esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis and Machine Learning Models  
#### 2.2.1 Data Analysis  
In this study, Jupyter Notebook was utilized as a tool for creating and sharing documents that contain live code, equations, visualizations, and narrative text. It is commonly used by practitioners and researchers in the fields of data analysis, machine learning, and scientific computing. Before using a dataset for machine learning, data analysis, and feature extraction techniques (as shown in Figure 2) are employed to select the most relevant and informative features for the task at hand. Techniques, such as visualizing the correlations between the features and the target variable, are useful, and visualization libraries, such as Pandas' scatter matrix and Altair, which is a declarative visualization library for Python. It supports a wide range of chart types, including bar charts, line charts, scatter plots, and heat maps. It also provides support for faceting and layering, which allows users to create more complex visualizations.  
These tools can aid in understanding data and selecting a suitable machine-learning model. However, it is important to consider the nature of the different machine learning models and how they handle different types of problems. The research utilized experimental data from three materials collected from previous studies [20,21,23]. The experimental  
Figure 1: Specim’s geometry dimensions in mm (**a**) without crack (**b**) with crack [19].  
Figure 2: The flow chart of analysis steps.  
data consists of four features: temperature \({}^{\circ}\)C, crack location mm, amplitude mm, natural frequency Hz, and a predicted value: crack depth mm. The data were plotted using a scatter matrix, pair plot, and grid plot to investigate the relationship between the features and the predicted value, which indicated that the task was a regression problem. Pre-processing of the data was conducted using libraries, such as Pandas and NumPy, to clean and format the data, perform basic statistical analysis, and use techniques, such as Variance Threshold, to drop features with constant or near-constant values. The data were also scaled using libraries, such as Standard Scaler, to improve the training time of the model and ensure that the features were at a comparable scale.  
After cleaning and preparing the data, they are typically split into a training set and a validation set. The training set is used to train the model, while the validation set is used to evaluate the performance of the model on unseen data, ensuring that the model generalizes well to new, unseen data.
# A Lightweight Detection Method for Meretirix Based on an Improved YOLOv8 Algorithm
## 3 Improved Lightweight Method for Clam Detection
### DyHead  
DyHead (Dynamic Head) is a multi-dimensional dynamic attention mechanism detection head that integrates scale-aware, spatial-aware, and task-aware mechanisms, enabling adaptive fusion of multi-scale features [20]. Figure 7 shows the structure of the DyHead module. The detection head of the YOLOv8n model is improved to enable scale-aware dynamic detection head attention, with the corresponding formula for the attention function calculation as follows:  
\[W(F)=\pi_{\text{C}}(\pi_{\text{S}}(\pi_{L}(F)\cdot F)\cdot F)\cdot F \tag{5}\]  
In the formula, \(\pi_{\text{C}}\) represents task-aware attention, \(\pi_{\text{S}}\) represents spatial-aware attention, \(\pi_{L}\) represents scale-aware attention, and \(F\) represents the feature vector.  
By introducing the self-attention mechanism in three specific dimensions, the detection head's ability to perceive clam features is enhanced, improving the algorithm's detection performance on small targets. This includes scale-aware attention, spatial-aware attention, and task-aware attention, and their respective calculation formulas are as follows:  
\[\pi_{L}(F)\cdot F=\sigma(f(\frac{1}{\text{SC}}\sum_{\text{SC}}F))F \tag{6}\]  
\[\pi_{\text{s}}(F)\cdot F=\frac{1}{L}\sum_{l=1}^{L}\sum_{k=1}^{K}w_{l,k}\cdot F (l;p_{k}+\Delta p_{k};c)\cdot\Delta m_{k} \tag{7}\]  
Figure 6: C2f-Faster module structure diagram. The symbol “\(\star\)” indicates a convolution operation.  
\[\pi_{C}(F)\cdot F=max(\alpha^{1}(F)\cdot F_{c}+\beta^{1}(F),\alpha^{2}(F)\cdot F_{c })+\beta^{2}(F) \tag{8}\]  
In the formulas, \(\sigma\) represents the activation function, \(f\) represents the transformation function, \(P_{k}\) represents the position, \(\Delta P_{k}\) represents the position offset, \(\Delta m_{k}\) represents the additional weight, \(K\) represents the number of key points, and \(F_{c}\) represents the features segmented on the \(C\)-th channel.  
