esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Protocol to Secure E-Commerce Transactions
### Procedures of Proposed Protocol  
In building our protocol, we depend on the mechanism of encrypting and decrypting transaction requests. This is accomplished by employing symmetric and asymmetric encryption techniques. The FER is used to encrypt security parameters and request information. We generally enumerate the steps for the proposed protocol.  
1. Using an ELG asymmetric encryption technique, our protocol generates large random keys that are both private and public. The random keys 1024-bit are then divided into 256-bit chunks to accommodate the keys of FER algorithm.
2. This public key is used to encrypt e-commerce requests between \(\mathcal{M}\) and \(TS\). When e-commerce requests are transferred from \(TS\) to \(\mathcal{M}\) or vice versa, our protocol employs strong encryption with high encryption randomness, thus making it difficult to hack.
3. The FER and ELG keys will be used to decrypt the required information. \(\mathcal{M}/TS\) device receives a decrypted message that is intractable to perforate by a hacker.
4. We employ DLD technology to safeguard information, particularly \(\mathcal{M}\) information, against leakage throughout the key generation, encryption, as well as decryption procedures.
5. Concealing security parameters and request information on devices of networks, particularly keys, is critical in situations of device hacking.
# SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance
## 4 Results and Validation  
### Experimental Results  
#### 4.1.1 Pre-Training Results  
To explore and verify the possibility of model improvement more quickly, we conducted pre-training on the original pedestrian and vehicle dataset. As shown in Figure 7, the model closely approximates the fitting accuracy of the baseline model while showing significant improvement in training efficiency.  
By observing the model accuracy and convergence efficiency (Table 2), it can be inferred that this YOLOv8 model may outperform the baseline YOLOv8 on medium- to large-sized objects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Graphics Card** & **Random Access** & **Display** & **Experimental** \\  & **Model** & **Memory (RAM)** & **Memory** & **Environment** \\ \hline \multirow{2}{*}{Model training} & NVIDIA RTX & \multirow{2}{*}{30.1} & \multirow{2}{*}{16.9} & Python 3.10.12 \\  & A4000 & & & torch2.0.1 GPU \\ \hline \multirow{2}{*}{Model testing} & NVIDIA GeForce & \multirow{2}{*}{16} & \multirow{2}{*}{8} & Python 3.8.1 \\  & RTX 3060 & & & torch2.0.1 GPU \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environments for model training and testing.  
the Recall increased by 0.8%, while mAP0.5 and mAP0.5: 0.95 increased by 0.5 percent and 9 percent, respectively. Additionally, the introduction of the GSConv lightweight module alone also resulted in a certain degree of improvement in mAP. The combination of GSConv, Triplet Attention, and Soft-NMS reduced the model parameters by 5.6% and GFLOPs by 7.9%. Meanwhile, SGST-YOLOv8 improved by 1.2% in Precision, 3.1% in Recall, 2.0% in mAP0.5, and 3.2% in mAP0.5:0.95.  
Compared to Scenario 3, which contains the GSConv module, Scenario 6 of the improved model has a slightly higher number of parameters. However, overall, the model combination in Scenario 6 enhances the comprehensive capability of the model. Although there is a slight decrease in performance after introducing the GSConv convolution, the combination of GSConv and Triplet Attention has improved the accuracy compared to the baseline YOLOv8 model. This is because these two techniques complement each other in terms of performance. The Triplet Attention module reduces information loss by enhancing feature representation, thereby improving accuracy. It compensates for the minor impact on performance resulting from the introduction of the GSConv convolution and VoV-GSCSP lightweight module. At the same time, convolutional operations like GSConv and VoV-GSCSP can reduce the complexity of calculations, thereby improving the model's computational efficiency. This makes the model more efficient during both training and inference stages.
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### Coexistence and Interoperability
#### 3.5.1 Beacon Shifting  
A coordinator may transmit its beacons at different time offsets relative to the start of the beacon periods by including a Beacon Shifting Sequence field in its beacons (specific sequence details can be  
Figure 15: Coexistence Format of IEEE 802.15.6 [9].  
found in section 5.3.1.10 of the standard [9]). A coordinator should choose a beacon shifting sequence that is not being used by its neighbor coordinators to mitigate potential repeated beacon collisions and scheduled allocation conflicts between overlapping or adjacent WBANs operating in the same channel.
