esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of the Dental Pulp and Periodontal Ligament Stem Cells of the Yucatan Miniature Pig
###### Abstract  
Miniature pigs have been considered as a recommended large animal model for biomedical research. Mesenchymal stem cells offer promising potential for tissue regeneration. Recent studies have suggested that dental pulp stem cells (DPSCs) and periodontal ligament stem cells (PDLSCs) may provide more reliable strategies for the treatment of dental diseases using a cell-based tissue engineering approach. The aim of this study was to isolate and compare the characteristics of the DPSCs and PDLSCs of a miniature pig breed to the DPSCs and PDLSCs of a domestic farm pig breed. Stem cells of the DP and PDL were obtained from a male Yucatan miniature pig (nine months old) and a male domestic farm pig breed (six months old). The cell morphology, surface stem cell marker expression, proliferation, and osteogenic differentiation ability were evaluated. Under a light microscope, the DPSCs and PDLSCs of the miniature pig breed had morphologies similar to those of the domestic farm pig breed. The proliferation of PDLSCs in both animals showed no significant differences, except on day five, whereas the proliferation of DPSCs was significantly higher in the miniature pig breed. However, the osteogenic abilities of the DPSCs and PDLSCs from the miniature pig breed were significantly lower compared to the domestic farm pig breed. This observation emphasizes the need for the breed-specific optimization of an osteogenic differentiation culture protocol for Yucatan miniature pig DPSCs and PDLSCs before application to cell-based therapy for tissue engineering and regenerative medicine.  
miniature pig: stem cell; dental pulp; periodontal ligament 20 August 2021 9 October 2021 9  
muscle, and nerve cell production [8; 9; 10; 11]. They are also simple when attempting to create distinct cell lineages [12]. In addition, these stem cells have characteristics that suppress the immune reaction, as do other stem cells [13; 14]. On the basis of these features, many studies have utilized these cells from pigs, mainly in a dental disease model. Recently, many studies have suggested that dental stem cells, such as dental pulp (DP) and periodontal ligament (PDL) stem cells, may be useful in the treatment of dental diseases that require tissue regeneration [15; 16; 17; 18]. However, it has been unclear as to whether studies using DPSCs and PDLSCs from miniature pig breeds are more efficient than those using domestic farm pig breeds. Furthermore, knowledge of the characteristics of DPSCs and PDLSCs of miniature pig breeds is scant compared to the published work on domestic farm pig breeds. Therefore, the present study aims to investigate and compare the characteristics of the DPSCs and PDLSCs of a miniature pig breed to those of a domestic farm pig breed by evaluating the cell morphology, surface marker expression levels, proliferation rates, and osteogenic differentiation abilities.
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 2 PIC Evolution
SiN: Ligentec (Ecublens, Switzerland); Lionix (Enschede, Netherlands); Institute of Microelectronics of Barcelona IBM-CNM, \"Silicon Nitride Technology,\" (Barcelona, Spain). Furthermore, several reference institutions with state-of-the-art research in integrated photonics contributed to the technology developments, namely the University of California, Santa Barbara (UCSB) with the available California NanoSystems Institute (CNSI) Nanostructure Cleanroom Facility (NCF); the Kavli Nanoscience Institute (KNI) laboratory at the California Institute of Technology (Caltech), the Fraunhofer Heinrich-Hertz-Institut (HHI); the ePIXfab, INTEC department-Photonics Research Group at Ghent University; the Nanophotonics Technology Center (NTC) at the Universitat Politecnica de Valencia (UPV); etc. Beyond the generic availability of design/fabrication, it is necessary to further develop and invest in packaging technologies to reach simpler, standardized, and efficient approaches, thus making it accessible for users and/or companies. Currently, the technology-associated limitations are mainly related to the lack of general design rules that ease the encapsulation and allow successful testing of the manufactured photonic chips [6]. This last integration step (packaging) is imperative for labs and companies that are trying to enter/grow in the integrated photonic technology and market. A consortium model aiming to establish PIC packaging processes under European commission Horizon 2020 funding is currently being conducted [62] to facilitate the development of this PIC technologic branch through the development of standard technologies to provide access to affordable, mature, and scalable packaging solutions [63].
