esa-sceva/satcom-chunk-collection huggingface dataset:
# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 3 Related Work  
Thus far, there has been limited work on node partitioning in massive MIMO networks in the sense we consider in this paper. There is a substantial body of work on node selection, starting with multiuser MIMO (MU-MIMO) systems [8; 9; 10; 11] even before the advent of massive MIMO. However, the goal of these studies was to choose some subset of the available nodes to be served at a given point in time, typically to maximize the sum rate of the system. Although this is often referred to as node scheduling, it is not true scheduling in that nodes that are not served are not scheduled to be served in future; the nodes are rather partitioned into those that are served and those that are not, with the assumption that as channel conditions change, different nodes will be served over time. We are, however, interested in node partitioning for the purpose of then creating a schedule for all node groups to be served over time. As such, multiple groups are created, and all nodes must be included in at least one group for the partition to be valid. To avoid confusion, we will therefore reserve the term _scheduling_ to mean arranging the service of nodes or node groups over time such that all nodes or groups are eventually served, and use the term _selection_ to mean selecting some subset of nodes for service at a given point in time.  
Furthermore, our objective is not to maximize the sum rate of the system. In many IoT use cases, high throughput is not needed. Rather, the aim is often to serve all node demands with minimal energy usage. Here, the rate provided to an individual node does have some relation to this goal, since a high achievable data rate can be used rather to reduce bit errors and thus increase reliability, instead of maximizing throughput. Fewer bit errors mean fewer retransmissions, and thus energy saved. Beyond a certain point, however, nodes do not benefit from a higher SINR as they only have a small amount of data--for example, sensor or control data--to transmit or receive. As such, we aimed to find a node grouping that maximizes the minimum SINR of any node, rather than maximizing the sum rate.  
Despite these differences, work on node selection is relevant to our work, since a full node grouping can be regarded as an extension of the binary partitioning of nodes into those that are served and those that are not, and can use similar approaches. We will first discuss the extensive body of work on node selection, and then consider the more limited existing work on scheduling and random access in massive MIMO systems.
# Quantifying Auditory Presence Using Electroencephalography
## 3 Results
### Experiment 1
In the row direction of Table 3, the dichotomous data of presence score and EEG indices came from the same participant, indicating they should be treated as pairs in the correlation analysis. For this reason, we utilized the McNemar test within the range of chi-squared tests to be more specific [47; 48]. Table 4 presents the results of the McNemar test. We could see that there was a significant difference (\(p<0.05\)) between presence score and the absolute powers (theta, alpha and beta), suggesting that they were not statistically correlated. Instead, there was no significant difference (\(p>0.05\)) between presence score and the four power ratios, indicating a significant correlation between them. In addition, the Pearson correlation coefficient was also obtained with the McNemar test. We could see that presence score only showed a significant (\(p<0.05\)) correlation with T/B among all seven indices, with the correlation coefficient being 0.683. It suggested that if the presence score for program A was higher than B, it would be highly possible that the T/B for program A was also higher than that for B. On the contrary, the correlation was insignificant (\(p>0.05\)) for the other six indices. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**EEG Index** & **Theta** & **Alpha** & **Beta** & **T/A** & **T/AB** & **T/B** & **A/B** \\ \hline \(p\) & 0.569 & 0.079 & 0.02 * & 0.642 & 0.098 & 0.056 & 0.438 \\ \hline \hline \end{tabular}  
* Significance level of 0.05.  
\end{table} 
Table 2: Results of the Wilcoxon signed rank test in Experiment 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Participant** & **Presence Score** & **Theta** & **Alpha** & **Beta** & **T/A** & **T/AB** & **T/B** & **A/B** \\ \hline
2 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 1 \\
3 & 1 & 0 & 0 & 0 & 1 & 1 & 1 & 1 \\
4 & 1 & 0 & 0 & 0 &  
To summarize, we first found significant differences in the presence score and beta power when presenting different programs, proving that participants were able to detect the difference between the two programs. Then, we investigated the correlation between presence score by subjective questionnaires and EEG indices by objective measurements. Considering both the results of the McNemar test and the Pearson correlation coefficient, we found that among all EEG indices used in this study, only T/B showed a significant correlation with the presence score.
