esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Ground Measurements and Parameters Estimation  
All ground measurements data were integrated into 8-day time step for the comparability in temporal resolution with the MODIS data products (Figure 1). Daily Tmin, range from \(-20.4\)\({}^{\circ}\)C to 8.9 \({}^{\circ}\)C, which was much lower than the default upper limit (Tmin_max, 12.02 \({}^{\circ}\)C) in the BPLUT (Table 1 and Figure 1a). In general, VPD fluctuated around 0.15 \(\pm\) 0.10 (mean \(\pm\) SD) Kpa except for a few measurement days accessible to the VPDmin (0.65 Kpa) (Table 1 and Figure 1b). The supreme PAR appeared on July and August (DOY120-240) that the seasonal average could reach 18.6 \(\pm\) 4.2 umol photons\({}^{-1}\) m\({}^{-2}\) day\({}^{-1}\) (equal to 4.2 \(\pm\) 0.9 MJ m\({}^{-2}\) day\({}^{-1}\)) (Figure 1c).  
The consecutive LAIMC was stimulated from the linear regression between the consecutive 8-day time step LAIMC and field measurement LAI (LAIG) in measurement days (LAIGMC = 2.33 \(\times\) LAIMC \(-\) 0.23, \(R^{2}\) = 0.84, \(p\) < 0.001, \(n\) = 23) (Figure 1d). The LAIMC underestimated the vegetation condition in this alpine swamp meadow by an average 39.2% except a few days during the non-growing season. The LAIMC reached its maximum on the August with multi-years average 5.99 m\({}^{2}\) m\({}^{-2}\) (\(\pm\)0.64 SD) from 2009 to 2012 (Figure 1d). As Figure 1e illustrated, the FPAR\({}_{\rm M}\) also generally underestimated the  
Figure 1: Seasonal variation of ground meteorological measurements data and the FPAR from 2009 to 2013. All data are integrated into 8-day time step and showed with the first day of each 8-day interval, the error bars in (**a**) and (**b**) are standard deviation for corresponding mean values. (**a**), Tmin is the daily minimum temperature; (**b**), VPD is the daily vapor pressure deficit; (**c**), PAR is photosynthetically active radiation; (**d**), the consecutive LAIMC is derived from linear regression between ground LAIGC measurements and MODIS LAI (LAIG) in LAIGC measurement days (\(n\) = 23); (**e**), FPAR\({}_{\rm M}\) is directly obtained from MODIS products, while FPARG is calculated from LAIGC in e based on Beer-Lambert law.  
FPAR\({}_{\rm G}\) by an average 22.2% during the whole study period. Seasonal FPAR trend was similar to LAI (Figure 1d,e).  
The MOD17A2 algorithm carried out an integrated \(\varepsilon_{\rm max}\) estimation of 1.78 g C MJ\({}^{-1}\) for this alpine wetland from 2009 to 2013 (\(R^{2}\) = 0.78, \(p\) < 0.01, \(n\) = 23). Alternatively, the light response curve derived from the Michaelis-Menten equation provided annual-scale \(\varepsilon_{\rm max}\) estimation for 1.70, 1.80, 1.33, and 1.59 g C MJ\({}^{-1}\) from 2009 to 2012, respectively (Figure 2a-d and Table 1). Two \(\varepsilon_{\rm max}\) estimate methods failed to generate significant difference in \(\varepsilon_{\rm max}\) values (paired \(t\)-test, t\({}_{0.025}\) = 2.2, df = 4, \(p\) = 0.1) during study period. Therefore, we assumed the \(\varepsilon_{\rm max}\) value in 2013 was 1.78 g C MJ\({}^{-1}\) when calculated GPP_MODR3 and GPP_MODR4 (Table 1).
## 3 Results
### Adaptive Network-Based Fuzzy Inference System (ANFIS)  
The surface roughness of the machined Al6061 aluminum blocks has been predicted using the ANFIS algorithm. Data in Table 1 have been employed to train the model's varying membership functions described in Section 3: membership function shapes, FIS generators, and optimization methods. In total, 24 data from the experimental results have been considered for the training, while six data have been isolated for the testing of the ANFIS model. The pictorial views of the model structure are depicted in Figure 8, and the results generated by different ANFIS configurations are recorded in Table 3 in terms of the root mean square error (RMSE) and regression values (R\({}^{2}\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{3}{c}{**Results (RMSE/R\({}^{2}\) Trained/R\({}^{2}\) Tested)**} \\ \cline{2-4}
**Hidden Layers** & **10 Neurons** & **11 Neurons** & **12 Neurons** \\ \hline
1 & 0.06128/0.3538/0.7687 & 0.0518/0.8314/8466 & 0.03653/0.8587/0.9984 \\ \hline
2 & 0.3919/0.891/0.9369 & 0.04487/0.9578/0.9396 & 0.06832/0.964/0.5667 \\ \hline
3 & 0.0907/0.8425/0.8885 & 0.07422/0.9754/0.7466 & 0.1035/0.8919/0.8342 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANN training results.  
