (37, 1.05\%, 1.76\%) \\
3 & (23, 1.08\%, 1.46\%) & (72, 0.97\%, 1.37\%) & (14, 0.53\%, 0.81\%) \\
4 & (52, 1.44\%, 3.52\%) & (84, 1.27\%, 3.46\%) & (39, 0.95\%, 1.34\%) \\
5 & (32, 0.73\%, 1.78\%) & (67, 0.69\%, 1.81\%) & (32, 0.46\%, 0.77\%) \\ \hline \hline \end{tabular} Note: The values in the table represent (\(N_{h}\), RLP-_V_, REP-_V_).  
\end{table}
Table 2: Results of Practical Implementation.  
Figure 15: (**a**) The site of implementation and (**b**) the welder machine with the W-CAN node.