esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The relationships between instrumental quality indices and sensory properties of Shine Muscat grapes harvested 16, 18, 20, and 22 weeks after full bloom (WAFB) were evaluated. The sweetness scores showed similar trends to the total soluble solids (TSS) content of berries. However, the sourness, astringency, and firmness scores showed no significant differences among the samples. The flavor score showed a trend similar to that of sweetness perception. The higher flavor score in the berries harvested at 20 and 22 WAFB seemed to result from the development of floral monoterpene compounds, in particular linalool and its derivatives, with ripening. Principal component analysis indicated that the sensory flavor characteristics of Shine Muscat grapes harvested from 16 to 22 WAFB changed from fruity and herbaceous to floral with the alteration of key active aroma compounds with ripening. From the results, it was concluded that sweetness and flavor are the most important quality indices affecting consumer preference for Shine Muscat grapes. The instrumental measurement of sugar content and aroma compounds could provide an excellent quality assessment of ripened Shine Muscat grapes.  
K.-O.C.: Study design, Methodology, Investigation, Writing--original draft/ review and editing; Y.Y.H.: Study conception and design, Methodology, Writing--original draft/review; S.J.P.: Study conception and design, Methodology; D.H.L.: Study conception and design, Methodology; S.J.K.: Study conception and design, Methodology, Writing-original draft/review; D.I.: Methodology, Investigation, Writing--original draft/review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was carried out with the support of \"Cooperative Research Program for Agriculture Science and Technology Development\" (Project No. PJ015758012021), Rural Development Administration, Republic of Korea.  
All subjects gave their informed consent for inclusion before they participated in the study.  
Data Availability Statement:Data is contained within the article.  
The authors declare no conflict of interest.  
Figure 4: Schematic representations of (**a**) a radar plot of hedonic sensory properties of Shine Muscat grapes harvested at 16, 18, 20, and 22 weeks after full bloom (WAFB), and (**b**) heatmap of Pearson’s correlation coefficient matrix between sensory attributes.
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 4 Experiments
### Evaluation Criteria  
In the field of crowd counting, the mean absolute error (MAE) can indicate the accuracy of the model, and the root mean square error (RMSE) can represent the robustness of the model. In this research, MAE and RMSE are employed as the evaluation criteria. The formulas of evaluation can be listed as follows.  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert Pre_{i}-GT_{i}\rvert \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(Pre_{i}-GT_{i})^{2}} \tag{6}\]  
\[Pre_{i}=\sum_{p=1}^{p}\sum_{q=1}^{Q}z(p,q) \tag{7}\]  
Among them, \(N\) is the count of heads in a test sample, \(Pre_{i}\) indicates the predicted density map, and \(GT_{i}\) indicates the marked head counting by manual in the given scenario. \(z\) (\(p\), \(q\)) represents the pixel value of the point (\(p\), \(q\)), and \(P\) and \(Q\) indicate the length and width of the estimated density map, respectively.
# Experiments and Design of an Anti-Disaster Support System for Apple Orchards
## 4 Design of the Anti-Disaster Support System
### Safety Review  
The safety of the apple tree support was examined by comparing the yield moment of the support pipe, the resistance moment of the apple tree, and the required moment due to the wind load, as shown in Equation (1). Figure 19 is the flowchart showing the safety review procedure of the support pipe for apple trees. An example of the safety review for the fence support system is presented in Appendix A. An anti-disaster fence support for apple trees can be designed in accordance with the flowchart. If the support is found to be unsafe, the support pipe can be reselected according to pipe size in standard [11] to have the safety of the new support system re-examined. The wind velocity by locality is to be selected pursuant to the design standard [21].  
Figure 18: Estimation of the area required for moment transformation.  
Figure 20: Mimetic diagram of fence support.  
Figure 19: Flowchart for safety review of the support system design.  
