esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenges of Using SDR with Dynamic Process Data
### Quantifying Trajectory Mixing  
We propose a quantitative measure of the quality of neighborhoods in terms of the _trajectory mixing_ by employing the information-theoretic notion of entropy. Consider any data instance, \(x\), which belongs to trajectory \(\Gamma_{i}\). Let \(p_{j}\) be the fraction of \(k\) nearest neighbors of \(x\) that lie on a trajectory \(\Gamma_{j}\). The entropy of the \(k\)-neighborhood of the data instance \(x\) is calculated as  
\[H_{x}^{k}=\sum_{\forall j,p_{j}\
eq 0}-p_{j}\log_{2}p_{j}. \tag{3}\]  
Similarly, we can define the \(k\)-neighborhood entropy for a trajectory \(\Gamma_{i}\) as the average of \(k\)-neighborhood entropy for all data instances on the \(i\)th trajectory, i.e.,  
\[H_{\Gamma_{i}}^{k}=\frac{1}{m_{i}}\sum_{x\in\Gamma_{i}}H_{x}^{k}. \tag{4}\]  
Neighborhood entropy is directly related to the mixing of the trajectories in the proximity of a given data instance. If the neighborhood entropy of an instance is high, it means that the nearest neighbors of that instance exist in a large number of trajectories, indicating a strong mixing of the trajectories. On the other hand, if the entropy of a data instance is low, it means that the nearest neighbors lies on a single or very few trajectories, indicating a low level of mixing.  
Figure 4: Pairwise distances of all points with \(\chi=3.0\) and from six trajectories for \(\phi\in\{0.50,0.52,0.54,0.56,0.58,0.6\}\) visualized in two ways. (Please view in color). (**a**) Distance matrix \(\mathbf{D}\) for all data grouped by \(\phi\) and ordered by time step along both axes. Distances in subblocks along the main diagonal denote interpoint distances within a fixed \(\phi\)-value trajectory. Off-diagonal subblocks highlight distance between points lying on disjoint trajectories. (**b**) Distance matrix \(\mathbf{D}\) with rows grouped by \(\phi\) and ordered by time step. Entries in row \(i\) are sorted by increasing distance from \(x_{i}\) and colored according to their \(\phi\) value. Clusters of similar color nearest the left edge reflect \(k\)-NN having common \(\phi\)-value for sufficiently large \(k\).
# Challenges in Physical Layer Security for Visible Light Communication Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
VLC Visible Light Communication  
PLS Physical Layer Security  
RF Radio Frequency  
WEP Wired Equivalent Privacy  
WPA WiFi Protected Access  
LED Light Emitting Diode  
SISO Single-Input Single-Output  
MISO Multiple-Input Single-Output  
CSI Channel State Information  
PP Poisson Point Process  
SNR Signal-to-Noise Ratio  
SINR Signal-to-Interference-plus-Noise Ratio  
SOP Secrecy Outage Probability  
FoV Field-of-View  
ZF Zero-Forcing
## 3 Proposed Method
### Second Stage: MYO and LVC Segmentation
#### 3.2.3 Refined Segmentation  
The refined segmentation substage uses the U-Net to segment MYO and LVC based on the newly extracted ROIs obtained through mask aggregation. This network was chosen based on the experiments performed. The U-Net used has the same architecture described in Section 3.1. Therefore, this network receives the new ROIs generated in the preceding substage and obtains the LVC and MYO masks. These masks will be combined  
Figure 5: Mask aggregation process for the extraction of a new ROI.  
with those generated by the RV segmentation step, whose details will be presented in the following section.
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
###### Abstract  
Aluminum alloy LF 21 has a strong ability to reflect electromagnetic waves. LF 21 waveguide slit array structure is widely used in waveguide radar antenna. The stiffness of the slit array structure is relatively weak. So, the structure is prone to deformation under the cutting force in the conventional milling process. Micro-milling technology can realize high-precision machining of mesoscale parts/structures and is a potential effective machining technology for the waveguide slit array structure. However, the diameter of the micro-milling cutter is small, and the feed per tooth is comparable to the arc radius of the cutting edge, so the micro-milling cutter is prone to wear. In addition, the effects of elastic recovery of material, the minimum cutting thickness and friction of cutting dead zone on micro-milling force cannot be ignored. A simulation model of micro-milling aluminum alloy LF 21 processes based on DEFORM 3D is built by combining the theory of cutting and the technology of process simulation. Prediction of tool wear is achieved. The quantitative relationship between the arc radius of the cutting edge and tool wear is clarified for the first time. The authors built an improved cutting force model in micro-milling LF 21 considering tool wear and cutter runout with the minimum cutting thickness as the boundary. The validity of the built micro-milling force model is verified by experiments.  