## 3 Results
### Wettability of the Investigated Implants  
The wettability of the SLA-treated, and GRGD coated, IDCT-Ti implants was assessed by the dripping and immersion test as shown in Figure 5. The average contact angle in the dripping test for the SLA-treated implant was 75.5 \(\pm\) 6.8\({}^{\circ}\) (deionized water, Figure 5a) and 135.1 \(\pm\) 2.8\({}^{\circ}\) (blood, Figure 5b), while the IDCT-Ti/GRGD-1 implant in both deionized water and blood was nearly 0\({}^{\circ}\). Furthermore, both deionized water and blood were raised along the thread of the IDCT-Ti/GRGD-1 implant to an average height of 2.5 \(\pm\) 0.2 mm  
Figure 4: The result of the live/dead cell assay after the L929 cells were cultured in different concentrations of the GRGD peptide for 24 h. Magnification = 10\(\times\).  
and 3.0 \(\pm\) 0.3 mm, after being immersed for 20 s, respectively. Obviously, both deionized water and blood appeared to be wetting and adhering to the IDCT-Ti/GRGD-1 implant as compared with the SLA-treated implant. A similar finding could also be observed when the Ti implant was subjected to a coat with a higher concentration of GRGD peptide (i.e., IDCT-Ti/GRGD-2 and IDCT-Ti/GRGD-3 implants).
# Experimental Study on the Condensation Heat Transfer on a Wettability-Interval Grooved Surface
## 3 Results and Discussion
### Droplet Dynamic Behavior
#### 3.1.2 Spontaneous Suction and Directional Drainage via Grooves
As shown in Figure 7c, the whole process lasted about 10.5 s from the slippage initiation to the complete disappearance of the liquid block. It is noted that the downward flow of the liquid film in the groove could also result in a downstream spontaneous suction effect, which would further accelerate the removal of the condensate on the wettability-interval grooved surface. The spontaneous suction effect and directional drainage behaviors via grooves were unique phenomena on the wettability-interval grooved surface. In humid air, the process  
Figure 7: Directional drainage behaviors via microgrooves: (**a**) Schematic diagram of directional drainage behaviors; (**b**) Removal of the liquid block due to directional drainage behaviors; (**c**) Removal of the liquid block during downward sliding. of shedding was usually a result of the coupling effects. A typical shedding process on the wettability-interval grooved surface was selected as an example, as shown in Figure 8.  
On the wettability-interval grooved surface, dropwise condensation occurred on the hydrophobic ridge, and the droplets reached the maximum droplet radius by either direct condensation or coalescence. When the droplet on the hydrophobic ridge came into contact with the liquid film in the hydrophilic groove, the droplet was sucked into the hydrophilic microgroove under the action of surface tension, which also induced the coalescence of droplets on the adjacent hydrophobic ridge. The coalescence process was rapid, and only lasted for 0.3 s. The coalescence accelerated the downward movement speed of the liquid film in the microgroove, and the other downstream droplets on the hydrophobic ridge were merged by the rapid downward liquid film in the microgroove, resulting in increased volume after the coalescence. After 1.8 s, the liquid block across the hydrophobic area gradually detached from the surface under the action of directional drainage via hydrophilic microgrooves, renewed surface for new dropwise condensation, and the downward liquid film in the groove would continue to suck the droplets on the downstream hydrophobic ridges. From the above analysis, it can be seen that the wettability-interval grooved surface showed efficient drainage performance under multiple actions.
