esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The authors of [22] introduced the explicit congestion notification (ECN) algorithm, which must be used with the AQM algorithm. When the network node discovers congestion, it sets flag bits in the data packet, and the sending node adjusts the rate based on the flag bits in the received data packet. This scheme can effectively reduce packet loss and improve the accuracy of the end node's perception of congestion. The authors of [23] introduced the XCP scheme. On packet departure, the sender attaches a consensus header to the packet. The XCP controllers on the router make a single control decision for every average RTT (the control interval). We have summarized the relevant studies in Table 1. The proposed scheme in this article adopts a transmission rate control method based on network node bandwidth allocation. The sending, network, and receiving nodes each calculate the transmission rate that can be provided. The sending node negotiates the transmission rate with the network node and receiving node with negotiation messages to obtain the maximum transmission rate the current network state can provide. The network nodes notify the sending node to adjust the transmission rate to adapt to network changes through rate adjustment messages. Compared with the methods of only sending node control and only receiving node control, the participation of network nodes enables the transmission rate to be closer to the maximum transmission rate that the current network state can provide. Compared with other network nodes participating in transmission rate control methods, our scheme allows network nodes to provide more accurate information than the ECN method. Compared with the XCP scheme of adding control information at the head of each data packet, this paper uses negotiation and adjustment messages for control, separating control information from data and reducing the processing complexity of data packet forwarding.
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
## 3 Results and Discussion
### Prediction of the Water Retention Curves
#### 3.4.1 Sand and Sand Mixtures  
The predicted drying path of the WRCs of each sand or sand mixture are presented in Figure 10. Note that for the hydrophobized sand, the WRC prediction at CA\({}_{\text{mean}}\) + CA\({}_{\text{SD}}\) was not shown, as the model is not valid for CAs above 90\({}^{\circ}\) (the cosine of CA becomes negative).  
Figure 10 shows that the WRC predictions using a CA of zero degrees are not useful for any sand mixture except for the natural sand, where they provide a somewhat accurate and possibly useful approximation. However, a more accurate prediction was given when non-zero values of the CA were considered in the model, with the data appearing to fall near the right bound of the prediction range (for a contact angle of CA\({}_{\text{mean}}\) \(-\) CA\({}_{\text{SD}}\)). However, for the hydrophobic sand, the measured WRC generally did not lie close to the right bound prediction; however, it was more accurate than assuming a CA of zero. Additionally, below a degree of saturation of around 0.1, the model consistently tended to underestimate the water volume in the material. This is a common problem when predicting WRCs and is likely due to imperfect estimation of the pore sizes in the materials and ignoring water adsorption [28]. Despite this, Figure 10 shows that considering a material's receding CA in its WRC prediction improved the prediction accuracy. This highlights how a simple modification to allow consideration of non-zero CAs in WRC predictions may be useful for hydrophobic materials.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Material**} & \multicolumn{4}{c}{**Model Parameters That Depended on CA**} & \multicolumn{2}{c}{**Constant Model**} \\  & **(Value at 0\({}^{\circ}\), Value at Mean CA \(-\)1 SD, Value at Mean CA \(+\)1 SD)** & \multicolumn{2}{c}{**Parameters**} \\ \hline  & \(\alpha\) & **n** & **m** & **e** & \(\theta_{\tau}\) \\ \hline Natural sand & 3.0, 3.2 \({}^{1}\), 3.4 \({}^{2}\) & 8.5, 8.7 \({}^{1}\), 8.7 \({}^{2}\) & 0.9 \({}^{*}\) & 0.669 & 0 \\
