esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Project and Layout of Measurement Points
### Pit Construction Conditions  
The pit construction is divided into three conditions as shown in Table 2, and the schematic diagram of the construction process is shown in Figure 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Construction Conditions** & **Construction Process** \\ \hline Condition 1 (enclosure construction) & Reinforcement of the trench wall on both sides of the ground wall; partially grouped pile construction; enclosure construction of diaphragm wall. \\ \hline  & First earth excavation, first concrete support, trestle concrete, concrete maintenance; \\ Condition 2 (earth excavation construction) & second earth excavation, second concrete support, \\  & concrete maintenance; \\  & third earth excavation, third concrete support, concrete maintenance; \\  & fourth earth excavation, setting bedding, and waterproof layer. \\ \hline  & Concrete maintenance, removal of the third course of support, \\  & replacement of the top plate of the third basement wall, steel pipes \\  & and steel sections, and construction of diagonal bracing in Zone I; \\ Condition 3 (basement support construction) & concrete maintenance, removal of the second course of support, \\  & replacement of the top plate of the second basement wall, steel pipes \\  & and steel sections; \\  & concrete maintenance, removal of the first course of support, \\  & replacement of the top plate of the first basement wall, steel pipes and \\  & steel sections. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of deep foundation pit construction conditions.  
Figure 2: Schematic diagram of the construction process: (**a**) enclosure construction; (**b**) earth excavation construction; and (**c**) basement support construction.
## 1 Introduction
Online viscometers have been tested in pilot mills, and an overview of their performances is provided in References [15; 16]. However, there is still a need for an online viscometer that can be installed permanently in the mill [17]. Few papers have been devoted to the application of time-domain nuclear magnetic resonance (TD-NMR), for the analysis of black liquor [15; 16; 18]. Citing the revealed correlations between several NMR relaxation parameters (Free Induction Decay (FID) magnitudes, spin-lattice T\({}_{1}\), and spin-spin T\({}_{2}\) relaxation times) and SC and viscosity [15; 18], TD-NMR has been considered a potential industrial soft sensor for the viscosity and SC determination of black liquor [15; 18]. However, a general relationship between the NMR relaxation times and the viscosity of black liquors originating from different pulps has not been found [15; 18]. On the other hand, a general correlation between the FID magnitude and SC for several different pulps has been revealed in a narrow range of SCs [18], although to the authors' best knowledge, there have been no further investigations of this finding. Although the TD-NMR technique has not often been applied for online or in-line process analysis and control, the potential for online TD-NMR use has been shown in the laboratory [19] and in harsh industrial environments like mines [20]. TD-NMR systems for the online measurements of onboard ship fuel and for the online characterization of animal slurry have also been proposed [21; 22]. In the current work, the objective was to demonstrate the feasibility of online TD-NMR use in industrial environments and to apply the TD-NMR for the measurement of the SC of black liquor.
## 4 Mathematical Model  
### Assumption  
Before further research on the problem, to facilitate the research of the problem and the establishment of the model, the following assumptions are made:  
1. When orders within the same pallet belong to the same container, these orders are considered as one consolidated order.
2. Once the pallet-picking task is opened, it can only be closed when all the associated orders are completed.
3. There is a limit to the number of pallets that can be picked simultaneously by each picking station, and this paper assumes it to be three.
4. The operation time for actions such as retrieving containers, handing over containers, and picking out orders is not considered.
5. The stacker is a single load and can only retrieve one container at a time.
6. The stacker is assumed to drive at a constant speed, without considering the acceleration and deceleration process. Its constant lifting and lowering speed are expressed as \(V_{y}\), the maximum horizontal speed is expressed as \(V_{xm}\), and the ideal horizontal movement speed with the best energy consumption is expressed as \(V_{0}\).
7. Each handover point at the aisle port can only store up to one container.
8. AGV is a single-load vehicle that can only transport one container at a time and drives at a constant speed along a straight line at a speed of 1 m/s without considering acceleration and deceleration processes.
9. AGVs adopt the predetermined acceleration and deceleration strategy to avoid collisions, with the same additional energy consumption paid each time.
