esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Designing Nominal SAR Operational Parameters at Nominal Altitude
### Design Results on Nominal SAR Operational Parameters  
The procedure described in the previous sections are used to calculate the SAR operational parameters for each swath. Table 3 shows the nominal SAR operational parameters that are calculated for each swath. It also shows performances that are predicted for the sliding spotlight mode. In the parameter calculation and the performance prediction, the nominal altitude, incidence angle range, and swath width are 520 km, 20-55\({}^{\circ}\), and 5.3 km, respectively. Also, the Tx duty ratio, hamming window coefficient (\(\alpha\)) in range and azimuth are 30 %, 0.77 and 0.54, respectively.  
Figure 9 shows the SAR performances at the nominal altitude. All the performances satisfy the requirements in Table 1 for the whole range of the incidence angle.  
Figure 8: PRF determination for SW#5: (**a**) Area in magenta: allocable PRF zones, area in green: interferences from transmitting returns, area in blue: nadir returns; (**b**) area in red: determined PRF at 8956.5 Hz.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **SW\#1** & **SW\#2** & **SW\#3** & **SW\#4** & **SW\#5** & **SW\#6** & **SW\#7** & **SW\#8** \\ \hline Incidence angle at center (\({}^{\circ}\)) & 20.03 & 25.14 & 29.91 & 35.16 & 39.90 & 45.14 & 50.00 & 54.93 \\ PRF (Hz) & 11839.6 & 8549.9 & 11755.5 & 11874.6 & 8956.5 & 6849.7 & 5742.9 & 4691.9 \\ Tx pulse length (us) & 25.33 & 35.09 & 25.52 & 25.26 & 33.49 & 43.79 & 52.24 & 63.93 \\ Maximum azimuth steering angle (\({}^{\circ}\)) & 1.65 & 1.63 & 1.60 & 1.56 & 1.51 & 1.47 & 1.42 & 1.43 \\ Image acquisition time (s) & 5.96 & 6.07 & 6.19 & 6.37 & 6.56 & 6.84 & 7.16 & 7.78 \\ NESZ (dB) & \(-26.41\) & \(-25.57\) & \(-24.69\) & \(-24.35\) & \(-23.77\) & \(-22.98\) & \(-21.96\) & \(-20.96\) \\ RAR (dB) & \(-31.42\) & \(-34.87\) & \(-26.06\) & \(-24.64\) & \(-28.63\) & \(-28.61\) & \(-25.93\) & \(-21.25\) \\ AAR (dB) & \(-38.49\) & \(-33.16\) & \(-38.19\) & \(-38.40\) & \(-32.60\) & \(-27.85\) & \(-26.36\) & \(-24.50\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nominal SAR operational parameters and corresponding performances at nominal altitude.
# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
## 2 Materials and Methods
### Accuracy Assessment of Image Harmonizing  
In this study, we used two observation images (DOY 178 and DOY 227) to verify the accuracy of the model. Owing to these two images were not involved in image harmonizing process, the  
Figure 3: Schematic diagram of spatio-temporal integration by improved the Kalman filtering algorithm with the ESTARFM model.  
verification observation data and synthetic data were independent of each other. We compared the NDVI images observed by satellite with the harmonized NDVI images, and calculated the prediction residuals (\(\Delta PR_{K}\)) of each pair of images to evaluate the accuracy of the fused images. The prediction residual images of ESTARFM model and improved Kalman filtering model and their probability density images were analyzed to evaluate the ability of the two models to fuse data. The prediction residual was computed as:  
\[\Delta PR_{K}=\frac{NDVI_{obs}-NDVI_{hak}}{NDVI_{obs}} \tag{13}\]  
where \(\Delta PR_{K}\) is the prediction residual at pixel level, \(NDVI_{obs}\) is the pixel value in the observation image, and \(NDVI_{hak}\) is the pixel value of harmonized image. The improved Kalman filter model and the data analysis algorithms were running under MATLAB (The Mathworks, Inc., Natick, MA, USA) environment.
