esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Anatomical Leg Length Discrepancy Using the Plug-in-Gait Model
## 3 Results  
Symmetricity is presented in Figure 1a. Lower limb measurements in all subjects were found symmetrical for both methods, including femur and tibia segmental length, X-ray total leg length and clinical leg length. Similar distributions were found between sides (Figure 1b). No differences were found for the femur t(14) = 1.06, \(p\) = 0.3 and tibia t(14) = \(-\)0.76, \(p\) = 0.45 segmental length for both methods when measuring discrepancies between sides (Table 1). In addition, no difference was found when comparing total X-ray leg length discrepancy to clinical leg length discrepancy t(14) = 1.33, \(p\) = 0.2 (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Segment** & **Left (Mean)** & **Right (Mean)** & **Mean Discrepancy (SD)** & **Significance** \\ \hline PGM Femur length & 372 & 371 & 0.66 (13.17) & \(p\) = 0.30 \\ X-ray Femur length & 410 & 413 & \(-\)2.93 (9.63) & \(p\) = 0.30 \\ \hline PGM Tibia length & 367 & 368 & \(-\)1.60 (19.42) & \(p\) = 0.45 \\ X-ray Tibia length & 331 & 329 & 2.06 (7.01) & \(p\) = 0.45 \\ \hline X-ray total leg length & 753 & 754 & \(-\)1.0 (14.71) & \(p\) = 0.2 \\ Clinical leg length & 802 & 799 & 2.33 (14.12) & \(p\) = 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Plug-in-Gait-model (PGM) versus X-ray segmental length, total X-ray length, clinical leg length and discrepancies (mm). Significance represent comparison of mean discrepancies. Level of significance—\(p\) < 0.01.  
Figure 1: _Cont._  
High correlations were found between methods when measuring segmental length (Figure 2); left femur length, r = 0.808, \(p\leq 0.001\), left tibia length, r = 0.963, \(p\leq 0.001\), right femur length, r = 0.868, \(p\leq 0.001\), right tibia length, r = 0.933, \(p\leq 0.001\). A significant difference was found in both methods when measuring the femur and tibia segmental length (\(p<0.001\)) (Figure 3). The femur X-ray length was found to be significantly longer than the femur PGM length measurement; conversely, the tibia X-ray length was found to be significantly shorter than the tibia PGM length.  
Figure 1: (**a**) Symmetricity of 15 participants for all lower limb measurements in both methods. Patient ID 1–10 children, 11–15 adults; (**b**) Density plot comparing distribution between sides for each measurement. PGM: Plug-in-Gait-model.  
Figure 2: Correlations between femur and tibia PGM and X-ray segmental length.
## 2 Materials and Methods
### Color Parameters  
The measurement was carried out using a Color Quest XE colorimeter (HunterLab 166, HunterLab, Reston, VA, USA) equipped with a xenon flash lamp. Glass vucettes with an optical path of 1 cm were employed for the measurements. The results were reported in accordance with the CIE \(L^{*}a^{*}b^{*}\) system, utilizing illuminant D65 and 10\({}^{\circ}\) observer. The data were expressed using the Hunter scale parameters: \(L^{*}\) (lightness/darkness), \(a^{*}\) (redness/greenness) and \(b^{*}\) (yellowness/blueness) values. The total color difference (\(\Delta E\)) was calculated using Equation (2), where \(L^{*}\), \(a^{*}\) and \(b^{*}\) represent the values of the control sample:  
\[\Delta E=\sqrt{\left(L^{*}-L_{0}^{*}\right)^{2}+\left(a^{*}-a_{0}^{*}\right)^{ 2}+\left(b^{*}-b_{0}^{*}\right)^{2}} \tag{2}\]
# Secured MCDM Model for Crowdsource Business Intelligence
## 6 Discussion
### Sensitivity Analysis  
The GRA results are not weight normalized but min and max normalized. Hence, the sensitivity analysis is first carried out on the AHP implementation, where the weight change can be followed up with a change in the Consistency Ratio (CR) and the final ranking. However, as per the original assessment of the AHP method based on the expert opinion, the features were ranked as per the importance: Features > Cost > Warr-anty > Model.  
