esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Confusion Matrix  
Figure 5 shows the confusion matrix in the \(x\)- and \(z\)-directions. Figure 5a,b show the results at an SPTR of 10 ps and 100 ps, respectively. The results in the \(y\)-direction were omitted because of symmetry. The readout pitch was fixed at 3.0 mm. As the number of events in the test dataset was 39,625, the maximum number of events in diagonal elements was considered to be 7925. In the confusion matrix in the \(z\)-direction at an SPTR of 100 ps, the diagonal elements had expanded to nondiagonal elements.  
\begin{table}
\begin{tabular}{c c c} \hline
**SPTR (ps)** & **Readout Pitch (mm)** & **Classification Accuracy (\%)** \\ \hline
0 & 0 & 96.1 \\
10 & 3 & 80.3 \\
100 & 3 & 25.0 \\ \hline \end{tabular}
\end{table}
Table 2: Classification accuracy at representative conditions.  
Figure 5: Confusion matrix in the \(x\)- and \(z\)-directions at an SPTR of (**a**) 10 ps, and (**b**) 100 ps, respectively. Results in the \(y\)-direction were omitted because these were almost same as those in the \(x\)-direction. Readout pitch was fixed at 3 mm. As the number of events in the test dataset was 39,625, the maximum number of events in diagonal elements is considered to be 7925.
# A Blockchain-Based Supervision Data Security Sharing Framework
## 3 Blockchain-Based Framework for Secure Sharing of Engineering Supervision Data
### Rapid Retrieval Mode for Engineering Supervision Data  
Due to the business communication between the supervision party and various construction departments, a large amount of data will be generated, and all data corresponding to the same file number will be stored in a dispersed database. If traditional data retrieval frameworks are used, database management software will screen full-text data based on file _ID_ as the retrieval criteria to obtain target data. The data retrieval time is longer due to the high time complexity and excessive interaction with the hard disk. And as the amount of data increases, the efficiency of data retrieval will further decrease. The nodes on the proposed framework chain will maintain a hash table to record the position of the supervision data summary in the consortium blockchain, and achieve fast retrieval by locating the block where the data are located.  
1. For file data scattered in the database, all data related to the file can be quickly found if the block positions of all data corresponding to the file can be obtained. An index data structure is established for file _ID_ and block number based on the above data retrieval characteristics. There are two variables in this data structure: one is used to record the file _ID_, and the other is a collection class used to store the block numbers of all database records related to the file.
2. In the traditional framework for fast data retrieval, node _ID_, and block number information are maintained by creating a new B+tree index structure. If querying file data file _ID_ with 01, it is inefficient to retrieve information from the database according to the retrieved block number.
3. By establishing a hash index with the file _ID_ as the primary key, the encapsulation class of the file _ID_ and block number is stored in the hash table. Firstly, all block numbers related to the file _ID_ with 01 are searched in the hash table in memory, and then file data in the database are searched based on the set of block numbers. Compared to the solution framework of creating a new B+tree index, this framework can reduce disk I/O times by half. According to the storage characteristics of hash tables, compared to I/O interaction, the search time of hash tables can be ignored and mainly determined by the number of disk and memory interactions, which can effectively improve retrieval efficiency.
## 4 Methodology
### Validation and Metrics  
Standard DL model validation techniques such as train-test division or k-fold cross validation are not useful for evaluating and validating time series. They ignore the temporal behavior of this type of problem. For this reason, to validate our proposed model we used the Walk-Forward Validation (WFV) technique [91,92].  
Taking into account the above, WFV takes the last training-set as time lagged to make a forecast; thus, it compares the output of this model with the unseen data (testing-set). Moreover, this technique allows taking different time lags to evaluate in the model; furthermore, it works well in conjunction with the sliding window method.  
One of the most commonly used metrics to evaluate the performance of a forecast is MAPE since it is easy to understand and explain. However, the use of this indicator is restricted in the case of having null or close to zero measured values, which would provide undefined or very extreme values [8]. For this reason, to compare the forecasting performance of our model, we used the following metrics: coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and coefficient of variation of root mean square error (CVRMSE). It should be noted that the CVRMSE metric is especially used in this research, since the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), the International Performance Measurement and Verification Protocol (IPMVP), and the Federal Energy Management Program (FEMP) established this indicator as a metric  
Figure 6: Architecture proposal and training flow of the model for forecasting the daily energy consumption of the HVAC system in buildings.  