## 3 Results
### Optimization and Modeling in Energy Systems
#### 3.1.2 Originality  
The originality in this domain lies in how recent models effectively integrate uncertainty and transition from theoretical frameworks to practical applications. For example, the stochastic-robust model for fully renewable communities also introduces practical solutions for day-ahead scheduling, incorporating P2P transactions among prosumers [28; 29; 2]. This represents a notable departure from traditional, deterministic approaches. Hybrid methodologies, such as those combining robust optimization with CVaR, demonstrate a novel ability to address financial and operational risks simultaneously. These frameworks provide actionable insights even in scenarios with limited data, addressing challenges previously considered insurmountable [4; 30].  
Another innovative aspect is the emphasis on privacy-preserving decentralized systems. Distributed platforms employing blockchain and algorithmic solutions enable participants to share minimal information while collectively optimizing operations. This dual focus on efficiency and security responds to critical challenges in the energy market [18; 31]. Additionally, recent models increasingly leverage emerging technologies like AI and deep learning. For instance, a hybrid model utilizing Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks has shown remarkable accuracy in predicting controllable loads, facilitating more effective resource management in distributed systems [24; 32; 33]. Such applications signify a shift towards practical, technology-driven solutions that address real-world complexities.
## 2 Calculation Methods
### Density Functional Theory Simulation  
Density functional theory (DFT), a quantum computational method used to determine the reaction enthalpy by calculating the ground state energy, has been widely applied in the study of environmentally friendly insulating gases [2; 28; 29]. To verify the validity of the PMVE/N\({}_{2}\) mixture gas decomposition pathway, transition state searches and reaction enthalpy calculations were performed [30]. In this study, all structures were optimized using the Gaussian 16 software package [31], the three-parameter hybrid function B3 LYP [32], and the 6-311 G++ (d, p) basis set [33; 34], and [PERSON]'s D3 BJ dispersion [35] was also considered. This calculation method has been shown to provide good agreement with experimental data from previous studies [28]. At the same computational level as the geometry optimization, the zero point energy (ZPE) was calculated, and ZPE and enthalpy corrections were performed to improve the accuracy of the results [36]. The reaction energy can be calculated as follows:  
\[\Delta E=E_{Products}-E_{Reactants} \tag{2}\]  
where \(\Delta E\) represents the reaction energy, \(E_{Products}\) is the energy of the products calculated after geometric optimization, and \(E_{Reactants}\) is the energy of the reactants calculated after geometric optimization.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis
### Text Recognition
The segmentation-based method is an effective and simple approach, but pixel-level annotation is necessary. Sequence-based recognition is another popular method that produces good outcomes but has the drawback of being complex. Table 8 provides a more detailed comparison of the state-of-the-art text recognisers' performance on benchmark datasets with irregular text. None of the displayed results were generated from a lexicon. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Methods** & **Anno.** & **ICDAR15** & **SVTP** & **CUTE80** \\ \hline ASTER [131] & word & 0.761 & 0.785 & 0.795 \\ ScRN [133] & word, char & 0.784 & 0.811 & 0.906 \\ CA-FCN+data [134] & word, char & - & - & 0.799 \\ CAPNet [135] & word, char & 0.766 & 0.788 & 0.868 \\ SAR [136] & word & 0.788 & 0.864 & 0.896 \\ TextScanner [137] & word, char & 0.835 & 0.848 & 0.916 \\ SRN [138] & word & 0.827 & 0.851 & 0.878 \\ DPAN [139] & word & 0.855 & 0.890 & 0.919 \\ CDistNet [140] & word, char & 0.860 & 0.887 & 0.934 \\ MATRN model [141] & word, char & 0.828 & 0.906 & 0.935 \\ S-GTR [142] & word & 0.873 & 0.906 & 0.947 \\ PARSeq [143] & word & 0.896 & 0.957 & 0.983 \\ STN-CSTR [144] & word & 0.820 & 0.862 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy results of text recognition methods on ICDAR15, SVT Perspective and CUTE80. “Anno.” is short for required annotations.
