esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Removal of Pharmaceuticals during Wastewater Treatment
### Removal of Different Pharmaceuticals by Advanced Oxidation Processes
Ozone is a powerful oxidizing agent (E0 = 2.07 V), capable of acting both directly and indirectly. The direct use of ozone without catalyst or irradiation is ozonation, while the additional use of a catalyst or photoactivation is considered AOP [1]. The removal of drugs such as antibiotics, estrogens, and neutral pharmaceuticals has been treated with [PERSON] et al. [118] in different types of water for the same pH = 8 and a temperature of 20 \({}^{\circ}\)C. For a synthetic wastewater, they found more than 90% of removal for all pharmaceutical products in 2 min with concentration of ozone of 5.5 mg/L. Regarding wastewater, surface water, and wastewater treatment plants, WWTPs, the same percentage of more than 90% removal for antibiotics, estrogens, and neutral pharmaceuticals was achieved with ozone doses of 2.05, 1.11, and 1.30 mg O\({}_{3}\)/mg DOC, respectively. Moreover, the simple use of ozone for water treatment shows good results, which were confirmed by [PERSON] et al. [119]. They noted that a total removal of Bezafibrate BZF drugs in the aqueous solution with the initial concentration of 0.5 mmol/L could be reached just after 10 min of treatment using the dose of 0.73 mmol/L of ozone (Table 3). However, degradation almost never results in complete mineralization. In addition, the oxidation of endocrine-distrupting compounds EDCs with comparable chemical structures such as Bisphenol A, 17\(\upmu\)-Estradiol, and 17\(\upmu\)-Ethynyl Estradiol have been studied by [PERSON] et al. [120]. They reported that, when usingozone at the concentration of 30 mmol/L, 99% transformation would be expected in less than 2 s. However, a residual estrogenic response could still be present because of the oxidation byproducts. They showed a transformation after 10 min. [PERSON] et al. [121] were interested in the effect of an oxidative pre-treatment with ozone, dose of 20 mg O\({}_{3}\) /g TSS, on the removal of pharmaceutical and personal care products (PPCPs) during the anaerobic digestion of sewage sludge. They found an almost total elimination of all PPCPs of 99%, except for carbamazepine, which was only up to 60% removed under thermophilic conditions. Therefore, they concluded that ozonation could represent an economically feasible solution to improve sludge stabilization. Therefore, non-steroidal anti-inflammatory drugs such as indomethacin were removed by [PERSON] et al. [122] after just 7 min with the lowest dose of ozone. Concerning the antineoplastic drug 5-fluorouracilacil, [PERSON] et al. [123] also studied its degradation under solar irradiation. In fact, the results showed that the ferroxalate system was more efficient for this degradation and mineralization.
## 2 Materials and Methods
### Method
#### 2.3.2 ET Calculation  
A Surface Energy Balance System (SEBS) has been proposed for calculation of ET based on Earth observation satellite data and weather information at proper scales [36]. The surface energy balance is usually given as follows:  
\[R_{n}=G_{0}+H+\lambda E \tag{4}\]  
where \(R_{n}\) is the net radiation (W/m\({}^{2}\)), \(G_{0}\) is the soil heat flux (W/m\({}^{2}\)), \(H\) is the turbulent sensible heat flux (W/m\({}^{2}\)), \(\lambda E\) is the turbulent latent heat flux (W/m\({}^{2}\), \(\lambda\) is the latent heat of vaporization (\(\lambda=2.49\times 10^{6}\) J/kg), and \(E\) is the actual evapotranspiration [kg/(m\({}^{2}\cdot\)s)]).  
The formula to estimate the net radiation is shown as follows:  
\[R_{n}=(1-\alpha)R_{\text{s}nd}+R_{\text{t}nd}-\varepsilon\sigma T_{0}^{4} \tag{5}\]  
where \(\alpha\) is the albedo, \(R_{\text{s}nd}\) is the downward solar radiation, \(R_{\text{t}nd}\) is the downward longwave radiation, \(\varepsilon\) is the emissivity of the surface, \(\sigma\) is the Stefan-Bolzmann constant (\(5.67\times 10^{-8}\) W-m\({}^{-2}\cdot\)K\({}^{-4}\)), and \(T_{0}\) is the surface temperature (K).  
