esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Experimental Evaluation
#### 4.3.2 Joint Estimation
The theoretical curve corresponds to an additive white Gaussian-noise channel assuming Gray-coding and only nearest neighbor errors [109]. \(\mathrm{BER}=10^{-2}\) for the joint-wavelength case compared to the independent case. The difference between the joint schemes is only 0.05 dB. Although the performance gains were small, they are consistent for the tested frequency spacings and OSNR values. This indicates that the results were not a statistical artifact. The fact that the performance gain is small means that phase noise was not a limiting factor in the tested system. Instead, our results serve as an indication that the phase coherence between the received channels was good enough to enable joint phase estimation. In phase-noise limited systems, such as low-baud rate and higher-order modulation-format systems, larger performance gains can be expected, which has been demonstrated in other phase-locked systems [104] and predicted using simulations [99; 103; 105]. Although we have discussed master-slave methods and joint phase estimation separately, these two methods can be readily combined if several subchannels are present. The design strategy could then be to perform joint phase estimation on a subset of the channels, enough to reach the desired phase noise tolerance, and then use the jointly estimated phase for the remaining subchannels in a master-slave fashion. Then, both increased phase-noise tolerance and complexity reduction can be achieved.
## 3 POD Results
### Comparison among Different POD Strategies
The bumps at around 10,000-20,000 s that are usually caused by the orbit errors lying in the ground-only clock offsets become much smoother in the results of the Joint 2 solutions. For the IGSO and GEO (C59) satellites, the stability derived from the Joint 2 clock is greatly improved compared to the other POD strategies. It is noted that the clock stability of satellite C59 obtained by the Joint 1 POD without geometry-free observables shows unexpected worse performance than that of the ground-only POD. Therefore, the performance of the satellite clock offsets estimated by the Joint 2 strategy indicates the rationality and necessity of adding both clock-free and geometry-free ISL observables. Figure 4: Two-day overlapping differences of clock offsets estimated by ground-only POD, Joint 1 POD, and Joint 2 POD. Figure 5: Overlapping Allan deviation of the satellite clock offsets estimated by: ground-only POD (**a**); Joint 1 POD (**b**); and Joint 2 POD (**c**). Although the accuracy of IGSO and GEO satellites using Joint 2 strategy is slightly worse than that of the Joint 1 strategy on the basis of the orbit overlapping differences, which only represents an internal consistency accuracy, the radial component in orbit overlapping differences, the SLR residuals and the characteristic of satellite clock offsets demonstrate that the orbit determination accuracy of the Joint 2 POD strategy is higher than that of Joint 1 POD. In the following sections, the Joint 2 POD strategy is adopted for three types of BDS-3 satellites to analyze whether the number of stations required for orbit determination can be reduced and whether the influence of stations distribution can be reduced or even eliminated after the introduction of ISL.
## 4 Experimental Results
### Recovery of Images  
The recovery of images analyzed on 210 images from the USC-SIPI dataset is described here. The proposed RDH algorithm was able to recover 97.37% of aerials, 98.55% of sequences, 67.19% of textures, and 94.87% of miscellaneous images, successfully. The overall recovery for all the 210 image dataset was 88.1%. Figure 7 depicts the results related to the image recovery according to the image category.
## 3 Results and Discussion
### Physical Properties
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **Thickness, (mm)** & **Disintegration Time, (s)** \\ \hline CMC—0 & \(0.056\pm 0.002\) & \(100\pm 1\) \\ FRG—12 & \(0.065\pm 0.005\) a & \(134\pm 3\) b,d. \\ FRG—15 & \(0.078\pm 0.001\) a & \(150\pm 4\) b,c \\ FRG—18 & \(0.086\pm 0.004\) a & \(189\pm 6\) b,c,d \\ FRG—21 & \(0.095\pm 0.003\) a & \(197\pm 7\) b,c,d \\ \hline \hline \end{tabular} Mean values in the same column with different letters indicate statistically significant differences (ANOVA/ Tukey’s test, \(p<0.05\)). a \(p\leq 0.05\) vs. CMC—0; b \(p\leq 0.05\) vs. FRG—12; c \(p\leq 0.05\) vs. FRG—15; d \(p\leq 0.05\) vs. CMC—0; e \(p\leq 0.05\) vs. FRG—12; f \(p\leq 0.05\) vs. FRG—15; g \(p\leq 0.05\) vs. CMC—0; h \(p\leq 0.05\) vs. FRG—12; k \(p\leq 0.05\) vs. FRG—15; j \(p\leq 0.05\) vs. FRG—15.  
