esa-sceva/satcom-chunk-collection huggingface dataset:
# Steering Mirror System with Closed-Loop Feedback for Free-Space Optical Communication Terminals
## 1 Introduction
Finally, TNO is also working on an ISL version called LEOcat [11], with a size of 190 \(\times\) 190 \(\times\) 250 mm beyond the CubeSat platforms. Steering mirrors are critical technologies for ensuring stability in pointing and tracking. Figure 1 illustrates the optical architectures of terminal technologies incorporating steering mirrors. When the same steering mirror is employed for both the transmitter and receiver, the steering functionality must compromise between speed (to correct for satellite jitter), size (to decrease the natural divergence induced by diffraction for the transmitter beam or maximize the beam collection capability on the receiver channel), and stroke amplitude (linked to the Field of Regard (FoR)). In a duplex co-located PAT system, as depicted in Figure 1a, a single mirror is used to steer both the transmitted and received optical signal. While this configuration offers the advantage of a simpler design [9; 10], it typically requires a separate beacon channel, along with the use of dichroic beamsplitters to separate the send and receive beams. These beam splitters necessitate the use of different data wavelengths on opposing terminals. Alternatively, a duplex bistatic configuration, shown in Figure 1b, features separate transmitter and receiver optical systems. This approach provides greater versatility in decoupling the channels, allowing customization of the FoV and divergence for each path to meet the specific requirements of both the transmitter and receiver. This paper describes a steering mirror design which can be used on the receiver side of the ISL. It incorporates a two-axis mirror to increase the angular FoR of the receiver satellite, enabling the use of a small and fast Avalanche Photodiode Detector (APD). Smaller APDs have a higher cutoff frequency, and current commercial technology achieves up to 4 GHz on a 40-micron photosensitive area. Commercial off-the-shelf (COTS) actuators and optical encoders are used, offering a simple and compact solution for future CubeSat laser communication terminals.
## 4 Discussion
### Uncertainty  
It should be noted that the lower spatial resolution of the available soil moisture product (0.25\({}^{\circ}\)) unavoidably affected the accuracy of SWUE, especially for highly heterogeneous ecosystems. The ECV-SM data only represented the surface soil moisture condition [9]; thus, the estimation of SWUE might ignore deeper soil water used by plants. In addition, ECV-SM products cannot accurately capture the soil moisture dynamics in dense vegetation areas, which limits the reliability of SWUE. There still exists the problem of missing data in the CCI soil moisture product. In this study, due to the lack of CCI soil moisture data, especially before 2007, we could not obtain and analyze longer time-series SWUE in northeast China. NDVI, LST, precipitation, and land surface phenology parameters were chosen as the representative factors when evaluating the relationship among SWUE, and environmental and phenological changes. Some other variables such as soil evaporation and irrigated water supplies may also be associated with the ecosystem SWUE. Moreover, the correlation analyses between SWUE and the above impact factors were preliminary and better-fitting mathematical methods should be explored in further study.  
In addition, the land surface phenology results varied because different data sources and models were adopted. In many previous studies, the analysis of LSP would not include the cropland because it was highly affected by human activity. Due to the large area of cropland and the low resolution of estimated SWUE data, this study was conducted on the whole region including cropland. The phenological parameters were compared with the studies of [PERSON] et al. [23] (used Global InventoryModeling and Mapping Studies (GIMMS) NDVI3g data), [PERSON] et al. (2019) (used Advanced Very High Resolution Radiomete (AVHRR) NDVI data), and [PERSON] et al. (2010) (Table 1). The deviations may be attributed to human interference and variations in natural conditions. The results of this study were reasonable.
## 4 System Model and Description of the Proposed Protocols
### Channel Capacity Model in BF-SPR-Three  
The Channel Capacity (cc) of the acoustic channel is formulated using Equations (6) and (7) as in [24].  
\[cc=B\cdot\log_{2}(1+SNR), \tag{6}\]  
\[AC(PER)=\frac{cc}{1-H_{2}(PER)}, \tag{7}\]  
here, \(B\) represents the bandwidth of the acoustic channel, \(SNR\) is used to denote signal to noise ratio (calculated using Equation (8)). Meanwhile, \(PER\) is used to represent the number of bits without any error and \(H_{2}\) is a binary entropy function using PER, which is defined as \(H_{2}=(PER-1)\log_{2}(1-PER)(PER\log_{2}PER)\).  
\[SNR=\frac{P/A(Dis,Feq)}{NP^{Dis}(Feq)}, \tag{8}\]  
here, \(P\) is the power of the acoustic signal and \(Dis\) is the distance among \(n-1\) and \(n\)th nodes. Whereas, SNR follows the additive white gaussian noise channel.