## 2 System Model
### Joint Optimization Problems  
The task is determined, whether it is processed locally or offloaded to the MEC server for processing, by the offloading decision variable \(\lambda\). When \(\lambda=0\), it means that the task is computed locally, and when \(\lambda=1\), it means that the task is offloaded to the MEC server for computation. Thus, for the offloading task of vehicle \(m\), the total energy consumption incurred by the system is \(E(m)=(1-\lambda)E_{m}^{LOC}+\lambda E_{m}^{MEC}+E_{n}^{CONS}\), and the total delay is \(T(m)=(1-\lambda)T_{m}^{LOC}+\lambda E_{m}^{MEC}+T_{n}^{CONS}\).  
In this paper, the minimum weighted sum of the energy consumption and delay incurred by joint task offloading and blockchain consensus is used as an optimization problem with the aim of minimizing the system overhead. The problem model is expressed as:  
\[O(m)=\eta E(m)+\gamma T(m) \tag{24}\]  
\[s.t.\begin{cases}&C_{1}:\lambda\in\{0.1\}\\ &C_{2}:T_{m}^{up}\leq T_{m}^{stop}\\ &C_{3}:S_{k}<S_{p}\\ &C_{4}:T_{m}^{LOC}+T_{m}^{MEC}+T_{n}^{CONS}\leq T_{m}^{max}\\ &C_{5}:T_{n}^{CONS}\leq T_{cons}^{max}\end{cases} \tag{25}\]  
where \(\eta\) and \(\gamma\) denote the coefficients weighing energy consumption and delay, where \(0\leq\eta\leq 1\), \(0\leq\gamma\leq 1\), and \(\eta+\gamma=1\). In practical applications, the selection of the weighing factors needs to be determined in the context of specific scenarios and requirements [25]. \(C_{1}\) indicates that the vehicle can only choose one offloading decision: local offloading or MEC server offloading; \(C_{2}\) indicates that it is ensured that the vehicle completes the offloading task before driving away from the coverage area of the RSUs; \(C_{3}\) indicates that the size of the block has to be larger than the size of the total transaction, thereby ensuring that only one consensus process exists; \(C_{4}\) means that the total delay in processing the offloading task must not exceed the maximum tolerable delay of the system; and \(C_{5}\) means that the blockchain consensus process has to be completed within a specified time.
## 3 Preliminaries
### Data-Forwarding Phase
#### 4.2.3 Coordination Mechanism  
The coordination mechanism of FCM-OR is similar to that of the ORW protocol, with some minimal changes. When the sender transmits a packet, it waits for a while to receive an ACK. Suppose it does not receive an ACK after a specific waiting time. In that case, it retransmits the packet to ensure that at least one forwarder receives the packet and forwards it. Meanwhile, suppose one forwarder node obtains the same packet a second time. In that case, it sends an ACK with a 50% probability. After receiving at least one ACK, the sender finally discards the packet and returns to its duty cycle scheduling. In our protocol, we allowed the sender to retransmit a packet once to save energy. Meanwhile, the awaiting forwarder node will discard the packet provided the ACK of another forwarder node is overheard while a potential forwarder is waiting for the forwarding decision, and it has a smaller FR value compared with that of the current node.  
Because there are fewer active nodes when routing through low-density areas, especially when duty-cycled MAC is in effect, sender waiting time may increase even though redundant transmissions are decreased. This creates a trade-off between responsiveness in real time and energy efficiency. This delay increase, which is further discussed as a future optimization target in Section 7 and this issue is observed in Section 5.2.11.
## 3 Results  
### Spatiotemporal Simulation of Forest Carbon Cycle  
Based on the improved InTEC model, the carbon cycle of forest ecosystems in Hangzhou was simulated for 20 years from 2001 to 2020 by integrating the climatic inversion data of Hangzhou forests into the InTEC model. The result is displayed in Figures 3-5, which show the spatiotemporal distribution of GPP, NPP, and NEP.  