Using the allowable stress design method, Tables 4 and 5 show the standard specifications for the apple tree support system in consideration of wind velocity by locality after selecting the members pursuant to the specification of KS D 3760 [11]. According to Table 4, the fence support and matrix support in the case of the circular hollow section (CHS) steel pipe can withstand wind velocities of up to 30 m/s and 38 m/s, respectively. It was reasoned that the support system was limited by wind velocity due to the sectional property of the CHS steel pipe. Therefore, the standard specification of the apple tree support system was proposed anew according to Table 4 by selecting a rectangular hollow section steel pipe as a member of the support system. Additionally, the strut wire, trellis wire, and mat wire of the matrix support system were specified, as well as the specification of turnbuckle to bring about tension on the wire. Although the trellis wire was designed to be \(\omega\)2.38, as shown in Figure 5, its specification should be determined on the basis of the structural design principle. The factors of influence on the tension of the wire are the initial tension during its installation and the wind load to the apple tree. The additional tension due to wind load is considered to be the main factor of influence. The maximum tension of the trellis wire was the tension as measured at the point of the collapse angle of the apple tree (10.4\({}^{\circ}\)).
## 2 Laboratory Experiments
### Measurement Technique  
The fast evolution of the dam-break flow was recorded through a Sony Nex-7 camera with a 50-fps acquisition frequency at 1920 \(\times\) 1080p resolution, positioned with the field of view focused on the downstream part of the channel. The reservoir water was colored using methylene blue dye [12]. The whole channel was lighted with fluorescent lamps located at the bottom. To better control the ambient light, tests were performed in a dark room, all of the sides of the channel were coated with black materials to reduce light reflection, and a white sheet of Plexiglas was put under the glass bottom.  
Ultrasonic distance sensors (UDS) were used to obtain the time evolution of the water levels (i.e., the stage hydrographs). They measure distances by sending acoustic waves and measuring their return time [58; 59; 60; 61; 62]. Specifically, five MicrosonicTM sensors were used in the present study (one Mic+35/IU/TC and four Mic+25/IU/TCs). Measurement locations of the sensors are shown in Figure 2. The Mic+35/IU/TC sensor (P1) was used to obtain the water level variation upstream of the gate, since it has a higher measurement range (6.5-35 cm) and a slower response time (64 ms). The four Mic+25/IU/TC sensors (P2-P5) were used downstream in order to be able to measure fast water level changes, due to their lower measurement range (3-25 cm) and quicker response time (32 ms). More information about the UDS can be found in previous studies [63].
## 2 Data Collection  
To be able to train and evaluate the classification system, a comprehensive annotated data set was collected. We chose Nokia N900 mobile phones for data collection, the main reason being easy access to sensor data via application programming interfaces and the open source nature of the Maemo platform. Two data collection campaigns were arranged. The first campaign, with 21 users, was arranged to collect a basic training set with full annotations, providing enough data for the design and validation of the feature extraction and pattern recognition algorithms. During the first campaign, the users were requested to manually input their current activity and environment from the mobile phone user interface (UI). The second campaign, with 10 users, was arranged for testing the adaptation of algorithm parameters for each individual user. The campaign was done with context recognition software running and with only binary yes, no input, indicating whether or not a recognition result was correct or not, required from the user. It should be noted that the first campaign required much more effort from the test persons since the users were providing full annotation compared to the binary yes/no answers in the second campaign.
# Thermal Performance Analysis of a Double-Pass Solar Air Collector: A CFD Approach
## 2 Materials and Methods  
### U-Shape Double-Pass CPC Description and Physical Model  
The proposed solar air heater is a variant of the flat-plate receiver Compound Parabolic Concentrator (CPC) conceptualized as a U-shape double-pass heat exchanger. Figure 1a shows the evaluated geometry dimensions and the inlet and outlet positions, whereas, in Figure 1b, the CPC cross-section is shown. The CPC is tilted \(24^{\circ}\) since it is the latitude of the City of interest (Durango, Mexico) and consists of a flat-plate receiver, two reflectors, a cover, and a duct. The first pass of the airflow inside the CPC occurs in the cavity formed by the receiver, two reflectors, and the cover, while the second pass is in the duct section. The aperture area where the solar radiation enters the CPC is \(0.42\) m\({}^{2}\), while the area where it is absorbed is \(0.20\) m\({}^{2}\).  
