esa-sceva/satcom-chunk-collection huggingface dataset:
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 3 Proposed Watermarking Scheme
### Watermark Detection
#### 3.3.3 Message Extraction  
Watermark detection is an iterative search for candidate LSFRs. As long as watermark information is detected in one LSFR, the watermark detection of the captured image is completed. Each time, one candidate LFSR is orientation normalized and discrete Fourier transformed. According to the nature of the DFT coefficients, although we do not know the original size, the radius of watermark locations will not vary as long as the area corresponding to the feature scale has not varied. However, the feature scale and its corresponding area will vary slightly, resulting in a slight variation in the radius of watermark locations. Therefore, the searching area will be between \(R_{i}\in(R-10,R+10)\) at a step of 1 pixel. Besides, we also need to consider the variation in the feature orientation. As we have investigated in Section 3.1, the orientation variation is primarily less than five degrees. Therefore, the starting position is between \(\left(-5^{\circ},+5^{\circ}\right)\) of the initial position at each radius \(R_{i}\) at a step of one degree.  
The correction of perspective distortion will inevitably cause some shift of the coefficients and imperfections in resampling. This results in a variation in the coefficient of the adjacent point. An example is shown in Figure 10. In addition, because the feature orientation will vary, the starting position cannot be located directly. Therefore, each time, the maximum magnitude value of the candidate positions and their neighborhoods are extracted to obtain the message \(V\).  
Based on local statistical feature, the extracted message \(w\) is defined as:  
\[w(i)=\left\{\begin{array}{ll}1&if\ V(i)\geq\overline{M}_{w}+k_{2}\sigma_{w} \\ -1&if\ V(i)<\overline{M}_{w}+k_{2}\sigma_{w}\end{array}\right. \tag{17}\]  
where \(\overline{M}_{w}\) and \(\sigma_{w}\) define the mean value and the standard deviation of all the magnitudes in the range of \(\left\{R_{i}-2,R_{i}+2\right\}\). \(V(i)\) is the extracted maximum value within \(3\times 3\) magnitudes, \(k_{2}\) is a parameter used to determine the threshold for message extraction.  
The extracted \(w\) is compared with the pseudorandom sequence \(W\) generated by the secret key to calculate the number of erroneous bits. The watermark detection is positive if the number of erroneous bits is below the predefined threshold \(T\). If the detection is negative, the iterative process continues.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 4 Cellular Networks
### 5g Nr-V2x  
In addition to congestion intolerance, a major drawback of C-V2X with respect to its utilization for EV charging management is that it only provisions for broadcast communications and, hence, does not allow establishing dedicated sessions for direct exchange of vehicular information between the network entities. Accordingly, a further advanced cellular V2X network is required to address different variations of vehicular communication requirements including unicast communications to maintain the privacy of EV charging information. This has motivated the development of 5G V2X technologies that leverage on existing LTE, LTE-advanced (LTE-A) and LTE C-V2X, while fulfilling additional requirements of ultra-high reliability, high mobility, lower latency, and higher density connections [38]. This is particularly addressed in 3 GPP Release 16 and is referred to as 5G New Radio (NR)-V2X.  
NR-V2X technology offers several advantages over LTE C-V2X. The sub-6 GHz and mmWave frequency ranges offer a single-user bandwidth of 100 and 400 MHz, respectively, which are both significantly higher than the maximum LTE bandwidth of 20 MHz [82]. Accordingly, the higher bandwidth and higher bandwidth efficiency, together with the smaller control overhead allows NR-V2X to offer higher data rates for vehicular communications in comparison with C-V2X [58]. In addition, NR-V2X allows operation in modes 1 and 2, both of which allow EVs to communicate directly with one another. The difference between the two modes is in the resource allocation, which is performed by the 5G base station (gNodeB) in mode 1 but is autonomously performed in mode 2 allowing device-to-device (D2D) vehicular communication [57]. Nevertheless, the key distinguishing feature of NR-V2X for enabling EV charging coordination and management is the support for unicast, groupcast, and broadcast messages [56; 57]. In this way, an EV user can initiate multiple dedicated communication sessions with the charging infrastructure, an energy sharing vehicle or other neighboring vehicles without overloading other network entities.  
The technical specifications of DSRC, LTE C-V2X, and 5G NR-V2X are summarized and compared in Tables 3 and 4.
# Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving
## 1 Introduction  
High-quality infrared images play a crucial role in scenarios such as auto-driving, fault diagnosis, and fire detection [1; 2; 3; 4]. However, the quality of infrared images obtained in real-life scenarios tends to be poor due to environmental effects and the limitations of infrared thermal imaging technology [5]. The low contrast and unclear texture details of infrared images largely increase the difficulty of subsequent processing, such as detection, perception, and location [1; 6; 7; 8]. Low-contrast infrared images lead to large deviations in target localization, so the first step in target localization is to increase the contrast of the image. Traditional infrared image enhancement methods are mainly divided into three types: histogram-based methods, transform function-based methods, and transform domain-based methods. Most of the traditional methods need to set parameters artificially, which enormously reduces the flexibility of applications [9]. Moreover, it takes a longer time to process larger-resolution images via traditional methods. Predicting the target and background precisely is crucial to achieve the aim of improving image contrast robustly. Unlike with fixed filters, we try to learn the filters suitable for extracting the target and background sub-images using a data-driven method. Inspired by the ability of convolutional neural networks (CNNs) in the image classification field, we propose a novel approach to predicting target and background features using filters learned by a CNN for infrared image enhancement.  
In this paper, we propose a convolutional neural network model to enhance the quality of infrared images. The model consists of two parts: a feature extraction module and animage enhancement module. We consider low-contrast infrared image enhancement as a supervised learning problem, and the model learns the end-to-end mapping between low- and high-contrast images, directly. Then, the targets and background clutters are predicted from the extracted multiscale feature images by the learned feature extraction module. Finally, the weak infrared image is enhanced by zooming in on the target while removing background clutter in the image enhancement module.  
The contributions of our work can be summarized in three ways.  
(1) Convolutional neural networks consisting of a feature extraction module and image enhancement module are applied to infrared image enhancement.  
(2) The low- and high- contrast images are considered the input and output of the model for training. To overcome the lack of a large amount of training data, the brightness and clarity of the infrared images are randomly reduced to form sample pairs.  
(3) Extensive experiments show that our method can not only effectively improve the quality of infrared images, but also reduce processing time.
## 4 Experimental Evaluation of the GAGAN Model
### Experiment Setup and Tests  
The dataset employed in the experiments is the CelebFaces Attributes Dataset (CelebA), which contains over 200,000 celebrity images, each annotated with 40 attributes. It features diverse pose variations and background clutter, encompassing 10,177 unique identities, 202,599 face images, and 5 landmark locations. CelebA is suitable for tasks such as face attribute recognition, face detection, landmark localization, and face editing. Only the images were considered for GAGAN training. Specifically, 2000 random images were selected from the available 202,599, at a resolution of 128 \(\times\) 128 pixels. This choice facilitated adequate visualization and manageable training time, significantly reducing the computational time compared to using the entire dataset.  
Both models DCGAN and GAGAN were trained on a subset of 2000 images from the CelebA dataset for 50 epochs, using the same batch size, optimizer, and hyperparameters. This consistent setup, including identical epochs, datasets, and architecture, was designed to ensure that any differences in performance could be linked exclusively to the design and training strategies of the two models.  
The proposed GAGAN model was developed and trained in Python with initial training conducted on Google Colab, utilizing a T4 GPU to evaluate its capacity for handling the model's requirements. The primary focus of the experimental setup was to optimize the discriminator's weights, as it plays a key role in distinguishing real images from generated ones. By fine-tuning the discriminator, we aimed to enhance the feedback loop to the generator, thus improving the quality and diversity of generated images, while addressing issues such as mode collapse.  
However, this initial setup encountered challenges, including limited memory capacity and long training times that extended over several hours, even with a reduced dataset. These constraints highlighted the need for a more robust computational environment to support efficient model training and parameter optimization within the GAGAN framework.  
To address these issues, we adopted an iterative optimization approach, adjusting various parameters such as dataset size, the number of epochs, image resolution, latent dimension, and learning rates for both the generator and discriminator. Despite these adjustments, the training process remained time-consuming, frequently taking between 3 and 7 h. To resolve this, we transitioned to an A100 GPU, enabling a more efficient training process and faster parameter adjustments. Ultimately, the final configuration was achieved by using a reduced dataset of 2000 images at a 128 \(\times\) 128 resolution. With a smaller population size and fewer generations, we reduced training time to approximately one hour, achieving the target results without the complications observed with larger datasets.
# Risk-Aware Wireless Positioning in
## 2 Preliminary: Various Wireless Positioning Schemes
### Time of Arrival (ToA)  
The ToA scheme is a wireless positioning method that estimates a signal source position from the arrival times of the positioning signal at the receivers. In this case, all the signal sources and receivers should be synchronized [17; 18], while the positioning signal should contain a time stamp.  
