esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Simulation Setup  
The underlying aim of the study is to efficiently and precisely locate any defect or damage, such as cracks in steel, using simulation, which imitates the photoacoustic imaging technique. The simulation setup is shown in Figure 3. The target object is a steel cylindrical pipe with a base radius of 10 mm, a thickness of 1 mm, and an axis length of 100 mm, as depicted in Figure 3a. A crack is formed in the center of the steel rod. The edge diagram of the steel pipe under observation and the corresponding crack are shown in Figure 3b. Figure 3c shows the cross-sectional view of the steel pipe with global origin. The radii of the inner and outer steel surfaces are 9 and 10 mm, respectively. The crack is 2 mm wide and 15 mm long, as displayed in the two-dimensional surface view in Figure 3d. The mesh size is an important parameter in FEM simulations; generally, a fine mesh and a smaller time step result in more accurate results, but are associated with high processing costs in terms of computing time and resources. To obtain adequate accuracy and high efficiency, the maximum element size and time step are adopted according to Reference [40], where they can be mathematically expressed as:  
\[\Delta I=\frac{\lambda_{min}}{20}, \tag{1}\]  
where \(\Delta I\) is the element size and \(\lambda_{min}\) is the minimum wavelength of the propagating wave. The simulation time step is defined as:  
\[\Delta t=\frac{1}{20f_{max}}, \tag{2}\]  
where \(\Delta t\) is the simulation time step and \(f_{max}\) is the highest frequency of interest. Table 1 lists the elastic and mechanical properties of the steel for the simulation settings. Abaqus CAE is utilized to develop the 3D model of the setup, and cracks are localized to verify the proposed technique. A mechanical force at the excitation point actuates acoustic waves to imitate the photoacoustic imaging modality and applies a load in the simulation. At the designated sensing points, the generated acoustic waves that travel through the pipe are measured. Data at sensing points are logged and saved for further analyses. The scheme of this study is to develop a numerical model of the data acquired through the PAI setup of steel pipes in pristine conditions with cracks at different locations. The key feature of this idea is to generate ample responses of pipes with known crack locations to approximate the entire pipe by a combination of these responses. Then, when a new response with an unknown damage location is presented to the designed model, it estimates the crack location by interpolating the best match of the data with the existing information.  
Figure 2: Block diagram of a photoacoustic imaging system.
###### Abstract  
In recent years, climate change has greatly affected agricultural activity, sustainability and production, making it difficult to conduct crop management and food security assessment. As a consequence, significant changes in agricultural land and land cover (LC) have occurred, mostly due to the introduction of new agricultural practices, techniques and crops. Earth Observation (EO) data, cloud-computing platforms and powerful machine learning methods can certainly support analysis within the agricultural context. Therefore, accurate and updated agricultural land and LC maps can be useful to derive valuable information for land change monitoring, trend planning, decision-making and sustainable land management. In this context, this study aims at monitoring temporal and spatial changes between 2001 and 2021 (with a four 5-year periods) within the Chi River Basin (NE-Thailand). Specifically, all available Landsat archives and the random forest (RF) classifier were jointly involved within the Google Earth Engine (GEE) platform in order to: (i) generate five different crop type maps (focusing on rice, cassava, para rubber and sugarcane classes), and (ii) monitoring the agricultural land transitions over time. For each crop map, a confusion matrix and the correspondent accuracy were computed and tested according to a validation dataset. In particular, an overall accuracy \(>\) 88% was found in all of the resulting five crop maps (for the years 2001, 2006, 2011, 2016 and 2021). Subsequently the agricultural land transitions were analyzed, and a total of 18,957 km\({}^{2}\) were found as changed (54.5% of the area) within the 20 years (2001-2021). In particular, an increase in cassava and para rubber areas were found at the disadvantage of rice fields, probably due to two different key drivers taken over time: the agricultural policy and staple price. Finally, it is worth highlighting that such results turn out to be decisive in a challenging agricultural environment such as the Thai one. In particular, the high accuracy of the five derived crop type maps can be useful to provide spatial consistency and reliable information to support local sustainable agriculture land management, decisions of policymakers and many stakeholders.  
agriculture; land cover; change detection; Earth Observation; multi-temporal image; Landsat; Google Earth Engine (GEE); random forest 2023154339[[https://doi.org/10.3390/rs15174339](https://doi.org/10.3390/rs15174339)]([https://doi.org/10.3390/rs15174339](https://doi.org/10.3390/rs15174339))
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
###### Abstract
The rest of this paper is organized as follows: Section 2 provides an overview of related works, Section 3 presents the proposed SET algorithm, Section 4 illustrates the performance evaluation and the benchmark of the proposed SET algorithm, and finally, Section 5 concludes the entire work.