The analysis of the position of the air inlet and outlet in the cavity consisted of the study of four configurations that were positioned concerning the height of the cavity (h\({}_{\text{cav}}\)): (a) inlet \(\frac{1}{4}\)h\({}_{\text{cav}}\)-outlet \(\frac{1}{4}\)h\({}_{\text{cav}}\) (Down-Down), (b) inlet \(\frac{1}{4}\)h\({}_{\text{cav}}\)-outlet \(\frac{3}{4}\)h\({}_{\text{cav}}\) (Down-Up), (c) inlet \(\frac{3}{4}\)h\({}_{\text{cav}}\)-outlet \(\frac{3}{4}\)h\({}_{\text{cav}}\) (Up-Up), and (d) inlet \(\frac{3}{4}\)h\({}_{\text{cav}}\)-outlet \(\frac{1}{4}\)h\({}_{\text{cav}}\) (Up-Down). The air inlet and outlet configurations are shown in Figure 2.  
Figure 1: U-shape double-pass CPC solar heater. (**a**) Geometry; (**b**) Cross-section.
## 3 Datasets and Methods Used in SATIDS  
### Database Description  
The performance of an AI-powered IDS is dependent on the quality of the dataset used for its training. The lack of an up-to-date, real-world generated dataset is one of the key obstacles in training effective AI-based IDS. The main reason that public data sets are not available for the intrusion detection area is for privacy and legal considerations. To verify the proposed system's performance in two different environments, it was trained and evaluated using two of the most recent realistic datasets, namely ToN-IoT and InSDN datasets which are introduced in the following subsections. These datasets were selected to test the system robustness and generalization and to say with a certain confidence that the proposed IDS behaves well in both SDN and IoT environments. The ToN-IoT dataset simulates an IoT environment with 43 features and nine types of attacks, while the InSDN dataset enables some cyberattacks using OpenFlow protocols in the SDN architecture, with 79 features and eight types of attacks.
# Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine
## 2 Theory and Principle
### MFI Classifier Based on SVM  
The SVM [25] is used to build the MFI classifier for processing the extracted signal constellation diagram features. The robustness of the classification algorithm is improved by constructing a linear segmentation hyperplane with maximum boundaries to classify all data correctly. A training dataset \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots,(x_{M},y_{M})\}\) containing \(M\) samples is given. The samples' feature vectors and labels are \(x_{i}\) and \(y_{i}\), respectively, where the feature vector satisfies \(x_{i}=\left(x_{i}^{(1)},x_{i}^{(2)},\cdots,x_{i}^{(m)}\right)\) with \(m\) features and the labels satisfy \(y_{i}\in\{-1,+1\}\). To train the maximum margin model with parameters \(w\in R^{D}\) and \(b\in R\), it solves the following optimization problem:  
\[\begin{split}&\underset{w,b}{\text{\emph{min}}}\frac{1}{2}\|w\|^{2} \\ &\text{Subject to }y_{i}\Big{(}w^{T}x_{i}+b\Big{)}\geq 1,\ i=1, \cdots,N\end{split} \tag{9}\]  
According to the feature vector \(x_{i}\) and Lagrange multiplier pairs \(\lambda_{i}\), \(\lambda_{i}^{*}\)[26], the classification decision function is calculated by Equation (10):  
\[f(x)=\sum_{m=1}^{M}\left(\lambda_{i}-\lambda_{i}^{*}\right)\langle x_{i},x \rangle+b \tag{10}\]  
The classifier's performance is measured by comparing the predicted results with \(y_{i}\), and the classification accuracy is expressed by Equation (11):  
\[P=\frac{1}{M}\sum_{m=1}^{M}\text{II}(f(x_{i})=y_{i}) \tag{11}\]  
where II denotes the module turns the digital signal into an analog signal. In the Mach-Zehnder modulator (MZM), four signals are modulated to two optical carriers with different polarization states. The optical carriers are generated by a continuous-wave (CW) laser with a center frequency of 193.1 THz and divided into different polarization states by a polarization beam splitter (PBS). Then, the two polarization states of the modulated optical signal are combined into one using a polarization beam combiner (PBC). The transmission links are 80 km and 120 km single-mode fiber (SMF) with an attenuation coefficient of 0.2 dB/km and a dispersion coefficient is 16.75 ps/nm/km. The amplification gains of the erbium-doped fiber amplifier (EDFA) are 16 dB and 24 dB, respectively. Then, the optical signals are transmitted through SMF and amplified by EDFA, which can completely compensate for the fiber attenuation loss in the process of optical signal transmission. Two optical signals, I and Q, are obtained at the receiver, and the photodiode is used to modulate the optical signals into analog signals. Next, after the coherent receiver, the analog signal is converted into a digital signal by the analog-to-digital converter (ADC) module. Further, dispersion compensation (DC), resampling, timing recovery, and constant mode algorithm (CMA) equalization are performed sequentially at the DSP. Finally, the required constellation diagrams are generated. The parameters of the coherent optical communication system are shown in Table 1.