of the goodness of fit of a mathematical model with respect to the data or reference estimates measured in different operating conditions of an HVAC system [93; 94; 95; 96]. Specifically, if the CVRMSE forecast error of the model is less than 0.30 it is considered valid and adequate for engineering purposes. Next, we define the equations for the error metrics  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{\text{m}}\left(\hat{y}_{i}-y_{i}\right)^{2 }}{\text{m}}} \tag{7}\]  
\[\text{CVRMSE}=\left(\frac{\text{RMSE}}{\overline{y}}\right)*100\% \tag{8}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{\text{m}}\left(\hat{y}_{i}-y_{i}\right)^{2}}{ \sum_{i=1}^{\text{m}}\left(y_{i}-\overline{y}\right)^{2}} \tag{9}\]  
where m is the number of samples, \(y_{i}\) is the expected value of the i-th point, \(\overline{y}\) is the mean of the expected value, and \(\hat{y}_{i}\) is the predicted value.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 5 Performance Evaluation
### Linear Topology  
We define an ad-hoc network with a group of 5 fixed nodes aligned horizontally separated by variable lengths fulfilling a total length of 370 m in 20 s of simulation. We consider a linear topology because this topology can be applied to real scenarios such as petroleum pipes and orchards [1]. Node 0 will be the server and node 4 the client, as depicted in Figure 2.  
Figure 3 shows the results obtained from the simulations with power control and without it.  
Figure 1: Link between network and physical layer.  
Figure 2: Linear topology.  
As can be seen, the throughput is noticeably greater when using the power control mechanism in the first test, so the functioning of the designed power control mechanism can be validated. This is because the default transmission power, which is fixed in the \"without mechanism\" case is not enough to create a link between nodes 2 and 3. In the second test, the throughput obtained without using the power control mechanism achieves an almost ideal transmission; this happens because in the second test, the distance between node N2 and node N3 was reduced to 115 m, distance at which a transmission with minimum losses is possible.
## 4 Discussion
It should be emphasized that the effect of both angle changes on energy production in Central Europe was declared by other experimental results which were obtained during the several years in Czech Republic, specifically in the region of Brno and Prague. Identical methods of processing and evaluation of experimental data were used. It was found that in the region of Nitra in Slovakia the optimal angle of inclination is 34.5\({}^{\circ}\), in the region of Brno in the Czech Republic it is 34.7\({}^{\circ}\), and in the region of Prague the optimal tilt angle is 35\({}^{\circ}\).
# Removal of Phosphate from Aqueous Solution by
## 2 Materials and Methods
### Experimental Data Analysis  
#### 2.6.1 Adsorption Capacity and Removal Rate  
The equilibrium adsorption capacity and removal rate of phosphate were determined based on the following formula:  
\[q_{e}=\frac{V(C_{0}-C_{e})}{m} \tag{1}\]  
\[R=\frac{C_{0}-C_{\mathrm{e}}}{C_{0}}\times 100\% \tag{2}\]  
where \(q_{e}\) (mg/g) denotes the equilibrium adsorption capacity, \(V\) (L) represents the volume of the solution, \(m\) (g) is the mass of the material, \(C_{0}\) and \(C_{e}\) (mg/L) represent the initial and equilibrium concentrations of phosphate, and \(R\) (%) represents the percentage of phosphate removed.
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Interoperability  
Facilitating efficient communication and data sharing across varied devices in smart buildings is a significant problem owing to the existence of disparate protocols and technologies. The presence of many communication protocols might result in silos, preventing devices from different manufacturers from communicating or collaborating effectively. Despite continuous initiatives to establish standards such as IEEE 802.15.4 and Matter, achieving broad acceptance continues to pose a hurdle. Moreover, disparate devices may use distinct data formats, complicating integration and analysis. The absence of compatibility may impede the operation of IoT devices, limiting the overall efficacy and efficiency of smart buildings.
## 5 Discussion and Future Research Trends
### Remarks
In addition, researchers are required to generate effective and surprisingly automated approaches to classify the training data. * In the future, it is necessary to merge the one-stage and two-stage detectors to achieve the best outcome because both detectors have their own benefits. For instance, one-stage algorithms are fast while two-stage algorithms have influential accuracy. * In contrast to YOLO-based algorithms, the detection speed of Faster R-CNN still requires to be upgraded and gradually put forward in upcoming research. * To train a framework of high quality, in the future, there is a prerequisite to change the portable datasets of high quality into vast datasets. * To discuss every feature regarding the employment of UAVs in target detection, we plan to extend the study, involve more work, and compute the latest approach in the future. * There are also some other YOLO versions such as YOLOv5 which were introduced in May 2020 after two months of the YOLOv4 version. Recently, a few papers have been published based on YOLOv5 on some custom datasets due to efficient memory in the training process. Meanwhile, the other versions YOLOv6, YOLOv7, and YOLOv8 are still in the improvement phase in terms of training speed and computational cost. Therefore, no paper has been published yet based on those versions. In the coming years, our entire focus will be on these versions to handle different issues.
# Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
## 2 The Proposed Algorithm
### The Proposed Online Inspection System  
In addition to the development of algorithms, the online evaluation of the proposed algorithms in an Internet of Things (IoT) system [25] for manufacturing [26] is also considered in this study. Figure 7 shows the basic architecture of the IoT system, which consists of illumination devices, a surface inspection platform, and a computer server. The trained neural network models for different products are stored in the server. The proposed system is deployed in the surface inspection platform. Given a product, the corresponding model can be downloaded from the cloud server to the inspection platform for the defective detection operations. When a defective sample is identified, the images of the defective samples will also be delivered to the server for subsequent quality management.
## 7 Experiment and Analysis
### Application Effect Analysis  
This method has been applied in a bank's key personal settlement business, analyzing real-time alerts related to the business, providing timely positioning results of abnormal objects during malfunctions and sending them to operation and maintenance personnel via email. In addition, a large-screen view has been developed, which supports drilling down to view the running details of suspicious objects. The actual positioning accuracy rate of this method is about 82%, and the analysis time is less than 2 min.
## 2 Materials and Methods
### Isolation and Identification of the Active Substances  
The _A. xylocarpa_ leaves (1400 g) were extracted using the same method described earlier. The aqueous methanol leaf extracts were concentrated in _vacuo_ (40 \({}^{\circ}\)C) to produce an aqueous solution for further isolation and identification. The isolation methods were modified from those performed by [PERSON] et al. [47]. The separated fractions obtained from each isolation step were examined for their activity using an _L. sativum_ bioassay as the same assay method described above. After adjusting the pH to 7 using 1 M phosphate buffer, the aqueous solution was partitioned with ethyl acetate at an equal volume and separated into aqueous and ethyl acetate fractions. The ethyl acetate fraction was subjected to silica gel column chromatography (60 g, silica gel 60, 70-230 mesh; Nacalai, Kyoto, Japan), and eluted with stepwise gradient mixtures of _n_-hexane: ethyl acetate (9:1 to 2:8 (_v/v_), 150 mL), and methanol for final elution (300 mL). The most active fraction (F5) was then chromatographed on a Sephadex LH-20 column (100 g; GE Healthcare, Uppsala, Sweden). The mobile phase was used as stepwise water: methanol mixtures (8:2 to 2:8 (_v/v_), 150 mL), and methanol (300 mL). The most active fraction (F2) eluted with 40%aqueous methanol. Therefore, fraction 2 was loaded onto a reverse-phase C\({}_{18}\) cartridge (1.2 \(\times\) 6.5 cm; YMC-Dispo Pack AT ODS-25; YMC Ltd., Kyoto, Japan). The cartridge was eluted step-by-step with water: methanol mixtures (9:1 to 2:8 (_v/v_), 15 mL), and methanol (30 mL). The most active fraction was eluted with 20% aqueous methanol (F1) and finally purified using reverse-phase HPLC (column size 10 mm i.d. \(\times\) 500 mm S-5 \(\upmu\)m; ODS AQ-325, YMC Ltd.), eluted at a flow rate of 1.5 mL/min with 20% aqueous methanol, and detected at a wavelength of 220 nm. Finally, two isolated substances were found, and their chemical structures were assessed using spectral analysis.
## 3 Results
### Differences Between DSI Groups  
We divided participants into three groups based on their DSI values. Nine participants were allocated in the high DSI group (>1.00), nine were in the low DSI group (<0.80), and twenty-four participants were in the middle group (0.80 \(<\) DSI \(<\) 1.00). No statistically significant differences in the homogeneity of variance existed among groups within the [PERSON]'s test were observed, and thus equal variances were assumed. Statistically significant differences were found between groups in IMPT peak force (\(F\) = 12.562; \(p\) \(<\) 0.001), horizontal jump (\(F\) = 5.621; \(p\) = 0.007), lateral jump (\(F\) = 7.753-9.503; \(p\) = 0.000-0.002) and specific lateral jump (\(F\) = 5.872-7.049; \(p\) = 0.000-0.002). Additional post hoc tests showed significant differences between high and low DSI groups (\(g\) = 1.66) and mid and low DSI groups (\(g\) = 1.93) in IMPT peak force. Regarding horizontal jump, the post hoc test showed a significant difference between the mid and low DSI groups (\(g\) = 1.34). In the lateral jump and basketball-specific lateral jump, the post hoc test showed a significant difference between the high and low DSI group (\(g\) = 0.89-1.37) and the mid and low DSI group (\(g\) = 1.28-1.70).  
