esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Detailed Analysis  
Figure 5 illustrates the annual WT and PV power curtailment before and after coupling integration obtained by optimization. It is evident from the figure that after coupling integration, the amount of curtailed WT and PV power has significantly decreased. Specifically, there is no wind curtailment throughout the year, and solar curtailment occurs only during a few instances in the winter, with the highest curtailment at about 42.1 MW. Before coupling integration, both WT and PV power had curtailment power throughout the year, with higher curtailment in the winter compared to the summer. The maximum curtailment power reaches 111.6 MW for WT and 155.6 MW for PV, indicating a lower utilization rate of RESs and a waste of energy. The high installed capacity of PV in the test system may lead to substantial load variations during the daytime, making it difficult for both TPUs and the power grid to handle this fluctuation efficiently. The analysis shows that coupling integration not only enhances the utilization rate of RESs but also reduces the maximum curtailment of WT and PV power.  
Figure 6 presents the annual output of TPUs before and after coupling integration obtained from optimization. The figure illustrates that after undergoing flexibility retrofit and coupling integration, TPUs exhibit a significant improvement in their output range and regulation capability. This allows TPUs to rapidly adjust their output in response to real-time WT and PV outputs, thereby optimizing the accommodation of RESs. Specifically, during summer at about 600-12.000 time-steps, when PV output variation is substantial, coupling integration and retrofitting TPUs demonstrate quick adjustment capabilities due to improved ramping performance, with a significantly wider output change range than before retrofitting and coupling integration. In winter, retrofitting lowers TPUs' minimum output, allowing for prioritized RES generation over TPUs compared to the pre-retroit scenario, thus reducing WT and PV curtailment. Additionally, the output curve from Figure 6a reveals that retrofitted TPUs possess even greater regulatory potential in the summer, which can accommodate more WT capacity. These findings verify the effectiveness of flexibility retrofits of TPUs, as well as the coupling integration of RESs and TPUs in fully leveraging the potential of TPUs, especially in accommodating significant volumes of RESs while maintaining system efficiency.  
Figure 5: Comparison of renewable energy curtailment. (**a**) After coupling integration with retrofitted TPUs; (**b**) Before coupling integration and without retrofitted TPUs.
# Small-Size Square Ring 1-Bit Reconfigurable Transmitarray Unit Cell for C-Band Applications
## 1 Introduction
The unit cell can be used for transmitarray design with a beam-steering capability and can be scaled from C-band to a higher frequency range.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 5 Modeling Device-to-Device Communications in SimuLTE
### Modeling Resource Allocation
Mode selection algorithms can be written by extending the D2 DModeSelectionBase module residing at the MAC layer of the eNB. Such module periodically runs a doModeSelection() function that produces the communication modes to be used in the next period for all the ongoing D2D peerings. Custom mode selection policies can be implemented by inheriting the structure of the D2 DModeSelectionBase module and overriding the doModeSelection() method. An example of a mode selection algorithm can be found in [28].
## 2 Geologic Background and Methods  
### Geologic Background  
The Gonghe Basin, where the study area is located, is a faulted basin from the early Neoproterozoic Era. It is bordered by several folded uplift zones, including Qinghai Nanshan to the north, Hekashan to the south, Era Mountain to the west, and Wariguan Mountain to the east [14,15]. To the east is the Wariguan Mountain Fold Rise Belt. These uplift zones are magmatic tectonic features formed by the intrusion of magmatic rocks. The intrusive bodies--predominantly from the Indo-Chinese-Yanshan period--were emplaced into the Middle and Lower Triassic System and include granite, granodiorite, quartz diorite, and porphyritic granite [16,17]. The mean value of geothermal flow in the Gonghe Basin is about 1.2%. The average geothermal heat flow in the Gonghe Basin is 102.2 mW/m\({}^{2}\), which is far higher than the average value of 60.4 mW/m\({}^{2}\) in mainland China [18], demonstrating that the Gonghe Basin is a distinct high-thermal anomaly area [19].  
