esa-sceva/satcom-chunk-collection huggingface dataset:
# Framework for Malware Triggering Using Steganography
## 3 Methodology
### Hiding Javascript Exploit Code and Geolocation Code in Image Pixels  
The exploit code, with the geolocation code, is implanted in a PNG image with the Steggolpit package's Pixel Encoder tool (iterative_encoding.html) to obtain the encoded image. To embed the code in the image, the stegosploit tool employs Least Significant Bit (LSB) Steganography. This does not change the color sufficiently to enable detection by the human eye. The LSB approach is one of the simplest and most common techniques used in the spatial domain. It conceals the harmful code in the least significant bits of the image's pixels, making the distortions caused by the insertion process imperceptible.  
An image is made of an array of pixels. Each pixel can have three color channels (red, green, and blue), and each channel is made up of one byte, an 8-bit value that produces 256 discrete levels of color, and a grayscale image with one black color channel [32]. Figure 4 shows an original image and its bit plane.  
The \"iterative_encoding.html\" tool lets us select the bit layer (0 to 7), grid size, and channel (red, green, blue, or grayscale) to use for the encoding purpose. This tool allows us to process any bit layer other than the LSB layer. Still, it is suggested that bit layers 0, 1, and 2 are the most suitable for steganography because this creates no optical aberration in the image. Our experiment implanted the code at bit layer 2 with a 3 \(\times\) 3 pixel grid size and the grayscale channel. An HTML5 Canvas was used to perform the steganographic encoding in the browser. The resulting image, with the bitstream encoded on layer 2, exhibits no visual aberration, even when extremely expanded. It should be noted that the exploit code is not present in the encrypted image as strings because the exploit code is steganographically encoded in the image pixels themselves.
# 3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics
## 2 Materials and Methods  
### Overall Framework  
In this paper, we trained the 3D V-Net using the above-mentioned three distance-based loss functions and the regional loss function jointly. Figure 1 shows the 3D liver and tumor segmentation framework utilized in this paper.  
In the training stage, the 3D data was fed into the V-net model for feature extraction, then the regional and the three distance-based loss functions (which were denoted as \(Loss_{Reg}\), \(Loss_{BD}\), \(Loss_{HD}\), and \(Loss_{SDM}\)) were combined through variable weight values and used to jointly train the liver and tumor segmentation model. The loss functions in this paper were denoted as:  
\[Loss_{BD}=\alpha Loss_{Reg}+(1-\alpha)Loss_{Boundary} \tag{1}\]  
\[Loss_{HD}=\alpha Loss_{Reg}+(1-\alpha)Loss_{Hausdorff} \tag{2}\]  
\[Loss_{SDM}=\alpha Loss_{Reg}+(1-\alpha)Loss_{Signed\ Distance\ Map} \tag{3}\]  
where \(Loss_{Reg}\) was the regional loss function utilized in the original V-net architecture, these loss functions will be described in detail in the next paragraph. Among them, the distance-based loss function was used to assist the regional loss function to fine-tune the training models. Therefore, at the beginning of training, \(\alpha\) was set to 1, which indicated that the models were trained only utilizing regional loss, and the distance-based losses were not involved in the calculation of the loss function. When the training reached the plateau, the \(\alpha\) gradually decreased until it reached a value of 0.01. As found in these experiments, such a training strategy was more effective than a joint training strategy from scratch. In the next paragraph, we will expand on the details of the algorithm.
###### Abstract  
Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human-computer interaction. However, several challenges arise in outdoor settings, including varying lighting conditions, occlusions caused by obstacles, environmental noise, and the complexity of differentiating between similar activities. This study presents a hybrid deep learning approach that integrates human activity recognition and localization in outdoor environments using Wi-Fi signal data. The study focuses on applying the hybrid long short-term memory-bi-gated recurrent unit (LSTM-BIGRU) architecture, designed to enhance the accuracy of activity recognition and location estimation. Moreover, experiments were conducted using a real-world dataset collected with the PicoScene Wi-Fi sensing device, which captures both magnitude and phase information. The results demonstrated a significant improvement in accuracy for both activity recognition and localization tasks. To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. Additionally, carrier frequency offset (CFO) and cyclic shift delay (CSD) preprocessing techniques were implemented to mitigate phase fluctuations. The experiments were conducted in a line-of-sight (LoS) outdoor environment, where CSI data were collected using the PicoScene Wi-Fi sensor platform across four different activities at outdoor locations. Finally, a comparative analysis of the experimental results highlights the superior performance of the proposed hybrid LSTM-BIGRU model, achieving 99.81% and 98.93% accuracy for activity recognition and location prediction, respectively.  