## 3 MHSA-1D CNN-Bi-LSTM Interference Cancellation Scheme
### MHSA-1D CNN-Bi-LSTM Interference Cancellation Algorithm  
The signal sequence typically allows for extracting only a limited number of features by a single model. To effectively mitigate the ISI introduced by the FTN, we propose a hybrid model, namely MHSA-1D CNN-Bi-LSTM, which combines 1D CNN, a Bi-LSTM network, and an MHSA mechanism. The network structure is illustrated in Figure 5.  
Initially, the FTN signal sequence \(S_{FTN}(t)\) is utilized as input for the neural network. A 1D CNN is utilized to extract and organize feature vectors associated with information such as ISI and noise into sequences. Subsequently, the Bi-LSTM network is split into two independent LSTM networks, where these identical LSTMs generate forward and reverse hidden state sequences from the input sequences. The LSTM selectively forgets information through the forgetting gate using the previous time node's output \(h_{format}[t]\) and the current time node's input \(x_{t}\). It then determines which new information will be stored in the current candidate memory cell \(c_{format}[t]\) to obtain the latest memory cell \(c_{backward}[t]\). The predecessor memory cell \(c_{backward}[t-1]\) is updated accordingly. By combining output value \(o_{t}\) of the output gate with the current moment memory cell state \(c_{backward}[t]\), we get the LSTM's output value \(h_{backward}[t]\). This is followed by a linear transformation with a weight matrix dimension matching that of the input data dimension, ensuring consistent dimensions for subsequent operations. An MHSA layer is introduced for a \"head-splitting\"  
Figure 4: Calculation process of the double-head self-attention mechanism.  
Figure 5: The structure of MHSA-1D CNN-Bi-LSTM network (Blue represents the input and output signals; purple represents the 1D-CNN; light yellow represents the normalization layer; green represents the Bi-LSTM network; red represents the multi-head self-attention mechanism layer; and orange represents the fully connected layer).  
operation after this step. Through the MHSA mechanism, distinct weights are assigned to each LSTM unit's output \([h_{1},h_{2},\ldots,h_{T}]\), enabling selective attention to different time steps during sequence processing. This facilitates the efficient selection of prediction information relevant to achieving current task goals. Finally, operations are performed within each self-attention mechanism and their outputs are concatenated for linear transformations before normalization.
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 4 Proposed Algorithm for Dynamic UE Scenario
### MDP Formulation
Action: The action \(a_{t}\) at the time step \(t\) is constructed as the optimized variables of problem \((P1)\), i.e., the active beamforming, \((\mathbf{W})_{t}\), and the passive beamforming vectors of all deployed IRSs, \(\{(\{\mathbf{\theta}_{t}\}_{t=1}^{L})_{t}\}\). Thus, the action can be defined as \[a_{t}=\{(\mathbf{W})_{t},(\{\mathbf{\theta}_{t}\}_{t=1}^{L})_{t}\}\ \in A.\] (19)  
Figure 3: Designed model-based framework: (**a**) DRL framework with DDPG agent; (**b**) proposed DDPG actor–critic layers. Correspondingly, the instant action dimension is simply \(2(MK+LN)\), where the complex-valued form is considered. 3. Reward: Since the total transmit power minimization at the source is the objective function of the presented optimization problem, linking the objective function and the reward will permit the achievement of MDP's goal of increasing long-term rewards. Accordingly, the instant reward feedback by the environment in this model is defined as the overall energy efficiency, i.e., the ratio between the total achieved rate and total transmitted power. Considering the SINR constraints, the SINR of each UE should be above the SINR threshold to satisfy the required QoS threshold. When the resulting UE's SINR is below the threshold, a penalty is applied to help the agent to alter the inappropriate beamforming. In our designed reward function, we impose the penalty value as zero or can simply assume that an unsuitable action leads to the Inf value of the corresponding total transmit power. Hence, the instant reward at each time step \(t\) can be calculated as follows: \[r_{t}= \begin{cases}0,&\text{if }SINR_{k}<\gamma_{k},\forall k\in\mathcal{K},\\ \frac{\sum_{k=1}^{K}R_{k}}{\sum_{k=1}^{K}\|\boldsymbol{w}_{k}\|^{2}},&\text{ otherwise.}\end{cases} \] (20)
4. Policy: The policy adopted in this work is a deterministic policy that learns to select the best possible actions of active and passive beamforming depending on different states, as will be discussed in the following section.
