esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Energy Consumption  
This network performance metric displays the total energy utilized by all of the active nodes in the network throughout each cycle. Based on simulation results, Figure 7a-c illustrate that the suggested ESO-GJO model outperforms PSO-LSA,EPOA-CHS, LEACH-IACA, and LEACH-ANT. By taking the objective function's energy impact into account, the ESO-GJO algorithm reduces network energy consumption and lengthens the network's lifespan.  
Figure 7: Energy consumption in each round. Scenarios with (**a**) 100 nodes; (**b**) 200 nodes; (**c**) 300 nodes.
## 4 Energy and Power Optimization Allocation  
In practical ISAL networks, network resources such as energy and bandwidth are often constrained. Therefore, in order to improve the accuracy of localization and sensing as much as possible, it is necessary to optimize the allocation of network resources. In this section, we mainly discuss the optimal allocation of power in the single-slot ISAL networks, as well as the optimal allocation of energy between time slots in the multi-slot ISAL networks. We propose two resource optimization schemes--a step-by-step scheme and an integrated scheme--and summarize the suitable scenarios for each of them.  
Firstly, we introduce the meanings of the step-by-step and the integrated scheme, respectively. For an ISAL network, assuming that the parameters are \(\boldsymbol{\theta}=[\boldsymbol{\theta}_{1}^{\mathrm{T}},\cdots,\boldsymbol{ \theta}_{N}^{\mathrm{T}}]\), in the integrated optimization scheme, we simultaneously optimize and allocate resources to all of the N variables in \(\boldsymbol{\theta}\); in the step-by-step optimization scheme, we only optimize and allocate resources to one single variable in \(\boldsymbol{\theta}\) and apply the resource optimization allocation result of the previous variable as prior knowledge to the optimization of the next variable. This converts the multi-variable optimization problem into multiple single-variable optimization problems and solves them step-by-step, which can avoid the optimization difficulties caused by dealing with multiple variables; however, it also comes at a cost of high time complexity.  
Based on the statement above, we need to minimize the time complexity of the step-by-step scheme when dealing with multi-variable optimization problems such as those in ISAL networks. In addition to designing optimization algorithms with low time complexity, this article explores the resource allocation of ISAL networks with different topologies and finds the regularity of energy allocation for step-by-step optimization schemes in the networks. This provides some theoretical reference value for practical step-by-step optimization schemes in ISAL networks. In addition, this article also analyzes the energy allocation between time slots in the dual-slot ISAL networks.
###### Abstract
Using direct georeferencing, camera positions are directly measured, generally using GNSS or RTK GNSS, while information on camera attitude may be provided by an IMU or estimated using tie points in a bundle block adjustment [21; 22; 23; 24]. Surprisingly, most of the applications of direct georeferencing concern UAV photogrammetry (GNSS-assisted aerial triangulation), whereas it is certainly more challenging to accurately position a moving platform. To our knowledge, only one previous attempt [25] was made, in an urban environment, to use terrestrial GNSS-assisted photogrammetry without GCP or using IMU. The system they present is made of a GNSS receiver rigidly tied to a pre-calibrated photogrammetric camera. Besides, the approach is complicated by the radial eccentricity existing between the GPS antenna and the camera, which must be accurately determined and required a total station and a retroreflective prism. All of this contributes to limiting the applicability of their approach. The present article describes a new method of RTK GNSS-assisted terrestrial SfM photogrammetry, which does not require GCPs and can be easily and efficiently implemented in a variety of situations. The method was specifically developed to simplify SfM photogrammetric surveys in natural environments. It is tested here for coastal cliff monitoring, while using both a Reflex camera and a Smartphone camera. The quality of the reconstructions is assessed by comparison to a synchronous TLS acquisition.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
###### Abstract  
In recent years, routing problems have attracted significant attention in the fields of operations research and computer science due to their fundamental importance in logistics and transportation. However, most existing learning-based methods employ simplistic context embeddings to represent the routing environment, which constrains their capacity to capture real-time visitation dynamics. To address this limitation, we propose a deep reinforcement learning-based decision-making framework (DRL-DM) built upon an encoder-decoder architecture. The encoder incorporates a batch normalization fronting mechanism and a gate-like threshold block to enhance the quality of node embeddings and improve convergence speed. The decoder constructs a dynamic-aware context embedding that integrates relational information among visited and unvisited nodes, along with the start and terminal locations, thereby enabling effective tracking of real-time state transitions and graph structure variations. Furthermore, the proposed approach exploits the intrinsic symmetry and circularity of routing solutions and adopts an actor-critic training paradigm with multiple parallel trajectories to improve exploration of the solution space. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate that DRL-DM consistently outperforms heuristic and learning-based baselines, achieving up to an 8.75% reduction in tour length. Moreover, the proposed method exhibits strong generalization capabilities, effectively scaling to larger problem instances and diverse node distributions, thereby highlighting its potential for solving complex, real-life routing tasks.  