The ToA scheme is illustrated in Figure 2. In a two-dimensional plane, there are three BSs and a single user and the BSs attempt to measure the user position. Meanwhile, the user is at position \((x,\ y)\) and the three BSs are at \((X_{1},\ Y_{1})\), \((X_{2},\ Y_{2})\) and \((X_{3},\ Y_{3})\), respectively. From the time of arrival (ToA) at the \(i\)th BS (denoted by \(\tau_{i}\)), the \(BS_{i}\) can determine the distance between them, as given by  
\[d_{i}=c\cdot\tau_{i}, \tag{3}\]  
where \(c\) is the speed of light. Then, the position of the user \((x,y)\) satisfies that  
Figure 1: The Angle of Arrival (AoA) scheme.  
\[\sqrt{(x-X_{i})^{2}+(y-Y_{i})^{2}}=d_{i}\quad\forall i\in\{1,2,3\}. \tag{4}\]  
Thus, the BSs can determine the user position because it is at the intersection of the circles centered at each BS with the corresponding radius of \(d_{i}\). Note that although we explained the ToA scheme in synchronous networks, they are also studied in asynchronous networks [19; 20].
## 2 LPWAN Technologies
### LoRaWAN  
As a small start-up company, Cycleo introduced the LoRa (Long-Range) communication network in 2009 [10]. Three years later, the company was acquired by the Semtech corporation [11]. Since then, LoRa Wide-Area Networks (LoRaWANs) have been deployed all over the world to provide long-range and low-power communication for IoT networks [12].  
The LoRaWAN physical layer is closed-source and proprietary. It utilizes Chirped Spread Spectrum (CSS) modulation in the unlicensed Sub-1 GHz frequency bands [13]. The operational bandwidth can be either 125, 250, or 500 kHz. Moreover, the allowed data rate varies from 300 bits per second (bps) to 50 kbps [14; 15]. Accordingly, the characteristics of a LoRa signal, i.e., the carrier frequency, the bandwidth of the signal, and the modulation technique, allow LoRa networks to establish communication links up to 15 km in rural areas [2]. Besides that, LoRa sub-GHz has an extension, namely LoRa 2.4 GHz, which was announced in 2020. This LPWAN technology has the benefit of using the widely used unlicensed 2.4 GHz Industrial, Scientific, and Medical (ISM) band and, therefore, is not subject to duty cycle restrictions. The technology is based on the same CSS technique, but the bandwidths, spreading factors, and, therefore, data rates are expanded. Nevertheless, the advertised communication ranges seem to be higher compared to any other technology that is operating in this band [12].
## 3. Results
### Nutritional Characteristics  
#### 3.5.1 Total Amino Acids Profile  
The total amino acid content and composition per 100 g of CSPSP are detailed in Table 9. The total amino acid content per 100 g of CSPSP amounted to 14,336.0 mg. Among the key amino acids, each comprising more than 8% of the composition, were leucine (1204.8 mg, 8.4%), lysine (1296.9 mg, 9.1%), aspartic acid (1546.3 mg, 10.8%), and glutamic acid (2464.8 mg, 17.2%).  
The total essential amino acid content per 100 g of CSPSP equated to 6908.2 mg, which accounted for approximately 48.2% of the total amino acid content. In terms of specific amino acids, threonine and lysine, both considered essential amino acids, comprised 661.9 mg (4.6%) and 1296.9 mg (9.1%) per 100 g of CSPSP, respectively. It is worth noting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **RCS** & **CSPSP** \\ \hline  & Moisture & 80.6 \(\pm\) 0.2 b & 76.1 \(\pm\) 0.5 a \\  & Crude protein & 16.0 \(\pm\) 0.1 b & 14.8 \(\pm\) 0.2 a \\  & Crude lipid & 1.3 \(\pm\) 0.3 a & 5.4 \(\pm\) 0.6 b \\ composition (g/100 g) & Ash & 1.5 \(\pm\) 0.2 a & 2.2 \(\pm\) 0.0 b \\  & Carbohydrate & 0.6 & 1.5 \\ \hline Energy (kcal) & 82.4 & 115.9 \\ Salinity (g/100 g) & 0.9 \(\pm\) 0.0 a & 1.1 \(\pm\) 0.0 b \\ pH & 6.80 \(\pm\) 0.0 a & 6.98 \(\pm\) 0.0 b \\ Volatile basic nitrogen (VBN) (mg/100 g) & 12.0 \(\pm\) 0.4 a & 20.2 \(\pm\) 0.8 b \\ \hline \hline \end{tabular}
\end{table}