The formula to evaluate soil heat flux is parameterized as:  
\[G_{0}=R_{n}[\Gamma_{c}+(1-f_{c})(\Gamma_{s}-\Gamma_{c})] \tag{6}\]  
where the ratio of soil heat flux to net radiation \(\Gamma_{c}\) = 0.315 for a full-vegetation canopy [37] and \(\Gamma_{s}\) = 0.05 for bare soil [38]. An interpolation was conducted between these limiting cases by applying the fractional canopy coverage, \(f_{c}\). \(f_{c}\) can be calculated by the following equation:  
\[f_{c}=\left(\frac{NDVI-NDVI_{min}}{NDVI_{max}-NDVI_{min}}\right)^{2}\]  
where \(NDVI_{max}\) and \(NDVI_{min}\) represent total vegetation coverage (\(f_{c}\) = 1) and bare land (\(f_{c}\) = 0).  
\[ET=8.64\times 10^{7}\times\frac{\Lambda\,R_{n}}{\lambda\rho_{w}} \tag{7}\]where \(ET\) is the actual evaporation on a daily basis (mm d\({}^{-1}\)), \(\lambda\) is the latent heat of vaporization (\(\lambda=2.49\times 10^{6}\) J/kg), \(\rho_{w}\) is the density of water (1.000 kg m\({}^{-3}\)), and \(\Lambda\) is the evaporative fraction [36].
# Fault Diagnosis Algorithm of Beam Pumping Unit Based on Transfer Learning and DenseNet Model
## 2 Fault Diagnosis Model and Application Method  
### Sucker Rod Pump Fault Diagnosis  
The polished rod indicator diagram is often used to judge downhole working conditions. The ground polished rod dynamometer can draw the relationship between the load and displacement of the polished rod through the test data, that is, the polished rod indicator diagram. The fault type of the oil well can be judged by calculating and analyzing  
Figure 1: Distribution diagram of actual working condition data.  
the indicator diagram. Researchers in various countries expand the scope of interpretation by improving the precision of the dynamometer and the interpretation method of indicator diagrams. As the main means to judge the sucker rod pump's fault, the optical rod dynamometer has been widely used until today because of its advantages of convenient operation and use. Figure 2 shows the theoretical indicator diagram under static load.  
In Figure 2, \(\lambda\) is the stroke loss; \(P_{\text{liquid}}\) is the gravity of the liquid column above the pump; \(P_{\text{rod}}\) is the gravity of the sucker rod in the liquid; \(P_{\text{static}}\) is the static load on the polished rod. The indicator diagram is surrounded by closed line segments, and the area represents the work done by the pumping unit in one reciprocating motion. The dynamometer diagram of the pumping unit is close to a parallelogram under normal working conditions. In the production operation, it is different to compute due to the complex underground working environment of the pumping unit. In addition to the manufacturing and installation of the pumping unit itself, downhole geological conditions such as fluid, sand inclusions, liquid gas, and other factors can also cause the indicator diagram to have a variety of different shapes.  
A total of 15 fault types are studied in this paper: plunger up bump, plunger down bump, insufficient fluid supply, double vale loss, fixed vale loss, broken rod, normal operating conditions, gas effect, pump leakage, plunger coming out, swimming vale loss, sand effect, heavy oil effect, wax effect, pump and eject. Figure 3 is an indicator diagram of six common faults, where the horizontal axis represents the displacement and the vertical axis represents the load.