# Galerkin's Spectral Method in the Analysis of Antenna Wall Operation
## 3 Radiation Field Analysis of Unrestricted Antenna Array  
We consider the case of a periodic antenna array in the plane \(z=0\) in the directions of the \(x\)- and \(y\)-axes. Thus, the antenna cells with the carrier function \(h_{mn}\) cover the entire plane \(z=0\), i.e., the plane (\(x\),\(y\),\(0\)) with the carrier function \(A(x\),\(y)\):  
\[\sum_{m,n}h_{m,n}(x,y)=H(x,y)=1 \tag{64}\]  
Similarly, the metal parts of the antenna are distributed periodically over the entire plane \(z=0\) in individual array cells (\(m\),\(n\)) and cover the area with the carrier function \(A(x\),\(y)\):  
\[\sum_{m,n}a_{m,n}(x,y)=A(x,y) \tag{65}\]  
\[a_{mn}(x,y)=1\ for\ (x,y)\in\ parts\ of\ the\ metallic\ cell\ (m,n) \tag{66}\]  
\[a_{mn}(x,y)=0\ for\ (x,y)\ \
otin\ parts\ of\ the\ metallic\ cell(m,n) \tag{67}\]  
We consider the case of an antenna wall composed of perfectly conducting planar elements, placed in a homogeneous, isotropic, lossless dielectric medium. The solution of the considered scattering problem comes down to determining the generalized scattering matrix of the antenna placed in this way. The generalization to the case of an antenna \"immersed\" in a dielectric layered structure is realized by constructing the generalized scattering matrix as a cascade composition of scattering matrices corresponding to individual layers (planar discontinuities of the antenna). Let us summarize below the spectral representations of individual components of the electric field above or on the antenna surface \(z=0\).
## 4 Evaluation Methodology
### Evaluation Methodology  
Since the data used in our experimentation come from a real case study, their quantity is limited, which in turn restricts the range of applicable techniques. Nevertheless, for comparative purposes, we have integrated three distinct approaches into our analysis. These approaches encompass classical Natural Language Processing (NLP) techniques, including those based on bag-of-words and key phrases, as well as contemporary methods that leverage implicit knowledge, such as embeddings. We believe that these methodologies exemplify the range of text representation methods to be employed for similarity calculation. Therefore, we compute similarity between the query and the posts/questions using different strategies: (1) similarity between bags of words, (2) similarity between word phrases and (3) similarity between word embeddings.  
As the evaluation metric, we have used _precision at k (P@k)_, which is a typical information retrieval metric defined as the number of relevant documents among the \(k\) first documents in the retrieval ranking. We set \(k\) to 5. Evaluation is performed manually by four experts in data structures and algorithms, and includes both the evaluation of the recommendations of reinforcement activities from previous tests and the evaluation of the recommendations of related posts from the course discussion forum.  
We evaluate the recommendations using two different criteria for defining when a post or activity is considered as relevant for a given query:  
1. The strict evaluation criterion or problem-related recommendation: a question from a test is relevant to the student query if it refers to the same concept or problem solved or it uses the same data structure or scheme. This is a very restrictive criterion that considers only questions strongly related to the query.
2. The relaxed evaluation criterion or scheme-related recommendation: it allows the recommendation of questions concerning the same data structure or scheme but a different problem, since they may also be of interest to the student to reinforce learning. For example, if the student is asking about the resolution of the backpack problem using the greedy scheme, the recommender may suggest activities regarding other problems solved using the greedy strategy (e.g., Dijkstra, or the exchange problem).
# Synchronization of Acoustic Signals for Steganographic Transmission
###### Abstract  
Steganography is a technique that makes it possible to hide additional information (payload) in the original signal (cover work). This paper focuses on hiding information in a speech signal. One of the major problems with steganographic systems is ensuring synchronization. The paper presents four new and effective mechanisms that allow achievement of synchronization on the receiving side. Three of the developed methods of synchronization operate directly on the acoustic signal, while the fourth method works in the higher layer, analyzing the structure of the decoded steganographic data stream. The results of the research concerning both the evaluation of signal quality and the effectiveness of synchronization are presented. The signal quality was assessed based on both objective and subjective methods. The conducted research confirmed the effectiveness of the developed methods of synchronization during the transmission of steganographic data in the VHF radio link and in the VoIP channel.  
audio steganography; audio watermarking; security; data hiding; synchronization +
Footnote †: journal:  
1
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 4 Design Analysis
### Buoyancy  
One essential aspect analyzed in the USV design process is the buoyant force generated by the USV. The buoyant force needs to be calculated to determine whether the buoyant force of USV can lead the USV to float. The criteria, which have to be met is the buoyant force of the USV, is greater than the weight of the USV (Fb > W). The buoyant force equations are presented in Equations (22)-(24). Where Fb is buoyant force (N), \(\rho\) is water density (kg/m\({}^{3}\)), g is the acceleration of gravity (m/s\({}^{2}\)), and V\({}_{\rm Submerged}\), different from the total volume of USV (0.005316 m\({}^{3}\)), is the sum of USV's Frame, Thrusters, and Hulls volume (m\({}^{3}\)) or the volume of the submerged part.  
