esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 5 Experimental Evaluation and Discussion
### SOC Curve Change during Convergence  
The SOC and difference change curves in the training process of different algorithms are shown in Figure 11. As shown in Figure 11a, the SOC variation trends of the machine learning algorithm and physical aging experiment are generally consistent, indicating that our algorithm follows the basic battery aging rules. Meanwhile, although all algorithms end training at 80% of battery capacity, the SOC of the PCA\(-\)DDQN algorithm is slightly higher than the results of most algorithms in many cycles, which can be explained by the fact that the agent, in reinforcement learning, modifies the policy based on a series of state\(-\)action\(-\)reward processes toward an enormous reward value. In contrast, the traditional heuristic scheduling algorithms take the error difference as an indicator for updating parameters and do not update them in the corresponding range, with high model fluctuations. The PSO algorithm is better at dealing with continuously varying variables, and the GA algorithm has more advantages in discontinuously varying problems [42]. In other cases, the RL agent performs slightly lower than that of the heuristic algorithm due to the \(\varepsilon\) greedy strategy of the agent in selecting action. The \(\varepsilon\) value set in this study is 0.9, which means that, in the action selection process, there is a 90% probability of using the previous optimal solution to select the action with the maximum Q value. There is a 10%  
Figure 10: (**a**) Comparison of average rewards of different reinforcement learning algorithms; (**b**) rewards during training of different reinforcement learning algorithms; (**c**) performance of different algorithms under temperature constraint; (**d**) performance of different algorithms under \(\Delta IR\) constraint.  
probability of random selection, which allows the agent to search a larger space and avoid the agent falling into the local optimum. As can be seen in Figure 11b, there are capacity changes during battery aging, and the SOC trajectories of the proposed method, with the partial heuristic scheduling algorithms, fluctuate in a relatively narrow range, while the other algorithms fluctuate within a more extensive range, which is not conducive to the overall cycle life of the battery. In addition, the results in Figure 11 show that none of the overall changes in SOC are evident in the initial stage of the cyclic aging experimental process, which is because the early battery physical and chemical degradation does not immediately lead to capacity decay, and the capacity decay process is accelerated when a certain critical point is reached.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### AI Algorithms in Aquaculture
#### 3.2.4 Application of Fuzzy Logic in Aquaculture  
Fuzzy logic is a computational method that emulates human reasoning, making it particularly useful for managing uncertainty and imprecision in complex and dynamic systems like aquaculture. In contrast to conventional binary logic, which operates on strict true or false conditions, fuzzy logic enables more flexible decision-making by incorporating varying degrees of truth. This flexibility makes it particularly useful for optimising aquaculture operations. For example, a study in [29] combines U-Net with a neuro-fuzzy system optimised through particle swarm optimisation (PSO) to accurately detect fish diseases in underwater photos, facilitating timely intervention and disease management. Similarly, research in [53] employs fuzzy inference systems in combination with FFAUNet segmentation to optimise feeding operations in aquaculture, minimising waste while increasing fish growth. A study referenced in [54] employs a fuzzy neural network integrated into an expert system to identify fish diseases. This approach combines real-time data with expert insights to facilitate early disease detection and improve treatment planning. Such implementations demonstrate the versatility and efficacy of fuzzy logic in boosting operational efficiency, sustainability, and productivity within the aquaculture industry.