75\% natural sand & 3.0, 4.0, 6.0 & 8.5, 7.0, 9.0 & 0.9, 1.3, 0.8 & 0.684 & 0 \\
50\% natural sand & 3.0, 5.0, 14.0 & 8.5, 8, 8 & 0.9, 0.9, 1.0 & 0.682 & 0 \\
25\% natural sand & 3.0, 5.0, 25.0 & 8.5, 6.0, 6.5 & 1.1, 2.0, 2.2 & 0.684 & 0 \\ Hydrophobic sand & 3.0, 13.0, \(-\)52 & 8.5, 6.5, 8.5 & 1.1, 2.0, 1.0 & 0.681 & 0 \\ Rock 212\(-\)300 & 5.7 \({}^{*}\) & 23.1 \({}^{*}\) & 0.2 \({}^{*}\) & 0.802 & 0 \\ Rock 150\(-\)212 & 2.2 \({}^{*}\) & 120 \({}^{*}\) & 0.04 \({}^{*}\) & 0.802 & 0 \\ Rock 75\(-\)150 & 1.3 \({}^{*}\) & 8 \({}^{*}\) & 0.6, 0.65, 0.75 & 0.804 & 0 \\ Rock 50-75 & 0.6
## 4 Results and Discussion  
On account of the influence of the number of training samples on the prediction performance of the proposed model, the dataset in case Ani-3 is selected to obtain 16 CNN models with difference size of training samples. The prediction datasets are identical for the 16 models, with 120 samples. Figure 6 displays the variation of R\({}^{2}\) and RMSE values for \(N_{CH}\) when the number of training samples increases from zero to 420. It is evident that the R\({}^{2}\) value increases and the RMSE value decreases quickly with the increasing number of training samples. These changing trends gradually flatten out as the number of training samples exceeds 210. The R\({}^{2}\) and RMSE values slightly fluctuate when the training samples arrive at 350, demonstrating that 420 training samples are sufficient to provide stable prediction performance of the surrogate model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(N_{CL}\) & \(K_{CL}\) & **Pooling Type** & \(N_{CH}\) & \(N_{CM}\) & \(N_{CH}\) & \(N_{CM}\) \\ \hline
2 & \(2\times 2\) & Maximum & 0.9048 & 0.8996 & 0.2768 & 0.2210 \\
2 & \(2\times 2\) & Average & 0.9420 & 0.9417 & 0.2501 & 0.1938 \\
# Effective Data Utilization in the Context of Industry 4.0 Technology Integration
## 1 Introduction
### Cloud Computing  
Cloud computing, in collaboration with IoT, enables the storage, processing and analysis of vast amounts of data, thereby contributing to the realization of large-scale simulations of various aspects of industrial organizations [6; 16]. It is a flexible and inexpensive technology that provides services including infrastructure, hardware, software, platforms and other information within information technology. Users can use the provided services by utilizing the application through computers and storage servers [5]. Cloud technologies serve as tools to enable worldwide access to information [20]. [PERSON] [25] in his research stated that cloud computing is one of the most used technical solutions for data collection, data processing and its subsequent use in the business environment.  
Several major cloud computing platforms can be used in industrial IoT and Big Data analysis. The key features of these platforms are security, data storage capacity and flexibility for client requirements [26]. By using the services of cloud platform providers, industrial organizations are not forced to acquire their own IT infrastructures for data storage. By shifting the responsibility to external companies, industrial organizations are able to reduce their operational costs, increase security and gain the flexibility to scale up IT infrastructures based on business needs [27].  
Cloud Computing is a technology that provides reliable storage and management of data thus providing opportunities to exploit the potential offered by the Internet of Things and Cyber-Physical Systems [24].