The planning demonstration of the diversion of the \"Yellow River to Xining\" tunnel through the Kunlun Massif in the northeastern part of the Gonghe Basin and the Qilian Massif boundary zone is located in the northeast edge of the Tibetan Plateau geothermal anomaly area, with a complex geological structure, strong new tectonic movement, and intense hydrothermal activity. The tunnel crosses several deep fractures, including the Qinghai Nanshan Fracture, the Daotang River Fracture, and both the southern and northern edges of the Laji Mountain Fracture (Figure 1). [PERSON] and colleagues believe that the vigorous recent neo-tectonic activity facilitates the upward migration of deep heat along these fractures. In the study area, there are a number of deep tectonic movements, such as the East Kunlun and the northern edge of the Laji Mountains, and a number of deep faults, such as the northern edge of the Laji Mountains. In the study area, the development of igneous sequences and metamorphic sedimentary rocks from the East Kunlun and West Qinling orogenic belts is evident. Magmatic activities--primarily intrusions and eruptions--are governed by the region's geological structures. The predominant rock types consist of metamorphic sedimentary rocks from the Middle and Late Triassic periods, along with Indo-Chinese granite and graniodiorite [20]. A portion of the tunnel traverses granite formations. The tunnel traverses areas with granite distribution, where the high radioactive heat generation rate of granite is notable. In certain sections, the overlying rock exceeds a thickness of 1000 m. The lacustrine fine-grained elastic rocks--characterized by their small grain sizes, low permeability, and poor thermal conductivity--offer excellent thermal insulation, thereby heightening the risk of heat damage [21]. This further elevates the potential for thermal damage.
## 2 Monitoring Project Overview
### Analysis of Experimental Data Processing
#### 2.3.1 Data Pre-Processing

Table 2: Monitoring equipment parameters. method. Common data anomaly processing methods, namely the horizontal processing method and the vertical processing method, are used to refine the data. The horizontal processing method is suitable for data with strong continuity, while the longitudinal processing method is more appropriate for data with obvious periodicity. In the dust-related data, the particle concentration data exhibit strong continuity, and the horizontal processing method is used for correction. The specific operation involves comparing the data from one hour before and after the data anomaly point to determine if the difference between the data before and after the point exceeds the threshold. If it exceeds, it is judged as abnormal data. Generally, the anomalous point is replaced by the mean of the two sets of data before and after. The threshold is one-third of the mean of the entire sample data. \[|F(d,t)-F(d,t-1)|>\Omega(t) \tag{2}\]  
\[|F(d,t)-F(d,t+1)|>\Omega(t) \tag{3}\]  
\[F(d,t)=\frac{|F(d,t-1)+F(d,t+1)|}{2} \tag{4}\]  
where \(\Omega(t)\) and \(\Omega(t)\) represent thresholds, \(F(d,t)\) represents the data at time \(t\) on day \(d\), while \(F(d,t-1)\) and \(F(d,t+1)\) represent the data at the previous and next moments, respectively. The meteorological data for temperature and humidity exhibit clear daily periodicity in average changes. Therefore, abnormal temperature and humidity data are corrected using a longitudinal processing method. The specific operation involves comparing the data at this point with the data from recent days to determine if the difference exceeds the threshold and is greater than the average value of the sample data. If the value exceeds the threshold, the sum of the threshold and the average value is used to replace the abnormal data. If the difference exceeds the threshold but is less than the average value of the sample data, the difference between the threshold and the average value is used instead [33]. \[|F(d,t)-m(t)|>\ell(t) \tag{5}\]  
\[F(d,t)=m(t)+\ell(t),F(d,t)>m(t) \tag{6}\]  
\[F(d,t)=m(t)-\ell(t),F(d,t)<m(t) \tag{7}\]  
where \(\ell(t)\) represents one third of the mean value \(m(t)\) of the data from the past 5 days. Secondly, in meteorological monitoring data, the rainfall monitoring data represent the cumulative rainfall for the day. These data need to be averaged to determine the rainfall at specific times, and the rainfall data should be cleared promptly once the rain stops.
