esa-sceva/satcom-chunk-collection huggingface dataset:
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method
### Classification and Regression
#### 3.3.1 Classification Branch  
The classification branch is primarily responsible for predicting the classification response map. The IoU-Aware Classification Score (IACS) is used as the training objective for the classification loss function. Additionally, the varifocal loss \(L_{vfl}\) is employed as the training loss function. \(L_{vfl}\) is a dynamically scaled binary cross-entropy loss that utilizes an asymmetric training sample weighting approach. This involves reducing the weight of negative samples and increasing the weight of high-quality positive samples to address the issue of class imbalance during training. The classification and varifocal loss functions are expressed as shown in Formula (5) [35]:  
\[L_{cls}=L_{vfl}(p,IoU(b,\hat{b})) \tag{5}\] \[L_{vfl}(p,q)=\begin{cases}-q(qlog(p)+(1-q)log(1-p))\ q>0\\ -xp^{\gamma}log(1-p)\ \ q=0\end{cases}\]  
where \(p\) represents the predicted IACS, \(q\) represents the target score calculated from the intersection over union (IoU) of \(b\) and \(\hat{b}\). \(b\) represents the predicted bounding box position, and \(\hat{b}\) represents the actual bounding box position.
# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 2 Related Works
### Cameras for Object Detection
They effectively reduce the complexity of the network, but accurate estimation is still challenging. [PERSON] et al. [24] proposed Rotation-Mask Deformable Convolution (RMDC) to address the problem of rotation and distortion in top-view fisheye images. The method adaptively rotates the convolution filter and introduces a center-fixed deformable convolution. The learning capability of the convolution kernel and the object detection accuracy of the top-view fisheye images are improved. In addition, a UAV equipped with a fisheye camera can obtain a blind-free field of view. A UAV equipped with a fisheye camera plays an important role in environmental monitoring, disaster prevention and relief, object detection, etc. [PERSON] et al. [3] utilized a 360-degree camera on a UAV to obtain a blind-free field of view, which can be used for a significant advantage in forest fire monitoring. The DeepLab V3+ network is utilized to segment flames and smoke in an image, which effectively achieves the detection and localization of fire areas. [PERSON] et al. [6] constructed an autonomous landing system for UAVs. The system combines a fisheye camera with a wide field of view and a stereo camera with depth imaging to acquire rich visual information. In particular, the system employs YOLOv3 to directly detect objects in fisheye images, robustly realizing autonomous landing of mobile unmanned ground vehicles (UGVs). Currently, there are few fisheye image datasets of real scenes. As the demand for a wide field of view increases, the fisheye image dataset of real scenes needs to be expanded. In addition, the wide viewing angle of fisheye cameras leads to distortion and discontinuity of objects in the captured fisheye images. Existing object detectors mainly detect regular images, and distorted and discontinuous objects in fisheye images pose a challenge to object detectors.
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 3 Experimental Verification
### Comparison of Results among Different Multi-Point Vibration Response Prediction Methods

Table 4: Root mean square error (RMSE) of the predicted multi-point vibration response for different models. Figure 4: Energy dB error of the different methods. (**a**) t1 = 1. MIMO LS-SVR. (**b**) t2 = 5. MIMO LS-SVR. (**c**) t1 = 1. MIMO LS-SVR with loads. (**d**) t2 = 5. MIMO LS-SVR with loads. (**e**) t1 = 1. Transmissibility matrix. (**f**) t2 = 5. Transmissibility matrix. (**g**) t1 = 1. Transfer function with loads. (**h**) t2 = 5. Transfer function with loads. (**i**) t1 = 1. Multiple linear regression (MLR). (**j**) t2 = 5. MLR. (**k**) t1 = 1. MIMO Neural network. (**l**) t2 = 5. MIMO Neural network. multi-point vibration response using the measured multi-point vibration response (transmissibility matrix, MIMO LS-SVR) was better than the methods that predicted the unmeasured multi-point vibration response using known loads (transfer function with loads and MIMO LS-SVR with loads) at most points, which indicates that the prediction methods that predict the unmeasured response using the measured response still have good prediction performance when the uncorrelated multi-source loads are unknown, even better than some methods for predicting response using known loads. 