esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Hormones  
Residual hormones have become a source of major concern because they can affect the biological activity of non-targeted organisms. These compounds are a potential risk for wildlife and humans through the consumption of contaminated food or water. The most potent active EDCs present in the environment are steroids, which can be formed naturally by humans and wildlife or produced synthetically. At low concentrations, steroidal hormones alter the endocrine system, changing the growth, development, and/or reproduction of exposed animals. These changes may be expressed later in the life cycle or in future generations. Therefore, determining the fate and distribution of steroids and their conjugates in the environment is critical because they are potential sources of active oestrogens after dissociation in wastewater treatment plants or the influx of treated wastewater directly into surface waters [83].  
Analytical methods with high sensitivity, selectivity and resolution must be developed to determine low concentrations of these substances and to overcome matrix complexity.  
Although there are many extraction techniques for liquid samples, solid-phase extraction is the most common. Other miniaturisation techniques have also been employed to analyse hormones in water samples: SPME, IT-SPME, DLLME and SBSE [84]. However, LC coupled with DAD or a fluorescence detector (FD) was used for detection; a unique study employing microextraction techniques coupled to LC-MS was published by [PERSON] _et al._[85] (Table 3). Five oestrogens were analysed in environmental waters by IT-SPME with a Supel-Q PLOT capillary column. The recoveries under the optimal conditions ranged from 86.1% to 106.8%, and the LOD ranged from 2.7 to 11.7 \(\mathrm{ng\cdot L}^{-1}\).
## 5 The Testbed  
To show that our SDN framework can identify the changing traffic patterns and increase the QoS of an IoT network by immediately applying a pre-calculated network configuration optimized to the new pattern, we implemented our framework as an SDN application on OpenDaylight and tested it on an SDN testbed. A photo of the testbed is shown in Figure 2. A plot of the testbed topology is shown in Figure 3. In the testbed, there were 9 SDN core switches, where eight were MikroTik RouterBOARD 750 Gr3 switches with 1 Gbps ports, and the other one was a Pica8 SDN switch with 1 Gbps and 10 Gbps ports. A Spinent hardware traffic generator was used for generating realistic traffic and analyzing the traffic statistics like packet drop rate and delays. The Spinent hardware traffic generator sent the generated traffic to a non-SDN Dell switch through a 10 Gbps optical link. The Dell switch distributed the generated traffic to MikroTik switches. The Pica8 switch forwarded the collected traffic to the sink of the Spinent traffic generator through a 10 Gbps optical link. To make the MikroTik switches SDN-capable, their firmware was replaced with an OpenWRT embedded operating system and Open vSwitch switching software. Our SDN application, the Spinent TestCenter application, the VTN Coordinator application, and the OpenDaylight SDN controller were installed on a computer that communicates with the SDN switches and the traffic generator via a separate network for the out-of-band control of the testbed.  
Figure 2: A photo of the testbed.
## 3 Results
### Crop Yield Prediction
#### 3.1.2 RTM Integration with CGMs  
Integration of RTMs with CGMs is extremely limited, with only 11 available studies. Most of the studies have different CGMs integrated and lacked repetitive studies to validate the findings of individual studies with other research outputs featuring the integration of the same RTMs and CGMs. However, this also provides an opportunity for researchers to work on this area so that an integrated approach with more reliability for CYP may be evolved. Analysis of the selected articles (11) showed that only four types of RTMs were integrated with 10 different types of CGMs. These RTM models were PROSAIL (7) [19; 23; 46; 47; 48; 49; 50],  
Figure 3: Graphs showing RTM-based CYP characteristics. (**a**) Radiative transfer models used; (**b**) crop traits investigated; (**c**) countries of study; (**d**) crops investigated. 6S (2) [51; 52], SLC (1) [53] and FLiES (1) [54]. These RTMs were integrated with CGMs, namely APSIM (2), Wheat Grow/Info Wheat Grow (2), Oryza (1), WOFOST (1) and others (5). CGMs grouped under other categories were Aquacrop-OS, PROMET, Geo-CropSim, SAFY and BESS. This grouping helped with the ease of data handling, understanding and analysis. Among crops, the maximum number of integration studies was found for wheat (6), followed by rice (2), maize (2) and one study on maize and soybean. Crop traits used for CYP for integration of RTMs and CGMs were LAI (7), radiation flux density (RFD) (2), chlorophyll ab +dry matter (Cab + Cm) (1), fraction vegetation cover (FVC) (1) and gross primary productivity (GPP) (1). These investigations were distributed geographically as China (4), Italy (2), the USA (1), Germany (1) and Others (3). Other countries were South Korea, Vietnam and India. Research studies depicted that PROSAIL (RTM), APSIM (CGM), Wheat (Crop), LAI (Crop Traits) and China (Country) were distinct features relevant to this integration approach. Graphs showing the percentage distribution of research papers in terms of RTMs, CGMs, retrieved crop traits used, crops and country-wise distribution are given below in Figure 4.  