\end{table} 
Table 3: Physical properties of sublingual films based on CMC with _G. biloba_ ethanol extract (GFD). a higher concentration of GFD (Tables 2 and 3). Similar results were reported by [PERSON] and coauthors [15], suggesting that increasing the hydrophobic drug (dihydroergotamine mesylate) amount in maltodextrin-pullulan sublingual films significantly increased the disintegration time. SEM images of CMC-0 and FRG--15 are shown in Figure 1. SEM analysis was done only for the sample coded FRG--15, since it showed higher elongation and tensile strength, and a faster disintegration time, which are important physicochemical properties for production. To understand the morphology of freeze-dried extract after incorporation as well their distribution into the film, SEM images of film formulations were obtained. Blank film (CMC--0) shows uniform structure. Moreover, the SEM images of prepared sublingual film show the uniform surface morphology of the FRG--15 sample with homogenous distribution of GFD and with no crystalline structures visible on SEM images.
## 4 Establishment of the Model
### Finite Element Calculation Model  
The finite element model presented in this paper is an idealized three-dimensional model used to calculate the flux and loss of the magnetic material surrounded by the coil. The loss is mainly concentrated on the magnetic material surrounded by the coil, and the loss at the corners is extremely low, meaning the error is within a controllable range. Firstly, a three-dimensional model of the Epstein frame is established according to the design parameters in the finite element calculation software COMSOL Multiphysics 6.0 [26]. The magnetic properties of the 0.10 mm ultra-thin silicon steel sheet and the amorphous alloy measured above are added to the three-dimensional model, including the magnetization curve and density. The magnetic field-physical field interface is integrated in order to combine the calculation model with the three-dimensional model of the Epstein frame, and the corresponding excitation is applied to the coil. The model is then meshed.
## 4 Numerical Results
### Angular Protection with Distance Protection: Restriction Zone
 } & No Protection & 3463 & \(-\)47.59 dB & 1450 & 41.87\% & 19.64 dB \\  & 45\({}^{\circ}\) & 3503 & \(-\)47.64 dB & 1455 & 41.53\% & 20.55 dB \\ \cline{1-1}  & 90\({}^{\circ}\) & 3624 & \(-\)47.79 dB & 1504 & 41.50\% & 19.26 dB \\ \cline{1-1}  & 180\({}^{\circ}\) & 3654 & \(-\)47.82 dB & 1143 & 31.28\% & 18.65 dB \\ \cline{1-1}  & 270\({}^{\circ}\) & 3686 & \(-\)47.87 dB & 809 & 21.95\% & 17.11 dB \\ \hline \hline \multirow{4}{*}{ \begin{tabular}{c} **UMi** \\ **(30 km \(\times\) 30 km)** \\ \end{tabular}  } & No Protection & 21,831 & \(-\)55.59 dB & 10,960 & 50.20\% & 26.79 dB \\ \cline{1-1}  & 45\({}^{\circ}\) & 22,036 & \(-\)55.63 dB & 11,022 & 50.02\% & 27.74 dB \\ \cline{1-1}  & 90\({}^{\circ}\) & 22,509 & \(-\)55.72 dB & 11,156 & 49.56\% & 25.02 dB \\ \cline{1-1}  & 180\({}^{\circ}\) & 22,603 & \(-\)55.74 dB & 8910 & 39.41\% & 24.43 dB \\ \cline{1-1}  & 270\({}^{\circ}\) & 22,697 & \(-\)55.75 dB & 6211 & 27.36 These tables reveal that a higher beam shut-off range results in lower BS interference, leading to a smaller exclusion zone. Then, the total number of BSs that can be deployed increases. The interference threshold for the restriction zone slightly changes when the beam shut-off range increases. The number of BSs in the restriction zone continues to decrease when the protection level increases. The percentage of deployed BSs requiring Tx power control also decreases when the beam shut-off range increases. The mean Tx power reduction in the restriction zone first increases when using the 45\({}^{\circ}\) beam shut-off range compared with when angular protection does not exist. The power decreases when the level of angular protection increases. When using the same beam shut-off angle, the exclusion zones of the 8 \(\times\) 8 and 16 \(\times\) 16 arrays become smaller than that of the 4 \(\times\) 4 array. Therefore, the total number of BSs that can be deployed increases. In addition, when using the same 45\({}^{\circ}\) and 90\({}^{\circ}\) beam shut-off angles, the 8 \(\times\) 8 array has the highest percentage of deployed BSs that require Tx power control. However, when using 180\({}^{\circ}\) and 270\({}^{\circ}\) beam shut-off angles, the antenna array with the largest number of elements has the lowest percentage of deployed BSs that require Tx power control. With the same beam shut-off angle, the 4 \(\times\) 4 and 8 \(\times\) 8 antenna arrays have a very similar mean Tx power reduction in the restriction zone. The 16 \(\times\) 16 antenna array has the lowest mean Tx power reduction in the restriction zone. Therefore, in UMi, the most efficient angular protection in terms of the number of deployable BSs and the power reduction is achieved using a 16 \(\times\) 16 antenna array.
