esa-sceva/satcom-chunk-collection huggingface dataset:
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 3 System Model
### Experimental Channels
#### 3.2.1 Water Tank  
The static channel experiment is based on an experimental water tank (2 m \(\times\) 1 m \(\times\) 1 m) situated on the L@bisen Yrcea-Ouest (Brest, France) premises and depicted in Figure 3. At the transmission side, a Neptune Sonar Limited (Kelk, United Kingdom) D/26 is used, whereas at the reception side, we use a Bruel & Kjaer (Narum, Denmark) 8104 hydrophone. The range between the projector and the hydrophone is 1 m. Channel parameters are summarized in Table 1, and estimated CIR is depicted in Figure 4a. Large channel delay spread is caused by multiple reflections on tank boundaries. Furthermore, due to the tank configuration, CIR is assumed static during the experimentation.  
Figure 2: Frame structure.  
Figure 4: Channel impulse response for tank and NOF1 channels, the cyclic prefix tolerance as the dotted line.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Water Tank Channel** & **Watermark NOF1 Channel**[18] \\ \hline Center frequency & 23 kHz & 14 kHz \\ Signal bandwidth & 6 kHz & 8 kHz \\ Transmission range & 1 m & 750 m \\ Water depth & 50 cm & 10 m \\ RMS channel delay spread [17] & 16 ms & 3.3 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel parameters.  
Figure 3: Experimental water tank.
## 5 Simulation Experiments  
### Simulation Environment  
This experiment is based on SDN and the IEEE 802.15.4 protocol for wireless sensor networks. The network emulator for the framework is Mininet [30], and the controller is Floodlight [31]. Floodlight runs on a server with an AMD Opteron processor 6348 and 16 GB memory. The server is installed with Linux kernel version 2.6.32. Mininet runs on a separate server, and the servers are connected by a 10 Gbps Ethernet network. Details appear in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Network Parameters** & **Values** \\ \hline Number of nodes & 50, 75, 100, 125, 150, 180 \\ Network area & 500 m \(\times\) 500 m \\ Radio range & 10 m–40 m \\ Initial energy of nodes & 10 j–20 j \\ Size of each packet & 512 bytes \\ \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
The simulation metrics are:  
1. Overall Message Cost (OMC)--This is a summation of messages sent by all of the nodes in the network. \(\text{msg}_{\text{k}}\) denotes the total number of messages sent by \(\text{n}_{\text{k}}\) throughout the simulation period. \[\text{OMC} =\sum\text{msg}_{\text{k}}\] \[\text{n}_{\text{k}} \in\text{NW}\]
2. Overall Energy Consumed (OEC)--This is a summation of energy consumed in all of the nodes in the network. \[\text{OEC} =\sum\left(\text{m-en}_{\text{k}}-\text{r-en}_{\text{k}}\right)\] \[\text{n}_{\text{k}} \in\text{NW}\]
3. Network Throughput (NT)--This is the percentage of data packets that were successfully delivered to their respective destinations. t-p is the number of packets transmitted throughout the communication session, and d-p is the number of packets successfully delivered to their destinations throughout the simulation run. \[\text{NT} =\left(\text{d-p}/\text{t-p}\right)\times 100\]
4. Average Delay (AD)--This is a summation of delay faced by all of the packets in the network, divided by the number of packets transmitted. PAC is the set of all of the packets transmitted throughout the simulation. d-l(pac) denotes the delay suffered by packet PAC: \[\text{AD} =\sum\text{d-l(pac)}/\mid\text{PAC}\mid\] \[\text{pac} \in\text{PAC}\]