Figure 4: Graphs showing RTM and CGM integration-based CYP characteristics. (**a**) RTMs used; (**b**) CGMs used; (**c**) crop traits used; (**d**) crops investigated; (**e**) countries of study. A flow chart showing the overall summarized findings and characteristics of the RTMs used for CYP is given below in Figure 5.  
Due to the limited number of studies relevant to RTM applications for crop yield prediction, the summarized information of each study is given in Table 3 to highlight the specific findings.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 6 Simulation Results
### Network Performance at Uniform Traffic Load
The network energy utilization of the EE-CR protocol is between 0.4 and 0.45, which is even lower than that of the DT protocol (i.e., from 0.5 to 0.6). The reason for this phenomenon is that the cooperative transmission employed in the EE-CR protocol leads to extra energy consumption of cooperative nodes besides their own data packet transmissions, which intensifies the uneven energy consumption of each node in the network and leads to the early energy exhaustion of some nodes. Namely, its network energy is not fully utilized. The network energy utilization of the PO-CMAC protocol is close to 1. That is to say, the residual energy of the network is very little and its network energy is fully used. This is also one of main reasons that the PO-CMAC protocol performs better than the DT and EE-CR protocols in terms of network lifetime and the number of data packets transmitted by each node. From Figures 9 and 10, when \(M=1\), the network lifetime is the longest and the number of data packets transmitted by each node is the largest. Furthermore, as \(M\) increases, both of them decrease. This is because when the network traffic load is uniform, different nodes have the same arrival rates and then consume the energy in a similar way. In this case, it only needs to select one cooperative node with the best channel quality in the cooperative node selection phase and then balances the residual energy of the sender and this cooperative node through the LETPO algorithm. By this mechanism, both high energy utilization of the network and high energy efficiency of the nodes are guaranteed, and thus the network lifetime is further extended. Figure 11 also shows this point. When \(M=1\), the energy utilization of the PO-CMAC protocol is nearly 1 and there is no more room for performance improvement. Moreover, when \(M=1\), the overhead is the least and the energy efficiency is the highest. Therefore, the network lifetime is the longest.
## 2 Materials and Methods
### Processing of Welding Pool Image  
To overcome the impact of the high arc light in conventional PAW welding that typically employs a constant current, the PC processes the image of the weld pool, which is captured by the CMOS camera. However, due to the intense arc light, observation of the weld pool becomes difficult. To reduce the effects of the strong arc light, the authors adopted a pulsed current for the welding procedure. This allows for the reduction in the welding current, specifically when the camera shutter is opened, enabling better visibility and more explicit observation of the weld pool. The welding current is only 10 A while the camera shutter is open. The welding current increases to 240 A after closing the shutter. Figure 5 shows the image of the weld pool grabbed in pulsed welding current. The detection area of the weld pool in the welding process is shown in Figure 6. Since the molten area is shot from the rear, the direction of the welding progress is the upper side of the image. The black area at the upper edge of the image corresponds to the welding torch. From this image, it can be clearly confirmed that the keyhole is formed under the torch. It can also be seen that the plasma airflow is pushing down the molten metal. Thus, the keyhole and the surrounding molten metal can be clearly observed in the image. The molten metal is expelled by the plasma airflow, causing it to move in a backward direction along the wall of the keyhole. The arc heat also turns part of the side wall of the level into molten metal. As the welding progresses, the molten metal is pulled toward, and the boundary line between the fluids is considered to appear as a boundary line.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Mn** & **Si** & **C** & **S** & **P** \\ \hline \(>\)2.5 \(\times\) C & - & \(<\)0.23 & \(<\)0.035 & \(<\)0.035 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of the base material from JISG 3106 [wt. %].  