10. The power limit of AGVs and the capacity limit of the buffer zone are not considered.
# Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
## 3 Numerical Simulation Analysis
### Wind Load Simulation Method  
The harmonic synthesis method is employed to simulate the time-varying fluctuating wind speeds across multiple segments of the transmission tower. In this approach, the wind field is modeled as a one-dimensional multivariate Gaussian random process, denoted by the vector \(\left(u(t)=\left\{u_{1}(t)\;u_{2}(t)\ldots u_{n}(t)\right\}^{T}\right)\), where its cross-spectral density matrix \(S(\omega)\) and cross-correlation function matrix \(R(\omega)\) are semi-positive definite complex matrices. The Cholesky decomposition of \(S(\omega_{l})\) can be expressed as  
\[S(\omega_{l})=H(\omega_{l})H^{*T}(\omega_{l}) \tag{2}\]  
where \(H^{*T}(\omega_{l})\) is the conjugate transpose matrix of \(H(\omega_{l})\). The argument of the off-diagonal elements is given by  
\[\theta_{jm}(\omega)=\tan^{-1}\Biggl{\{}\frac{Im\left[H_{jm}(\omega)\right]}{ \text{Re}\left[H_{jm}(\omega)\right]}\Biggr{\}} \tag{3}\]  
Figure 4: FEM model of transmission tower.  
Figure 5: Modal analysis.
## 4 Accurate CROWDMATCH Based on Coalition Games
Proof: Initially, let us consider that the matching configuration of crowdworkers, denoted as \(\Lambda^{*}\) and determined by the Accurate CROWDMATCH algorithm, does not exhibit Nash-Individual stability. In this context, the following conditions must be satisfied: (i) \(\exists n\
otin\mathcal{N}_{m},\forall m\in\mathcal{M},m=\arg\max_{m^{\prime} \in\mathcal{M}}\{U_{m^{*}}(m^{*}\cup\{n\})-U_{m^{*}}(m^{*})|U_{m^{*}}(m^{*}\cup \{n\})-U_{m^{*}}(m^{*})>0\}\); (ii) \(\exists n\in\mathcal{N}_{m}\), satisfying \(U_{m}(m\setminus\{n\})>U_{m}(m)\); (iii) \(\exists n\in\mathcal{N}_{m},\exists m^{\prime},m\
eq m^{\prime}\), satisfying \(U_{m}(m\setminus\{n\})+U_{m^{\prime}}(m^{\prime}\cup\{n\})>U_{m}(m)+U_{m^{ \prime}}(m^{\prime})\); and (iv) \(\exists n\in\mathcal{N}_{m}\), \(\exists n^{\prime}\in\mathcal{N}_{m^{\prime}}\), and \(m\
eq m^{\prime}\), satisfying \(U_{m}((m\setminus\{n\})\cup\{n^{\prime}\})+U_{m^{\prime}}((m^{\prime}\setminus \{n^{\prime}\})\cup\{n\})>U_{m}(m)+U_{m^{\prime}}(m^{\prime})\). However, in the Accurate CROWDMATCH algorithm, if any of the aforementioned conditions holds true, the crowdworkers will adhere to the correspondingswitching rules described in Definition 4. Consequently, the matching of the crowdworkers cannot be considered final, as they will persistently modify their associations with crowdsourcers by following these switching rules. This discrepancy challenges our initial assumption, leading to the deduction that the Accurate CROWDMATCH algorithm converges to a Nash-Individually stable matching among the crowdworkers and the crowdsourcers. ```
1:Input\(\Lambda_{\text{initial}}\) from Approximate CROWDMATCH Algorithm
2:Output: Optimal Matching \(\Lambda^{*}\)
3:repeat
4: Randomly select a crowdworker \(n\) and its crowdsourcer \(m\)
5:if\(n\) does not belong to any crowdsourcer then
6:\(m=\arg\max_{m^{*}\in\mathcal{M}}\{U_{m^{*}}(m^{*}\cup\{n\})-U_{m^{*}}(m^{*})| U_{m^{*}}(m^{*}\cup\{n\})-U_{m^{*}}(m^{*})>0\}\)
7:\(\mathcal{M}=\{\mathcal{M}\setminus\{m\}\}\cup\{m\cup\{n\}\}\)
8:else
9: Another crowdsourcer \(m^{\prime}\), \(m^{\prime}\
eq m\) is randomly selected
10:if\(N_{m}\leq N_{m^{\prime}}^{max}\)then
11:if\(U_{m}(m\setminus\{n\})+U_{m^{\prime}}(m^{\prime}\cup\{n\})>U_{m}(m)+U_{m^{ \prime}}(m^{\prime})\)then
12:\(\mathcal{M}=\{\mathcal{M}\setminus\{m,m^{\prime}\}\}\cup\{m\setminus\{n\} \}\cup\{m^{\prime}\cup\{n\}\}\)
13:endif
14:else
15: Randomly select a crowdworker \(n^{\prime}\) of crowdsourcer \(m^{\prime}\)
16:if\(U_{m}((m\setminus\{n\})\cup\{n^{\prime}\})+U_{m^{\prime}}((m^{\prime}\setminus \{n^{\prime}\})\cup\{n\})>U_{m}(m)+U_{m^{\prime}}(m^{\prime})\)then