Table 8: Proximate composition, energy, and salinity of RCS and CSPSP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**EAA** & **RCS** & **CSPSP** & **NEMA** & **RCS** & **CSPSP** \\ \hline Threonine & 687.7 (4.6) & 661.9 (4.6) & Aspartic acid & 1587.9 (10.6) & 1546.3 (10.8) \\ Valine & 710.2 (4.7) & 654.2 (4.6) & Serine & 654.6 (4.3) & 677.6 (4.8) \\ Methionine & 210.4 (1.4) & 306.0 (2.1) & Glutamic acid & 2511.7 (16.7) & 2464.8 (17.2) \\ Isoleucine & 776.4 (5.2) & 700.3 (4.9) & Proline & 834.5 (5.5) & 699.5 (4.7) \\ Leucine & 1288.9 (8.6) & 1204.8 (8.4) & Glycine & 831.6 (5.5) & 668.8 (4.7) \\ Phenylalanine & 671.2 (4.5) & 656.8 (4.6) & Alanine & 955.6 (6.4) & 812.9 (5.7) \\ Histidine & 352.3 (2.3) & 363.5 (2.5) & Cysteine & 92.1 (0.6) & 101.0 (0.7) \\ Lysine & 1362.0 (9.1) & 1296.9 (9.1) & Tyrosine & 348.4 (2.3) & 456.9 (3.2) \\ \cline{2-4} Arginine & 1154.6 (7.7) & 1063.8 (7.4) & Sub-total & 7816.4 (51.9) & 7427.8 (51.8) \\ \cline{2-4} Tryptophan & ND & ND & Total & 15,030.1 (100.0) & 14,336.0 (100.0) \\ \hline Sub-total & 7213.7 (48.1) & 6908.2 (48.2) & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Total amino acids profile (mg/100 g) and ratio (%) of RCS and CSPSP.  
that the primary limiting amino acid in CSPSP was methionine, a sulfur-containing amino acid, except for tryptophan, which was not detected.
###### Abstract  
The adjustment of the natural frequency of a micro crystal resonator (MCR) is the key requirement in its manufacturing, which determines the actual performance of the product. Laser processing is a new method for MCR frequency modulation, and its process parameters directly affect the frequency modulation effect. Aiming at the laser processing equipment for MCR frequency modulation, the structure of the laser processing control system for MCR frequency modulation is designed by analyzing the requirements of a laser processing control system. The working flow of the laser processing control system for MCR frequency modulation is studied, the motion control method of the laser processing platform for MCR frequency modulation is proposed, and the laser processing control system for MCR frequency modulation is designed. By analyzing the laser characteristics for MCR frequency modulation, an orthogonal experiment is carried out to optimize the laser processing parameters for MCR frequency modulation.  
micro crystal resonator; laser processing; frequency modulation; control system; orthogonal experiment +
Footnote †: journal: Applied Sciences
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model  
### WBAN Architecture and Requirements  
In this section, we present the WBAN model and the communication tier that has been used in this work. A nursing home has been considered as the application scenario. The real dimensions of the physical space can vary, but for simplicity, we consider a hall 10 m in width and 10 m in length. This area has been logically partitioned into smaller sections, called 'zones'. Each zone has its own gateway acting as a base station, giving wireless coverage in a 5 \(\times\) 5 m\({}^{2}\) area. The gateway is fixed (stationary) at the center of the zone and it is assumed that it has a fixed source of power, i.e., wall power. The nodes in each zone correspond to individual patients. A patient can have multiple sensors fitted or attached to his/her body. All these sensors are sending their data to the central sensor node located at waist height; this central node periodically transmits the collected data to the gateway. This concept is shown in Figure 1.  
The overall hall, zones, gateways and the corresponding nodes are illustrated in Figure 2 (here at the start of the simulation, right after they have been deployed). In this work, we consider the IEEE 802.15.6 standard that specifically targets WBANs [22]. To accommodate the huge application range of WBANs, this standard defines three physical layer options for WBANs, namely narrowband (NB), ultra-wide band (UWB) and human body communications (HBC) [22]. Each physical layer standard has its own design requirements. In this work, we consider only the NB physical layer on the 2.4-2.483 GHz band. This band is termed the industrial and medical (ISM) band. Each gateway is monitoring this band in the 2.4-2.483 GHz range. Other requirements and features of the system are given in Table 1[22; 28].  
Figure 1: Wireless body area network.  
For simplicity, we use only 20 channels in our simulations. The range of possible data rates for IEEE 802.15.6-compliant WBANs is quite wide (121.4 Kbps-971 Kbps), but to evaluate the system under demanding requirement conditions, we have chosen a data rate of 971.4 kbps in our simulations.