Figure 7: ANN regression line.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 5 Simulation Evaluation
### Performance Metrics  
In the following representation, we use \(m\) to indicate the total number of interest issued by consumers during the whole simulation, \(n\) to indicate the number of satisfied interest that consumer obtains the requested data.  
1. _Satisfaction Ratio_: it is defined as \(\dfrac{n}{m}\). This metric indicates the ratio of issued interested and satisfied interest at the application level. Higher satisfaction ratio means more interested data are received, i.e., the interests are satisfied.  
Figure 7: Simulation Scenario.  
2. _Average Hops_: it is defined as \(\frac{\sum_{i=1}^{n}{h_{i}}}{n}\), where \(h_{i}\) indicates the count of interest hops and data hops for a satisfied interest. This metric reflects how many times interest and data packets need to be forwarded to get an interest satisfied. Less hops means a shorter path.
3. _Normalized Transmission Overhead_: it is defined as \(\frac{\sum_{i=1}^{n}{Int_{i}}+\sum_{i=1}^{n}{Data_{i}}}{n}\), where \(Int_{i}\) is the count of transmitted interest generated by each issued interest, and \(Data_{i}\) is the count of transmitted data generated by each returned data. Then their sum is normalized by the total number of satisfied interest to indicate the overhead cost for a certain satisfaction ratio. The normalization shows the average transmission overhead for each request. This metric can measure the transmission cost and bandwidth usage of the network. Less normalized transmission overhead means an interest can get satisfied at a lower cost.
4. _Average Delay_: it is defined as \(\frac{\sum_{i=1}^{n}{t_{i}}}{n}\), where \(t_{i}\) indicates the delay between when the interest is issued by the consumer and when the returned data arrives at the consumer. Shorter average delay means interest can be satisfied faster, which is important for those time-critical applications.
## 4 Discussion
Phosphorylation of molecules in NF-\(\kappa\)B and MAPKs pathways can be readily observed in inflammatory lung disorders such as pneumonia and COPD [32]. The pathogenesis of COPD with immune infiltration, mucus overproduction, fibrosis, and airway remodeling and inflammation is associated with JNK, ERK1/2, and p38 MAPKs [33]. ERK1/2 is implicated in pathological disease such as chronic inflammation and cancer and is concerned with cell growth, survival, migration, proliferation, and differentiation [34]. p38 MAPK is intimately linked to inflammation and is required for the generation of IL-1\(\beta\), IL-6, and TNF-\(\alpha\)[35]. This study showed that JGT reduced NF-\(\kappa\)B, ERK1/2, and p38 MAPK phosphorylation levels in COPD-induced lung tissues, suggesting that JGT reduces inflammation by preventing NF-\(\kappa\)B and MAPKs signaling pathway activation. Histological examination of biopsy specimens from COPD patients has revealed structural abnormalities, such as airway wall thickening, subepithelial fibrosis, and increases in smooth muscle mass [36]. Fibroblast growth factor (FGF), TNF-\(\alpha\), insulin-like growth factor (IGF), IL-8, and peculiarly, TGF-\(\beta\) are essential elements in this process [37]. TGF-\(\theta\) has been shown to increase collagen and fibronectin production by fibroblasts, suggesting its importance in the progression of peribronchial fibrosis. According to research, TGF-\(\theta\) has been related to smooth muscle cell proliferation and mucus hypersecretion [38; 39; 40]. MMP-9, a member of the MMP family, is generated by smooth myocytes, mast cells, epithelial cells, and fibroblasts, all of which are significant mediators of the extracellular matrix metabolism and airway wall remodeling [41; 42]. Expression of MMP-9 is also higher in several types of lung cells of individuals with IPF (idiopathic pulmonary fibrosis) [43]. MMP-9 is the most potent profibrotic mediator and has a bidirectional connection with TGF-\(\beta\)1. As a reaction to TGF-\(\beta\)1 generated in the lung epithelium, IPF lung fibroblasts produce MMP-9, which in turn activates latent TGF-\(\beta\)1 to increase the active TGF-\(\beta\)1 pool [44]. Peribronchial fibrosis following airway remodeling is thus most likely the result of a TGF-\(\beta\)-mediated process comparable to uncontrolled repair. The current investigation discovered that JGT therapy significantly lowered increased levels of TGF-\(\beta\) and MMP-9 in LPS and CS--exposed mice, indicating that the beneficial impact of JGT on COPD may potentially be connected to airway remodeling. Notably, prior studies have shown that NF-\(\kappa\)B and MAPKs signaling regulate MMP-9 expression. MMP-9 expression is promoted by the IL-1\(\beta\) and TNF-\(\alpha\) through NF-\(\kappa\)B, ERK1/2, and p38 MAPK signaling pathway [42; 45]. JGT inhibits airway remodeling caused by CS and LPS-induced COPD via partially regulating the phosphorylation of NF-\(\kappa\)B, ERK1/2, and p38 MAPKs.
