esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We propose a long-term parameterization scheme for two critical parameters, zero-plane displacement height (\(d\)) and aerodynamic roughness length (\(z_{\rm 0m}\)), that we further use in the Surface Energy Balance System (SEBS). A sensitivity analysis of SEBS indicated that these two parameters largely impact the estimated sensible heat and latent heat fluxes. First, we calibrated regression relationships between measured forest vertical parameters (Lorey's height and the frontal area index (FAI)) and forest aboveground biomass (AGB). Next, we derived the interannual Lorey's height and FAI values from our calibrated regression models and corresponding forest AGB dynamics that were converted from interannual carbon fluxes, as simulated from two incorporated ecological models and a 2009 forest basis map These dynamic forest vertical parameters, combined with refined eight-day Global LAnd Surface Satellite (GLASS) LAI products, were applied to estimate the eight-day \(d\), \(z_{\rm 0m}\), and, thus, the heat roughness length (\(z_{\rm 0m}\)). The obtained \(d\), \(z_{\rm 0m}\) and \(z_{\rm 0h}\) were then used as forcing for the SEBS model in order to simulate long-term forest evapotranspiration (ET) from 2000 to 2012 within the Qilian Mountains (QMs). As compared with MODIS, MOD16 products at the eddy covariance (EC) site, ET estimates from the SEBS agreed much better with EC measurements (\(R^{2}\) = 0.80 and RMSE = 0.21 mm\(\cdot\) day\({}^{-1}\)).  
Keywords:roughness length; forest vertical parameters; Surface Energy Balance System (SEBS); evapotranspiration; remote sensing +
Footnote †: journal:
## 4 Tests and Results  
Subsequently, three experiments are conducted to evaluate the performance of character recognition in the presence and absence of bio-inspired retinal preprocessing.  
The first experiment involves extracting digits from vehicle license plates and binarizing them before applying an OCR algorithm. The second experiment utilizes the proposed CNN model for character recognition. Lastly, the third experiment employs an SNN model generated from the CNN model.  
For the experiments, 40 sample images were captured using a Huawei P20 smartphone (which incorporates a 20-megapixel camera), and the samples were analyzed on a desktop computer featuring an AMD Ryzen 3700 processor, 32 GB of RAM, and an Nvidia RTX-3080 GPU.
## 4 The Proposed Model
### Virtual Relational Knowledge Graph (VRKG) Construction
#### 4.1.1 Virtual Relation Clustering  
Given the original relation embedding matrix \(\mathbf{R}=\left[\mathbf{r}_{1},\dots,\mathbf{r}_{|\mathcal{R}|}\right]\in \mathbb{R}^{d\times|\mathcal{R}|}\), we define a virtual relation center matrix \(\mathbf{V}=[\mathbf{v}_{1},\dots,\mathbf{v}_{K}]\in\mathbb{R}^{d\times K}\), where \(K\) is the number of virtual relation clusters. Each original relation \(\mathbf{r}_{p}\) is softly matched to virtual centers via temperature-scaled attention:  
\[\alpha_{pk}=\frac{\exp(\frac{\mathbf{r}_{p}^{\top}\mathbf{v}_{k}}{\tau})}{ \sum_{j=1}^{K}\exp(\frac{\mathbf{r}_{p}^{\top}\mathbf{v}_{j}}{\tau})},\quad \forall k\in\{1,...,K\}, \tag{5}\]  
where \(\tau\) is the temperature parameter controlling assignment sharpness.  
The reconstructed virtual relation embedding for \(\mathbf{r}_{p}\) is then defined as  
\[\tilde{\mathbf{r}}_{p}=\sum_{k=1}^{K}\alpha_{pk}\cdot\mathbf{v}_{k}. \tag{6}\]  
Figure 1: Overview of the proposed GLARA model.  
To ensure clustering consistency and semantic coherence, we impose a regularization term that penalizes reconstruction loss:  
\[\mathcal{L}_{\text{cluster}}=\sum_{p=1}^{|\mathcal{R}|}\left\|\mathbf{r}_{p}- \bar{\mathbf{r}}_{p}\right\|_{2}^{2}. \tag{7}\]  
This regularization term, \(\mathcal{L}_{\text{cluster}}\), is used during the clustering phase to encourage the model to learn consistent and coherent virtual relation clusters. However, it is not directly included in the final loss function since its effect is implicitly integrated into the global optimization framework through the clustering process. The primary optimization focuses on improving the recommendation task, while \(\mathcal{L}_{\text{cluster}}\) ensures the semantic consistency of the virtual relation embeddings during the clustering step. This approach allows the model to focus on the recommendation objective while maintaining the integrity of the semantic relationships learned during clustering.
