esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Real-Time Evaluation of HRI with BCI
### Application of BCI in Different Fields of HRI  
To illustrate the application of BCI combined with the potential psychological effects in HRI, we next describe a few typical scenarios from different HRI fields for exemplary purposes.  
Figure 2: General steps for developing BCIs for HRI evaluation and optimization.  
The increasing use of robotic assistance in medicine calls attention to their critical influences on humans, which might even endanger the life of patients under extreme circumstances. The extent of the motor priming effect of a robotic nurse's movement on a human surgeon could be tracked with a BCI approach. A high degree of motor priming effect indicates that the surgeon might be seriously affected by the robotic movement [42], therefore the velocity of the robot's movement could be set to an appropriate range, matching the habit of the human surgeon in this situation, rather than as fast as possible, in order to avoid detrimental consequences.  
Another field with extensive application of robotics is the manufacturing industry. Indeed, joint action for humans and industrial robots has not only been investigated in studies [91; 92], but also become a reality in factories [93]. When an industrial robot and a human worker assembly capital goods together, it is important for both of them to form a unified representation of their shared task, which could be monitored be means of a BCI. If the BCI outcome indicates insufficient task co-representation for the human worker (e.g., weak motor planning EEG activities before the movement of the robot), the robot should be adjusted to display more predictable behaviors in line with the human partner's expectation, which has been proved to facilitate joint action performance in HRI [43].  
Meanwhile, the social facilitation effect induced by robotic presence is ubiquitous in most fields of HRI, including the aforementioned scenarios. With a BCI to evaluate the magnitude of such effect in real time, the electronic monitoring function of the robot could be emphasized when beneficial to human performance and weakened when detrimental, resulting in improved productivity.
## 3 Experiments
### Performance Characterization  
In this paper, Accuracy, Precision, Recall, and F1-Score are selected as performance evaluation metrics with the following formulas:  
\[Accuracy=\frac{TP}{TP+FP+TN+FN} \tag{19}\]  
\[Precision=\frac{TP}{TP+FP} \tag{20}\]  
\[Recall=\frac{TP}{TP+FN} \tag{21}\]  
\[F1-Score=\frac{P\cdot R}{P+R} \tag{22}\]  
where TP (True Positive) is normal attack, TN (True Negative) is normal traffic, FN (False Negative) is attack categorized as normal traffic, FP (False Positive) is normal traffic categorized as attack, \(P\) is precision, and \(R\) is recall.
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
## 2 Seal Calculation Model  
### Seal Structure and Force Analysis  
Figure 2 shows a schematic diagram of seal installation in a separable sealed pressure vessel. In accordance with the operational necessities of the separable pressure vessel, it is imperative that the upper and lower spherical shells are not rigidly interconnected by means of bolts or other permanent fastening components. The sealing structure is mainly composed of the upper spherical shell, lower spherical shell, lip seal ring, and convex seal ring, as shown in Figure 3. The specific seal dimensions are shown in Figure 4.  
Figure 1: Principle prototype of the separable sealed pressure vessel.  
After installation and pre-tightening of the fastening band, the upper and lower spherical shells of the separable pressure vessel are brought into contact, and the seal is compressed vertically, resulting in a balance between the resistance force and the compression force of the fastening band. At this point, the seal is not completely pressed into the groove. Afterward, as water pressure is applied, the upper spherical shell gradually exerts downward pressure until both the upper and lower spherical shells are tightly fitted, and the seal is vertically compressed to its limit. As a result, the seal used in the separable pressure vessel can be categorized into two conditions: the first condition pertains to shallow water with low water pressure, where the vertical compression of the seal is subject to variations based on the water depth; the second condition pertains to deep water operations, wherein the lip seal is fully compressed into the groove as the underwater vehicle descends to a certain depth. The vertical compression of the seal reaches its maximum, and the water pressure acting on the lip seal opening and inner cavity proportionally increases with the water depth.  
The vertical compression force between the upper and lower spherical shells can be calculated by Equation (1).  
\[F=F_{\text{preload force}}+PS \tag{1}\]  
Figure 4: Seal dimensions.  
Figure 3: Lip seal sealing structure.  