The scale-aware attention module enhances multi-scale feature representation ability by dynamically fusing semantic features of different scales. This module introduces a linear function \(\mathrm{f(\cdot)}\), equivalent to a \(1\times 1\) convolution operation, and combines it with the hard-sigmoid activation function \(\mathrm{\sigma(x)=max(0,\,min(1,\,x+1/2))}\) to efficiently filter features. The spatial-aware attention module further strengthens the consistency modeling of spatial position information and feature hierarchy on top of feature fusion. It uses deformable convolution to sparsify the attention distribution, focusing on the cross-layer feature relationships at the same spatial location. The task-aware attention module dynamically adjusts the activation state of feature channels based on specific task requirements to enhance the model's adaptability to multi-task scenarios.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 6 Performance Results
### Reliability  
The RPE method is investigated for a WSN with links that experience packet drops. Following the same investigations as LFC [16], we assess links with PRR 70% increasing in increments of 10% until a 100% PRR. RPE, with \(\tau=8\) slots, is compared with the Single Path scenario. It was found that all the scenarios involving packet replication showed a negligible difference in reliability as from the sinks point of view the delay \(\tau\) does not affect the PRR. Hence, in Figure 9 SP is only compared to RPE \(\tau=8\) for simplicity.  
Figure 8: Nodes with corresponding path and hop count.  
Figure 9 shows that RPE increases reliability compared to Single Path. Single Path was found to have a reliability of 88.24% when the link quality was 70%. At the same link quality, RPE increases the reliability to 98.65%. Moreover, the reliability of WSN with links with link quality of 80% shows significant improvements as Single Path had a packet reception ratio of 97.7% compared to RPE with a success rate of 99.95%. Lastly, at 90% link quality, the gap in reliability shrinks to a difference of 0.09% as RPE delivers a 100.0% delivery ratio. However, there is packet loss involved, and over time statistically, a packet will be dropped.
## 4 Experimental Results
### Surface Structure Analysis  
Figure 8 shows the SEM results for the surface structure of the absorbent containing CuO on the surface of \(\gamma\)-alumina and macro-porous alumina. Macropores were dispersed uniformly over the prepared macro-porous alumina surface. As expected, such large pore sizes were not observed over the \(\gamma\)-alumina, and the macropores are the morphology in which PMMA used as a template was decomposed and removed. On both supports, the CuO absorbent was seen at high CuO loadings (>10 wt %). Morphologies that can be regarded as crystal growth are observed over the surface of the samples loaded with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Amounts of CuO, wt \%** & **Surface Area, m\({}^{2}\)/g** & **Total Pore Volume, cm\({}^{3}\)/g** & **Pore Diameter, nm** \\ \hline \multirow{4}{*}{CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\)} & 5.0 & 180.5 & 0.4280 & 9.5 \\ \cline{2-5}  & 10.0 & 168.2 & 0.3992 & 9.5 \\ \cline{2-5}  & 15.0 & 151.1 & 0.3213 & 8.5 \\ \hline \multirow{4}{*}{CuO/macro-porous-Al\({}_{2}\)O\({}_{3}\)} & 5.0 & 202.8 & 0.3020 & 6.0 \\ \cline{2-5}  & 10.0 & 188.3 & 0.2959 & 6.3 \\ \cline{2-5}  & 15.0 & 148.5 & 0.2202 & 5.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Surface and pore properties of the Cu-based desulfurization sorbents by the N\({}_{2}\)-adsorption method.  
Figure 7: Adsorption (blue) and desorption (red) isotherm of Cu-based desulfurization sorbents by the N\({}_{2}\)-adsorption method: (**a**) 5 wt % CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\); (**b**) 10 wt % CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\); (**c**) 15 wt % CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\); (**d**) 5 wt % CuO/macro-porous Al\({}_{2}\)O\({}_{3}\); (**e**) 10 wt % CuO/macro-porous Al\({}_{2}\)O\({}_{3}\); and (**f**) 15 wt % CuO/macro-porous Al\({}_{2}\)O\({}_{3}\).  
