esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
First, as shown in Table 1, batch-learning-based models show a drastic drop in accuracy when deployed in the real world over long-term periods without any model update. Second, to discover time-invariant features, we conducted a chi-square test for independence. A Chi-square test compares two categorical variables in a contingency table to investigate whether they are related. In our case, each categorical variable was generated using features based on the raw data during each period of data collection. As a within-subject experiment, we performed a chi-square test on each user's features extracted from the new dataset. From this test, we can identify the features that are time invariant: 7 features out of 50 (five users \(\times\) ten features) show low correlation (independence) between the two sets of data gathered during each period. Third, we designed windowing and time-invariant feature-based online learning (WTOL) to overcome the significant accuracy drop over time against a long-term dataset. Considering the time-invariant features discovered by statistical tests, WTOL partially uses the collected dataset to reduce the variation in features when the model is trained. In other words, time-variant features should be partially used to perform online learning because the data trend is different over time. Owing to adaptive windowing according to the time sensitivity, WTOL outperforms batch-learning models and simple online learning models by up to +24.7% and +14.3% in terms of the F-score, respectively. The main contributions of our work are summarized as:  
* To investigate users' behavior changes over time and the effect of these changes on the quality of models, we collected in the wild notifications from five users who participated in the previous experiments. For 63 days, on average, 11,258 notifications were gathered 593 days after the end of the original experiment. To our knowledge, this is the first in the wild dataset in terms of multiple devices and long-term periods. * We discovered time-invariant features among 50 features (five users \(\times\) 10 features) by conducting a chi-square test for independence. * To the best of our knowledge, this study is the first to highlight the problem that the accuracy of the trained model drastically drops when it is deployed in the real world;it also states the reasons. To compensate for the accuracy drop from batch learning, we present a method based on online learning of windowing-time-invariant features.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion  
Extensive experiments and comparative analyses were carried out on the constructed multi-source apple canopy image dataset of apple diseases and pests to verify the effectiveness of the proposed method.
## 2 Materials and Methods
### Study Area  
The southeastern part of the Republic of Serbia was chosen as the area of interest (AOI) (Figure 3). The area covered 1218 km2 (42 \(\times\) 29 km) with the central point at 575,100 and 4,710,500 (34T UTM/WGS84) or 21.9147 E, 42.5429 N (WGS84) coordinates.  
The city of Vranje is located in the centre of the study area. In a southwest-to-northeast direction, the region is intersected by the South Morava River, which creates a vast, flat region. The region's northwest consists of hilly terrain, whereas the southeast is dominated by mountainous terrain. The lowest point is situated in the valley of the South Morava River, on the northern boundary 331 in above sea level (a.s.l.). The highest point is the mountain peak Kocurac (Zladovacka Planina mountain) in the southeastern part of the test area, which is 1558 m a.s.l. (Figures 2 and 3). The study area's vegetation consists of seasonal crops, meadows, pastures, and woodlands. In the northwest and southeast parts of the region are forests. Most forest cover comprises deciduous species (beech, oak, and others), whereas coniferous woods (spruce, fir, and others) comprise a significantly minor portion of the land area. According to geomorphological properties, lowland plains are primarily used for agriculture, where cultivated arable crops such as wheat, maize, and others are planted [22].  
Figure 1: Improving Forest Detection Using Machine Learning and Remote Sensing workflow chart.  
Figure 2: Vranje and its surroundings—Area of interest. Map created using ESA remote sensing data and © OpenStreetMap contributors open data [22; 23; 24].