## 3 Results
### TSS Model
#### 3.1.2 Comparison and Verification of TSS Models  
Several empirical and semi-analytical algorithms have been proposed to estimate TSS concentration, from clear open sea waters and slightly turbid coastal waters to highly turbid inland waters. However, the performance of different models cannot be directly compared because of the differences in band positions and bandwidth designs between Landsat and other sensors, such as MODIS and MERIS. Taking these factors into account, Table 4 lists representative TSS retrieval algorithms of the same type of sensor to facilitate the comparison and verification of the performance and accuracy of the model. Here, we were able to use the simulated \(R_{rs}\) and field measurement data from the aggregated data in Table 1 to validate these models, in which the calibration dataset and the verification dataset were used to recalibrate the model parameters and validate the accuracy of all chosen models, respectively. The model algorithm and verification results are shown in Table 4.  
The validation results shown in Table 4 indicate that the average relative errors between the estimated and measured values of these models were 47.28%, 31.28%, 27.36%, 63.54%, and 34.16%. The accuracies of those models were low and could not meet the requirement of accuracy for TSS estimation. Contrarily, the proposed model calibrated in this study (whether it is a band ratio model or a three-band model) has the highest accuracy. The \(RMS\)s and \(MREs\) of all validation data for the band ratio model are 6.39 mg/L and 19.62%, and 6.24 mg/L and 18.02% for the three-band model. The two models calibrated in this paper have the best validation accuracy among all models. Besides, the determination coefficients in the band ratio model and three-band model are 0.84 and 0.81, respectively, thus these models can still explain the TSS concentration variation more than 80%. In summary, compared to the second-order band ratio model, the first-order three-band model may better suit estimating the TSS concentration in Hedi Reservoir.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**From**} & \multirow{2}{*}{**Study Area**} & \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Validation**} \\ \cline{5-6}  & & & & \(N\) & \(RMSE\) **(mg/IMRE \((\%)\)** \\ \hline [PERSON] et al. (2018) & Orinoco River & OLI Bands 5 & TSS = \(1.35512\)\(\mathrm{s}-2.9385\) & 15 & 10.79 & 47.28 \\ [PERSON] et al. (2009) & Yangtze River & ETM Bands 4 & TSS = \(3.18236\)\(\mathrm{h}\)\(\mathrm{h}\)\(\mathrm{h}\)\(
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 2 Methods  
### SARIMA Model  
The SARIMA model is an ARIMA model considering season influence, which can predict the data with seasonal characteristics [20; 35].  
The ARIMA model was proposed by [PERSON] and [PERSON] in the 1970s [36], and it is a single-product autoregressive moving average process, in which a random process containing \(d\) unit roots is firstly assumed, and it can be transformed into a stable autoregressive moving average process after \(d\) times difference [37]. In other words, the time series are all stationary series in the autoregressive AR model, the moving average MA model, and the autoregressive moving average ARMA model. However, time series behave as d-order unit root processes in many cases. Therefore, we first need to perform differential processing on the data to convert them into a static time series before establishing the model. The ARIMA (\(p\), \(d\), \(q\)) model is as follows:  
\[\left(1-\sum_{i=1}^{p}\alpha_{i}L^{i}\right)(1-L)^{d}y_{t}=\alpha_{0}+\left( 1+\sum_{i=1}^{q}\beta_{i}L^{i}\right)\epsilon_{t} \tag{1}\]  
where \(p\) is the non-seasonal autoregressive order, \(d\) is the non-seasonal difference order, \(q\) is the non-seasonal moving average order, \(\alpha_{i}\) is the autoregressive term coefficient, \(\beta_{i}\) is the moving average term coefficient, \(L\) is the lag operator, \(\epsilon_{t}\) is the white noise series, \(\alpha_{0}\) is the constant term coefficient, \(i\) is the coefficient number, and \(y_{t}\) is the time series value at time \(t\).  
