esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characteristics of Extruded Corn Snacks
#### 3.2.2 Quality of Extruded Corn Snacks with Fruit Pomace
On the other hand, when the parameter b* was investigated, it was noted that samples with pomace showed lower values compared to extrudates made from commeal only, which had a value of 38.39. The highest value of the b*parameter was measured in extrudates with a 20% addition of rosehip (27.99) and the lowest in extrudates with a 20% apple pomace addition [77]. The introduction of fruit pomaces into snacks resulted in a decrease in WI values, indicating a shift from white color (Table 6), which was increasing with the share of added pomaces. The greatest decrease was observed for extrudates prepared with the addition of chokeberry (ECHB) and the smallest for those with cherry pomace (ECH) addition. Moreover, the color difference (\(\Delta\)E) between the control and other investigated samples was evaluated. Similarly, for WI, the same trend was observed--an increasing share of pomace resulted in increased \(\Delta\)E values. In addition, the greatest differences between control and experimental samples were observed for ECHB samples, whereas the smallest were observed for ECH. It can be assumed that a \(\Delta\)E value up to 2.3 corresponds to a just noticeable difference (JND) in color between the two samples [78; 79]. Such a low difference was only observed between EBC-10 and ECHB-05 (1.8) (Table S1). According to the aforementioned authors for \(\Delta\)E values < 3:5, an inexperienced observer also noticed the difference, and such a situation occurred between the ECHB-10 and EBC-20 samples. Data related to the hardness measurement of selected corn extrudates are summarized in Table 7. The addition of fruit pomaces resulted in decreased hardness (cutting force) as compared to the control sample. Such behavior could be explained by the partial replacement of starch polymers, which were subjected to melting during the extrusion process, forming a matrix for other ingredients in the extruded snack. Such replacement resulted in a more brittle structure, and less effort was required to cut the extrudate. In a study by [PERSON] et al. [57], a decrease in corn snack hardness was also observed when defatted blackcurrant seeds were added (an average decrease of 53% compared to
# Approaches and Challenges in Internet of Robotic Things
## 3 Related Work
### Secure Communication Mechanisms for IoRT  
Secure communication among IoRT devices is one of the primary concerns for industries, as well as society. Commonly used secure communication mechanisms for IoRT are as follows:  
* Trust-based mechanisms,
* Encryption-based mechanisms.
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 1 Introduction
In the same category, another recent work utilizes the concept of optimal measurement device placement to dictate the structure of the neural network [17]. Another work used neural networks to make state _forecasts_ instead of estimation such as [18]. These works show that neural networks can indeed be used for state estimation. However, for large, practical systems, the neural networks will tend to be large, heavy, and computationally taxing as well. This paper proposes taking the neural network application three steps further by (i) adopting the historical measurements and states to improve the accuracy of state estimation, especially for those states near transformers, ii) utilizing the standardized model optimization techniques to lighten the neural network for state estimation, and (iii) expanding its target application to the state estimation for hybrid AC/DC distribution systems. To visualize the thought process, this paper is structured as follows: Section 2 discusses the basic intuition on power system state estimation and the conventional method: weighted least squares state estimation (WLS-SE). It also shows the main objective of state estimation, i.e., to minimize the error between the calculated and the measured system state. Section 3 discusses neural networks and its learning process which involves minimizing errors between initial and target predictions and using those errors to update its own parameters. The section also ties machine learning to state estimation by showing where it can be used within state estimation and the advantages it has over the conventional WLS-SE. Section 4 carefully lays out the process of developing and verifying the proposal from data preparation all the way through performance evaluation. Section 5 provides insight into the performance of the proposed neural networks and Section 6 provides a summary, conclusion, and the topic's possible future works.
# Numerical Study of Dig Sequence Effects during Large-Scale Excavation
## 5 Discussion
### Determination of the Optimal Excavation Scheme  
To objectively judge the advantages and disadvantages of different excavation sequence schemes, we defined the foundation pit excavation safety coefficient \(\mathrm{K_{es}}\) for comprehensive evaluation. \(\mathrm{K_{es}}\) consists of the displacement of the foundation pit, \(\mathrm{K_{1}}\); the time required for construction, \(\mathrm{K_{2}}\); the deformation rate of the foundation pit, \(\mathrm{K_{3}}\); the volume of the plastic zone of the foundation pit, \(\mathrm{K_{4}}\); the maximum principal stress for DW, \(\mathrm{K_{5}}\); and the maximum moment at DW monitoring point, \(\mathrm{K_{6}}\). The specific composition of each factor is shown in Table 3. The weight of each factor was 0.5, 0.1, 0.1, 0.1, 0.1, and 0.1, respectively. Subsequently, all the results were processed via min-max normalization [41]. The specific formula is shown in Equation (1), and the results were obtained as shown in Table 4.  