5. Percentage of alive nodes per established communication session (PALCS)--This isthe summation of the number of alive nodes (ALVN(CS)) in session CS multiplied by 100 anddivided (by total number of nodes in optimal route in the same session \(\times\) total number of established sessions). SES is the set of all of the sessions established throughout the simulation time. route(CS) is total number of nodes in an optimal route in session CS. \[\text{PALCS} =\sum\left(\text{ALVN(CS)}\times 100\right)/\left(\mid\text{ SES}\mid\times\text{route(CS)}\right)\] \[\text{CS} \in\text{SES}\]
## 2 Materials and Methods
### Translation Estimation  
By estimating the most approximate normal direction, the corresponding BBX and the matching image position \(C_{t}=(x_{t},y_{t})\) are also obtained. To align the projected model with the matched object, the virtual camera is translated along the vector \(\mathcal{C}_{t}\mathcal{C}_{B}\) regarding the projection viewer. Because of the translation is performed in the 3D virtual projection environment, the scale of the model keeps unchanged, but the view angle is transformed according to the 3D translation operation. The 3D translation scale is calculated by the proportion of \(\mathcal{C}_{t}\mathcal{C}_{B}\) with the pixel size \(l_{M}\) of the marker in \(\mathcal{I}_{c}\), which are both available. Therefore, the translation offset matrix T\({}_{o2}\) could be expressed by:  
Figure 5: Initial pose guesses obtained by the bounding box projection. (**a**) Six orthographic surfaces on the bounding box of a CAD model. (**b**) Perspective projection of bounding box (BBX) surfaces using \(T_{M}\).  
\[\mathrm{T}_{o2}=\left[\begin{array}{ccccc}1&0&0&\frac{\|\widetilde{C}_{\mathrm{c}} \cdot C_{\mathrm{R}}\|}{M}(x_{t}-x_{B})\\ 0&1&0&\frac{\|C_{\mathrm{c}}\cdot C_{\mathrm{R}}\|}{M}(y_{t}-y_{B})\\ 0&0&1&0\\ 0&0&0&1\end{array}\right], \tag{5}\]  
and the projection matrix after the translation is:  
\[T_{R}=T_{M}\cdot T_{o1}\cdot T_{o2}. \tag{6}\]  
After the translation of the virtual camera, the CAD model is coarsely aligned with the image ROI of its corresponding real product as shown in Figure 6.
# Modeling Epithelial Homeostasis and Perturbation in Three-Dimensional Human Esophageal Organoids
###### Abstract  
**Background:** Esophageal organoids from a variety of pathologies including cancer are grown in Advanced Dulbecco's Modified Eagle Medium-Nutrient Mixture F12 (hereafter ADF). However, the currently available ADF-based formulations are suboptimal for normal human esophageal organoids, limiting the ability to compare normal esophageal organoids with those representing a given disease state. **Methods:** We have utilized immortalized normal human esophageal epithelial cell (keratinocyte) lines EPC1 and EPC2 and endoscopic normal esophageal biopsies to generate three-dimensional (3D) organoids. To optimize the ADF-based medium, we evaluated the requirement of exogenous epidermal growth factor (EGF) and inhibition of transforming growth factor-(TGF)-\(\beta\) receptor-mediated signaling, both key regulators of the proliferation of human esophageal keratinocytes. We have modeled human esophageal epithellial pathology by stimulating esophageal 3D organoids with interleukin (IL)-13, an inflammatory cytokine, or UAB30, a novel pharmacological activator of retinoic acid signaling. **Results:** The formation of normal human esophageal 3D organoids was limited by excessive EGF and intrinsic TGF\(\beta\)-receptor-mediated signaling. Optimized HOME0 improved normal human esophageal organoid formation. In the HOME0-grown organoids, IL-13 and UAB30 induced epithelial changes reminiscent of basal cell hyperplasia, a common histopathologic feature in broad esophageal disease conditions including eosinophil esophaggitis. **Conclusions:** HOME0 allows modeling of the homeostatic differentiation gradient and perturbation of the humanesophageal epithelium while permitting a comparison of organoids from mice and other organs grown in ADF-based media.  
