esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2
## 2 Methods
### Elliptic Fourier Analysis (EFA)
#### 2.2.1 Calculation of Elliptic Fourier Descriptor (EFD)  
After picture preprocessing, we get the closed product profile curve, which can be regarded as the motion trajectory of the moving point and expressed as two following functions of the arc length (\(l\)) from a point to the starting point on the profile curve [23]:  
\[\left(\begin{array}{c}x(l)\\ y(l)\end{array}\right)=\frac{1}{2}\left(\begin{array}{cc}a_{0}\\ c_{0}\end{array}\right)+\sum_{n=1}^{N}\left(\begin{array}{cc}a_{n}&b_{n}\\ c_{n}&d_{n}\end{array}\right)\left(\begin{array}{c}\cos(\frac{2 nml}{L})\\ \sin(\frac{2 nml}{L})\end{array}\right) \tag{1}\]  
where \(x(l)\) and \(y(l)\) are continuous piecewise linear functions; \(\frac{a_{0}}{2}\) and \(\frac{c_{0}}{2}\) are the abscissa and ordinate of the profile center point, respectively; \(n\) is the number of ellipse Fourier harmonics; \(N\) is the maximum number of ellipse Fourier harmonics; \(l\) is the arc length parameter; \(L\) is the profile perimeter; \(a_{n}\), \(b_{n}\), \(c_{n}\) and \(d_{n}\) represent the elliptic Fourier coefficients of the \(n\)th harmonic and can be defined as follows [17]:  
\[\begin{array}{c}a_{n}=\frac{L}{2n^{2}n^{2}}\sum\limits_{p=1}^{K}\frac{ \Delta np}{\Delta np}\left[\cos(\frac{2 nmlp}{L})-\cos(\frac{2 nmlp-1}{L})\right] \\ b_{n}\frac{L}{2n^{2}n^{2}}\sum\limits_{p=1}^{K}\frac{\Delta np}{\Delta np} \left[\sin(\frac{2 nmlp}{L})-\sin(\frac{2 nmlp-1}{L})\right]\\ c_{n}=\frac{L}{2n^{2}n^{2}}\sum\limits_{p=1}^{K}\frac{\Delta np}{\Delta np} \left[\cos(\frac{2 nmlp}{L})-\cos(\frac{2 nmlp-1}{L})\right]\\ d_{n}=\frac{L}{2n^{2}n^{2}}\sum\limits_{p=1}^{K}\frac{\Delta np}{\Delta np} \left[\sin(\frac{2 nmlp}{L})-\sin(\frac{2 nmlp-1}{L})\right]\end{array} \tag{2}\]  
where \(K\) is the total number of sampling points of the profile; \(\Delta x_{p}\) and \(\Delta y_{p}\) are the displacements from the point \(p\) to the point \(p+1\) along the X-axis and Y-axis directions, respectively, then:  
\[\Delta l_{p}=\sqrt{\Delta x_{p}^{2}+\Delta y_{p}^{2}} \tag{3}\]  
Thus, product profile can be described by an elliptic Fourier coefficient matrix containing \(n\) harmonics, which is given as  
\[F=\left[\begin{array}{cccc}a_{1}&b_{1}&c_{1}&d_{1}\\ \vdots&\vdots&\vdots&\vdots\\ a_{n}&b_{n}&c_{n}&d_{n}\end{array}\right] \tag{4}\]  
\(F\) is the EFD that approximates the profile curve by controlling the harmonic number \(n\). When \(n\) takes different values, the fitting effect of profile curve will be different. In general, a few low-frequency harmonics maintain the global features of profile curve, and high-frequency harmonics affect its finer features.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 1 Introduction
Clients are assigned to different community clusters based on their data distribution similarities, enabling those within the same cluster to share a federated model. This method effectively reduces the negative impact of non-IID data on federated learning models. However, existing clustered federated learning algorithms have limitations. Classic clustering methods like K-Means [25], the iterative federated clustering algorithm (IFCA) [26], and CFL often require significant computational resources, which can hinder their practicality. To address the challenges of high computational complexity, performance limitations, and privacy concerns in traditional clustered federated learning, this paper proposes a load-forecasting framework specifically designed for residential energy users. It introduces an innovative federated clustering method and explores a personalized adaptive differentially private clustered federated learning algorithm. The framework selects weather and time factors as key load-related variables, constructs a user dataset, and establishes a load-forecasting model. Case studies demonstrate that the proposed algorithm surpasses existing mainstream federated algorithms in predictive performance. The key contributions of this paper are as follows:  
1. A federated learning load-forecasting framework tailored for residential energy users with heterogeneous data is proposed. 2. A method for calculating the similarity of load patterns while ensuring privacy is introduced. This involves constructing a complex network and developing a novel federated clustering method based on the Louvain algorithm, which offers lower computational costs and does not require pre-specifying the number of clusters (K). 3. An adaptive gradient-clipping differentially private clustered federated averaging algorithm, called pADP-FedAvg, is proposed, along with a differential privacy analysis. 4. Weather and time factors are identified as key load-related variables, a user dataset is constructed, and a federated learning load-forecasting LSTM model is established. The effectiveness and advantages of the proposed methods are validated through case studies.