# Image Segmentation Framework for Detecting Adversarial Attacks for Autonomous Driving Cars
## 3 Methodology
### U-Net Model for Mask Segmentation  
[PERSON] et al. [60] introduced U-net, a convolutional neural network designed for biomedical image segmentation, such as tumor detection. U-net is particularly effective for segmenting small objects and has two key components: the encoder and the decoder. The encoder uses convolutional layers to extract features, followed by pooling layers to reduce spatial dimensions. The decoder then upsamples these features with transposed convolution layers and refines the output. Skip connections link intermediate layers of the encoder and decoder paths to preserve spatial information of the image, such as edges and boundaries. The encoder, decoder, and skip connections form the U-shaped architecture In our work, the U-net model generates segmentation masks that isolate the main object from the background of each image, on the one hand, and identify adversarial inputs, on the other hand.
## 4 Numerical Results and Discussion
### Large Air Bubble Scenario  
In Figure 5, we consider the average BER in the presence of large air bubble-induced fading for different water turbidity levels. As can be observed, the considered antacid concentration levels are similar to the small air bubble scenarios. Both analytic and asymptotic results demonstrate behavior similar to Monte Carlo simulation results, indicating the accuracy of the exact and asymptotic closed-form expressions obtained in Equations (9) and (13), respectively. In addition, Monte Carlo and analytic results support the convergence of the asymptotic solution, which is again faster for less turbid water cases. It can be observed that the asymptotic behavior of the scenario with a concentration of 14 mg/L of antacid does not fit as fast and accurately as the rest of the scattering scenarios. This is because this scenario exhibits a higher diversity order, i.e., a greater slope in the BER curve. Nonetheless, asymptotic results can be used as a tight upper bound across the presented SNR range.  
As discussed in Section 4.2, the behavior of the average BER can be categorized into two regions. Before 60 dB, the average BER is lower when considering a tap water scenario, where the scattering effect is insignificant. However, after 60 dB, low average BER results are achieved in most turbid water scenarios. At 40 dB, the average BER for 14.5 mg/L is \(9.1\times 10^{-2}\), while for 0 mg/L, it is \(2.1\times 10^{-2}\). However, at 70 dB, the average BER for 14.5 mg/L is \(9.4\times 10^{-7}\), while for 0 mg/L, it is \(4.2\times 10^{-5}\). Although the scenarios with 3.6 mg/L and 7.3 mg/L of antacid show a higher average BER than the tap water scenario in all the evaluated SNR ranges, after 60 dB, the 7.3 mg/L scenario shows a lower average BER than the 3.6 mg/L scenario. This should be attributable to an increase in the absorption coefficient, i.e., a higher pathloss, in 3.6 mg/L and 7.3 mg/L scenarios with respect to the clear water case. However, it seems that the scattering coefficient remains constant for both cases.  
In Figure 6, the outage performance results of the UOWC system under large air bubbles for different levels of water turbidity are plotted. Monte Carlo simulations results validate the analytic and asymptotic results obtained from Equations (21) and (22). The outage probability confirms the conclusions obtained from the above-average BER performance.  
Figure 4: Outage performance when considering an empirical UOWC channel in the presence of small air bubble-induced fading under different levels of antacid concentration at a link distance of 3 m and a wavelength of 520 nm.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 4 Intelligent Channel Selection Scheme  
Because of the dynamic nature of jammer interference patterns, conventional anti-interference algorithms often struggle to cope effectively. Deep reinforcement learning, as a -potent decision-making tool within artificial intelligence, is capable of adapting to dynamic environments and achieving optimal outcomes. However, deep reinforcement learning algorithms strongly depend on hyperparameters, which can be a limiting factor in their application. Therefore, this section proposes an intelligent channel selection scheme based on Bayesian optimization within a Discrete Soft Actor-Critic (BO_DSAC) framework. This approach is designed to facilitate channel selection for relay drones, thereby mitigating the impact of dynamic interference on the communication of cluster drones.