Considering the seasonal factors, the SARIMA (\(p\), \(d\), \(q\)) (\(P\), \(D\), \(Q_{m}\) model is as follows:  
\[\left(1-\sum_{i=1}^{p}\phi_{i}L^{i}\right)\left(1-\sum_{i=1}^{p}\Phi_{i}L^{mi} \right)(1-L)^{d}(1-L^{m})^{D}y_{t}=\alpha_{0}+\left(1+\sum_{i=1}^{q}\theta_{i }L^{i}\right)\left(1+\sum_{i=1}^{Q}\Theta_{i}L^{mi}\right)\epsilon_{t} \tag{2}\]  
where \(P\) is the seasonal autoregressive order, \(D\) is the seasonal difference order, \(Q\) is the seasonal moving average order, \(m\) is the number of periods (monthly data \(m=12\), quarterly data \(m=4\)), \(\phi_{i}\) is the non-seasonal autoregressive coefficient, \(\Phi_{i}\) is the seasonal autoregressive coefficient, \(\theta_{i}\) is the non-seasonal moving average coefficient, and \(\Theta_{i}\) is the seasonal moving average coefficient.
# First Approach in Analysis of Tool Wear When Milling Additive Manufacturing (AM) Parts  
[PERSON]  
1 Faculty 2: School of Engineering--Technology and Life, FIW Berlin University of Applied Sciences,  
Wibelheimhofstrasse 75A, 12459 Berlin, Germany; [EMAIL_ADDRESS] (A.P);  
fiona.sammler@thw-berlin.  
processes like L-PBF, WAAM, 3 DPMD (3D plasma metal deposition), and conventionally cold rolled variants of the steel using milling tests and point towards the dependence of the milling process of 316L on the manufacturing process [12]. The hardness of the 316L variants increases following this order: 3 DPMD, cold rolled, WAAM, and L-PBF [12]. The cutting forces are found to be higher for 316L that has been cold rolled and WAAM, and lowest for L-PBF [12]. The authors explain these dependencies according to their distinct microstructures: L-PBF (lenticular), WAAM (dendritic), 3 DPMD (elongated), and cold rolled (homogeneous) [12]. [PERSON] et al. state that WAAM process parameters such as travel speed, wire feed speed, and interpass temperature affect postprocessing machining [13]. Shorter cooling times during the WAAM process lead to hard and smooth as-deposited surfaces and therefore result in better chip formation and a better surface finish after machining [13].  
In summary, the milling of AM parts differs from the milling of conventionally manufactured parts due to inherent AM process characteristics and the resulting distinct microstructure and material properties of those parts. Postprocessing therefore requires suitable tools and milling process strategies. Moreover, the microstructure and mechanical properties of AM parts are often anisotropic and heterogeneous. Current research does not entirely offer solutions to taking these aspects into account. Also, the removal of support structures, which are used to support the overhanging structures of AM parts and often have lattice geometries, has until now not been a focus of research work.  
The aim of this work is a first approach to investigating the milling of AM parts by examining two application-related cases of the mechanical postprocessing of AM: the milling of the surface layer of WAAM parts and the milling of segmented L-PBF support structures, in both cases compared to the milling of solid AM material. These investigations focus on a first approach to tool wear analysis and the resulting forces seen during the milling processes.