## Appendix A Statics Analysis of the Buoy-Ballast System  
The entire system is assumed to move at a constant velocity \(v\) driven by the ROV in the left-right direction. Assuming that all forces intersect at \(B_{1}\), the compensation of forces exerted on the left buoy is calculated as  
\[W_{buoy}+T_{B1}^{y} = 0 \tag{1}\] \[-F_{d}^{buoy}-T_{1}+T_{B1}^{x} = 0 \tag{2}\]  
where \(T_{B1}^{x}\) and \(T_{B1}^{y}\) are the components of the force \(\mathbf{T_{B1}}\) that is exerted by the cable portion \(B_{1}C\) onto the left buoy. \(F_{d}^{buoy}\) is the intensity of the drag force exerted by the fluid on the buoy. \(T_{1}>0\) is the magnitude of the horizontal scalar force exerted by the cable that links the left buoy to the remote station. Assuming that all forces intersect at \(B_{2}\), the compensation of forces exerted on the right buoy is calculated as \[W_{buoy}+T_{B2}^{y} = 0 \tag{11}\] \[-F_{d}^{buoy}+T_{2}+T_{B2}^{x} = 0 \tag{12}\]  
where \(T_{B2}^{x}\) and \(T_{B2}^{y}\) are the components of the force \(\mathbf{T_{B2}}\) that is exerted by the cable portion \(B_{2}\)C onto the right buoy. \(T_{2}\) is the algebraic horizontal scalar force exerted by the cable that links the right buoy to the ROV. In the case of the figure, the intensity \(T_{2}\) is positive since the ROV pulls on the cable to move from left to right. Taking into account a drag force of intensity \(F_{d}^{CB_{1}}\) and \(F_{d}^{CB_{2}}\), resp., on the cable portions \(CB_{1}\) and \(CB_{2}\), the compensation of forces exerted on the ballast is  
\[-W_{ballast}-T_{B1}^{y}-T_{B2}^{y} = 0 \tag{13}\] \[-F_{d}^{ballast}-F_{d}^{CB_{1}}-F_{d}^{CB_{2}}-T_{B1}^{x}-T_{B2}^ {x} = 0 \tag{14}\]  
Equation (13) is actually not useful since it was already set that \(W_{ballast}=2.W_{buoy}\), which can be found again using Equations (12) and (13). In addition, the cable stiffness must be taken into account in the compensation of the efforts exerted on cable portions \(CB_{1}\) and \(CB_{2}\). Taking \(C\) as a reference point, the compensation of the moments of force and torques exerted on \(CB_{1}\) is expressed as  
\[\left[\mathbf{r_{CB_{1}}}\times(-\mathbf{T_{B1}})+\frac{1}{2}\mathbf{r_{CB_{1} }}\times\mathbf{F_{d}^{CB_{1}}}+\mathbf{T_{qB_{1}}^{s}}+\mathbf{T_{qC,right}^ {s}}\right].\mathbf{z}=\mathbf{0}\]  
and on \(CB_{2}\) as  
\[\left[\mathbf{r_{CB_{2}}}\times(-\mathbf{T_{B2}})+\frac{1}{2}\mathbf{r_{CB_{2 }}}\times\mathbf{F_{d}^{CB_{2}}}+\mathbf{T_{qB_{2}}^{s}}+\mathbf{T_{qC,left} ^{s}}\right].\mathbf{z}=\mathbf{0}\]  
where \(\mathbf{r_{CB_{1}}}\) and \(\mathbf{r_{CB_{2}}}\) are the position vectors of \(B_{1}\) and \(B_{2}\), resp., with regard to \(C\), and  
* \(\mathbf{T_{qB_{1}}^{s}}\) is the cable bending torque exerted onto the branch \(B_{1}C\) at \(B_{1}\), and \(\mathbf{T_{qB_{2}}^{s}}\) is the cable bending torque exerted onto the branch \(B_{2}C\) at \(B_{2}\). * \(\mathbf{T_{C,right}^{s}}\) and \(\mathbf{T_{qC,left}^{s}}\) are the cable bending torques exerted by the right and left parts, resp., at \(C\). These torques are proportional to a bending stiffness coefficient named \(C_{s}\).
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 3 Proposed Autoencoder-Based Image Transmission System
### Machine Perception of Images  
Since there is no widely recognized image quality metric specifically designed for machines and SCs, another CNN model is trained to evaluate the received images based on their classification accuracy. Classification accuracy is a critical metric, as it directly reflects the machine's ability to interpret and act on the transmitted information. This approach ensures that the system's performance is measured not just by traditional image quality metrics but by how effectively it enables machines to extract and utilize the intended semantic content from the images. Figure 5 demonstrates the proposed CNN image classification model to perceive the images. As shown in Figure 5, the input shape of \((28,28,1)\), two convolutional layers (kernel size \(=(3,3)\)) with a ReLU activation function, two max pooling layers (pool size \(=(2,2)\)), and three dense layers with a ReLU activation function, followed by a final dense layer with a sigmoid activation function, are used for the CNN classification test setup.  
