esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sensing and Regulatory Processes during Acid Stress
A decrease in the intracellular pH (pH \(\
eg 4\)) prompts a reversible structural rearrangement in GadB which disrupts the covalent bond between the His465 and PLP, resulting in the exposure of the active site and enabling the decarboxylation of glutamate while recruiting the protein to the membrane [118; 121; 122]. High-resolution X-ray crystal structures of GadC [123] and AdiC [124; 117; 125] have been solved and a model for substrate binding and conformational changes associated with the AdiC transport cycle has been proposed. Current knowledge on the structures, transport mechanisms, and regulation of these acid resistance associated antiporters have been recently reviewed [126]. Interestingly, the antiporters of each decarboxylation system in _E. coli_ possess an invariant glutamic acid in the intermembrane domain that has been proposed to act as a pH sensor [117]. The model proposes that under extreme acidic conditions (between pH 2-3) AdiC assumes a conformation that exposes Glu208 to the acidic periplasm, resulting in its protonation. Once the conformation changes during substrate transport, Glu208 faces the less acidic cytosol returning to its deprotonated form [117]. Similarly, GadC is active at pH below 6.5 which promotes the rearrangement in the C-terminal region leaving the antiporter channel exposed [123]. This system has been previously reviewed [127; 10]. The aforementioned invariant Glu is conserved in _L. monocytogenes_ ArcD (Lmo0037 Glu205) and also in GadT1 and GadT2 (Lmo0448 Glu212 and Lmo2362 Glu213) ([PERSON], [PERSON] and [PERSON], unpublished data), which further supports the crucial role of this residue in low pH detection in antiporters from both bacterial species. In _E. coli_ CadC, a ToxR-like DNA-binding transmembrane protein, has a periplasm-spanning C-terminal domain that acts as acid sensor through five negatively charged residues (Asp198, Asp200, Glu491, Glu468, and Asp 471) that are protonated under environmental low pH [128; 129; 130]. CadC activity is modulated by LysP, a lysine permease [131] that interacts with CadC enabling its activation in the presence of both lysine and low pH, resulting in the transcriptional activation of the _cadAB_ operon [130; 131; 132; 133; 134]. CadA expression is induced at low pH, high lysine concentrations, and anaerobic conditions. Its enzymatic activity also depends on its oligomerization state and it is further regulated by the alarmone ppGpp [37; 135]. The transcriptional regulator ArgR, a repressor of the arginine biosynthetic pathway in _L. monocytogenes_, is also involved in the acid stress response to lactic acid in this bacterium. The expression of \(\sigma^{\text{B}}\) was repressed by ArgRwhen bacteria were exposed to acidic conditions in an arginine-dependent manner [136] (Figure 1B). Finally, there is some evidence that the LisRK two-component system may also be involved in acid stress resistance in _L. monocytogenes_[137], but further studies will be required to better understand the role the LisRK regulon plays in acid stress resistance in this bacterium.
## 8 Most Significant EEG- and Motor Imagery-Based Studies in the Last 7 Years  
Table 2 lists the results of the most significant EEG and motor imagery-based studies using deep learning methodologies to classify between different motor simulation tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Brand** & **Model** & **Number of Channels** & **Intended Use** \\ \hline Emotiv & Emotiv EPOCH & 5–14 channels & Used in research and for personal use \\  & Emotiv Insight & & \\ BIOPAC systems & EEG100C & 16 channels & Used in sleep studies and evoked responses \\ OpenBCI & OpenBCI 32-bit & 4–21 channels & Used in BCI and biosensing \\  & OpenBCI Cyton & & \\  & OpenBCI Ganglion & & \\  & Ultracortex BCI & & \\ Narosky & Brainwave & 1 channel & Used in neurogaming and meditation \\  & Mindflex & & \\  & Mindwave & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of EEG devices.  
Figure 10: Most popular datasets.  
In recent years, several notable studies have emerged in the field of EEG and motor imagery-based deep learning models for stroke rehabilitation. For instance, ref. [85] introduced an Adaptive CNN model applied to the BCI competition IV dataset 2a, achieving a classification accuracy of 93.20%. This approach dynamically adjusts convolutional layers to enhance feature extraction from EEG signals, showing promise in handling variability in EEG data. Similarly, ref. [86] proposed an attention-based CNN model, tested on data collected from 30 subjects, which achieved 88.75% accuracy. The attention mechanism improves the model's ability to focus on relevant features, enhancing classification performance.  
