esa-sceva/satcom-chunk-collection huggingface dataset:
# An Initial Dot Encoding Scheme with Significantly Improved Robustness and Numbers
## 2 Coded Mark Design and Detection
### Coded Mark Detection
The final step is to decode the number of the encoded marker point by the position information of the code point. Taking a 12-bit code point as an example, as shown above Figure 2, since we have determined the number of coded bits and the location of the code point. The difference between the position information of the starting point and the position information of the center point can decode the rotation angle of the marking point, which is used to realize the rotation invariance of the article coding scheme. The first bit of the code point is in the same line as the start point and is closest to the start point. Then, the number of the marker points is calculated clockwise. Here, the decoded value is composed of a 12-bit binary number, wherein if the code point is a hollow dot, it is 1 and the solid dot is 0. Finally, we decode the data represented by the coded marker points for subsequent matching and reconstruction.
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 3 Results
### Quantitative Results
#### 3.3.1 Experimental Results of Different Datasets  
This section sets a benchmark for our framework on CUB200-2011, CARS196, and SOP datasets. Recall results are reported for the first five ranks on CUB200-2011 and CARS196 datasets, and they are reported in recall of rank 1, 10, 100, 500, and 1000 on the SOP dataset with the instance-level label. The experimental results show that our approach achieves a recall of 0.9414, 0.8541, and 0.9806 with the first ranking and mAP of 0.9379, 0.8804, and 0.9141 on the CUB200-2011, CARS196, and SOP datasets, respectively, details are given in Table 2. SOP dataset gives two types of labels: class labels with 22,634 categories and super-class labels with 12 categories. The super-class labels indicate the type of product, while the class label varies according to each product, that is, different views of the same product. Our result also shows a high recall value with instance image retrieval, and we obtain recall at the first rank of 0.947 on the SOP dataset.
## 3 Analytical Model
### BS Power Consumption  
Generally, BS consumes two types of power: static power consumption and transmit power consumption [3]. Then, for the \(k\)th tier, a BS power consumption can be given as follows:  
\[P_{k}=P_{k,s}+\xi_{k}P_{k,t}, \tag{9}\]  
where \(P_{k,s}\) is the static power consumption of a BS in the \(k\)th tier, which is caused by signal processing, battery backup, as well as site cooling, and is independent with the BS transmit power. \(P_{k,t}\) is the transmit power of a BS for data transmission in the \(k\)th tier, and \(\xi_{k}\) is the load-dependent power consumption coefficient of a BS in the \(k\)th tier.  
Note that the transmit powers of each MBS in ABS subframe and Non-ABS subframe are different. Therefore, the network power consumption in ABS subframe and Non-ABS subframe will not be the same. Thus, we decompose the network power consumption into two parts: the network power consumption in ABS subframe \(P_{abs}\) and the network power consumption in Non-ABS subframe \(P_{non\_abs}\). In particular, the transmit power consumption of MBS for data transmission in ABS subframe is assumed to be zero due to its silence in the ABS subframe. Combined with Equation (9) and the density of MBS and PBS, \(P_{abs}\) and \(P_{non\_abs}\) can be obtained in the following, respectively:  
\[\begin{array}{l}P_{abs}=\lambda_{m}P_{1,s}+\lambda_{p}P_{2,s}+A_{p_{c}}\lambda _{u}P_{2,t}\\ =\lambda_{m}P_{m,s}+\lambda_{p}P_{p,s}+A_{p_{c}}\lambda_{u}P_{p,t},\end{array} \tag{10}\]\[\begin{split} P_{non\_abs}&=\lambda_{m}P_{1,s}+A_{m_{u}} \lambda_{u}P_{1,t}+\lambda_{p}P_{2,s}+A_{p_{c}}\lambda_{u}P_{2,t}\\ &=\lambda_{m}P_{m,s}+A_{m_{u}}\lambda_{u}P_{m,t}+\lambda_{p}P_{p,s }+A_{p_{c}}\lambda_{u}P_{p,t}.\end{split} \tag{11}\]  
Considering the ABS ratio \(\theta\), the network total power consumption can be derived as:  
\[P_{total}=\theta P_{abs}+\left(1-\theta\right)P_{non\_abs}. \tag{12}\]
# A Survey of Secure Time Synchronization
## 3 Current Time Synchronization Methods
### Global Positioning System (GPS)  
The global positioning system is a navigation system based on 32 satellites, which originally used 24 satellites, that has been developed by the U.S. Department of Defense (DoD) [36]. It provides accurate location and time information, in all weathers, anywhere on the earth or near the earth where there is an unobstructed line of sight to three or more GPS satellites. GPS time synchronization (Figure 2) provides accuracy in the order of 200 ns, but the hardware is expensive and it is a power-hungry device [37]. In addition, to work properly and accurately, three of its satellites must be in the line of sight at all times. Currently, at least four satellites are in the line of sight all the time, as shown in the figure below; however, this may not be possible in some cases such as inside buildings or underwater [21].  