outdoor environment; human activity; deep learning; Wi-Fi signals +
Footnote †: journal: _Algorithms_
## 1 Introduction  
Recently, with the drastic increase of data traffic in datacenters (DCs) and the evolution of high-performance computing (HPC), high-speed (\(>\)25 Gbit/s) data communication are required. In the case of conventional electrical interconnects, high-speed and long distance (\(>\)10 m) transmission is challenging due to large transmission loss [1]. On the other hand, optical interconnects have the advantage that high-speed signals can be transmitted further than in an electrical one with low propagation loss. Therefore, as the optical interconnects for rack-to-rack data transmission, active optical cables (AOCs) have been developed and widely applied [2; 3; 4; 5; 6]. More recently, for high-density signal transmission, it has become necessary to increase the total input/output (I/O) capacity. To meet that need, we previously proposed a 25.78-Gbit/s \(\times\) 12-ch AOC in the same size as the form factor of QSFP28 [7]. However, to meet system requirements, 4-ch and 1-ch AOCs are also required.  
To achieve high density signal transmission with 4-ch AOCs, it is necessary to reduce the form-factor of AOCs compared with our previous form-factor (12-ch AOC) or the standard form factor of QSFP28 [7; 8]. Miniaturizing the form-factor of an AOC, and therefore reducing its surface area, reduces its heat dissipation efficiency. In addition, miniaturizing the form factor also increases crosstalk noise because high-speed signal transmission lines are fabricated closely together. Therefore, to realize 4-ch AOCs with compact form factor, techniques to achieve higher-heat dissipation and lower inter-channel crosstalk have to be improved.  
In this study, we developed a 25.78-Gbit/s \(\times\) 4-ch AOC integrated with a newly ultra-compact form factor. The proposed AOC has a small size of 5.2 cm\({}^{3}\) as a result of applying newly developed high-density packaging techniques, namely, a 0.5-mm pitch electrical connector and high-heat dissipation structure. Furthermore, optimizing signal transmission lines and ground vias improved Sinter-channel crosstalk characteristics. In our experiments, the proposed AOC demonstrated25.78-Gbit/s error-free optical transmission under all-channels (4-ch) operation at high temperature (case temperature: \(T_{\rm c}\) = 70 \({}^{\circ}\)C).
## 5 Analysis of Chatter Stability of Two Boring Bar Models
### Comparison of Chatter Stability for Different T/D Ratios  
To compare the chatter stabilities of these two models for different T/D ratios, the number of layers was set to 4, 8, and 12. The corresponding T/D ratios were 0.0111, 0.0222, and 0.0333, respectively. Other parameters, such as the boring bar length, inside radius, and composite type (carbon), and ply angles were set with similar values to those in Section 5.2. The chatter stability lobes of these two composite boring bar models are shown in Figure 11.  
As shown in Figure 11, the chatter stability lobes of the composite boring bars for different T/D ratios were different. The chatter stability lobe lies at the top when T/D = 0.0333, and the chatter stability lobe lies at the bottom when T/D = 0.0111. When T/D = 0.0222, the curve lies in the middle position. The bigger the T/D ratio is, the deeper the cut is. Moreover, with the same L/D ratio, the curves of the shear model (which are labeled with subscript S) always lie below the non-shear boring bar curves. Thus, the cutting depth of the boring bar decreases when considering the shear deformation and rotational inertia. In particular, when T/D = 0.0333, the difference between these two models is the greatest. When T/D = 0.0111, the difference between these two models is a minimum. Therefore, it can be concluded that the greater the T/D ratio, the greater the influence of shear deformation and rotational inertia on cutting stability is.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 1 Introduction
Some of the enabling technologies for network slicing include software defined network (SDN), network function virtualization (NFV), machine learning, artificial intelligence, etc. SDN and NFV are envisioned as key technologies to execute the network slicing. The paper also present an overview of network slicing for the smart port 5G use case. A wide range of network infrastructure and a diverse range of services needs to be provided within the area of the seaport. This could range from offering enhanced broadband applications (e.g., virtual reality and augmented reality for passengers on cruise ship), critical services (traffic lights and water control gates at seaport), and massive connectivity (massive number of sensor) [7]. Hence, we describe the smart port scenario based on network slicing by integrating ML techniques with SDN/NFV architecture in order to manage the resources more effectively and efficiently.
