esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
At the global level, meat consumption has doubled in the last 20 years, and it is expected to grow by another 13% per year by 2028 [1]. Meat represents an important source of nutrition for many people around the world. Globally, pig meat is the most popular, but poultry production is growing the most quickly. The economic increase is an important factor in meat consumption. Income growth enables the purchase of meat, which is typically a more expensive source of protein. In this context, consumer preferences for meat origin and meat production system, including traceability and animal diet, are increasing faster than in the past. Therefore, they are willing to pay a higher price for meat that meets their individual standards and specific requirements. However, due to the COVID-19 pandemic, food prices were rising, and now, in the context of the East European conflict, the meat price index was up 15% during this year [2], and it is a concern that mislabeling (false declaration of geographical origin or farming system) of food commodities could occur. Consequently, there is a continuous requirement for the development of reliable analytical methods that can be used for authentication goals [3]. In this regard, by combining stable isotopes with multi-element analysis and then applying statistical treatments, reliable information about the geographical origin of various food products such as honey [4, 5], milk [6, 7], meat [7, 8], and fruit distillates [9, 10] could be obtained. The isotopic fingerprint of animal tissues and products is the summation of feeds ingested throughout their life. The \({}^{18}\)O/\({}^{16}\)O and \({}^{2}\)H/\({}^{1}\)H ratios reflect the consumed water and subsequently the isotopic signature of the local precipitation from the production area, depending on the distance to the ocean and the altitude above the sea, while the \({}^{13}\)C/\({}^{12}\)C ratio reflects mainly differences in the food resources. The ratio \({}^{13}\)C/\({}^{12}\)C of plant material is determined primarily by the photosynthesis type of the plant but also by the local and temporal climate conditions under which it has grown, and through the food chain, the isotope fingerprint is correspondingly transferred to animal material [8; 11].  
In this context, one aim of the present study was to determine the isotopic (\(\delta^{13}\)C, \(\delta^{2}\)H, and \(\delta^{18}\)O) and elemental (Na, M, K, Ca, Fe, Zn, Rb, Cu, Cr, B, Ni, Ba, Mn, Se, Mo, Li, Sr, V, Co, La, Nb, Pd, In, Pb, Sn, As, Sb, and Cd) profiles of 120 meat samples (49 chicken and 71 pork) from different Romanian regions. The second objective was related to building the chemometric models for chicken and pork meat differentiation according to: (1) their geographical origin and (2) the animal breeding system (animals coming from yard rearing systems versus animals coming from conventional/industrial farms). A feature selection step was applied prior to the construction of the PLS-DA models in order to identify the variables having the highest discrimination power.
# Evaluation of BDS-2 and BDS-3 Satellite Atomic Clock Products and Their Effects on Positioning
## 3 Data Collection  
Satellite clock products provide effective data to evaluate clock stability. As one of IGS analysis centers, Wuhan University (WHU) has been providing precise clock products for BDS-3 together with BDS-2 since 1 January 2019. Then, GeoForschungsZentrum (GFZ) started providing both BDS-2 and BDS-3 clock products since 28 November 2019. Therefore, the precise clock products generated by WHU and GFZ with a 30 s sampling interval were selected to calculate ADEV and analyze the clock datum bias between BDS-2 and BDS-3. Meanwhile, the broadcast ephemeris clock products of the same 30 s interval obtained by the second-order polynomial interpolation were also evaluated.  
Moreover, seven international GNSS monitoring and assessment systems (iGMAS) and three MGEX tracking stations, which can track BDS-2 and BDS-3 satellites, were used to further calculate the ISB and evaluate the performance of PPP based on broadcast ephemeris. The experiments were performed with the FUSING (FUSing IN GNSS) software [37]. The FUSING software was developed for high precision real-time GNSS data processing [38], multi-sensor navigation [39] and atmospheric modeling [40]. The station distribution is illustrated in Figure 1. All of them are located in the Asia Pacific due to the limitation of BDS-2 regional service. The time period of selected data in this paper is from modified Julian day (MJD) 58815-59214 for a total of 400 days.