## 4 Illustrative Example of Proposed Method
### Determination of the Optimal Wavelet Basis
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Wavelet Basis** & **June** & **August** & **October** & **December** & **Average Ranking** \\ \hline db4 * & 8001.23 & 8602.58 & 9591.84 & 8503.50 & 15.25 \\ db5 & 7999.58 & 8589.13 & 9599.54 & 8495.31 & 14.25 \\ db6 & 8018.02 & 8613.19 & 9594.12 & 8466.70 & 16 \\ db7 & 8018.29 & 8612.75 & 9578.43 & 8458.29 & 13 \\ db8 & 7991.01 & 8588.05 & 9602.97 & 8471.25 & 11.5 \\ db9 & 7996.80 & 8603.29 & 9590.40 & 8467.05 & 12 \\ sym4 ** & 8005.19 & 8643.32 & 9589.52 & 8512.81 & 17.75 \\ sym5 & 8006.20 & 8604.73 & 9608.01 & 8477.20 & 16.75 \\ sym6 & 7985.82 & 8617.04 & 9567.02 & 8471.13 & 10.5 \\ sym7 & 7989.02 & 8582.14 & 9590.40 & 8496.14 & 10.5 \\ sym8 & 7984.43 & 8610.07 & 9569.49 & 8453.37 & 6.75 \\ sym9 & 7986.99 & 8597.47 & 9588.57 & 8442.58 & 6 \\ sym10 & 7985.72 & 8605.50 & 9567.13 & 8444.88 & 6.25 \\ sym11 & 7989.75 & 8573.70 & 9597.81 & 8460.48 & 8.25 \\ sym12 & 7984.51 & 8600.23 & 9573.49 & 8453.88 & 5.25 \\ sym13 & 7981.59 & 8584.90 & 9609.61 & 8436.08 & 6.25 \\ coif3 ** & 7996.46 & 8611.83 & 9586.39 & 8471.45 & 13.25 \\ coif4 & 7985.78 & 8607.17 & 9592.58 & 8461.40 & 10.75 \\ coif5 & 7982.60 & 8597.93 & 9599.51 & 8464.90 & 8.25 \\ coif6 & 7985.44 & 8601.74 & 9605.49 & 8479.89 & 12.25 \\ coif7 & 7991.55 & 8601.05 & 9617.08 & 8486.84 & 15 \\ coif8 & 7993.86 & 8606.54 & 9627.60 & 8491.81 & 17.25 \\ bior1.1 *** & 8312.55 & 8962.69 & 9860.08 & 8920.30 & 24 \\ bior1.3 & 8550.51 & 9184.83 & 10,100.92 & 9148.77 & 26.25 \\ bior2.2 & 8825.44 & 9446.93 & 10,489.33 & 9285.45 & 29 \\ bior2.4 & 8587.86 & 9216.15 & 10,269.83 & 9060.60 & 27 \\ bior3.1 & 14,081.81 & 14,898.99 & 16,232.34 & 14,702.62 & 33.75 \\ bior3.3 & 11,079.45 & 11,797.47 & 13,029.48 & 11,624.38 & 32 \\ rbio1.1 *** & 8312.55 & 8962.69 & 9860.08 & 8920.30 & 24 \\ rbio1.3 & 8167.93 & 8777.54 & 9805.52 & 8679.98 & 23 \\ rbio2.2 & 8986.85 & 9700.68 & 10,704.76 & 9607.84 & 30 \\ rbio2.4 & 8641.32 & 9304.25 & 10,266.36 & 9186.08 & 27.75 \\ rbio3.1 & 13,956.77 & 14,854.77 & 15,890.53 & 14,853.39 & 33.25 \\ rbio3.3 & 11,034.62 & 11,733.42 & 12,886.81 & 11,584.53 & 31 \\ \hline \end{tabular} Abbreviations: * dbN—Daubechies wavelet family, ** symN—Synthesis wavelet family, *** ‘coliN—Coffiß wavelet family, *** ‘biorNr.Nd—Biorthogonal wavelet family, *** ‘riboNr.Nd—Reverselior wavelet family.  
\end{table} 
Table 3: SI values of wavelet basis in different months.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 5 FSS Measurements
### Angular Stability  
As an FSS is exposed to electromagnetic radiation, it ideally acts like a filter that is independent of polarization and angle of incidence. However, in real scenarios, the electromagnetic waves might be incident with various angles and polarization. This necessitates the design of FSSs that work for all the incident angles. The reflection/transmission properties should be stable as the angle of polarization and incidence changes when the electromagnetic waves impinge the FSS. Considering an indoor environment, the signals might experience multiple reflections off walls and furniture before reaching the FSS. Figure 9 shows a unit cell consisting of a Jerusalem cross FSS alongside its reflection and transmission response. However, harmonics are present for higher ranges of incident angles.  
Stable frequency response of an FSS can be achieved by selecting appropriate elements or changing FSS layers or dielectric substrates [132; 133; 134]. A double-layer configuration has been investigated for reducing the angular sensitivity [135; 136; 137; 138; 139]. In order to achieve higher angular stability, FSSs with unit cell sizes much smaller than the wavelength are of great interest. Such small elements avoid the excitation of higher-order brag modes and result in higher stability of the frequency response over the oblique incidences. Different methods such as using lumped elements, fractal unit cells, meandered unit cells, etc., have been utilized to miniaturize the unit cell. By reducing the electrical sizes of the constituting element to a subwavelength level and producing resonance by a combination of the miniaturized non-resonant elements, highly stable frequency responses under oblique incidence angles are achieved.
# Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution
## 2 Methodology and Principle  
### Basic Theory on LSM  
The forward problem in the classical migration imaging can be expressed as:  
\[d=Lm, \tag{1}\]  
where, \(L\) is the linear operator in the linear Born simulation, which expresses the relation between underground reflectivity model m and seismic scattering data \(d\). The migration images can be obtained by projecting the adjoint Born operator \(L^{T}\) into the seismic scattering data \(d\):  
\[I=L^{T}d. \tag{2}\]  
The adjoint Borning operators \(L^{T}\) can correctly reverse the propagation effects on the travel time and phase. However, due to problems such as limited observation apertures and limited data frequency band, the adjoint operator is not a good approximation for the inverse Born operators, which leads to unpreserved amplitude and low spatial resolution.  
The LSM enables to provide high-fidelity images by minimizing the difference between synthetic and observed data, and the misfit function of the least-squares migration can be defined as,  
\[\mathrm{C}(m)=\frac{1}{2}||Lm-d_{obs}||_{2}^{2}. \tag{3}\]  
When the objective function reaches the minimum, the reflectivity model \(m\) satisfies the following equation:  
\[I=\left(L^{T}L\right)=Hm. \tag{4}\]  
In this equation, \(H\) is the Hessian operator, which is the second derivative of the misfit function (3). This equation is always named as the Newton normal equation, which establishes the basic principle for the LSM in the imaging domain and physically reveals that the migration image obtained by the conventional migration method is the Hessian blurring version of the subsurface reflectivity model m. Thus, the seeking reflectivity model can be realized using the generalized inverse under the sense of the norm \(L_{2}\).  
\[m=\left(L^{T}L\right)^{-1}I=H^{-1}I. \tag{5}\]
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
## 1 Introduction  
Wireless local area network (WLAN) technologies are an essential feature of everyday life for the Internet of Things (IoT); the Internet of Everything (IoE) using wireless fidelity (Wi-Fi) is also beginning to emerge [1; 2; 3; 4]. For IoT applications such as sensors and smart grid networks, WLAN technologies have been evolving to support a wide radio range and high energy efficiency by taking advantage of narrowband wireless transmission techniques for IEEE 802.11 af/ah in the sub-1 GHz frequency [3; 4; 5; 6; 7; 8; 9]. However, narrowband transmission in WLAN might suffer from severe signal distortion due to the carrier frequency offset (CFO) in the reduced subcarrier space, which is relatively larger than that of legacy WLANs in 2.4/5 GHz industrial scientific and medical (ISM) bands.  
In this paper, for reliable communication links, digital pre-distortion of the carrier frequency offset is proposed to improve the data transmission rate by accurately estimating the CFO of a data packet transmitted through a wireless channel in a wireless communication system. The digital pre-distortion of the CFO is a method of transmitting data, channel-adaptively estimating the CFO of data packets, predistoring the CFO at the transmitter, and training the CFO to reduce the residual offset between Tx and Rx. Evaluation results demonstrate that the proposed method can minimize packet error rates by accurately estimating and compensating for a CFO in a wireless channel environment for a wireless communication system. This paper makes the following two key contributions:  
* This is the first work to mitigate the effect of CFO at the transmitters for more reliable communications, improving narrowband WLANs targeted for IoT devices.
* The proposed digital predistortion of CFO yields substantial improvements in the packet error rate and residual CFO values.  
The remainder of this paper is organized as follows. In Section 2, wireless systems in narrowband are introduced. In Section 3, problematic radio frequency (RF)/analog impairments are analyzed, and related works are presented in Section 4. In Section 5, the digital CFO predistortion scheme is proposed. In Section 6, the evaluation results are presented and, finally, Section 7 draws conclusions.
## 3 Results
### Classification Results for High and Low NMSS Score Groups  
Mean QSM values for the high and low NMSS score groups are shown in Table 4. The difference between QSM values in these groups was only 0.5% for all ROIs (high NMSS score group = 0.112 \(\pm\) 0.02 ppm; low NMSS score group = 0.113 \(\pm\) 0.03 ppm).  