###### Abstract  
The objective of this study was to analyze the effects of variables such as pre-crash emergency braking and inclined posture on human injuries in autonomous vehicle collisions using an active human model and through crash analysis. To achieve this, the MADYMO (MAhematical DYNAmic MOdels) active human model was validated for predicting occupant responses during pre-crash emergency braking. Its biofidelity during crash conditions was also validated. Additionally, the model was validated under component-level impact conditions to ensure its suitability for predicting occupant injuries. Two autonomous vehicle-relevant crash scenarios reconstructed based on actual accident conditions were selected. Variations in collision conditions, such as collision angles, overlaps, and relative collision speeds, were applied to selected crash scenarios. A finite element vehicle-to-vehicle crash analysis was performed to obtain the crash pulse. Using the validated crash analysis model, a parametric simulation study was conducted by applying variations to parameters such as emergency braking, seat-related parameters, and muscle activity. Finally, the impact of each variable on injury risk was analyzed using the Wilcoxon rank sum test. Analysis results showed that a reclined posture and a seat track position located 300 mm rearward from the baseline seat track position had a significant impact on injuries. Evaluation results on the effects of these variables can contribute to the development of safety evaluation standards for autonomous vehicles, such as crash safety regulations, by crash safety assessment organizations.  
autonomous vehicle collision; active human body model; parametric simulation study; sensitivity analysis; pre-crash; reclined posture 2025  
156492  
[PERSON]  
1 Department of Mechanical Design Engineering, Tech University of Korea,  
Sibeung-si 15073, Republic of Korea; [EMAIL_ADDRESS]  
2 Department of Advanced Railroad Vehicle Research, Korea Railroad Research Institute, Uiwang 16105,  
Republic of Korea  
2 [PERSON]ADDRESS]  
3 These authors contributed equally to this work.
## 4 Discussion
What it was possible to ascertain in this study, confirming the preliminary data shown by [31] based on a minor number of samples, is that the variability of floral scents is not related to the geographical distance between populations, nor to the main environmental characteristics of the growth sites. However, even if based on only three samples, the observed variation of the VOCs emitted by the same individual in two consecutive years would seem to indicate a poor genetic determinism for this phenomenon. The mechanisms behind this variability could be related to an intrinsic plasticity of metabolic pathways that lead to the synthesis of VOCs in _B. robertiana_, but no studies have specifically investigated this aspect thus far. Several of the detected compounds, such as verbenone and \(\alpha\)-zingiberene, are known to act as pheromones [33; 34]; however, this specific function probably is not specifically used by _B. robertiana_. These results may suggest that there is no adaptation of floral scent to local environments or specific communities of pollinators. In fact, the wide spectrum of VOCs emitted can allow _B. robertiana_ to attract different species of insects also belonging to very distant taxonomic groups, as evidenced by some studies on its pollinators [24; 25] relying on a large plethora of possible pollinators. The strategy of _B. robertiana_ to attract pollinators many involves early flowering, showiness and long-lasting inflorescence, traits that can be advantageous for exploiting the first insects that emerged from winter hibernation. floral scent, a key trait for interaction between plants and insects [35; 36], plays an important role for floral mimicry in deceptive species. In this context, a huge variation in flora scent, such as that highlighted by _B. robertiana_, can be considered an effective strategy for a rewardless, but allogamous, species to avoid that visiting insect learn to avoid such flowers. In fact, some studies [37] has been highlighted as rewardlessness can be a dangerous strategy [38]. Despite the causes of rewardlessness are still little known, just a study on _B. robertiana_ showed for the first time the reproductive advantage of the lack of nectar [24]. However, it must be stressed again that this reproductive advantage can only occur if pollinating insects do not learn to associate the floral signals of a species with the lack of nectar inside the flowers.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 4 Applications of ML in D2D Communications
### ML for Power Control  
PC is an important interference management related topic that ML can handle in D2D. In [20], two PC algorithms based on supervised and unsupervised learning were proposed in D2D scenarios. The authors proved the importance of ML in D2D communication by comparing two ML algorithms with conventional PC methods in terms of computational complexity, throughput, and energy efficiency. In [21], the problem of D2D PC is addressed in the case of known channel gains between two D2D users and BSs, while the channel gains among the two users are unknown. A complete automatic power allocation method for Internet of things (IoT)-D2D communication based on DL are proposed in [22]. They designed a distributed DL structure that trains the devices as a group but each device works independently and attained near optimal performance. The authors of [23] suggested a mean-field multiagent deep RL model that permits the devices to learn online PC strategies in a fully distributed manner, i.e., a selfish strategy; each node operates independently.