## 2 Materials and Method
### Method
#### 2.2.1 Signal Acquisition Circuit Design  
The system is powered by an isolated external power supply and extracts the AC-coupled signal components from the vehicle's cigarette lighter interface. Notably, the device shares a common ground reference with the cigarette lighter socket, maintaining electrical continuity while preventing unintended load coupling with the alternator. Since the rotational speed measurement is based on a frequency analysis of the coupled AC signal present in the cigarette lighter interface, a dedicated signal acquisition circuit was designed to extract valid signals that meet the requirements for the microcontroller's internal A/D conversion.  
Figure 1: Automotive RPM measurement schematic.  
The designed signal acquisition circuit shown in Figure 2 incorporates resistors \(R_{1}\) and \(R_{6}\) as current-limiters to prevent system damage from excessive input current, while capacitor \(C_{1}\) isolates the DC component to ensure only the AC signal is acquired. Resistors \(R_{2}\) and \(R_{3}\) form a DC bias circuit that elevates the signal to 1.65 V to match the optimal input range of the STM32 microcontroller's A/D converter operating between 0 and 3.3 V, with the 1.65 V DC offset enabling effective AC signal acquisition. To accommodate potential variations in AC signal amplitude across different vehicle models, a dual-stage amplification circuit was implemented to facilitate simultaneous two-channel A/D acquisition, with the system software automatically selecting the optimal channel for subsequent calculations. Capacitors \(C_{2}\) and \(C_{3}\) were incorporated in the amplification circuit to ensure the selective amplification of only the AC components while rejecting the DC offsets, thereby maintaining signal integrity throughout the acquisition process.
## 8 Navigation  
UGVs navigation can be defined as the task of mapping the surrounding environment, localizing the robot position in space, path-planning, applying the movement, and avoiding dynamic obstacles [84]. Navigation capabilities are crucial for any UGV, regardless of its purpose in field operations [85]. These combinations of activities are critical and computationally expensive to achieve in an agricultural context, which requires the need to navigate effectively through heterogeneous environments, which can vary significantly due to natural and artificial obstacles, terrain features, and different weather conditions. Considering each aspect separately, it is possible to define the constraints, challenges and available solutions.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 4 Results and Discussion
### Solution
#### 4.1.2 Subsequent Access to the Main Server of APPIFY  
After [PERSON] signs up for the application to the main server of APPIFY, explained above in Section 4.1.1, there could be three reasons for subsequent access to the main server. The first is to renew the certificate \(cert(Alice13)\). [PERSON] may require obtaining a new certificate due to the change of the user device, adding a new authorized device, or loss of the previous certificates. For renewing the certificate, [PERSON] needs to sign in by using her user identity Alice13 and password \(psw\).  
The second reason is to update service settings and user profile. [PERSON] can use either her existing certificate to sign in or her user identity and password pair for this access. After a successful update, the main server of APPIFY should issue a new certificate \(cert(Alice13)\), according to the updated service settings.  
The third reason for subsequent access to the main server is that no suitable MEC host is deployed near [PERSON]. If she wants to use APPIFY, but there is no MEC with APPIFY around her, she should receive the service from the main server of APPIFY. [PERSON] can use either her existing certificate to sign in or her user identity and password pair for this access. In this case, the main server does not need to renew the certificate.  
When [PERSON] uses her certificate for signing in to the main server, the access could be done using TLS (transport layer security) with mutual authentication based on the certificates. The certificate \(\textit{cert}(\textit{Alice}13)\) should be sent to the main server after the TLS handshake to protect the privacy of [PERSON] in this case. Note that [PERSON] receives a certificate \(\textit{cert}(\textit{Vk}_{main})\) for the main server during the registration.  
[PERSON] could use her username and password to sign in to the main server. The procedures are similar except that [PERSON] sends her username and password instead of authenticating with a certificate after the server-authenticated TLS session is established.