### Study Area Location and Background  
Geographically, the study points along the R37 road are located approximately 15 km south of Burgersfort town in the Sekhukhune District Municipality of Limpopo Province. The region of investigation is confined within the green demarcation, as shown in Figure 1. The topography of the study area is dominantly steep hilly or mountainous terrain [22]. The region is reported to experience a semi-arid climate with hot summers and mild winters. Historically, the region has seen periods of drought and inconsistent rainfall, influencing agricultural productivity, according to [23; 24]. Over recent decades, climate change has exacerbated weather extremes, with more frequent heatwaves and shifting rainfall patterns [24]. The area's water resources are heavily reliant on seasonal rains, making it vulnerable to prolonged dry spells. Mining is a centric economic driver in the region, with large-scale operations from companies like Samancor Chrome and Platinum Mines influencing the region's infrastructure and development.  
Limpopo Province, and particularly the Sekhukhune District, experiences a semi-arid climate with annual rainfall primarily concentrated in the summer months (November to February). The district typically receives between 400 mm and 600 mm of rain annually, with significant variability due to topography (see Figure 2). The eastern and mountainous regions tend to receive more rainfall compared to the drier, western areas. Rainfall in Sekhukhune is strongly seasonal, with frequent droughts and occasional heavy storms that can lead to flash floods, affecting agriculture and infrastructure. Climate change is intensifying these extremes, increasing the need for adaptive measures.  
Figure 1: Location of the study along the R37 road.  
Figure 2: Mean annual rainfall variability in Limpopo province, modified from [24].
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 3 Optical Filter Shift Detection and Data Augmentation
### Data Augmentation  
We evaluated the detection performance by adding constellation data reproduced by the DC-GAN. For data augmentation, a DC-GAN-generated model is created with reference to a model created by using only measured constellation data. The model diagram of the DC-GAN for this experiment is shown in Figure 13. In addition, the DC-GAN training parameters used in this experiment are shown in Table 2 below.  
Figure 12: Snapshot of real-time telemetry (on the network).  
Figure 13: DC-GAN model diagram.
## 4 Preprocessing Module
### Position Prediction  
Detection can obtain the locations of each GSE and aircraft in a single frame, and associating the positions of the same target in consecutive frames is an indispensable precondition for the spatiotemporal information extraction for each target. Predicting the target position in the following frame based on the target position in the current frame can reduce both the search range and improve the accuracy of the match during association. To predict the target position, a state vector based on the detection results for a single frame is initialized [33]. \(\text{x}=\left[x_{c},y_{c},\delta,h,\Delta x_{c},\Delta y_{c},\Delta\delta, \Delta h\right]^{T}\), where \(\delta=w/h\), \(\left[x_{c},y_{c},\delta,h\right]\) denotes the position and shape of the bounding box, and \(\left[\Delta x_{c},\Delta y_{c},\Delta\delta,\Delta h\right]\) represents the change of state of objects between adjacent frames. The state-transition matrix \(\text{A}\in\mathbb{R}^{8\times 8}\) is defined as  
\[\text{A}=\begin{bmatrix}\text{I}_{4\times 4}&\text{I}_{4\times 4}\\ \text{0}_{4\times 4}&\text{I}_{4\times 4}\end{bmatrix}, \tag{4}\]  
where, \(\text{I}_{4\times 4}\) and \(\text{0}_{4\times 4}\) are identity and zero matrixes with \(4\times 4\), respectively. The matrix of the transition from the state space to the observation space is \(\text{C}=\left[\text{I}_{4\times 4}\ \text{0}_{4\times 4}\right]\). According to the state model in the \(t\)-\(1^{th}\) frame, the estimated state model of the object in the \(t\)th frame is \(\hat{\kappa}_{t}=Ax_{t-1}+Q\), where \(Q\in\mathbb{R}^{8}\) is a noise vector. According to the theory of Kalman filtering [34], the predicted result in the current frame is  