\[\rm{Fb}=\rho gV_{Submerged} \tag{22}\]  
\[\rm{Fb}=997\frac{kg}{m^{3}}\times 9.81\ \frac{m}{s^{2}}\times 0.005002\ m^{3} \tag{23}\]  
\[\rm{Fb}=48.92\ N \tag{24}\]  
From the results of this calculation, where W = 48.76 N, the value of Fb > W was found. It is concluded that the USV is floating. However, due to the fact that the value of buoyant force (Fb) is approximately the USV's weight, almost all of the hull and the Frame are submerged.
## 3 Invalid Action Masking
### Masking Changes Policy Gradient
 \tag{20}\]  
Since \(\
abla_{\theta}\sum_{a_{i}\in\mathcal{A}}\pi_{\theta}(a_{i}|s_{t})=\
abla_{ \theta}1=0\), (20) is written as:  
\[\
abla_{\theta}\log\pi_{\theta}^{\varDelta}(a_{t}|s_{t})=\
abla_{\theta}\log \pi_{\theta}(a_{t}|s_{t})+\frac{\sum\limits_{a_{i}\in\mathcal{A}}\
abla_{ \theta}\pi_{\theta}(a_{i}|s_{t})(1-\varDelta(a_{i}|s_{t}))}{\sum\limits_{a_{j} \in\mathcal{A}}\pi_{\theta}(a_{j}|s_{t})\varDelta(a_{j}|s_{t})}. \tag{21}\]  
According to the definition of masking function (7), we have \(1-\varDelta(a_{i}|s_{t})=0,\ \forall a_{i}\in\mathcal{A}_{\text{v}}(s_{t})\) and \(\varDelta(a_{i}|s_{t})=0,\ \forall a_{i}\in\mathcal{A}_{\text{inv}}(s_{t})\). Substituting the above equation into (21), we obtain that  
\[ \begin{split}\
abla_{\theta}\log\pi_{\theta}^{\varDelta}(a_{t}|s _{t})&=\
abla_{\theta}\log\pi_{\theta}(a_{t}|s_{t})+\frac{\sum \limits_{a_{i}\in\mathcal{A}_{\text{inv}}(s_{t})}\
abla_{\theta}\pi_{\theta}(a _{i}|s_{t})}{\sum\limits_{a_{j}\in\mathcal{A}_{\text{inv}}(s_{t})}\pi_{\theta}(a _{j}|s_{t})}\\ &=\
abla_{\theta}\log\pi_{\theta}(a_{t}|s_{t})+\frac{1}{\mathcal{I }(s_{t};\theta)}\sum\limits_{a_{i}\in\mathcal{A}_{\text{inv}}(s_{i})}\
abla_{ \theta}\pi_{\theta}(a_{i}|s_{t}),\end{split}  \tag{22}\]  
where \(\mathcal{I}(s_{t};\theta)=\sum_{a_{j}\in\mathcal{A}_{\text{v}}(s_{t})}\pi_{ \theta}(a_{j}|s_{t})\). Substituting (22) into (17), we obtain that  
\[ \begin{split} g_{\text{mask}}&=\mathbb{E}_{\tau\sim \pi_{\theta}^{\varDelta}}\left[\sum\limits_{t=0}^{H}\left(\
abla_{\theta}\log \pi_{\theta}(a_{t}|s_{t})+\frac{1}{\mathcal{I}(s_{t};\theta)}\sum\limits_{a_{i }\in\mathcal{A}_{\text{inv}}(s_{t})}\
abla_{\theta}\pi_{\theta}(a_{i}|s_{t}) \right)\hat{A}_{t}\right]\\ &=g_{\text{naive}}+\mathbb{E}_{\tau\sim\pi_{\theta}^{\varDelta}} \left[\sum\limits_{t=0}^{H}\frac{\hat{A}_{t}}{\mathcal{I}(s_{t};\theta)}\sum \limits_{a_{j}\in\mathcal{A}_{\text{inv}}(s_{t})}\
abla_{\theta}\pi_{\theta}(a _{i}|s_{t})\right]\end{split} \]This completes the Proof of Theorem 1. Theorem 1 illustrates that the gradient produced by invalid action masking differs from the naive policy gradient. Specifically, the residual item is the sum of gradients corresponding to invalid actions. The experimental results derived by [PERSON] et al. [23] show that once the masking is removed, the performance of the original policy \(\pi_{\theta}\) will be unstable. We infer that the residual item in the invalid action policy gradient results in the poor performance of the original policy. In the rest of this paper, we propose a novel algorithm for invalid action masking based on Theorem 1.
# Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
## 4 Result
### Experimental Set-Up  
The experiments are performed on two different virtual machines (VMs) supported by two heterogeneous GPUs, mainly used for GPU computing. These heterogeneous GPUs are NVIDIA Fermi C2075 (released in 2010) and NVIDIA Kepler K40c (released in 2012). OpenNebula [48] is used as a Virtual Infrastructure Manager (VIM), and the KVM is the used hypervisor, as shown in Table 8. Additionally, the Operating System (OS) used is Linux CentOS in the VM. Moreover, the used CUDA compiler version is 7.5. The used GPUs have distinctive features and resources, as shown Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline
**VM Characteristics** & **VM1** & **VM2** \\ \hline CPU & Intel Xeon E5-2630 v3 2.4 GHz & Intel Xeon E5-2630 v3 2.4 GHz \\ \hline VCPU & 8 & 8 \\ \hline RAM Size & 32 GB & 64 GB \\ \hline GPU & NVIDIA Fermi C2075 & NVIDIA Kepler K40c \\ \hline Hypervisor & KVM \\ \hline CUDA Compiler Version & 7.5 \\ \hline OS & Linux CentOS \\ \hline VIM & OpenNebula \\ \hline \end{tabular}
\end{table}
Table 8: Two VMs’ details in the cloud testbed.  
\begin{table}
\begin{tabular}{c c} \hline
**GPU Details** & **Fermi C2075** & **Kepler K40c** \\ \hline CUDA Cores & 448 & 2880 \\ \hline SMs & 14 & 15 \\ \hline Cores/SM & 32 & 192 \\ \hline Core Frequency (MHz) & 1150 & 745 \\ \hline Memory Size (GB) & 6 & 12 \\ \hline Max Power Consumption (W) & 225 & 235 \\ \hline Max Threads/Block & 1024 & 1024 \\ \hline Max Warp/SM & 48 & 64 \\ \hline Max Thread Blocks/SM & 8 & 16 \\ \hline ECC Mode & Enabled & Enabled \\ \hline \end{tabular}
\end{table}
Table 9: Fermi C2075 and Kepler K40c GPUs’ characteristics.  
Moreover, the VMs connected with the GPUs were set with different RAM sizes. For both VMs, the physical RAM was allocated to the virtual RAM. In all models, we selected application sizes to be compatible with both GPUs' resources, since the Kepler GPU and the connected VM have better resources and performance, and to enable fairness between the GPUs as well. Additionally, the nvprof profiling tool measured the time of data transfer between the RAM and the GPU, which is included in the execution time. This work does not consider the impact of other factors on energy consumption when GPU applications are executed in a cloud-computing infrastructure, including I/O traffic and networking, since the majority of power consumption is consumed by processing units.  
To measure the model accuracy with the actual value for each application in the testing set, the absolute error percentage is utilized, and it is calculated by the following formula:  
\[\text{Error}\;(\%)=\frac{Predicted-Actual}{Actual}\times 100 \tag{8}\]
## 5 Key Enablers
### Software-Defined Controllers for Edge Cloud
#### 5.3.6 Reinforcement-Learning-Based SDN Controller for Load Balancing  
The network traffic dynamically changes due to the types of applications and services, locations of the users, and uneven demand for network resources. To handle such traffic imbalance, predicting traffic patterns is very important. However, forecasting traffic patterns is highly challenging due to short-term fluctuations, complex long-term trends, and the dynamic nature of networks. [PERSON] et al. [51] proposed a reinforcement learning  
Figure 12: New hierarchical edge cloud SDN controller system [70].  
scheme for balancing controller loads while minimizing switch migration. To determine if a controller is overloaded or not, they use the following equations.  
\[L_{ij}(t)=\sum\limits_{i=1}^{n}\lambda_{vi}(t)x_{ij}(t) \tag{4}\]  
\[L^{I}(t)=\frac{\sum\limits_{j=1}^{k}L_{cf}(t)}{k} \tag{5}\]  
\[LR_{cj}(t)=\frac{L_{cj}(t)}{L^{I}(t)} \tag{6}\]  
where, \(L_{cj}(t)\) is instantaneous load, \(L^{I}(t)\) is ideal load, \(LR_{cj}(t)\) is the ration of the instantaneous load and ideal load. \(\lambda_{vi}\) is the packet-in count at a controller from switch \(vi\) in time \(t\). If the ratio is greater than \(1\), it indicates an overloaded controller [51].  