Further advancements include [87], which utilized a Graph-CNN on the BCI competition IV dataset 2b, achieving an accuracy of 89.60%. This study highlights the effectiveness of graph-based methods in capturing spatial relationships in EEG data. Ref. [88] presented a Hybrid CNN-RNN model applied to the PhysioNet EEG MI Dataset, with a notable accuracy of 94.50%. This hybrid model leverages the strengths of both CNNs and RNNs in feature extraction and temporal sequence learning, respectively.  
Lastly, ref. [89] introduced a Transformer-based model for EEG classification on the BCI competition IV dataset 2a, achieving an accuracy of 92.30%. The Transformer architecture, known for its success in natural language processing, demonstrates its potential in EEG signal analysis by effectively capturing long-range dependencies.  
These recent studies underscore the rapid advancements in applying deep learning techniques to EEG and motor imagery data, pushing the boundaries of stroke rehabilitation research and offering new avenues for enhancing patient outcomes.
# Modeling and Experimental Verification of Induction Heating of Thin Molybdenum Sheets
## 3 Design of Electro-Thermal Simulation Model of Induction Heating
The setting for the simulation is as follows:  
* The current flowing through the coil is 150 A
* The thickness of the ferrite core is 2:1:12 mm
* The operating frequency of the applied field is 25 kHz
* Distance (gap) Between sheet and heating element is 3 mm  
From Figure 7, the fluctuations of the electromagnetic loss density located at the molybdenum sheet are seen during the parametric change of the thickness of the ferrite core. This fact is related to the variations of the ferrite core's electromagnetic excitation when its thickness is being changed. Thus, it is valid here that for the given thickness of the ferrite core and the applied field's input parameters, a proper value of the heating element's inductance is achieved while providing optimal magnetic path and coupling between molybdenum sheet and heating element. It ensures that the volumetric loss density within the  
The last parametric evaluation focuses on investigating the impact of the value of the applied electromagnetic field (Figure 9). Figure 8: Distribution of the volumetric loss density within the molybdenum sheet at different geometry of the ferrite thickness. Figure 9: The dependency of maximal electromagnetic volumetric losses within the molybdenum sheet on the operating frequency. These relationships have been investigated for the constant distance between the heating element and sheet and for the ferrite's constant geometry. The values were selected based on previous results, whereby the solution with the highest volumetric loss on the sheet was selected. The settings for these types of simulations are as follows:  
* The current flowing through the coil is 150 A. * The thickness of the ferrite core is 7 mm. * The applied field's operating frequency is 20:5:40 kHz (initial frequency 20 kHz, step-change 5 kHz, and final frequency 40 kHz). * Distance (gap) between sheet and heating element is 3 mm. Figure 10 shows that with the increase of the EM field's operating frequency, the volumetric losses within the molybdenum sheet increase almost linearly. The experiment was realized for the range from 20 kHz up to 40 kHz. Within this range, the saturation of ferrite material is avoided. The optimal operation point for this experiment was reached at 40 kHz. Above this value, the magnetic saturation will cause a decrease in the value of inductance; thus, the coupling shall also be disturbed, and the required heat rate within the molybdenum sheet shall be insufficient.
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
###### Abstract  
Global Navigation Satellite System (GNSS) operation can be affected by several environmental factors, of which ionospheric scintillation is one of the most significant. Scintillation is usually characterized by two indices, namely the amplitude scintillation index (S4) and phase scintillation index (\(\sigma_{\varphi}\)). However, these two indices can only be generated by specialized GNSS receivers, which are not widely available all around the world. To popularize the study of scintillation, this article proposes to use more accessible parameters, namely multipath (MP) and rate of change of total electron content index (ROTI), to characterize scintillation. Using GPS data obtained on six days in total from three stations, namely PRU2 and SAOOP located in Sao Paulo, Brazil and SNA0P located in Antarctica, respectively, both the time series plots and 2D maps were generated to investigate the relationship of scintillation indices (S4 and \(\sigma_{\varphi}\)) with MP and ROTI. To prevent the effect of the real multipath error, a 30-degree satellite elevation mask is applied to all the data. As the scintillation indices S4 and \(\sigma_{\varphi}\) have a sampling interval of 1 min, MP and ROTI are calculated with the same sampling interval for a more direct comparison. The results show that the structural similarity (SSIM) and correlation coefficient (CC) between parameters was greater than 0.7 for 70% of outputs. In addition, the variogram and cross-variogram are applied to investigate the spatial structure of the MP, ROTI, S4 and \(\sigma_{\varphi}\) in order to support the results of SSIM and CC. With outputs in three forms, promising spatial and temporal relationships between parameters was observed.  