## 3 Results
### Petri Dish Experiment
#### 3.1.2. Sprout Length Analysis  
Results on sprout length (Figure 6) show the distribution of sprout length across three analyzed treatment groups, Control with 0% MP, LDPE 0.01%, and LDPE at 1% concentrations in the Petri dish experiment. Both LDPE 0.01% and LDPE 1% treatments exhibit slightly lower median sprout lengths compared to the Control, indicating a potential negative influence of LDPE on early sprout development. However, the observed differences are relatively modest.  
The overall variability is comparable across treatments, only the LDPE 1% group shows slightly greater dispersion, suggesting increased variability in response at this concentration.  
The results presented in Table 1 show that there are no statistically significant differences in sprout height between the treatments in the Petri dish experiment. Specifically, the comparison between LDPE 0.01% and the Control has an estimate of \(-\)0.08234, with a confidence interval ranging from \(-\)0.36323 to 0.19854 and a \(p\)-value of 0.812, indicating no significant difference between these two treatments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison** & **Estimate** & **Lower CI** & **Upper CI** & \(p\) **Value** \\ \hline LDPE 0.01\%-Control & \(-\)0.08234 & \(-\)0.36323 & 0.19854 & 0.812 \\ LDPE 1\%-Control & \(-\)0.02403 & \(-\)0.30966 & 0.26161 & 0.974 \\ LDPE 1\%-LDPE 0.01\% & 0.05831 & \(-\)0.22732 & 0.34395 & 0.930 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Duncan test for differences in sprout length between treatments in the Petri dish experiment.  
Figure 6: Boxplot illustration of the distribution of length (in mm) of sprouts from the Petri dish experiment across three treatments, Control (C), LDPE 0.01%, and LDPE 1%, with a \(p\)-value of 0.812 indicating no significant difference. The central bold line within each box represents the median sprout length, while the box itself spans the interquartile range (25 th to 75 th percentile). Whiskers indicate the minimum and maximum values within 1.5 times the interquartile range. The dot represents outliers, where for each treatment, some individual measurements were much lower or higher than the majority of the data.  
Similarly, the comparison between LDPE 1% and the Control shows an estimate of \(-0.02403\), with a confidence interval from \(-0.30966\) to \(0.26161\) and a \(p\)-value of \(0.974\), which also suggests no significant effect of LDPE 1% on sprout height relative to the Control. Finally, the comparison between LDPE 1% and LDPE 0.01% shows an estimate of \(0.05831\), with a confidence interval ranging from \(-0.22732\) to \(0.34395\) and a \(p\)-value of \(0.930\), further confirming that there is no significant difference in sprout height between these two LDPE treatments.  
In summary, the data suggest that the different concentrations of LDPE do not have a statistically significant impact on sprout height in the Petri dish experiment.
## 3 Results and Discussion
### Field-Effect ICEO of a Janus micropillar Subjected to a Controllable Gate Voltage
#### 3.3.1 Effect of AC Gate-Voltage Bias on ICEO Mixing- and Pumping-Flow Components
Although a larger \(\chi\) at a low frequency is of great benefit for sample mixing (Figure 6C), it works at the price of a reduced throughput due to the quite weak u\({}_{\mathrm{x}}\) when the signal frequency is no more than 100 Hz (Figure 6A). Thus, great care should be taken in the proper choice of AC exciting frequency for multidirectional on-chip sample handing. We then compared the responses of the IZP and the ICEO slipping to the signal frequency on the metal- and dielectric-phase surfaces of the central Janus pillar, as shown in Figure 6D. Both the IZP and the ICEO streaming on the dielectric end (the dashed  
Figure 6: The calculation results of the AC field-frequency effect on the resulting pump u\({}_{\mathrm{x}}\) and mixing-flow behavior u\({}_{\mathrm{y}}\) orthogonal to each other due to Janus AC-FFT, in which the metal phase of a Janus micropillar of varying radii is exposed to an arbitrary gate voltage amplitude V\({}_{\mathrm{G}}\) = 0.9V\({}_{\mathrm{D}}\) = 1.8 V for a given driving voltage V\({}_{\mathrm{D}}\) = 2 V. (**A**) Frequency-dependent ICEO pump speed along channel length for different pillar radii. (**B**) Frequency-dependent ICEO mixing velocity along channel width for distinct pillar radii. (**C**) Frequency-dependent mixing/pumping-velocity-component ratio u\({}_{\mathrm{y}}\)/u\({}_{\mathrm{x}}\) under varying pillar radii. (**D**) A quantitative comparison of tangential ICEO slipping and induced zeta potential between the dielectric- and metal-phase surfaces of the Janus micropillar of a given