## 4 Parametric Study
### Effect of the Axial Force  
The axial force (\(N\)) is always an important factor affecting the joint rotational behavior [6; 9; 32]. To investigate the effect of \(N\) on the rotational stiffnesses of the joint with EG and the joint without EG, the joint-rotation-angle-versus-bending-moment relationships with conditions that subject the joint to different axial forces are obtained. The joint-rotation-angle-versus-sagging-moment relationships with the axial forces of 500, 1000 and 1500 kN are presented in Figure 13. The joint-rotation-angle versus hogging-moment relationships with axial forces of 500, 1000 and 1500 kN are presented in Figure 14. According to the results in the two scenarios, by decreasing the axial force, the calculated rotation angles in the condition of the same bending moment are increased significantly. Therefore, according to Equation (23), the rotational stiffnesses of the two joints both tend to be significantly decreased. Moreover, with the condition of the same axial force, since the EG can have an effect on softening the joint rotational behavior, the average rotational stiffness of the joint with EG is smaller than that of the joint without EG. Moreover, with decreasing the axial force, the average distance between the rotation-angle-versus-bending-moment curves of the two joints subject to the same bending moment is increased gradually, thus indicating that the softening effect tends to be more significant.  
Figure 13: Effect of \(N\) in sagging-moment scenario.
## 3 The Equations of ADFTV and the Mathematical Solutions of FOAM
In Equations (7) and (8), the terms \(\mu_{1}=\mathrm{f}_{1}\bigg{(}\mathrm{X}_{\mathrm{C}\mathrm{I}\mathrm{n}}, \mathrm{J}_{\mathrm{X}_{2}\mathrm{AT}},\mathrm{\rho},\mathrm{L}_{\mathrm{A} \mathrm{T}}\bigg{)}^{2},\mathrm{I}_{\mathrm{X}_{2}\mathrm{GT}}\bigg{)}\), \(\mu_{3}=\mathrm{f}_{3}\bigg{(}\mathrm{X}_{\mathrm{C}\mathrm{B}\mathrm{n}}, \mathrm{J}_{\mathrm{X}_{2}\mathrm{AB}},\mathrm{\rho},\mathrm{L}_{\mathrm{A} \mathrm{B}}\bigg{(}\frac{\mathrm{L}_{\mathrm{AB}}}{\mathrm{I}_{\mathrm{B}}} \bigg{)}^{2},\mathrm{I}_{\mathrm{X}_{2}\mathrm{C}\mathrm{G}\mathrm{B}}\bigg{)}\) express the internal parametric excitations of GT and GB (see Equations (A13)-(A16) in Appendix B), while the terms \(\Gamma_{1}=\mathrm{g}_{1}\bigg{(}\frac{\mathrm{A}\mathrm{T}\mathrm{T}}{\mathrm{ X}_{\mathrm{C}\mathrm{I}}\mathrm{T}},\frac{\mathrm{M}_{\mathrm{e}}}{\mathrm{I}_{ \mathrm{X}_{2}\mathrm{GT}}}\bigg{)},\Gamma_{3}=\mathrm{g}_{5}\bigg{(}\frac{ \mathrm{A}\mathrm{B}\mathrm{I}_{\mathrm{T}\mathrm{A}\mathrm{B}}}{\mathrm{X}_{ \mathrm{C}\mathrm{G}\mathrm{R}}},\frac{\mathrm{M}_{\mathrm{H}}}{\mathrm{I}_{ \mathrm{X}_{2}\mathrm{C}\mathrm{G}\mathrm{R}}}\bigg{)},\Gamma_{5}=\mathrm{g}_{5 }\bigg{(}\frac{\mathrm{A}\mathrm{B}\mathrm{I}_{\mathrm{T}\mathrm{A}\mathrm{ B}}}{\mathrm{X}_{\mathrm{C}\mathrm{R}}},\frac{\mathrm{M}_{\mathrm{H}}}{\mathrm{I}_{ \mathrm{X}_{2}\mathrm{C}\mathrm{G}\mathrm{R}}}\bigg{)}\) express the external cubic and quintic external excitations for GT and GB (see Equations (2) and (A10) in Appendix B). The functions \(\mathrm{f}_{i},i=1,2\) and \(\mathrm{g}_{i},i=1,2,3\) have the variables mentioned above represent the physical sources of these internal and external excitations. Injecting the relations (7) in Equation (4) and repeating the same manipulation by injecting the relations (8) in Equation (5) yields  
\[\ddot{\varphi}_{1}++\overline{\Omega}_{1}^{2}\varphi_{1}=-2\zeta\overline{ \Omega}_{1}(1+\alpha_{1}+\alpha_{2}\cos 2\varphi_{1})\dot{\varphi}_{1}-\overline{\Omega}_{1}^{2}(2 \alpha_{1}+\alpha_{1}^{2}+2\mu_{1}\cos 2\varphi_{1})\varphi_{1}+\Gamma_{1}\varphi_{1}^{3}, \tag{9}\]  