## 4 Analysis
### Assessment of Robustness of SD Model Outputs
\begin{table}
\begin{tabular}{c c c} \hline
**Target Model** & **Responsive** & **Sensitivity Results 95\% Confidence** \\
**Variables** & **Parameters** & **Interval** \\ \hline \multirow{2}{*}{Adopt unsuitable equipment:} & Perception, Max number of accidents, & \\  & Probability Valve Closed & 0.92 \(\pm\) 0.004 (dimensionless) \\ \hline \multirow{2}{*}{Improper safety organisation} & Perception, Max number of accidents, & \\  & Probability Valve Closed & 1.29 \(\pm\) 0.233 (dimensionless) \\ \hline \multirow{2}{*}{Inefficient timely control} & Perception, Max number of accidents, & \\  & Probability Valve Closed & 0.25 \(\pm\) 0.212 (dimensionless) \\ \hline \multirow{2}{*}{Inefficient emergency plan} & Perception, Max number of accidents, & \\  & Probability Valve Closed & 1.24 \(\pm\) 0.226 (dimensionless) \\ \hline \multirow{2}{*}{No check rules} & Perception, Max number of accidents, & \\  & Probability Valve Closed & 0.29 \(\pm\) 0.211 (dimensionless) \\ \hline \multirow{2}{*}{Not comply with instructions} & Perception, Max number of accidents, & \\  & Probability Valve Closed & 0.30 \(\pm\) 0.219 (dimensionless) \\ \hline \multirow{2}{*}{Not obey standards} & Perception, Max number of accidents, & \\  & Probability Valve Closed & 1.29 \(\pm\) 0.236 (dimensionless) \\ \hline \end{tabular}
\end{table} 
Table 8: Results of Monte Carlo Sensitivity analysis of responsive parameters. Results from this analysis indicate that all targets show similar ranges of variations around their own average value, except 'Adopt unsuitable equipment' which has a very low variability of 0.04. The results of the MC simulations show that all the target variables exhibit either a low or moderate response to changes in the responsive parameters. This indicates a high degree of robustness in the SD model and hence, the model outcomes can be accepted with confidence.
###### Abstract  
Chinese characters carry a wealth of morphological and semantic information; therefore, zero-shot Chinese character recognition with the morphology of Chinese characters has drawn significant attention. The previous methods are mainly based on radical-level decomposition or stroke-level decomposition, which usually cannot capture adequately the structural and spatial information of Chinese characters. In this paper, we develop a stroke-based autoencoder (SAE), to model the sophisticated morphology of Chinese characters with a self-supervised method. Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence. This pre-trained SAE model can predict the stroke image series for unseen characters, as long as their strokes or radicals are in the training set. We have designed two contrasting SAE architectures on different forms of stroke images. One is fine-tuned on existing stroke-based method for zero-shot recognition of handwritten Chinese characters, and the other is applied to enrich the Chinese word embeddings from their morphological features. The experimental results validate that after pre-training, our SAE architecture outperforms other existing methods in zero-shot recognition and enhances the representation of Chinese characters with their abundant morphological and semantic information.  