\[X_{new}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{1}\]  
where \(X_{new}\) is the normalized value, \(X_{max}\) is the maximum value of the sample data, and \(X_{min}\) is the minimum value of the sample data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compositional Factor** & **Weight** & **Note** \\ \hline Displacement of the & & The displacement maxima in 6 directions XYZ in the model, maximum settlements at \\ foundation pit (\(\mathrm{K_{1}}\)) & 0.5 & 4 monitoring points, maximum horizontal \\  & & displacements on 4 survey lines \\ \hline Time required for & & The total steps required to complete \\ construction (\(\mathrm{K_{2}}\)) & 0.1 & pit excavation \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Foundation pit \\ excavation safety \\ factor (\(\mathrm{K_{es}}\)) \\ \end{tabular} } & Deformation rate of & \\ \cline{2-3}  & the foundation pit (\(\mathrm{K_{3}}\)) & 0.1 & The maximum deformation rate in the model \\ \cline{2-3}  & The volume of the plastic zone & & The “shear-now”, “shear-past”, “tensile-now”, and “tensile-past” volumes in the modeled \\  & of the foundation pit (\(\mathrm{K_{4}}\)) & 0.1 & plasticity region \\ \hline Maximum principal & & Maximum principal in the shell structure stress \\  & stress for DW (\(\mathrm{K_{5}}\)) & 0.1 & Maximum moment of the monitoring points in \\ \hline Maximum moment at & & the shell structure \\ \hline \hline \end{tabular}
\end{table}
Table 3: Components of the foundation pit excavation safety coefficient \(\mathrm{K_{es}}\).  
Figure 10: Foundation pit construction site.  
From the above results, it can be seen that schemes C and E have significant advantages, and E is the optimal scheme.
## 2 Materials and Methods  
### Gold Nanoparticles (AuNPs) Synthesis  
Chloroauric acid trihydrate (HAuCl4 3H2O), sodium citrate trihydrate, sodium carbonate, sodium hydrogen carbonate, hetero-bifunctional poly(ethylene glycol)s (HS-PEG5 kDa-X, X: COOH, NH2 or OCH3 and HS-PEG20 kDa-COOH), bovine serum albumin (BSA) and FITC (fluorescein-5-isothiocyanate isomer 1) were purchased from Sigma-Aldrich (St. Louis, MO, USA) and used without further purification. KCN was purchased from Chemapol. All glassware used in the syntheses were thoroughly cleaned in aqua regia (HCl: HNO3 = 3:1 _v/v_), rinsed in ultrapure water, and oven-dried prior to use.  
The synthesis of spherical citrate-capped gold nanoparticles was employed, adopting a seeded growth approach developed by [PERSON] et al. [17]. Briefly, 150 mL of 2.2 mM sodium citrate was heated to boiling in a 250 mL three-neck round-bottomed flask. When the solution started to boil, 1 mL of HAuCl4 25 mM was added under vigorous stirring. After 10 min, the color of the solution turned soft pink, indicating the formation of AuNP seeds. For the growing steps, the temperature of the solution was decreased to 90 \({}^{\circ}\)C, and 1 mL of sodium citrate 60 mM and 1 mL of HAuCl4 25 mM were successively added. This process was repeated every 30 min for a total of 3 times, thus resulting in a new generation of nanoparticles. When the reaction was finished, the sample was diluted by extracting 55 mL of the sample and adding 53 mL of ultrapure water and 2 mL of 60 mM sodium citrate. The extracted sample was characterized, and the remaining one was used as seeds for the next generation. For this study, 2 nd generation (24 nm) and 6 th generation (56 nm) AuNPs were used.  
The PEGylation of AuNPs was performed as described below. HS-PEG-X (X: COOH, NH\({}_{2}\), OCH\({}_{3}\)) with MW = 5 kDa and HS-PEG-COOH with MW = 20 kDa were dissolved in water at a concentration of 5 mg/mL and added to 10 mL of AuNP solution to a ratio of 10 PEG molecules per nm\({}^{2}\). The solutions were stirred overnight at room temperature, and the excess PEG molecules were washed by centrifugation (three times) [18].