15 wt % CuO. These crystal structures of CuO were confirmed by XRD (see Figure 6). When CuO was supported up to approximately 10 wt %, CuO was present in the dispersed state on the surface of the supports, but in the case of the 15 wt % loading, CuO crystals with a needle shape formed during the thermal treatment. The surface area of CuO corresponding to the active site of the absorbent decreased due to the growth of CuO crystals, which increased the performance as a sulfur absorbent. Overall, \(\sim\)10 wt % of CuO on the supports would provide the optimal sulfur absorption capacity because of the uniform dispersion of CuO on the supports.
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 4 Case Study: 3D Printing
### Data Integration Through Databases  
To start with, it is crucial to choose the appropriate database, and for our case study, we opted for the publicly accessible Okobau.dat database, given its wealth of material data pertinent to our specific scenario.  
Table 1 presents an excerpt of LCI data for construction materials, including their lifecycle modules, global warming potential (GWP) in kg CO\({}_{2}\)-eq, and ozone depletion potential (ODP) in kg CFC-11-eq. Materials such as wood fiber insulation (A2) have moderate GWP (0.367) and very low ODP (7.33 \(\times\) 10\({}^{-10}\)). Fiber cement facade panels (C2) show low GWP (0.042) and negligible ODP (1.08 \(\times\) 10\({}^{-15}\)). Clay plaster (A5) has the highest GWP (7.926) but similarly low ODP (2.30 \(\times\) 10\({}^{-15}\)). Fly ash (A1-A3) has no measurable impact (GWP and ODP both 0). Cement types (A1-A3) show moderate GWP: 0.688 for CEM IV 42.5 and 0.795 for CEM II 42.5, with low ODP values (around 3.8 \(\times\) 10\({}^{-15}\) for both).  
The modules represent different stages or activities in the product's life cycle, categorized according to standardized frameworks such as EN 15804 [81]. These modules define the boundaries within which environmental impacts are assessed for specific materials or processes. They are typically organized into distinct phases: production (A), construction (B), usage (C), and end of life (D). Each phase is further divided into specific modules, for example, A1 (raw material supply, which includes the extraction and processing of raw materials) and A2 (the transportation of raw materials to the manufacturing site).
## 3 Simulation and Results
At this time, the PO-8 QAM and AE-8 QAM show almost the same performance, which means that the PO-8 QAM can achieve an optimal transmission performance like the AE-8 QAM under the ideal AWGN channel, in line with the theoretical model of the PO algorithm. In the second type of simulation channel, the comparison of BER performance versus the estimated SNR of the entire channel with different shaping schemes is shown in Figure 4. It is worth noting that the signal-to-noise ratio at this time is estimated using the EVM algorithm [34]. Similarly, the three shaping schemes have poor performance at a very low SNR, and the constellations in region (i) also exhibit great distortion. Additionally, due to the addition of nonlinearity, the performance at this time is worse than that in the Gaussian white noise channel under the same SNR. The constellations in the three regions of Figure 4 are more severely distorted than those in Figure 3, and there are more obvious nonlinear distortions in the constellations. These distortions reflect the unique characteristics of the nonlinear simulation channel compared to the Gaussian channel. It is noted that as the SNR increases, both the AE-8 QAM and PO-8 QAM also show better performances than the Norm-8 QAM, but at the same time, the AE-8 QAM starts to perform better than the PO-8 QAM. This means that the AE-8 QAM has a stronger ability to resist nonlinearity and adaptive ability for specific channels. Figure 3: BER versus SNR with different shaping schemes in AWGN channel. We then test the BER of the three shaping schemes by adjusting the Vpp parameters of the simu-VLC channel. The signal-to-noise ratio of Gaussian white noise is fixed at 15 dB. Figure 5 shows the BER versus simulated Vpp with different shaping schemes in the simu-VLC channel. As Vpp increases and the nonlinear effect of the system becomes more and more significant, the AE-8 QAM achieves a significantly lower BER than the other two schemes. When the Vpp is too large and the nonlinear effect of the entire channel is very strong, the upper limit of the correct rate of transmission is limited, and the gain of the AE-8 QAM compared with the PO-8 QAM is not further improved. Owning to the lack of adaptability to different channel conditions, the Norm-8 qm always presents the worst performance. Although the PO-8 QAM has a certain adaptive ability, it only makes adjustments to the theoretical signal-to-noise ratio, and cannot consider the specific damage caused by the channel. Therefore, it will have a poorer performance than the AE-8 QAM.