###### Abstract  
This study proposes a method based on image segmentation for accurately identifying liquid aluminum leakage during deep well casting, which is crucial for providing early warnings and preventing potential explosions in aluminum processing. Traditional DeepLabV3+ models in this domain encounter challenges such as prolonged training duration, the requirement for abundant data, and insufficient understanding of the liquid surface characteristics of casting molds. This work presents an enhanced DeepLabV3+ method to address the restrictions and increase the accuracy of calculating liquid surface areas for casting molds. This algorithm substitutes the initial feature extraction network with ResNet-50 and integrates the CBAM attention mechanism and transfer learning techniques. The results of ablation experiments and comparative trials demonstrate that the proposed algorithm can achieve favorable segmentation performance, delivering an _MIoU_ of 91.88%, an _MPA_ of 96.53%, and an inference speed of 55.05 FPS. Furthermore, this study presents a technique utilizing OpenCV to accurately measure variations in the surface areas of casting molds when there are leakages of liquid aluminum. In addition, this work introduces a measurement to quantify these alterations and establish an abnormal threshold by utilizing the Interquartile Range (IQR) method. Empirical tests confirm that the threshold established in this study can accurately detect instances of liquid aluminum leakage.  
image segmentation; identification of liquid aluminum leakage; transfer learning; attention mechanism +
Footnote †: journal: Applied sciences
## 3 Proposed Methods
### Dynamic Convolution and C2f_DynamicConv  
Dynamic Convolution (DynamicConv) was introduced by Microsoft researchers in 2020. It begins by defining a mechanism called the dynamic perceptron, which is then integrated into convolution operations to create the DynamicConv module. The concept of the dynamic perceptron is derived from the static perceptron, represented as \(\mathbf{y}\), and \(\mathbf{y}=g(\mathbf{W}^{T}\mathbf{x}+\mathbf{b})\), where \(\mathbf{W}\) is the weight matrix, \(\mathbf{b}\) is the bias vector and \(g\) is the ReLU function. Therefore, the dynamic perceptron is a multiple (K) linear functions group and defined as  
\[\mathbf{y}=g(\widetilde{\mathbf{W}}^{T}(\mathbf{x})\mathbf{x}+\widetilde{ \mathbf{b}}(\mathbf{x})) \tag{1}\]  
Therein, \(\widetilde{\mathbf{W}}(\mathbf{x})\) and \(\widetilde{\mathbf{b}}(\mathbf{x})\) are defined as  
\[\widetilde{\mathbf{W}}(\mathbf{x})=\sum\limits_{k=1}^{K}\pi_{k}(\mathbf{x}) \widetilde{\mathbf{W}}_{k} \tag{2}\]  
\[\widetilde{\mathbf{b}}(\mathbf{x})=\sum\limits_{k=1}^{K}\pi_{k}(\mathbf{x}) \widetilde{\mathbf{b}}_{k} \tag{3}\]  
where \(\pi_{k}\) represents the attention weight for the \(k\)-th linear function in the group \(\left\{\widetilde{\mathbf{W}}^{T}\mathbf{x}+\widetilde{\mathbf{b}}\right\}\), and \(0\leq\pi_{k}(\mathbf{x})\leq 1\), \(\sum\limits_{k=1}^{K}\pi_{k}(\mathbf{x})=1\), since the input matrix \(\mathbf{x}\) is variable, the attention weights \(\left\{\pi_{k}(\mathbf{x})\right\}\) are not fixed either. This makes the defined perceptron dynamic and more flexible compared to the static version.  
The process of DynamicConv is illustrated in Figure 4, which closely resembles the dynamic perceptron. It consists of K convolution kernels, all having the same size and identical input/output dimensions, aggregated by the attention weights \(\left\{\pi_{k}(\mathbf{x})\right\}\). As shown in Figure 4, the input data first flows into the attention module to compute the attention weights. Simultaneously, the data are passed through the Conv module, which also contains K dynamic kernels. Finally, the processed data goes through Batch Normalization (BN) and the ReLU activation function to produce the output.  
Dynamic convolution functions as a versatile plugin that can seamlessly integrate into any convolutional neural network. Therefore, we incorporated it into the C2f module, which plays a critical role in the architecture of YOLOv8. The resulting C2f_DynamicConv module, as shown in Figure 5, replaces the Bottleneck with DynamicConv, making it more flexible and adaptive to other modules within the YOLOv8 network:  
Figure 3: The structure of M-FPN-PAN.