For learning and predicting traffic, they use a Q-learning algorithm in reinforcement learning and the following equation is the updated Q-learning rule they used in their experiment.  
\[Q_{n+1}(s,\,a)=(1-a(n))Q_{n}(s,a)+a(n)\Big{[}G(s,a)-l_{m}G_{mg}(s,a)+\gamma_{ a^{\prime}}^{min}Q_{n}(s^{\prime},a^{\prime})\Big{]} \tag{7}\]  
where, \(s\) is state and \(a\) is action or event. \(l_{m}G_{mg}(s,a)\) represents the cost acquired for taking action \(a\) in state \(s\), weighted by the Lagrange multiplier \(l_{m}\). \(Q_{n}(s,a)\) represents the \(Q\)-value in reinforcement learning. Through the realization of our research, we reviewed several research works closely related to this research topic and present reinforcement-learning-based approaches for computation offloading, route optimization, and resource allocation [54; 58].
# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
## 4 Experiment Results and Analysis
### Experimental Results
As illustrated in Figure 12, box_loss quantifies the discrepancy between predicted and ground-truth bounding boxes, with lower values indicating higher detection accuracy. cls_loss measures the divergence between predicted and ground-truth class labels, where reduced values reflect improved classification precision. Furthermore, dfl_loss formulates continuous coordinate prediction as discrete probability distribution prediction, enabling more accurate coordinate localization. Minimized values of this loss signify enhanced prediction fidelity. As shown in Figure 13, in the SFE-YOLO model, C represents bottom-resting targets (objects settled on underwater surfaces), while F denotes suspended targets (objects floating in water without settling). In the YOLOv11n network, Class 1 is designated for bottom-resting targets and Class 2 for suspended targets. Comparing the left and right subfigures, the SFE-YOLO model demonstrates substantial improvements in precision and completeness under full-target detection conditions compared to the baseline model. Additionally, its performance curves exhibit enhanced smoothness, reflecting the model's superior stability and robustness in dynamic environments. Figure 12: Training and validation losses and metric progression. The final experimental results are visualized in Figure 17. The first column shows the original images. The second column displays the baseline detection results. The third column presents the SFE-YOLO detection results. The first image row demonstrates that the baseline YOLOv11n model produces substantially more false alarms for bottom-resting targets than SFE-YOLO. Analysis of the second and third rows indicates that the baseline model underperforms the improved version in detecting feature-ambiguous bottom-resting targets while also yielding less confident predictions for suspended targets. Notably, the fourth row highlights the enhanced model's superior detection accuracy in target localization. Figure 16: Comparison of mAP50 between SFE-YOLO and YOLOv11n. \[P=\frac{TP}{TP+FP} \tag{14}\]0 Some visualization results.
## 1 Introduction
utilizes a dual-branch transformer architecture with different input rates to separately encode motion information and skeleton context, achieving a MPJPE of 38.9 mm on the Human3.6M dataset [22]. [PERSON] et al. proposed Root-GAST-Net, a geometry-aware spatiotemporal model that integrates human detection and depth estimation for multi-person 3D absolute pose estimation, with an MPJPE of 39.8 mm on Human3.6M [23]. [PERSON] introduced a transformer-based framework (LiftFormer) to lift 2D keypoints to 3D coordinates, reporting a MPJPE of 40.2 mm [24]. In 2021, [PERSON] et al. developed HybrIK, a hybrid approach that combines inverse kinematics and deep learning for the estimation of 3D pose and shape, achieving an MPJPE of 41.6 mm on Human3.6M [25]. A summary of the reviewed models and demonstrated resutls is given in Table 1.  
Despite these promising advancements, there remains substantial room for improvement, particularly in enhancing the temporal and spatial synchronization of joints to achieve more accurate and robust 3D human pose estimations. While the HumanEva dataset is comprehensive and provides valuable resources for 3D human pose estimation, tasks involving different camera setups may require additional investigations within their existing frameworks. This underscores the importance of further exploring new architectures, conditions, and camera setups to enhance model performance under varied viewing or clothing conditions.