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
###### Abstract  
Drones are widely used for wildlife monitoring. Deep learning algorithms are key to the success of monitoring wildlife with drones, although they face the problem of detecting small targets. To solve this problem, we have introduced the SE-YOLO model, which incorporates a channel self-attention mechanism into the advanced real-time object detection algorithm YOLOv7, enabling the model to perform effectively on small targets. However, there is another barrier; the lack of publicly available UAV wildlife aerial datasets hampers research on UAV wildlife monitoring algorithms. To fill this gap, we present a large-scale, multi-class, high-quality dataset called WAID (Wildlife Aerial Images from Drone), which contains 14,375 UAV aerial images from different environmental conditions, covering six wildlife species and multiple habitat types. We conducted a statistical analysis experiment, an algorithm detection comparison experiment, and a dataset generalization experiment. The statistical analysis experiment demonstrated the dataset characteristics both quantitatively and intuitively. The comparison and generalization experiments compared different types of advanced algorithms as well as the SE-YOLO method from the perspective of the practical application of UAVs for wildlife monitoring. The experimental results show that WAID is suitable for the study of wildlife monitoring algorithms for UAVs, and SE-YOLO is the most effective in this scenario, with a mAP of up to 0.983. This study brings new methods, data, and inspiration to the field of wildlife monitoring by UAVs.  
drones; aerial image dataset; wildlife detection; performance evaluation; attention +
Footnote †: journal: Applied Sciences
# Generation of Data-Driven Expected Energy Models for Photovoltaic Systems
## 3 Results and Discussion
### Low-Capacity Systems  
The model performance of the AIT for low-capacity systems was generally comparable to that of high-capacity systems, although the improvements were not as high. Across all low-capacity sites, the AIT model's goodness of fit improved by 0.165 in \(R^{2}\) (IEC: 0.74; AIT:  
Figure 7: Percent error as a function of irradiance shows that the ‘additive interaction’ model outperforms the IEC standard across both high-capacity (**a**) and low-capacity (**b**) systems. Lines indicate mean values, while shaded region captures one standard deviation. The ‘additive interaction’ model performs best (\(\delta\approx 0\)) at 500–1100 \(\frac{W}{m^{2}}\) and at 200–1000 \(\frac{W}{m^{2}}\) for high-capacity and low-capacity systems, respectively.  
0.90) and 0.61 in log \(RMSE\) (IEC: 3.35; AIT: 2.75) (Figure 6a,b). Out of the 31 low-capacity sites (comprising 50K hours), the IEC-based model outperformed the trained models in 4 systems (Table 3). In some of the low-capacity systems, the measured energy is much higher than expected (Figure 5). The tendency of the IEC model to overpredict likely describes why this model performs better for some of the lower-capacity systems.  
The \(\delta\), on average, is 4.42 and 15.37 for the AIT model and IEC model, respectively. Similar to the high-capacity system, the percent error values are greater at the extremes (Figure 7b). However, the standard error is generally higher in the low-capacity systems, as evidenced by wider standard deviation bars across the irradiance levels (Figure 7b).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dependent Variable** & **Model** & \multicolumn{4}{c}{**Coefficients**} & \multicolumn{4}{c}{**Model Summary**} \\ \hline  & & **B** & **SE** & **Beta** & **Sign.** & \(\mathbf{R^{2}}\) & **Sign.