To provide time synchronization, GPS devices communicate with satellites [38]. To do so, a GPS receiver is required in each device, which is impractical, especially for wireless devices, because of hardware costs and power constraints [39]. Nodes receive real time information from the GPS and synchronize themselves as follows: Every node has its own hardware clock; denote the value of node \(i\)'s hardware clock by \(Hi(t)\). We assume that every node's hardware clock has bounded drift \(\rho<11\). For all nodes \(i\),  
\[\forall t:1-\rho\leq\left(\frac{dH_{i}(t)}{dt}\right)\leq 1+\rho \tag{3}\]  
where \(t\) is real time, \(d\) is distance, and \(\rho\) is bounded drift. Each node computes a logical clock value by using its hardware clock and received message from other nodes. Note node \(i\)'s logical clock value at time \(t\) by \(Li(t)\). The clock synchronization algorithm tries to make sure that the logical values of the nodes are close to real time and close to each other's values [40].  
The accuracy of GPS time synchronization will vary at different times, depending on the number of satellites that can communicate with a receiver. In addition, it takes some time for a GPS message to propagate the entire network [36]. For example, A GPS message is received at the node \(i\) by an input action gps(\(t\))\(i\). The purpose of the message is to inform \(i\) that the current real time is \(t\). However, it can take longer for the message to reach node \(i\) in a big network and node \(i\) may receive this message after the real time \(t\)[40]. Expensive hardware such as a GPS receiver and the high energy requirement of the GPS protocol result in using increased bandwidth and computational power, which makes it unsuitable because sensors have limited battery power, limited bandwidth availability, limited storage space, and limited computational power.
## 1 Introduction  
Single-crystal sapphire optical fiber is frequently used in high temperature and corrosive environments for sensors monitoring properties such as temperature [1; 2; 3] or gas concentration [4]. While the resilience of sapphire in extreme environments is rivaled by very few optical materials, its large refractive index relative to air results in high modal volume at typical sensing wavelengths. This contributes to higher loss, increased modal dispersion, decreased distributed sensing resolution, and decreased detection sensitivity than can be achieved by silica-based counterparts [5; 6] or preclude the application of some sensing schemes altogether [7].  
A cladding is employed in most optical fiber applications to regulate the index disparity between the core and its surroundings to reduce modal volume. To date, however, successful claddings have not been demonstrated for sapphire that will survive the extreme conditions in which it is typically utilized. At temperatures approaching or exceeding 2000 \({}^{\circ}\)C, many candidate claddings will delaminate due to thermal expansion mismatch or diffuse into the core, altering optical properties (not to mention those materials which would form an excellent sapphire cladding from an optics perspective but cannot survive hot and corrosive environments).  
Ordered-array photonic crystal fibers (PCFs) have been demonstrated using sapphire [8; 9] with reduced modal volume but still rely on commercially-available sapphire fiber, which are 50-70 \(\upmu\)m in diameter at the smallest. Engineered cross-sectional geometries for the reduction of modal volume have also been theoretically proven recently [10; 11], but practical fabrication methods for these designs have not yet been developed. Therefore, in lieu of a cladding or fabrication of PCFs and engineered fibers, one remaining method for producing sapphire optical fiber that will support only low order modes is to drastically reduce its diameter well below the minimum diameter commercially available.  