## 2 Method
### Study Tools and Measurements  
#### 2.4.1 Smartphone Application MindsCare  
In this study, we used a smartphone usage management application as a study tool. In a previous research we developed the Smartphone Overdependence Management System (SOMS), which is a background data collecting system, designed to deliver problematic use prevention and diagnosis based on scientific evidence [26]. The results of our previous study using SOMS showed that by using such an application data could identify problematic smartphone usage [27; 28]. Therefore, SOMS was extended as a user-friendly interface application in MindsCare so that it can interact with users and provide intervention service. MindsCare is an android application that was developed for prevention of problematic smartphone use. The user received personalized health care services through the application. When the user installed the system on the smartphone, the application started to measure users' data relating to smartphone usage, estimated usage of downloaded applications, and saved usage records in real time. The users were able to set a daily usage goal for improving self-control, received educational image contents and relaxation therapy related to problematic smartphone use, were informed on local counseling centers  
Figure 2: Participant enrollment flow diagram.  
(I WILL center; internet addiction prevention counseling center in Korea), were provided messaging function to communicate with the treatment expert, and conduct a survey (on various psychological scales such as anxiety, depression, stress, impulsivity, self-control, problematic smartphone use, etc.) for self-evaluation at weeks 0, 8, and 12 through the MindsCare application. These functions are classified as automatic intervention. A web monitoring system was also developed to receive MindsCare users' data every 10 min. The usage pattern was analyzed from the stored usage time data, and the user could receive a personalized result analysis' service. The treatment expert provided personalized intervention by monitoring users' data from the developed website. These functions are classified as manual interventions. Therefore, MindsCare performed an integrated intervention function that can help alleviate the problematic use of smartphones and their allied psychological symptoms (See Figure 3).
# Sentiment Analysis on Algerian Dialect with Transformers
## 3 Data Collection and Analysis
### N-Gram Analysis  
We have analyzed our dataset to look for the most common unigrams and bigrams [31]. Table 4 reveals the top 10 most common unigrams on each class, as well as on the whole dataset. Note that both Arabic and Algerian dialect stop words were removed. While, Table 5 shows the top 10 most common bigrams in each class and on the whole dataset.  
Before concluding the top n-grams in the above tables, we performed some text preprocessing, including stop word removal; basic stemming, such as removing cumulative redundant words; and cleaning punctuation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Training** & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} & \multicolumn{2}{c}{**All**} \\ \hline  & **No. Samples** & **(\%)** & **No. Samples** & **(\%)** & **No. Samples** & **(\%)** & **No. Samples** & **(\%)** \\ \hline Negative & 11,175 & 35.5 & 1608 & 35.7 & 3174 & 35.3 & 15,957 & 35.5 \\ Neutral & 7040 & 22.3 & 1030 & 22.9 & 2023 & 22.5 & 10,093 & 22.4 \\ Positive & 13,285 & 42.2 & 1862 & 41.4 & 3803 & 42.2 & 18,950 & 42.1 \\ \hline Total & 31,500 & 100 & 4500 & 100 & 9000 & 100 & 45,000 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the dataset with different splits and classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Total Samples** & **Percentage (\%)** \\ \hline Pure Arabic letters & 38,108 & 84.684 \\ Pure Latin letters & 5699 & 12.664 \\ Mixed & 1193 & 2.652 \\ \hline Total & 45,000 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of samples by type of letters used.  
As you can see in Tables 4 and 5, we see predictable words that are very common in Algerian text, and especially in social media comments. Interestingly, in the negative and positive class columns on both tables, we see some negative and positive words, respectively.