This assumption produced a consistency ratio of 1.5% for the attributes under subjective weight assumptions.The original feature importance distribution is shown in Figure 4.  
The sensitivity analysis for GRA is discussed with the Figure 5. The diagram shows how the attributes contribute to the estimation of the Gray Relational Grade and the final ranking. The rank is expressed in the X-axis and the corresponding values of the grade  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Step** & **Steps of Algorithm** \\ \hline
1 & Establish user authorization. \\
2 & function hasIssueRole(role,account) public view \\
3 & require(account! = address(0), “Roles: this account is the zero address”); \\
4 & return role.bearer[account]; \\
5 & end function \\
6 & Assign authorization to user account \\
7 & action addIssueRole(role, account) public onlyOwner \\
8 & require(has(role, account), “Roles: this account already has role”); \\
9 & role.bearer[account] = false; \\
10 & end function \\
11 & Dataset authority revokes role authorization \\
12 & function renounceIssueRole(role,account) public onlyOwner \\
13 & require(has(role, account), “Roles: this account does not have role”); \\
14 & role.bearer[account] = false; \\
15 & Extending the access structure externally \\
16 & function par Extension(role,account) \\
17 & require(has(system parameters,role,extended role), “Roles: this account role has been extended”); \\
18 & role.bearer[account] = false; \\
19 & end function \\ \hline \hline \end{tabular}
\end{table}
Table 10: Environment-Based Attribute Access Control model(EBAACM) Algorithm.  
Figure 4: Attributes with Weight Representation in AHP.  
are represented in the Y-axis in terms of percentage. As per the contribution, the feature importance follows the following order: Warranty > Cost > features > Model.  
The Warranty is the primary contributing factor for the highest rank, comprising 70%, followed by cost at around 50%, then features and model around 35% and 20%, respectively.
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 3 Community Density
### Reasoning Community Density Alleviates the Modularity Resolution Limitation
\[Q_{R_{1}}=\frac{1}{NE}\bigg{(}(L(u)+L(v))*1*1*(1-\frac{L(u)*L(v)}{NE})\bigg{)}= \frac{1}{NE}*0=0, \tag{16}\]  
\[Q_{R_{2}} =\frac{1}{NE}\bigg{(}\sum_{i=1}^{L(u_{1})}\sum_{j=1}^{L(v_{1})} \delta_{i,L(v)}\delta_{L(u),j}(1-\frac{L(u)*L(v)}{NE})+\sum_{i=1}^{L(u_{2})} \sum_{j=1}^{L(v_{2})}\delta_{i,L(v)}\delta_{L(u),j}(1-\frac{L(u)*L(v)}{NE}) \bigg{)}\] \[=\frac{1}{NE}\{E_{1_{\text{in}}}*1*1*(1-\frac{L(u)*L(v)}{NE})+E_{ 1_{\text{out}}}*1*0*(1-\frac{L(u)*L(v)}{NE})+E_{2_{\text{in}}}*1*1*\] \[(1-\frac{L(u)*L(v)}{NE})+E_{2_{\text{out}}}*1*0*(1-\frac{L(u)*L( v)}{NE})\}\] \[=\frac{1}{NE}(0+0+0+0)\] \[=0\]  
As Equations (16) and (17) show, the modularity of the two community detection results calculated by [PERSON]'s modularity is 0. However, \(R1\) and \(R2\) have different community detection but \(Q_{R_{1}}=Q_{R_{2}}\). In \(R1\), divide the network into a community. Logically, the best partition is the one where all the nodes are put in a single cluster. So, there are no cuts, no edges between clusters. So, the modularity proposed by [PERSON] cannot effectively judge the merits and demerits of the results. Let us use the community density proposed in this paper to calculate the two partition results. \(D_{R_{1}}\) represents the community density divided according to R1, and \(D_{R_{2}}\) represents the community density divided according to R2. For R1, any node pair \((u,v)\) in community \(G\), all node pairs containing \(G\) are within the community, so the calculation formula of node pair \((u,v)\) density \(D_{(u,v)}\) is as follows (18). \[D_  
After calculation, \(D_{R_{1}}\
eq D_{R_{2}},D_{R_{1}}-D_{R_{2}}=\frac{1}{2}\). Therefore, community density can be used to evaluate the merits and demarcations of such results. Effectively alleviate the resolution limit of modularity.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 2 VNE Problem Decomposition and Modeling
### Problem Description
#### 2.1.8 VNE Problem Complexity  
There is a multitude of computational problems of various categories. However, the computational complexity theory focuses mainly on decision problems where the answer is rather simple, such as the binary outcome of affirmative or negative responses [30]. An algorithm may be categorized as efficient when it utilizes a polynomial amount of computational resources. If \(P\) is the class of decision problems that can be solved efficiently, then \(NP\) could be identified as the class of problems for which there is an algorithm of polynomial time, able to verify whether a given solution is correct or not.  