There were no differences between the groups regarding sprint and CoD performance (\(p\) = 0.073-0.996). Nevertheless, linear sprint results tended toward better performance in the high DSI group, while curvilinear results tended toward better performance in the low DSI group. Furthermore, for vertical jumps, ANOVA showed significant differences in CMJ height (\(F\) = 3.848; \(p\) = 0.030) and CMJ peak force (\(F\) = 5.489; \(p\) = 0.008), with no significant differences being observed in DJ parameters (\(F\) = 0.382-1.776; \(p\) = 0.183-0.685). Further post hoc tests revealed significant differences between the mid and low DSI group (\(g\) = 0.94) in CMJ height and between high and mid DSI groups in CMJ peak force (\(g\) = 1.22). The results for one-way ANOVA and post hoc tests are shown in the table below (Table 3).
## 5 Conclusions and Future Work
However, it is challenging to find a closed-form solution which is globally optimal and applies to the general case of 3D trilateration based on more than 3 landmarks. This will be considered in our future work. Moreover, we target the development of an automatic localization system for mobile robot self-localization based on the proposed single-camera trilateration scheme. The experimental system shown in Figure 16 has provided the necessary onboard hardware for the proposed scheme. Our future work will focus on the development of the software system and supporting vision algorithms. In addition, over the years of research and development, numerous localization algorithms/approaches in different categories have been proposed in the literature, from deterministic to probabilistic, from ranging-based to image-based. The work of this paper is carried out within the scope of trilateration, with the intention to provide a convenient addition to this category of localization approaches. Meanwhile, the development in other categories of localization approaches are highly significant, e.g., the approaches in the big category of computer vision-based localization [79; 80; 81]. Our future work on localization may expand into those categories, and comparison study among related approaches is expected. Conceptualization of the presented approach, funding acquisition, research supervision, derivation and finalization of the complete algorithm of position and orientation estimation, performance analysis, paper writing, Y.Z. ; derivation of the position estimation algorithm of the presented approach, W.L. ; experimental study, X.L. and X.Z. This research was partly supported by National Science Foundation, grant number1360873. The authors declare no conflict of interest.
## 2 Materials and Methods
### The Moderate Resolution Imaging Spectroradiometer-Based Vegetation Photosynthesis Model  
#### 2.4.1 Model Structure  
As a RS-based vegetation photosynthesis model for simulating GPP [33], the VPM model is based on the conceptual partitioning of chlorophyll and non-photosynthetically active vegetation at the canopy. Air temperature (Ta), the EVI, LSWI, and PAR are the input data of the VPM model (Figure 2).  
VPM estimates GPP over the photosynthetically active period of vegetation [33; 36]:  
\[GPP=\varepsilon_{\text{g}}\times FPAR_{\text{chl}}\times PAR \tag{7}\]  
where parameter \(\varepsilon_{\text{g}}\) is the light use efficiency (LUE) (\(\mu\)mol CO\({}_{2}\)/\(\mu\)mol photosynthetic photon flux density, PPFD), which is affected by leaf phenology, temperature, and water; parameter PAR is the photosynthetically active radiation (\(\mu\)mol PPFD), and parameter \(FPAR_{\text{chl}}\) is the fraction of PAR absorbed by leaf chlorophyll in the canopy.  
In the VPM model, \(FPAR_{\text{chl}}\) is calculated as [10; 23; 33; 36; 39; 40]:  
\[FPAR_{\text{chl}}=\alpha\times EVI \tag{8}\]  
where EVI is the Enhanced Vegetation Index, which is calculated from the reflectance values of the eight-day MODIS dataset; \(\alpha\) is the coefficient in the EVI-\(FPAR_{\text{chl}}\) linear function, which is set to be 1.0 [33; 36].  