radius R = 5 \(\upmu\)m as a function of the field frequency. lines) are about \(\log_{10}(\mathrm{R}/\lambda_{\mathrm{D}})\approx 2\) orders of magnitude smaller than that on the conducting side (the solid line in Figure 6D), which is consistent with the results of the preliminary scaling analysis in Equations (23)-(28). Similar to the calculation results in Figure 4G for the unbiased powering mode, the ICEO's flow velocity on the dielectric surface reaches its maximum value at a frequency that is much higher than that on the conducting portion. In contrast with the velocity-relaxation peak in the middle-frequency band, the IZP reaches a stable low-frequency plateau, with its magnitude mainly decided by the applied gate-voltage offset \(\mathrm{\triangle V_{G}=V_{G}-V_{D}/2=1.8-2/2=0.8~{}V}\) and diminishes to zero with a high-frequency limit. Based on the analysis above, we prefer to choose a moderate driving frequency of 200 Hz within the effective range from 100 to 1000 Hz and a moderate pillar size of \(\mathrm{R=5~{}\mu m}\) in order to guarantee a sufficient flow flux \(\mathrm{u_{x}}\) and nontrivial velocity ratio \(\mathrm{\chi=u_{y}/u_{x}}\) at the same time, which can also help suppress bipolar electrochemical reaction to a certain extents.
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 4 Experiments and Analysis
It is discernible that as channel noise increases, there is a noteworthy reduction in transmission rate. Our proposed  
Figure 6: Transmission rate versus transmission power. method consistently outshines the comparative approach, showcasing higher transmission rates at both low and high power levels. It is also noteworthy that at transmission power levels below 0.2 watts, our proposed method exhibits a higher transmission rate when the noise variance \(\sigma_{b}\) is 1.5 compared to existing methods at \(\sigma_{b}\)=1. This suggests that our proposed method achieves faster transmission rates under low power conditions, with more pronounced improvements in transmission rates compared to high-power scenarios. Figure 7 illustrates transmission rates under different levels of covertness using our proposed method and the comparative approach. This figure illuminates a trend wherein as covertness increases, both methods experience declines in transmission rates. Notably, our proposed method consistently maintains a superior position compared to the comparative approach, effectively enhancing covertness while sustaining competitive transmission rates. A critical consideration arises from the comparison of results in Figure 5a,b. The substantial divergence in covertness metrics between the proposed method and the comparative approach underscores the challenge of directly comparing transmission rates under equivalent covertness conditions. Our proposed method stands out by simultaneously achieving superior covertness and higher transmission rates, underscoring its effectiveness in optimizing both aspects of covert communication. In summary, our findings demonstrate the intricate interplay between transmission rate, transmission power, and covertness, with our proposed method consistently achieving enhanced covertness and superior transmission rates across diverse operational scenarios. In this section, we first validated the accuracy of our Taylor series expansion method. Subsequently, we compared existing methods to our proposed optimization method in terms of covertness performance under different channel coefficients and noise conditions for the sender, as well as transmission rates under different channel coefficients and noise conditions for the receiver. Finally, in the same environment, we compared the covertness performance and transmission rate of our proposed method to those of existing methods. Under the same covertness indicator, our proposed method has a higher transmission rate. This implies that surveillance videos can be covertly transmitted at a higher rate, corresponding to higher video clarity and shorter video transmission times.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 2 Related Work
### Planning
#### 2.4.3 Motion Planning  
Motion planners receive a goal point coupled with sensor data and try to find a valid motion sequence to reach the goal without violating any constraint, such as not hitting any obstacle along the way. Usually, motion planners are compared based on their computational efficiency (the process run time and its scalability with the environment growth) and completeness (its ability to find the optimal solution in a finite time) [3]. The most notable motion planning methods are the Cell Decomposition Methods (CDMs), the Potential Field Methods (PFMs), and the Sampling-Based Methods (SBMs).  