# Evaluating Biosedimentation for Strength Improvement in Acidic Soil
## 3 Results and Discussions
### Macro-Mechanical Results
#### 3.2.3 Specific Gravity and Density  
The soil characteristics of the D45 and D60 soil specimens were considerably improved following treatment, as indicated in Table 7. The bulk density for D45 and D60 rose from 1560 kg/m\({}^{3}\) to 1710 kg/m\({}^{3}\) and 1670 kg/m\({}^{3}\), respectively. For both D45 and D60, the void ratio fell substantially. D60, on the other hand, maintained the same specific gravity of 2.51 as natural soil (N). Furthermore, the modulus of elasticity (Young's modulus) for both D45 and D60 was considerably increased, reaching up to two times that of natural soil (N). That was owing to the filling of soil voids by CaCO\({}_{3}\), as well as a reduction in the water content, both of which occurred as a result of the electrokinetic effect, as validated by research [37; 38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Code** & **C\({}_{\mathbf{V}}\)** & **k** & **m\({}_{\mathbf{v}}\)** & **a\({}_{\mathbf{v}}\)** \\ \hline
**(Unit)** & **(m\({}^{2}\)/year)** & **(m/s)** & **(m\({}^{2}\)/ton)** & **(m\({}^{2}\)/ton)** \\ \hline N & 2.55778 & \(1.9\times 10^{9}\) & 0.02916 & 0.07146 \\ D45 & 8.10157 & \(8.2\times 10^{9}\) & 0.01110 & 0.01792 \\ D60 & 3.62619 & \(6.4\times 10^{9}\) & 0.01027 & 0.02255 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Conducted and derived permeability and compression parameters for N, D45 and D60.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Code** & **w\({}_{\mathbf{c}}\)** & **w\({}_{\mathbf{LL}}\)** & **w\({}_{\mathbf{PL}}\)** & **PI** & **LI** & **CI** & **USCS** \\ \hline N & 67 & 63.66 & 32.57 & 31.1 & 1.151 & \(-\)0.101 & OH \\ D45 & 49.07 & 43.37 & 23.94 & 19.44 & 1.52 & \(-\)0.24 & OL \\ D60 & 48.02 & 43.88 & 27.27 & 16.62 & 1.89 & \(-\)0.15 & OL \\ \hline \hline \end{tabular}
\end{table}
Table 4: Afterberg limits and soil classification for untreated soil (N) and treated distances of 450 mm (D45) and 600 mm (D60).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Code** & **C\({}_{\mathbf{C}}\)** & **CR** & **C\({}_{\mathbf{\alpha_{c}}}\)** & **C\({}_{\mathbf{r}}\)** \\ \hline N & 0.27995 & 0.10450 & 0.03351 & 0.01462 \\ D45 & 0.24186 & 0.10555 & 0.03852 & 0.03282 \\ D60 & 0.20796 & 0.09424 & 0.04121 & 0.02371 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Conducted and derived consolidation parameters for N, D45 and D60.
## 4 Discussion
### Using an Acoustic Camera in Extremely Turbid Environment  
The first use of an acoustic camera for the purposes of assessing the abundance and diversity of marine organisms in the extremely turbid waters of French Guiana was described. All rocky areas between Cayenne and the Approuague River Estuary, representing 12 km of coastline, were surveyed and recorded with the P900-130 BlueView acoustic camera. The camera allowed overcoming limitations imposed by extreme turbidity [6]. Marine species with a characteristic shape (ray, turtles, and sharks) were easily identified, even at a species level, based on their silhouette (for example, manta ray and long nose ray). However, these criteria were not adequate for species with similar morphology, such as most fish, for which other criteria, such as the swimming behavior, were needed. Fish swimming behavior was previously used as an additional character in acoustic-mediated identification [49]. However, it was not possible to identify all fish species with a P900-130 BlueView, in particular, species smaller than 40 cm.  
Acoustic cameras are useful in some situations, but usefulness depends on the scientific question. The goal of many studies using acoustic cameras has been to study the total abundance of fish in habitats of limited visibility [8; 14; 15; 19], or to focus on one species that was morphologically different from all other species [17; 26]. Most studies attempting identification to a species level were carried out with a DIDSON camera at a high frequency (1.8 MHz) [3; 9], which is twice the frequency of the P900-130 BlueView. Higher frequencies typically produce clearer images; thus, it would be interesting to compare both types of acoustic cameras in French Guiana.  
Nevertheless, the use of the ProViewer provides sufficient resolution of certain marine animals, those with distinct morphology and/or behaviors, for identification and measurement and is therefore useful for assessment of stock status for those species. In addition, for some species, juveniles can be distinguished from adults (e.g., marine turtles, sharks and goliath groupers) because maturity is a function of size. In French Guiana, such information could be used to develop an effective program for marine ecosystem management.
## 2 RadCom System Model and Processing
### Signal Propagation  
Let \(x_{pb}(t)=x(t)e^{i2\pi f_{c}t}\) be a passband transmitted signal, where \(f_{c}\) is the carrier frequency, and \(B\) is the bandwidth of \(x\). Considering multipath propagation, the passband received counterpart of \(x_{pb}\) is expressed as:  
\[y_{pb}(t)=\sum_{j=0}^{L_{ch}-1}h_{j}(t)x(t-\tau_{j}(t))e^{i2\pi f_{c}(t-\tau_{j }(t))} \tag{1}\]  
where \(L_{ch}\) denotes the number of propagation paths \(\tau_{j}(t)\), and \(h_{j}(t)\) is the time-varying channel gain associated to the \(l\)-th path. This model also accounts for multitarget cases.  