# Modeling and Control of Satellite Formations: A Survey
## 2 Multiple-Input-Multiple-Output Formation Architecture
The technical result of the invention is to provide a given configuration of the AES system, observed from certain places on the Earth's surface.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Results for Real-World RS Hazy Images  
In this section, we present a qualitative comparison of the dehazing performance of different methods using real-world RS hazy images. The results are depicted in Figure 3. Figure 3a shows two aerial hazy images labeled as \"image 1\" and \"image 2,\", respectively. These images were selected from [PERSON] et al.'s work [38] and Flickr. Figure 3b-o show the dehazed results obtained using various methods. The original hazy images appear blurry and have low contrast. However, after applying the dehazing process, the image quality improves significantly. Nonetheless, different methods remove haze to varying extents. DehazeFlow, as a supervised learning-based method, fails to remove haze properly in Figure 3e, suggesting poor generalizability to real-world RS image dehazing. The result obtained by ZID in Figure 3j exhibits an apparent color shift on the roof of \"image 1.\" FADE and USIDNet have over-enhancement issues, as can be seen in \"image 1\" of Figure 3d,i, respectively. In Figure 3f,g, SKKTDN and DWGAN produce unnatural-looking dehazed images with color saturation loss. _DCP_ and RefineDNet show good dehazing performance, but the results in Figure 3b,h display a slight dark color. CEEF delivers a well-dehazed image with full detailed information in Figure 3c, although \"image 1\" suffers from slight color distortion. All zero-shot dehazing methods, except for ZID, generate natural and pleasing dehazed images. However, in Figure 3k,j,m, DDIP, YOLY, and ZIR exhibit undesired bright flares on the top right of \"image 2.\" Our proposed method, as shown in Figure 3n, outperforms the other dehazing methods in terms of dehazing and color consistency when compared to the reference result by Photoshop in Figure 3o. Therefore, the embedded _DCP_ module assures that our network has effective haze removal ability, while the RefineNet corrects the _DCP_'s shortcomings. Moreover, the zero-shot learning manner guarantees the good generalizability of our model while processing the complex real-world RS hazy images.
## 6 Practical Applications and Future Perspectives  
The present study opened up some hypotheses that could not be answered using this method. For example, what causes the kinetic of Xc/h after a team sports training session, or why it seems that CK is correlated with R/h and not with Xc/h.  
Despite these hypotheses, and more focused on the real applicability of BIVA in the sports field, we observed how the patterns of change in the whole-body analysis and in the muscle-localized analysis were completely different. On the one hand, whole-body BIVA is especially sensitive to changes in body hydration, which is important in high performance since a significant loss of body water means lower performance. On the other hand, the strength of ML-BIVA lies in the information that it provides on muscle cellular health and integrity, although the kinetics of some parameters are unclear and need to be further studied. The correlation between R/h and CK could help physical trainers to control training load, for example, by establishing thresholds that could determine the intra-session intensity.  
Regarding whole-body BIVA protocols, the foot-to-hand tetrapolar technique at 50 kHz is commonly used. However, with muscle-localized BIVA it is not clear where the electrodes should be placed on each muscle, and it is imperative to standardize a protocol. For this reason, we propose to follow the SENIAM project guidelines [39], that were designed initially for surface electromyography but can also be used in ML-BIVA.  
In the present study, we only analyzed the bioelectrical values of the right thigh; however, it may also be interesting for future studies to address the possibility of analyzing both thighs and checking the possible differences between them, such that ML-BIVA could also become a method to assess asymmetries, as was shown by [PERSON] et al. [60] with segmental specific BIVA.  
Finally, the muscle-localized data were normalized by the subject's height, because this is the predominant method in the current ML-BIVA studies; however, we believe that normalizing by muscle length would be more appropriate, as was executed by [PERSON] et al. [61].