17:\(\mathcal{M}=\{\mathcal{M}\setminus\{m,m^{\prime}\}\}\cup\{(m\setminus\{n\}) \cup\{n^{\prime}\}\}\cup\{(m^{\prime}\setminus\{n^{\prime}\})\cup\{n\}\}\)
18:endif
19:endif
20:endif
21: Update \(m\) to the current crowdsourcer of \(n\)
22:if\(U_{m}(m\setminus\{n\})>U_{m}(m)\)then
23:\(\mathcal{M}=\{\mathcal{M}\setminus\{n\}\}\cup\{m\setminus\{n\}\}\)
24:endif
25:until no further updates of the crowdworkers
```  
**Algorithm 2** Accurate CROWDMATCH Algorithm
# Effects of Reflectance of Backsheets and Spacing between Cells on Photovoltaic Modules
## 5 Conclusions  
In conclusion, the effects of gap spaces between PV cells and backsheets were investigated. For the study of gap spaces, broad gaps of 2.5 mm and 6 mm for the MBB and shingled module, respectively, improved I\({}_{\text{Sc}}\)s leading to an increase in PV module power and efficiency. In terms of backsheet, the white backsheet showed superior performances compared to the black and B-W hybrid backsheets. This is because the white backsheet reflects visible light and the reflected light is absorbed into PV cells again. The results suggest that broad gap spaces and white backsheet enhance module efficiency. For commercial production, however, too broad a gap space reduces space for PV cells and leads to fewer PV cells incorporated into PV modules. Therefore, the balance between the number of PV cells and gap spaces with white backsheet is substantially crucial to obtain high PV module efficiency.  
The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/app12010443/s1, Figure S1: Illustration of the light pathway irradiated from the front glass. It is absorbed by PV cells.  
Conceptualization, H.L. and S.H.K.; methodology, H.L. and S.H.C.; software, S.H.C.; validation, J.M. and D.Y.J.; formal analysis, H.L.; investigation, S.H.C.; resources, H.L.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and S.H.K.; visualization, H.L.; supervision, S.H.K.; project administration, S.H.K.; funding acquisition, S.H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the New & Renewable Energy of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resources from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20213030010240& 20213030010290).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Optimization of ML Models and Results
### Model Performance
#### 4.2.2 Results of Models for Y-PPV Prediction  
To better demonstrate the performance of the first three models for the prediction of Y-PPV, the results of the prediction of the validation set using GWO-GBR, GA-GBR, and PSO-GBR are shown in Figure 9. In addition, the parameters of these models are shown in Table S2.  
As shown in Figure 9a-f, the R\({}^{2}\) of GWO-GBR, GA-GBR, and PSO-GBR were 0.9147, 0.8894, and 0.8771, respectively. The R of GWO-GBR, GA-GBR, and PSO-GBR were 0.9589, 0.9589, and 0.9683, respectively. The RMSE of GWO-GBR, GA-GBR, and PSO-GBR were 0.0954, 0.1086, and 0.1145, respectively. The MAE of GWO-GBR, GA-GBR, and PSO-GBR were 0.0734, 0.0938, and 0.0886, respectively. According to Figures 6d-f and 9b,d,f, the  
Figure 8: The prediction performance of X-PPV: (**a**) the measured and predicted results of GWO-GBR; (**b**) the confidence band and prediction band of GWO-GBR; (**c**) the measured and predicted results of GA-GBR; (**d**) the confidence band and prediction band of GA-GBR; (**e**) the measured and predicted results of PSO-GBR; (**f**) the confidence band and prediction band of PSO-GBR.  
hybrid models had higher R\({}^{2}\) and R and lower RMSE and MAE compared to the standalone models. This indicates that the hybrid model had a higher performance of Y-PPV regression prediction than the standalone model. The evaluation indicator R\({}^{2}\) of GWO-GBR was higher than the other models, and the R of GWO-GBR was similar to the other models. The RMSE and MAE were all lower than the other models. This shows that GWO-GBR had the best performance for Y-PPV regression.  