3. Compared with the MIMO neural network method and MLR, the predictive performance of MIMO SVR is much higher than that of MIMO NN and MLR, because these three methods predict the unknown response with known response when the load is unknown, so it can be seen from the Tables 2-4 that SVR is more suitable for the application in this situation. 4. As shown in Figure 4 and Tables 2-4, MIMO LS-SVR-based method has the best result compared with other methods on the indicators of the over 3 dB and MAPE, indicating that the predictive ability of MIMO LS-SVR-based method is better than that of other methods on the indicators of relative error; and on RMSE, MIMO LS-SVR-based method also has a good predictive effect. Although it was not as high as that for the transmissibility matrix-based methods, the MIMO LS-SVR-based method predicted a response without the transmissibility matrix and regardless of whether the structures were linear or nonlinear. 5. It can be seen from Table 5 that the fewer the measurement points, the worse the accuracy of the LS-SVR model, but even if there are only two measurement points (equal to the number of uncorrelated load sources), the LS-SVR model can still maintain high accuracy. Moreover, it is found from Table 6 that the LS-SVR model can maintain high performance even when there is only one measurement point, which indicates that the LS-SVR model has high applicability.
## 2 Materials and Methods
### Cytotoxicity (MTT) Assay  
For the cytotoxicity testing of HeLa, A549, Hepa 1-6, and RAW264.7, cells were harvested with trypsin, resuspended in media containing 10% FBS at a density 0.5 \(\times\) 10\({}^{6}\) cell/mL, and plated on 96-well cell culture plates. After 24-28 h of growth, after 80% confluence has been reached, the cell culture medium was changed to serum-free and nanoparticles were added at a concentration of 5.0 \(\upmu\)g/mL. After 24 h, the cytotoxicity of NPs was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay [43] based on the ability of a mitochondrial dehydrogenase enzyme in viable cells to cleave the tetrazolium rings of the pale yellow MTT and form a dark blue formazan crystal. The number of surviving cells is directly proportional to the level of the formazan product created, which can then be quantified by reading absorbance at a wavelength of 570 nm with a multiwell scanning spectrophotometer (ELx800 Absorbance Reader, Biotek, Inc., Winooski, VT, USA). Briefly, after cells were incubated with a nanoparticle-containing media for 24 h, 10 \(\upmu\)L MTT solution was added to the medium and the mixtures were incubated for 4 h at 37 \({}^{\circ}\)C, followed by addition of 100 \(\upmu\)L DMSO to dissolve the purple crystals with gentle pipetting. The absorbance at a wavelength of 570 nm was read on the ELx800 Absorbance Reader (Biotek). In control samples cells were cultured with medium only. Cell viability was calculated using the equation:  
\[\text{Cell~{}viability}\ (\%)\ =\frac{([OD]test-[OD]blank)}{([OD]control-[OD]blank)} \times 100\% \tag{1}\]  
where [OD]test, [OD]control, and [OD]blank represented the absorbance values of the wells with cells and NPs, cells without NPs, and without NPs and cells, respectively. For each experiment the absorbance was the average value measured from 12 wells in parallel. Four independent experiments have been performed in case of each cell line used.
## 2 Experimental Approach
### Waveform Data Processing  
The typical AE waveform and its data processing are shown in Figure 2. AE waveforms were recorded as a single data file by PCI-2; each data file recorded one AE waveform that represents one fracture event. The available AE waveform data was analyzed through Fast Fourier transformation (FFT) and transformed into amplitude spectra conventionally. With the help of a MATLAB (R2016a, MathWorks, Natick, MA, USA) programming process, the dominant frequencies of AE waveforms were calculated through a batch process and exported to Excel automatically.
###### Abstract
From our reliable theory, by double-pass pumping through the gain medium and increasing the pump power to 25 W, we confirmed that the highest optical-to-optical conversion efficiency of 55.9% and slope efficiency of 59.6% can be achieved using high-loss crystals. The analysis also shows that even if the experimental conditions deviate from the optimum, the efficiency reduction is limited to a few percent. These efficiencies are not only the highest among Ti:sapphire lasers, they are so high that they well exceed over half of the quantum limit of the Ti:sapphire lasers of approximately 70%.