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 4 Discussion
### Influence of LAI and Soil Moisture on \(\sigma^{\circ}\) Calculated from Sentinel-1 Data.  
The assessment of LAI using ENVISAT MERIS and ASAR data was presented by [PERSON] et al. in [44; 62; 63]. This was done for the wetland habitats distinguished from the classification of MERIS image. LAI prediction, applying ALOS PALSAR HV data, was presented by [PERSON] et al. in [43; 65; 66]. The obtained results showed that with the increase of LAI there was an increase in the backscattering coefficient, which indicates the low signal attenuation by vegetation in L-band, contrary to results obtained in C- and X-band. For LAI assessment, the ASAR mode with higher incidence angles gives better results, which have been examined by authors in other studies [67], applying VV IS6 data for the agriculture area. In the present study, the influence of LAI and SM on \(\sigma^{\circ}\) calculated from Sentinel-1 data has been examined. Two linear multiply regression models of \(\sigma^{\circ}\) VH that include these two components as the independent variables have been developed. Comparing the results of model (2) and model (3), it has to be noted that the influence of LAI on \(\sigma^{\circ}\) VH  
Figure 14: Soil moisture difference map created by applying dates: 10 July 2015 and 11 June 2015.  
value is much stronger when soil moisture values are low (<40%). The obtained results are promising and will be validated in the future work using new satellite and in-situ data.
# Software Project Risk Identification Based on Scrum Artifacts
## 3 Risk Identification Methodology for Scrum Projects  
The proposed risk identification methodology (Figure 1) consists of two main stages: adapting the identification framework to the specific project and analyzing the risks based on the project data during project execution. Further stages of risk management are not analyzed, as our approach is concentrated on risk identification and ensuring that this process is structured, automated, and aligned with Agile project management and, specifically, Scrum principles.  
In the proposed methodology, the terms 'Project Team' and 'Team member' encompass a broader range of participants than the traditional Scrum roles. In addition to the core Scrum roles (Product Owner, Scrum Master, and Development Team), the team may include product managers, product designers, business analysts, DevOps engineers, and  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Criteria** & [22] & [25] & [26; 27] & [28] & [30] & [31; 32] & **This Study** \\ \hline Designed for Agile/Scrum Projects & + & + & + & + & + & + \\ \hline Team participation in risk identification & + & + & + & & + & + \\ \hline Risk classification/categorization/taxonomy & + & + & + & + & + & + \\ \hline Focus on Machine Learning & & & & + & + & \\ \hline Analyzes risk identification & + & + & + & + & + & + \\ \hline Integration with project management tool (e.g., Jira) & & & + & & & + \\ \hline Threshold-Based risk detection & & & & & + & + \\ \hline Analyzes attributes/artifacts/risks relations & + & & & & + & + \\ \hline Aligned with Scrum artifacts & & + & + & + & + \\ \hline Implemented in a software tool & & + & & + & + \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of the analyzed risk management approaches to our proposed framework (a ‘+’ indicates that the criterion has been met).  
other relevant stakeholders. Any individual who contributes to the project's success is considered a team member. To enhance collaboration and clarify role alignment, the RACI (Responsible, Accountable, Consulted, Informed) matrix [2] can define and allocate responsibilities among team members. Additionally, all team members need to understand the risk identification principles. Active engagement in applying these principles within their areas of responsibility is essential to fostering a risk management culture throughout the project lifecycle.  
The prepared framework, which encompasses a risk classifier, Scrum project artifacts, and attributes, is based on practical experience and analyzed research in the risk management area. The risk classification, lists of project artifacts and their attributes, and the relationships between attributes and risks are proposed as a basis for further improvement and adaptation of the project team according to the specifics of the project.