Figure 4: V-shaped groove of the base material.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Nozzle Diameter** & **Shielding Gas** & **Back Shielding** & **Standoff** & **PLASMA Current** & **Plasma Gas** \\  & **Flow Rate** & **Gas Flow Rate** & **Standoff** & **Flow Rate** \\ \hline
4.0 [mm] & 10 [L/min] & 10 [L/min] & 4 [mm] & 235 [A] & 10 [L/min] \\ \hline \end{tabular}
\end{table}
Table 3: Welding conditions.  
Since the sensitivity of the CMOS camera sensor disappears at approximately 1000 nm, an infrared (IR) 950 nm bandpass filter was utilized as an interference filter. To block all visible light and exclusively permit the transmission of infrared light, an IR 950 nm bandpass filter was employed, thus clarifying the contrast between the molten and non-molten areas. The weld pool changes slowly at low velocities in PAW, so images were taken every 200 ms in the seam tracking experiment.
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 4 The NoC Workflow
### Multicast and Broadcast  
In the usage scenarios of heterogeneous multi-core SoC, such as wireless base stations, there is usually a demand for data multicasting and broadcasting. PCCNoC has multicasting function and broadcasting function.  
The multicast packet structure is specified in Figure 7c. When using the multicasting function, the multicasting number P is set during the route establishment period. During the routing process, every time a routing target is reached, it sends a transmission grant (TG) to the routing source. The routing source node starts data transmission after receiving P TG signals. After the routing source node finishes sending data, it sends an unlink signal to all routing targets. For multicasting and broadcasting, the routing source node can only be a data sender. Figure 10 shows an example of how multicasting is used in a digital fronthaul end (DFE) NoC system.  
Broadcasting packets are used to quickly transmit data or control information to the entire system for global reconstruction. The routing source transmits identical data to all nodes. When the broadcast indicator is set to high, other connections in the whole network are cleared. The source routing is set as the data sender, and all other nodes in the whole NoC are set as data receiver. The broadcasting packet has the highest priority. The broadcast packet structure is specified in Figure 7d.  
Figure 10: A DFE SoC with proposed NOC architecture using multicasting. The source PE generates data and performs data pre-processing. Target PEs perform data post-processing. The filters are ASIC modules. The application mapped in this diagram is a four-antenna transmission circuit for 60 MHz bandwidth. The direction of the arrows represents the data flow direction. PE3\(\sim\)PE7 receive multicasting data from PE1.
## 4 OWC Technologies for the 5G, 6G, and IoT Solutions
### Surveys of OWC-Based 5G and IoT Systems

Table 5: Summary of the current research trends in the OWC based on the 5G and IoT systems. In [76], the authors present the integration of 5G New Radio (NR) with VLC downlink architecture. It combines complementary upcoming 5G NR and VLC wireless technologies. The data transmission of the 5G NR frame over VLC is implemented. The three-dimensional hybrid RF/VLC indoor IoT system in [81], a homogeneous Poisson point process is adopted to model to the distribution of the terminals. The light EH model is considered after introducing the LOS propagation model for VLC. At each of the devices at room, light EH is conducted by using PDs and the harvested energy is adopted for the transmissions over the RF uplink. This paper presents the key advances of OWC technologies to meet the future demands considering 5G, 6G, and IoT systems that are not yet presented in any other review literature. All the OWC technologies are considered and the ways through which each technology can contribute to reach the goal of 5G, 6G, and IoT systems are clearly presented in this article.