## 3 Experimental Results and Discussion
### Compressive Strength  
The compressive strength values of concrete specimens are shown in Figure 3. The data revealed that the freeze-thaw cycles reduced the compressive strength of standard concrete specimens, and concrete surface reinforcers were able to improve the compressive strength of freeze-thaw-deteriorated concrete specimens. The compressive strength of concrete could be increased by up to 15.67% after the application of the surface reinforcer. The surface reinforcer type, the action duration, and the number of applications had a significantly positive effect on the promotion of compressive strength. To be specific, the compressive strength decreased from 48.61 MPa to 46.39 MPa after 80 freeze-thaw cycles, with a reduction rate of 4.57%. Surface reinforcer type A exhibited a better strength-promoting effect compared to surface reinforcer types B and C. For instance, for the repaired concrete specimen, after experiencing four applications and a 7 d action duration, the compressive strength of concrete repaired by surface reinforcer type A was 52.47 MPa, which was higher than that of 51.67 MPa and 51.03 MPa for surface reinforcer types B and C. In addition, with the same number of applications, the compressive strength increased with the increase in the action duration. Moreover, for the repaired concrete specimen, after experiencing four applications, the compressive strengths of the repaired concrete specimens under the action duration of 3 d, 7 d, and 14 d were 50.33 MPa, 52.47 MPa, and 53.66 MPa, respectively, which improved by 8.5%, 13.1%, and 15.7%, compared to the deterioration specimen of 46.39 MPa. In addition to the type and action duration of surface reinforcer, the number of applications affected the compressive strength of concrete specimens. For example, the compressive strengths of B-2-7, B-4-7, and B-8-7 were 49.61 MPa, 51.67 MPa, and 50.28 MPa, respectively, which increased by 6.9%, 11.3%, and  
Figure 1: Experimental procedure of pore structure.  
8.4% compared to the deterioration specimen. Therefore, excessive applications of surface reinforcer could not produce any obvious enhancement of compressive strength, and the effect of action duration on the degree of increase in amplitude of repaired concrete was higher than that of number of applications in this study.  
Figure 2: Stress–strain curves under different test conditions. (**a**) Stress–strain curves under different surface reinforcer types. (**b**) Stress–strain curves under different durations. (**c**) Stress–strain curves with different application numbers. (**d**) Three stages of concrete stress–strain curve.
## 1 Introduction  
Silicon photonics is known as a promising platform for high speed applications, and in recent years, the maturity of the technology has allowed the proposal of photonic chips with the necessary components [1; 2; 3; 4]. Even if several investigations are still ongoing [5], silicon Photonics is now a proven technology for high speed short reach optical interconnects with several companies proposing 100 GBASE compatible products [6; 7; 8]. As data rates increase from 10 G to 25 G to 100 G per wavelength [9], the design of these optical links relies on a careful balance between several parameters such as for instance the receiver sensitivity, the modulator efficiency or the laser power. This balance depends upon many system level parameters (e.g., tolerable link loss) but also technological parameters (e.g., doping levels of the active regions) and external constraints (e.g., power consumption). In this paper, we focus on the Mach-Zehnder Modulator (MZM) used in such links, operating at 1310 nm, and propose a method to optimize its technological and physical parameters independently of the other system building blocks.  
Based upon the plasma dispersion electro-optical effect, the active region of the modulator creates a phase shift, which is then converted into an intensity modulation by using an interferometricstructure. In order to obtain the best specifications for a targeted application, a compromise between insertion loss, modulation bandwidth and efficiency must be found. This optimal point is difficult to obtain without a proper method or compact model, because it depends on various parameters. In a previous work, we bypassed this issue by developing a compact model for the conception of a ring modulator in order to find the optimal point in the most general case [10]. This paper completes this discussion by detailing a similar method to obtain the best parameters for the MZM, which we apply to short reach interconnects, with 25 Gbps On-Off Keying (OOK) modulation at 1310 nm. The objective of this study is thus to determine the configuration of the MZM which leads to the largest tolerable path loss for the overall link, and see the reachable performances for the modulator.  