Figure 2: Seal installation position.  
where \(F_{\text{preload force}}\) denotes the initial preload force that is supplied by the fastening band, \(P\) denotes the actual water pressure exerted on the lip seal at a particular water depth, and \(S\) denotes the projected area of the spherical shell.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
###### Abstract  
The fabrication of lightweight, ultra-thin, low power and intelligent body-borne sensors leads to novel advances in wireless body area networks (WBANs). Depending on the placement of the nodes, it is characterized as in/on body WBAN; thus, the channel is largely affected by body posture, clothing, muscle movement, body temperature and climatic conditions. The energy resources are limited and it is not feasible to replace the sensor's battery frequently. In order to keep the sensor in working condition, the channel resources should be reserved. The lifetime of the sensor is very crucial and it highly depends on transmission among sensor nodes and energy consumption. The reliability and energy efficiency in WBAN applications play a vital role. In this paper, the analytical expressions for energy efficiency (EE) and packet error rate (PER) are formulated for two-way relay cooperative communication. The results depict better reliability and efficiency compared to direct and one-way relay communication. The effective performance range of direct vs. cooperative communication is separated by a threshold distance. Based on EE calculations, an optimal packet size is observed that provides maximum efficiency over a certain link length. A smart and energy efficient system is articulated that utilizes all three communication modes, namely direct, one-way relay and two-way relay, as the direct link performs better for a certain range, but the cooperative communication gives better results for increased distance in terms of EE. The efficacy of the proposed hybrid scheme is also demonstrated over a practical quasi-static channel. Furthermore, link length extension and diversity is achieved by joint network-channel (JNC) coding the cooperative link.  
wireless body area networks; two-way relay cooperation; energy efficiency; quasi-static channel; joint network-channel coding +
Footnote †: journal: Sensors
# Compressed Nonlinear Equalizers for 112-Gbps Optical Interconnects: Efficiency and Stability
## Principle of Pruned Nonlinear Equalizers
### Volterra Series and Neural Network-Based Equalizer
Function \(softmax(\cdot)\) is used to convert the results of output layer to probability distribution for each class. Finally, \(argmax[\cdot]\) is decision function, which returns an index of the maximum value of output probability vector. \(y\) is final result indicating a specific symbol of information. Loss functions of ANN, including mean square error (MSE) loss function, logarithmic loss function and cross-entropy loss function, are important for training model and final performance. The MSE loss function usually shows slow convergence and is seldom used in classification task. In this work, back propagation (BP) algorithm combined with cross-entropy loss function are used to train NNE to achieve better classification performance. The structure of the 3-layer NNE is shown in Figure 1b, where \(NNE(N_{1},N_{2},N_{3})\) represents a 3-layer NNE with \(N_{1},N_{2},N_{3}\) as neuron number of input, hidden and output layer. Similar to VE, computation complexity for NNE is defined as number of multiplication operation as calculated in Equation (5),  
\[Complexity=N_{1}\times N_{2}+N_{2}\times N_{3}+N_{3}\times N_{4}+\cdots \tag{5}\]
# A Study on the Stoichiometry of Casein/Chitosan Gel Complexes as a Delivery System for Quercetin
###### Abstract  
As a well-known plant flavonol, quercetin possesses a diverse range of biological properties. These include its ability to act as an antioxidant, reduce inflammation, and exhibit anticancer effects. Consequently, it finds extensive application in numerous models related to wound healing. However, the poor physicochemical characteristics of the molecule (which include low solubility, stability, and permeability) eventually reduce its bioavailability at the targeted sites. A variety of nano formulations with great therapeutic potential have been created in order to get around these obstacles on the way to successful therapy. The current investigation aims to examine the properties of nano- and micro-sized casein/chitosan gel polyelectrolyte complexes (PECs) with respect to their potential for quercetin loading and release. Four different types of hydrogel particles at pH 6 and different casein/chitosan charge ratios were synthesized; namely, 1:1, 2:1, 4:1, and 6:1 in excesses of casein. The attractive electrostatic interactions between the oppositely charged polyelectrolytes were proved by FT-IR spectroscopy. The process yield increased from 37.5% to 72.5% in excesses of casein. The gel particle's size varied between 377 nm and 5.72 um depending on the casein/chitosan stoichiometry. The morphology of the obtained gel polyelectrolyte complexes was found to be spherical, based on scanning electron microscopy and atomic force microscopy analysis. The quercetin loading efficiency was above 95% for all investigated hydrogel complexes. Investigation of the physical state of the loaded polyphenol by the differential scanning calorimetry and X-ray powdered diffraction technique suggested the occurrence of partial recrystallization phenomena. The quercetin release test was performed in phosphate buffer (pH 5.5) at 32 \({}^{\circ}\)C and permanent stirring at 50 rpm. A zero-order model was used to describe in the best way the release kinetics. The reported casein/chitosan complexes loaded with quercetin may find application in wound healing as a concomitant treatment.  