As shown in Figures 3-5, the spatiotemporal distributions of GPP, NPP, and NEP were obviously heterogeneous, with higher values concentrated in the southeast and northwest region, while lower values are observed in the southwest. Additionally, significant temporal variability was evident within the same region across different years.  
Based on Figures 3-5, the calculation can obtain the trend of the interannual average GPP, NPP, and NEP of Hangzhou forests from 2001-2020, as shown in Figure 6. Analyzing Figure 6, we can see that the annual mean GPP, NPP, and NEP all show a pronounced decreasing trend. Specifically, the annual mean GPP decreased at a rate of 22.29 g C m\({}^{-2}\)yr\({}^{-1}\) (\(p<0.01\)), the annual mean NPP decreased at a rate of 9.88 g C m\({}^{-2}\)yr\({}^{-1}\), and the annual mean NEP decreased at a rate of 15.84 g C m\({}^{-2}\)yr\({}^{-1}\) (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Latent Variable** & **Explicit Variable** & **Unit** \\ \hline \multirow{4}{*}{Climatic factors} & Pre & mm \\  & Tmin & °C \\  & Tmax & °C \\  & Rad & W M\({}^{-2}\) \\  & SOS & day \\  & EOS & day \\  & LOS & day \\  & GPP & g C m\({}^{-2}\) yr\({}^{-1}\) \\ Carbon cycle & NPP & g C m\({}^{-2}\) yr\({}^{-1}\) \\  & NEP & g C m\({}^{-2}\) yr\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Construction of PLS path model.  
Figure 4: NPP spatial distribution.  
Figure 3: GPP spatial distribution.
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 2 Object Detection
### Two-Stage Object Detection Algorithms  
The advent of two-stage object detection algorithms marked a significant shift in the field, bringing about transformative changes. This approach introduced a distinct separation between region proposal and object classification stages, addressing computational inefficiencies prevalent in earlier methods [22]. The family of R-CNN (Region-Based Convolutional Neural Network) algorithms, including R-CNN, Fast R-CNN, and Faster R-CNN, played a pivotal role in establishing and refining this innovative approach.  
R-CNN, presented by [PERSON] et al. in 2014 [23], introduced a novel strategy by proposing regions of interest (ROIs) using a selective search and then classifying objects within these regions through a convolutional neural network (CNN). While promising in terms of accuracy, this method required faster processing speeds due to the sequential nature of region proposal and classification. The subsequent refinement, Fast R-CNN, addressed this challenge by merging the region proposal and feature extraction stages, sharing computation across different regions, and enhancing efficiency [24].  
The culmination of this two-stage approach occurred with the introduction of Faster R-CNN by [PERSON] et al. in 2015 [25]. This approach seamlessly integrated the region proposal process into the network architecture using a Region Proposal Network (RPN), enabling end-to-end training. Faster R-CNN not only achieved remarkable accuracy but also significantly reduced the processing time, establishing itself as a cornerstone in object detection [26,27].