## 1 Introduction  
Being able to model and eventually forecast population density and crowd distributions in a smart city scenario is mainly about studying and analyzing data in space and time domain. A very useful kind of data able to describe human presence and mobility in urban and rural areas is mobile phone data. There are different scientific works that have been studying mobile phone data in depth thus revealing the different patterns that emerge [1; 2; 3; 4]. Another line of research for aggregated mobile phone data such as in [5] deals with modelling them as a time series, so as to explore their predictability by using different forecasting methods. Although a large part of predictive analytics is based on modelling and developing forecasting algorithms, there is still a lack of understanding of which forecasting method is best for which time series forecasting scenario. The forecasting methods that we use in this study belong to two broad categories: statistical approaches and deep learning approaches. As we show in this work, both categories have advantages and disadvantages, thus the purpose of our study is not to establish an absolute winner between the two. Instead, we aim to underline the strengths and weaknesses of each category of methods and, most importantly, to investigate which one might be the most appropriate for a specific forecasting scenario.  
Following this direction, the main contribution of our work is that of evaluating the forecasting performance of different statistical and deep learning methods in predicting mobile activity and thus the crowd distribution represented by such activity in aggregated mobile data. More in detail in Section 2, we present the aggregated dataset, its extension in space and time as well as its main features. In Section 3 we present the statistical and deep learning methods that we use for performing forecast. In the same section we illustrate how we proceed with model evaluation and the error forecasting measures that we use. In Section 4 we illustrate three different forecasting scenario and the experiments we perform in order to explore such scenario. In Section 5 we go through the related work on relevant forecasting methods and finally in Section 6 we present our conclusions.
## 4 Regression Analysis Results  
Regression analysis methods were used to generalize the LDP curves obtained via the numerical analysis. The empirical formula for the LDP proposed by [PERSON] [8] (Equation (4)) can be generalized as Equation (13) containing two parameters (\(\alpha\), \(\beta\)):  
\[\frac{u_{r}}{u_{r}^{m}}=\left[1+\exp\left(\frac{-x/R_{T}}{\alpha}\right)\right] ^{-\beta} \tag{13}\]  
where \(\alpha\), \(\beta=parameters\) related to rock mass conditions, \(\alpha\) and \(\beta=f(RMR,\ p_{0})\);  
\(u_{r}=\) radial displacement at distance \(x\) from the face;  
\(u_{r}^{m}=\) maximum radial displacement;  
\(x=\) distance from the face;  
\(R_{T}=\) tunnel radius.  
Parameters \(\alpha\) and \(\beta\) are functions of the RMR and initial stress state \((p_{0})\) indicating a rock mass condition, as determined via the numerical analysis results. The values of \(\alpha\) and \(\beta\) in Equation (13) were obtained using regression analysis. Regression analysis was performed using MATLAB, and the results are listed in Table 2. The analysis was performed in the range of \(\alpha\) = 0.898-2.416 and \(\beta\) = 1.361-2.851 depending on the rock  
Figure 8: Variation of LDP with initial stress conditions: (**a**) RMR = 10; (**b**) RMR = 30; (**c**) RMR = 50; and (**d**) RMR = 70.  
quality and the initial stress conditions. These results were obtained using the extended values at \(\alpha=1.1\) and \(\beta=1.7\) of the empirical equation for the LDP proposed by [PERSON] (2016), which showed the best fit for the tunnel internal displacement measurements reported by [PERSON] et al. (2017). The value of \(\alpha\) is proportional to the RMR and tends to be inversely proportional to the initial stress. The value of \(\beta\) exhibits a tendency to decrease with an increase in the RMR; however, it shows no clear correlation with the initial stress.  
Figure 9 presents the results of the regression analysis for \(\alpha\) and \(\beta\). The correlation was excellent, and the parameters for Equation (13) were analyzed as shown in Equations (14) and (15):  
\[\alpha=0.305\ln(\text{Po}/\text{RMR}^{1.5})+2.419 \tag{14}\]  
\[\beta=2.926\,\exp(-0.01\text{ RMR}). \tag{15}\]  
Figure 9: Regression analysis results for parameters (**a**) \(\alpha\) and (**b**) \(\beta\).