esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Energy in Urban Planning: Lesson Learned and Recommendations from Six Italian Case Studies
## 1 Introduction
### Framework and Aim of the Work  
This paper presents part of the findings related to Italian case studies within the _Subtask C--Case Studies and Action Research_ (STC) framed in the International Energy Agency (IEA) Solar Heating and Cooling Program (SHC) Task 51 _\"Solar Energy in Urban Planning\"_[29, 30, 31]. During the whole duration (2013-2017), the international experts working in Task 51 (i.e., architects, urban planners, public authorities, researchers, etc.) promoted through their work the integration of active and passive solar solutions in the built environment. Furthermore, the collection of international case studies through a common template carried out within STC allowed to create an overview of solar energy in urban planning by highlighting potentialities and fragilities and by summarizing lessons learned for urban stakeholders,public authorities, and researchers. The developed template is here introduced and used to present six Italian case studies, which make use of solar energy in different ways and are characterized by different built environments. Five of them deal with consolidated and new urban areas, with photovoltaic or solar thermal panels fully integrated or applied to the buildings' envelopes, while one case provides an example of an agricultural PV system and its relationship with the landscape. Different from [31] where an international overview is given, in this paper, the Italian case studies are presented more in detail, and specific conclusions are drawn for solar energy implementation in the Italian context. Additionally, a comparison between the different built environments through similarities and differences is provided.  
In the next section, an overview of the Italian energy planning legislation is given, followed by the presentation of the case studies and their discussion. Finally, the limitations of the study are presented, together with a conclusive summary of the main lessons learned and recommendations.
## 3 Parameter Analysis and Optimization Design
### Parameter Analysis
#### 3.1.4. Impact of the Damping Coefficient and Damping Exponent  
This section expounds the variation law of \(f_{d}\) and \(f_{E}\) with the damping index \(\alpha\) as well as the damping coefficient \(c\). From Figure 9(a),b, we obtained a conclusion that \(\eta_{d}\) gradually decreases when increasing the damping coefficient \(c\) or index \(\alpha\). Therefore, \(f_{d}\) gradually decreases, while \(f_{E}\) first increases and then reduces. Therefore, it is necessary to select appropriate index \(\alpha\) and damping coefficient \(c\) to enable \(f_{E}\) to reach the largest value. Under different exponent \(\alpha\), the variation law of \(f_{E}\) with the change in the damping coefficient \(c\) is shown in Figure 9(c). \(f_{PE}\) in Figure 9(c) refers to the peak value of the energy dissipation coefficient \(f_{E}\). Figure 9(d) presents the curve of \(k_{d}^{\prime\prime}/k\) and \(\eta_{d}\) corresponding to \(f_{PE}\) in Figure 9(c) with the index \(\alpha\). According to Figure 9(d), it is indicated that \(f_{E}\) reaches the maximum value when the \(c\) and \(\alpha\) make \(k_{d}^{\prime\prime}/k=1+k_{N}/k\) or \(\eta_{d}=1/[2(1+k_{N}/k)^{1+\alpha}]^{1-0.5\alpha}\).
## 4 Channel Selection Algorithm Based on Multi-Armed-Bandit Algorithm
### Learning Rules of the Multi-Armed-Bandit Algorithms  
Figure 5 illustrates a series of flows from the determination of the transmission channel to the data transmission based on the MAB algorithm when each node treats the ACK frame from the gateway as a reward for the MAB problem. The node periodically repeats the wakeup mode for data transmission and learning and the sleep mode for suppressing power consumption. The process shown in the red frame in this flow is the channel-selection process. A node updates the learning parameters such as the number of data transmission attempts \(N\) and the number of successful messages \(R\) according to whether ACK can be received or not after data transmission. The following sections introduce some major MAB algorithms that can be used as the channel-selection algorithm in this process.
