esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Results
### Simulation  
We conduct a comparative analysis of the proposed Hierarchical Stackelberg strategy against two benchmark approaches: the baseline control model and the conventional Stackelberg model. This evaluation aims to assess the performance improvements introduced by incorporating hierarchical coordination mechanisms. Firstly, the baseline model represents a traditional fixed-time signal control system that does not give priority to emergency vehicles (EVs) and does not facilitate adaptive coordination. It serves as a reference point for assessing the value of game theory and hierarchical strategies. In the Stackelberg standard model, the Stackelberg single-level game also includes the Traffic Management Center (TMC), which acts as the leader, and the regular vehicle reacts as the follower. Although the model introduces interaction optimization, the vehicle types (e.g., EVs and RVs) do not differ, and its response is limited in priority scenarios.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 1 Introduction
### Definition and Research Needs
The demand-side changes will include power-to-X conversion and further energy sector coupling, whereby electricity systems play central roles in large-scale coupling to thermal, natural gas, and hydrogen systems. Load coupling will provide more options to compensate for VRE intermittency and inflexibility. The social transition to digitalization will facilitate demand-side changes, further increasing demand-side participation in future power grids [21]. Moreover, the sites of VRE production will become closer to the locations of energy consumption. This implies the need for a distributed paradigm of power grid control and estimation, rather than a centralized paradigm, whereby bulk power is delivered to remote load centers in a unidirectional fashion via high-voltage, long transmission lines. In regions with significant levels of distributed VRE sources, distribution management systems (DMSs) require special attention; these have not traditionally reflected the sources of active generation and the controllable loads [22]. When VRE sources are aggregated across larger distribution areas using high-voltage transmission lines, a hybrid approach that combines the best of the centralized and distributed paradigms will be required in practice for the realization of 100% renewable electricity. Another essential technology is that of power electronics converters [2, 4, 14, 15], by which many VRE sources are interfaced at points of common coupling (PCCs) spread across power networks. Power electronics converters for VRE sources differ from the traditional synchronous machines used by fossil fuel generators, with respect to synchronization, programmability, dynamic responses, power quality, and protection. Therefore, the siting, control methods, and operating characteristics of VRE inverters significantly affect power grid stability and reliability and, further, transmission line expansion and even electricity markets and regulations.
# Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation
## 2 Image Acquisition System Design  
The principle of the machine vision-based O-ring inspection method is to obtain a clear image of the seal using an industrial camera, and then apply the technology of image processing to obtain the dimensions of the inner and outer diameters, as well as the wire diameter. Therefore, capturing a clear image of the seal is the first step in the implementation of the measurement system [5].  
The structure of the image acquisition system is shown in Figure 1, which mainly consists of high-definition industrial camera, inspection platform, lighting system, etc. The O-ring is placed on the inspection platform, the front and back lighting LED lights are turned on, the seal is irradiated, and then a high-definition camera is used to get a clear image of the O-ring. In order to meet the accuracy requirements of O-ring dimension measurements, the camera has a resolution of \(3664\times 2748\) pixels. The lens is an M1614-MP2, which has focal length of 16 mm [6].
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 3 Results
### Spectral Indices  
The spectral indices extracted from the control plots stayed within the 'no-change' region for both remote sensing data sources throughout the duration of the experiment (Figure 2). By contrast, within the treated plots, spectral indices derived from both platforms exhibited a significant decline over time. Indices derived from satellite imagery showed a marked reduction 75 days after treatment that was well below the \"no-change\" region for NDVI and RENDVI and marginally below this region for GNDVI. For UAV data all indices dropped below the no-change region 29 days after treatment. This decline continued throughout the duration of the experiment for NDVI but levelled out 52 days after treatment for the other two indices (Figure 2).  
Analysing the changes in spectral indices within the canopies of tree clusters of different sizes provides insight into the sensitivity of each sensor to physiological stress expression at different spatial scales. Spectral indices derived from the UAV sensor showed the most marked changes over time in NDVI. For all treatment cluster sizes, there was a marked decline in NDVI that moved below the no-change region 29 days after treatment and continued to decline until the end of the experiment (Figure 3). Values of GNDVI and RENDVI dropped below the no-change region at day 29. However, after an initial decline, values of these indices stabilised after day 53 for most cluster sizes and exhibited a sharp increase for the single tree clusters 64 days after treatment. The magnitude of reductions in all indices scaled positively with cluster size. By day 87, reductions in GNDVI, NDVI, and RENDVI were respectively, 0.89, 8.66, and 2.41 for single tree clusters and 3.18, 11.93, and 5.28 for clusters with 16 trees. Spectral indices derived from the satellite imagery were not sufficiently sensitive to detect treatment changes in smaller clusters of trees. All indices remained within the 'no-change' zone until 13 days after treatment. For cluster sizes of four or more trees, values for all indices were below the 'no-change' zone for the next imagery acquisition 75 days after treatment. In contrast, index values for clusters of two or fewer trees showed very little change 75 days after treatment. For these smaller clusters, there was only marginal movement below the 'no-change' zone for RENDVI in single tree clusters (Figure 3). All indices showed greater reductions as cluster size increased and changes were very muted for the two smallest clusters. Reductions in GNDVI, NDVI, and RENDVI 75 days after treatment ranged from 0.29, 0.38, and 0.53 for single tree clusters to 2.01, 5.84, and 5.40 for clusters with 16 trees. For both sensors and for all indices no decline below the 'no-change' zone was observed for the control plots where no herbicide was applied.