Chinese character; self-supervised learning; zero-shot recognition; pre-training model; semantic enhancement
## 3 Results and Discussion
### Equation of Air Leakage Rate in the Tunnel Air Duct  
The leakage of the ventilation duct results in a lower supply of air volume to the working face than the air volume of the fan. In order to estimate the effective air volume of the working face, this study established three models with different hole sizes to explore the impact of air duct leakage on the tunnel air supply volume.  
Three different hole sizes were selected based on a range of around 1% air leakage rate per hundred meters. In total, 30 ventilation duct models with varying lengths of 100-3000 m were established for each type of hole. The air volume results at the end of each model have been organized, as shown in Figure 8. Each blue dot represents the calculation result of a tunnel model.  
The effective air volume estimation function is shown in Figure 8. According to the above research results, the airflow velocity of the duct hole leakage near the fan is relatively  
Figure 8: Calculated average air velocity in tunnels: (**a**) 0.08 \(\times\) 0.06 m holes; (**b**) 0.08 \(\times\) 0.10 m holes; (**c**) 0.08 \(\times\) 0.15 m holes.  
Equation (6) can be used to calculate the actual air supply of the tunnel working face during tunnel construction.  
\[y=\gamma exp\left(\frac{x}{\alpha}\right) \tag{7}\]  
Equation (7) can estimate the distance that can be ventilated based on the actual air volume of the working face. The further calculation results can serve as the basis for fan selection.  
The coefficients \(\alpha\), \(\beta\), and \(\gamma\) are parameters related to the size of duct openings. Table 2 lists the average air velocity and leakage rate for different hole sizes.  
According to Table 2, the effective air volumes on the working face for different hole sizes differ under the same fan volume. Therefore, different hole sizes lead to different air-volume losses. The increase in longitudinal dimensions has a significant impact on air leakage. Thus, materials of different strengths should be considered for different duct directions to reduce the loss of air volume.
## 4 Experiments  
First, extensive ablation studies on the object detection task using the VisDrone2019 dataset are conducted to compare the performance of the MFFCESSC-SSD with that of the conventional SSD in terms of accuracy and efficiency and to test the effect of each component. In addition, using this dataset, the MFFCESSC-SSD is compared against other algorithms such as Cascade R-CNN, CenterNet, SyNet, and so on to evaluate the improvement in object detection. Experiments are conducted on the Aerial Rat-Hole dataset (ARH2000), which was acquired by M200 UAVs (DJI-Innovations, Shenzhen, China), for April 2020 and April 2021 to further validate the MFFCESSC-SSD framework.
## 1 Introduction
First, during the penetration, the huge inertial force exerted by the impact applied to the pile and its dynamic nature strengthens the uncertainty of the plugging state and the internal stress state of the soil plug. Additionally, the huge uncertainty in the bearing capacity of the pile is also problematic after installation, which stems from the uncertainty of the stress state throughout the installation process. In this context, present standards or manuals suggest a design method that estimates the inner skin friction to be equal to the outer skin friction and assumes it to act along the entire length of the soil plug [21; 22]. These conclusions, made without consideration of the internal stress state of the soil plug, are known to be highly conservative. There are only limited studies that directly investigate the internal state experimentally [23], and numerical studies lack comprehensive validation of the pile penetration model that covers both the soil-soil and soil-pile interaction while simulating impact driving. The difficulty in experimentally measuring the internal stress condition further complicates solving the problem in this regard. Alternatively, among previous numerical study methodologies, the Finite Element Method (FEM) and the Finite Difference Method (FDM) have an inherent disadvantage as they are unsuitable for analyzing soil behavior during pile installation simulations without any additional supportive techniques [24; 25; 26; 27; 28]. Although the Discrete Element Method (DEM) can be utilized to investigate large-displacement analysis of soil behavior, previous studies have primarily focused on verifying the behavior of the pile itself, and they lack sufficient validation of the soil-pile interaction during the pile installation process [10; 29; 30; 31]. Therefore, this research aims to investigate and analyze the stress state within the soil plug of open-ended piles during impact driving using the Particle Flow Code (PFC) software (v.6.0). Specifically, it seeks to address the uncertainties and challenges associated with the plugging effect in OEPs, which significantly affect their structural integrity and load-bearing capacity. The study aims to provide insights into the internal stress conditions of the soil plug, which are crucial for accurately predicting the behavior of OEPs under various installation conditions. Moreover, the study verified the DEM model through comprehensive comparisons with the author's experimental results [32]. While the previous DEM analysis only validated the soil-soil interaction, this study enhanced the accuracy and reliability of its research results by verifying both soil-soil and soil-pile interactions. By employing PFC simulations, the research intends to advance our understanding beyond the current conservative design methods and traditional numerical models, thereby contributing to improved methodologies for the design and construction of open-ended piles.