## 3 Results and Discussion
### Influence Factor Analysis for PC Properties
#### 3.2.5 Freeze-thaw Cycles  
The results of freeze-thaw cycles test are shown in Figure 13. The number of freeze-thaw cycles is 40 to 80 for PC. It can be seen that aggregate size is the primary factor influencing PC freeze-thaw property. A small aggregate is more favorable for the freeze-thaw resistance of PC. Small aggregate provides greater specific surface area and there is more cement mortar between aggregates. Therefore the bonding force is stronger and resistance to the freeze-thaw cycles will be better. The number of freeze-thaw cycles decreases 20 times when the aggregate size changes from 4.75 mm to 16 mm. When aggregate size is the same, the number of freeze-thaw cycles decreases with the increase of designed porosity. High porosity means more water content and larger frost expansion force, which reduces resistance to the freeze-thaw cycles. W/B ratio has no significant impact on the freeze-thaw cycles of PC. Because the void structures of the PC and ordinary concrete are completely different, the freeze-thaw damage patterns of the two are obviously different.
## 3 Contextual-Bandit-Based Reminder Care System
### Complex Activity Detection  
For a system to recommend appropriately, it needs to be aware of the user's activities to enable it effectively carry out desired recommendations. Despite the extensive studies on Human Activity Recognition (HAR), most existing studies rely on wearable sensors for the detection of simple activities, which are inadequate to support the detection of complex activities. Therefore, further sources (e.g., environmental sensors and home appliance sensors) are incorporated to help the system's accurate detection of complex activity. In our previous work [12], we designed a preliminary reminder care system based on the detection of complex activities. We conduct recommendations via three main steps:  
Figure 3: Overview of the proposed methodology.  
Figure 2: The three main concepts in the Contextual Bandit Approach are (1) State, which represents the current situation for the user; (2) Action, which provides the required item that meets the user’s need; and (3) Reward, which considers feedback for the system to improve the quality of recommendations.  
Elementary activity recognition: In this approach, the common configuration of DeepConvLSTM was used as the classifier to detect elementary activities. The DeepConvLSTM was configured to four convolutional layers with feature maps and two LSTM layers with 128 cells. This stage was tested on two public datasets PAMAP2 dataset [33] and PUCK dataset [34]. The result shows that DeepConvLSTM achieved a promising accuracy of 77.2%.
* Ontology for complex activity recognition: After achieving the detection of elementary activities, we built an OWL (OntologyWeb Language3) model, which includes the artefacts, locations, environment, and activities required to define things involved in the interaction. From the Aris scenario, preparing a cup of tea could involve changes in the motion sensor (local environmental sensor), status of the kettle in triggering the usages, and time period for this activity, which would rarely be in the early morning before sunrise. From the example, we can extract numbers of context: First, from the motion sensor referring to [PERSON] place (the kitchen); Secondly, the item context where the kettle has been used; and finally, the time context of when this activity took place.
* Rule-based orchestration: This step utilizes the output from the two previous steps for the detection of complex activities. A set of rules produced based on the previous ontological models are implemented. Following the [PERSON] tea preparation illustration, we can create an ontological rule in a descriptive language as:
## 6 Results, Implementation, and Testing
### Real-Time Testing with Continuous Data  
Continuous, unseen data were used to test the IMU-based detection method, aiming to replicate a real-time setup. Integrating the machine learning model into the microcontroller of the SafeStride system facilitated the experimentation with features transmitted to the MCU via serial communication. To ensure efficient data processing, a graphical interface was developed, enabling the loading and visualization of continuous samples and their subsequent transmission to the controller, as can be seen in Figure 8. The implementation employed a sliding window technique, employing a window size of 160 samples with a 50% overlap. Subsequently, the data were received by the microcontroller, classification was performed, and the corresponding result was returned. By comparing the recorded and transmitted data, highly favorable outcomes were obtained.  