\[\ddot{\varphi}_{3}++\overline{\Omega}_{3}^{2}\varphi_{3}=-2\zeta\overline{ \Omega}_{3}(1+\alpha_{3}+\alpha_{4}\cos 2\varphi_{1})\dot{\varphi}_{3}-\overline{\Omega}_{3}^{2}(2 \alpha_{3}+\alpha_{3}^{2}+2\mu_{3}\cos 2\varphi_{3})\varphi_{3}+\Gamma_{3}\varphi_{3}^{3}+ \Gamma_{5}\varphi_{3}^{5}. \tag{10}\]  
Equations (9) and (10) represent the FTV of the GT and GB in APPR that include the following effects: the kinematic and geometric nonuniformity of quasi-homokinetic tripod's joints, the nonuniformity of AIGM and AIMM for the elements of AD and the nonuniformity of external harmonic excitations of the engine, and the reaction torque induced by the wheel.
## 3 Results
### Description of the Selected Soil Improvement Techniques
#### 3.2.1 Chemical Stabilization  
Chemical stabilization is one of the various improvement techniques, and it can modify soil properties by adding chemically active materials [16; 35].  
Various materials act as chemical stabilizers, including calcium-based stabilizer materials (CSMs) [47]. CSMs exhibit pozzolanic properties. Widely used CSMs include lime, cement, fly ash (FA), ground granulated blast furnace slag (GGBS), and bottom ash (BA) [47]. Other reported materials include calcium sulfoaluminate (CSA) cement [48], alkali-activated binders (AABs) derived from precursors such as fly ash, slag, metakaolin, or quarry dust [35], calcium chloride (CaCl\({}_{2}\)) [16], potassium chloride (KCI) [35], quarry dust [35; 49], groundnut shell ash [47], and cement kiln dust (CKD) [17; 49; 50]. Choosing the right stabilizer is crucial for the sustainable construction of civil structures on expansive soils. This technique has been practiced for decades to mitigate problems related to expansive soils' swelling and shrinkage behavior [49].  
The effects observed with these stabilizers include the following:  
* Plasticity: The addition of CSMs and other stabilizers, such as lime and AABs, reduces soil plasticity, as evidenced by decreased liquid limit (LL) and plasticity index (PI) [16; 17]. Lime is particularly suitable for materials with a PI greater than 10% to reduce plasticity [47].
* Compaction: Properties such as maximum dry density (MDD) and optimum moisture content (OMC) are evaluated [16; 35]. Adding CaCl\({}_{2}\) and KCl increases MDD and decreases OMC, resulting in greater strength [16]. Depending on the precursor used, AABs can increase or decrease MDD and OMC [35]. Adjusting lime percentages is crucial to optimize stabilization without compromising soil compaction [17].
* Bearing Capacity and Strength: Improvements in bearing capacity (CBR) [16; 17] and UCS [35; 47; 49] are observed. Cement provides the highest strength among the CSMs [47]. Fly ash has also been shown to increase the strength of expansive soils with a 10% addition [47]. The strength achieved depends on the amount of stabilizing agent incorporated and the type of material treated [47]. There is a positive relationship between motor values and CBR [17].
* Volumetric Change (Swelling/Shrinkage): Chemical stabilization controls and decreases swelling properties [16; 17; 35; 49]. A decrease in swelling is observed as the percentage of lime application increases [17]. AABs restrict expansive characteristics [35]. However, an increase in swelling has been identified, especially at lower levels of lime application, suggesting the need for a balanced approach to the quantity used [17]. It is important to carefully consider swelling in the design of lime mixes [17].