## 2 Materials and Methods
### Voice Discrimination Threshold in Noisy Conditions (VDTn)  
The SNR at which the VDTn was determined was different for adults and children. Specifically, for the young adults, VDTn was determined at the same SNR for all participants and was set at \(-5\) dB. This was based on previous studies on the Hebrew Matrix in young NH adults, which showed that their mean speech reception threshold in noisy conditions (that is, the SNR that yields 50% correct recognition of speech in noisy conditions, or the SRTn) is \(-8\) dB, with a low amount of variability [32; 49]. To test VD in SNRs that yield adequate speech recognition on the psychometric function, based on a slope of approximately 1.8 [59], we doubled the intensity of the sentences (i.e., we increased the sentences that were originally extracted from the Matrix test by 3 dB, resulting in the VD test being conducted at SNR of 5 dB) [60]. In contrast, previous studies with children showed considerably larger between-subject variability of SRTn compared to adults [32]. Therefore, SRTn was evaluated on an individual basis for each child (mean SRTn = \(-\)3.33 dB \(\pm\) 3.62 dB). In addition, for the children, we added \(+5\) dB to the SRTn, resulting in a mean SNR of \(+1.67\) dB \(\pm\) 3.62 dB for the VD test, to ensure ease of listening based on a pilot study we conducted.
# Off-Cloud Anchor Sharing Framework for Multi-User and Multi-Platform Mixed Reality Applications
## 5 Experiments
### First Experiment: Local HoloLens-HoloLens Communication  
In this experiment, the performance of the proposed framework is evaluated using two HoloLens devices in a Local Area Network (LAN) environment. The goal was to measure the time required for creating, transmitting and loading spatial anchors of various sizes between the two devices in a total of four scenarios. For such a purpose, one device acted as the host, while the other one was connected as a client. Specifically, the experiments were conducted using two Microsoft HoloLens 2 HMD and a TP-LINK router with the following configuration:  
* Microsoft HoloLens 2 (Qualcomm Snapdragon 850, Wi-Fi (IEEE 802.11 ac (2x2))), 4 GB of LPDDR DRAM).
* Router: TP-LINK Archer C5400 (IEEE 802.11 ac), 2.4 GHz connection.  
The four evaluated scenarios utilized a different anchor size: small, medium, big and room-size. For each scenario, the experiment was repeated 20 times to ensure data consistency, obtaining a total of 80 recordings. The following key metrics were collected: exported spatial anchor size (in MB), time in seconds required to create and export the anchor on the host device (i.e., acting as server), transmission time from server to the client, and the time taken by the client to load the received anchor into the scene.  
The obtained results are depicted in Figure 8. As it can be observed in this figure, a consistent increase in time and memory usage can be observed as the spatial complexity of the anchor grows, as detailed in Table 1. In the small-anchor scenario, the anchor size remains minimal (oscillating between 3.0 to 3.6 MB), resulting in very low processing times across all stages. For medium-size anchors, the size ranges from 6.0 to 6.7 MB, leading to significantly longer transmission times, with the minimum time being 25.92 s and reaching up to 26.1 s in some executions. The client loading times also rise, almost doubling the ones from small anchors, with values between 1.4 and 2.2 s. In the big-anchor scenario, anchor sizes keep growing, ranging from 10.1 to 10.8 MB. As expected, the transmission times increased to values between a minimum of 43.3 s and maximum of 43.6 s. Meanwhile, client loading times range from 2.4 to 5.2 s, nearly tripling in comparison to the small-anchor scenario, although the server export time remained relatively stable. Finally, the room-size anchor scenario presents the largest data volumes, with anchor sizes of up to 20.8 MB. In  
Figure 7: Three-dimensional meshes of the spatial mappings captured by the HoloLens for each experiment: (**a**) Small anchor. (**b**) Medium anchor. (**c**) Big anchor. (**d**) Room-sized anchor.  
this case, the clients receive times starting from 85.01 s and peaking to 86.02 s, while load times extend from 4.83 up to 7.3 s.