micro-milling; aluminum alloy LF 21; force model; tool wear +
Footnote †: journal: Medical Engineering
## 2 Electromagnetic Radiation Testing Method
### Constructing a Three-Dimensional Electromagnetic Model Environment
This comprehensive arrangement method ensures coordination and cooperation among various modules, making the entire testing process more efficient and reliable. Figure 8: Three-dimensional schematic diagram of the connected vehicle testing model. Finally, based on the three-dimensional model, we replicated the environment in electromagnetic software, as shown in Figure 9. Since we already analyzed and determined the antenna to be used in Chapter 1, during the model reconstruction process, the model was directly imported from the antenna library. When importing the model, it was necessary to set the solver type of each model to \"MLFMM\" to ensure consistency. However, mesh generation is a crucial step, as it directly affects the accuracy of the simulation and the consumption of computational resources. After importing the model, \"Automatic meshing\" should be clicked, and the system automatically generates the mesh for the model. If errors such as problematic triangles or other mesh generation issues occur during runtime, it is necessary to identify the specific erroneous surfaces in POSTFEKO and make local modifications accordingly. Another important point to note is that all models must use \"Media\" of \"Perfect Electric Conductor\" and \"Perfect Magnetic Conductor\". The selected base station transmitting antenna, the GNSS transmitting antenna, and the vehicle-mounted receiving antenna should be positioned at their respective locations. After all the models are imported and properly positioned, it is essential to select all models and perform a Boolean operation, specifically the \"Union\" function in the toolbar. This step is of significant importance. Subsequently, the frequency required for the simulation should be set. Based on the frequency ranges of 4G and GNSS, the frequency range is ultimately set between 1 and 1.6 GHz. Finally, the solution type should be configured, typically selecting \"Voltage Source\". Under \"Configuration Specific\", \"Far Fields\" and \"Near Fields\" should be chosen from the \"Requests\" section, as these are the solution results relevant to this study. At this point, the setup of the simulation software is essentially complete. It is worth noting that the proposed method in this study requires testing in a fully anechoic chamber. Therefore, when conducting simulations in the electromagnetic simulation software FEKO, the default setting assumes an environment equivalent to a fully anechoic chamber. However, if conditions such as ground reflections need to be incorporated, these can be manually configured within the software to suit specific requirements. Next, the coupling coefficient S-parameter values between the transmitting antenna and the vehicle-mounted receiving antenna were simulated.
## 2 Materials and Methods  
### BTF Set Up  
The experimental work was done in a lab scale BTF operated in co-current gas flow mode. Therefore, the gas inlet was located at the top of the column, with the gas and liquid flowing in parallel. The trickling liquid velocity (TLV) was 10 m h\({}^{-1}\) and mimic biogas (mixture of H\({}_{2}\)S and balance to N\({}_{2}\)) was fed to the system. The bioreactor was made of transparent PVC and packed with 5/8\" Pall rings (Pall Ring Company, UK). The column had a height of 70 cm and internal diameter of 7.14 cm, with working volume of 2.8 L. The temperature was kept constant at 30 \({}^{\circ}\)C by a cooling thermostat ([PERSON], Germany) using a heater exchanger in the recirculation medium. pH was controlled at 7.4 using an ON/OFF control (Multimeter 44, Crison, Spain) on peristaltic pumps by automated addition of NaOH 5 M or H\({}_{3}\)PO\({}_{4}\) 0.66 M. The system was controlled and monitored using the LabVIEWTM platform (National InstrumentsTM, USA) with cDAQ Chasis (NI-9184) with three modules: NI-9208 (current input module), NI-9264 (voltage output module) and NI-9375 (digital I/O module).  