## 3 Results and Discussion
### Mixing Behavior in Laser-Arc Hybrid Test Welds
As a whole, test welds LAH3 and LAH4 exhibited the best overall results of the laser-arc hybrid test welds, corresponding to homogenous filler metal distribution throughout the whole weld cross-section. If intensive and homogenous mixing in the root portion is preferred, test weld LAH4 with trailing wire feeding showed more even mixing behaviour than test weld LAH3. [PERSON] et al. [17] used modelling and simulations in mixing and fluid flow studies for full penetration laser-gas metal arc hybrid welding of 10 mm thick closed butt-joints with leading and trailing torch configuration. In their studies, the longitudinal sections of the simulated weld pool showed different behaviour of flow patterns and temperature contours between the trailing and leading torch. In the leading torch configuration, the modelling results indicated a solidifying peninsula growing horizontally at the mid-thickness range of weld pool, which partly sectioned the melt pool to upper and lower half. The mentioned solidification peninsula may obstruct the circulation of the melt flow between the weld surface and root, which in turn may reduce the mixing of weld metal. This melt pool behaviour with solidification peninsula was not seen in the trailing torch simulations. Consequently, with the trailing torch configuration the mixing at the root portion of weld was observed to be more intensive than with the leading torch. The mentioned observations in the reference [17] may also explain why the trailing wire feeding results of laser-arc hybrid experiments presented in this work showed enhanced mixing behaviour near the weld root portion compared to the corresponding results achieved with the leading wire feeding.
## 4 Experiment and Result Analysis  
### Data Acquisition and Processing  
The performance of the proposed MRWG BiAtten model is examined on two datasets, i.e., the BikeNYC dataset and the TaxiNYC dataset. Both datasets are associated with traffic flow demand, including time, region, demand request times, etc. In addition, motor vehicle accident data of New York City (NYC) are added to analyze the impact of abnormal events. The NYC dataset consists of the traffic records of in total 60 days in 2016, including 8000 traffic accidents per month, where each record contains the location of the vehicles and the start and end times of the journey. Table 1 is a summary of the two datasets.  
The first 45 days of the dataset are used as the training data, and the remainder for the test set. Before the training, min-max scaling is used to unify the data in the range of [0, 1], and the prediction output is de-normalized for evaluation. The Local CNN networks have a space pool size of m = 7, a [\(7\times 7\)] convolution kernel. In our proposed model, the dropout rate is set to \(d_{prate}\) = 0.1, and the number of stacked model layers and \(N\) multi-head attention modules is set as 4 and 8, respectively. The performed hardware environment contains 1 \(\times\) NVIDIA GeForce RTX 3080(10G) GPUs and 2 \(\times\) Intel Xeon Silver 4214R CPU.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **TaxiNYC** & **BikeNYC** \\ \hline Time duration & 1 January 2016–29 February 2016 & 1 July 2016–29 August 2016 \\ \hline Sampling interval & 15 min & 15 min \\ \hline City region scope & 10 \(\times\) 20 & 10 \(\times\) 20 \\ \hline Vehicle number & 24,000+ & 8200+ \\ \hline Max flow & 928 & 168 \\ \hline Total records & 23 million & 3.2 million \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset details.  
The learning rate is set as 0.001 and the batch size is fixed as 64. The training epoch is set as 500 to ensure convergence and the Adam optimizer is used to train the model.
# Current Status of HbA1c Biosensors
## 2 HbA1c Biosensors
### Affinity Biosensors Based on Boronic Acid Derivatives
#### 2.1.3 Biosensors Based on 3-Aminophenyl Boronic Acid (APBA)
Under the optimized condition, the SPR signal shows a linear response to HbA1c in the range of 0.43-3.49 \(\upmu\)g/mL and with the detection limit of 0.01 \(\upmu\)g/mL.
# Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
## 5 Conclusions  
This paper primarily investigates target detection and parameter estimation methods for clutter avoidance in the down-looking mode of airborne radar. A novel working method of multi-PRF orthogonal signal staggered-transmission is also proposed. Each PRF of the radar has a designated DFC signal, and the DFC signals of different PRFs are mutually orthogonal. By performing a matched filtering on the echo signal, the target echoes corresponding to each PRF are obtained. After applying the DFT on the target echoes, RD results with ambiguity are obtained. By combining the range and Doppler information of the surveillance region, the corresponding cells in each ambiguous RD are identified. The first pulse emission timing of each PRF is then used for coherent processing and resolves the ambiguity. Compared to the severe velocity ambiguity of the traditional LPRF method, the method in this paper can accurately extract the target's velocity information. Furthermore, compared to the severe range ambiguity of the traditional HPRF method, the method in this paper can accurately extract the target's range information. The problem of insufficient signal energy utilization in traditional multi-PRF is also effectively addressed in this paper: the phase-coherent processing of all pulse signals is completed, effectively extending the range of the radar and increasing the probability of detecting low SNR signals.  