polic electrolyte gel complexes; casein; chitosan; drug delivery +
Footnote †: journal: _Sciences_  
00233
## 3 Results
### Surgical Completeness of Robotic Thyroid Surgery  
The surgical completeness of robotic TT with CCND was evaluated in terms of the number of retrieved lymph nodes and ablation and control sTg levels (Table 3). The mean number of retrieved LNs retrieved was seven. Of the 194 study subjects, 159 (82.0%) underwent RAI remnant ablation. After excluding patients with positive anti-Tg Ab levels, 133 and 108 patients were evaluated for ablation and control sTg levels, respectively. The proportions of patients with an ablation sTg \(<\) 10 ng/mL or a control sTg \(<\) 1 ng/mL were 94.0% and 91.7%, respectively. The surgical completeness presented as the number of retrieved LNs and ablation/control sTg levels did not significantly differ between before and after the learning curve end-point (120 th case).  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & **Number of Patients (_n_ = 194)** \\ \hline Operation time, min, mean \(\pm\) SD & 174.9 \(\pm\) 36.1 \\ Blood loss, ml, mean \(\pm\) SD & 4.3 \(\pm\) 3.2 \\ Postoperative hemorrhage, \(n\) (\%) & 0 (0) \\ Temporary hypoparathyroidism, \(n\) (\%) & 71 (36.6) \\ Permanent hypoparathyroidism, \(n\) (\%) & 2 (1.0) \\ RLN injury, \(n\) (\%) & 3 (1.5) \\ Infection, \(n\) (\%) & 2 (1.0) \\ Seroma, \(n\) (\%) & 0 (0) \\ Chyle leakage, \(n\) (\%) & 5 (2.6) \\ Tracheal injury, \(n\) (\%) & 0 (0) \\ \hline \multicolumn{3}{c}{SD = standard deviation; RLN = recurrent laryngeal nerve.} \\ \end{tabular}
\end{table}
Table 2: Technical outcomes of robotic thyroid surgery.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & **Overall** & **Before** & **After** & \multicolumn{1}{c}{_p-Value_} \\ \cline{2-5} \multicolumn{1}{c}{**Characteristics**} & **(_n_ = 194)** & **(_n_ = 119)** & **(_n_ = 75)** & **\(p\)-Value** \\ \hline Retrieved LNs, & 7 & 6 & 8 & 0.135 \\ \(n\), median (range) & (0–24) & (0–24) & (0–24) & \\ RAI remnant ablation, & 159 & 107 & 52 & \\ \(n\) (\%) & (82.0) & (89.9) & (69.3) & \(<\)0.001 \\ Anti-Tg Ab (+), & 30 & 21 & 9 & 0.289 \\ \(n\) (\%) & (15.5) & (17.7) & (12.0) & \\ Ablation sTg, ng/mL \({}^{*}\), & 0.55 & 0.54 & 0.76 & 0.899 \\ median (range) & (0.08–47.10) & (0.08–47.10) & (0.08–17.80) & \\ Ablation sTg \(<\) 10 g/mL \({}^{*}\), \(n\) (\%) & 1257133 & 81/88 & 44/45 & 0.259 \\  & (94.0) & (92.1) & (97.8) & \\ Control sTg, ng/mL, & 0.08 & 0.08 & 0.08 & 0.872 \\ median (range) & (0.08–16.50) & (0.08–16.50) & (0.08–6.30) & \\ Control sTg \(<\) 1 ng/mL, & 99/108 & 6874 & 31/34 & 1.000 \\ \(n\) (\%) & (91.7) & (91.9) & (91.2) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surgical completeness of robotic thyroid surgery, overall, before, and after the learning curve end-point.