The mineral medium composition was (g L\({}^{-1}\)) [15]: NH\({}_{4}\)Cl (1.0), K\({}_{2}\)HPO\({}_{4}\) (2.0), MgSO\({}_{47}\)H\({}_{2}\)O (0.8), NaHCO\({}_{3}\) (1.78) as carbon source, trace elements solution SL-4 (5 mL L\({}^{-1}\)) and a solution of FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (2 g in 1 L H\({}_{2}\)SO\({}_{4}\) 0.1 N) (10 mL L\({}^{-1}\)). The trace element solution (SL-4) composition was: EDTA (0.5 g L\({}^{-1}\)), FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (0.2 g L\({}^{-1}\)) and trace element solution SL-6 (100 mL L\({}^{-1}\)). The composition of the trace element solution (SL-6) (g L\({}^{-1}\)) was: ZnSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (0.1), MnCl\({}_{2}\)4H\({}_{2}\)O (0.03), H\({}_{3}\)BO\({}_{3}\) (0.3), CoCl\({}_{2}\)\(\cdot\)6H(0.2), CuCl\({}_{2}\)-2H\({}_{2}\)O (0.01), NiCl\({}_{2}\)-6H\({}_{2}\)O (0.02), Na\({}_{2}\)MoO\({}_{4}\)-H\({}_{2}\)O (0.03). Sodium nitrite was used as an electron acceptor.  
The outlet H\({}_{2}\)S concentration was measured using an electrochemical sensor (SureCell, Euro-Gas Management Service, UK). Further information about the operation principle of the anoxic BTF can be found elsewhere [12; 13; 16].
## 2 Research Model and Hypothesis
### Research Hypothesis 1: DA as a Prerequisite for BDAC  
BDAC is a dynamic capability since it possesses the characteristics of dynamic capabilities, e.g., repeatability, which create an experience that is difficult to imitate and thus create valuable assets that enhance competitive advantage [56]. However, to extract value from BDA, data must be available; a lack of data can be critical to overall performance and, hence, competitive advantage [57]. In addition, lost data cannot be recovered after being identified [49]. Therefore, firms aim to maximize the availability of their data to enhance their BDAC [58] by addressing related issues, such as data source, size and collection method [57].  
Advanced Industry 4.0 based manufacturing technologies like cyber-physical systems and human-robot collaboration with sensing devices, e.g., depth camera, proximity sensors, radio frequency identification (RFID), etc., can connect to the IoT to generate big data [4; 5; 6; 7; 8; 9; 10; 11; 12; 29] that can be connected to the cloud to ensure DA [26]. In addition, management information systems, such as enterprise resource planning (ERP), can be integrated with BDA [28; 30]. In this regard, advanced wireless networks are supposed to support real-time DA, e.g., 5G [32; 33]. However, DA is an essential factor that contributes to the effectiveness of BDA [59]. Therefore, since higher DA promotes better data analytics, we propose that DA is an enabler of BDAC. Hereafter, we propose the following hypothesis:  
**Hypothesis 1 (H1).**_Higher DA can positively enhance BDAC._
## 4 Results and Discussion
### Visual Explanations with LIME  
Figure 15 shows the explanations generated with LIME for sample instances of pediatric chest radiographs. Lung masks are applied to the explanations to localize only the ROI specific to the lung regions. The explanations are shown as follows: (i) Superpixels with the highest positive weights and the rest are greyed out; and, (ii) superpixels superimposed on the extracted lung regions. We observed that the explainer focused on the regions with high opacity. The model differentiates bacterial and viral pneumonia by (i) showing superpixels with the highest positive activations in the regions of lobar  
Figure 14: Visual explanations through average-grad-CAM: (**a**) Bacterial and viral CXR (top and bottom); (**b**) Average-grad-CAM localizing class-specific ROI with bounding boxes highlighting the regions of maximum activation; (**c**) Automatically segmented lung masks; (**d**) Average-grad-CAM localizing class-specific ROI with the extracted lung regions.  
Figure 13: Visual explanations through average-CAM: (**a**) Bacterial and viral CXR (top and bottom); (**b**) Average-CAM localizing class-specific ROI with bounding boxes highlighting the regions of maximum activation; (**c**) Automatically segmented lung masks; (**d**) Average-CAM localizing class-specific ROI with the extracted lung regions.  
consolidations for bacterial pneumonia; and, (ii) diffuse interstitial patterns across the lungs for viral pneumonia. We also observed that a number of false positive superpixels are reported. The reason is that the current LIME implementation uses a sparse linear model to approximate the model behavior in the neighborhood of the explained predictions. However, these explanations result from a random sampling process and are not faithful if the underlying model is highly non-linear in the locality of predictions.
# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 1 Introduction
While these methods mitigate the problem of excessive sensitivity to textual instructions, a notable drawback is that they typically require independent training for each specific task, increasing implementation complexity and limiting their potential for widespread application. Moreover, in practical operations, this leads to significant changes in image details, contradicting the goal of pursuing high precision and detail integrity in editing and thus failing to meet user demands for maintaining image detail. Although existing methods have progressed in some aspects of image editing, they still have significant limitations and cannot simultaneously balance high precision and operational convenience. Our research introduces a dual attention control method aimed at addressing the limitations of current DMs in high-precision image editing. This method ingeniously integrates cross-attention and self-attention control during the denoising phase of diffusion models to achieve high-precision image editing effects. Our method proves that the operation of attention control in the diffusion process can achieve high-precision editing tasks, as shown in Figure 1. Additionally, our method can achieve specified style transformations without optimizing or fine-tuning. More importantly, our technology is a zero-shot solution; unlike specific image editing techniques, our method can be applied to any text-to-image DMs based on attention mechanisms without requiring fine-tuning or optimization. Our main contributions are as follows:  
(1) To address the limitations of current DMs in high-precision image editing, we propose a dual attention control method to achieve high-precision image editing, which cleverly combines cross-attention and self-attention control in the denoising stage of the diffusion model to achieve the effect of high-precision image editing. (2) We designed a cross-attention control module, which combines the attention map of the original image and the attention of the edited image by introducing weight parameters so that the edited image can effectively preserve the structure of the input image without losing the details of the original image. (3) We designed a self-attention control module that effectively maintains the details and style of the input image by applying self-attention control at \"coarse\" and \"fine\" layers, thus meeting high-precision editing requirements. Figure 1: Our method achieves perfect editing of large objects with good control over details and style.
# The Park of Emotions\": A Serious Game for the Development of Emotional Intelligence in Children
## 3 Results
### Analysis of Covariance of the Intervention on Participants' Emotional Intelligence  
Results for univariate analysis of variance using data from the pre-test phase indicated that there were no significant differences in the EG and CG before the intervention regarding the total score of the scale. The same results were observed for all the subscales before the intervention. On the other hand, ANCOVA post-test results confirmed the existence of significant differences between groups, as EG participants had significantly higher scores compared to CG participants in total score (\(F(1,434)=41.282,p<0.001\)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Pre-Test**} & \multicolumn{4}{c}{**Post-Test**} \\ \cline{2-11}
**Variables** & \
\begin{tabular}{c} **Experimental** \\ **Group** \\ \end{tabular} &
\begin{tabular}{c} **Control** \\ **Group** \\ \end{tabular} & & & \multicolumn{4}{c}{**Experimental**} & \multicolumn{4}{c}{**Control**} \\  & **Mean (SD)** & **Mean (SD)** & **F (I,A34)** & **\(p\)** & **\(\eta^{2}\)** & **Mean (SD)** & **Mean (SD)** & **F (I,A34)** & **\(p\)** & \(\eta^{2}\) \\ \hline ES & 18.96 (3.36) & 18.75 (3.33) & 0.434 & 0.510 & 0.001 & 22.05 (2.02) & 20.28 (2.95) & 59.567 & \textless{}0.001 & 0.121 \\ ERE & 17.52 (2.60) & 17.86 (2.77) & 1.740 & 0.188 & 0.004 & 19.44 (2.67) & 18.78 (2.86) & 9.016 & 0.003 & 0.020 \\ SA & 17.66 (2.49) & 18.09 (2.87) & 2.871 & 0.091 & 0.007 & 18.76 (1.99) & 18.33 (2.35) & 7.261 & 0.007 & 0.016 \\ SM & 17.57 (3.79) & 17.51 (3.87) & 0.031 & 0.861 & 0.000 & 18.53 (2.38) & 17.44 (3.00) & 20.688 & \textless{}0.001 & 0.046 \\ SAED & 20.48 (3.83) & 20.82 (3.53) & 0.926 & 0.337 & 0.002 & 22.39 (2.27) & 21.16 (3.01) & 34.432 & \textless{}0.001 & 0.074 \\ SSEE & 19.83 (2.91) & 19.95 (2.78) & 0.191 & 0.662 & 0.000 & 20.23 (1.71) & 19.74 (2.13) & 9.473 & 0.002 & 0.021 \\ SACT & 19.84 (3.05) & 20.25 (2.69) & 2.193 & 0.019 & 0.005 & 19.87 (2.30) & 19.19 (2.46) & 13.261 & \textless{}0.001 & 0.030 \\ TRA & 19.15 (2.68) & 19.28 (2.65) & 0.235 & 0.628 & 0.001 & 19.26 (2.02) & 19.00 (2.18) & 2.455 & 0.118 & 0.006 \\ EUN & 22.33 (3.48) & 22.75 (3.03) & 1.784 & 0.182 & 0.004 & 23.21 (2.43) & 22.54 (2.77) & 11.307 & \textless{}0.001 & 0.025 \\ TOTAL & 173.36 (20.33) & 175.27 (18.98) & 1.026 & 0.312 & 0.002 & 183.73 (13.51) & 176.47 (15.79) & 41.282 & \textless{}0.001 & 0.087 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Means and standard deviations for total score and subscales of the emotional intelligence scale for experimental and control group during the pre-test and post-test phases.  