# Captioning Transformer with Stacked Attention Modules
## 6 Quantitative Analysis  
As is shown in Figure 3, we select some samples from the local test set for reference. ATT is the Soft-Attention model, CT is our model, and GT is the ground-truth which has five sentences. We can see that our model can generate readable text content and maintain rich semantic information about the image. 5 please define. For example, in the first image, we can see the generated text \"a boat floating on top of a body of water\". The generated caption can successfully describe the boat and the water in the image, and even is nearly the same as the first ground-truth sentence. In the second image, we can see our model can recognize man, bench and pigeons in the image, and even more, it can find that a man is sitting on the bench.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **ROUGE-L** & **CIDEr** \\ \hline NIC (Resnet\_152) & 71.7 & 54.8 & 41.0 & 30.8 & 53.2 & 98.8 \\ Soft-Attention (Resnet\_152) & 73.4 & 56.9 & 43.1 & 32.6 & 54.4 & 103.5 \\ CT-C3-64a4n6 (Resnet\_152) & 72.9 & 56.7 & 43.3 & 33.1 & 54.9 & 107.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the CT model with the Resnet_152 as the encoder.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **ROUGE-L** & **CIDEr** \\ \hline CT-ms-o1-64a4n6 & 69.9 & 52.8 & 39.4 & 29.6 & 52.5 & 94.9 \\ CT-ms-o2-64a4n6 & 69.8 & 52.9 & 39.5 & 29.6 & 52.4 & 95.0 \\ CT-ms-o3-64a4n6 & 70.4 & 53.3 & 39.8 & 29.8 & 52.7 & 95.8 \\ CT-ms-o4-64a4n6 & 70.6 & 53.5 & 40.1 & 30.3 & 52.9 & 96.8 \\ CT-ms-o5-64a4n6 & 70.0 & 52.8 & 39.4 & 29.6 & 52.3 & 95.6 \\ CT-ms-o6-64a4n6 & 70.9 & 54.2 & 40.9 & 31.0 & 53.2 & 98.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the Multi-Level Supervision method with different super parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **ROUGE-L** & **CIDEr** \\ \hline CT-C3-64a4n2 & 72.6 & 56.1 & 42.6 & 32.3 & 54.5 & 104.4 \\ CT-C3-64a4n4 & 72.5 & 55.9 & 42.4 & 32.2 & 54.6 & 105.2 \\ CT-C3-64a4n6 & 73.0 & 56.9 & 43.6 & 33.3 & 54.8 & 108.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the CT model with the different decoder layers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **ROUGE-L** & **CIDEr** \\ \hline ST-C3-64a4n6 & 69.9 & 52.8 & 39.4 & 29.6 & 52.5 & 94.9 \\ CT-ms-o2-64a4n6 & 69.8 & 52.9 & 39.5 & 29.6 & 52.4 & 95.0 \\ CT-ms-o3-64a4n6 & 70.4 & 53.3 & 39.8 & 29.8 & 52.7 & 95.8 \\ CT-ms-o4-64a4n6 & 70.6 & 53.5 & 40.1 & 30.3 & 52.9 & 96.8 \\ CT-ms-o5-64a4n6 & 70.0 & 52.8 & 39.4 & 29.6 & 52.3 & 95.6 \\ CT-ms-o6-64a4n6 & 70.9 & 54.2 & 40.9 & 31.0 & 53.2 & 98.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the Multi-Level Supervision method with different super parameters.
## 3 Results and Discussion
### Application to In Vitro Plasma Stability Test and Preclinical PK Studies in Rats  
This qualified LC-MS/MS assay was applied to the in vitro plasma stability test and a preclinical PK study in rats. The results of the in vitro linker stability test are shown in Figure 5. The remaining (%) of acDrug in T-DM1 was calculated by relative ratio to the concentration of desulfurated DM1 at day 0, which was the initial incubation time. The results show that desulfurated DM1 as acDrug decreased dramatically to 36.97% at day 3, and then slowly decreased until day 7 in rat plasma.  
Rat plasma samples obtained from preclinical PK study after single intravenous administration of 3 mg/kg for T-DM1 were also analyzed using this qualified LC-MS/MS assay. The PK profiles and parameters for desulfurated DM1 are shown in Figure 6 and Table 4, respectively.  
As a result, the half-life (T\({}_{1/2}\)) was 4.56 \(\pm\) 1.11 days, the maximum plasma concentration (C\({}_{\text{max}}\)) was 1396.50 \(\pm\) 267.92 ng/mL, and the clearance (CL) was 22.55 mL/day/kg. From the PK perspective, the acDrug of T-DM1 as a desulfurated form appeared to show relatively higher CL than the total antibodies of conventional therapeutic monoclonal antibodies. As shown from the in vitro plasma stability results, it is assumed that the linker instability of T-DM1 in rat plasma might play a role to some extent regarding the higher CL in vivo.  