esophagus; organoids; epidermal growth factor; transforming growth factor-\(\beta\); basal cell hyperplasia; eosinophil esophagigitis; retinoic acid
# Uniform Page Migration Problem in Euclidean Space
## 2 Preliminaries  
Let \(M\) be a metric space with a distance function \(\delta\). For a given initial page location \(s_{0}\in M\), a sequence of requests \(r_{1},\ldots,r_{n}\in M\), and a page size \(D\in\mathbb{Z}^{+}\), the page migration problem is to compute page locations \(s_{1},\ldots,s_{n}\in M\) such that the cost function \(\sum_{i=1}^{n}\left(\delta\left(s_{i-1},r_{i}\right)+D\delta\left(s_{i-1},s_ {i}\right)\right)\) is minimized. We call \(s_{i}\) a server.  
An online algorithm must compute \(s_{i}\) without any information regarding \(r_{i+1},\ldots,r_{n}\), whereas an offline algorithm may compute \(s_{i}\) using the entire sequence of requests \(r_{1},\ldots,r_{n}\). An adversary against an online algorithm \(A\) generates a sequence of requests given to \(A\) and computes an output sequence for the generated requests. If \(A\) is deterministic, then the adversary generates requests using the definition of \(A\), or equivalently, the information of the actual behavior of \(A\), and computes its own output according to an optimal offline algorithm OPT. The deterministic algorithm \(A\) is c-competitive if \(cost_{A}\left(s_{0},\sigma\right)\leq c\cdot cost_{OPT}\left(s_{0},\sigma\right)+\alpha\) for the initial server \(s_{0}\) and any sequence \(\sigma\) of requests, where \(cost_{A}\) and \(cost_{OPT}\) are costs of \(A\) and OPT, respectively, and \(\alpha\) is a constant. For randomized online algorithms, there are two types of adversaries. An adversary is said to be oblivious if it generates requests in advance only using the definition of \(A\), i.e., without any information about the random behavior of \(A\) and computes its own output according to OPT. In contrast, an adaptive online adversary generates requests using information of the random behavior of \(A\) and computes its own output in an online fashion. The competitiveness of a randomized online algorithm against oblivious or adaptive online adversaries is defined in a similar way to that of a deterministic online algorithm, except that expected values are used for randomized costs.  
One commonly used technique for proving the competitiveness of an online algorithm \(A\) is to utilize a potential function \(\Phi\), which typically maps the situation at a point of time, such as the page locations of \(A\) and OPT, to a real value. More specifically, we suitably divide the sequence of the online processes of \(A\) and OPT into certain events. Our goal is to define the value of \(\Phi\) in such a way that the initial value of \(\Phi\) is at most some constant \(\beta\), \(\Phi\) is always at least some constant \(-\gamma\) and that \(\Delta cost_{A}+\Delta\Phi\leq c\cdot\Delta cost_{OPT}\) for any event, where \(\Delta\) denotes the change of values by the event. Summing the inequality over all events for an initial server \(s_{0}\) and a request sequence \(\sigma\), we have \(cost_{A}\left(s_{0},\sigma\right)\leq c\cdot cost_{OPT}\left(s_{0},\sigma \right)+\beta+\gamma\), which means that \(A\) is \(c\)-competitive.
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 4 Case Analysis
### Analysis of the Prediction Results  
The comparison between the prediction model proposed in this paper and the four models LSTM, BLSTM, CNN-BLSTM, and LVMD-DBFCM+CNN-BLSTM using three load evaluation indices, RMSE, MAE, and MAPE, is shown in Table 4 and Figure 17. From the results, we can see that the prediction models proposed in this paper have better prediction results than the other four models in all evaluation indices.  
Figure 18 shows the prediction graphs of the five prediction models. From the figure, it can be clearly seen that the pink line, representing the model proposed in this paper, fits so closely to the target curve represented by the black line, which can better reflect the trend of the target, indicating that the model proposed in this paper has the better prediction effect than addressed models. Figure 19 shows the prediction error comparison of the 5 models, from which it can be clearly seen that the model proposed in this paper, represented by yellow, has the lowest error.  
Figure 19: Comparison of prediction errors of the 5 models.  