\(\varepsilon_{\text{g}}\) is calculated by the maximum light use efficiency (LUE) \(\varepsilon_{0}\) (\(\mu\)mol CO\({}_{2}\)/\(\mu\)mol PPFD) and the scalars for the effects of temperature, water and leaf phenology on light use efficiency of vegetation.  
\[\varepsilon_{\text{g}}=\varepsilon_{0}\times T_{\text{scalar}}\times W_{\text {scalar}}\times P_{\text{scalar}} \tag{9}\]  
\(T_{\text{scalar}}\) is the scalar of temperature on photosynthesis, which is estimated by the Terrestrial Ecosystem Model (TEM) [51]; \(W_{\text{scalar}}\) is the impact of water on vegetation photosynthesis with \(LSWI_{\text{max}}\)[40]; and, \(P_{\text{scalar}}\) is the scalar for the impact of leaf phenology on photosynthesis within the canopy [39].  
\[T_{scalar}=\frac{(T-T_{\text{min}})(T-T_{\text{max}})}{\left[(T-T_{\text{min}})(T-T_ {\text{max}})\right]-\left(T-T_{opt}\right)^{2}} \tag{10}\]  
\[W_{scalar}=\frac{1+LSWI}{1+LSWI_{\text{max}}} \tag{11}\]  
\[P_{scalar}=\frac{1+LSWI}{2} \tag{12}\]  
where \(LSWI_{\text{max}}\) is the maximum LSWI during 2009-2010 [15; 26; 27]; \(T_{\text{max}}\), \(T_{\text{min}}\), and \(T_{\text{opt}}\) are the maximum, minimum, and optimal air temperature during photosynthesis, respectively.
# Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance
## 2 Modeling of Variable-Load Quadrotor
\begin{cases}\check{x}=[F_{I}^{\rm F}(\cos\psi\sin\theta\cos\phi+\sin\psi\sin \phi)-k_{a}\check{x}]/(M+m)\\ \check{y}=[F_{I}^{\rm F}(\sin\psi\sin\theta\cos\phi-\cos\psi\sin\phi)-k_{a} \check{y}]/(M+m)\\ \check{z}=[F_{I}^{\rm F}(\cos\theta\cos\phi)-k_{a}\check{z}]/(M+m)-g\\ \check{\phi}=\tau_{x}^{\rm F}/J_{xx}+[J_{yz}(\check{\theta}^{2}-\check{y}^{2} )+(J_{yy}-J_{zz})\phi\dot{\psi}+J_{xz}(\dot{\phi}\dot{\theta}+\ddot{\psi})+J_{ xy}(\ddot{\theta}-\dot{\psi}\dot{\psi})]/J_{xx}\\ \ddot{\theta}=\tau_{y}^{\rm F}/J_{yy}+[J_{xx}(\dot{\psi}^{2}-\dot{\psi}^{2})+ (J_{zz}-J_{xx})\phi\dot{\psi}+J_{xy}(\dot{\psi}\dot{\psi}+\ddot{\psi})+J_{yz}( \ddot{\psi}-\dot{\phi}\dot{\theta})]/J_{yy}\\ \ddot{\psi}=\tau_{z}^{\rm F}/J_{zz}+[J_{xy}(\dot{\psi}^{2}-\dot{\theta}^{2})+ (J_{xx}-J_{yy})\dot{\psi}\dot{\theta}+J_{yz}(\dot{\psi}\dot{\psi}+\ddot{\theta })+J_{xz}(\ddot{\psi}-\dot{\theta}\dot{\psi})]/J_{zz}\end{cases} . \tag{14}\]  
The inherent underactuation and strong dynamic coupling in quadrotor systems mean that any actuator fault propagates disturbances across all 6-degree-of-freedom motion states. When a fault occurs, the actual actuator lift force \(F_{I}^{F}\) is obtained through Equation (12). Using Equation (13), the total lift and individual moments under actuator fault conditions can be computed, which drive the flight through the dynamics model (14). 3 Design of Event-Triggered Cascade Active Disturbance Rejection Control with Prescribed Performance  
The quadrotor represents an underactuated system with four inputs governing six degrees of freedom. This configuration inherently prevents complete decoupling within the system. Through a cascaded outer-inner loop structure, the controller achieves implicit decoupling between positional and attitudinal kinematic relationships. Displacement control along the \(x\)- and \(y\)-axes must be achieved through attitude angle regulation. Based on this principle, we propose a dual-loop cascade active disturbance rejection control (CADRC) scheme with prescribed performance control (PPC). An event-triggered mechanism is introduced to reduce computational redundancy, while PSO is employed for online controller parameter self-tuning. The control system structure is shown in Figure 4. The outer-loop position controller generates the desired pitch angle \(\theta_{d}\), roll angle \(\phi_{d}\), and \(U_{1}\), which are then passed to the inner-loop attitude controller for conversion into \(U_{2}\), \(U_{3}\), and \(U_{4}\).