attention mechanism; deep reinforcement learning; encoder-decoder; routing +
Footnote †: journal: Applied sciences
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis
### Analysis of Imperceptibility
#### 5.2.2 Model Translation and Rotation  
In the practical use of 3D models, operations such as translation and rotation are often performed. Therefore, performing translation and rotation attack testing was a significant factor in detecting the robustness of watermarking algorithms. In the experiment, the watermarked data was translated as a whole, and the watermark was detected. The results are shown in Table 5 where it can be seen that the watermark information can be completely detected.
## 3 Results and Discussion
### Antioxidant Activities of Rice Stem Cell Extracts  
The diameter of the discharge region of the \(\mu\)-DBD plasma device with the duty cycle used in this study was 60 mm; the plasma characteristics were reported in a previous paper [26]. Through the previous studies, we were able to confirm that the plasma device in this study used about 1.27 kV and 50 mA of root mean square voltage and current and that the electrical discharge energy delivered to the rice stem cells in a 1 s treatment was 1.55 W [26].  
Figure 3: Measured O\({}_{3}\) and NO\({}_{2}\) concentrations according to the time variant. Changes in O\({}_{3}\) (black line) and NO\({}_{2}\) (blue line) concentrations during plasma discharge 15 min.  
Figure 2: Frequency of callus induction from rice embryonic cells.  
To test whether plasma treatment could affect the physiological aspects of RSCE, we analyzed the total phenolic compound content of rice stem cells. The content of phenolic compounds is closely related to their antioxidant activity [29; 30; 31]. The total phenol content of the rice stem cell extract was analyzed using the Folin-Ciocalteu reagent method, and the TPC content was compared according to the plasma treatment time (Figure 4). The concentrations of total phenolic compounds in the rice stem cells showed a tendency to increase with plasma treatment (Figure 4). As the N\({}_{2}\) plasma treatment time increased, the polyphenol concentration also increased. The total antioxidant activity of the callus extract treated with plasma for 1 min (7.2 mg/g) and for 3 min (7.6 mg/g) was increased by about 1.5 times compared with the control (5 mg/g). Antioxidant activity increased in proportion to plasma treatment time up to 3 min, but in the 5 min (5.2 mg/g) treatment, antioxidant activity decreased compared with the 1 and 3 min treatments and the content was similar to the untreated control. Additionally, there was no significant difference between the 1 and 3 min plasma treatments.  
The total phenol content (TPC) of the rice stem cell extract was analyzed using the Folin-Ciocalteu reagent method, and the TPC content was compared according to the plasma treatment time. Absorbance was measured at 765 nm using a UV-vis spectrophotometer. A standard curve was obtained using the absorbance values of gallic acid (reference polyphenol), and the total phenol amount in each sample was estimated as gallic acid equivalents (mg GAE/g).
## 2 Materials and Methods  
### Study Area and Experimental Design  
The trial was conducted in the experimental station of the University of Bari (Italy) located at Policoro (40\({}^{\circ}\)10\({}^{\prime}\)20\({}^{\prime\prime}\) N; 16\({}^{\circ}\)39\({}^{\prime}\)04\({}^{\prime\prime}\) E; altitude: 15 m above sea level). The soil texture was classified as silt loam (sand 8%, silt 68%, clay 24%), according to the USDA [19]. Since 2005, a two-year rotation of durum wheat with faba bean in a split-block design with three field replications has been introduced. Treatments were as follows: i) no tillage and no fertilization (NT), ii) NT and crop fertilization (30 kg P\({}_{2}\)O\({}_{5}\) ha\({}^{-1}\): NTF), iii) minimum tillage (20 cm deep subsoiling in late August and 15 cm deep disk narrowing in November) and no fertilization (MT), iv) MT and crop fertilization (MTF), v) conventional tillage (35 cm deep moldboard blowing in late August and 15 cm deep disk narrowing in November) and no fertilization (CT), and vi) CT and crop fertilization (CTF). More details about treatments and soil properties are reported elsewhere [20; 21].  
After more than a decade of trial, each faba bean plot (30 \(\times\) 30 m) was sampled in July 2017 at three different depths (0-30, 30-60, and 60-90 cm) using an auger, after the harvest, and the removal of aboveground crop residues. Due to the soil homogeneity, nine sub-samples have been collected from each plot using a grid sampling scheme.