Figure 4: Proposed end-to-end M2M communication system.  
Figure 5: Proposed CNN model for image classification.  
The main idea is to transfer the latent vector through a channel and determine what it implies from the M2M perspective after reconstructing it from the transmission. Since the idea of the SC is to increase the actual information content at a lower data rate, the autoencoder can be used to transmit the minimum number of bits while preserving the original quality of the data. The encoder takes the input data and compresses them into a lower-dimensional representation. The latent vector is then transferred through a channel. On the receiver side, the decoder takes the compressed representation and reconstructs the original data. The reconstructed data are used for image recognition via CNN as shown in Figure 5.
# Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication
## 2 CEHBC System Architecture  
### CEHBC System  
In general, the WCE system is composed of one transmitter (Tx) and one receiver (Rx) with multiple receiving electrodes, because the WCE Tx capsule moves in the human body in real time. Figure 1 shows an example of the use of the WCE system. The capsule endoscope transmits the image data while moving in the body, and the receiver continually requests data reception utilizing the receiving electrodes that are attached to the human body. There are three system limitations. First, the capsule endoscope must be operable for a long time without supply of additional external power. Second, high-resolution images are required for accurate diagnosis. Third, because of the signal transmission in the body, the characteristics of the reception signal are changed by the distance and position between the capsule endoscope and the electrodes. To overcome these limitations, we proposed the following three methods for the new CEHBC system. First, we used the frequency selective digital transmission (FSDT) modulation method in the HBC to implement ultra-low power consumption and to transfer the image data stably at a high data rate. Second, we proposed using the selection algorithm to choose the best combination of electrodes among the plurality of receivingelectrodes. Third, we applied the LineSync algorithm to synchronize and rematch the polarity of the signal for stable data transfer.
## 3 Results
### Isolation and Identification of the Intracellular Pigment  
To determine the nature of the pigment, we conducted pigment isolation from both the wild-type and \(\Delta\)PMA1 strains, followed by analysis using TLC, UV-Vis, and infrared spectrophotometry techniques. An initial visual inspection (Figure 5A) revealed that the pigments extracted from both strains appeared identical to the naked eye. Subsequently, we employed TLC chromatography (Figure 5B) with a melanin standard and the pigments isolated from the two strains. The congruent chromatographic patterns strongly indicated the presence of melanin in the preparations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strain** & **Culture Medium** & **Growth Rate (\(\mu\))** & **Generation Time (g)** \\ \hline \multirow{2}{*}{FB2} & YPD & 0.297 \(\pm\) 0.04 h\({}^{-1}\) & 2.34 h \\  & MM & 0.271 \(\pm\) 0.08 h\({}^{-1}\) & 2.55 h \\ \multirow{2}{*}{\(\Delta\)PMA1} & YPD & 0.282 \(\pm\) 0.09 h\({}^{-1}\) & 2.45 h \\  & MM & 0.247 \(\pm\) 0.09 h\({}^{-1}\) & 2.80 h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetics parameters of growth of wild strain FB2 and FB2-PMA1 mutant. Cells were cultured in YPD or mineral media for 72 h.  
Figure 4: Growth in YPD of the wild-type FB2, and mutant \(\Delta\)PMA1 cells of _U. mydis_. Cells were cultured in YPD (FB2, black circle; APMA1, red circle) and minimum media (FB2, inverted green triangle; \(\Delta\)PMA1, yellow triangle) for 72 h. The growth of both strains was manipulated in the same way as mentioned in the Methods section.  
For a more comprehensive analysis of the pigment, we generated UV and IR spectra from two separate preparations. As illustrated in Figure 5C, the UV-Vis spectra of eumelanin and the two samples displayed considerable overlap, with all three spectra exhibiting an absorbance band peaking at 230 nm. This peak is a characteristic feature shared by commercial melanin and the fungal melanin found in _Yarrowia lipolytica_[39].  