Figure 5: In vitro linker stability of T-DM1 and remaining (%) of acDrug in T-DM1, calculated from desulfurated DM1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Assessment**} & \multirow{2}{*}{**Statistics**} & \multicolumn{2}{c}{**Low QC**} & \multicolumn{2}{c}{**Medium QC**} & \multicolumn{2}{c}{**High QC**} \\  & & **(42.74 ng/mL)** & **(427.36 ng/mL)** & **(854.72 ng/mL)** \\ \hline \multirow{3}{*}{Short-term stability (Room temperature, 6 h)} & Mean concentration & 46.15 & 436.99 & 874.26 \\  & Accuracy (\%) & 108.00 & 102.25 & 102.29 \\  & Precision (\%, CV) & 4.90 & 2.14 & 9.75 \\  & n & 3 & 3 & 3 \\ \hline \multirow{3}{*}{Long-term Stability (\(-80\)\({}^{\circ}\)C, 3 months)} & Mean concentration & 40.57 & 398.93 & 866.68 \\  & Accuracy (\%) & 94.93 & 93.35 & 101.40 \\  & Precision (\%, CV) & 22.55 & 5.08 & 4.12 \\  & n & 3 & 3 & 3 \\ \hline \multirow{3}{*}{Freeze-thaw Stability (\(-80\)\({}^{\circ}\)C, 3 cycles)} & Mean concentration & 40.63 & 429.47 & 910.96 \\  & Accuracy (\%) & 95.07 & 100.49 & 106.58 \\ \cline{1-1}  & Precision (\%, CV) & 8.00 & 0.75 & 6.70 \\ \cline{1-1}  & n & 3 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stability tests of T-DM1 in rat plasma under various conditions.
## 1 Introduction
In this respect, we have investigated a high-conductivity GAF and used it as an antenna design in our recent work. In addition, we designed a miniaturized GAF FSS, which has the advantages of lightweight, low profile and good flexibility [31; 32; 33; 34]. In this work, we designed a 5G MIMO antenna array based on GAF and employed GAF FSS and isolation strips to improve the isolation between MIMO antenna elements. The GAF has a high conductivity of \(1.1\times 10^{6}\) S/m and low density of \(1.48\) g/cm\({}^{3}\). Moreover, the thermal conductive ability of GAF is much higher than copper foil. The GAF MIMO antenna array consists of four identical antenna elements arranged horizontally with an interval of \(0.4\lambda\). The GAF antenna element working at \(3.5\) GHz has a good radiation capability with a maximum realized gain of \(2.87\) dBi. After deploying the decoupling structure of GAF FSS and isolation strips, the isolation between MIMO antenna elements is increased by more than \(10\) dB, and the reflection coefficient and radiation pattern are corrected. All results prove that GAF and its electronics are in line with the development requirements of 5G wireless communication.
## 3 Results
### Microbial Metabolic Activity  
In CeD subjects, the amount of SCFAs was comparable to other groups (Table 2). However, the relative amount of acetate (% of total SCFAs) was higher in the control group compared to the NCGS group (\(p\) = 0.03). No statistically significant differences were observed in the proportions of propionate or butyrate between the groups. In addition, the amounts of ammoniacal nitrogen and \(\beta\)-glucuronidase activity were comparable between the study groups (Table 2).  
Figure 1: Total microbiota profiles of study subjects were comparable between the groups as assessed by Principal Component Analysis (PCoA). CED celiac disease (\(n\) = 19), NCGS non-celiac gluten sensitivity (\(n\) = 10) and CTRL healthy controls (\(n\) = 14).  