# Trajectory Planning of Robotic Arm Based on Particle Swarm Optimization Algorithm
## 2 Trajectory Planning  
### Overview of Trajectory Planning  
Trajectory planning typically focuses on two research areas: mobile robots and industrial robots. In the context of the mobile robot, trajectory planning mainly refers to the path trajectory planning of the robot. For example, whether the robot has a map or not, the kind of path trajectory to follow is determined at this level. For industrial robots, trajectory planning encompasses two directions. Firstly, it involves planning the curvilinear trajectory of the end of the manipulator, detailing the curvilinear profile of the manipulator during motion displacement, velocity, and acceleration. As for the trajectory planning, it is also divided into Cartesian space planning and joint space planning according to the coordinate system reference. In more detail, the joint space planning is mainly oriented toward a joint manipulator. In this configuration, the displacement, the angular displacement, the velocity, and the acceleration of each joint of the manipulator are used to compute the time function. Furthermore, the desired pose of the end actuator is achieved by computing the relevant parameters. However, the calculation process is simpler and does not cause singularity problems at the level of the manipulator. Yet, the obtained results after using the joint space planning are difficult to be transferred to the three-dimensional space, and the posture of the manipulator will change along with the velocity. The second representation is the Cartesian space planning. It is usually deployed in the end actuator to achieve straight lines, arcs, and other determined curves; moreover, it can intuitively observe the motion trajectory of the end of the manipulator.  
In trajectory planning, the equations modelling the displacement, velocity, acceleration, and other variables are set according to the time parameters, and the function relationship between the displacement of each joint and time can be computed through the inverse kinematics equation of the manipulator.  
Moreover, in this work, a four-DoF manipulator is analyzed in which three DoF comprise translational pairs. Each step of the manipulator is a motion from the starting point to the end point, that is, the trajectory planning from the start to the end points, and the parameters such as displacement, velocity, and acceleration must meet the smoothness and continuity characteristics. In this work, the Cartesian space planning algorithm is deployed to plan the trajectory of the four-DoF manipulator described earlier.
## 3 MATLAB and ADAMS Co-Simulation
### Simulation of DDPG Algorithm in Active Vibration Control
#### 3.3.2 Reward Function  
The setting of the reward function is crucial; a good design can not only speed up the training process of the reinforcement learning algorithm but also avoid the situation of reward-hacking and convergence to a local optimum. The control objective for active vibration reduction is that the absolute speed of the payload is infinitely close to zero. Then, the reward function can be set as a linear function of \(v_{\text{p}}\) in simulation, and its expression is shown in Equation (17):  
\[R=-\big{|}v_{\text{p}}\big{|}-0.1{v_{\text{p}}}^{2} \tag{17}\]
## Materials and Methods
### Experimental Design  
Three vibrotactile stimulators were attached to three phalanges (P1, P2, and P3) of a randomly selected finger (index, middle, ring, or little finger) of the right hand before starting the experiment. One session comprised three blocks (Figure 2), each of which included a rest (30 s), stimulation (30 s), and response phase (9 s). During the rest phase, subjects were asked to sit comfortably with their eyes closed, and did not receive any vibration stimulus. In the stimulation phase, subjects received 30 s of vibration stimulation to one of the randomly selected phalanges (P1, P2, or P3) on a specific finger (index, middle, ring, or little). Since there were three stimulation phases per session, all three phalanges could be sequentially stimulated. During the response phase, a response button was placed in the subjects' left hand and they were asked to press the button corresponding to the phalange that had been stimulated. This was to confirm whether the subject had correctly identified the stimulated location. This session was repeated three times for each of the selected fingers, during which all three phalanges of the selected finger were stimulated three times each. This procedure was repeated for the remaining three fingers. The order of finger/phalange stimulation was counterbalanced to prevent repetitive effect and carryover effect; additionally, there was a 5-min break between sessions. With three sessions per finger, all participants completed a total of 12 sessions (3 sessions/finger \(\times\) 4 fingers = 12 sessions). According to the subjects' responses, which were recorded in the response phase, functional images of the eight subjects (age = 26.6 \(\pm\) 2.9 years) that demonstrated a 100% correct response rate were analyzed.  