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 5 Performance Analysis and Evaluation
### Outlier Detection with LOF  
Table 2 illustrates a subset of the computed precision and recall values for some trajectories with LOF. The first column of Table 2 provides the trajectory identifier (named \"File Name\" in GEO); columns 2 and 3 provide the precision and recall values that have been computed for each trajectory. The number of detected outliers is provided in column 7, while column 8 holds the number of neighbours. For this sample, it can be seen that the values reached for both precisions are usually high (99% or even 100%). Moreover, 0 values mean that the precision or recall could not be computed by LOF.  
LOF depends on an adequate calibration of MinPts in order not to have a significant impact on outlier detection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Precision** & **Recall** & **Accuracy** & **F-Score** & **N Reg** & **Outliers** & **Neighb** \\ \hline
0.996 & 0.822 & 0.820 & 0.901 & 907 & 6 & 4 \\ \hline
0.000 & 0.000 & 0.000 & 0.000 & 243 & 7 & 1 \\ \hline
1.000 & 0.769 & 0.800 &  
LOF intrinsically depends on K to determine the scale of local neighbourhoods; however, LOF does not differentiate outliers, and unlike DBSCAN is influenced by the size of the dataset, as can be seen in the correlation provided in Figure 5, which shows a comparison of the dependency of both algorithms on the respective parameters.  
Figure 5 shows how the statistical measures (mean, median, quartiles, interquartile distance, and the number of points of each trajectory) are correlated with each other and the clusters obtained through DBSCAN and LOF.  
The illustration of the detected outliers with LOF is illustrated in Figure 6.  
For each point, LOF decides whether it is an outlier or not by checking whether the LOF is close to the value 1. If this value is much higher than 1, it is considered an outlier factor, while if it is close to 1, it is a normal point.
## 3 Results and Discussion
### Self-Healing Performance
#### 3.1.1 Repair Effect of Mechanical Damage and Breakdown Strength
In addition, the interface area between the microcapsule and matrix can introduce new structural defects, which can not only increase the number of PD points, but also form relatively concentrated low-density areas [39]. Therefore, doping microcapsules can reduce the breakdown strength of polyethylene and increase its dispersion. In composites, the scratch can break the wall structure of the microcapsule in the polyethylene matrix and reduce the breakdown strength of the composite, which is similar to that of pure polyethylene. The thermal conditions and capillary action can cause the DCPD core material to flow into the defect site. The liquid DPCD contacts with the catalyst and reacts in the damaged area to form the PDCPD solid, whose thermal deformation temperature is 120 \({}^{\circ}\)C [40]. Moreover, the density and dielectric constant of PDCPD are similar to those of polyethylene. Thus, PDCPD can homogenize the local high field of the defect and decrease the local low-density area, reducing the electromechanical stress and the disorder of structure [10; 37; 39], thus, improving the electrical insulation performance of the matrix. Therefore, compared with pure LDPE, the composite has obvious self-healing effects on the microstructure and breakdown strength of mechanical damage. The repair efficiency for scratch width was 82-100%, and the breakdown strength could be restored to about 94.4% of the original state. In Figure 3d, the self-healing effect of the damaged microcapsules surrounding area is more obvious, and the crack is almost completely repaired. The main reason is that the damaged area near the microcapsules can preferentially contact a large amount of repairing agent, and the reaction rate between DCPD and the catalyst is relatively fast, causing the DCPD to already begin to react in the diffusion process. The fast reaction speed can better meet the timely requirement of the insulating material for damage treatment. At present, the research on self-healing insulating material is still in the exploratory stage. The experimental results show that the composite prepared in this paper does have the ability to self-heal mechanical structural damage. Although a small part of insulation strength is sacrificed, the composite can quickly restore the insulation performance after damage. Thus, the composite still has good research value in lower voltage application scenarios.