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 2 System Components  
The hydrogen production system is composed of the offshore wind farm, for electricity production, the electrolyzer, for hydrogen production, and the hydrogen storage system.
# Cross-Domain Access Control Model in Industrial IoT Environment
## 5 Experimental Verification and Analysis
### Grouping Strategy Matching Algorithm  
In order to improve the matching rate of attributes and strategies, this paper proposes a grouping strategy retrieval algorithm, which avoids comparing unnecessary strategies by grouping them to improve the matching rates of strategies. In the experimental part, 25 strategies in 11 groups were randomly generated. The numbers of attributes of these 11 sets of strategies were 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, and 15. Moreover, this was repeated 20 times to take the average. The average matching times of the strategy sets with different numbers of attributes were compared and analyzed by the traditional method and the strategy grouping method. The results are shown in Figure 8.  
As shown in Figure 8, the strategy matching times were relatively close in the beginning. However, with the increase in the number of attributes, the traditional strategy  
Figure 8: Strategy matching time comparison.  
Figure 7: Effects of node online time on reputation.  
matching time increased rapidly. The grouping strategy matching time was better than the traditional method.
## 2 Materials and Methods
### Experimental Setup
#### 2.3.1 Benchmark Pipeline  
To maintain a controlled experimental environment and ensure fairness among all experiments, we implemented the pipeline shown in Figure 3. The time series data were normalized to the interval \([0,1]\) and a sliding window approach was applied, depending on the requirements of each method. While AE, GANF and TranAD require input data to be given as subsequences with fixed length \(L\), MDI and MERLIN require the entire time series, along with a range of subsequence lengths \(L_{min}\) and \(L_{max}\).  
In the case of the MDI and MERLIN methods, the range of subsequence lengths used was arbitrarily set to \(L_{min}=75\) and \(L_{max}=125\) time steps. For TranAD, the subsequence length \(L=10\) and stride \(s=1\) were used, according to the experiments in [(13)] for the time series taken from the UCR anomaly archive. For GANF, a subsequence length of \(L=100\) was chosen, which is in the middle of the range \(L_{min}\) and \(L_{max}\), and stride \(s=10\) based on [(12)]. As for the AE method, the subsequence length \(L=10\) and stride \(L=10\) were determined empirically. RRCF does not require the data to be given as subsequences. A table summarizing the configurations used in our experiments can be found in Appendix A.2.  
The normalized time series or subsequences were then used as input for the respective anomaly detection method, which calculates an anomaly score. MERLIN is an exception in this regard, as it returns only the anomalous subsequences. As the scores produced by the different methods are very heterogeneous, we employed a method called _Peak Over Threshold (POT)_[(44)] to determine a suitable threshold for classifying subsequences as normal or anomalous. This approach was also used in previous works such as [(13; 45; 40)].  
Figure 3: Benchmark pipeline used in the experiments.
## 4 Multi-Attribute Machine Learning Model Selection
### Gradient Boosting Regressor (GBR)  
Gradient boosting is one of the most popular machine learning algorithms for discrete datasets [34]. Compared with linear models, the tree-based methods Random Forest and Gradient Boosting are less impacted by outliers [35]. Gradient Boosting is a robust machine learning algorithm that combines both the Gradient Descent algorithm and Boosting [36]. Gradient Boosting tries to optimise the mean square error (MSE) or the mean average error (MAE).  
Among its advantages, one can identify its capability to deal with missing data and its ability to fit the nonlinear relationship between the data and the features. At the same time, it allows the optimisation of different loss functions. The method trains multiple decision tress in a sequential process, starting from a tree with a weak prediction and improving the over-all model's prediction capacity by adding another decision tree that is upgraded by modifying the weights of the first decision tree. Therefore, the new model has a better performance, given by the combination of the two decision trees [37]. After that, the error (also known as residual) given by the model's prediction is evaluated. If the error is not satisfactory, a new tree is introduced to better classify the data and reduce the error. Theprocess is repeated for a imposed number of iterations in order to minimize the error and obtain a better prediction.  