Figure 17: Comparison of the RMSE, MAE, and MAPE results for 5 models’ predictions.
## 5 Analysis and Discussion
### Qualitative Hypotheses and Future Work  
Although slip was not observed in our experiments, the measured reduction in DCS deceleration suggests that conditions leading to slip (i.e., abrupt velocity changes) are effectively mitigated. While Sections 3.1 and 3.2 primarily introduce the conceptual design principles of the C-STS rather than a purely theoretical model, they still elucidate how lowering deceleration in the DCS phase can lessen the likelihood of exceeding friction limits and hence reduce the risk of slip.  
While our explanations are consistent with general mechanical principles--concerning inertia, timing, and torque demands--they remain qualitative. More rigorous validation, possibly involving multi-body simulations, torque-sensor data, or additional sensor instrumentation, would help quantify each proposed factor. Nevertheless, the recurring improvement observed in mid/high-speed conditions under C-STS, alongside its apparent drawbacks at low speed, provides critical insight for future design refinements. For instance, if one seeks to harness C-STS effectively at low speeds, it may be necessary to ensure that torque demand remains high enough prior to contact transition, or to implement a variable-stiffness mechanism that dynamically adjusts spring behavior. Moreover, certain simplifying assumptions--such as ignoring directional changes in the CoR trajectory or assuming minimal torque needs in flat rolling--should be revisited with advanced dynamic modeling and experimental measurements, potentially leading to more robust and versatile solutions.
## 2 _Drosophila_ ALS Models  
Approximately 10% of ALS cases are familial ALS and the other 90% are sporadic [1]. More than 30 ALS-causative genes, such as _chromosome 9 open reading frame 72_ (_C9 orf72_), _superoxide dismutase 1_ (_SODI_), _TAR DNA-binding protein 43_ (_ TDP-43_), and _fused in sarcoma_ (_FUS_), have been identified [16; 17]. These genes play important roles in the pathogenesis of both familial and sporadic ALS.  
Various _Drosophila_ models of ALS targeting _SODI_[18], _FUS_[19], _VAMP-associated protein B_ (_VAPB_) [20], _TDP-43_[21], _Valosin-containing protein (VCP)/p97_[22], _Ubiquilin-2_ (_UBQLN2_) [23], _C9 orf72_[24], and some of their _Drosophila_ homologues have been established and analyzed [8]. Of note, many of these _Drosophila_ ALS models exhibit common phenotypes such as larval crawling defect, adult climbing defect, aberrant morphology of synapse at the neuromuscular junction (NMJ), and aberrant eye morphology (rough eye phenotype) [8]. Genetic screening using ALS model flies identified several genes that genetically interact with causative genes of ALS. The genes involved in the EGFR signaling pathway, such as _rhomboid-1_, _rhomboid-3_, or _mirror_, genetically interacted with _cabeza_ (_caz_), a _Drosophila_ homologue of _FUS_[25]. Genetic screening also identified the _wallenda_ gene, encoding a conserved MAPKKK, as a modifier of TDP-43 [26]. A genetic link between _caz_ and _hippo_ (_lpo_), the _Drosophila_ orthologue of human _Mammalian sterile 20-like kinase 1_ and 2, has also been reported [27]. In addition to a role in tumor suppression, current studies revealed a novel role of Hippo in synaptic development that may be independent of Yorkie, a downstream target of the canonical Hippo pathway [28]. Aberrant morphology of mitochondria, such as fragmentation, is reported both in ALS patients and _Drosophila_ expressing human TDP-43 [29; 30]. Genes involved in mitochondrial dynamics, such as _Drosophila Mitofusion_ (_dMfn_)/_Marf_, _Opa1_, and _Drp1_, were identified as modifiers of TDP-43 or FUS-induced phenotypes in _Drosophila_[29].
# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 3 Materials and Methods
### Workflow of the Proposed Method  
The workflow of the proposed EMD-LightGBM collision classification method is illustrated in Figure 3. Initially, the data resource comprised collision vibration data acquired through the insertion and collision of each point on the collision point template. Subsequently, normalization was applied to the six-axis vibration signals to eliminate scale differences between the vibration features across different axes. The typical six-axis collision vibration signals and their corresponding normalized forms are shown in Figure 4. Here, we adopted the term 'effective period', as introduced in [15], to describe the actual length of the training data. Additionally, in consideration of signal variations during the collision, the effective period encompassed the length of the time series preceding the collision. For ease of comparison and analysis, this work standardized the length of the time series preceding the collision for vibration signals at different collision points to 50 sampling points. Following preprocessing, the data were partitioned into training and testing sets. To ensure there was no leakage of information from the testing set to the training set, distinct joint configurations were employed during the acquisition of the testing set, which were entirely different from those used to obtain the training set in this study. To enhance model performance while reducing computational complexity, feature selection was essential. In this work, we initially used the Relieff [34] method to rank the importance of predefined features. Subsequently, redundant features were eliminated using LightGBM, KNN, SVM, and FNN methods. The use of these four methods was primarily for fairness in model validation and comparison. During the model validation and comparison stage, the comparative methods mentioned in [8], KNN, SVM, and FNN, were employed. In practical applications, it is suggested to consider using LightGBM independently to remove redundant features. It is worth noting that our feature selection process involved signals without introducing noise. After identifying suitable features, signals with noise underwent EMD. This was followed by a denoising reconstruction process to generate new vibration signals. The resulting vibration signals then underwent feature extraction based on the chosen feature representation. Following this, the LightGBM model was trained using the extracted features. In addition, during testing, the same EMD and reconstruction process for signals containing noise applied in the training process was used on the test data. The identical feature representation method was directly applied to the reconstructed signals of the testing set for feature extraction. The extracted features were then utilized to evaluate the trained model.
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 5 Discussion
### Future Research Directions  
VidBlock demonstrates significant potential as a decentralized streaming platform, but further research is required to address scalability and performance challenges in larger and more complex network scenarios. Future work will focus on conducting experiments in high-concurrency environments and networks with thousands of geographically distributed nodes. These tests aim to assess key metrics such as latency, fault tolerance, and bandwidth utilization under realistic and demanding conditions.  
Moreover, optimizing relay node selection based on real-time network metrics, including latency, node reliability, and bandwidth, can further enhance VidBlock's performance. This approach will be particularly valuable for live streaming scenarios that span diverse geographical regions, ensuring a seamless user experience across different network conditions.  
In addition, incorporating adaptive streaming protocols into VidBlock's framework could enable automatic adjustments to video quality based on dynamic network states, improving overall user satisfaction. Future research may also explore integrating decentralized identity systems and token-based incentives for relay nodes, encouraging greater user participation while maintaining network integrity.  
By expanding its experimental scope and exploring innovative enhancements, VidBlock can strengthen its position as a scalable and resilient decentralized streaming solution in the Web3.0 ecosystem.
## 2 Data and Methods
### Feature Extraction  
To identify the most important variables, we used three major types of feature selection methods, namely, statistical feature methods, regression coefficients, and principal component analysis. Using statistical methods, we calculated the information value (IV) and variance inflation factor (VIF) for each feature. The IV provides an estimate of the predictive ability of the variable, while the VIF value provides a collinearity estimate of the variable with other variables in the model [17; 18]. In order to reduce the multicollinearity and improve the predictive ability of the features, we deleted the attribute VIF \(\geq\) 10 and IV \(<\) 0.01 and deleted the attribute VIF \(\geq\) 10 and IV \(<\) 0.001 from the data. In addition, we also used four regression models for feature selection, namely, stepwise regression based on AIC criterion, stepwise regression based on BIC criterion, Lasso regression, and Elastic Net regression. These techniques were chosen because they had been shown to be effective in previous user default prediction studies [19; 20; 21]. The third method is principal component analysis. The dimensions of the data after using different feature selection algorithms are shown in Table 2.