Figure 1: MR-compatible vibrotactile stimulator. (**a**) Conceptual diagram of operation; (**b**) actual view of stimulator (sandbag is under the hand).
## 3 Results
### Case Study
#### 3.1.4 Representative Weather Conditions  
Electrical load demand greatly depends on meteorological conditions. The representative climatological conditions of the area are determined by considering these conditions at the zones associated with the primary loading areas, specifically the cities of Cuernavaca, Puebla, Veracruz, Villahermosa, and Poza Rica. Subsequently, the weighted average value of these factors--namely temperature, relative humidity, global horizontal irradiance (GHI), pressure, wind speed, and precipitable water--is calculated in accordance with Equation (5).
## 1 Introduction
These functions impose diverse optimization challenges, ranging from simple unimodal functions that test convergence speed to intricate multimodal and hybrid landscapes that stress exploration capabilities. The experiments highlight that JADEDO achieves competitive or superior results in terms of both solution quality (precision) and convergence speed. Statistical significance, assessed via the Wilcoxon sum-rank test, underscores JADEDO's consistent advantage over several established optimizers. Beyond synthetic benchmarks, we demonstrate the practical value of JADEDO in two domains. First, we address three engineering design problems with high stakes and multiple constraints: the pressure vessel, the spring, and the speed reducer. In each case, JADEDO attained highly competitive designs, underscoring its potential to reduce costs, maintain reliability, and meet strict physical constraints. Second, we apply JADEDO to an attack-response optimization problem, where timely and cost-effective countermeasures against potential threats must be identified. JADEDO excels in this domain by swiftly converging on strategies that minimize both risk and resource expenditure.
## Appendix A Sort I Error Analysis
\begin{split}\Pr\biggl{(}\left\lvert T(\mathbf{Y}(i),c_{i}) \right\rvert>\left\lfloor n\delta_{\beta}\right\rfloor\biggr{)}&= \sum_{l=\left\lfloor n\delta_{\beta}\right\rfloor+1}^{n}\binom{n}{l}e^{l}(1- \varepsilon)^{n-l}\\ &\leq\frac{\left(n\delta_{\beta}+1\right)(1-\varepsilon)}{n \delta_{\beta}-n\varepsilon}\cdot 2^{-n}\left[T_{\varepsilon}\left(\frac{\left\lfloor n \delta_{\beta}\right\rfloor+1}{n}\right)-H\left(\frac{\left\lfloor n\delta_{ \beta}\right\rfloor+1}{n}\right)\right]\\ &=\frac{\left(\delta_{\beta}+\frac{1}{n}\right)(1-\varepsilon)}{ \delta_{\beta}-\varepsilon}\cdot 2^{-n}\left[T_{\varepsilon}\left(\frac{\left\lfloor n \delta_{\beta}\right\rfloor+1}{n}\right)-H\left(\frac{\left\lfloor n\delta_{ \beta}\right\rfloor+1}{n}\right)\right]\\ &\triangleq\zeta_{1,n}.\end{split}  \tag{12}\]  
Observe that the exponent of exponential term is always _strictly_ positive, since for \(\varepsilon\in(0,1/2)\), the arguments of \(T_{\varepsilon}(\cdot)\) and \(H(\cdot)\) are strictly less than \(1/2\). That is, we have the following  
\[T_{\varepsilon}\Bigl{(}\Bigl{(}\left\lfloor n\delta_{\beta}\right\rfloor+1 \Bigr{)}\Big{/}n\Bigr{)}>H\Bigl{(}\Bigl{(}\left\lfloor n\delta_{\beta}\right \rfloor+1\Bigr{)}\Big{/}n\Bigr{)}. \tag{13}\]  
The argument is as follows:  
\[ \begin{split}\frac{l}{n}=\frac{\left\lfloor n\delta_{\beta} \right\rfloor+1}{n}\leq\frac{\max\left\lfloor n\delta_{\beta}\right\rfloor+1}{ n}&\overset{(a)}{<}\frac{\left\lfloor n\max\left(\varepsilon+ \beta(1/2-\varepsilon)\right)\right\rfloor+1}{n}\\ &\overset{(b)}{<}\frac{\left\lfloor n/2\right\rfloor+1}{n}\overset {(c)}{\leq}\frac{n/2+1}{n},\end{split}  \tag{14}\]  
which is strictly less than \(1/2\) in the asymptotic, i.e., as \(n\to\infty\), where \((a)\) and \((b)\) follows by the same arguments given for (14), and \((c)\) follows since \(\lfloor x\rfloor\leq x\) for real \(x\). Therefore, the difference for the evaluation of \(T_{\varepsilon}(\cdot)\) and \(H(\cdot)\) for a given fix argument is always a _strict_ positive value; see Figure 7. Hence, \(P_{\varepsilon,1}(i,\mathsf{K})\leq e_{1}\), \(\forall i\in\mathbb{T}_{\mathsf{K}}\) holds for sufficiently large \(n\) and arbitrarily small \(e_{1}>0\). Thereby, the sort I error probability satisfies \(P_{\varepsilon,1}(i,\mathsf{K})\leq\zeta_{1,n}\leq e_{1}\). This complete the analysis for the sort I error probability.