## 2 Elastoplastic Constitutive Relationship Theory
### Iterative Formula of Stress and Strain Increment Under Different Boundary Conditions  
The principal stress and principal strain can be used to simulate common variables in laboratory true triaxial and conventional triaxial tests. The approach to solving the constitutive model's stress-strain increment iteration equations under different boundary conditions, also known as different stress paths, involves solving a system of equations consisting of the third-order compliance matrix form of the stress-strain increment Equation (16), the intermediate principal stress coefficient Equation (18), and the stress path equation. The maximum principal strain increment, \(d\epsilon_{1}\), is used to express the intermediate principal strain increment, \(d\epsilon_{2}\), the minimum principal strain increment, \(d\epsilon_{3}\), the maximum principal stress increment, \(d\sigma_{1}\), the intermediate principal stress increment, \(d\sigma_{2}\), and the minimum principal stress increment, \(d\sigma_{3}\). From Equation (14), the stress-strain equations expressed in the third-order compliance matrix form can be obtained as follows:  
\[\{d\epsilon_{i}\}=\left[C_{ij}\right]\{d\sigma_{j}\},i=1,2,3,\ \ \ j=1,2,3 \tag{15}\]  
Of course, Equation (15) can also be expressed in the following form:  
\[\begin{cases}\begin{aligned} & d\epsilon_{1}\\ & d\epsilon_{2}\\ & d\epsilon_{3}\end{aligned}\end{cases}=\begin{bmatrix}C_{11}&C_{12}&C_{13}\\ & C_{21}&C_{22}&C_{23}\\ & C_{31}&C_{32}&C_{33}\end{bmatrix}\begin{cases}\begin{aligned} & d\sigma_{1}\\ & d\sigma_{2}\\ & d\sigma_{3}\end{aligned}\end{cases} \tag{16}\]  
In experiments, the maximum principal strain increment, \(d\epsilon_{1}\), is generally a known quantity, representing the axial strain. To express the remaining five variables in terms of \(d\epsilon_{1}\), two additional equations are needed. One equation can be derived from the stress path, while the other can be obtained using the intermediate principal stress coefficient, \(b\), or the stress Lode angle, \(\theta\). The definition of the intermediate principal stress coefficient, \(b\), is given as follows:  
\[b=\frac{\sigma_{2}-\sigma_{3}}{\sigma_{1}-\sigma_{3}} \tag{17}\]  
where \(b\) is a constant, provided as an initial condition. This relationship remains valid throughout the increment process:  
\[b=\frac{d\sigma_{2}-d\sigma_{3}}{d\sigma_{1}-d\sigma_{3}} \tag{18}\]The increment formulation is independent of the constitutive model, and different models use different compliance matrices. During loading and unloading, the compliance matrix, \(\left[C_{ij}\right]\), is taken as the elastoplastic compliance matrix and the elastic compliance matrix, respectively. The following sections present the incremental formulations for some commonly used stress paths.
# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 4 Validation Test
### Object Tracking Results  
The shaking table test was conducted based on the described test setup, and recorded videos were analyzed using seven different algorithms (KLT, C8 RT, KCF, MedianFlow, MIL, MOSSE, and TLD). The tracking video with the region of interest (ROI) area is shown in Figure 7. As a result, KLT (Figure 7), KCF (Figure 7), MIL (Figure 7), and MOSSE (Figure 7) successfully tracked the object during the shaking table test. However, the C8 RT (Figure 7), MedianFlow (Figure 7), and TLD (Figure 7) algorithms showed that the ROI deviated from the initially designated object.  
Figure 8 shows the time-displacement curve of the KLT algorithm that succeeded in tracking (Figure 8) and the TLD algorithm in which the ROI deviated from the initially designated object (Figure 8). The time-displacement curve of the C8 RT algorithm in which the ROI deviated from the initial designated object can confirm that the displacement was measured abnormally.  
The frequency response function (FRF) was calculated, as shown in Figure 9, using the displacement measured by the four object tracking algorithms that successfully tracked the object. Based on the FRF calculated using the four object tracking algorithms, the dynamic characteristics of the structure were confirmed, the results of which are shown in Table 2. The dynamic characteristics of the structure shown in Table 2 are used as the fitness function in the FE model updating, and the details are described in the next section.  
Figure 8: Time-displacement curve using different object tracking algorithms.  
Figure 7: Vision-based displacement measurement: (**a**) region of interest (ROI) at the initial frame, (**b–h**) ROI at the final frame using different tracking algorithms.  
Figure 9: Comparison of the transfer function using different object tracking algorithms.
###### Abstract  
It is well documented that excess phosphorus in source waters is a major contributor to harmful algal bloom formation. While there are many approaches to controlling algal populations in reservoirs, including a variety of phosphorus reduction approaches (e.g., sequestration of legacy phosphorus with alum or clay products), addressing physical phosphorus loading upstream is considered less often. Water treatment residuals (WTR) containing alum, a common waste product of conventional surface water treatment, have been shown to retain the ability to capture phosphorus even after the WTR'sludge' is formed and removed from the sedimentation process. This research designed and tested a refillable, reusable in-stream phosphorus cartridge system which beneficially reutilizes WTR'sludge' to sequester instream phosphorus and remove it from the water when spent media is replaced. This reduces in-stream phosphorus entering into the reservoir without permanently adding additional materials to the waterbody and provides measurable results as to the amount of phosphorus removed. The ten sampling events during the first year's field assessment indicated that the gates removed a total of 556.31 g of reactive phosphorus (PO4\({}^{3-}\)) and it is anticipated that the actual phosphorous removal was even greater. Other watershed managers can implement the same approach using their own WTR to capture in-stream phosphorus.  
water treatment residual; phosphorus; harmful algal blooms +
Footnote †: journal: Applied  
1  
Design and Preliminary Testing of an In-Field Passive Treatment System for Removing Phosphorus from Surface Water.