# Creep Model of Weakly Cemented Soft Rock Considering Damage and Secondary Development in FLAC3D
## 3 Creep Damage Constitutive Model Considering Damage for Weakly Cemented Soft Rock
### Improved Nishihara Model Considering Damage  
[PERSON] et al. [42] analyzed the Nishihara model based on the principle of energy conservation, drawing a conclusion of its absence in explaining the conversion of external work into kinetic energy during the accelerated creep stage. To address this limitation, they introduced a winding element in series with the Nishihara model, hence establishing an improved Nishihara model capable of describing the accelerated creep stage of rock. The creep formula for the winding element is outlined as follows:  
\[\varepsilon=\frac{\lambda\sigma}{2\rho h^{2}}t^{3} \tag{3}\]  
It can be observed that the strain of this element is proportional to the cube of time (\(t^{3}\)), enabling its correlation with the accelerated creep stage. In the equation, \(\sigma\)--stress at time \(t\); \(\lambda\)--correction coefficient, controlling the intensity of accelerated creep, dimensionless; \(\rho\)--density of the specimen; and \(h\)--height of the specimen.  
Based on Equations (1) and (2), the creep formula for the winding element considering damage can be derived as follows:  
\[\varepsilon_{d}=\frac{\lambda(\sigma-\sigma_{S})}{2\rho h^{2}[1-D(t)]}t^{3} \tag{4}\]  
Connected with the Nishihara model, this model is intended to represent a creep damage constitutive model considering damage, as illustrated in Figure 8. The combined effect of the Nishihara model and this element during the accelerated creep phase is expressed as \(\varepsilon=\varepsilon^{e}+\varepsilon^{w}+\varepsilon^{ep}+\varepsilon^{g}\).  
Figure 8: Improved Nishihara creep model considering damage.  
Figure 7: Stress–strain curve of triaxial compression test [46].  
The constitutive equation is as follows: when \(\sigma<\sigma_{\rm s}\), the constitutive equation is identical to that of the Nishihara model; when \(\sigma\geq\sigma_{\rm s}\), an additional winding element is incorporated into the model.  
\[(A+\frac{1}{E})\ddot{\sigma}+(\frac{1}{\eta_{2}}-3 At^{2}+\frac{1}{\eta_{1}}+ \frac{K}{\eta_{1}G}+A\frac{K}{\eta_{1}})\dot{\sigma}+\frac{K}{\eta_{1}}\frac{( \sigma-\sigma_{\rm s})}{\eta_{2}}-(6 At+3A\frac{K}{\eta_{1}}t^{2})\sigma=\ddot{ \epsilon}+\frac{K}{\eta_{1}}\dot{\epsilon} \tag{5}\]  
Creep equations are as follows:  
\[\left\{\begin{array}{l}\epsilon=\frac{\sigma}{[1-D(t)]G}+\frac{\sigma}{K[1- D(t)]}(1-e^{-\frac{K}{\sigma^{\rm m}}t})\ \ \ \ \ (\sigma_{\rm D}<\sigma_{\rm s})\\ \epsilon=\frac{\sigma}{[1-D(t)]G}+\frac{\sigma}{K[1-D(t)]}(1-e^{-\frac{K}{ \sigma^{\rm m}}t})+\frac{(\sigma-\sigma_{\rm s})}{\eta^{\sigma p}[1-D(t)]}t+ \frac{\lambda(\sigma-\sigma_{\rm s})}{2\rho\hbar^{2}[1-D(t)]}t^{3}\ (\sigma_{\rm D}\geq\sigma_{\rm s})\end{array}\right. \tag{6}\]
# Superconducting Sub-Terahertz Oscillator with Continuous Frequency Tuning
## 3 Results and Discussion
### Traditional FFO
A significant increase in the spectral linewidth is due to the effect of noise self-pumping [59; 60; 61] and transition to the chaotic regime [62; 63]. A detailed discussion of these effects is beyond the scope of this article. Figure 2a shows the IVCs of the Nb-AlO\({}_{x}\)-Nb junction measured at different magnetic fields, while Figure 2b demonstrates one IVC taken at a fixed magnetic field. The dependence of the differential resistance Rd on voltage V for the curve from Figure 2b is presented in Figure 2c. One can see that there are jumps between steps that make operation impossible in some frequency ranges (marked by bars in Figure 3c). Furthermore, at the edges of the steps, regions of a sharp increase in the differential resistance appear, which leads to a significant broadening of the emission line. The special shape of the FFO [64] is beneficial  
Figure 2: (**a**) IVCs of the Nb-AlO\({}_{x}\)-Nb FFO measured at different magnetic fields induced by the current in integrated control line (I\({}_{\text{CL}}\) = 20–40 mA); (**b**) IVC of the Nb-AlO\({}_{x}\)-Nb FFO measured at fixed value of I\({}_{\text{CL}}\) = 29.6 mA; (**c**) differential resistance R\({}_{\text{d}}\) calculated from (**b**) using linear fitting of the IVC over 3 points at each voltage value. for suppression on the Fiske steps in the Josephson junctions. We used the same shape for the ends of the FFO (see Figure 3); although in the range of magnetic fields required for the FFO operation, the Fiske steps were still presented on the FFO IVCs. It should be mentioned that continuous frequency tuning and even phase locking with spectral ratio up to 90% was realized for specially designed Nb-AlN-NbN FFO in the frequency range 490-606 GHz (below V\({}_{\text{g}}\)/3) [18; 29]. It became possible due to FFO IVCs bending at low I\({}_{\text{b}}\) and high voltages (just below V\({}_{\text{g}}\)/3) caused by dispersion and surface losses [65] by using a specially developed procedure to find the optimal Fiske step nearest to required LO frequency [18; 19].