The time-varying delay is due to the varying motion between transmitter (RadCom system) and receiver (targeted vehicle), and is given by the following expression:  
\[\tau(t)=\beta\frac{d(t)}{c}=\beta(\frac{d_{0}}{c}+\frac{v}{c}t) \tag{2}\]  
Figure 1: RadCom time-division multiplexing (TDM) approach.  
where \(c\) is the speed of light, \(d_{0}\) is the distance between RadCom transmitter and targeted vehicle at \(t=0\). For simplicity, we omit the \((.)_{0}\) subscript. \(v\) is the relative speed at \(t=0\)[16]. In this case, acceleration and higher-order motion were ignored. We introduced parameter \(\beta\) to model both the communication system and the radar using the same equation. In this case,  
\[\begin{cases}\beta=1&for\quad communication\\ \beta=2&for\quad radar\end{cases}\]  
Substituting (2) in (1), it follows that baseband received signal \(y(t)=y_{pb}(t)e^{-\beta\pi f_{d}t}\) is  
\[y(t)=\sum_{j=0}^{L_{cb}-1}h_{j}(t)x((1-\beta\frac{\tau_{d}}{c})t-\tau_{j})e^{- 2\pi f_{d}\tau_{j}}e^{j2\pi f_{d_{j}}t} \tag{3}\]  
where \(\tau_{j}=\beta\frac{d_{0}}{c}\) is a constant delay, \(f_{d_{j}}=-\beta f_{c}\frac{\tau_{d}}{c}\) is the motion-induced frequency shift, also known as the Doppler shift (Doppler shift is function of the carrier frequency and angle of arrival \(\theta_{l}\) of the \(l\)-th path, such that \(f_{j}=f_{d_{j}}cos\theta_{j}\)[17], \(f_{d_{j}}\) is the maximal shift in this case). The term \(s=\beta\frac{\tau_{d}}{c}\) is known as the time-scale factor [18].  
Equation (3) implies that the received signal is the sum of attenuated Doppler-shifted (in frequency), time-stretched/-compressed (due to time scaling) and phase-shifted delayed copies of the transmitted signal. Phenomena of multipath propagation considering Doppler effects can be modeled as convolution with a filter, given as:  
\[h(t,\tau)=\sum_{j=0}^{L_{cb}-1}h_{j}(t)e^{-j2\pi f_{c}\tau_{j}}e^{-j2\pi f_{d_ {j}}t}\delta((1-s)t-\tau_{j}) \tag{4}\]  
Equation (4) is the impulse response of the channel. Its discrete-time counterpart considering a critically sampled model can be given as follows:  
\[h[n,\tau]=\sum_{j=0}^{L_{cb}-1}h_{j}e^{-j2\pi f_{c}\tau_{j}}e^{-j2\pi f_{d_{j }}nT_{s}}\delta[(1-s)n-\tau_{j}/T_{s}] \tag{5}\]  
where \(T_{s}\) stands for sampling time, notation \((nT_{s})\triangleq[n]\) is adopted, and \(n\) stands for the time index.
## 1 Introduction  
Recently, X-ray Free Electron Lasers (XFELs), such as the Linac Coherent Light Source (LCLS) [1] and the SPring-8 Angstrom Compact Free Electron Laser (SACLA) [2], have successfully generated brilliant, femtosecond X-ray pulses, achieving an ultrahigh resolving power in both space and time that opens up new frontiers in various scientific fields [3; 4; 5]. Currently, the Self-Amplified Spontaneous Emission (SASE) scheme [6; 7] is widely utilized for generating XFEL pulses. SASE-XFEL pulses have many narrow spike structures originating from the stochastic nature of the electron beam used as light source. The spike structures reflect important characteristics of the XFEL beam: the width and the number of spikes in the whole spectrum closely relate with the XFEL pulse duration and the longitudinal mode number, respectively. To measure spike structures, single-shot measurements with a dispersive X-ray spectrometer are required, because spike structures change shot by shot [8; 9; 10; 11]. However, it was difficult to fully resolve the spike structure with a typical width of \(\sim\)100 meV over the entire pulse spectrum covering a few tens of eV. In this paper, we report on the development of a single-shot spectrometer covering the whole XFEL spectrum with fully-resolved spike structures.  