\[x_{t}=\hat{\kappa}_{t}+K_{t}(y_{t}-C\hat{\kappa}_{t}), \tag{5}\]  
where \(y_{t}\)= \([x_{c},y_{c},\delta,h]^{T}\) is the final result in the current frame that is obtained by detection and association. \(K_{t}\in\mathbb{R}^{8\times 4}\) is the gain of the Kalman filter and is computed as  
\[K_{t}=\hat{P}_{t}C^{T}(C\hat{P}_{t}C^{T}+R)^{-1}, \tag{6}\]  
where R\(\in\mathbb{R}^{4\times 4}\) is the covariance matrix of the observation noise. \(\hat{P}_{t}\) is the predicted covariance matrix of the state vector in frame \(t\) and is calculated as  
\[\hat{P}_{t}=AP_{t-1}A^{T}+W, \tag{7}\]  
where W\(\in\mathbb{R}^{8\times 8}\) is the covariance matrix of the system noise, \(P_{t-1}\) is the covariance matrix in the previous frame, and the covariance matrix \(P_{t}\) in the current frame needs to be updated according to \(P_{t}=(I-K_{t}C)\hat{P}_{t}\). The initialization of the covariance matrix \(P_{0}\), R, and W for surveillance videos of No.734 and 939 are defined as  
\[P_{0}=\left[\begin{array}{cccccc}10&0&0&0&0&0&0\\ 0&10&0&0&0&0&0\\ 0&0&10&0&0&0&0\\ 0&0&0&10&0&0&0\\ 0&0&0&0&10000&0\\ 0&0&0&0&0&10000&0\\ 0&0&0&0&0&0&10000\end{array}\right],R=\left[\begin{array}{cccccc}1&0&0&0\\ 0&1&0&0\\ 0&0&10&0&0\\ 0&0&0&0&10000&0\\ 0&0&0&0&0&10000\end{array}\right], \tag{8}\] \[W=\left[\begin{array}{cccccc}1&0&0&0&0&0&0\\ 0&1&0&0&0&0&0\\ 0&0&1&0&0&0&0\\ 0&0&0&1&0&0&0\\ 0&0&0&0&0.01&0&0\\ 0&0&0&0&0&0&0.0001\end{array}\right].\]
## 3 Results
### The Dynamics of uC2C Levels after TKR  
A change in uC2C concentration over 12 months was shown in Figure 3. Although the median value of uC2C was significantly increased for three months after the TKR compared to the preoperative value (\(p\) = 0.012, Wilcoxon paired test; Table 2), the median uC2C fell to  
Figure 2: The association of uC2C levels with the radiological severity of kOA in the preoperative period in (**A**) the whole group, (**B**) males, and (**C**) females (boxplot with 10 th–90 th percentiles, Wilcoxon paired test). KL—grades of radiographic knee osteoarthritis by the Kellgren–Lawrence scoring system; NS—the highest grade of knee OA changes in two knee joint compartments (tibiofemoral and/or patella–femoral joints) by the Nottingham system (NSy).  
the preoperative level after 12 months. This dynamic applied to men, whereas in females, the uC2C was lower a year after surgery compared to preoperative levels (Figure 3).  
The individual dynamics of uC2C over the course of the year ranged from an 87% decrease to a 120% increase in uC2C levels (Figure 4). We also found that the significant (20%) increase in post-TKR concentration of uC2C was obvious in patients with lower baseline (preoperative) levels of uC2C (16 out of the first 30 ranked cases). Conversely, we found a remarkable number of cases (12 out of the last 29 ranked cases) with a significant decrease in the post-TKR period among those with a higher baseline value of uC2C.  
Taking a 20% change in the biomarker as the basis for grouping, we demonstrated that 28% of patients (17 out of 60) were \"descenders\", 32% were \"ascenders\" (19 out of 60), and the remaining 40% belonged to the \"stable\" group. We demonstrated a statistically significant difference between the uC2C values of \"ascenders\" and \"descenders\" both pre-TKR and 12 months after TKR (\(p\) = 0.00022 and \(p\) = 0.021, respectively, Wilcoxon test; Figure 5). We found the same statistically significant difference between \"ascenders\" and \"descenders\" in males (\(p\) = 0.019 for pre-TKR and \(p\) = 0.035 for one year post-TKR, Wilcoxon test; Figure 5C), but not in females (\(p\) = 0.10 for pre-TKR and \(p\) = 0.053 for one year post-TKR, Wilcoxon test; Figure 5B). However, the subgroups did not differ in uC2C levels at the three-month postoperative timepoint.  