## 4 Use Case  
Mobile networks are nowadays almost vital in the daily life of most people. This type of network is deployed by establishing telephone antennas located at strategic points to cover a maximized area. Currently, connection bands are divided into 2G, 3G and 4G, and in the near future, 5G connections are expected to be deployed in some areas. Usually, the range of coverage of data networks is inversely proportional to their speed. For example, the 2G connection has a low speed, compared to the other connections, but it has a wider coverage.  
Most users who have an Internet connection on their mobile phones use them to obtain information about places, restaurants, social networks, etc. However, in hiking tourism, most information, such as weather, track status, local flora and fauna and recommendations on activities related to trekking, is not available in many cases.  
As an example of the application of the proposal, we now consider the case of a region on La Palma Island (see Figure 4(a)) in the Spanish region of Santa Cruz de Tenerife, where many outdoor activities are offered for hikers [30] in different varieties of trails approved and enabled for their use. Some of them are even enabled for people with reduced mobility.  
Trails on La Palma (see Figure 4(b)) represent 7.23% of the island surface and are classified as follows:  
* Green: These are local trails, of less than 10 km, approved and verified by the issuing authority.
* Yellow: These are short distance trails, with a length between 10 and 50 km, but generally with quite a few slopes.
* Red: These trails are long-haul, over 50 km, so they are designed for several hours or even days.  
Figure 6 shows three images representing the different coverages (from 2G to 4G) in separate layers. Although 2G technology has a greater range, it has a worse coverage because there are not as many 2G antennas as 3G or 4G, for example. As shown in Figure 6, there are some areas where there is no coverage at all.  
Figure 5: La Palma island and its possible tracks.  
Figure 7 shows an overlap of the trail layer over the 2G + 3G + 4G map.  
According to Figure 7, approximately 11.52% of the island has no access to an Internet connection of any type (white areas in the image). Working with this composition and using a technique based on the difference between layers of the image so that if there is a point in the layer A that is present in the layer B, that point is painted with a certain color, as seen in Figure 8, it is possible to graphically visualize which trails have network coverage, and which trails do not have it. In the case shown in Figure 8, we can see the red color indicating the number of trails (0.54% of the image surface) that have no coverage. This means that 85.036% of the trails have network coverage, while 14.937% of the trails do not have any coverage. The FatBeacon protocol is proposed here to solve the problem that hiking tourism faces in this latter case.  
Figure 6: Example of trail routes and signal coverage.  
Figure 7: Map of La Palma island and the 2G + 3G + 4G overlaid coverage map.
## 5 Methods and Models
### Event Reporting and Global Event Detection  
Data aggregation is an important element for improving transmission efficiency. Thus, it is carried out at the SN levels for local events and at the BS level for global events. Local event frameworks are summarized in Table 4. A frame of environmental information has been defined as \(0\times 0a\), with the _Limit_ field of \(0\times 01\) if a parameter overruns. Otherwise it is 0, and the _Type_ field represents the parameter type, and _PaRm_ is configured with specific values of tracked parameters. Besides this, SN runs an algorithm for localization and geographic positioning, and it updates 32 bits for the x and y coordinates.  
After receiving aggregated data from SNs, the RN is set to 0\(\times\)0b to maintain the identity of the environment related data. Further, transmission from RNs is done after adding the identity of the SN. The _Num_ field is the number of SNs, and _Addr_ represents the IP address of the SN. As data collision is always a considerable factor in WSN routing, both _Num_ and _Addr_ are helpful for avoiding data collision. There is no need to change the frame monitored in Table 4. Moreover, the data is sent directly to RNs without aggregation for overruns, to distinguish them as urgent transmissions of emergency data. Table 5 shows the frame format of RNs.  
The local guidance and tracking information (LGTI) is updated by the BS for SNs ((LGTI)) each time [LGTI] is tagged with a sequence number of _SeqF_. Whenever there is new information for the BS, it inserts this information into the _LocalizInfo_ of [LGTI], _SeqF_, and also adds a number of packets to the data flooding, as shown in Table 6. The broadcasted packets contain information in the form of a list. Whenever an RN \(i\) receives _LocalizInfo_, it matches the received information with the previously received information. It also compares the _SeqF_ with the previous _SeqF_. A higher _SeqF_ indicates that the received packet contains new information for this node (_LGTIi_). Thus, it updates information and revises _SeqF_ in a similar manner. Then, the RN checks for the information related to subsequent SNs. If it is empty, then it is ignored, otherwise it updates SN.