We previously demonstrated a wet-acid etching method which was successfully applied to reduce the diameter of a sapphire optical fiber to less than 800 nm while maintaining a smooth, consistent surface [12]. This method, which summarily prescribes the immersion of sapphire fiber into a mixture of sulfuric and phosphoric acids at temperatures exceeding 340 \({}^{\circ}\)C, was employed again in the work presented below to produce an air-clad sapphire optical fiber demonstrating support of only the fundamental LP\({}_{01}\) mode at wavelengths of 783 nm and greater.
# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
## 3 _Ekf_ Based Model
### Process of \(Ekf\)  
Recall that we use the \(EC\) as the filter target, and regard \(i_{t}\) and \(h_{t}\) as the prediction value and measured value, respectively. Thus, we can obtain the equations of state time propagation and state measurement propagation as follows:  
\[\left\{\begin{array}{ll}X(t|t-1)=i(t)+r,\\ Z(t|t-1)=h(t)+q,\end{array}\right. \tag{11}\]  
where \(r\) is the process standard deviation, and \(q\) is the measurement standard deviation.  
Then we can get the Jacobian matrix \(F_{t}\) and \(H_{t}\).  
\[\left\{\begin{array}{ll}F_{t}=\frac{\partial i}{\partial t},\\ \\ H_{t}=\frac{\partial h}{\partial t}.\end{array}\right. \tag{12}\]  
The covariance updating equation of \(P(t|t-1)\) is described below.  
\[P(t|t-1)=F_{t}P(t-1|t-1)F_{t}+Q, \tag{13}\]  
where \(F_{t}\) is the transpose matrix of \(F\), \(Q\) is the process variance.  
According to the setting profile and feedback current, the current optimal estimation \(X(t|t)\) can be obtained.  
\[X(t|t)=X(t|t-1)+K_{g}(t)(Z(t|t-1)-H_{t}X(t|t-1)), \tag{14}\]where \(K_{g}(t)\) is Kalman gain, and it can be calculated using the following equation.  
\[K_{g}(t)=P(t|t-1)H_{t}^{T}(H_{t}P(t|t-1)H_{t}^{T}+R)^{-1}, \tag{15}\]  
where \(R\) is the measurement variance.  
To keep the \(EKF\) running until the end of the system process, the covariance updating equation of \(P(t|t)\) is needed.  
\[P(t|t)=(I-Kg(t)H_{t})P(t|t-1), \tag{16}\]  
where \(I\) is a unit matrix.  
The Equations (11)-(16) are the process of \(EKF\) in the \(MP\).
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
###### Abstract  
The Panama Canal, inaugurated in 1914, continues to play a pivotal role in global maritime connectivity. In 2016, the Canal underwent a significant expansion, reshaping maritime transit by accommodating larger vessels and reinforcing its strategic importance in international trade. The objective of this study is to identify a suitable time series statistical model to forecast the number of vessels transiting the Panama Canal. The three approaches employed were the following: the Autoregressive Integrated Moving Average (ARIMA) model, the Holt-Winters (HW) exponential smoothing method, and the Neural Network Autoregressive (NNAR) model. The models were compared based on forecasting errors to evaluate their predictive accuracy. Overall, the NNAR model exhibited slightly better predictive performance than the SARIMA (1,0,1) (0,1,1) model in terms of error, with both outperforming the Holt-Winters model by a significant margin.  
time series; SARIMA; Holt-Winters; forecasting; neural network; forecast accuracy; Panama Canal; maritime transit Article
## 2 Triterpenoids
### 8,9-ene-triterpenoids
#### 2.2.5 C24 Triterpenoids  
Nishitoba and the research group have studied the bitter triterpenoids from the fruiting bodies of _G. lucidum_ since 1985. Besides C30 and C27 triterpenoids, C24 triterpenoids including lucidones A (**435**), B (**436**), C (**437**) and H (**438**) were also identified from this fungus [175; 197; 290; 298], and they discussed how to produce these C24 triterpenoids. Nishitoba et al. [175] proposed that lucidones A (**435**), B (**436**) and C (**437**) might be artefacts and that lucidones B (**436**) and H (**438**) were derived from methyl sandstone N (**9**) and methyl ganoderate O (**10**) under the alkaline conditions used during the isolation procedure. In order to confirm this deduction, they used 1 M KOH to treat compounds methyl  
Figure 13: Chemical structures of several C27 triterpenoids (**364–**431**).  