## 1 Introduction
Conventional approaches often neglect long-term dependencies, resulting in fragmented data sequences that fail to accurately reflect the underlying temporal dynamics. The significance of this paper lies in its innovative approach to preprocessing strongly time-correlated data, such as the weather data utilized in experiments. Firstly, this study contributes to the time efficiency of data processing and the speed of regression in a time series analysis. This advantage is evident in the faster regression capabilities, allowing the model to achieve desired loss scores in significantly less time compared to traditional methods. Secondly, this paper enhances the accuracy and reduces the error of data predictions in time series analysis. This improvement is demonstrated by lower scores in squared error loss functions when employing regression model forecasting tasks, a standard metric for assessing performance. The Cycling technique achieves lower function scores, indicating a more accurate representation of the data. Finally, this study highlights the adaptability and customizability of time series analysis. Traditional preprocessing techniques often provide a one-size-fits-all solution, which may not be optimal for specific analytical tasks. In contrast, the Cycling Layers can be tailored to capture the regular features of different time series data by adjusting the cycling period. Furthermore, the architecture of the Cycling Self-Attention Layer allows for customization, enabling users to modify and train models specific to their objectives. This paper aims to (1) evaluate the effectiveness of different preprocessing techniques, including positional self-attention mechanisms and traditional statistical methods, in enhancing the performance of deep learning models for time series data, and (2) compare the performance of these methods, particularly in tasks related to anomaly detection and forecasting. Section 2 reviews the related work about the dataset and presents the theoretical framework for integrating positional self-attention mechanisms into autoencoders. Section 3 describes the methodologies employed, including the specific models and evaluation metrics used. Section 4 discusses the dataset used and the results of the research, comparing the performance of the positional self-attention method with traditional preprocessing techniques. Section 5 concludes the paper by summarizing the findings and discussing the implications for future research and potential applications.
## 7 Discussions and Future Directions
### Facilitating Practice and Standardization for 6G and Beyond-5G  
Telecommunications networking research and development often transition to practice via standardization. While 3 GPP introduces the notion, objectives, and requirements of the flying base station (called UxNB base station) for the upcoming 6G networking [7; 8], further standardization effort enabled by research and development in flying base station will facilitate practice and deployment of flying base station. The flying base station can also be particularly useful for 6G and beyond-5G to provide line-of-sight communication using its mobility, as the mmWave communication signal cannot propagate through physical barriers, unlike the signals we currently use which are in lower frequency bands.
# Noises in Double-Differenced GNSS Observations
## 2 Materials and Methods
### Spectral Analysis  
For the purposes of this paper, the L-S periodogram method was used for spectral analysis and to estimate the type of noise present in the DD data. This method has particularly broad applications in astronomy and geodynamics and allows the frequency spectrum to be calculated for non-uniformly sampled or intermittently sampled data. The gaps in the analyzed DD time series are mainly due to the signal blocking caused by the horizon obscuration for low satellites. On the other hand, during the formation of double differences, the reference satellite DDs are not calculated. This results in there being gaps in the days of several tens of minutes.  
The power spectrum of various natural phenomena can be approximated by the power-law rule:\[P(f)\propto f^{k} \tag{6}\]  
where \(P\) is a power spectrum, \(f\) is the signal frequency, and \(k\) is the spectral index. The spectral index value corresponds to specific noise in the analyzed signal. White noise is \(k=0\), flicker noise (pink noise) is \(k=-1\),and random walk is \(k=-2\)[49]. Noises with a fractional spectral index within the range \(-3<k<-1\) are called \"fractional random walk\" or \"fractional Brownian motions\" and are typical of nonstationary processes. Stationary processes with \(-1<k<1\) are called \"fractional white noise\" or \"fractional Gaussian\" [35]. The value of k can be calculated as the slope of a straight line fitted in the power spectrum after the transformation of frequency and power to the logarithmic scale. This simple method of estimating the spectral index \(k\) produces satisfactory results, especially for large time series lengths [29].