## 3 Monitoring Results and Analysis  
### Tunnel Displacement  
Figure 12 has shown the development process of the tunnel displacements in section DK68+295. It can be seen that the arch settlement and the horizontal convergence both increased sharply in the first 30 days; the deformation then slowed down and reached a staged steady state. However, subsequent construction stages caused varying degrees of increase in the monitored values. It is worth noting that from the later stage of excavation of the lower bench to the early stage of excavation of the rock pillar, i.e. from about day 575 to day 630, the arch settlement decreased. This can be attributed to the unloading that was caused by excavating a large amount of rock mass, which caused the bottom of the tunnel to uplifting; as a result of this, the cumulative settlement was reduced. As the construction progressed to the 12 th stage, the monitoring points were damaged due to installation of the vault's secondary support. The final monitoring values of the arch settlement and the horizontal convergence were 18.9 mm and 9.2 mm, respectively.  
Figure 11: Layout of the monitoring points.  
Figure 12: Curves of the tunnel displacement over time.  
Monitoring the displacement of a tunnel is necessary during its construction. Therefore, two adjacent monitoring sections DK68+290 and DK68+300 were selected to be used as control. The arch settlements of DK68+290 and DK68+300 were 14.2 mm and 12.1 mm, and the horizontal convergences of these two sections were 11.5 mm and 8.5 mm, respectively. Table 5 has shown the proportions of the tunnel displacement for the three monitoring sections at different stages during construction. Significant differences in the tunnel displacements were identified during the different stages of construction. The arch settlements during the excavation of TH and UB accounted for 35.1-54.0% and 21.6-35.2% percent of the total arch settlements, respectively. It can be seen that the arch settlements were mainly caused by the excavation of TH and UB. The horizontal convergences mainly occurred during the excavation of UB, MB and LB, the proportions of which were 30.6-42.3%, 17.8-31.5% and 14.3-26.8%, respectively. The Arch settlements and the horizontal convergences were small during the other excavation stages. In general, for the large tunnel displacements during the excavation of TH, UB, MB and LB, the risks during the tunnel's construction were higher. Therefore, it is necessary to strengthen the construction management and site monitoring in order to ensure the safety of the aforementioned construction stages.
## 2 Material and Methods
### Antidiabetic and Antiobesity Activity  
\(\upalpha\)-amylase inhibition activity was measured according to the method of [PERSON] et al. (2023) [23]. Porcine pancreatic \(\upalpha\)-amylase (220 U/mL) was dissolved in 100 mM phosphate buffer (pH 5.6). To measure \(\alpha\)-amylase inhibitory activity, a mixture of 0.25 mL of \(\alpha\)-amylase solution and extract was first incubated in a water bath at 40 \({}^{\circ}\)C for 15 min. Then, 0.5 mL of 1% starch solution was added. After 10 min, the reaction was stopped by adding 1 mL of 3,5-dinitrosalicylic acid and heating for 10 min. The mixture was then made up to 10 mL with distilled water and the absorbance was measured at 415 nm against a blank.  
For \(\alpha\)-glucosidase inhibition activity measurement, 0.04 mL of \(\alpha\)-glucosidase (1 U/mL) and 0.2 mL of sample were added to 0.9 mL of a 0.1 mol/L phosphate buffer at pH 6.8 [23]. After incubation at 37 \({}^{\circ}\)C for 5 min, 0.04 mL of 1% sucrose was added. The reactions were incubated at 37 \({}^{\circ}\)C for 50 min, stopped by adding 0.2 mL of 3,5-dinitrosalicylic acid (DNS) and heated for 10 min. Absorbances were tested at 540 nm.  
Lipase activity was measured using 4-nitrophenyl acetate as a substrate [23]. The reaction mixture contained 0.44 mL of a 100 mM TRIS-HCL buffer, pH 7.6, 0.02 mL of the enzyme (10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, pancreatin from porcine pancreas), 0.02 mL of the studied extract and 0.04 mL of the substrate (10 mmol/L 4-nitrophenyl acetate). The change in absorbance was measured at 410 nm.  
The ability of the tested samples to inhibit \(\alpha\)-amylase, \(\alpha\)-glucosidase and lipase activity was calculated according to the following equation:  
\[\mathrm{I}\left(\%\right)=\left[\mathrm{[A0-A1]/A0}\right]\times 100\]  
where A0 is the absorbance of the control sample and A1 is the absorbance of the test sample. The IC\({}_{50}\) values (mg/mL), calculated from the regression curve, indicate the extract concentration inducing 50% radical scavenging.