## 1 Introduction
Moreover, since the FD mode significantly degrades the system performance, besides effective SIC techniques, there should be other solutions such as power allocation to compensate for this degradation. Motivated by the previous works, in this paper, we aim to derive the exact closed-form expression of the SEP of the SM-FD relay system with DF protocol applied at the relay to enhance the system performance. Based on the derived expression of SEP, we can determine the optimal power allocation for the FD relay to reduce the RSI impact on the SEP performance. So far, this is the first work that successfully derives the exact closed-form expression of SEP and use it for optimizing the power allocation for SM-MIMO-FD relay systems. The main contributions of this paper can be summarized as follows:  
* We analyze the SM-MIMO-FD relay system where SM is used at the source and the relay nodes under the impact of the RSI caused by the imperfect SIC. Unlike previous works, we derive the exact closed-form expression of SEP for the system over the Rayleigh fading channel. * We propose an algorithm to calculate the optimal transmission power of the FD relay. Based on this algorithm, we obtain the optimal power allocation for the considered system. The proposed optimal transmission power algorithm significantly improves the SEP performance, especially in the low SNR region. Additionally, using the derived expression of SEP, we can also examine the influences of the number of transmitting/receiving antennas and the RSI on the system performance in the case with and without optimal power allocation. The rest of this paper is organized as follows. Section 2 presents the system model. Section 3 provides the detailed derivations of the closed-form expression of SEP. The optimal power allocation algorithm for the FD relay is developed in Section 3. Section 4 presents the analytical and simulation results for performance evaluation. Finally, Section 5 concludes the paper.
# Coded-GFDM for Reliable Communication in Underwater Acoustic Channels
## 3 System Architecture
### GFDM Transceiver Architecture  
The proposed GFDM model is presented in Figure 2. Suppose the input bit stream is passed through a mapper to create multiple symbols in \(2^{\mu}\)--QAM constellations with \(\mu\) being the order of modulation. The mapped symbols are represented by \(\overset{\rightarrow}{c}\). The total number of carriers used are denoted by \(K\) and \(M\) represent the sub-symbols. The serial stream is then converted to \(N\) parallel streams represented [8] by \(C=\left(\overset{\rightarrow}{c}_{0}^{T},\ldots,\overset{\rightarrow}{c}_{M -1}^{T}\right)^{T}\) where \(N=K\times M\). The output data then passes through the pulse shaping filter followed by the IFFT operation defined as:  
\[g_{k,m}[n]=g[(n-mK)\ mod\ N]e^{i2\pi\frac{f}{k}n} \tag{3}\]  
It is pertinent to mention that the pulse filter expressed above as \(g_{k,m}[n]\) is circularly shifted in the time domain while using a modulo operation. While several pulse-shaping filters exist in literature, we only use Raised Cosine (RC) filters in this work. The frequency response of an RC filter is given by:  
\[G_{RC}[f]=\frac{1}{2}\bigg{[}1-\cos\bigg{(}\pi\text{lin}_{\text{a}}\bigg{(} \frac{f}{M}\bigg{)}\bigg{)}\bigg{]} \tag{4}\]  
The transmitted signal can be represented as follows:  
\[x[n]=\sum_{k=0}^{K-1}\sum_{m=0}^{M-1}g_{k,m}[n]c_{k,m}\ \ n=0,1,\ldots,N-1 \tag{5}\]  
The above expression can also be written in a matrix form as follows:  
\[\overset{\rightarrow}{x}=A\overset{\rightarrow}{c} \tag{6}\]  
where \(A\)[30] has dimensions of \(KM\times KM\) dimensions with each vector in columns given \(\overset{\rightarrow}{8}_{k,m}\) represented by \((g_{k,m}\ [n])^{T}\) and vector \(\overset{\rightarrow}{c}\) has dimensions of \(KM\times 1\). Equation (5) contains A such that:  
\[A=\left(\overset{\rightarrow}{g}_{0,0}^{\rightarrow}\cdots\overset{ \rightarrow}{g}_{K-1,0}^{\rightarrow}\overset{\rightarrow}{g}_{0,1}\cdots \overset{\rightarrow}{g}_{K-1,M-1}\right) \tag{7}\]  
Figure 2: Proposed coded-GFDM transceiver architecture.  