Conceptualization, X.S. and L.Z.; methodology, X.S., X.W. and Q.Z.; software, X.S., X.W., and Q.Z.; validation, X.S., J.W. and L.Z.; formal analysis, X.S., J.W. and L.Z.; investigation, X.S. and X.W.; writing--original draft preparation, X.S.; writing--review and editing, X.S., X.W. and J.W; visualization, X.S.; supervision, L.Z.; project administration, L.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 23: The detection error of the target in different operating modes. (**a**) Distance measurement error. (**b**) Distance measurement error (scaling). (**c**) Velocity measurement error. (**d**) Velocity measurement error (scaling).  
**Funding:** This research was funded in part by the National Natural Science Foundation of China under Grant 62101603 and Grant 62201623 and in part by the Notional Nature Science Foundation of Guangdong under Contract 2021A1515011979.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their suggestions and comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Cluster Analysis  
The test results of the samples corresponding to the two sub-tanks were averaged, and these average values obtained were used as a basis for analysis. In order to analyze the comprehensive effects of rainfall intensity, slope gradient and vegetation coverage type on soil erosion and nutrient loss on slope surface, clustering analysis method in data mining was adopted [35].  
Clustering is the process of dividing data objects into classes or clusters, which makes the objects in the same cluster have a high degree of similarity, while the objects in different clusters have a high degree of difference. As a function of data mining, cluster analysis can be used as an independent tool to obtain data distribution, to observe the characteristics of each cluster, and to focus on certain specific clusters for further analysis. However, the clustering analysis of data needs to calculate dissimilarity matrix first. The dissimilarity matrix is usually represented by an \(n\times n\) table:  
\[\left(\begin{array}{ccccc}0&&&&\\ d(2,1)&0&&&&\\ d(3,1)&d(3,2)&0&&\\ \vdots&\vdots&\vdots&\ddots&\\ d(n,1)&d(n,2)&\cdots&\cdots&0\end{array}\right) \tag{4}\]  
where \(d(l,j)\) is the measurement difference or divergence between objects \(l\) and \(j\), and is usually a non-negative value. The more similar or \"close\" objects \(l\) and \(j\) are, the closer the value is to 0; the more different the two objects, the greater the value. Since \(d(l,j)=d(j,i)\) and \(d(l,i)=0\), we have the above matrix. The dissimilarity is evaluated based on the attribute value describing the object, usually using a distance metric. In addition, \(d(l,j)\) is generally calculated by Euclidean distance ([PERSON] and [PERSON], 2006):  
\[d(i,j)=\sqrt{\left(x_{i1}-x_{j1}\right)^{2}+\left(x_{i2}-x_{j2}\right)^{2}+ \cdots+\left(x_{in}-x_{jn}\right)^{2}} \tag{5}\]  
where \(l=(x_{i1},x_{i2},\ldots,x_{in})\) and \(j=(x_{j1},x_{j2},\ldots,x_{jn})\) are two \(n\)-dimensional data objects.  
The calculation method of dissimilarity varies according to the variable describing the object. Furthermore, the method of ratio-scaled variable was selected to calculate the dissimilarity matrix according to the experimental results. The ratio-scaled variable takes a positive measurement value on a nonlinear scale (such as an exponential scale). Through logarithmic transformation of the ratio-scaled variable, the value obtained can be regarded as an interval value, and the distance calculation method is used for processing. Thus, the dissimilarity between objects described by ratio-scaled variable can be calculated.  
The dissimilarity matrix related to various index (runoff yield, sediment yield, TN content, TP content) was calculated to determine the similarity between different treatment methods.
## 4 Prototype Implementation
### Healthcare Services Prototype  
Three prototype services have been implemented within the healthcare domain for patient-centric well-being as proof-of-concept. The services are developed as information consumers that make use of information from the IIP. Figure 10 shows an end-to-end perspective of the developed service prototypes. Information is gathered from several devices relayed through an application gateway running on an Android smartphone and forwarded to the IIP. All implemented services subscribe to information channels of interest and provide services to different healthcare actors.  
Figure 10: End-to-end perspective of implemented service prototypes.  
Figure 9: High-available and scalable IIP prototype deployment.  
Two commercial off-the-shelf wireless Bluetooth medical devices are currently used for gathering the vital signs (_i.e._, Nonin Onyx II 9560 for blood oxygen saturation (SpO2) and pulse rate data, Vitalograph copd-6 bt for spirometry data).