** \\ \hline  & Const & \(0.017\) & \(0.001\) & & \(0.001\) & & \\  & RMSSDRel3 & \(1.320\) & \(0.001\) & \(0.785\) & \(0.001\) & & \\ RMSSDRec1 & BFExe2 & \(-0.149\) & \(0.001\) & \(-0.369\) & \(0.001\) & \(1.000\) & **0.001** \\  & BFExe6 & \(0.041\) & \(0.001\) & \(0.085\) & \(0.001\) & & \\  & BERel4 & \(-0.008\) & \(0.001\) & \(-0.029\) & \(0.005\) & & \\ \hline  & Const & \(0.054\) & \(0.001\) & & \(0.003\) & & \\  & HRExe2 & \(-0.001\) & \(0.001\) & \(-0.824\) & \(0.003\) & & \\ RMSSDRec2 & BFExe4 & \(0.186\) & \(0.002\) & \(1.230\) & \(0.008\) & \(1.000\) & **0.002** \\  & BFExe3 & \(-0.099\) & \(0.002\) & \(-0.845\) & \(0.012\) & & \\  & RMSSDRel3 & \(-0.104\) & \(0.005\) & \(-0.248\) & \(0.025\) & & \\ \hline  & Const & \(-0.026\) & \(0.002\) & & \(0.008\) & & \\ RMSSDRec3 & BFExe5 & \(0.128\) & \(0.001\) & \(0.728\) & \(0.001\) & 0.999 & **0.001** \\  & Fat Mass & \(-0.001\) & \(0.001\) & \(-0.924\) & \(0.001\) & & \\  & Height & \(5.75\times 10^{-5}\) & \(0.001\) & \(0.036\) & \(0.037\) & & \\ \hline  & Const & \(0.071\) & \(0.010\) & & \(0.005\) & & \\ RMSSDRec4 & HRExe1 & \(-0.001\) & \(0.001\) & \(-0.903\) & \(0.002\) & 0.978 & **0.001** \\  & BFExe5 & \(0.078\) & \(0.017\) & \(0.405\) & \(0.018\) & & \\ \hline  & Const & \(-0.044\) & \(0.008\) & & \(0.012\) & & \\ RMSSDRec5 & RMSSDRel5 & \(0.589\) & \(0.054\) & \(0.905\) & \(0.002\) & 0.979 & **0.003** \\  & BFExe4 & \(0.118\) & \(0.020\) & \(0.479\) & \(0.010\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Multiple linear regression analysis for the averaged RMSSD over 3 min segments during post-exercise recovery in the supine position.
## 3 State of the Art  
### IoT Communication Technologies for the Internet of Things  
Low-power wide area networks (LPWANs) have become a popular communication technology for the Internet of Things (IoT) due to their low power consumption and wide coverage area. Two of the most popular LPWAN technologies are LoRaWAN and NB-IoT [17; 18; 19].  
LoRaWAN (Long Range Wide Area Network) is a wireless communication protocol based on the LoRa modulation technique. LoRaWAN has the ability to communicate over long distances, typically up to 10 km in rural areas and up to 2 km in urban areas. It operates in unlicensed frequency bands, which makes it a cost-effective solution. The LoRaWAN  
Figure 2: Design Science Research Process; Own illustration, based on [13].  
protocol is open-source and has a large community of developers. LoRaWAN is primarily used for battery-powered devices that require low data rates, such as environmental monitoring sensors, smart parking systems, and asset tracking [20, 21].  
NB-IoT (Narrowband IoT) is a cellular technology designed for IoT devices within 5G cellular networks. It is a standardization effort by the 3 GPP and is based on the LTE (Long-Term Evolution) technology. The NB-IoT uses a narrow bandwidth of 200 kHz and can operate in licensed or unlicensed frequency bands. The main advantage of the NB-IoT is its ability to operate in areas with weak signal strengths and in underground locations. It can also support high data rates and has a low latency, making it suitable for applications that require real-time data, such as industrial automation and smart cities [22, 23]. Table 1 summarizes the technical properties of LoRaWAN and NB-IoT.  
One of the main advantages of LoRaWAN is its long-range communication capabilities, which make it suitable for use in cases that require devices to be deployed in remote areas, such as environmental monitoring or asset tracking. Additionally, the unlicensed frequency bands used by LoRaWAN make it a cost-effective solution, as no licensing fees are required. However, the trade-off for this long-range communication is a low data rate and higher latency, which may not be suitable for applications that require real-time data [24].  
On the other hand, NB-IoT offers high data rates, low latency, and reliable connectivity in areas with weak signal strengths. Its cellular network infrastructure also provides a level of security and reliability that may not be possible with LoRaWAN. However, the licensing fees and higher deployment costs associated with the NB-IoT may make it less cost-effective than the LoRaWAN for certain applications [24].  
In conclusion, both the LoRaWAN and NB-IoT have their advantages and limitations, and the choice between them will depend on the specific requirements of the application. The LoRaWAN is best suited for applications that require long-range communication and low data rates, while NB-IoT is ideal for applications that require real-time data and operate in areas with weak signal strength.