This is followed by the addition of a cyclic prefix and the signal is then converted to analog for transmission. At the receiver side, the received signal goes through an analog to digital converter, and then the cyclic prefix is removed. FFT operation is performed to obtain:  
\[\overrightarrow{y}=HA\overrightarrow{c}+\overrightarrow{n} \tag{8}\]  
If the CSI is assumed to be known, we multiply each side with \(H^{-1}\) and the estimated symbols \(\overrightarrow{c}\) are represented as:  
\[\overrightarrow{c}=Z\widetilde{y} \tag{9}\]  
where \(Z\) is a \(KM\times KM\) matrix and its computation influences the choice of receiver design to be implemented. We consider two approaches, a matching filter (MF) receiver and a zero-forcing (ZF) receiver.
## 3 Post-Transcriptional and Translational Regulation in Integrons  
While the transcription regulations of integrase and gene cassettes have been widely approached, there is much less information concerning the translational mechanisms within integrons. It will be discussed below the mechanisms of post-transcriptional and translational control of gene cassettes unraveled, so far, in class 1 integrins, which involve translational coupling, translational attenuation, and regulatory RNAs.
## 4 Conclusions  
In this study, we present improved tensile shear load results of bonded CFRP and pre-inscribed patterned galvanized steel. We first carried out a parametric study to optimize the laser processing parameters for joining CFRP and zinc-coated steel for non-patterned cases. Then, using the optimized fusion bonding parameters, we investigated the effect of the pre-inscribed rectangular grid patterns on the surface of zinc-coated steel prior to laser fusion bonding.  
The following conclusion can be made from this study:  
1. The optimized laser fusion bonding parameters for this study were found to be \(P_{1}\) = 0.7 kW, \(z_{1}\) = 10 mm, \(d_{1}\) = 3 mm, \(N_{1}\) = 2, and \(P_{\mathrm{c}}\) = 5 MPa.
2. The number of scan passes \(N_{2}\) during the surface patterning process results in controllable pattern depth but uncontrollable hump height and pattern width.
3. The optimal processing parameters for the pre-inscribed rectangular pattern on the zinc-coated steel were found to be \(P_{2}\) = 0.45 kW, \(d_{2}\) = 500 \(\upmu\)m, and \(N_{1}\) = 1.
4. An increased tensile shear load is expected for all the grid pitch sizes with a maximum increase value of 100% over the sample with no patterns (4 kN to 8 kN).
5. The increased tensile shear load for joints with the pre-inscribed patterns is the result of the increased bond surface area between the PEEK matrix and the zinc-coated steel.
6. Increasing \(N_{2}\) values decrease the tensile shear load of the joints due to an increased amount of recondensed particles and the formation of cavities.  
We hope that the results presented in this study advance the efficiency and the effectiveness of joining CFRP and zinc-coated steel, leading to the wide use of CFRP in many areas of engineering.  
Figure 9: The failure surfaces and the tensile shear load test results of joints between CFRP and zinc-coated steel formed by four different methods: self-piercing rivet, laser fusion bonding with and without the pre-inscribed patterns, and adhesive bonding.  
**Author Contributions:** Conceptualization, D.-H.K.; Methodology, N.-K.J. and T.-E.J.; Software, N.-K.J. and T.-E.J.; Validation, N.-K.J. and T.-E.J.; Formal Analysis, D.-H.K.; Investigation, N.-K.J. and T.-E.J.; Resources, D.-H.K.; Data curation, D.-H.K. and J.K.; Writing--Original Draft Preparation, D.-H.K. and J.K.; Writing--Review and Editing, J.K.; Visualization, N.J. and T.-E.J.; Supervision, D.-H.K.  
**Funding:** This work was supported by the Korea Institute of Industrial Technology and the Ministry of Trade, Industry, and Energy of the Republic of Korea and by the Ministry of Science and ICT (No. 2019M3A6B3032325, 2016R1D1A1B03935743, 2015R1A6A1A03031833).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Fuzzy Impedance Controller
### Fuzzy Logic Design  
The main objective of the fuzzy impedance controller is to estimate the impedance parameter \(B\). Based on the learning law of ILC in Equation (7), the fuzzy system is designed consisting of two inputs: the gripping force error \(\Delta F_{e}\) and the closing speed \(\dot{X}_{e}\). The output is the damping \(B\). The parameter \(K\) is calculated by Equation (2) after the damping \(B\) is estimated.  
Figure 4 shows the triangular membership functions of inputs and output. The number of fuzzy regions of inputs \(\Delta F_{e}\), \(\dot{X}_{e}\), and output \(B\) is \(i\), \(j\), \(k\), respectively. The inputs \(\Delta F_{e}\) and \(\dot{X}_{e}\) are recorded at the time when the fingers just touched the object for the first time, so these parameters have negative values and magnitude depending on the closing speed and object hardness. The output \(B\) is always positive. Based on the dataset collected from the data collection as presented in Section 4.1, the maximum magnitudes of the inputs and output are determined to set the values of \(Am\), \(Bm\), and \(Cm\). The number of fuzzy regions \(i\), \(j\), and \(k\) are experimentally adjusted so that the fuzzy controller achieves high accuracy.  