GNSS; scintillation parameters; multipath; ROTI; relationship +
Footnote †: journal: Sensors
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Result
#### 3.2.1 Metrics Visual Analysis of Classification Results
Among the four sets of segmentation results, different degrees of shadows exist on different features within the original image, and among the three models, only SwinUnet can better reduce the shadow effect and segment the features, while the other  
Figure 11: Comparison of transformer’s segmentation results in the Vaihingen data set. The red boxes are where the differences between the three network segmentations are large. two models produced more incorrect segmentation for the shadow part, but SETRnet produced less of this situation compared with TransUnet. This situation may be due to the transformer's insufficient learning ability for shadows. At the same time, we can see a significant improvement in the segmentation effect of SwinUnet and SETRnet for vehicles in the Potsdam data set. It shows that with the expansion of the data set scale, the sample size of the ground objects increases, and the segmentation accuracy of the transformer for small-scale ground objects also increases.
# Imperceptible-Visible Watermarking to Information Security Tasks in Color Imaging
## 4 Experimental Results
### Dataset Detail and Binary Watermark Pattern  
The dataset used to evaluate the performance of the proposed algorithm is obtained from the uncompressed color image dataset (UCID) [36], which contains 1338 uncompressed color images with 512 \(\times\) 384 size and TIFF format. As shown in Figure 9, three binary watermark patterns are used for the performance evaluation, where Figure 9a,c are two owner logotypes with sizes of 150 \(\times\) 150 and 134 \(\times\) 39, respectively. Figure 9b corresponds to a QR code of 116 \(\times\) 116 size and error correction label L that corresponds to a 7% of loss information; additionally, to specifically evaluate QR codes, error correction levels M, Q, and H are capable of information decoding with 15%, 25%, and 30% of loss information [37], respectively.
# Technologies and Fabrication of Intelligent Packaging for Perishable Products
## 2 Intelligent Packaging Techniques  
Different kinds of technologies have been utilized for developing intelligent packaging, targeting to track and identify products, monitoring the status of products for maintaining the quality of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Keywords** & **2014** & **2015** & **2016** & **2017** & **2018** \\ \hline \multicolumn{5}{c}{(a)} \\ \hline Active Packaging & 2246 & 2636 & 2757 & 2628 & 2968 \\ Intelligent Packaging & 483 & 718 & 815 & 1098 & 1292 \\ Indicator and Intelligent Packaging & 14 & 23 & 31 & 29 & 40 \\ Sensor and Intelligent Packaging & 208 & 280 & 306 & 412 & 521 \\ Radio-frequency identification (RFID) and Packaging & 289 & 308 & 243 & 309 & 311 \\ Printing and Intelligent Packaging & 29 & 35 & 46 & 61 & 80 \\ \hline \multicolumn{5}{c}{(b)} \\ \hline Active Packaging & 0 & 0.540166 & 0.707756 & 0.529086 & 1 \\ Intelligent Packaging & 0 & 0.290482 & 0.410383 & 0.760198 & 1 \\ Indicator and Intelligent Packaging & 0 & 0.346154 & 0.653846 & 0.576923 & 1 \\ Sensor and Intelligent Packaging & 0 & 0.230032 & 0.313099 & 0.651757 & 1 \\ RFID and Packaging & 0.676471 & 0.955882 & 0 & 0.970588 & 1 \\ Printing and Intelligent Packaging & 0 & 0.117647 & 0.333333 & 0.627451 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: (a) Recent publications about intelligent packaging. (b) Recent publications about intelligent packaging after normalization.  
Figure 1: Publication trends: (a) active packaging and intelligent packaging (b) main techniques in intelligent packaging.  
products, while differentiating one product from another. In this section, identification techniques and sensing methods are mainly reviewed.