# Guidelines towards Information-Driven Mobility Management
## 3 Mobility Management Functional Aspects
To further assist such evolution, it is also relevant to remind that IP-based mobility management approaches have been designed keeping in mind support of mobility from a source-driven perspective. On later phases, adjustments of the centralised solutions for support of simultaneous mobility [13] as well as non-simultaneous mobility have been introduced. This is the case, for instance, of the _Return Routability Procedure_ for MIPv6 solutions, intended to assist CN mobility. As also stated in Section 2, efforts towards the evolution of IP-based mobility management is approaching a distributed vision, having in mind the support of mobility for the different entities, where IPv6 is the underlying protocol. In such context, solutions have looked into MAP selection and discovery; forwarding path and signalling management; and exchange of control information to assist faster handovers (e.g., better selection of identifiers to use on the new attachment locations).
_applied sciences_  
_Article_  
**Prediction of Maximum Tunnel Uplift Caused by Overlying Excavation Using XGBoost Algorithm with Bayesian Optimization**  
**[PERSON] 1, [PERSON] 1,*(r)**, [PERSON] 1, [PERSON] 1(r) and [PERSON] 2(r)  
**Abstract: The uplifting behaviors of existing tunnels due to overlying excavations are complex and non-linear. They are contributed to by multiple factors, and therefore, they are difficult to be accurately predicted. To address this issue, an extreme gradient boosting (XGBoost) prediction model based on Bayesian optimization (BO), namely, BO-XGBoost, was developed specifically for assessing the tunnel uplift. The modified model incorporated various factors such as an engineering design, soil types, and site construction conditions as input parameters. The performance of the BO-XGBoost model was compared with other models such as support vector machines (SVMs), the classification and regression tree (CART) model, and the extreme gradient boosting (XGBoost) model. In preparation for the model, 170 datasets from a construction site were collected and divided into 70% for training and 30% for testing. The BO-XGBoost model demonstrated a superior predictive performance, providing the most accurate displacement predictions and exhibiting better generalization capabilities. Further analysis revealed that the accuracy of the BO-XGBoost model was primarily influenced by the site's construction factors. The interpretability of the BO-XGBoost model will provide valuable guidance for geotechnical practitioners in their decision-making processes.**  
**Keywords:** tunnel uplift prediction; overlying excavation; machine learning; Bayesian optimization; model visualization
## 3 Results
### Linear Regression  
In the first stage of modelling, linear regression parameters were estimated based on a set of design data obtained from architectural offices. This set consisted of 245 one- and two-story residential buildings with multi-pitched roofs. The final, optimal form of the linear model was created after rejecting 26 outliers and dropping two irrelevant variables--knee--wall height and boiler room; the reliable fit of this model to the data is 88%. Table 2 shows the detailed results of the multiple regression analysis for a sample of 219 observations, described by 5 explanatory variables.  
The standardised regression coefficients (\(\beta\)-weights) prove that the \(AC\) variable (built-up area) has the greatest influence on the explanatory variable, being, thus, the most effective predictor of floor area. The results of the regression model parameter significance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(N\) = 219 & \(\beta\) & \(\sigma(\beta)\) & \(a\) & \(\sigma(a)\) & \(t(213)\) & \(p\)-Value \\ \hline intercept & & & 12.080 & 8.301 & 1.455 & 0.147 \\ A\({}_{\rm C}\) & 1.086 & 0.040 & 0.532 & 0.019 & 27.363 & \textless{}0.001 \\ H & 0.091 & 0.025 & 3.432 & 0.955 & 3.596 & \textless{}0.001 \\ G\({}_{\rm A}\) & \(-0.260\) & 0.039 & \(-0.403\) & 0.061 & \(-6.660\) & \textless{}0.001 \\ S\({}_{\rm N}\) & \(-0.158\) & 0.079 & \(-9.111\) & 4.580 & \(-1.989\) & 0.048 \\ R\({}_{\rm S}\) & 0.236 & 0.078 & 0.453 & 0.150 & 3.022 & 0.003 \\ \hline \end{tabular} \(\text{R}=0.938\)\({\rm R}^{2}=0.881\)Adj. \({\rm R}^{2}=0.878\)F(5, 213) = 314.48 \(p<0.001\) SE: 8722.  
\end{table}
Table 2: Multiple Linear Regression estimates for the building designs dataset (model D). Source: own calculation.  
Figure 1: Features distributions in the data: (**A**) new designs sample, (**B**) old designs sample, (**C**) existing buildings sample; black dots represent validation data.  
tests (the last two columns of Table 2) confirm the adequacy of the five exogenous variables. In the rest of the article, the model is referred to as Model \(D\).