The fuzzy impedance controller employs Mamdani If-Then rules as the following form:  
The \(r^{th}\) rule: If \(\Delta F_{e}\) is \(A_{u}\) and \(\dot{X}_{e}\) is \(B_{v}\) then \(B\) is \(C_{v}\), where \(u=1,\ldots,i\); \(v=1,\ldots,j\); \(w=1,\ldots,k\)  
The dataset will be preprocessed to filter noise based on the maximum overshoot \(F_{p}\) and the final force error \(F_{fc}\). Its final data records will be calculated to create the rule base.  
Figure 4: Membership functions of inputs and output.
# Design of Shadow Filter Using Low-Voltage Multiple-Input Operational Transconductance Amplifiers
## 2 Proposed Circuit
### Proposed First Shadow Filters
The natural frequency (\(\omega_{o}\)) and the quality factor (\(Q\)) of filters are expressed as follows:  
\[\omega_{o}=\sqrt{\frac{g_{m1}g_{m2}}{C_{1}C_{2}}} \tag{12}\]  
\[Q=(1+g_{m3}R_{1})\sqrt{\frac{g_{m2}C_{1}}{g_{m1}C_{2}}} \tag{13}\]  
It can be observed that the quality factor can be controlled via \(g_{m3}R_{1}\). According to Equation (4), \(g_{m1}\), \(g_{m2}\), and \(g_{m3}\) can be individually controlled via the bias currents \(I_{set1}\), \(I_{set2}\), and \(I_{set3}\) corresponding to their respective \(g_{mj}\) (\(j\) = 1, 2, 3). For independent control of the parameters \(\omega_{o}\) and \(Q\), capacitors \(C_{1}\) and \(C_{2}\) can be fixed. The parameter \(\omega_{o}\) can be controlled by \(I_{set}\) (where \(I_{set}=I_{set1}=I_{set2}\)), while the parameter \(Q\) can be independently adjusted via \(I_{set3}\) or \(R_{1}\). However, varying \(\omega_{o}\) using \(I_{set}\) may impact the stability, power consumption, and dynamic range of the original second-order filter. Therefore, the primary purpose of the first shadow filter is to control only the parameter \(Q\) via an external amplifier. If independent control of both \(Q\) and \(\omega_{o}\) is required, this can be achieved using the second  
Figure 4: Proposed first shadow filter using MI-OTAs. proposed shadow filter. In this case, varying both \(\omega_{o}\) are \(Q\) will only affect the power consumption of the filter. Equations (9)-(11) demonstrate that varying the filter response's quality factor by a factor of \(1+g_{m3}R_{1}\) also scales their transfer functions by the same factor. This adjustment enables the achievement of a unity passband gain. Unlike previous shadow filters (e.g., [8; 15; 16; 17; 20; 21; 23; 25; 26; 29; 31; 32]), in which variations in the quality factor resulted in changes to the passband gain of the transfer function, the proposed design maintains a consistent passband gain despite such variations.
## 5 Results
Figure 11: The image array is acquired by the laboratory macroscopic far-field Fourier ptychography setup with scanning pupils of 4.5 and 2 mm. (**a**) the image array with a scanning step of 4.5 mm and 61% overlap ratio; (**b**) the image array with a scanning step of 2 mm and 84.5% overlap ratio. Figure 12: **(Top panel)** The recovered experimental data when the camera moved 4.5 mm with a red frame each time. (**Bottom** panel) The recovered experimental data when the camera moved 2 mm with a blue frame each time. (**a1**) the reconstructed HR intensity image of the object with scanning step 4.5 mm; (**b1**) the zoomed-in HR intensity image of Group 9; (**c1**) the reconstructed phase of the object with scanning step 4.5 mm; (**d1**) the zoomed-in phase image of Group 9; (**a2**) the reconstructed HR intensity image of the object with scanning step 2 mm; (**b2**) the zoomed-in HR intensity image of Group 17; (**c2**) the reconstructed phase of the object with scanning step 2 mm; (**d2**) the zoomed-in phase image of Group 17. To allow a clearer comparison of the image quality of the high-resolution intensity images in the three cases, we compared the resolution and calculated the corresponding signal to noise ratio (SNR) [14], as shown in Table 1. As we can tell from the table, the partition reconstruction can improve the image resolution and the SNR at the same time.