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 1 Introduction
To meet these challenges, this study introduces a two-level Adaptive Path Planner (APP), which integrates advanced optimization and adaptive planning methods specifically designed for fixed-wing UAVs. The APP consists of a Global Path Planner (GPP), responsible for generating energy-efficient, kinematically feasible paths, and a Local Path Planner (LPP), which dynamically recalculates routes in response to changing environmental conditions or unforeseen obstacles. This paper examines two distinct scenarios related to air pollution in Poland and the Arctic. The first scenario focuses on smog profiling in Poland. In 2018, the WHO air pollution database ranked Zywiec as the second most polluted city in Poland, with the PM2.5 levels exceeding 40 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\). Zywiec was also identified as the fourth most polluted city in the European Union [21]. The second scenario focuses on the BC concentrations over the Kongsvegen glacier in Svalbard. This methodological approach is critical for ensuring mission safety and reliability while minimizing energy consumption. The APP leverages a robust environment modeling approach to account for terrain, wind conditions, and airspace constraints, enabling real-time adaptability during missions. By combining path optimization with adaptive re-planning capabilities, the APP enhances operational efficiency, demonstrating its potential as a transformative tool for long-endurance pollution monitoring missions. The rest of the paper is organized as follows. Section 2 reviews the related work in the field of UAV path planning, highlighting the existing approaches and their limitations. Section 3 presents the proposed methodology, detailing the structure and functionality of the APP. Section 4 discusses the experimental setup, including test scenarios and evaluation metrics, followed by a presentation of the results. Section 5 includes a comprehensive discussion of the results achieved by the APP in simulations and in real conditions. Moreover, that section outlines the future research directions. Section 6 concludes the paper.
## 4 Voting Algorithm
### Voting Procedure
#### 4.3.2 Vote Accumulation Stage  
When the neighbor subscriber \(E_{x}\) subscribes and receives the revocation request packet, it first verifies the signature and valid time stamp \(T_{i}\) of the revocation request packet from \(E_{i}\). If the signature is incorrect or the valid time expired, \(E_{x}\) discards the message. Otherwise, \(E_{x}\) extracts the revocation reason to judge and vote, generates its own voting information \(VM_{x}\) as Formula (8), and send the generated voting information to the revocation initiator \(E_{i}\). After successfully receiving and counting the voting information, \(E_{i}\) calculates the latest accumulative voting threshold \(th_{cnew}\) as Formula (9), the latest security weight \(SW_{xnew}\) of the successful voting user as Formula (10) and it adds \(UID_{x}\), \(SW_{xnew}\), and \(\alpha_{\text{x}}\) to the KVWL.  
\[Sig_{x}=SIG(H(CN_{i},UID_{x},\alpha_{x},Pub_{x},SW_{x})) \tag{7}\]  
\[VM_{xr}=(Sig_{x},CN_{i},SW_{x},\alpha_{x},UID_{x},Pub_{x}) \tag{8}\]  
\[th_{cnew}=VW_{i}\times\alpha_{i}+VW_{x}\times\alpha_{x} \tag{9}\]  
\[SW_{xnew}=SW_{x}+1 \tag{10}\]  
\(E_{i}\) continues to record new voting messages according to the above rules until the \(th_{c}\) reaches the set threshold \(th_{c}\), which is as shown in Formula (11)  
\[\sum_{k\in R}VW_{k}\times\alpha_{k}\geq th_{cs} \tag{11}\]  
where \(th_{cs}\) is the setting threshold value for the revocation algorithm. If \(th_{cs}\) is not reached within the valid time stamp \(T_{i}\), the revocation event fails.