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Simulation in Simulation Software
#### 4.1.3 Simulation of Protocol Stack  
Next, we simulated the functions of protocol stack by building an LSP in the network. First, we used network manager to send a request to build an LSP.  
As shown in Figure 15, the request message will be sent to node 1, requiring the node to build an LSP with a bandwidth of 1100 M to the node with the destination address of fe80:0:0:000b::. According to the topology shown in Figure 11, we found that the destination node has a node ID of 11 (0x 000B). Having received the LSP request, the routing protocol first calculates the route path meeting the bandwidth requirement from node 1 to node 11. Then, the signal protocol builds the LSP according to the route path.  
Figure 16 shows the label distribution of the signal protocol. It can be seen that the route path consists of nodes 1, 4, 7 and 11. According to the topology of the simulation network, the route path obviously meets the bandwidth requirement of the LSP. This means that the routing protocol can indeed calculate the route path meeting requirement of the bandwidth. The figure also shows FIB or LFIBs that are created by the signal protocol. It is clear that the information of the FIB or LFIBs is accurate and the LSP has already been built by the signal protocol.  
Figure 14: Neighborhoods of node 7 and node 11.  
Figure 15: Network manager screen for LSP request in simulation network.  
Based on the simulation and analysis above, it can be seen that the protocol stack has successfully implemented the previously designed functions.
## 4 Experiments and Analysis
### Comparisons with the State-of-the-Art Methods  
Foggy weather adaptive detection is a common and challenging task for cross-domain object detection, and the object detector must be robust in all conditions. Therefore, we evaluated the robustness of the model under foggy weather variations by converting the Cityscapes dataset to the Foggy Cityscapes dataset.  
The Cityscapes dataset was used as the source domain, while the Foggy Cityscape dataset was used as the target domain. In the experiments, the model was trained with the labeled images from Cityscapes and the unlabeled images from Foggy Cityscapes. We report the testing results on the validation set of Foggy Cityscapes.  
We performed Source only and Oracle experiments with YOLOv5 (marked by \(*\)). Source only indicates the model was trained with only the source domain data, and Oracle indicates the model was trained with labeled data from the source and target domains. _Ours w/\(\mathcal{L}_{cls}\)_ indicates the cross-entropy loss was used in the cross attention strategy to align the source and target features. _Ours w/trans_ indicates the model was trained with the transformer encoder layer only.  
As shown in Table 1, our method outperformed other advanced methods. The state-of-the-art method SFA [23] achieved a 41.3% mAP, while our method achieved a 43.3% mAP, gaining a +2.0% improvement. This result shows that our method had a stable ability to solve foggy weather adaptive detection. Our method did not achieve the highest performance in the 'truck' and 'bus' categories. Although it did not perform better than the state-of-the-art methods in these categories, our results were very close to these methods. In addition, the AP of a few categories was relatively lower but CAST-YOLO(Ours) was substantially improved compared to the Source only detection, and the confusion matrix in Figure 6 confirms this. Meanwhile, compared with _Ours w/Cl\({}_{cls}\)_ and _Ours w/trans_, _CAST-YOLO(Ours)_ was also substantially improved in these categories. We attributed this to the proposed cross-attention strategy transformer and the use of a more advanced convolutional block attention module.