In Figure 5D, the IR spectrum displayed a broad absorption band spanning from 3200 to 3300 cm\({}^{-1}\), signifying the presence of hydroxyl groups (O-H) and -NH bonds [34; 40]. The peak observed at 1718 cm\({}^{-1}\) can be attributed to the vibrations of C=O or COOH groups, while the peak at 1620 cm\({}^{-1}\) corresponds to the vibrations of aromatic groups C=C and COO-. Additionally, a shoulder observed at 2300 cm\({}^{-1}\) indicates N-H stretching. A comparative analysis of the infrared spectra indicated the presence of bands associated with phenolic groups (3200-3400 nm), further corroborating that the pigment is indeed melanin.  
Figure 5: Identification of the intracellular pigment. (**A**) Pigment extracted from cells cultivated in YPD for 72 h. (**B**) Thin layer chromatography of the pigment isolated. (**C**) UV spectra of the isolated pigment and melanin standard with a 230 nm maximum absorption. (**D**) IR spectra of commercial melanin and the isolated pigment.
# A Review on Recent Trends and Applications of IoT in Additive Manufacturing
###### Abstract  
The Internet of Things (IoT) is a new way of communicating that is changing the way things are monitored and controlled from a distance. Gradually, companies want to digitalize their production processes and implement control and monitoring systems on the shop floor. On the basis of the Industry 4.0 concept, internet features and database services have been incorporated into processes in order to reinvent manufacturing. This study proposes a proof-of-concept system for the management of additive manufacturing (AM) machines, where an internet integration of beacon technology in the manufacturing environment enables the rapid and intuitive interchange of production data retrieved from machines with mobile devices in various applications. Even though AM technologies can be used to customize the final product, they cannot be used to make a lot of 3D-printed jobs at once for commercial usage. Therefore, this research-based study aims to understand IoT technologies to improve the understanding and reliability of AM processes and 3D print smart materials in large quantities for manufacturers around the world. This study demonstrates the significance of the successful use of internet-based technologies in AM by examining its practical consequences in various fields. This paper gives an overview of IoT-based remote monitoring and control systems that could solve problems in AM, particularly in digital twin, human augmentation (HA), 3D biopirnets, 3D scanners, input parameters optimization, and electronics fields. IoT in AM makes production processes more efficient, reduces waste, and meets customer needs.  
additive manufacturing; IoT; welding +
Footnote †: journal: _Journal of Applied Soft
## 2 State-of-the-Art: IAR Applications for Training and Assistance  
### IAR Training Systems  
Well-trained operators have a major impact on productivity. IAR can help during training processes by providing contextual information and step-by-step instructions when performing specific tasks. Moreover, IAR applications can help after the execution of a task in order to monitor the performance of the trainee. Such support and feedback are very important when training workers to operate machinery like the one used for assembling in sequence, since they reduce the time and effort dedicated to check documentation [22] and enhance the accuracy (e.g., by reducing the error rate in assembly tasks [23]) and efficiency of the performed task. Therefore, IAR can reduce training time for new employees and lower their skill requirements by significantly decreasing the impact of previous experience in the learning process. Moreover, the displayed instructions can be adapted to the previous experience of the workers. In this regard, IAR emerges as a human-centered tool to assist non-expert and less skilled operators in executing new tasks.  
There are some interesting examples of the benefits of IAR training in the literature. For instance, in [16], the authors evaluated a virtual training system for the assembly of a pneumatic cylinder through both AR and Virtual Reality (VR) applications. Based on a 16 people evaluation, the results showed significant differences in the system usability between AR and VR, but no discrepancy in terms of ergonomics and the perceived task load. As a result, some recommendations for the design of AR-based systems were indicated. Such guidelines were given in terms of task, user, information, interaction, and technology adequacy.
# Manipulating Stress Responses during Spaceflight Training with Virtual Stressors
## 5 Results
### NASA-TLX: Workload  
The main effect of stressor level on workload (\(N=39\)) was significant, \(F(1.63,62.1)=12.5\), \(p<0.001\), with a large effect size, \(d=1.15\) (Figure 11). Pairwise comparison indicated the workload was significantly higher (\(p=0.002\), \(d=0.7\)) for participants in high stressor (\(M=57.5\), \(SD=17.6\)) compared to low stressor (\(M=45.1\), \(SD=17.8\)), significantly higher (\(p<0.001\), \(d=0.61\)) for high stressor compared to medium stressor (\(M=46.9\), \(SD=17.4\)), but not significantly (\(p=0.99\)) different for medium stressor compared to low stressor.  