## 3 Case Study and Parametric Analysis
### Factors Influencing the Sealing Capacity of the EPDM Gasket
#### 3.3.3 Service Time  
EPDM gaskets are subject to aging and creep during a long service, and the contact stress will experience a gradual relaxation over time, which will further lead to an aggravation of the leak. This paper uses the EPDM aging model provided by [PERSON] et al. [40] to calculate the leakage evolution of EPDM in service for 100 years.  
\[p(t)=p_{0}\times 1.308\exp(-0.004t^{0.4687}) \tag{28}\]  
Figure 11: Effects of the joint offset and gap coupling on leakage; the curves from top to bottom are the relationship between leakage and offset when the joint gap is 11.24 mm, 9.73 mm, 8.21 mm, and 4.42 mm respectively. The four points of the dotted line are the maximum offsets when the groove slope is 30 degrees; they are 2.55 mm, 4.74 mm, 5.62 mm, and 6.49 mm, respectively.  
The calculations agree well with the qualitative analysis. Figure 12a shows the leakage versus service time under conditions of different joint gaps within the service time of one year reported in [10]. Figure 12b shows the relationship between leakage and service time in 100 years. All five lines in Figure 12a indicate that the leakage increases with the aging of sealing materials and all five curves show the same trend illustrated by Figure 12b. The difference lies in the fact that larger gaps have more initial leakage volume (leakage at a service time of 0). The fitted curve of leakage of the second Dapu Road measured at a different time is in accordance with calculated lines. The measured leakage is almost distributed below the calculation of the joint gap of 8 mm (the maximum allowable joint gap of this tunnel design). This conclusion verifies the reliability of the model. The fitting curve of leakage monitoring of the second Dapu Road Tunnel shows a good agreement with the calculation slope.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 4 Analog-Digital Receiver Design
### Iterative Analog-Digital Equalizer
 \tag{13}\]  
where \(\mathbf{H}_{k}=\left[\mathbf{H}_{u,k}\mathbf{H}_{u,u}\right]_{1\leq u\leq U} \in\mathbb{C}^{N_{\emph{rx}}\times U}\) is the concatenated equivalent channel, and \(\mathbf{W}_{\text{\emph{add}},k}^{(i)}=\mathbf{W}_{\text{\emph{add}},k}^{( i)}{(\mathbf{W}_{a}^{(i)})}^{H}\). From (13), we can identify three error terms: (1) the residual intersymbol interference (ISI); (2) the error from the incorrect estimate made by \(\hat{\mathbf{c}}_{k}\) of the signal \(\mathbf{c}_{k}\); and (3) the part that corresponds to the channel noise. From (13), as we can see in the Appendix A, we obtain the corresponding mean square error for the \(k\)th subcarrier. \[ \begin{split}\text{MSE}_{k}^{(i)}&=\mathbb{E}\Big{[} \Big{|}\overline{\mathbf{c}}_{k}^{(i)}-\mathbf{c}_{k}\Big{|}_{2}\Big{]}\\ &=\|\mathbf{W}_{\text{\emph{add}},k}^{(i)}\mathbf{H}_{k}-\mathbf{ B}_{d,k}^{(i)}\overline{\mathbf{\Psi}}^{(i-1)}-\mathbf{I}_{U}\|_{p}^{2}\sigma_{u}^{2}+ \|\mathbf{B}_{d,k}^{(i)}(\mathbf{I}_{U}-\big{|}\overline{\mathbf{\Psi}}^{(i- 1)}\big{|}_{2})^{1/2}\|_{F}^{2}\sigma_{u}^{2}+\|\mathbf{W}_{\text{\emph{add}},k }^{(i)}\|_{F}^{2}\sigma_{n}^{2}.\end{split}  \tag{14}\]  
From (14), we can obtain a semi-analytical BER approximation for an M-QAM constellation with Gray mapping, given by [34]  
\[\text{BER}=\frac{\alpha}{US}\sum_{u=1}^{U}\sum_{k=1}^{S}\text{Q}\left(\sqrt{ \beta\big{(}\text{MSE}_{k,u}^{(i)}\big{)}^{-1}}\right), \tag{15}\]  
where \(\alpha=4(1-1/\sqrt{M})/\log_{2}[M]\), and \(Q(. )\) denotes the Q-function. \(\text{MSE}_{k,u^{\prime}}^{(i)}\), such that \(\sum_{u=1}^{U}\text{MSE}_{k,u}^{(i)}=\text{MSE}_{k}^{(i)}\), is the mean square error on samples \(\overline{\mathbf{c}}_{k,u}^{(i)}\) at iteration \(i\). The optimization problem can be given by  