Figure 9: The prediction performance of the Y-PPV: (**a**) the measured and predicted results of GWO-GBR; (**b**) the confidence band and prediction band of GWO-GBR; (**c**) the measured and predicted results of GA-GBR; (**d**) the confidence band and prediction band of GA-GBR; (**e**) the measured and predicted results of PSO-GBR; (**f**) the confidence band and prediction band of PSO-GBR.  
According to Figure 9b,d,f, the confidence band and prediction band of GWO-GBR, GA-GBR, and PSO-GBR for Y-PPV prediction were similar, which illustrates that the uncertainty performance of the X-PPV prediction models was similar [27]. Therefore, the GWO-GBR model had the best performance of Y-PPV regression.
## 1 Introduction
Given the characteristics of the long distances between port surveillance cameras and vessels, detecting small vessels still presents many challenges, especially when it is getting dark and when they are moving slowly. This defect is caused by significant interference from sea surface noise. Therefore, this study proposes an IMSM which takes into account the noise of the sea environment as well as the movement of vessels in the process of their real-time detection. The primary contributions of this paper are summarized as follows:  
* The proposed improved multi-structural morphology approach is designed based on physics and intensive mathematical contexts that result in the accurate detection of target vessels. * Deep Hough transform (DHT), together with OSTU-based adaptive threshold segmentation, enables the removal of the irrelevant lines/occlusions on images and converts them into binary maps. * The combination of weighted morphological filtering with neighborhood-based adaptive fast median filtering using the associated domain makes it possible to clearly locate and monitor vessel movements in real time. The rest of this paper is organized as follows: The development of the port vessel detection system is presented in Section 2. The multi-structural morphology filtering design is described in detail in Section 3. Section 4 focuses on and highlights the scientific applications of the IMSM approach, and Section 5 presents the conclusions of this study.
## 8 Conclusions  
For computer vision and AI researchers, the automated segmentation, recognition, and assessment of endometrial tissues remains a difficult task. In this work, we suggest using a metric-driven approach based on ensembles to automatically differentiate endometrial glands using group predictions. In order to create a training and testing dataset that is more statistically significant and to lower the computing resource needs, we also suggest an image processing phase. We used a dataset amassed during actual clinical practice to construct our experimental research. The suggested ensemble-based technique showed comparatively high endometrial gland identification accuracy. The ensemble-based model outperformed other designs, including U-Net, ResNet34-UNet, and U-Net++, with higher Dice score (0.95) and IoU (0.90) values. The metric-driven approach highlighted that the nuclei density is higher in the receptive samples. Endomet stromal cell nuclear size and cell-to-cell distance are potential candidate criteria for the metric-driven automated evaluation of the endometrial phase. Moreover, more nuclei can be detected next to endometrial glands than in other parts of the tissue. Future studies will focus on the segmentation of more endometrial cells and/or structures, such as stroma and blood vessels. The current dataset will also be augmented with new whole-slide images of endometrial biopsies and expert-based annotations in order to build a better and more precise segmentation algorithm.  
Conceptualization, V.R., A.S. and E.D.; methodology, A.K.-G.; software, V.R.; validation, A.M. and R.B.; formal analysis, A.S.; investigation, V.R.; resources, M.S. and A.M.; writing--original draft preparation, V.R.; writing--review and editing, all authors; visualization, A.K.-G.; supervision, E.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Estonian Research Council (grant PRG1076), Enterprise Estonia (grant no EU48695 and grant ReadUteru RE.5.02.22-0193), and also received funding from the Research Council of Lithuania (grant no 31V-72) and Horizon Europe (NESTOR, grant no. GA101120075).  
This study was approved by the Research Ethics Committee of the University of Tartu, Estonia (No. 340T-12).  
This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Research Ethics Committee of the University of Tartu, Estonia (approval date 19 April 2021, No. 340/T-12). The scanned endometrial tissue slides of the anonymized participants were obtained from the beREADY test laboratory (www.beready.ee, accessed on 24 June 2024), Competence Centre on Health Technologies (CCHT), Tartu, Estonia.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical issues.
# Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs
## 2 Related Works
### Computation Offloading  
The authors of [1] proposed a deep Q-learning [12] based offloading mechanism for the mobile fog computing environment [1]. In their frameworks, an application is decomposed into basic blocks and offloaded to many devices. They evaluated latency and energy consumption for varying computation demands and a number of devices. MAUI [13], ThinkAir [14], and CloneCloud [15] are the frameworks for offloading to the mobile edge cloud. In the frameworks, the scheduling algorithm decides whether to offload tasks or not and deploys them according to the policy of the cloud. AVE [16] is an ant colony optimization (ACO) [17] based task offloading framework for mobile devices that are connected with each other by dedicated short-range communications (DSRC). It assumes that tasks are context-free, although the tasks of an application are actually tightly connected among themselves. Note that the precedence relationships and data transfer cost between tasks must be considered to optimize the completion time of an application. However, the works mentioned above model an application as one task or multiple context-free tasks.  