One of the main drawbacks is that the GBR minimizes all errors, including the ones given by the outliers, that can cause overfitting. However, to address the overfitting issues, different methods, as regularization, setting a maximum depth and early stopping can be chosen [38; 39]. Another disadvantage is that this method is almost impossible to scale up, because each decision tree is trained based on the previous one and it is hard to parallelize the process. For processes that need to be scaled up, a scalable end-to-end tree boosting system called XGBoost is widely used by data scientists. The XGBoost reduces the run time and scales to billions of examples in distributed or memory-limited settings [40].  
Considering that the datasets under test are discrete, with a non-linear relationship between data and features, the GBR algorithm is a suitable method to train a machine learning model. For the four datasets under test, the overfitting issue was avoided by choosing a maximum depth of three and an early stopping coefficient to stop the training process when validation score is not improving after 20 iterations. The loss function imposed for the training process is the squared error.
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 6 Industrial Test of Gangue Grouting Filling Technology in Goafs
### The Process and Layout of Grouting Filling
#### 6.2.2 Grouting Filling Equipment and Parameters  
The ground filling station of Zhangjiamao Coal Mine adopts three stages of crushing, one stage of screening, one stage of buffering, two stages of stirring, and one stage of pumping equipment. The first-stage crusher is a through-type crusher, which can crush the washing gangue to less than 80 mm; the secondary crusher is a double-geared roller crusher, which can crush the gangue from below 80 mm to below 30 mm. The three-stage crusher is a squirrel cage crusher, which can crush the gangue from below 30 mm to below 3 mm. The flip-flow screen can realize the screening of the gangue below 3 mm; the volume of the deferred storage is 20 m\({}^{3}\), which can meet the continuous operation of crushing screening and stirring pumping. The mixer is a two-stage continuous overflow mixer, and the filling pump is a plunger pump.  
The filling pipeline route is as follows: filling station \(\rightarrow\) air shaft site in the second panel \(\rightarrow\) return air inclined shaft in the second panel \(\rightarrow\) joint roadway of return air inclined shaft and return air roadway in the second panel \(\rightarrow\) return air roadway \(\rightarrow\) return air roadway \(\rightarrow\) number 22202 auxiliary transportation roadway (number 22203 mining roadway) \(\rightarrow\) adjacent grouting borehole \(\rightarrow\) number 22202 goat. The total length of the filling pipeline is about 4.6 km, and the pipeline model is a \(\phi\)180 \(\times\) 15 mm seamless steel pipe.  
The adjacent grouting borehole spacing is 120 m, the borehole length is 28.0 m, the angle with the horizontal plane is 21\({}^{\circ}\), the borehole diameter is \(\phi\)200 mm, and a \(\phi\)180 mm protective casing is used to protect the adjacent grouting filling borehole.
## 6 Conclusions  
This work presents a path-loss-profiling method for UAV swarms with machine learning, and it provides receive-signal-strength prediction. The UAV-swarm-communication problem for search-and-rescue applications has been solved by the proposed procedure machine-learning approach. Initially, the path-loss profile is generated though the training of a random forest model-based received-signal-strength dataset. In order to find the reallocation of UAVs, the dataset was generated for UAV clustering with the concept of triangular-based swarm formation. After that, K-means clustering was applied for the prediction of cluster parameters for a UAV swarm. Furthermore, the inter-swarm euclidean distance was found through the dendrogram plots. Finally, the results of communication model for the complete UAV swarm were presented in heap maps and contour plots. Future work could be related to radio-frequency-interference mitigation through machine learning.  
Conceptualization, H.K.; Methodology, I.K. and M.H.A.-A.; Software, H.K.; Investigation, A.J.A.; Resources, S.U.R. and M.E.; Visualization, I.U.; Supervision, H.K.; Project administration, G.A. All authors have read and agreed to the published version of the manuscript.  
This work do not have any funding.  
The code is available on request.  
This work was supported by the EIAS Data Science and Block chain Lab., College of Computer and Information Sciences, Prince Sultan University, Riyadh Saudi Arabia.  
The authors declare no conflict of interest.