As was previously stated, the VNE problem can be formulated mathematically. Under the prism of this fact, VNE can be reduced to computer science optimization and decision problems known for their complexity, such as the multi-commodity flow (MCF) or k-multiway separator. Therefore, the NP-hardness of VNE is also established [21]. To avoid resulting in unsustainable execution times, VNE implementations usually resort to solving relaxed variants of the original formulations.
## 1 Introduction
SAR measurements are obtained through active remote sensing techniques and thus can provide observations that are not influenced by cloud or sunlight, which can influence the quality of optical datasets. S1 datasets showed great success in vegetation phenological studies, such as for estimating crop phenology [18,19], classifying mixed temperate forests [2], and determining meadow phenology [20]. Moreover, for forest phenology, some studies have started to use S1 backscatter measurements to explore forest temporal characteristics, such as the seasonal variations of temperate forest [1], forest biomass estimation [21,22] and forest damage degree [23]. Current studies of optical sensors have not been satisfied with the accuracy of phenological extraction (R\({}^{2}\) value of approximately 0.5) and are considering introducing S1 to improve the accuracy [20]. Therefore, it is necessary to assess the accuracy of phenological extraction with S1 backscatter measurements. To conclude, it is still unclear how accurate phenological retrieval based on S1 backscatter would be for forests of different types. Thus, this study investigates the accuracy of retrieving phenology for deciduous and coniferous forest sites across North America usingS1 backscatter measurements. An evaluation of the S1 derived phenological metrics was performed against an independent in-situ dataset obtained from the PhenoCam network. Additionally, an inter-comparison against S2 NDVI derived phenology was also performed.
# Global Power Dynamics in the Contemporary Space System
## 3 System Context--Intersystem Transition  
The space system has undergone a substantial metamorphosis since the conclusion of the Cold War era. It transitioned to a unipolar structure during the post-Cold War period, representing a departure from the previous simple bipolar configuration that characterized the Cold War era. In the early 21 st century, the space system underwent further transformation, giving rise to new space actors, dynamics, and trends.  
This process is also known as an intersystem transition, in which a prior system configuration changes into a new systemic order. This transition between different stages of history represents the exhaustion of an established order and the emergence of a new one. Significant differences in the actors, phenomena, and relations of the emerging order coexist with those of the decaying structure. This phenomenon is characterized as the period between the 'decadence' of a system and the 'emergence' of another [38].  
The intersystem transition in the space domain has demonstrated a resurgence of interest and development among multiple nation-states in defining national objectives in space. The contemporary global landscape of space exploration and development features the traditional space powers--the United States and the Russian Federation--as well as emerging space powers such as China, India, Japan, and the European Union. Additionally, a substantial group of nations with developing and ambitious space plans, including Turkey, Iran, Brazil, South Korea, the United Kingdom, and Pakistan, are present. This scenario represents a novel context in which multiple states are developing increasingly robust space programs and preparing for fierce competition.  