## 3 Materials and Methods
### Methodology
#### 3.5.2 Localization  
Some image patches of VHRShips contain multiple or very small ships that cannot be classified without extracting them from the background. This fact prevents implementing the classification algorithm directly on the image as a whole. The general approach to localizing the objects in the images is to divide the image into sub-images and propose the regions that can contain ship(s). While the sliding window is the most straightforward and least computationally efficient method to divide the images into sub-images, the selective search, the regional proposal network, and the anchor-based search are newer and more efficient approaches. In this research, YOLOv4 is chosen for the localization of ships [61]. Indeed, the YOLO architecture is an end-to-end solution that covers localization, detection, and classification. YOLO optimizes the classes and the bounding boxes (bBox) simultaneously, unlike the proposed HieD. However, in the proposed approach, only the bBox localization feature of YOLOv4 is used (Figure 2).  
For the localization stage, YOLOv4 provides a black-box procedure that gets the image labeled with ship existence and exports the bBox of the ship(s) in [x, y, width, length] format.  
YOLOv4 is trained and tested with different parameters during the optimization of the localization step. At first, YOLOv4 is trained with one class corresponding to the ship as the foreground and the rest as the background to force the network to focus on only the localization regression. However, the best result is achieved with two classes: the navy and the civilian ships. Additionally, to suppress multiple detections of the same  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Type** \\ \hline Network & Xception (MATLAB version 21.1.1) \\  & pre-trained with ImageNet database \\ Network input & \(416\times 416\times 3\) (original images are resized) \\ Network output & ship/non-ship (binary classification) \\ Training optimizer & Stochastic Gradient Descent with Momentum (SGDM) \\ Loss function & Cross-entropy \\ Data augmentation & Random rotation [0, 360] \& random X and Y reflection \\ Initial learning rate (LR) & 0.001 \\ LR drop frequency & Every 10 epochs \\ LR drop factor & 0.1 \\ Number of epoch & 50 \\ Mini-batch size & 8 \\ Detection threshold & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameters used in the detection stage.  
Figure 2: YOLOv4 architecture used for localization.  
ship, Non-Maximum Suppression (NMS) is applied [1]. The rest of the parameters to tune YOLOv4 for the localization stage are given in Table 6.
## 1 Introduction
In a recent study at KU Leuven [17], it has also been found that the average tool feed rate, average gap voltage and average amount of normal discharge pulses per unit time, which can be monitored during the micro-EDM drilling process, display a high degree of correlation to the MRR and TWR. In particular, the average tool feed rate has shown the highest correlation to the MRR and TWR ([PERSON]'s correlation coefficient above 0.8). In this paper, previous study is further extended to investigate the applicability of data-driven approaches for tool wear and material removal prediction in micro-EDM drilling. Multiple linear regression (MLR) is used to model the relationship between the material removal performance indicators (MRR and TWR) and the average amount of normal discharge pulses per time unit, average tool feed rate, and average gap voltage. These average values are monitored and recorded at regular steps during the drilling process. Different regression models are built, using data collected through process monitoring in addition or in replacement of the processing parameters for predicting the MRR and TWR. The advantages of data-driven approaches with respect to traditional regression models solely based on processing parameters are analysed. The MLR models are implemented into a process monitoring strategy, which is used for controlling the hole depth based on the in-process prediction of tool wear. The paper is structured as follows. Section 2 details the materials and methods applied in this research, including a description of the experimental setup for micro-EDM drilling. Section 3 provides detailed explanations about the regression models. Section 4 presents the in-line strategies for predicting the tool wear and controlling the hole depth. In Section 5, the experimental results are presented and discussed, focusing on the prediction accuracy of the regression models and precision of the control strategy based on in-process tool wear prediction. Some concluding remarks, including a summary of the main achievements and future research directions, are given in Section 6.
# Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR
## 4 Simulation Experiment
### Planning Trajectory Performance Evaluation
#### 4.2.3 Verification of Articulation Angle Constraints  
To test the effect of the algorithm articulation angle constraint, the articulation angle variation curves with time are plotted for the lane borrowing overtaking scenario with three vehicle speeds: high, medium, and low. Figure 16 shows the curve of articulation angle. From the curve, we can see that the maximum articulation angle is \(-20.30^{\circ}\) (positive and negative indicate the direction) for a vehicle speed of \(20\) km/h, \(-12.44^{\circ}\) for a vehicle speed of \(50\) km/h, and \(-5.96^{\circ}\) for a vehicle speed of \(80\) km/h. The higher the car speed, the smaller the maximum value of the articulation angle, which achieves the expected effect of the articulation angle constraint design and ensures the stability of the semi-trailer train driving at high speed.
## 2 The Proposed Pulse-OTFS Signal
### Derivation of ACF and PSD
#### 2.2.3 Analysis of PSD and ACF  
Since the properties of PSD and ACF strongly affect the navigation performance of the signal, we discuss the PSD and ACF with different parameters in this section. To understand the difference between the proposed signal and the traditional GNSS and Locata signals, we have included BPSK(5) and Pulse-BPSK(5) signals with a 10 MHz bandwidth. The delay bins \(M\) and the signal bandwidth \(B\) are the main parameters affecting the PSD and ACF of Pulse-OTFS, so we give the PSD and ACF results of Pulse-OTFS with signal bandwidths of 1 MHz and 10 MHz and delay bins of 1, 64, and 1023 in Figures 3 and 4, respectively.  
Figure 3 illustrates that the PSD main lobe of the Pulse-OTFS signal is flat and its out-of-band (OOB) emission decreases as the delay bin increases. Interestingly, the PSD degrades to that of a BPSK modulation with twice the bandwidth of Pulse-OTFS when the delay bin is equal to 1. Compared to BPSK and Pulse-BPSK, the signal energy of Pulse-OTFS is concentrated within the main lobe, and its OOB emission is much lower, which is an advantage in some bands where the spectrum is strictly guarded.  
As can be seen from Figure 4, for Pulse-OTFS signals with different parameters, the main peak of the ACF is steeper the wider the signal bandwidth is, while there is not much difference between the ACFs with different delay bins, except that its ACF when the delay bin is equal to 1 degrades to that of the BPSK modulation with twice the bandwidth, which is similar to the results in Figure 3. The main peak of the ACF of Pulse-OTFS in Figure 4 is  
Figure 2: PSDs of the Pulse-OTFS signal.  
steeper than that of BPSK and Pulse-BPSK for the equal bandwidth, which enables more precise determination of the time of arrival, resulting in enhanced delay resolution and measurement accuracy. This precision is particularly crucial for applications demanding exact positioning or synchronization. But one may ask the question, although the ACF of the Pulse-OTFS signal has a steep main peak, it has a lot of sidelobes; could this lead to false locking during tracking similar to the BOC signal? The answer is no because its S-curve has only one zero-crossing point; it can be found in the next section.
###### Abstract  
The pore mobilization characteristics of CO\({}_{2}\) when in shale reservoirs is an important indicator for evaluating the effectiveness of its application for enhanced recovery in shale reservoirs, and it is important to develop a comprehensive set of physical simulation methods that are consistent with actual field operations. This has underscored the need for efficient development techniques in the energy industry. The huff-n-puff seepage oil recovery method is crucial for developing tight oil reservoirs, including shale oil. However, the small pore size and low permeability of shale render conventional indoor experiments unsuitable for shale oil cores. Consequently, there is a need to establish a fully enclosed experimental method with a high detection accuracy to optimize the huff and puff process parameters. The NMR technique identifies oil and gas transport features in nanogaps, and in this study, we use low-field nuclear magnetic resonance (NMR) online displacement technology to conduct CO\({}_{2}\) huff and puff experiments on shale oil, covering the gas injection, well stewing, and production stages. After conducting four rounds of huff-n-puff experiments, key process parameters were optimized, including the simmering time, huff-n-puff timing, number of huff-n-puff rounds, and the amount of percolant injected. The findings reveal that as the number of huff-n-puff rounds increases, the time required for well stabilization decreases correspondingly. However, the enhancement in recovery from additional huff-n-puff rounds becomes negligible after three rounds, showing only a 1.16% improvement. CO\({}_{2}\) re-injection is required when the pressure falls to 70% of the initialization pressure to ensure efficient shale oil well development. This study also indicates that the most economically beneficial results are achieved when the injection volume of the huff-n-puff process is 0.44 pore volumes (PVs).  