## 2 Theoretical Background
### Thermal Inertia Evaluated In Situ via Remote-Sensing Thermal and Optical Images  
Remote-sensing soil water contents, SWC\({}_{\text{RS}}\), can be computed by substituting a simplified thermal inertia, the so-called apparent thermal inertia, \(AT^{*}\) [-], in place of the actual thermal inertia, \(P\):  
\[Kp=\frac{P-P_{dry}}{P_{wet}-P_{dry}}\approx\frac{ATI^{*}-ATI^{*}_{dry}}{ATI^{* }_{wet}-ATI^{*}_{dry}} \tag{5}\]  
The method requires two thermal images (acquired at different times), the solar irradiance reaching the ground, \(E_{0}\) [W m\({}^{-2}\)] and the shortwave albedo at noon, \(\alpha_{SW}\) [-]. The value of \(ATI^{*}\) is computed as:  
\[ATI^{*}=\frac{(1-\alpha_{SW})}{\Delta LST}\cdot E_{0}\cdot[cos(\omega_{t2}- \delta_{1})-cos(\omega_{t1}-\delta_{1})] \tag{6}\]  
where \(\Delta LST=LST_{t2}-LST_{t1}\) is the land surface temperature difference between two acquisition times, \(t_{2}\) and \(t_{1}\), \(\omega\) [s\({}^{-1}\)] is the angular velocity of the earth (7.27 \(\times\) 10\({}^{-5}\) rad s\({}^{-1}\)), and \(\delta_{1}\) [s\({}^{-1}\)] is the phase difference of soil temperature for the first order approximation of the mono-dimensional thermal diffusion equation of semi-infinite homogeneous soil [3]. Equivalence in Equation (5) is valid when solar altitude and phase difference can be assumed to be almost constant in time and space. This condition occurs when the Xue and Cracknell approximation [30] is used to calculate \(P\). Once \(Kp\) is calculated, SWC\({}_{\text{RS}}\) is computed using Equation (2).The thermal inertia method was applied to remote-sensing images acquired during a field experiment aimed to assess the SWC spatial distribution (see Section 3).
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
###### Abstract  
Deployment of surface-level gateways holds potential as an effective method to alleviate high-propagation delays and high-error probability in an underwater wireless sensor network (UWSN). This promise comes from reducing distances to underwater nodes and using radio waves to forward information to a control station. In an UWSN, a dynamic energy efficient surface-level gateway deployment is required to cope with the mobility of underwater nodes while considering the remote and three-dimensional nature of marine space. In general, deployment problems are usually modeled as an optimization problem to satisfy multiple constraints given a set of parameters. One previously published static deployment optimization framework makes assumptions about network workload, routing, medium access control performance, and node mobility. However, in real underwater environments, all these parameters are dynamic. Therefore, the accuracy of performance estimates calculated through static UWSN deployment optimization framework tends to be limited by nature. This paper presents the _Prediction-Assisted Dynamic Surface Gateway Placement_ (PADP) algorithm to maximize the coverage and minimize the average end-to-end delay of a mobile underwater sensor network over a specified period. PADP implements the Interacting Multiple Model (IMM) tracking scheme to predict the positions of sensor nodes. The deployment is determined based on both current and predicted positions of sensor nodes, which enables better coverage and shorter end-to-end delay. PADP uses a branch-and-cut approach to solve the optimization problem efficiently, and employs a disjoint-set data structure to ensure connectivity. Simulation results illustrate that PADP significantly outperforms a static gateway deployment scheme.  
underwater wireless sensor networks (UWSNs); dynamic surface gateway placement; optimization +
Footnote †: 22 March 2019; Accepted: 23 April 2019; Published: 28 April 2019
## 3 Experimental Studies and Discussions
### Voltage and Power Output Performance
The power output responses were measured under a constant excitation of 0.745 g, with different resistance loads around 100 Hz. The resulting voltage across the load resistance was measured and recorded, as shown in Figure 7.  
Figure 7 illustrates the relationship between the RMS output power and load resistance for the piezoelectric transducers, demonstrating that the optimal output power is contingent upon the specific resistance load. The transducers achieve their peak performance under their respective best-matching resistance loads, which are 70 k\(\Omega\), 60 k\(\Omega\), 100 k\(\Omega\), 70 k\(\Omega\), 100 k\(\Omega\), and 80 k\(\Omega\) for each transducer. Correspondingly, the peak RMS power outputs are 0.69 mW, 0.63 mW, 0.36 mW, 0.39 mW, 0.36 mW, and 0.56 mW, respectively. Collectively, the piezoelectric transducers can deliver a total power output of 2.99 mW under these  
Figure 6: Open circuit RMS voltage output of all the piezoelectric transducers. Figure 7: RMS power output performance of all the piezoelectric transducers. conditions. It is important to note that increasing the amplitude of the excitations can significantly boost the power output. It should be highlighted that the resistance loads were utilized to assess the power output capability. Although connecting the piezoelectric sheets to voltage-rectifying bridges and capacitors might diminish the output power, the proposed PEH still demonstrates significant potential to supply power to a wireless sensor node.