The article is divided into three sections. The notations and the main elements referred to in the optimization procedure are explained in the first part. In the second part, after having determined the required power for the receiver, the electro-optical performance parameters of the active region of the MZM are extracted, including bandwidth simulations. The compact model for the optimal parameters of the junction is also presented. Last, the third part details the optimization method through one example, and combines all the previous data to optimize the MZM in a more general case.
# Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN
## 4 Design of Multi-Constraint Multi-Strategy Hopping Algorithm
### Path Similarity Constraint  
Path similarity is a metric used to compare the similarity between two transmission paths. It can be used to compare path choices between two nodes in a network, and the calculation of transmission path similarity can be based on the proportion of the same part of the path or the same nodes. When executing path hopping, if the choice of the hopping path has a high similarity to the current transmission path, it will reduce the unpredictability of path hopping. To avoid this situation, when selecting a hopping path, this paper uses the Jaccard similarity coefficient to constrain the path, which is a measure used to compare the similarity between two sets. It measures the ratio between the intersection and the union of two sets. The equation is as follows:  
\[J(i,j)=\frac{|i\cap j|}{|i\cup j|} \tag{5}\]  
in which \(i\), \(j\) are only 0, 1 of the \(n\)-dimensional vector according to the path hopping space to build a node matrix \(L_{msn}\) of all paths, \(m\) represents all paths of the hopping space, \(n\) represents all nodes except the source and destination nodes contained in these paths, nodes in the path are counted as 1, and nodes not in the path are counted as 0. Assuming that \(M_{01}\) represents the number of nodes that do not belong to path \(i\) but belong to path \(j\), \(M_{10}\) represents the number of nodes that belong to path \(i\) but do not belong to path \(j\), \(M_{11}\) represents the number of nodes that belong to both path \(i\) and path \(j\), then Equation (5) can be expressed as  
\[J(i,j)=\frac{M_{11}}{M_{10}+M_{01}+M_{11}} \tag{6}\]  
According to Equation (6), calculate the similarity coefficient between paths, and create a path similarity matrix \(J_{m+m}\) to record the calculation results. From the equation, it can be seen that the similarity coefficient is between 0 and 1, and the smaller the value, the lower the similarity between the two paths and the fewer the number of overlapping nodes. When choosing a path, a threshold can be set for the similarity coefficient as shown in Equation (7), and paths that satisfy the constraint conditions are preferentially selected as alternative hopping paths.  
\[J(i,j)\leq\alpha \tag{7}\]  
In Equation (7), \(\alpha\) serves as the threshold for the path similarity coefficient, used to determine the similarity between alternative paths and the current path. Typically, \(\alpha\) is set to 0.5; setting it too high will expand the range of similarity between alternative paths and the current path, leading to a higher similarity between them. Conversely, setting it too low will narrow down the selection range for alternative paths, resulting in fewer alternative paths and reduced diversity in jumps and ultimately affecting the effectiveness of defense strategies.
## 6 Dependability for MCAs in 6G
### Dependability Analysis of 6G MCAs
#### 6.2.4 Security  
MCAs are constantly targeted by malicious attackers, either due to the importance of the data they handle, or because of their key role in some industrial or medical use cases. If not properly secured, attackers targeting a remote surgery system could execute commands that hinder the performances of the controlled devices, severely threatening human lives. Moreover, other scenarios, such as autonomous driving, are also susceptible to malicious attacks that could hinder many of the systems running on the vehicle, from communication devices to sensors and other integral parts of its systems. Although security aspects rely mostly on the underlying communication network, it is also worth noting that MCAs could provide some security aspects, such as controlling inter-element communications or defining which element can access what information in the system.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 2 Model and Algorithm
### Reward Function
#### 2.4.2 Thrust Distribution Agent's Reward  
The thrust distribution agent controls the opening degree of each of the nozzles, with the opening degrees of these nozzles being unknown variables. The known constraints include the 5-axis thrust equation and the total nozzle opening degree command. The reward function design is shown in Equations (14)-(19).  