So far, two methods have been developed for designing single-shot spectrometers of well-collimated XFEL beams. One uses a thin, bent analyzer crystal [9; 11]. The total spectral range can be simply tuned by controlling the bending radius, while possible lattice distortions induced by the bending can degrade the energy resolution. The other one combines a flat analyzer crystal and an elliptical total-reflection mirror to increase the angular divergence of X-rays. In this case, one can achieve high resolution of 14 meV at around 10 keV photon energy by using a higher-order diffraction for the analyzer, although a spectral range is limited to 4 eV due to the small angular divergence of 2.7 mrad [10]. In this study, we utilized a multi-layer mirror to achieve a large divergence of 22 mrad to measure a wide spectral range of 55 eV, while keeping the resolution sufficient to resolve the spike structures of a few hundred meV.
## 6 Implications
### Main Challenges  
Despite advancements in understanding the causes of energy inefficiency, several challenges remain that hinder effective optimisation. Addressing these issues requires ongoing research and the development of adaptive, scalable solutions that cater to the diverse nature of IoT applications:  
* Developing sophisticated offloading and scheduling algorithms is challenging, particularly in heterogeneous environments with diverse task requirements and dynamic conditions. Efficient task management demands real-time adaptability, which can be resource intensive.
* Minimising latency is critical for real-time IoT applications. However, achieving low latency without sacrificing energy efficiency is difficult, as shorter response times often require additional processing resources.
* IoT networks are inherently dynamic, with devices frequently joining or leaving the network. Managing these topology changes without increasing power consumption remains a major challenge, and predictive algorithms are needed to anticipate and adjust to these changes.  
* The diversity of IoT devices in terms of capabilities and communication paths complicates load balancing. Failure to balance workloads effectively can lead to energy inefficiencies and degrade overall system performance.
* Coordinating resources across communication, memory, and energy management layers is critical for IoT performance. Balancing these resources to avoid bottlenecks while maintaining system efficiency is a complex task.
* As IoT networks expand, congestion becomes a growing issue, affecting data transmission rates and increasing power consumption. Effective congestion control requires adaptive routing protocols that can dynamically respond to changing traffic patterns.
* Clustering reduces energy consumption by aggregating data at designated cluster heads before transmission. However, selecting optimal cluster heads and defining cluster sizes remains challenging, and techniques such as machine learning and fuzzy logic show promise but need further refinement.
* Identifying energy-efficient routing paths in heterogeneous IoT networks is essential. Developing advanced routing protocols that adapt to varying conditions and minimise energy usage is an ongoing challenge.
* Optimising bandwidth allocation is critical for maintaining Quality of Service (QoS) in IoT systems. Balancing bandwidth without compromising service delivery is particularly challenging in networks with limited capacity.
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 4 Operation of Proposed Protocol  
Figure 2 illustrates a basic UCMAC procedure with two cooperators (C\({}_{1}\) and C\({}_{2}\)) that are located between the source (S) and destination (D). UCMAC consists of two phases, _channel reservation_ and _data transfer_. In the channel reservation phase, the source reserves the channel and identifies the cooperators through their control packet responses. In the data transfer phase, the source transmits a data packet (DATA) attaching a list of cooperators sequenced by closeness to the destination. Then, referring to the list, the destination requests retransmission of erroneous packets to cooperators in the closest-one-first manner. Meanwhile, the cooperators store the data packet (denoted by xDATA in Figure 2) that is overheard during the source's transmission. The parameters carried by packets are denoted inside speech bubbles as shown in Figure 2 and the details along with time parameters like w_ACK and w_DATA are explained in the corresponding subsections. Table 1 defines some of the parameters in advance to make it easier to understand subsequent sections.
## 3 Methodology  
Figure 1 illustrates the methodology applied for establishing and assessing the prediction performance of the FV prediction models proposed.  
A preprocessing operation was conducted on the compiled dataset using the Mahalanobis distance (MD) technique to objectively detect and remove outliers (see Section 3.1). The screened and retained data were then divided into two subsets: one for model training (90% of the data) and one for model testing (10% of the data), followed by normalization. Machine learning (ML) algorithms were fine-tuned and applied to the training data. The trained models were tested on unseen data to evaluate their performance and assessed using overfitting analysis, scoring, and robustness analysis to select the best-performing model. Additionally, Shapley additive explanations (SHAP) analysis was used to assess feature importance, while partial dependence plots (PDF) were used to reveal the marginal effects of individual features, providing further insights into the best-performing model. Once the best predictive model was identified, it was applied to unseen data from a different well within one of the studied fields to further evaluate its practical generalizability  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Properties** & **Maximum** & **Minimum** & **Average** & **Skewness** & **Kurtosis** \\ \hline FD, pcf & 148 & 70 & 83.8477 & 2.6358 & 6.4458 \\ \hline MFV, sec/quart & 78 & 33 & 46.0324 & 1.7428 & 5.5073 \\ \hline FV, mL & 75.5 & 0.4 & 9.2294 & 3.5018 & 14.8659 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistical characterization of the data entries in order to forecast the FV.  