# Estimating Eddy Dissipation Rate with QAR Flight Big Data
## 2 Data and Methodology
### Air Turbulence Measurement  
#### 2.2.1 Vertical Overload  
In China's civil aviation industry, vertical overload increment is often used as a basis for diagnosis of air turbulence. Since the overload coefficients of the \(x\) direction (longitudinal acceleration of the aircraft) and the \(z\) direction (lateral acceleration of the aircraft) are generally small, they are often ignored, and only the overload of the \(y\) direction (aircraft vertical acceleration) is considered. The overload increment \(\Delta n\) (expressed as a multiple of gravity acceleration g) is expressed as Equation (1):  
\[\Delta n=VRTG-1 \tag{1}\]  
where \(VRTG\) is the vertical acceleration of the flight path point recorded in the QAR data, and is also expressed as a multiple of the gravity acceleration g [24]. When the overload increment \(|\Delta n|>0.2\)g, the point could be regarded as a potential air turbulence point [25]. Table 4 shows how the air turbulence intensity corresponds to an overload increment \(|\Delta n|\).  
VA reflects more of the turbulent response of an aircraft passing through the atmospheric turbulence area at different speeds [26]. Different type of aircrafts may have different acceleration responses when they pass through the same atmospheric turbulence zone, so this air turbulence intensity measurement is not suitable for objectively describing the intensity of true atmospheric turbulence [27; 28].
## 4 Results
### Research Questions Results
In fact, they have gone from being considered as heavy and expensive equipment to viable instruments for multiple scientific and commercial applications [19]. In this sense, based on the bibliographic review carried out, it is identified that civil applications are more widely disseminated in the scientific field; in fact, more than 80% of the reviewed works, which corresponds to approximately 334 references, present applications of unmanned vehicles in the civil environment, while only 51 works describe applications in the military or defense field. Figure 7 illustrates the percentages of the identified applications. When reviewing applications in the civil field, it was identified that the largest proportion of works related to USVs has been developed in the academic field, which is followed by works in the areas of environmental monitoring and bathymetry. Some minor applications correspond to the areas of disaster management [4; 20], fishing [21] and hydrography [22]. Figure 8 presents the percentage distribution of the main application areas of USV technologies in the civil field. Figure 6: Document type over the years. Figure 7: Scope of application of autonomous and unmanned surface vehicles. Within the applications in the academic area, works related to target tracking [23; 24] and trajectory tracking [25], obstacle avoidance [26], path planning [27; 28; 29] and control techniques [30; 31] stand out. On the other hand, in the environmental area, works stand out in the lines of water sampling/water quality [32; 33], nuclear/oceanic radiation [34; 35], current studies [36] and ecological protection [37], among others. Likewise, as part of the main applications of bathymetry, some works developed in the Arctic environment [38], in lakes [39; 40] and in the marine/coastal environment [41; 42; 43] stand out. In the military field, the largest proportion of works developed are related to applications focused on security and defense, surveillance and reconnaissance, interception/interdiction, and intruder detection. Figure 9 presents a summary of the main application areas of USVs in the military environment. In the area of security and defense, works related to maritime border security [44], port security [45] and merchant escort [46] stand out. As part of the works in the area of surveillance and reconnaissance, some are focused on patrolling hostile environments with civilian traffic [47] and coastal surveillance [48], among others. The distribution of applications identified in the selected articles is presented in Figure 10.  
Figure 8: Application areas of autonomous and unmanned surface vehicles in the civil field. Figure 9: Application areas of unmanned vehicles. For applications related to monitoring and data collection, the studies shown in Table 13 were identified, according to their application areas in the civil environment.
## 3 Extending the Telco USN-Platform towards the Semantic Sensor Web
### Extending the Observation Storage with a Semantic Web Repository  
The Observation Storage entity of the USN-Enabler was extended to make RDF observations received by the USN-Platform from smart devices (such as the human-generated observations received from HMI systems) shareable with third party applications. As represented in Figure 4, a SPARQL ([[http://www.w3.org/TR/rdf-sparql-query/](http://www.w3.org/TR/rdf-sparql-query/)]([http://www.w3.org/TR/rdf-sparql-query/](http://www.w3.org/TR/rdf-sparql-query/))) Endpoint was added including a server that implements a protocol to access repositories over HTTP so that different applications can query, update and access smart device RDF observations through HTTP requests. As we will detail when describing our example scenario and its experimental implementation, in order to test a basic level of reasoning on smart device observations the SPARQL Endpoint embedded in the USN-Enabler was used also to provide support for RDF Schema inferencing.  