## 3 Microwave Photonics Applications of DFT
### DFT for Microwave Spectrum Sensing
The equivalent temporal sampling rate of the method is inversely proportional to the channel spacing and the dispersion value [121]. It is challenging to further reduce the channel spacing based on the state-of-the-art fabrication technology. While larger dispersion value could improve the sampling rate, it also degrades the frequency measurement solution. On the other hand, it has been reported recently that, for a spectrally-sparse signal, compressive sensing technique enables recovery of the signal with sub-Nyquist sampling [123]. Compressive sensing with photonically-assisted random demodulation has been implemented based on temporal mixing with pseudo-random bit sequences (PRBS) [124, 125]. However, sophisticated high-speed electronics is usually required in the PRBS generator and mixer. Most recently, we have proposed a novel sub-Nyquist microwave spectrum sensing scheme that combines temporal channelization  
Figure 10: Schematic of real-time microwave spectrum sensing system based on DFT-enabled temporal channelization. and compressive sensing [41], featuring enhanced bandwidth with single-shot measurement capability while maintaining high measurement resolution. Compared to the system in [121], a compressive sensing module is incorporated in the system to manipulate the modulated optical pulse before it is sent to the optical channelizer for sampling. Figure 11a shows the schematic of the compressive sensing module. Random mixing, which is the key step in compressive sensing, is implemented in the spectral domain using a reflective spatial light modulator (R-SLM). A pseudo-random bit sequence (PRBS) pattern is imprinted on the R-SLM. The spectrum of the modulated optical pulse, which carries the microwave signal, can be effectively mixed with the PRBS at the R-SLM, without being converted back to the time domain [124,125]. The randomly mixed spectrum is finally sent to the optical channelizer for downsampling. More importantly, since each channel has a relatively flap response and a wider bandwidth than the spectral resolution of the R-SLM, the output of each channel is the sum of the consecutive elements of the demodulated signal, as shown in Figure 11b, assuring the perfect implementation of sample-and-hold integration. The challenge of implementing high-frequency integration or low-pass filtering in time domain methods [124,125] has been eliminated in the proposed system. Then the Nyquist-sampled microwave signal can be recovered from down-sampled measurements in the digital domain following a sparse signal recovery algorithm [126] thanks to compressive sensing.
# Smart Grid for Industry Using Multi-Agent Reinforcement Learning
## 2 Fundamentals
### Industrial Electricity Supply  
Based on German regulations, there are several options industrial companies can pursue in order to arrange their power supply. These options will be briefly outlined.  
* **Power procurement [19; 20]:*
* In this case, the manufacturing companies buy the required amount of electricity from external sources. The general billing interval of the entire electricity market is 15 min. There are two main options for procurement:
* **Utility company:*
* Industrial companies may directly rely on power utility companies, which in general provide constant prices, thus completely bearing the price risks.
* **Market trading:*
* Companies can actively take part in the electricity market, either trading directly or relying on a suitable aggregator. There are several electricity markets which can be characterized by their lead time. It is worth noting that there is always some lead time between order purchasing and the actual delivery and consumption of the electricity (see Figure 3). The shortest lead time is offered by the intraday market, in which electricity can be bought at least 5 min before delivery. In case a company has bought electricity within a specified interval, it must ensure that the power is actually consumed. In general, a tolerance bandwidth of \(\pm\)5 to 10% is granted. Otherwise, there is a risk of receiving high penalties.  
Figure 3: Available short-term energy markets in Germany, depending on the time of delivery t [20].  
Figure 2: Vision of the smart grid system for industrial companies.  
* **Self-supply [21; 22]:** Manufacturing companies may dispose of their own power generation facilities, which enable them to generate their electricity on-site. Depending on the relevant control characteristics, power plants can be divided into two groups: \(\bigcirc\) **Variable power sources (VPS):** The power generation of these sources is difficult to control because it is highly dependent on the current weather conditions. The only control option is to cut off their power. However, since VPS do not entail any working expenses, this is not recommended. The most important kinds of VPS are solar and wind power plants. \(\bigcirc\) **Controllable power sources (CPS):** The power output of CPS can be controlled and adapted within a specified range. In the manufacturing field, combined heat and power plants (CHP) are widely used and are assigned to this category.
* **Battery systems:** Batteries consist of electrochemical, rechargeable cells and are very suitable for storing electricity for several hours or days [23]. However, the cells suffer from degradation over time. The extent of the degradation strongly depends on the charging cycles the battery is exposed to [24]. As a result, there exist several modelling approaches for considering the degradation process within a battery control strategy [25].