## 2 Methodology  
To investigate the efficacy of an alternator with a core composed of SMC material with high-carbon steel particles [25], a prototype was developed using fused filament fabrication (FFF) [41] with a printer Prusa-MK35 [42]. The process utilized a 1 mm nozzle and a layer height of 0.3 mm. Additionally, two other cores were manufactured to perform a comparative analysis, namely one simulating an air core with PETG [24], a material with low magnetic conductivity, using FFF with a 0.4 mm nozzle and 0.2 mm of layer height, and another with iron, known for its high magnetic and electrical conductivity, using traditional manufacturing methods. The choice of PETG is justified based on its insulating properties, which are like those of air. Additionally, PETG is widely used in 3D printing due to its mechanical toughness, heat resistance, and ease of printing. The prototype's design enables easy assembly and disassembly by attaching it to the stationary shaft of a VIV wind turbine, characterized by its dimensions of 0.85 m in height and 0.07 m in diameter, weighing 1.70 kg; it operates effectively at wind speeds around 7 m/s. This prototype comprises several essential parts: a housing for the copper winding, a winding produced from a 0.4 mm diameter enameled copper wire, the core (including a central cylinder, a top cover, and a bottom cover), and two permanent magnets attached to the core (on the top and bottom covers).
## 4 Experiment
### Implementation Details  
In this work, four mainstream shadow compensation methods are used for comparison with the proposed method: MSR [43] (Multi-Scale Retinex), SC-CycleGAN [16] (Shadow Compensation via Cycle-Consistent Adversarial Networks), MF [8] (Multi-exposure Fusion) and ISR [44] (Interactive Shadow Removal). The MSR processes images with Gaussian blur and then calculates the logarithmic difference between the original image and the Gaussian blurred image to obtain the Retinex image. The MSR is achieved by applying multiple \(\sigma\) values to a single image, where the \(\sigma\) sequences are [10; 15, and 200] for the Airport dataset and [15; 80, and 200] for the Houston dataset. The SC-CycleGAN model uses the default parameters such as the learning rate and batch size. The MF employs the exposure settings provided in the original publication, where the exposure coefficients for the Airport dataset and the Houston dataset are [0.8, 1, 1.2] and [0.5, 1, 1.5], respectively. The ISR distinguishes between shadowed and non-shadowed areas through manual annotation, requiring only the selection of a portion of pixels from both shadowed and non-shadowed areas during implementation, without strict regulations.  
In our method, after threshold segmentation on the shadow feature map, morphological operations are required to address isolated pixels and small pixel regions. Specifically, this is accomplished through basic operations such as dilation, erosion, and removal of small connected regions. During the exposure fusion stage, the exposure coefficients are derived from the degree of attenuation at shadow edges. The stages of shadow attenuation \(k\) for the Airport dataset and the Houston dataset are 3 and 4, respectively. In the edge fusion stage, the area \(A_{p}\) is obtained by expanding \(p\) pixels inward and outward from the shadow edges, with \(p\) being 3 for both the Airport and the Houston dataset.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 2 Methodology
### Sensor Model (RPCs)  
The sensor model of an Earth-observing satellite defines the relationship between satellite image coordinates and ground coordinates for use in GIS data. Since IKONOS' launch in 1999, the RFM (rational function model) has remained the most popular equation for sensor modeling. The RFM equation, provided as Equation (1), calculates satellite image coordinates \((l,s)\) based on the given ground coordinates \((\phi,\lambda,h)\)[1]. This equation requires a total of 80 coefficients, distributed as 20 coefficients each for \(a,b,c\), and \(d\), collectively known as RPCs. These RPCs encompass the modeling of focal length, lens distortions, acquisition angles, orbit errors, and target topographic reliefs as polynomial coefficients  
\[l=\frac{a^{T}u}{b^{T}u}L_{S}+L_{O},\quad s=\frac{c^{T}u}{d^{T}u}S_{S}+S_{O} \tag{1}\]  
with  
\[U=\frac{\phi-\phi_{O}}{\phi_{S}},V=\frac{\lambda-\lambda_{O}}{\lambda_{S}},W= \frac{h-h_{O}}{h_{S}},\]  
\(u=[1\quad V\quad U\quad W\quad VU\quad VW\quad UW\quad V^{2}\quad U^{2}\quad W ^{2}\quad UVW\quad V^{3}\quad VU^{2}\quad VW^{2}\quad V^{2}U\quad U^{3}\quad UW ^{2}\quad V^{2}W\quad U^{2}W\quad W^{3}]^{T}\)  
\(a=[a_{1}\quad a_{2}\quad\ldots\quad a_{20}]^{T},b=[1\quad b_{2}\quad\cdots \quad b_{20}]^{T},c=[c_{1}\quad c_{2}\quad\cdots\quad c_{20}]^{T},\ d=[1\quad d _{2}\quad\cdots\quad d_{20}]^{T}\)  
where \((\phi,\lambda,h)\) represent ground coordinates, such as latitude, longitude, and ellipsoidal height. \((l,s)\) denote the corresponding satellite image coordinates, such as row and column. \((U,V,W)\) represent the normalized ground coordinates, respectively. \((\phi_{O},\lambda_{O},h_{O},S_{O},L_{O})\) and \((\phi_{S},\lambda_{S},h_{S},S_{S},L_{S})\) denote the offset and scale factors used to normalize latitude, longitude, height, column, and row.  