Figure 4: The individual dynamics of uC2C values (pre-TKR and 12 months after TKR) ranked by baseline uC2C value.  
Figure 3: The dynamics of uC2C before and 3 or 12 months after TKR in (**A**) the whole group, (**B**) males, and (**C**) females (boxplot with 10 th–90 th percentiles, [PERSON] paired test).  
In summary, although the median value of uC2C a year after the TKR was similar to the preoperative value, a variety of individual changes existed. The excretion of uC2C could decrease, increase, or remain constant. A higher baseline value of C2C implied a tendency to diminish as a result of TKR.  
Association of UC2C Levels with Improvement of Subjective and Objective Limitations in the Postoperative Period
# AI-Based Smart Monitoring Framework for Livestock Farms
###### Abstract  
Smart farms refer to spaces and technologies that utilize networks and automation to monitor and manage the environment and livestock without the constraints of time and space. As various devices installed on farms are connected to a network and automated, farm conditions can be observed remotely anytime and anywhere via smartphones or computers. These smart farms have evolved into smart livestock farming, which involves collecting, analyzing, and sharing data across the entire process from livestock production and growth to post-shipment distribution and consumption. This data-driven approach aids decision-making and creates new value. However, in the process of evolving smart farm technology into smart livestock farming, challenges remain in the essential requirements of data collection and intelligence. Many livestock farms face difficulties in applying intelligent technologies. In this paper, we propose an intelligent monitoring system framework for smart livestock farms using artificial intelligence technology and implement deep learning-based intelligent monitoring. To detect cattle lesions and inactive individuals within the barn, we apply the RT-DETR method instead of the traditional YOLO model. YOLOv5 and YOLOv8 are representative models in the YOLO series, both of which utilize Non-Maximum Suppression (NMS). NMS is a postprocessing technique used to eliminate redundant bounding boxes by calculating the Intersection over Union (IoU) between all predicted boxes. However, this process can be computationally intensive and may negatively impact both speed and accuracy in object detection tasks. In contrast, RT-DETR (Real-Time Detection Transformer) is a Transformer-based real-time object detection model that does not require NMS and achieves higher accuracy compared to the YOLO models. Given environments where large-scale datasets can be obtained via CCTV, Transformer-based detection methods like RT-DETR are expected to outperform traditional YOLO approaches in terms of detection performance. This approach reduces computational costs and optimizes query initialization, making it more suitable for the real-time detection of cattle maintenance behaviors and related abnormal behavior detection. Comparative analysis with the existing YOLO technique verifies RT-DETR and confirms that RT-DETR shows higher performance than YOLOv8. This research contributes to resolving the low accuracy and high redundancy of traditional YOLO models in behavior recognition, increasing the efficiency of livestock management, and improving productivity by applying deep learning to the smart monitoring of livestock farms.  