Figure 7: Confusion matrix with the performance of the Random Forest Classifier on the test dataset.  
In contrast to traditional machine learning testing methods, this experimental setup distinguishes itself by employing continuous data instead of pre-processed and curated static samples. Within this framework, various critical aspects were comprehensively evaluated, including accurate step detection, mitigating missteps or indications of falls arising from random motion, and the immediate triggering of fall events when they occur. Real-time laboratory tests demonstrated the satisfactory performance of the trained algorithm, thereby confirming the robustness of both the model and the training dataset. Additionally, this experiment convincingly demonstrated the feasibility of implementing complex machine learning algorithms on an IoT device, particularly for tasks involving real-time and continuous data processing.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 6 Positioning Experiments
### Discussion  
In the experiment with the robot, a good positioning result was obtained by combining the dead reckoning (which is accurate in a short time) with the IMES radio-based positioning (which is not accurate, but gives an absolute position) with the EKF. However, in consideration of the results shown in Figure 12, the Kalman filtering might not be completely suitable for the IMES radio-based positioning with CNR. The Kalman filtering method basically assumes a zero-mean Gaussian noise for both the prediction and correction process. However, the error of the robot's wheel encoders (slippage) would not be a zero-mean Gaussian, and as seen in Figure 1, the horizontal distribution of distance plots centering the red line is also not a Gaussian distribution. Therefore, in order to increase the positioning accuracy, it might be better to use an estimator that can cope with a non-Gaussian noise, such as a particle filter.  
In the pedestrian experiment, the EKF could reduce the error accumulation caused by the dead reckoning, but the estimated position was biased to the right side, as shown in Figure 13, and caused a large positioning error. As also can be seen in Figure 13, the trilateration performance was very low; especially, the position estimation results were distributed outside the actual path. One possible reason for the low trilateration accuracy is that, because of the existence of a human body between the transmitter and receiver antennas, the power of the IMES radio (CNR) decreased, and as a result, the distance between transmitter and receiver antennas was calculated as longer than the actual distance. In this case, probably, it is difficult to model the noise distribution, because it varies according to the pedestrian posture; that is, to cope with this problem, a more complicated system model would be required.
## 1 Introduction
The signals from bridge structures are influenced by intrinsic and extrinsic factors (e.g., the rich structural information, the existence of high-level noise effect), which lead to inaccurate and spurious results by using RP and RQA. On the other hand, the choice of parameters of RP, especially the crucial recurrence threshold, mainly depends on the goal of the analysis [22; 23], which seriously restrict the application of RP and RQA in civil engineering. Recently, [PERSON] et al. [24] and [PERSON] et al. [25] made some worthy contributions in this area where the complex traffic flow signals had been dealt with by using a combination of empirical mode decomposition (EMD) and the RP method. The multiscale framework has been considered a promising solution to explore recurrence dynamics in complex systems [1]. However, an undesirable disadvantage of EMD is the mode mixing effect when applied in the field of signal processing [21; 26]. This means that oscillations or frequencies with the same time scale are assigned to different decomposed modes or the oscillations of disparate amplitudes that appear in one mode [27]. In order to lessen this problem, the ensemble EMD (EEMD) [28] has been proposed as an adaptive noise-assisted method capable of providing a partial solution to the mode-mixing problem. However, this method may produce spurious frequencies due to the added white noise and has difficulties estimating closely spaced frequencies [27]. In this regard, two new works have been done in this paper. First, an improved EEMD method is developed to alleviate the mode-mixing problem of EEMD in order to meet the requirements of the previously mentioned high-level noise and dense modal characteristics. By taking advantage of the uniform distribution of the probability density function of the added white noise in the full frequency domain, a new white noise assistance way is introduced to the EMD in order to ensure the single-mode characteristic of intrinsic mode functions (IMF). Second, by combining the method of \"surrogate data\" [29] and the hypothesis testing scheme of nonstationary and nonlinear synchronization, a data-driven RQA method is proposed in this paper to meet the requirements of the previously mentioned data. The data-driven RQA method is applied to a single-mode signal, which is corresponding to each IMF, the true recurrence mode, and the recurrence quantification values are obtained. Then, verification is carried out based on a 1:20 scale model of the cable-stayed bridge experiment. The results associated with the proposed method in this paper are compared with that of conventional non-stationary and nonlinear test methods. The structure of the paper is organized as follows: Section 2 provides the background of EEMD and introduces the improved EEMD. Section 3 introduces the method of data-driven RQA. In Section 4, a real scale model of cable-stayed bridge is utilized, the comparison between the conventional method, and the proposed method is performed. Section 5 discusses and concludes the present study.