Figure 1: Flowchart of the study.
## 4 Simulation Experiments and Result Analysis
### Analysis of Simulation Experiments  
Experiments related to short-term flows that often occur in heterogeneous vehicular networks are conducted to compare the performance of ICCDRL with NewReno, Cubic, Westwood and DRL-based DCC for different BERs and different numbers of data flows. The better performance of ICCDRL is verified by the variation of QoS performance metrics such as congestion window size, link utilization, and throughput. Moreover, several graphs are generated to verify the high performance of the proposed congestion control strategy.
## 4 Results and Discussions  
The comparative performance of training and testing accuracy for different CNNs for classification schemes are shown in Figure 11. It can be noted that, for three classification schemes, DenseNet201 is producing the highest accuracy for both training and testing. For normal and pneumonia classification, the test accuracy was 98%, while, for normal, bacterial, and viral pneumonia classification, it was 93.3%, and, for bacterial and viral pneumonia classification, it was found to be 95%. Figure 12 shows the area under the curve (AUC)/receiver-operating characteristics (ROC) curve (also known as AUROC (area under the receiver operating characteristics)) for different classification schemes, which is one of the most important evaluation metrics for checking any classification model's performance. It is also evident from Figure 12 that DenseNet201 outperforms the other algorithms.  
Figure 13 shows the confusion matrix for outperforming Densenet201 pre-trained model in for three different classifications.  
Table 4 summarizes the performance matrix for different CNN algorithms tested for the three different classification schemes. DenseNet201 outperforms other models in three different classification schemes in terms of different performance indices.  
The authors have also compared the results in the paper with the results of recently published works in the same problem. [PERSON] et al. [21] reported the detection of pneumonia using a deep learning technique called CXNet-m1, which showed sensitivity, precision, and accuracy of 99.6%, 93.28%, and 96.39%, respectively. [PERSON] et al. [29] evaluated the performance of VGG16 CNNs to classify pneumonia into normal, bacterial, and viral pneumorias. This showed sensitivity, precision, and accuracy of 99.5%, 97.7%, and 96.2%, respectively for pneumonia detection; however, 98.4%, 92%, and 93.6%, respectively, for distinguishing viral and bacterial pneumorias. Table 5 summarized the comparison of others works in pneumonia and types of pneumonia detection from chest X-ray images.  
Figure 11: Comparison of training and testing accuracy for normal pneumonia (**A**), normal, bacterial, and viral pneumonia (**B**), and bacterial and viral pneumonia (**C**) classification using different models.  
Figure 12: Comparison of the receiver-operating characteristics (ROC) curve for normal pneumonia (**A**), normal, bacterial, and viral pneumonia (**B**), and bacterial and viral pneumonia (**C**) classification using CNN-based models.  
Figure 13: Confusion matrix for (**a**) normal pneumonia, (**b**) bacterial and viral, and (**c**) normal, bacterial, and viral pneumonia classification using DenseNet201.  
It is evident that DesneNet201 exhibits the highest accuracy of all recent studies to the best of our knowledge, which could be useful in developing a prototype that can automatically classify results into normal, bacterial and viral pneumonia. Training the network using a larger database and working on an ensemble of the pre-trained CNN algorithms might increase the detection accuracy, which can be done as a future study.