\[ \begin{split}&\left(\mathbf{W}_{a}^{(i)},\mathbf{W}_{\text{\emph{add}},k ^{\prime}}^{(i)}\,\mathbf{B}_{d,k}^{(i)}\right)\\ &=\text{arg}\ \text{min}\ \sum\limits_{k=1}^{S}\text{MSE}_{k}^{(i)}\\ &\text{s.t.}\ \sum\limits_{k=1}^{S}\text{diag}(\mathbf{W}_{\text{\emph{add}},k}^{(i)}{(\mathbf{W}_{a}^{(i)})}^{H}\mathbf{H}_{k})=\mathbf{S}\mathbf{I}_{U} \\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\mathbf{W}_{a}^{(i)} \in\mathcal{W}_{a},\end{split}  \tag{16}\]  
where \(\mathcal{W}_{a}\) denotes the set of feasible analog coefficients, i.e., the \(N_{\emph{rx}}\times N_{\emph{rx}}^{\emph{RF}}\) matrices with constant-magnitude entries. The amplitude constraint in (16) is justified by the fact that, if we only consider the MSE minimization, then it may lead to biased estimates [10]. Note that the optimization of (16) considers as a metric the average MSE of the \(S\) subcarriers.
# Determining Factors of Fixed Offshore Platform Inspections in Indonesia
## 2 Conceptual Background
### Fixed Offshore Platform Inspections
This is carried out with a comparison of VoI, life cycle cost (LCC), and a reliability-based optimization approach [31]. A Bayesian network method is also used in other studies [11; 32; 33]. Thus, despite their costly nature, fixed offshore platform underwater inspections may be conducted to prevent fatigue-related failure. Traditionally, underwater inspection activities utilize rope access; however, this conventional method introduced safety risks for inspectors, and the industry is shifting toward the use of unmanned subsea ROVs (remotely operated vehicles) to perform interventions [34]. Unmanned and/or autonomous vehicles may be used to inspect the underwater conditions of fixed offshore structures, thus allowing authors to assess structural integrity, entanglements, marine growth intensity, and other factors influencing the hydrodynamic load [35]. Previous Research on Fixed Offshore Platform Inspections  
Previous research on inspections of fixed offshore platforms is presented in Table 1. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Author** & **Purpose** & **Variable** & **Method** \\ \hline
1 & [PERSON] [16] & Risk assessment and inspection plan development & Risks and consequences \\ \hline
2 & [PERSON] [29] & Reviewing the evolution of inspections of aging oil and gas facilities & Inspection strategies and industry challenges & Risk-based assessment \\ \hline
3 & [PERSON] et al. [17] & Identify and discuss maintenance challenges and maintenance practices for subsea oil production systems & Failure and maintenance \\ \hline
4 & [PERSON] [28] & Discuss the development and application of inspection techniques for fixed and floating platforms as well as jack-up drilling rigs & Reliability-based inspection \\ \hline
5 & [PERSON] and [PERSON] [7] & Implementation of risk-reliability-based underwater inspection & Probability of failure \\ \hline
6 & [PERSON] et al. [27] & Assess severity and recommend corrective action & Database of platform \\ \hline
7 & [PERSON] [36] & Sketching a health monitoring system to solve the problem of limitations and deficiencies of sensor networks as well as solve localization problems & Database of platform \\ \hline
8 & [PERSON] et al. [31] & Development of maintenance optimization by utilizing value of information (VoI) calculations and Bayesian decision optimization & Lifetime fatigue reliability, LCC, and VoI & Other methods (expert system; EMA and CMCM; Bayesian networks; and cost-benefit analysis) \\ \hline
9 & [PERSON] and [PERSON] [10] & Analyze the effect of time variations structural capacity on optimal time intervals for the inspection and maintenance of offshore platform & Cost, structural demand, and time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous research.