## 4 Analysis of the Main Objectives
### Postural Control  
Four out of the nine studies included in our review assessed postural balance using different evaluation scales. None of the studies used the same method for evaluating postural balance [26; 27; 28; 30]. The overall stability index (OSI) was used to calculate the individual stability of participants in [PERSON] et al., and they obtained significant improvement in the intervention group (\(p\) = 0.005). [PERSON] et al. used the Single Leg Balance test (SLB) at the start and at the end of the rehabilitation period for evaluation, which revealed a significant time effect of the SLB test on the right foot (\(p\) = 0.041). Significant group-by-time interaction was observed for the SLB test right foot (\(p\) = 0.033). For studying postural control [28], [PERSON] et al. used two tests, one considered a very important Sensory Organisation Test (SOT) and the Motor Control Test (MCT). The Composite Equilibrium Score (CES) is a part of SOT, which quantifies the overall postural stability or center of gravity (COG). Following the analysis of the Composite Equilibrium Score (CES), which represents percentages from pre- to post-intervention, notable differences were noted within the EG. Specifically, an increase of 8.21 points pre-treatment to the post-treatment evaluation was described (\(p\) < 0.001). In contrast, the same test did not yield significant results for the CG (\(p\) = 0.123). Additionally, in the same study, the Motor Control Test (MCT) evaluation between baseline and post-intervention revealed significant differences for the EG (\(p\) = 0.005). No significant differences were found in this concern for the CG. One study utilizes Directional Control (DCL) to assess the ability to control the direction of body sway, and it is the only study that did not obtain significant results between groups after the intervention [26].
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
###### Abstract
So, far, there has not been a recognized universal evaluation index for detecting bipartite network communities. The existing evaluation indexes are mostly based on the modularity of bipartite network [11; 16; 17] and make some improvements to it. However,  
Figure 1: Bipartite network example—South Africa companies bipartite network. due to the modularity resolution limitation [18], modularity cannot judge the merits and demerits of community detection results in some particular cases. In this article, our main contribution is:  
* A new evaluation index (Community Density \(D\)) is proposed, which alleviates the problem of modularity resolution limitation and evaluates the bipartite network's merits and demerits from a new point. * A new community detection algorithm (Density Sub-community Node-pair Extraction (DSNE)) is proposed. It has the advantages of a micro-network model, adopts a new merging strategy for community merging, and can detect the bipartite network's overlapping community structure. * The experimental results show that the proposed algorithm is superior to other algorithms' accuracy and effectiveness.
## 2 Methods
### Simplified Static-Equivalent Model
#### 2.1.2 Wind Loads  
The specific reference standards for calculating the action of the wind are IEC 61400-1:2020 [2] and DNVGL-ST-0126 [3]. The thrust force (\(F_{\text{Th}}\)) that the wind exerts on the rotor is simplified as a static punctual force acting at its level:  
\[F_{\text{Th}}=\frac{1}{2}\,\rho_{\text{a}}\,A_{R}\,C_{T}\,(\bar{U}+u)^{2} \tag{5}\]  
where \(\rho_{\text{a}}\) is the air density, \(A_{\text{R}}\) is the rotor swept area, \(C_{T}\) is the thrust coefficient, and \(\bar{U}\) and \(u\) are the mean and turbulent components of wind speed. The latter value was computed by following the turbulence models described in the reference standards [2,3], which are also summarized in [6].  
On the other hand, the distributed force acting along the parts of the structure above the mean sea level was calculated as follows [21]:  
\[dF_{\text{wind}}=\frac{1}{2}\rho_{\text{a}}C_{D}D_{i}U(z)^{2} \tag{6}\]  
where \(C_{D}\) is the drag coefficient (a value of 1.2 is considered in accordance with [22]), \(D_{i}\) is the segment diameter, and \(U(z)\) is the mean wind velocity at the corresponding height \(z\) from the sea level.