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
## 2 FSBDG Structure of NLECSLs
### Littman Structure of NLECSLs  
In the Littman structure, as shown in Figure 2, the FSBDG position is fixed; the wavelength of the semiconductor laser can only be changed by adjusting the position of the plane mirror. The laser output beam direction is constant, the linewidth becomes narrow, but the output laser power is less than Littrow structure of ECSL. In the Littman structure, the collimated laser grazes onto the FSBDG, and the incident angle of the beam is large. Compared with Littrow structure, when the laser irradiated the grating, more diffracted lasers are generated, anyone of them laser output linewidth is narrower [16].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Central** & **Tuning** & **SMSR** & **Line Width** & **Output** & **Publication** \\
**Wavelength** & **Range** & & & **Power** & **Time** \\ \hline
1080 nm & 100 nm & - & 4.2 kHz & 83 mW & 2016 [12] \\ \hline
525 nm & 11.0 nm & - & 0.08–0.18 nm & 400 mW & 2018 [13] \\ \hline
1550 nm & 209.9 nm & 65 dB & - & 48.9 mW & 2019 [14] \\ \hline
780 nm & 3.6 GHz & 48 dB & 540 kHz & - & 2021 [15] \\ \hline \hline \end{tabular} Note: “-” denotes that the data are not available.  
\end{table}
Table 1: The performances of Littrow structure of NLECSLs.  
Figure 1: Littrow structure of NLECSLs.  
In 2018, [PERSON] et al. [17] reported a 222 nm tuning range, using a compact external cavity GaSb-based diode laser and micro-electro-mechanical system (MEMS) mirror. In the tuning range, the power range was 8-24 mW, the SMSR was 50 dB, and the mode hopping was controllable 18 GHz.  
In 2020, [PERSON] et al. [18] optimized the ECSL of 1550 nm bent waveguide based on GaSb with the MEMS with the concept of ECSL cavity, and realized the tuning range of 106 nm, covering the wavelength range from near infrared to MIR.  
In 2021, [PERSON] et al. [19] used a laser diode with a central wavelength of 2.02 \(\upmu\)m. The collimated laser beam passed the MEMS mirror at approximately the 45\({}^{\circ}\) angle. It was reflected onto the reflection grating. The arrangement of the optical components was chosen to achieve optimal illumination of the grating. In the gain chip with curved waveguide, both facets are accessible, where the laser beam is couplet out via the rear facet of the laser diode, resulting in a higher efficiency of the resonator, with an SMSR of 2.02 \(\upmu\)m and a central wavelength of 53 dB. Table 2 shows the performance of the Littman structure of NLECSLs. The Littman structure of NLECSLs provides an output beam with a stable direction. Tuning of the Littman structure of NLECSLs is realized by the rotating mirror. Since it does not change the incident angle to the grating, the direction of the output beam is stable.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Orthopedic Implant Infection Monitoring  
A planar implantable antenna for monitoring infection in an orthopedic implant is presented in Ref. [101] for the 860 to 960 MHz RFID ultra-high frequency (UHF) band, as  
Figure 17: Proposed wide-beamwidth circularly polarized implantable antenna for glucose monitoring presented in Ref. [99]. Copyright © _IEEE_.  
Figure 18: Injectable glucose sensing device with spiral antenna [100]. Reproduced with permission from an open-access article from _Scientific Reports_ (CC-BY).  
shown in Figure 19. The size of the antenna is 14 mm \(\times\) 6 mm \(\times\) 3 mm. It is fabricated on a FR4 substrate and achieves a peak gain of \(-\)22 dBi.