Figure 3: Relative abundances of bacterial phyla (% of total reads) in subjects with celiac disease (CED) (\(n\) = 19), healthy controls (CTRL) (\(n\) = 14) and subjects with non-celiac gluten sensitivity (NCGS) (\(n\) = 10). No statistically significant differences were observed between the study groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CeD (\(n\) = 19)**} & \multicolumn{2}{c}{**NCGS (\(n\) = 10)**} & \multicolumn{2}{c}{**CTRL (\(n\) = 14)**} \\ \cline{2-7}  & **Concentration** & **\% of Total SCFA** & **Concentration** & **\% of Total SCFA** & **Concentration** & **\% of Total SCFA** \\ \hline Fecal acetic acid (\(\mu\)g) & 2144 (1228) & 63\({}^{\text{\,\text{\,\text{\,\text{\,\text{\,\,\text{\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\
## 3 Materials and Methods
### SAR Data and Multi-Temporal InSAR Processing
The MT-InSAR processing workflow can be divided into two steps: the pre-processing and the main processing using GAMMA/IPTA. During pre-processing, parameter files for the 99 SLC images were created and used to perform orbital refinement. The SLC images were co-registered based on the image used as a reference (20/09/2017), and a single-reference stack of 98 differential interferograms was generated. The Shuttle Radar Topography Mission (SRTM) 1-arcsec (i.e., 30 m/pixel) Digital Elevation Model (DEM) [53] was used during pre-processing to subtract the topography contribution from the interferograms. Using the 99 co-registered SLC images, a multi-reference stack was also created, containing both single-pixel and multi-look differential interferometric phases. Multi-look differential interferometric phases (DS) were first generated, and afterwards, single-pixel differential interferometric phases (PS) were estimated using low spectral diversity and temporal variability of SLC intensity. Then, single-look and multi-look phases were combined into one list of both PS and DS. The multi-reference stack consists of 291 differential interferograms, pairing each scene with the 3 subsequent scenes (triple redundancy). The perpendicular baselines of those 291 pairs were quite short, with the longest baseline being 202 m. A high-accuracy Digital Surface Model (DSM) (5.5 m/pixel) provided by the Hellenic Mapping and Cadastral Organisation (Figure 1b) was also used for the IPTA in order to improve the quality of the output results. As reference point, the closest point to the GPS benchmark \"Methane South\" (i.e., MESO station in Figure 1a; 37 deg34'51.02562\" with 23 deg22'12.14638\" E) was selected. Then, height corrections and atmospheric phases were computed using the combined point list. The above model was refined by updating height corrections and atmospheric phases via iteration of the last step two more times. Finally, the phases were converted into deformation time series, which contained both PS and DS. Then, the deformation time series database was split into two separate ones, one for the PS and the other for the DS targets.
# High-Temperature Annealing Effects on Atomically Thin Tungsten Diselenide Field-Effect Transistor
###### Abstract  
Two-dimensional (2D) material-based devices are expected to operate under high temperatures induced by Joule heating and environmental conditions when integrated into compact integrated circuits for practical applications. However, the behavior of these materials at high operating temperatures is obscure as most studies emphasize only room temperature or low-temperature operation. Here, the high-temperature electrical response of the tungsten diselenide (WSe\({}_{2}\)) field-effect transistor was studied. It is revealed that 350 K is the optimal annealing temperature for the WSe\({}_{2}\) transistor, and annealing at this temperature improves on-current, field-effect mobility and on/off ratio around three times. Annealing beyond this temperature (360 K to 670 K) adversely affects the device performance attributed to the partial oxidation of WSe\({}_{2}\) at higher temperatures. An increase in  
Further, due to low thermal conductivity and heat capacity as well as high surface area, these materials are expected to endure temperatures higher than bulk materials such as Si and GaAs [32; 33; 34; 35; 36]. Therefore, it is essential to test the WSe\({}_{2}\) devices at high operating temperatures and monitor their performance under these conditions.  
In this work, we designed an experimental setup to investigate the changes in the electrical behavior of the WSe\({}_{2}\) device after being exposed to high annealing temperatures. The investigation was carried out using a homemade probe station that can also anneal the devices in a nitrogen environment so that there is no need to take out the device after annealing. The device was annealed to temperatures ranging from 300 K to 670 K (step size: 10 K), and the electrical characterization of the device was performed at 300 K after each cycle of annealing. It was found that 350 K is the optimal annealing temperature for WSe\({}_{2}\), which improves the device performance by eliminating the adsorbed species and chemical residue. Annealing at temperatures higher than 350 K adversely affects the device performance. The effects of annealing on the transfer characteristic curve, on/off ratio, field-effect mobility and hysteresis were investigated.
## 4 Risk Analysis for Current Protocols
### NMEA 2000
#### 4.2.4 Replay/Man-in-the-Middle  
Because NMEA 2000 operates on a broadcast principle, an inline device could be placed between the target node and the rest of the channel (bus). This malicious inline device would be able to pass along messages from either the target node or the rest of the channel in either direction. We have identified a layer of protection in that NMEA 2000 contains a 15-bit Cyclic Redundancy Check (CRC) [5] (inherited from CAN bus). However, this is designed to detect transmission errors, rather than deliberate tampering as CRC is not considered to be cryptographically secure.