Figure 1: Flowchart of the procedure applied for developing simple and hybrid ML models to predict FV in DF from two easily measured input variables, FD and MFV.  
and reliability. Appendix A.1 provides a detailed description of ML and optimization algorithms used, developed, and evaluated.
## Materials and Methods
### IMERG and CMAGrid Precipitation  
This study considers one satellite-based precipitation estimate and one in-situ rain gauge-based precipitation. Integrated Multi-Satellite Retrievals for GPM (IMERG) [66] is a global high-resolution (0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\)) gridded precipitation dataset that is created by combining information of the GPM satellite constellation. The latest version of IMERG (Version 06) is used in this study, which is bias-corrected by climatological gauge. IMERG is available from 2000 to the present and can be accessed from [[https://gpm.nasa.gov/data/imerg](https://gpm.nasa.gov/data/imerg)]([https://gpm.nasa.gov/data/imerg](https://gpm.nasa.gov/data/imerg)) (accessed on 11 July 2022). The CMAGrid is an official interpolation precipitation grid (0.50\({}^{\circ}\)\(\times\) 0.50\({}^{\circ}\)) published by the China Meteorological Administration (CMA) [67], which is interpolated by ANUSPLIN based on Thin Plate Spline (TPS) algorithm and the latest compilation of China's surface high-density stations (2472 national meteorological observation stations). CMAGrid is available from 1961 to the present and can be accessed from [[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)) (accessed on 11 July 2022).
## 3 Field Application
### Operation of Automatic Wireless Monitoring System  
Figure 9 is a screen capture showing the actual experience of measured data stored on the server through the managing program. When the sensor is selected from the sensor list, the information of the temperature and column shortening (or axial strain) of the vertical member in a building can be checked in real-time through tables and graphs. In addition, the data can be checked over a specified range by adjusting the period of measuring, and be stored as a file, allowing easy analysis of long-term tendencies. Information on each sensor can be checked, permitting convenient comparison and analysis of measured values for each sensor.  
Figure 9: Screen capture showing measured data stored on the server through web-based managing program.  
The screen captures showing the configuration of sensors and nodes installed at the 10 th floor of towers 1 and 2 are shown in Figure 10. The history of vertical shortenings measured at the 10 th floor of towers 1 and 2 over 88 days is shown Figure 11. The sensors installed on the 10 th floor were manually measured at the beginning of the measurement. Thus, the strain data in Figure 11 did not start at zero. Figure 11 shows that the strains temporarily increase or decrease during a few days. This phenomenon can be attributed to the influence on temperature and temporary construction loads, such as construction equipment and materials. In case of this study, there was no correlation between the measured temperature and the temporary strain fluctuations.  
Figure 10: Location of sensors, sensor nodes, and master node installed at 10 th floor of towers 1 and 2. (**a**) 10 th floor of tower 1; (**b**) 10 th floor of tower 2.  
Theoretically, there is no correlation among the sensors located in different buildings under construction because the shortening of vertical members is dependent on the respective applied loads. However, the strains measured by the sensors on the 10 th floor of towers 1 and 2 increased during the elapsed time. A similar tendency for towers 1 and 2, which may be attributed to the similarity between the construction schedules of towers 1 and 2, is shown in Figure 11. A correlation can theoretically exist between the shortening of the columns on the 10 th and 25 th floors in the same building. However, the proposed automatic sensing system was not used to obtain simultaneous measurements in this study because of the client's financial demands. The wireless sensing units employed for the automatic measurements on the 10 th floor of towers 1 and 2 were removed after 88 days of measurements and were re-installed on the 25 th floor of towers 1 and 2. In this study, the correlation between the shortening of the columns on the 10 th and 25 th floors in the same building could not be investigated.  
Figure 11: History of strains measured from sensors at the 10 th floor of towers 1 and 2. (**a**) 10 th floor of tower 1; (**b**) 10 th floor of tower 2.