CO to other devices requires data transfer overhead. The data transfer overhead is important, particularly when devices are connected with a wireless connection, like the vehicular environment. In the worst-case scenario, the data transfer time might overwhelm the task processing time, so several studies focused on reducing this overhead. For example, the authors of [18] proposed a method using federated learning (FL), in which local devices compute a deep learning (DL) model and a data center gathers the model updates. In [19], the authors suggested reducing the data transfer overhead of the FL method by introducing the random client selection mechanism. For that, federated distillation, based on knowledge distillation [20] and data augmentation [21], were adopted. However, the offloading schemes in [18; 19; 20; 21] are very specific and only for deep-learning algorithms, so they cannot be applied to general applications.  
OpenVDAP [22] classifies CO-possible services into four categories, (1) real-time diagnosis services, (2) driving assistance services, (3) in-vehicle infotainment services, and (4) third-party application services. It is very important, especially for real-time diagnosis and driving assistance services, to minimize the latency. The total latency of a service is related to the schedule length, but there has not been much work done on shortening the schedule length.
# Performance of Adaptive Bit-Interleaved Polar Coded Modulation in FSOC System
## 2 Adaptive Bit-Interleaved Polar Coded Modulation (A-BIPCM)
Let \(\text{C}_{1}\) and \(\text{C}_{2}\) be two linear codes of the same length, which \(\text{C}_{1}+\text{C}_{2}|\text{C}_{2}\) will be defined as:  
\[\text{C}_{1}+\text{C}_{2}|\text{C}_{2}\,=\{[x+y]|x\in\text{C}_{1},y\in\text{C}_ {2}\} \tag{11}\]  
Polar codes can be described by the following recursive equation:  
\[\text{C}_{m,j}=\text{C}_{m-1,2j}+\text{C}_{m-1,2j+1}|\text{C}_{m-1,2j+1} \tag{12}\]  
where \(m=1,\ldots,M\) and \(j=0,\ldots,2^{M-m}-1\), the initial condition is \(\text{C}_{0,j}=\{0,1\},j\in\text{A}\) and \(\text{C}_{0,j}=\{0\},j\in\text{A}^{\text{c}}\). The length of the polar code is \(N=2^{M}\) represented by \(\text{C}_{M,0}\). Interleaved polar codes are constructed by \(C_{m-1,2j}\), inserting an interleaver at the output of each encoder. The interleaver can be expressed as a matrix \(\Pi\), which \(\text{C}\Pi\) will be defined in this article as:  
\[\text{C}\Pi=[x\Pi\,|\,x\in\text{C}] \tag{13}\]  
where C\(\Pi\) represents the new sequence obtained by interleaved all codeword sequences C using an interleaver \(\Pi\). Therefore, the interleaved sequence can be described by the following recursive equation  
\[\text{C}_{m,j}=\text{C}_{m-1,2j}\Pi_{m-1}+\text{C}_{m-1,2j+1}|\text{C}_{m-1,2j +1} \tag{14}\]
## 5 Real Data Analysis
### The Airline On-Time Performance Data  
The airline on-time performance data contain detailed information on US domestic flights since October 1987. The original data are saved in individual files labeled by month. For illustration purposes, in this study, we use data from between October 1987 and August 2018, 371 files/months in total, which contain 182,751,882 flights. After removing the records with missing or invalid inputs, \(N\) = 179,528,198 is actually considered in this study.  
Following [43], we formulate the data into a binary classification problem aiming to model the flight delay status, a binary response ArrDelayLabel (arrival delay label), which is generated by cutting the continuous variable ARRIVAL DELAY at the 15-min point. For simplicity, the departure time block DepTimeBlk, originally a factor of 17 levels, is regrouped into a factor with 4 levels (1 for 12:00 a.m.-05:59 a.m., 2 for 06:00 a.m.-11:59 a.m., 3 for 12:00 p.m.-05:59 p.m., and 4 for 06:00 p.m.-11:59 p.m.). Moreover, three categorical variables (QUARTER, DayOfWeek, and DepTimeBlk) and three continuous variables (DISTANCE, DepDelay, and CRSTimeElapsed) are used as illustrations to explain the status of flight arrival delays (see Table S8 in the Supplementary Materials for a list of the variables).