In terms of F-scores, GP again demonstrated the highest (17.772), and PUT again demonstrated the lowest (5.005) (Table 3). We implemented SVM and LR models for high and low NMSS score classification. The optimal features for the SVM and LR models were GP and RN. With the selected two ROIs, the SVM model demonstrated accuracy, sensitivity, specificity, and AUC values of 0.79 \(\pm\) 0.03, 0.77 \(\pm\) 0.04, 0.85 \(\pm\) 0.06, and 0.68 \(\pm\) 0.04, respectively, while the LR model showed those of 0.76 \(\pm\) 0.04, 0.68 \(\pm\) 0.10, 0.88 \(\pm\) 0.07, and 0.63 \(\pm\) 0.06, respectively (Figure 4).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**ROI d**} & \multicolumn{4}{c}{**Mean QSM Value \(\pm\) SD, ppm**} & \multirow{2}{*}{**F-score**} \\ \cline{2-2} \cline{5-6}  & & & **High NMSS (_n_ = 12)** & & **Low NMSS (_n_ = 12)** & \\ \hline GP e  & 0.136 \(\pm\) 0.029 & 0.149 \(\pm\) 0.037 & 17.772 \\ RN f  & 0.106 \(\pm\) 0.028 & 0.099 \(\pm\) 0.035 & 10.544 \\ DN s  & 0.102 \(\pm\) 0.033 & 0.100 \(\pm\) 0.031 & 10.426 \\ SNr h  & 0.129 \(\pm\) 0.043 & 0.133 \(\pm\) 0.045 & 9.070 \\ SNr c  & 0.127 \(\pm\) 0.045 & 0.134 \(\pm\) 0.042 & 9.063 \\ CN j  & 0.074 \(\pm\) 0.027 & 0.067 \(\pm\) 0.023 & 7.846 \\ PUT k  & 0.111 \(\pm\) 0.042 & 0.107 \(\pm\) 0.056 & 5.005 \\ \hline \hline \end{tabular}
\end{table}
Table 4: QSM a values and F-scores b for patients with high or low NMSS c score.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multicolumn{8}{c}{**The Number of Features**} \\ \hline \hline  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Accuracy & & & & & & & \\  & SVM a  & 0.67 & 0.65 & 0.65 & 0.64 & 0.72 & 0.71 & 0.76 \\  & LR b  & 0.58 & 0.55 & 0.59 & 0.70 & 0.69 & 0.70 & 0.72 \\ \hline AUC c  & 0.58 & 0.45 & 0.40 & 0.48 & 0.62 & 0.63 & 0.70 \\  & LR b  & 0.40 & 0.37 & 0.36 & 0.59 & 0.58 & 0.62 & 0.64 \\ \hline Sensitivity & & & & & & & & \\  & SVM a  & 0.68 & 0.54 & 0.56 & 0.55 & 0.81 & 0.89 & 0.77 \\  & LR b  & 0.49 & 0.49 & 0.52 & 0.69 & 0.62 & 0.64 & 0.72 \\ \hline Specificity & & & & & & & \\  & SVM a  & 0.70 & 0.76 & 0.72 & 0.75 & 0.67 & 0.60 & 0.84 \\  & LR b  & 0.69 & 0.67 & 0.68 & 0.75 & 0.82 & 0.79 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results of SVM a and LR b from the training set.  
When the optimal SVM model (using GP and RN) and the LR model (using GP and RN) were compared, \(p\)-values for sensitivity and AUC were \(<0.05\); the \(p\) value for accuracy and specificity were 0.09 and 0.525, respectively.