# Local Ecological Knowledge (LEK) Can Guide Decision-Making in Inland Fisheries Management
## 3 Results
### Fishers' Ecological Knowledge
#### 3.4.3 Reproductive Period  
The fishers' responses regarding the spawning periods of the species are depicted in Figure 8. In the case of crayfish and common carp, there was a high agreement among their responses (as more than 70% of the interviewed fishers mentioned one or two specific months) regarding the main reproductive peak for the species. A medium agreement was estimated among their responses for the spawning period of European perch, wels catfish, northern pike, and roach. In general, according to the fishers, common carp, European perch, and roach reproduce during the spring season. Additionally, two fishers noted a second reproductive episode in common carp (a small portion of the population) in early autumn. They also mentioned that wels catfish reproduce during the summer. Finally, the fishers stated that Danube crayfish release their hatchings mainly during May and June (Figure 8). Overall, LEK agreed with scientific data concerning both the maturity length and the spawning period for most species.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{6}{c}{**Species**} \\ \cline{2-7}  & **Wels Catfish** & **Common Carp** & **European Perch** & **Roach** & **Northern Pike** & **Danube Crayfish** \\ \hline \(L_{m}\) PR & 60.0 \(\pm\) 17.32 & 32.5 \(\pm\) 5.00 & 12.6 \(\pm\) 2.07 & 11.7 \(\pm\) 3.03 & 30.0 & 6.8 \(\pm\) 2.28 \\ \(W_{m}\) PR & 2.07 \(\pm\) 1.417 & 0.97 \(\pm\) 0.358 & 0.18 \(\pm\) 0.083 & 0.15 \(\pm\) 0.080 & 1.35 \(\pm\) 0.629 & 0.03 \(\pm\) 0.013 \\ \(L_{m}\) literature & 87.5 (86–108) & 34.8 (25 \(\pm\) 36) & 16.3 (11 \(\pm\) 23.4) & 14.0 (8–7) & 39.9 (25–63) & 9 (7–7) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Length (\(L_{m}\), cm) and weight at first maturity (\(W_{m}\), Kg) \(\pm\) standard deviation based on the perspectives of interviewed fishers for the commercial catch species in Polyphytos Reservoir (PR). The mean \(L_{m}\) values and the range (in parentheses) at which \(L_{m}\) is attained based on literature [22, 23] are also provided.  
Figure 7: Length and weight at first maturity for the commercial catch species in Polyphytos Reservoir based on the statements of the interviewed professional fishers. The boxplots show the median (horizontal line), the mean value (\(\times\)), the 50% of the values (box), the highest and lowest values (outer lines), and the outlier values (dots). The number of responders is provided above each box.  
The Spearman correlation among the year of entering the employment, total catches/year, fisher's age, and LEK on reproduction revealed a potentially significant positive correlation between total catches/year and LEK on reproduction (Table 4).