\(\eta^{2}=0.87\)), as well as for all the subscale scores. The only exception was the subscale TRA (\(F(1,434)=2.455,p=0.118\)). The largest effect in EG participants from the intervention was presented in the subscales ES (\(F(1,434)=59.567,p<0.001,\eta^{2}=0.121\)) and SAED (\(F(1,434)=34.432,p<0.001,\eta^{2}=0.074\)).
# A Multilevel Road Alignment Model for Spatial-Query-by-Sketch
## 3 Materials and Methods
### Characteristics and Matching Approach for Road Scenes
#### 3.4.5 Computation of Similarity between Road Scenes  
The similarity of two road scenes is the sum of the similarities in each composite road that constitute the road scene, _intersection order_ (\(IO\)) and _topological relationship between roads without matching priorities_ (\(TRMP\)) detailed in above sections. The similarity between two road scenes can be obtained as follows:  
\[Sim_{SR}=w_{9}\cdot\sum_{i\subseteq n}Sim_{CRi}+w_{10}\cdot Sim_{IO}+w_{11} \cdot Sim_{TRMP} \tag{17}\]  
where \(Sim_{SR}\) represents the similarity between two road scenes; \(Sim_{CRi}\) represents the similarity of the i-th composite road that constitutes the road scene; and \(n\) is the number of composite roads that constitute the road scene. \(w_{9}\) is the weight assigned to the sum of all \(Sim_{CRi}\). \(Sim_{IO}\) and \(Sim_{TRMP}\) respectively represent the similarity of two road scenes in terms of the \(IO\) and \(TRMP\). \(w_{10}\) and \(w_{11}\) respectively represent the weight assigned to \(Sim_{IO}\) and \(Sim_{TRMP}\). It holds that \(w_{9}+w_{10}+w_{11}=1.0\). A larger \(Sim_{SR}\) corresponds to more similar road scenes. In our paper, the weights in Formula (17) have been all assigned the same value (\(\frac{1}{3}\)) since all the features have the same relevance. Other studies may turn these weights differently.
## 3 Internal Load Monitoring
### Training Impulse (TRIMP)  
TRIMP is a widely used method for assessing training load and can identify the time spent at each training intensity [3; 52; 53]. [PERSON] and [PERSON] [53] were the first to present the TRIMP model to quantify internal training load. Subsequently, other TRIMP models were introduced using different physiological parameters for TRIMP calculations [3; 54; 53].  
The TRIMP method by [PERSON] [55] involves multiplying the duration that individuals spend within designated intensity zones during the training session. These zones of intensities are determined according to the percentage of maximum HR elicited during exercise (Zone 1: 50-60%; Zone 2: 60-70%; Zone 3: 70-80%; Zone 4: 80-90%; and Zone 5: 90-100%). The TRIMP method proposed by [PERSON] et al. [56] is based on ventilatory thresholds and is divided into three phases or zones: Phase I, representing low intensity below the ventilatory threshold; Phase II, indicating moderate intensity between the ventilatory threshold and the respiratory compensation point; and Phase III, signifying high intensity above the respiratory compensation point. In the TRIMP model, each phase is assigned a coefficient multiplied by the training duration within each zone, resulting in the TRIMP score [57; 58].  
Lastly, the [PERSON] and colleagues modified TRIMP model, known as Stagno's TRIMP, relates blood lactate levels to HR zones [41]. It is important to note that all TRIMPmodels were initially developed for endurance sports, and further research is needed to address the applicability to sports demanding other physical abilities (e.g., power, and anaerobic capacity).