## 4 Results and Discussion
### Human-Induced Gait Load Performance on the Reference Footbridge
#### 4.1.2 Vertical Loads  
Similarly, the vertical gait loads induced by all TSs on a rigid surface were evaluated for 30.0 s. The induced lateral load (\(L_{L}\)) was normalized with respect to the weight (\(W_{0}\)) of each ST, yielding an average normalized lateral load of approximately 105.0% and a maximum of 125.0%. These pedestrian-induced gait loads by TSs are presented in Figure 7 (top left) for a 1.5 s range and the average normalized gait loads (\(\frac{\overline{L_{L}}}{\overline{W_{0}}}\)), with the respective standard deviation (\(\pm\sigma\)). In addition, the vertical frequency response assessment of the gait loads on the rigid surface determined a fundamental frequency band around \(f_{1}=1.75\) Hz, as shown in Figure 7 (top right). However, the vertical frequency response assessment of pedestrian-induced gait loads on the reference footbridge demonstrated that a frequential content was developed by TSs close to the first fundamental frequency, called \(f_{HSI}\cong 1.8\) Hz (purple band, Figure 7, middle), which allows a stabilizing action on the ST during the crossing of the reference structure, increasing its effects according to the test velocity. On the other hand, changes in vertical normalized pedestrian-induced gait load by TS\({}_{1}\) were identified at the different points of the reference footbridge due to dynamic HSI effects, which decreased (\(\approx-20.0\%\)) or increased (\(\approx+50.0\%\)) the anthropic loads in direct relation with the vertical structural response at the action point of the pedestrian compared to the overall gait-induced loads on the rigid surface. Finally, the peak vertical pedestrian-induced gait loads were determined for the three gait velocities evaluated, which reached overage-normalized gait loads and standard deviations of 156.56% and 15.58%, respectively.  
Figure 5: Behavior of human-induced lateral gait loads on rigid and flexible surfaces. Time history and spectral response of human-induced lateral gait loads on rigid surfaces **(top)**; spectral response of human-induced lateral gait loads on the reference footbridge for the three gait velocities: 1.20 **(middle left)**, 1.50 **(middle center)**, and 1.80 m\(\cdot\)s\({}^{-1}\)**(middle right)**; and ST\({}_{1}\)-induced lateral gait loads on the reference footbridge for the three gait velocities: 1.20 **(bottom center)**, 1.50 **(bottom left)**, and 1.80 m\(\cdot\)s\({}^{-1}\)**(bottom right)**, with the structural response behind (gray).
## 3 Discussion
### Metabolite Identification and Biological Significance
#### 3.2.3 Negative Mode  
In negative mode, there was only one model created that had an AUC equal or greater than 0.7, and this model predicts between low vs. moderate disease activity (Figure 4C). The features used in that model were identified, and similarly to positive-mode models, bile acids and phospholipids were tentatively identified.  
Bile acids (Table 5) include Glyco-beta-muricholic acid and Glycoursodeoxycholic acid. Bile acids were tentatively identified in models distinguishing between moderate disease activity and high disease activity. Bile acid metabolites in general are involved in lipid absorption and can mediate immune signaling [39; 40]. The receptors Farnesoid-X-Receptor (FXR) and G-protein bile acid receptor 1 (GPBAR1) are among multiple receptors that can be activated by bile acids, and are present on many immune cells [39; 40]. The activation of these receptors on macrophages, dendritic cells, and natural killer T cells can modulate immune response [40]. FXR activation can block NF-KB-mediated pro-inflammatory responses [39; 40]. GPBAR1 activation is also responsible for regulating pro-inflammatory responses in innate immune cells [40]. Furthermore, these receptors are involved in the regulation of the nucleotide-binding domain, leucine-rich-containing family, and pyrin domain-containing-3 (NLPR3) inflammasome [40]. A study by [PERSON] et al. found ester versions of these bile acids were significantly lower in PsA patients compared to psoriasis patients [39]. Phosphatidylcholine, phosphatidylethanolamine, their oxidized versions, and methyl phosphatidylethanolamine are phospholipids that were tentatively identified in negative mode.  
Another interesting feature used in this model is Indoxyl sulfate (Table 5), a metabolite of Tryptophan and a uremic toxin that accumulate in the body under chronic kidney disease [41; 42; 43]. This metabolite is associated with impairment of bone turnover via inhibition of osteoblast and osteoclast maturation [43]. Furthermore, this metabolite has been shown to enhance oxidative stress, thereby promoting pro-inflammatory cytokines [41; 42]. Thus, indoxyl sulfate could be a potential metabolite of PsA that exhibits inflammatory responses and bone-related health effects.