\[R_{F_{y}}=-\Big{|}F_{ycmd}(t_{n})-F_{y}(t_{n})\Big{|} \tag{14}\]  
\[R_{F_{z}}=-|F_{zcmd}(t_{n})-F_{z}(t_{n})| \tag{15}\]  
\[R_{F_{roll}}=-|F_{rollcmd}(t_{n})-F_{roll}(t_{n})| \tag{16}\]  
\[R_{F_{pitch}}=-\Big{|}F_{pitchcmd}(t_{n})-F_{pitch}(t_{n})\Big{|} \tag{17}\]  
\[R_{F_{yw}}=-\Big{|}F_{ywcmd}(t_{n})-F_{yw\_target}(t_{n})\Big{|} \tag{18}\]  
\[R_{val}=-|A_{tsumcmd}(t_{n})-A_{tsum}(t_{n})| \tag{19}\]  
Under normal circumstances, pressure control is prioritized, making the reward item for the total valve opening degree the primary concern. A switch function with a threshold \(T\) is implemented; only when this reward item reaches \(T\) will other thrust deviation rewards be considered. The thrust distribution agent's reward is finally expressed in Equation (20).  
\[R_{F}=\begin{cases}R_{val}+R_{F_{y}}+R_{F_{z}}+R_{F_{roll}}+R_{F_{pitch}}+R_{F_{roll}},\;ifR_{val}>T\\ R_{val},\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;1. Displacement Difference: Using the displacement difference as a reward component effectively penalizes significant deviations from the command during initial training, thus speeding up convergence. \[R_{opening\_deviation}=-|A_{itcmd}(t_{n})-A_{it}(t_{n})|\] (21) 2. Velocity Term: To match the valve core's movement speed with the target curve's slope, a velocity term is added. This requires transforming the observation space, using the displacement difference between new and old states to represent the target curve's slope. \[R_{opening\_synchronization}=-|(A_{itcmd}(t_{n})-A_{itcmd}(t_{n-1}))-(A_{it}(t_{ n})-A_{it}(t_{n-1}))|\] (22) 3. Steady-State Reward: An additional reward is given when both the displacement and speed rewards are satisfactory, promoting a state in which both closely follow the target curve. \[R_{opening\_bous}=1\ if\ R_{opening\_deviation}<\varepsilon_{3}\ and\ R_{opening\_ synchronization}<\varepsilon_{4}\ else\ 0\] (23) 4. Dynamic Weighting: To ensure accuracy, the displacement difference should account for at least half of the weight in the reward function. \[K_{opening}=0.5\textit{tanh}(|A_{itcmd}(t_{n})-A_{it}(t_{n})|)+0.5\] (24) Finally, the opening closed-loop agent's reward function is shown in Equation (25). \[R_{opening}=K_{opening}R_{opening\_deviation}+\left(1-K_{opening}\right)R_{opening\_synchronization}+R_{opening\_bous}\] (25)
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 3 Results
### ORA Airflow Pressure  
The ORA measurements are automatically provided within a frame range of 400 frames. The maximum of the provided discrete piston chamber pressure was reached on average after 42.5% of the measurement period (between frame 168 and 172, 15.288 and 15.652 ms). Using a frequency of 11 kHz, the ORA analysing time period was equivalent to 36.360 ms.  
The maximum airflow pressure of the ORA was 91.7 \(\pm\) 0.72 mmHg (95%CI 91.2 to 92.1 mmHg) with a duration of 23.061 \(\pm\) 0.1872 ms (95%CI 22.285 to 24.438 ms) applied to an area of 35 mm\({}^{2}\), Figure 9a-c. The maximum pressure, defined as 99% quantile lasted for 1.182 \(\pm\) 0.1659 ms (95% CI 1.149 to 1.212 ms).  
The pressure decreased by 39.8% per millimetre distance from the centre towards the peripheral edges of the airflow. Within a diameter of 2.5 mm around the maximum, the pressure reduced by 80% of the maximum, Figure 9a-c.