In cell decomposition methods, the environment's free space is divided into small cells, and the output is a sequence of adjacent cells that leads the initial position to the goal while avoiding obstacles. [PERSON] [78] used approximate cell decomposition, which does not set a fixed cell shape and size, to navigate efficiently. [PERSON] [79] combined CDM with probabilistic sampling to improve its scalability. Cell decomposition approaches, however, suffer from limited granularity, combinatorial explosion, and often generating infeasible solutions [80]. In potential field methods, an attractive potential is attached to goals while a repulsive one is attached to obstacles, and the robot follows the gradient direction generated by the combination of those fields. Both [PERSON] et al. [81] and [PERSON] et al. [82] showed that such approach is suitable for multi-robot navigation. Potential fields, however, suffer from often converging to local minima, trapping the robot midway [80].  
Sampling-based algorithms have attracted considerable attention due to their ability to scale well while also being probabilistic complete, namely if the number of samples converges to infinity, the probability of finding the optimal solution converges to 1. The most notable SBMs are Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT) [83]. Despite both being based on connecting points sampled randomly, the way both algorithms connect those points largely differ. PRM [84] maintains multiple graph expansions simultaneously, and have been shown to perform well on high dimensional spaces, while RRT [85] rapidly explores a single graph, making it more suitable for local planning on small sized maps. [PERSON] et al. [86] showed that under mild conditions, original PRM and RRT algorithms, most of the time, returned non-optimal values. The author then introduced asymptotically optimal versions of both methods, namely PRM* and RRT*. This advantage, however, is overshadowed by their relatively slow convergence, which hinders their usage in real-time scenarios. They also provided no theoretical guarantees for obtaining an optimal solution [87].
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 3 Background
### AS Relationships  
AS relationships determine how routing policies must be set up to absolve the business constraints, agreements and requirements. They may be complex and are not, usually, exposed for public access. However, it is possible to abstract the relationships among ASs by simplifying them into one of the two types:  
* Customer-to-provider: the relationship where the AS provider is paid by the customer AS to carry customer network traffic from or to other networks;  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Priority** & **Criterion** \\ \hline
1 & Prefer route with highest Local preference \\
2 & Path originated by a local router \\
3 & Path with shorter AS-path length \\
4 & Path with lowest origin code \\
5 & Lower MED values for routes from the same neighboring AS \\
6 & Prefer routes learned from external BGP (eBGP) rather than internal BGP (iBGP) \\
7 & Path with closest next-hop \\ (...) & Other BGP tie-break \\ \hline \hline \end{tabular}
\end{table}
Table 2: The BGP path selection decision process. AS, Autonomous System; MED, Multi-Exit Discriminator.  
Peer-to-peer: the ASs involved agree to share the costs of the connectivity among them, and then, all traffic between them is free of charge.  
Furthermore, the customer-to-provider relationship ensures that AS providers are paid by the customer regardless of the direction in which traffic flows. Thus, for a particular AS, it is preferable to first route traffic for customer links, then peer-to-peer links and finally to provider links (when it is acceptable). Because the volume of traffic matters to define a transit AS profit, it is mandatory to control how the inbound and outbound traffic is routed. Besides, for non-transit ASs' (stub ASs) control, the inbound traffic is mandatory to reach the full utilization of their inter-domain links.
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 2 Description of the New Concept Platform
### Direct Position Analysis
Since for every solution \(\theta\), unique values of \(\hat{\theta}\) and \(\tilde{\theta}\) exist, a unique location of the upper platform is identified for each solution \(\theta\). These solutions correspond to the possible assembly configurations of the structure represented in Figure 4 and, as a consequence, of the PRBM depicted in Figure 3a, provided that the six inputs \(\alpha_{i}\) and \(d_{i}\) have been assigned. However, for the sake of the present investigation, the only interesting solution will be the one compatible with rotations and displacements starting from the initial undeformed configuration represented in Figure 2a. This shows that the numerical approaches could be used more conveniently than the purely analytical ones, because they could converge to the actual configuration by using the undeformed pose as the starting guess. In the next section, finite element analysis (FEA) is applied to the compliant platform illustrated in Figure 2a, by assigning three different sets of input displacements.