shale oil; CO\({}_{2}\); huff and puff; on-line NMR; parameter optimization Article  
Optimization of CO\({}_{2}\) Injection Huff and Puff Process Reservoirs Based on NMR
# Contributions of TOR Signaling on Photosynthesis
## 2 TOR: A Key Regulator in Photosynthesis
### Light and TOR Signaling Pathway  
Plants are oxygenic photosynthesizers that use light energy to generate organic molecules from carbon dioxide (CO\({}_{2}\)) and water (H\({}_{2}\)O). The ability to absorb the light energy has a significant effect on the photosynthetic efficiency of plants. In addition, light-stimulated photomorphogenesis is a developmental process that transforms plants into a vegetative state, which is required for photosynthetic activity. Light regulates the expression pattern of genes that participated in cell proliferation, organ development, and differentiation of photosynthetic cells. Advancing research has shown that light is intimately linked with TOR activation.  
In _Arabidopsis_ shoot apexes, both light signals and the glucose are indispensable for continuously promoting auxin accumulation for TOR activation to facilitate conversion to photoautotropic growth in _Arabidopsis_. Light-auxin signal can activate ROP2, which then promotes TOR kinase activity [49]. In deetiolating _Arabidopsis_ seedlings, phytochrome A and cryptochromes perceive light to inactivate the negative regulator Constitutive photomorphogenesis 1 (COP1), and then leads to TOR activation and phosphorylation of downstream protein RPS6. The activation of TOR and RPS6 enhances cotyledon opening and transformation from young plant seedlings into the vegetative phase with photosynthetic activities [53]. This finding reveals the molecular mechanism of TOR activation in light-specific translational enhancement in plants de-etiolation.  
In addition, the sugar requirement for TOR-S6K-RPS6 activation was investigated in older etiolated seedlings. RPS6 phosphorylation was elevated by light exposure for 2 h, but not for 0.5 h. However, when seedlings were grown with 1% sucrose, RPS6 phosphorylation was promoted by light as short as 0.5 h [54]. A recent study demonstrated that the impairment of TOR activity (through RAPTOR1B mutations or TOR kinase active site chemical inhibition) in _Arabidopsis_, reduced the accumulation of the photoreactive chlorophyll precursor protochlorophyllide in darkness, but increased greening rate of etiolated seedlings after exposure to light [55].
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 4 Discussion  
We chose to employ a two-dimensional convolutional neural network (2D-CONV) to extract spectral features from birdsong, due to its significant advantages in capturing time-frequency characteristics. Birdsong signals contain rich information across both time and frequency domains. By leveraging sliding window operations, the 2D-CONV efficiently extracts local patterns from both dimensions, enabling it to capture complex time-frequency relationships, such as instantaneous frequency variations and energy distribution.  
Considering that speech signal processing models are typically optimized for the frequency range of 16-22 kHz, whereas birdsong has higher frequencies and a broader distribution, we opted not to use these models that are specifically designed for speech. This ensured that the diversity and nuances of birdsong signals were adequately captured [15]. The experimental results demonstrate that the multi-scale model offers significant advantages in capturing both detailed and global patterns in the frequency spectrum, leading to improved classification accuracy. However, to optimize computational efficiency, a balance must be struck between the number of scales and the size of the feature maps. Performance improvements tend to diminish as we transition from a dual scale to a multi-scale configuration.  
As seen in Figure 6, despite the severe imbalance in the sample sizes across species, we introduced weighted cross-entropy into the ResNet18 training process to address this issue, ensuring higher weights for under-represented species, and thus mitigating the negative effects of sample imbalance on model performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Input Type** & **Accuracy (\%)** \\ \hline (2 DCNN + 3 DCNN) + GRU + GRU & Image, continuous frame sequence & 95.88\% \\ SFLN(GRU) & Continuous frame sequence & 95.92\% \\ EfficientnetV1\_b0 & Mel-spectrum & 95.02\% \\ EfficientnetV2\_s & Mel-spectrum & 95.30\% \\ MFF-ScSEnet & Mel-Sinc spectrum & 96.66\% \\ Ours (multi-scale feature extraction and machine learning classification) & Log-Mel spectrum & 97.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with state-of-the-art models.  