The development of this new space system has resulted in several issues that impact the global system. These include the emergence of a new space race, the potential militarization and weaponization of space, the increasing commercialization and privatization of space, the lack of a global legal framework, and the utilization of space diplomacy and cooperation to create new international alliances.  
The contemporary transformations within the space sector are a consequence of the alterations to the fundamental parameters of the space system. These alterations are manifesting in a gradual and persistent intersystem transition that is reshaping the balance of power in the space system.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Optimal Decision
#### 3.3.4 Svm  
The support vector machine (SVM), as one of the classical supervised ML algorithms, is usually used for classification. Through a small amount of training data, SVM can find the optimum classification plane between two clusters, and the classifier model only depends on several support vectors. If the data are not linearly separable, a kernel trick can be used for nonlinear classification. Although SVM was originally used for binary classification, multiclass SVM strategies such as one-versus-one (OVO) and one-versus-all (OVA) have been proposed. In [68], SVM-based detection is proposed and demonstrated in VLC systems. Experimental results indicate that the SVM-based scheme has a 35% increase compared with the conventional decision scheme when there is a strong nonlinear effect. In [69], a constellation decision based on SVM is investigated in integrated optical fiber and VLC systems when there is random phase rotation.
# Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation
## 2 Related Work  
There are indoor-positioning methods other than indoor networks. Based on the intensities and luminaries of indoor light, some researchers extended the boundaries of indoor positioning. In [13], an experimental robot captured frequency information from four LED transmitters with different frequencies. The robot is equipped with a LiDAR sensor that maps the room and two cameras capable of measuring light intensity and frequency. Although the experimental results reached a mean error of 80 cm, this method appeared to be unsuitable for localization in large environments. In [14], the researchers used light luminaries to predict position. A LiDAR-based SLAM map was generated by a robot that reads the visible light and stores it with a real-time position estimated using the HTC Vive. To predict the robot's current location, they used KNN, support-vector regression (SVR), and decision trees. The robot used in the research was a vacuum robot with an added photodiode to measure light intensities, and they achieved great results in terms of positioning error, which was between 8.4 and 14.4 cm. However, the experiment was extremely expensive, as the HTC Vive tracking system was used only in one room, which had a 43.4 m\({}^{2}\) area, and the cost would be high if applied to the entire building floor. In addition, it is only used for positioning trained robots. In terms of indoor positioning, WiFi is not the only network that caught researchers' attention. The researchers in [3] developed a ZigBee network radio map and a nonlinear-regression model to locate the robot's location, which is good for dynamic environments due to its continuous database updates. In a 145 m\({}^{2}\) environment, the robot visited 531 reference points with a mean error of 0.81. Although the robot can be used in any dynamic environment, it cannot avoid moving obstacles. Other researchers chose Bluetooth RSS as fingerprints in indoor positioning, and in [15] LiDAR and a camera were used as wearable devices to scan the environment and create SLAM-based maps. The camera is responsible for labelling the rooms in the environment, and the helmet records Bluetooth signals to be used to estimate position in the future. Other researchers suggested RFID tags as a solution. A study in [16] achieved a 1.22 m error rate for RSS-data collection over a 45 m\({}^{2}\) environment by using RFID tags and two-wheel robots. Table 1 summarizes a set of previous studies in this field. Ref. [17] demonstrated the use of Bluetooth low-energy (BLE) beacons for constructing a location-aware infrastructure by using unsupervised learning methods. Data collection, storage, and post-processing techniques in [17] were of particular interest. Paper [18] suggested using support-vector regression (SVR) for mobile-target localization in indoor environments. The techniques discussed in [18] could be applied to the dataset collected in this paper and would lead to an interesting future study.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 4 Evaluation Use Case
### Scenario Description: Air Monitoring Application  
The evaluation scenario is based on a use case within the scope of Antwerp's City of Things testbed [38]. The objective of our air monitoring application is to show the current status of the environment in the City of Antwerp and alert citizens of ambient pollution through a notification system in near real time. Regarding the current literature in air monitoring applications for smart cities, the proposed approach in [39] must be highlighted. In the article, a novel cloud-based approach for air quality monitoring is discussed. The approach is based on the design of two sensor front ends: a stationary air quality sensor that connects to the cloud via Ethernet and General Packet Radio Service (GPRS) and a portable sensor that connects to the smartphone via Bluetooth 4.0.  