ganoderate N and methyl ganoderate O (**9** and **10**) for 30 min to yield compounds lucidones B and H (**436** and **438**), respectively, which were determined using 1D NMR (Figure 16). Peng et al. [193] isolated a series of C24 triterpenoids, lucidones A (**435**), B (**436**), H (**438**) and D-G (**439-442**) from the MeOH extract of _G. resinaceum_, which was treated with satd aq. Na\({}_{2}\)CO\({}_{3}\). This also indicated that these C24 triterpenoids were artefacts. Furthermore, lucidone H (**438**) was also formed by the PDC (pyridinium dichromate) oxidation of lucidone B (**436**) and ganoderenic acid G (**41**) following treatment with ozone followed by PDC [188].  
Four new nortriterpenoid with A C24 skeleton named as lucidones I-L (**443-446**) were isolated and identified from the fruiting bodies of _G. resinaceun._\(\alpha\)-Glucosidase inhibitory activity was examined for each compound and lucidones K (**445**) and L (**446**) displayed no significant \(\alpha\)-glucosidase inhibitory activity [299].  
The 8\(\alpha\),9\(\alpha\)-Epoxy-4,4,14\(\alpha\)-trimethyl-3,7,11,15,20-pentaoxo-5\(\alpha\)-pregnane (**447**) was isolated from the EtOAc extract of the fruiting bodies of _G. concinna_ Rvy. (Ganodermataceae) was also a C24 triterpenoid [271]. Compared with the above lucidones, an 8,9-epoxy group was present in **447**. Furthermore, the evaluation of cytotoxicity showed that it induced apoptosis in human promylocytic leukemia HL-60 cells [271]. In addition, Li et al. [66] isolated a new C24 natural compound ganodrenol C (**448**) from the ethanolic extracts of _G. lucidum_.  
Chemical structures of different C24 triterpenoids are shown in Figure 17.
## 2 Literature Review  
### Existing Studies Using Object Detection  
Several studies have employed deep-learning-based object detection to identify construction resources such as workers, equipment, and materials via camera imagery. For instance, [PERSON] et al. [9] developed a method to automatically detect multiscale objects (worker and excavator) in real time, achieving an accuracy of 91-95%. Similarly, [PERSON] et al. [10] employed Faster R-CNN to identify hazardous areas by analyzing the interactions between workers and heavy equipment on construction sites, reporting a mean Average Precision (mAP) of 92.55%. [PERSON] et al. [11] also used Faster R-CNN to detect excavators and proposed a framework for recognizing excavator work patterns, thereby facilitating the analysis of cycle times and productivity; their approach attained 90.9% precision and 89.2% recall.  
In these studies, Faster R-CNN offered higher inference speeds than the original R-CNN and demonstrated a superior overall performance. However, as a two-stage detector, it may have drawbacks for real-time processing of continuous imagery, limiting its practical applicability to construction safety. Consequently, additional research has explored one-stage detectors to develop detection models suited for construction sites and to address safety-related uncertainties. YOLO--renowned for its fast image processing capabilities--has become a popular choice in this regard [12]. For instance, [PERSON] and [PERSON] [13] compared various training data combinations using YOLOv2 and YOLOv3 for rapid construction object detection, finding that YOLOv3 trained on a crowdsourced dataset yielded the highest mAP (78.2%). [PERSON] et al. [14] developed a YOLOv3-based approach to detect excavators and dump trucks, reporting 82% accuracy. Additionally, [PERSON] and [PERSON] [15] proposed a RetinaNet-based algorithm for automatic action recognition of earthmoving equipment (e.g., excavators and dump trucks), obtaining an AP of 95.75% for excavators and 35.69% for dump trucks. Finally, [PERSON] et al. [1] proposed a YOLOv5-based model for detecting nine types of heavy equipment (e.g., bulldozers, dump trucks, and excavators), demonstrating high reliability with an mAP of 90.26%.  
Collectively, these studies demonstrate that deep-learning-based object detection can effectively identify construction equipment on-site. In particular, YOLO-based approaches consistently achieve robust accuracy and real-time detection speeds. However, variations in datasets across different investigations make direct comparisons of model performance difficult. Moreover, further refinements in both accuracy and computational efficiency are essential to meet real-world, high-throughput requirements--especially for detecting multiple types of equipment simultaneously. Consequently, the development of more computationally efficient and reliable detectors remains critical for practical, real-time monitoring in construction environments.