Providing meaningful RDF representations of smart device observations and HTTP access are two of the four recommended principles in the Linked Data approach [27], namely, (1) use Uniform Resource Identifiers (URIs) to identify things; (2) use HTTP URIs so that things can be referred to and looked up (\"dereferenced\"); (3) provide meaningful descriptions of things using standard formats such as RDF; and (4) include links to related URIs on the Web. Linked Data constitutes a significant advance in the realization of the Semantic Web, and it has also been shown that it may become of key importance in the development of Semantic Sensor Networks [28].  
To follow all four Linked Data principles the Telco USN-Platform was further extended in two complementary directions: (1) to manage smart device RDF annotations that assign URIs to different  
Figure 5: Example of an Electric Vehicle observation using O&M-OWL.  
entities in the O&M-OWL model (mainly: _featuresOfInterest_, _procedure_, _observedProperty_ and _location_), and (2) linking them to the large and increasing number of publicly available linked-data resources in the Semantic Web (for example, linking location URIs to dbpedia ([[http://dbpedia.org](http://dbpedia.org)]([http://dbpedia.org](http://dbpedia.org)))). This makes it possible to publish not only observation data but also semantic links between data and formal (and often universal) descriptions based on ontologies accessible on the Semantic Web. As a result of these semantic extensions, the USN-Platform can be seen as a Semantic Sensor Network Platform.
## 6 Conclusions  
* The air quality index (AQI) values of Lahore remained greater than 100 for 88.4% of the time, and greater than 150 for 54% of the time. It remained \"good\" for 12 and \"moderate\" for only 104 days in the last 3 years (2019-2021);
* The average PM\({}_{2.5}\) concentration in Lahore in 2020 was 167 ug/m\({}^{3}\), and 175 ug/m\({}^{3}\) in 2021, as determined through real-time data. The AQI value reached a maximum of 478 and 466, in 2019 and 2020, respectively;
* The average CO concentration for all sites in the data set was 41.1 millimol/m\({}^{2}\). The maximum value was for Jail Road (58.982 millimol/m\({}^{2}\)) and the minimum was at Jia Bagga (34.57 millimol/m\({}^{2}\)). NO\({}_{2}\) average accumulation was 0.1010 millimol/m\({}^{2}\), with the maximum value of 0.255 millimol/m\({}^{2}\) obtained from Jail Road, and the minimum value of 0.037 millimol/m\({}^{2}\) from Kot Abdul. The SO\({}_{2}\) concentration on average in Lahore was 0.428 millimol/m\({}^{2}\). The maximum value obtained was 0.747 millimol/m\({}^{2}\) at Jia Bagga, and the minimum was 0.232 millimol/m\({}^{2}\) at Karol. The CH\({}_{4}\) average value was 1.914 millimol/m\({}^{2}\), the maximum was 1.993 millimol/m\({}^{2}\) in Quaid-e-Azam Industrial Estate, and the minimum was 1.860 millimol/m\({}^{2}\) in Sundar Industrial Estate. The HCHO on average was 0.236 millimol/m\({}^{2}\). The maximum value was obtained from Main Boulevard at 0.376 millimol/m\({}^{2}\), and the minimum value was from Jail Road at 0.079 millimol/m\({}^{2}\);
* The average AOD value in Lahore was 0.98 um. The maximum value was 2.27 um from Jia Bagga, and the minimum was 0.46 um;
* Variations in the sum of the concentrations of CO, SO\({}_{2}\), and NO\({}_{2}\) determined through remote sensing coincided considerably with the respective AQI plot. Neither HCHO, CH\({}_{4}\), nor AOD had a similar relationship with AQI values;* The results showed that economic activities/emission sources have an impact on the type of pollutant in which they are participating most. CO, SO\({}_{2}\), NO\({}_{2}\), HCHO, CH\({}_{4}\), and AOD showed variation at different sites. Thus, the sites which contribute most to a particular pollutant can be identified, and respective mitigation can be adopted;
* The results suggested that AQI values during the week were higher than the weekend.