In contrast, our approach is based on the deployment of a set of air quality sensors mounted on the roofs of bpost's (Belgian postal services) delivery cars as an initial proof of concept. These sensors send measures of typical gases and climate data, such as temperature and humidity, which are then annotated with GPS locations. Furthermore, these sensors allow gathering real-time air quality information with broad city coverage, since each car is continuously driving around in the city.
# Pt-Based Nanostructures for Observing Genuine SERS Spectra of _p_-Aminothiophenol (PATP) Molecules
## 5 Conclusions  
In summary, genuine SERS spectra of PATP molecules were successfully observed with Pt-based hollow nanostructures, though Pt itself has weak SERS activity. In the reported SERS measurements, PATP molecule dimerisation did not occur, and vibrational modes of 1140, 1390 and 1432 cm\({}^{-1}\) related to DMAB molecules were not observed. The SERS spectra are in good agreement with the Raman spectrum of PATP powder, even the laser illumination time is prolonged. The laser illumination time and the concentration of PATP molecules in solution are both the key factors that affect the plasmon-driven chemical conversion from PATP to DMAB, due to the thermal effects of a laser focal spot. In this work, the illumination time and the tip molecule concentration show no influence on the isolated SERS spectra of PATP, demonstrating the stable SERS catalytic stability of Pt-based hollow nanostructures. Furthermore, this suggests that the substrate with its unique optical properties may be useful in other SERS detection processes to obtain genuine SERS spectra.  
This work was supported by the National Natural Science Foundation of China (NO. 51371015, 51331002, 11674023 and 21601013) and Beijing Municipal Science and Technology Project (NO. Z17111000220000).  
Figure 4: Time-dependent SERS spectra of Pt-based hollow nanostructures on Si, after adsorption of PATP from an ethanol solution, with different laser illumination time. The concentration of PATP in solution and laser irradiation time for collecting Raman spectra from these samples are: (**a**) 10\({}^{-5}\) M, 200 s; (**b**) 10\({}^{-5}\) M, 2000 s; (**c**) 10\({}^{-6}\) M, 200 s; (**d**) 10\({}^{-6}\) M, 2000 s; (**e**) 10\({}^{-7}\) M, 200 s; (**f**) 10\({}^{-7}\) M, 2000 s.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] participated in paper revision and made many suggestions. All authors read and approved the final manuscript. [PERSON] and [PERSON] contributed equally to this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
###### Abstract  
An enhanced fault diagnosis approach for rolling bearings with composite faults using an optimized Squeeze and Excitation ResNet (SE-ResNet) model is proposed. This method integrates grid search (GS), support vector regression (SVR), ensemble empirical mode decomposition (EEMD), and low-rank multimodal fusion (LMF) to effectively handle the signals of acoustic-vibration fusion. By combining these techniques, the aim is to improve the accuracy and reliability of rolling bearing fault diagnosis. Firstly, improved EEMD combined with GS-SVR and a window function is used for rolling bearing vibration signal decomposition. Singular value methods are used to filter and reconstruct the results. Secondly, Markov transition fields (MTFs) are used to encode vibration signals into 2D images. LMF is used for the fusion of vibration and sound signals. An improved Squeeze and Excitation ResNet50 network is proposed for feature identification and classification of rolling bearing composite fault data. Finally, the method undergoes rigorous testing and evaluation using rolling bearing data. The experimental outcomes demonstrate that, in comparison to traditional neural networks, the enhanced SE-ResNet, integrated with GS-SVR-EEMD and LMF, attains superior diagnostic accuracy. Additionally, the proposed approach can be effectively utilized for diagnosing rolling bearing composite faults.  
deep learning; fault diagnosis; neural network; ensemble empirical mode decomposition; low-rank multimodal fusion Article