## 4 Results and Discussion
### SSA Optimization Process for the SVR and ELM  
The SSA parameters were configured with a population size of 20, and the algorithm was designed to iterate a maximum of 100 times. The proportion of discoverers, labeled as \(PD_{Num}\), constituted 70% of the total population, while the proportion of sparrows assigned to detection and warning, labeled as \(SD_{Num}\), was 20% of the total population. The alarm threshold, labeled as \(ST\), was fixed at 0.6. The computational setup consisted of an AMD Ryzen 9 5900 HX processor with AMD Radeon(tm) Graphics operating at 3.30 GHz, equipped with 32 GB of RAM, running the Windows 11 operating system, and utilizing the MATLAB R2020b environment for simulation experiments. Figures 4 and 5 display the MSE convergence situations.  
Figure 3: The monthly price of pork in Beijing from 2010–2024.  
Figure 4: The convergence of the MSE in the SSA-SVR model.  
Figure 5: The convergence of the MSE in the SSA-ELM model.  
Figures 4 and 5 demonstrate that the MSE of the SSA-optimized SVR converged in three generations with a value of 0.1066. Furthermore, the MSE of the SSA-optimized ELM converged in 30 generations with a value of 0.0087. The training errors for both the SSA-optimized SVR and ELM models decreased progressively as the number of iterations increased, suggesting that the SSA was effectively escaping local optima in its search for the global optimum during the training phase, thereby enhancing the predictive accuracy of the models.
# An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier
###### Abstract  
Energy consumption is always a key feature in devices powered by electric accumulators. The power amplifier is the most energy-demanding module in mobile devices, portable appliances, static transceivers, and even nodes used in underwater acoustic networks. These devices incorporate a modulator, typically a pulse-width modulation (PWM) and a class-D power amplifier, for higher efficiency. We propose a technique to integrate the modulator of a transmitter and PW-modulator of a class-D amplifier to improve the overall efficiency of the system. This integrated set operates as an up-converter, phase modulator (PM), and binary phase-shift keying (BPSK) modulator under certain conditions. The theoretical concept is verified using Matlab and a model is designed and simulated in Simulink. For validation purposes, an electronic circuit is built and tested using Multisim. The results obtained by simulations and circuit implementation show that the proposed integrated system is an energy-efficient and cost-effective solution compared to conventional techniques.  
class-D amplifier; power efficiency; digital modulator; BPSK; QPSK; PWM Article
## 1 Introduction
Compared to traditional systems, the AN-GSDM framework, combined with the proposed joint optimization algorithm, maximizes security and offers numerous practical applications, including UAV military confidential communications, UAV private information image transmission, and UAVs serving as relay stations for private information transmission. For instance, in widely used 5G and upcoming 6G systems, UAVs acting as relay stations can transmit private information. Some distributed receiving stations may be compromised by external intrusions, turning them into eavesdroppers. In such cases, UAVs can obtain the location and channel information of legitimate receivers and, to some extent, the information of eavesdroppers. By introducing the AN-GSDM system and jointly optimizing the precoding matrix and power allocation factor, this approach enhances the spatial distribution of resources--such as the precoding matrix controlling the power and beam distribution of the useful signal, and the power allocation factor controlling the distribution of noise power. This ensures that sensitive information is protected from interception by eavesdroppers, thereby safeguarding the security of private information transmission. This approach aligns with the advancements in integrating UAVs into 5G and beyond networks, as discussed in the recent literature [23]. By incorporating the AN-GSDM framework, UAVs can efficiently manage multi-beam transmissions and artificial noise,optimizing power resources to ensure secure communication channels even in the presence of potential eavesdroppers. The remainder of this paper is organized as follows: Section 2 introduces the system model for AN-GSDM. Section 3 presents the proposed iterative algorithm for jointly optimizing the precoding matrix and power-control factor to maximize the secrecy rate. Section 4 provides simulation results to validate the analytical findings and illustrate the effectiveness of the proposed scheme. Finally, Section 5 concludes this paper, summarizing its key contributions and potential implications.