## 4 Model Verification and Comparison Based on 1 DCNN-BiLSTM-AdaBoost
### Evaluation Indicators
#### 4.1.2 Loss Function  
This paper uses the cross entropy loss function to evaluate the model, which combines the Softmax function and the negative log-likelihood loss and calculates the difference between the prediction result and the actual label by the model.  
\[\text{Loss}=-\frac{1}{N}\sum_{i=0}^{N-1}\sum_{k=0}^{K-1}y_{i,k}\ln p_{i,k} \tag{13}\]  
where \(y_{i,k}\) indicates that the true label of the \(i\)-th sample is \(k\), there are \(K\) label values and \(N\) samples, and \(p_{i,k}\) indicates the probability that the \(i\)-th sample is predicted to be the \(k\)-th label value.
## 2 Materials and Methods
### Proofreading Activity  
To determine if the AlaRS suppressor variants led to changes in mis-aminoacylation in vitro, all single and double mutant AlaRS variants were used for in vitro mis-servation experiments. To monitor mis-aminoacylation, 5 uM AlaRS was incubated with 5 uM tRNA\({}^{\text{Ala}}\), 750 uM [\({}^{3}\)H]-Ser, aminoacylation buffer (100 uM HEPES pH 7.2, 30 mM KCl, and 10 mM MgCl\({}_{2}\)), and initiated with 8 mM ATP. Mis-servation reactions were carried out for at 37 \({}^{\circ}\)C 15 min and radiolabeled signal was determined by TCA precipitation and subsequent quantification by scintillation counting.  
Beyond mis-aminoacylation, _trans_ proofreading activity was monitored to determine if the suppressor mutants altered aminoacyl-tRNA re-binding and subsequent proofreading. To generate pre-formed aa-tRNA\({}^{\text{Ala}}\), recombinant EF-Tu was first activated (EF-Tu expression construct was generously provided by Dr. [PERSON], Ohio State University). EF-Tu activation was performed by incubating 50 uM EF-Tu with 50 mM Tris pH 7.8, 100 uM DTT, 68 mM KCl, 6.7 mM MgCl\({}_{2}\), 2.5 mM phosphoenol pyruvate, 30 ug pyruvate kinase, and 500 uM GTP. EF-Tu activation reactions were incubated at 37 \({}^{\circ}\)C for 30 min and used immediately for mis-aminoacylation. Pre-formed Ser-tRNA\({}^{\text{Ala}}\) was subsequently generated by incubating 10 uM tRNA\({}^{\text{Ala}}\) with 950 uM (\({}^{3}\)H)-Ser, 5 uM activated EF-Tu, 5 uM _E. coli_ AlaRS C666A, 8 mM ATP, and aminoacylation buffer. Cognate Ala-tRNA\({}^{\text{Ala}}\) was pre-formed in buffering conditions as described above and with 150 uM (\({}^{14}\)C)-Ala, 200 nM _E. coli_ AlaRS, and 5 uM tRNA\({}^{\text{Ala}}\). Aminoacylation reactions were incubated for 1 h at 37 \({}^{\circ}\)C and subsequently quenched by adding an equal volume of acid phenol chloroform. Following acid phenol chloroform extraction, aa-tRNA\({}^{\text{Ala}}\) was EtOH precipitated, before finally being re-suspended in 100 mM sodium acetate pH 4.5. To monitor aa-tRNA\({}^{\text{Ala}}\) deacylation activity, 150 nM AlaRS variants were incubated with aminoacylation buffer and pre-formed aa-tRNA\({}^{\text{Ala}}\). Aliquots of the reaction were quenched on 5% TCA presoaked filter paper at various time points between 0 and 20 min. Quenched reactions were then washed 3x in 5% TCA, EtOH, dried, and quantified using scintillation counting. Percent deacylation was determined by the decrease in radiolabeled signal relative to T\({}_{0}\).  
To monitor ATP consumption, reactions including: 2 uM tRNA\({}^{\text{Ala}}\), 1x aminoacylation buffer, 1 uM AlaRS variants, 0.2 U PPiase, 3 mM (\({}^{32}\)P - \(\gamma\)) ATP, and either 10 mM alanine or 100 mM serine were incubated at 37 \({}^{\circ}\)C. At time points ranging between 3\({}^{\prime}\)-30\({}^{\prime}\), aliquots of the reaction mix were removed and quenched in an equal volume of glacial acetic acid. To monitor the consumption of ATP, migration of the quenched reactions were monitored by thin-layer chromatography on PEI cellulose plates. Plates were developed in dipotassium phosphate and imaged using phosphor imaging.