intelligent monitoring; smart farm; deep learning model; lesion detection +
Footnote †: journal: Applied sciences
## 2 Modeling
### Kinematics
#### 2.2.1 Kinematic Model
For an object like Tilt-X that moves in three dimensions, mathematical modeling typically requires considering the transformation relationships between coordinate systems, which are the BCF and the ECF. The transformation relationships for velocity and angular velocity are expressed as follows:  
\[v^{e}=R_{b}^{e}v^{b}, \tag{1}\]  
\[\omega^{e}=A_{b}^{e}\omega^{b}, \tag{2}\]  
where the transformation matrices for velocity and angular velocity are:  
\[R_{b}^{e}= \begin{bmatrix}c\theta c\psi&c\psi s\theta s\varphi-s\psi c\theta&c \psi s\theta c\varphi+s\psi s\varphi\\ c\theta s\psi&s\psi s\theta s\varphi+c\psi c\varphi&s\psi s\theta c\varphi-c\psi s \varphi\\ -s\theta&s\varphi c\theta&c\varphi c\theta\end{bmatrix} , \tag{3}\]  
\[A_{b}^{e}= \begin{bmatrix}1&t\theta s\varphi&t\theta c\varphi\\ 0&c\varphi&-s\varphi\\ 0&\frac{s\varphi}{c\theta}&\frac{c\varphi}{c\theta}\end{bmatrix} . \tag{4}\]
## 3 Proposed Mechanism
### Movement Representation  
In the case of navigation, the entire body of an agent has been considered a unit. The agent movement is defined as the turning angle and the displacement, as shown in Figure 6. The turning angle is defined as the angle between the previous direction of movement and the direction of the current movement. The distance between the current and the previous location of the agent is defined as displacement.
## 4 Methods (ASMAG Algorithm)
### Snow Cover Mapping, Snow Line Altitude and Snow Cover Ratio Retrieval
#### 4.2.2 Snow Line Altitude (SLA) Determination  
The snow line on a glacier is rarely a continuous line, as windblown snow, avalanche deposits, hollows and local shading can all cause locally variable snow accumulation and thus an inhomogeneous distribution of remaining snow patches. Hence, the lower boundary of the snow cover is often patchy and could be seen as a zone covering several elevation bands rather than a line at a distinct altitude (Figure 4). The method developed to calculate the SLA accounts for these variable characteristics and follows the recommendation by the World Meteorological Organization [57] to interpret this region as a zone rather than a line. As a first step, the reference DEM is converted to 20 m (100 m for Landsat MSS) elevation bins. The SC map from each glacier is then digitally intersected with each elevation bin starting at the lowest one. The algorithm checks if the first five elevation bins all have a SC larger than 50%. In this case, the lowest bin is selected as the SLA. If there is no result with five bins throughout the glacier, the algorithm selects only four bins, and finally only three. If there is again no result, the algorithm takes the lowest elevation bin where a SC percentage of more than 50% is found. This procedure of considering isolated snowfields is similar to the principle of deriving mass balance from field measurements where such regions are considered as well. However, whether such snowfields will result in a positive mass balance for the related elevation bin or not will depend on specific conditions [58] like snow thickness and density. Even if an elevation bin has more than 50% SC, it might still be possible that the mass balance of the elevation bin is negative or vice versa (i.e., positive if less than 50% is snow covered).
# Detection of Hidden Communities in Twitter Discussions of Varying Volumes
## 3 Models and Methods in SNA
### Description of Community Detection Methods
#### 3.2.7 The Speaker-Listener Propagation Algorithm  
GANXiS, aka SLPA [23], is another extension of the LPA [14] algorithm that consists of three stages, as described below.  
1. The memory of each node is initialized with the identifier of this node (unique label).
2. The steps are repeated until the stopping criterion is met: 1. one node is selected as a listener; 2. each neighbor of the selected node sends one tag following a certain conversation rule, e.g., choosing a random tag from its memory with a probability proportional to the frequency of occurrence of this tag in memory; 3. the listener accepts a label from a collection of labels received from neighbors following a specific listening rule, e.g., choosing the most popular label from what it has observed at the current stage.
3. Finally, post-processing based on in-memory labels of nodes is used to display communities.  
SLPA utilizes an asynchronous update scheme, i.e., when updating a listener's memory at time \(t\), some already-updated neighbors have memories of size \(t\) and some other neighbors still have memories of size \(t-1\). SLPA reduces to LPA when the size of memory is limited to 1, and the stopping criterion is the convergence of all labels. Thus, SLPA is a joint version of the LPA algorithm that is suitable for detecting overlapping (fuzzy) clusters, while the disjoint version of the algorithm may be employed for finding non-fuzzy clusters and is used here for comparative purposes. We will name the joint algorithm GANXiSo, and the disjoint one GANXiSd.