## 2 Materials and Experimental Procedures
### Sensor Signal Pre-Processing  
In this work, an autoencoder neural network was employed to process each acquired sensor signal with the aim of extracting features automatically. As this approach is based on the use of a feedforward network, a fixed input length is needed, which implies that all the acquired sensor signals must be characterized by the same number of samples. Accordingly, an online acquisition module was developed so that the acquisition is saved for each channel of the sensor signal acquisition system starting from the moment when the thrust force \(F_{2}\) reaches a threshold value \(F_{thr}\), as per Equation (1). This condition indicates the tool-workpiece contact and assures the actual start of the drilling process.  
\[F_{2}>F_{thr} \tag{1}\]  
The sensor signal acquisition is then stopped when a predefined number of samples \(n_{s}\) are acquired, in accordance with the process time duration calculated on the basis of the process parameters, as described in Equation (2).  
\[n_{s}=\frac{60\cdot h\cdot f_{s}}{f_{r}\cdot\omega} \tag{2}\]  
where \(h\) is the specimen thickness (mm), \(f_{s}\) is the sampling rate (samples/second), \(f_{r}\) is the feed (mm/rev), and \(\omega\) is the spindle speed (rev/min). The above-defined thresholds were employed to perform the segmentation procedure on the thrust force signal. All the other sensor signals, including torque, vibrations, and acoustic emission RMS, were segmented using the same start and end point identified on the thrust force signal. An example of signal segmentation performed according to this procedure is shown in Figure 3 for the thrust force signal of hole no. 1 for the experimental test T2 carried out at 6000 rpm--0.20 mm/rev.  
Figure 3: An example of thrust force signal segmentation for the first hole of the experimental test T2.  
Figure 2: Tool wear curves obtained via third-order polynomial interpolation of the measured values for the experimental tests T1, T2, T3, and T4 are reported in Table 1.
## 3 Evaluation with Expected Total Costs
### Probability Analysis
#### 3.1.2 Probability of Failure  
The probability of failure, \(P_{f}\), in the proposed method is expressed as the sum of the conditional probability in the case with the CM and that without the CM. When the CM was conducted, the conditional probability of failure was assumed to be \(P_{f:T}\). When a higher \(m_{obj}\) was observed and the CM was not conducted, the conditional probability of failure was re-evaluated with the \(\text{COV}_{\text{R}\text{P}}\).  
\[P_{f}=P_{f:wi}+P_{f:wo} \tag{10}\]  
\[P_{f:wi}=\int_{0}^{m_{T}{}^{a}}f_{\ln}\Big{(}x=m_{obj};\ln(m_{i}),s_{i}{}^{2} \Big{)}\cdot P_{f:T}dm_{obj}=P_{c}P_{f:T} \tag{11}\]  
Figure 4: Example of probability of countermeasure (\(\text{COV}_{\text{R}\text{C}}\) = 0.35, \(\text{COV}_{\text{Q}}\) = 0.10, \(\beta_{T}\) = 3.0). \(\text{COV}_{\text{R}\text{C}}\) and \(\text{COV}_{\text{R}\text{P}}\) are COV of the capacity estimation by the conventional and proposed methods, respectively.  
\[P_{f;ww}=\int_{m_{T^{d}}}^{\infty}f_{\rm In}\Big{(}x=m_{ub};\ln(m_{i}),{s_{i}}^{2} \Big{)}\cdot\Phi(-\beta_{ob})dm_{ob} \tag{12}\]  
where \(f_{\rm In}()\) is the lognormal probability density function (PDF), subscript _wo_ stands for without CM, and _wi_ stands for with CM.  