## 2 Chip Architecture
### ISC Chip Architecture and Circuits
The low-pass cut-off frequency f\({}_{\mathrm{CLP}}\) is determined by the bandwidth of the internal opamp and is given by g\({}_{\mathrm{m}}\)/2\(\pi\)C\({}_{1}\), where g\({}_{\mathrm{m}}\) is the transconductance of the CMOS internal operational transconductance amplifier (OTA). The low-pass cut-off frequency must be sufficiently higher than the carrier frequency to be tested and is chosen as about 10 MHz. The amplified signal is digitized with a comparator to produce a pulse signal  
Figure 4: Row selector for Communication Signal Bus. Figure 5: Bandpass amplifier and comparator. output. This approach is useful for the simplification of the total system because the external system can be implemented with digital circuits and software. On the other hand, for a long distance communication with weak optical signals, the analog waveforms of the amplifier outputs are digitized with high-sampling rate A/D converters and a digital equalizer should be applied for a better eye opening [12]. However, for 25-channel outputs necessary for the light source tracking, 25-channel A/D converters are necessary in the external system which results in a bulky system and large cost. The threshold voltage of the comparator is chosen as V\({}_{\text{COM}}\) so that the bandpass amplifier output is digitized at the zero-crossing point of the waveform.
## 3 Results and Analysis  
On the real and synthetic networks, take degree centrality, k-shell algorithm, WL algorithm, ego betweenness centrality, LLS algorithm and the proposed NBF algorithm as attack strategies and rank the nodes in the network by the five algorithms. Then, remove a fraction of top important nodes according to the ranking result in a static and dynamic manner. Analyze the changes of the maximum connectivity coefficient and the decline rate of network efficiency when the nodes are removed, and verify the effectiveness of the proposed algorithm at last. Static network attacks refer to the network nodes being removed in descending order of node importance calculated initially, regardless of the impact of network structure changes due to node removal. In contrast, dynamic network attacks  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & \(n\) & \(m\) & \(\langle k\rangle\) & \(C\) & \(L\) \\ \hline USAir & 332 & 2126 & 12.807 & 0.625 & 2.729 \\ Netscience & 379 & 914 & 4.823 & 0.741 & 6.026 \\ Infectious & 410 & 2765 & 13.488 & 0.456 & 3.631 \\ USAirport & 1574 & 28,236 & 21.901 & 0.505 & 3.113 \\ Yeast & 2375 & 11,693 & 9.847 & 0.306 & 5.094 \\ Power & 4941 & 6964 & 2.669 & 0.080 & 18.989 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic statistical properties of the six real networks, including network size (\(n\)), edge number (\(m\)), average degree \(\langle k\rangle\), network aggregation coefficient \(C\) and average shortest path length \(L\).  
mean that only the most important node is removed in each round of attack, and all node importance in the remaining network needs to be recalculated each time.
## 2 Materials and Methods
### K-Nearest Neighbor Algorithm and Statistical Analysis  
The machine learning algorithm k-nearest neighbor (KNN) was used for the classification and identification of the status of the cows. The k-nearest neighbor algorithm is a classical non-parametric method classification algorithm. In a KNN classifier, the model is not trained, but all instances are retained. The class of a new input is concluded by finding the k-closest adjacent training instances and computing the class connection of the training inputs. The new input is deduced as the class with the most vote among the k-nearest training instances in which \(k\) is a positive integer and is typically small. The main idea is if the majority of the k-closest values of the input samples belong to a specific category or class, then the new samples with closely similar values will belong to the same category. The KNN is easily implemented and suitable for multi-classification problems.  
For the classification process, the cross-validation method of leave-one-out was used, meaning that the data of the three individual cows were used for the training set, and another set of data from another individual cow was used for validating the set. The evaluation for the effect of classification was based on the accuracy, Kappa coefficient, and precision. The accuracy and precision were calculated as follows: (1) accuracy = (TP + TN)/ (P + N), and (2) precision = TP/(TP + FP); where: TP as true positive is the number of positive samples correctly classified as positive samples, TN as true negative is the number of negative samples correctly classified as negative samples, FP as false positive is the number of negative samples incorrectly classified as positive samples, P as the number of positive samples, and N as the number of negative samples. The Kappa coefficient is a calculation of classification accuracy. Kappa coefficient between 0 to 0.2 represents a very low level of consistency, 0.41 to 0.60 represents medium consistency, 0.61 to 0.80 represents a high degree of consistency, and greater than 0.80 represents complete consistency to the true value [54].  
The data preprocessing, preparation, and algorithm programming after the acquisition of data were conducted using python 3.7.0 [55]. The commonly used scientific computing toolkits, such as matplotlib and scikit-learn, were also based on python 3.7.0.  
The Pearson correlation between the parameters and the conditions of dairy cows was analyzed using the scientific plotting tool in python. The results of the practical application were analyzed by using the general linear model of SAS, and if differences were detected, data were further analyzed using Duncan Multiple Range Test (DMRT). The statistical significance level for all comparisons of the groups was set to 0.05 if not otherwise indicated.