The experimental results show that our approach outperformed the original ResNet18 model on both Dataset A and Dataset B, demonstrating that the proposed multi-scale feature extraction method possesses strong generalization capabilities and practical application value.
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 4 Pathogenesis of DFU  
The development of a diabetic foot ulcer generally occurs in three stages:  
1. Callus Formation: The initial stage involves the formation of a callus, a thickening of the skin under the foot that appears as a yellowish tinge. This condition is often caused by peripheral neuropathy, resulting from nerve damage in the foot, which can occasionally affect leg nerves as well. Symptoms of peripheral neuropathy include numbness, cramps, and muscle fatigue.
2. Motor Neuropathy: In this stage, motor neurons in the foot are damaged, leading to weakness and deformation of the feet.
3. Sensory Neuropathy: The final stage involves damage to sensory neurons, resulting in loss of sensation, which can lead to trauma, skin drying, and autonomic neuropathy. Frequent damage to the callus can result in subcutaneous hemorrhage, ultimately forming an ulcer [13].  
The progression of DFUs is heavily influenced by bacterial infections. Poor hygiene and foot damage in diabetic patients create ideal conditions for DFUs. Bacterial associations and biofilm formation play a significant role in the rapid progression of DFUs. Bacteria involved in DFUs can form biofilms, which contribute to the disease's severity. DFUs are classified into five grades based on their severity:  
* Grade 0: No visible ulcer
* Grade 1: Superficial ulcer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Occurrence of** & & **No of Articles:** & **No of Articles:** & **No of Articles:** \\
**Biofilms** & & **Biofilm Makeup** & & **Specific Species** \\ \hline \multirow{3}{*}{Present} & \multirow{3}{*}{66/88} & Provided & 45/66 & Provided & 42/45 \\ \cline{3-5}  & & Not provided & 21/66 & Not provided & 3/45 \\ \hline Absent & 22/88 & & Not applicable & Not applicable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global survey of articles on the incidence of biofilms in DFUs (2014–2024).  
* Grade 2: Deep tissue ulceration
* Grade 3: Abscess formation involving bone
* Grade 4: Gangrene formation at the toe
* Grade 5: Extensive gangrene and necrosis throughout the foot [27]  
About 32.4% of patients have Grade 1 ulcers, 29.2% have Grade 2 ulcers, and only 8.2% have ulcers graded higher than Grade 2 [28]. Developing effective therapies requires creating disease models based on both in-vivo and in-vitro studies to address the various grades of DFUs effectively. Figure 2 shows the pathology of diabetic foot ulcers (DFUs), including disease progression caused by recurrent infections and toxin production by biofilm forming pathogens leading to tissue necrosis and possible amputation.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 7 Conclusions
### Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project 5G  Fifth Generation Mobile Networks 5 GC 5G Core 5G-PPP 5G Infrastructure Public Private Partnership 5 QI 5G QoS Identifier 6G  Sixth Generation Mobile Networks AF  Application Function  AHP  Analytic Hierarchy Process AI  Artificial Intelligence  AMF  Access and Mobility Management Function AP  Access Point API  Application Programming Interface AR  Augmented Reality AUSF  Authentication Server Function BAM  Bayesian Attractor Model CAPEX  CAPital EXpediture CBR  Constant Bit Rate CI  Confidence Interval CLI  Command Line Interface CNN  Convolutional Neural Network COPRAS  Complex Proportional Assessment CU  Centralized Unit CUPS  Control User Plane Split DevOps Development and Operations DL  Downlink DPMM  Dirichlet Process Mixture Model DU  Distributed Unit EMCO  Edge Multi-Cluster Orchestrator E2E  End-To-End EPoP  Edge Point of Presence ETSI  European Telecommunications Standards Institute FAHP  Fuzzy Analytic Hierarchy Process GA  Genetic Algorithm gNodeB  Radio base station GNS3  Graphical Network Simulator 3 GUI  Graphical User Interface IETF  Internet Engineering Task Force IMT-2020  International Mobile Telecommunications-2020 (5G) IoT  Internet of Things ISP  Internet Service Provider