Figure 5 shows the \(P_{f}\) based on Equations (10)-(12), assuming that \(\rm COV_{R;c}=0.35\), \(\rm COV_{Q}=0.10\), and \(\beta_{T}=3.0\). When the initial SF is low, CMs are implemented in most cases, and \(P_{f}\) approaches the target probability, \(P_{f;T}\) (\(\approx\)\(10^{-3}\)). When the initial SF is high, \(P_{f}\) becomes inversely proportional to the initial SF, and is lower than \(P_{f;T}\). Thus, the proposed method always has a lower probability of failure than the target value.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 2 An Overview on Multi-Objective Optimization and Multi-Objective Evolutionary Computation
### Multi-Objective Chaotic Evolution Algorithm
#### 2.3.2 Multi-Objective Chaotic Evolution Algorithm  
EMO algorithms can effectively solve the MOP. The effectiveness of EMO's optimization process depends on the search algorithm's capacity to optimize results in addition to Pareto dominance and solution diversity [65]. By including chaotic evolution in the EMO algorithm, reference [66] proposed the multi-objective chaotic evolution algorithm (MOCE)  
Figure 1: The basic flow of Pareto-based MOEA. The optimal set of solutions from the previous generation is retained and added to the new generation so that it continues to converge to the true Pareto front. This figure is adopted from [6].  
with a powerful search capability. The MOCE algorithm combines the non-dominated sorting and crowding distance methods with the search capacity of the traditional CE algorithm. It does not only aim to maintain the number of Pareto solutions but also strives to maintain their diversity.
## 3 Circuit Implementation
### Predictive Zero Current Detection  
According to the time exponential rate P&O algorithm, the power change is obtained by comparing the MP on-time. Therefore, a high-precision ZCD is crucial.  
When the ZCD turns off MP slowly, a reverse current appears on the inductor for a time, as shown in Figure 7a. Because the drain-substrate PN junction of the MN forms a discharge path of the reverse current, the voltage at the right end of the inductor (V\({}_{\text{LX}}\)) will first fall to the negative PN junction voltage (\(-\)V\({}_{\text{D}}\)). When the ZCD turns off MP quickly, a little forward current remains on the inductor, as shown in Figure 7b. The forward current flows through the source-substrate PN junction of the MP. Therefore, the V\({}_{\text{LX}}\) will first jump to the sum of the substrate voltage and the PN junction voltage (V\({}_{\text{OUT}}+\)V\({}_{\text{D}}\)).  
Figure 6: The architecture of the proposed MPPT circuit.  
Based on the above analysis, there are two different situations of the V\({}_{\rm{LX}}\) after the MP is turned off, and a predictive ZCD is proposed as shown in Figure 8. When the MP is turned on, current source II starts to charge capacitor C1. Until V\({}_{\rm{C1}}\) is higher than V\({}_{\rm{TP}}\), DFF is set by the COM1 and the ZCD output signal GP_OFF turns off the MP. In each converter cycle, after the MP is turned off, V\({}_{\rm{LX}}\) is compared with V\({}_{\rm{OUT}}\)/2 by a dynamic comparator COM2. Then the 9-bit bidirectional counter (BDC) is triggered to read the result of the COM2. If the BDC input DIR is high, the BDC output TP<8.0> is plus one. Otherwise, the TP<8.0> is minus one. The TPn<8.0> is used to control whether a series of proportional-current mirrors are connected to resistor R2, which defines V\({}_{\rm{TP}}\).  
When V\({}_{\rm{LX}}\) is higher than V\({}_{\rm{OUT}}\)/2, which means the MP is turned off early in the cycle, TP<8.0> and V\({}_{\rm{TP}}\) will increase and the MP will be shut down later in the next cycle, and vice  
Figure 8: Predictive zero current detection.  
Figure 7: The discharge path of remaining current and waveforms after the MP is turned off (**a**) slowly and (**b**) quickly.  
versa. Therefore, the predictive ZCD continuously corrects the TP<8:0> in each cycle and the \(t_{MP}\) is set around the best turn-off time adaptively. Consequently, the high-precision predictive ZCD is achieved. Meanwhile, the digital output TP<8:0> can be handled easily by the following circuit.