## 2 Results
### Dual-Frequency Microwave Plasma Source  
[PERSON] et al. in [1] presented a dual-frequency microwave plasma source based on microwave coaxial transmission line, and 915 and 2450 MHz microwaves were used in this study. Two waves were delivered from two ports into the plasma reactor. One of these waves was used to excite the plasma and the other to regulate the plasma characteristics. In this system, the electron density and electron temperature of plasma can be controlled by feeding in different frequencies from the second port. In this way, different frequencies can selectively drive the plasma characteristics. The OES (optical emission spectroscopy) results confirmed that the particles with different energy levels showed different responses at different frequencies. The comparison of the characteristics was carried out between the single-frequency microwave plasma and the dual-frequency microwave plasma in the same device. The presented device is interesting because it allows to some extent to regulate such plasma parameters as electron density and electron temperature. The range of these parameters can be extended by using microwave sources with adjustable power and frequency.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 5 Discussion  
Compared to prior approaches, the proposed method exhibits clear advantages in addressing key age estimation challenges. CNN-based hierarchical models such as SSRNet [10] and BridgeNet [14] pioneered coarse-to-fine prediction pipelines, but they rely on fixed age group partitions that can introduce quantization errors. In contrast, our two-stage network dynamically adjusts the local age range around each face's initial age prediction, mitigating rigid group boundary issues and directly tackling the ambiguity between neighboring age labels. Furthermore, whereas SSR-Net and BridgeNet do not leverage global context to guide fine-grained predictions, our model integrates a semantic attention mechanism that uses the global age estimate to focus on discriminative, age-sensitive facial regions. This guided refinement of local features enables the proposed method to distinguish subtle differences between adjacent ages more effectively than previous CNN-based methods. Transformer-based architectures have also advanced age estimation performance. By leveraging a Swin Transformer in the global stage, our model similarly benefits from rich contextual modeling, attaining a competitive MAE of 2.18 on the same benchmark and surpassing most existing frameworks. Notably, the proposed approach achieves superior cumulative score for closely spaced age labels, as demonstrated by a CS(5) of 90.96% on the Morph II dataset, underscoring its strength in fine-grained age differentiation and further validating the effectiveness of the semantic attention-guided hierarchical design.  
Automatic age estimation remains crucial in biometric recognition and various real-world applications, which underscores the value of further improving accuracy and robustness. Despite its strong performance, the proposed two-stage model has a few limitations. First, the neighborhood radius for refinement is manually defined for each dataset, which may not optimally adapt to every individual. Second, the incorporation of a Transformer backbone and a dual-stage architecture results in a relatively large model, potentially hindering deployment on resource-limited devices. In future work, we plan to explore adaptive mechanisms that learn an optimal neighborhood radius dynamically and to investigate model simplification or compression techniques to reduce the parameter count. Addressing these issues would further enhance the practicality and applicability of the proposed approach in real-world settings.