## 4 Experiments and Discussion
### Experimental Setup
During the training of the DNN, [PERSON] was employed as the optimizer, and the initial learning rate was preset to 0.0001. In addition, the training was performed using an early stopping strategy, i.e., once the loss did not decrease after 10 iterations, the training was stopped immediately, and the model was saved to prevent model overfitting. All models in this work were built on the Pytorch, and all algorithms were run on the Intel Core i7-9750H CPU (2.6 GHz) and Geforce RTX2080 Max-Q GPU (8 GB). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Stretching Process** & **Central Wavelength Range** & **Stretching Rate and Length** \\ \hline Stretching stage I & 1544.8–1548.2 nm & 5 \(\upmu\)m once every 3 s \\ Stretching stage II & 1548.2–1550.8 nm & 10 \(\upmu\)m once every 3 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information about the stretching process of FBG sensors. Figure 3: (**a**) The reflection spectrum of the FBG sensor used has an initial state center wavelength of 1544.8 nm and a 3 dB bandwidth of 0.15 nm. (**b**) Changes in the reflection spectrum of the FBG sensor during stretching. The arrow indicates the direction of stretching, the stretching step in the red linear range (stretching step I) is 5 \(\upmu\)m, and the stretching step in the blue linear range (stretching step II) is 10 \(\upmu\)m. (**c**) Diagram of the LPG reflection spectrum as a filter with a central wavelength of 1566.7 nm. The blue dashed range (1538.7–1594.8 nm) is the range that can be used for FBG sensor filtering.
# TRAP: A Three-Way Handshake Server for TCP Connection Establishment
## 4 System Design
### System Implementation
#### 4.2.3 TCP Option Types  
In order not to conflict with any existing implementation of TCP stacks for compatibility reasons, we chose a reserved number, 0x56, as our TCP option type number.  
Both the server module and the client module rely on this option number to check if the packet contains a TRAP command. If a packet does not contain such option number, it is simply ignored as not being a TRAP command.  
We utilized Wireshark, the packet-dumping program, to monitor the packet flow [22]. Figure 10 shows an example of viewing the raw packet using Wireshark.  
Figure 10: Packet dump viewed by Wireshark.  
Figure 9: Example code of acquiring packets using Netfilter hooks.
# Improving Tire Specification Character Recognition in the YOLOv5 Network
## 3 Experiments
### Ablation Experiment  
To validate the performance improvement brought by the decoupled head, C3-Faster, and WIOU in the YOLOv5 network, ablation experiments were conducted. A total of five experiments were set up, including the original YOLOv5s network, YOLOv5s with a decoupled head and C3-Faster, YOLOv5s with a decoupled head and WIOU, YOLOv5s with C3-Faster and WIOU, and YOLOv5s with a decoupled head, C3-Faster, and WIOU. These experiments were conducted on the same device with the same parameters. The results of the ablation experiments for the different modules of the improved YOLOv5s are shown in Table 4.  
On the car tire dataset, the improved YOLOv5s network outperformed the original network model in the ablation experiment. The mAP of the improved YOLOv5s network was 97.1%, and the precision was 95.4%. Compared with the original model, the training  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Architecture** & **mAP (\%)** & **Precision (\%)** & **Training Time (h)** \\ \hline YOLOv5s & 93.4 & 93.3 & 7.435 \\ YOLOv5s + Decoupled Head & 95.6 & 93.9 & 7.510 \\ YOLOv5s + Decoupled Head + C3-Faster & 94.9 & 94.3 & 7.411 \\ YOLOv5s + Decoupled Head + WIOU & 93.8 & 95.0 & 7.510 \\ YOLOv5s + C3-Faster + WIOU & 95.5 & 94.4 & **7.281** \\ YOLOv5s + Decoupled Head + C3-Faster + WIOU & **97.1** & **95.4** & 7.341 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experimental results of the different modules for improving YOLOv5s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Architecture** & **mAP (\%)** & **Precision (\%)** & **Training Time (h)** \\ \hline YOLOv5s + GIoU & 91.2 & 91.1 & 7.354 \\ YOLO5s + DIoU & 92.7 & 89.3 & 7.621 \\ YOLO5s + CIoU & 93.4 & 93.3 & 7.435 \\ YOLO5s + WIoUv1 & 94.6 & **92.9** & 7.401 \\ YOLO5s + WIoUv2 & 95.0 & 92.1 & 7.231 \\ YOLO5s + WIoUv3 & **96.0** & 92.2 & **7.136** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experimental results of the different loss functions.  
time did not change much, but the other indicators were improved. The mAP increased by 3.7 percentage points, and the precision increased by 2.1 percentage points.