# Application of DMLF in Pulse Ranging LiDAR System
## 2 Methods
### Analysis of the Linear Fitting Near PGZCPs  
Through the above analysis, we already know the feasibility of the differential method and the optimal axial displacement \(d\) corresponding to the maximum sensitivity of the differential echo curve. Next, we select seven points near the PGZCPs in the differential echo signal curve to fit a line, according to the slope value k of the linear-fitting curve to verify the optimal axial shift \(d\). The simulation results are described as Table 1. \(\text{k}_{\text{max}}=0.0229\) when \(d=c\tau\), which also verifies the correctness of Figure 2a.  
After period research, we find that, sometimes, the zero-crossing point maybe not exactly zero, but instead is a very small value. Therefore, in order to confirm the zero-crossing position exactly, we take the intersection of the fitting curve of the points near PGZCPs and the zero axes as the true zero point, which is the stop-time moment of TOF.  
Figure 2b is shown the traditional peak method, and the DMLF echo signal curves at \(d=c\tau\), \(t_{r}\) is the stop-time moment that we expect.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Axial Shift (m)** & **d/4** & **d/3** & **d/2** & **d** & **3 d/2** & **2 d** \\ \hline
**Slope k (v/ns)** & 0.0078 & 0.0127 & 0.0193 & 0.0229 & 0.0173 & 0.0052 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The slopes of linear fitting curve at different axial shift.  
Figure 2: (**a**) Differential echo pulse curves for different axial shift; (**b**) The traditional peak method and DMLF TES, AES, and DES means traditional echo signal, axial shift echo signal, and differential echo signal, respectively.
## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.6 Evaluation Metrics for 3D Generation and Animation Techniques  
_Matching Distance Ratio (MDR):_ Matching distance ratio is an evaluation metric used in the context of inpainting 3D images. It quantifies the quality of the generated inpainted regions by calculating the ratio of points that are accurately matched between the generated and ground-truth images. The point encoder GAN [161] model, employed for inpainting 3D images, utilizes the matching distance ratio as an evaluation metric to assess the effectiveness and accuracy of the inpainted regions. A higher matching distance ratio indicates a higher level of accuracy in matching the points between the generated and ground truth images, indicating a better quality of inpainted regions.  
_MPJPE (Mean per Joint Positioning Error):_ MPJPE, or mean per joint positioning error, is an evaluation metric commonly used in the context of generating realistic human poses. It measures the average positional error between the joints of the generated pose and the ground-truth pose. GAN-Poser [51], a model for generating realistic human poses, employs MPJPE as an evaluation metric to assess the accuracy and realism of the generated poses. A lower MPJPE value indicates a higher level of pose accuracy and alignment with the ground truth.  
_Chanfer Distance:_ Chamfer distance is an evaluation metric utilized in text-guided 3D object generation tasks. It quantifies the dissimilarity between the generated 3D objects and the target objects based on their point cloud representations. The DREAMFUSION [133] model employs Chamfer distance as an evaluation metric to measure the dissimilarity between the generated 3D objects and the target objects described in textual descriptions. A lower Chamfer distance value indicates a higher level of similarity and alignment with the desired object.  
_Regression Ratio:_ Regression ratio is an evaluation metric used in the context of inpainting 3D images. It quantifies the accuracy of the generated inpainted regions by calculating the ratio of accurately regressed points. The point encoder GAN [161], a model for inpainting 3D images, utilizes regression ratio as an evaluation metric to assess the accuracy and quality of the generated inpainted regions. A higher regression ratio indicates a higher level of accuracy and fidelity in regressing the missing parts of the 3D images.  
_KID (Kernel Inception Distance):_ Kernel inception distance (KID) is an evaluation metric employed in the context of generating personalized animatable 3D faces based on text guidance. It measures the distance between the feature representations of the generated faces and the real faces using a pre-trained kernelized inception network. The DreamFace [102] model utilizes KID as an evaluation metric to assess the similarity and realism of the generated 3D faces.