Within the NASA-TLX subscales, mental workload was significantly different, \(F(1.7,66.2)=4.28\), \(p=0.022\), \(d=0.67\), with the high stressor (\(M=55.6\), \(SD=21.9\)) being marginally significantly higher than the low stressor (\(M=45.8\), \(SD=23.1\); \(p=0.064\), \(d=0.44\)). Physical workload was significantly different, \(F(2,90)=7.78\), \(p=0.001\), with the high stressor (\(M=45.5\), \(SD=26.6\)) being significantly higher than the low stressor (\(M=34.7\), \(SD=26.2\); \(p=0.01\), \(d=0.41\)). Temporal workload was significantly different, \(F(2,76)=14.1\), \(p<0.001\), with the high stressor (\(M=68.2\), \(SD=26.6\)) being significantly higher than the low stressor (\(M=47.8\), \(SD=23.8\); \(p<0.001\), \(d=0.81\)), high stressor being significantly higher than the medium stressor (\(M=58.4\), \(SD=25.3\); \(p=0.011\), \(d=0.38\)), and medium stressor being significantly higher than the low stressor (\(p=0.036\), \(d=0.43\)). Performance was significantly different, \(F(1.8,68.6)=5.22\), \(p=0.01\), \(d=0.74\), with the high stressor (\(M=63.4\), \(SD=32.8\)) being significantly higher than the medium stressor (\(M=43.7\), \(SD=32.9\); \(p=0.008\), \(d=0.6\)). Effort was significantly different, \(F(1.63,62.1)=5.1\), \(p=0.013\), with the high stressor (\(M=67.8\), \(SD=22.8\)) being significantly higher than the medium stressor (\(M=59.4\), \(SD=23.1\); \(p=0.001\), \(d=0.37\)), and the high stressor being marginally significantly higher than the low stressor (\(M=59\), \(SD=21.4\); \(p=0.053\), \(d=0.40\)). Frustration was significantly different, \(F(1.77,67.3)=6.53\), \(p=0.004\), with the high stressor (\(M=45.3\), \(SD=31.2\)) being significantly higher than the medium stressor (\(M=32.4\), \(SD=28\); \(p=0.012\), \(d=0.43\)) and low stressor (\(M=34.4\), \(SD=27.5\); \(p=0.043\), \(d=0.37\)).
## 2 Materials and Methods
### Practical Implementation of the Proposal  
The pseudocode presented in Algorithm 1 describes the control process based on the low-pass filter (LP, V2 GGlide) applied to a power system using fuzzy logic. This method uses as inputs the PV power (\(P_{PV}\)) and the state-of-charge (\(SOC_{LI}\)) of a lithium battery. Initially, the devices are connected using the Modbus protocol to read the initial values of these variables.  
Subsequently, a fuzzy inference system (FIS) is defined that includes two input variables, \(SOC_{LI}\) and \(P_{PV}\), and an output variable, \(\alpha\), which determines the relative weight between the previous smoothed value and the current value. For these variables, triangular membership functions (trimf) are configured and adapted to their operating ranges. In addition, fuzzy rules are added that allow the FIS to dynamically adjust the value of \(\alpha\) based on the current system conditions.  
During real-time processing, a continuous loop, with a sampling interval of 100 ms, reads the current values from the Modbus devices. The FIS evaluates these values to calculate the \(\alpha\) factor and apply the V2 GGlide method, which uses a low-pass filter to smooth out PV power fluctuations. The result is a compensated power (\(P_{PV}\)) that is written back to the Modbus devices. In addition, the state-of-charge of the lithium batteries is continuously monitored to ensure system stability. This process is repeated indefinitely, providing efficient, real-time power control while ensuring stable and predictable system behavior.  
```
0: \(SOC_{LI}\), \(P_{PV}\) \(\bullet\) Membership functions for \(SOC_{LI}\), \(P_{PV}\) \(\bullet\) Fuzzy rules \(\bullet\) Sampling time: \(t\) = 100 ms.
0: \(\bullet\)\(P_{PV}\) (Compensated power) Process: Initialization:
1. Read initial values from Modbus devices: \(SOC_{LI}\) (state-of-charge of lithium batteries). \(\circ\)\(P_{PV}\) (photovoltaic power).
2. Define Fuzzy Logic System (FIS): \(\circ\) Inputs variables: \(\bullet\)\(SOC_{LI}\) with range [0, 100]. \(\bullet\)\(P_{PV}\) with range [0, 15]. \(\circ\) Output variable: \(\bullet\)\(\alpha\) (smoothing factor) with range [0, 10]. \(\circ\) Configure triangular membership functions (trimf) for input and output variables. \(\circ\) Add fuzzy rules to adjust \(\alpha\) based on \(SOC_{LI}\) and \(P_{PV}\).
```  
**Algorithm 1**Pseudocode for implementing the V2 GGlide method