esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
Each of these Pareto curves can have one or more constellations assigned to it and are equivalent to what the GA considers their quality to be. The closer these Pareto curves are to the optimality point, the more desirable they are. The GA then takes the solutions within the best Pareto curves; they are defined as the \"parents\" and use them to create new solutions. Each constellation is encoded as a binary string. By recombination of the strings of selected \"parents\", new constellations, called \"children\", are created and will define the next generation with the previous best Pareto curves. Furthermore, a random mutation is introduced by randomly changing a single bit. A crowded-comparison operator is applied in order to incentivize diversity in the selected solutions leading the algorithm toward a uniformly spread-out Pareto-optimal front. This procedure is repeated at each generation until convergence has been hit or a select number of generations has been reached. The NSGA-II scheme is well-established and has been used in various constellation designs [36; 37; 38; 39; 40; 41].
## 1 Introduction
### Summary  
Our purpose in this work is to model the time-correlated error coupled in the clock drift estimations in the VT loop using the Allan variance. Then, an improved KF will be proposed based on the model. Furthermore, the field test experiments will demonstrate the performance of the proposed algorithms. More specific explanations will be described in the subsequent sections.  
The remaining of this paper is organized as follows. Section 2 introduces the process of modelling the proposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF) including the basic concepts of the Allan variance, power spectrum density (PSD) related to the clock estimation random noise, time-correlated error model based on 1 st-order Gauss-Markov (GM) process, the system model of the A-VTTCES-KF, the adaptive algorithm in terms of the process covariance matrix, and the numerically controlled oscillator (NCO) updating method; the field test results, using the GNSS SDR with the proposed A-VTTCES-KF algorithms plus the ordinary adaptive KF algorithms, and the commercial U-Blox receiver, are provided in Section 3; Section 4 is devoted to some analysis and discussions associated with the field test results; the last Section 5 concludes this work.
## 1 Introduction  
Container shipping is an important form of international trade logistics, and a great deal of goods are transported from the origin to the consumption place far across the ocean by container shipping [1]. However, the spread of COVID-19 has had a profound impact on container shipping and will even overturn the trend of container shipping in the future [2]. Since the third quarter of 2020, there has been a global shortage in the supply of empty containers, and major shipping companies are short of shipping space [3]. The advance booking period of Sino-European routes is about two weeks, and even the Sino-American routes are sold out. The empty container supply and lack of capacity directly leads to a rapid rise in container service charge, and the Shanghai Containerised Freight Index (SCFI) and the Freight Baltic Index (FBX) are obviously up. For example, the price of a container shipped from China to Europe has risen from $2000 to $15,000, a 7.5 times higher transportation cost than before [4]. The rapidly rising price of container transportation has brought heavy burden to international trade, and the price of all kinds of goods transported by container has also risen sharply. Therefore, improving the efficiency of container shipping is an important way to better the performance of international trade and reduce trade costs. Previous practitioners and scholars have conducted studies on improving the efficiency of container shipping from many aspects [5, 6, 7, 8]. This study focuses on the forecasting of port container throughput, because the accurate forecasting results of port container throughput can provide decision support for shipping companies, port owners, freight forwarders, and other container shipping participants. With the development of machine learning, a variety of sophisticated forecasting models were proposed based on machine learning algorithms. However, many up-to-date forecasting methods were not applied to forecast container throughput. It is necessary to compare the performance of advanced machine learning methods and conventional methods on container throughput forecasting. Therefore, the research question of this study is which of the existing forecasting methods is more accurate in forecasting container throughput.  
The main contributions of this study are as follows. First, the performance of nine different time series forecasting methods on a single time series is compared, including conventional methods and machine learning methods. Secondly, the forecasting method GRU, which is accurate for short time series forecasting results, is obtained through comparison, which provides experience for future forecasting research. Thirdly, it is found that the forecasting results of machine learning algorithms on short time series are not necessarily better than those of conventional methods, and the more complex models tend to produce less ideal forecasting results.
###### Abstract  
This paper describes the assessment of radiofrequency (RF) electromagnetic field (EMF) exposure from fifth generation (5G) new radio (NR) base stations in a commercial NR network in Bern, Switzerland. During the measurement campaign, four base station sites were investigated and the exposure induced by the NR massive multiple-input-multiple-output (MaMIMO) antennas was assessed at 22 positions, at distances from the base station between 30 m and 410 m. The NR base stations operated at 3.6 GHz and used codebook-based beamforming. While the actual field levels without inducing downlink traffic were very low (<0.05 V/m) due to a low traffic load and low antenna input powers of up to 8 W, setting up a maximum downlink traffic stream towards user equipment resulted in a time-averaged exposure level of up to 0.4 V/m, whereas the maximum extrapolated exposure level reached 0.6 V/m. Extrapolated to an antenna input power of 200 W, values of 4.3 V/m and 4.9 V/m, respectively, were obtained, which amount to 0.5-0.6% of the reference level recommended by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). In Bern, it was found that the impact of the NR network on the total environmental RF exposure was very limited; with maximum downlink, it contributed 2% on average. Finally, it was also concluded that extrapolation to the maximum exposure level can be done without prior knowledge of the radiation patterns, directly based on the measurement of the Physical Downlink Shared Channel (PDSCH) resource elements.  
codebook-based beamforming; measurement; mobile telecommunications; non-ionizing radiation; radiofrequency electromagnetic fields (RF-EMF); spectrum analyzer +
Footnote †: journal: Applied Sciences
## 2 Literature Review
### Train-Set Rescheduling Problem
In this model, there is a one-to-one responsibility relationship for train-sets, trip tasks, and maintenance. They designed a genetic algorithm to solve it. A case study shows that dispatching trains between stations in the hub can improve efficiency. The second scenario of TCPP is that if there are emergencies in railway operation, how to reschedule train-sets after the emergency. It is also called the rolling stock rescheduling problem (RSRP), which is similar in feature to the TCPP. [PERSON] et al. [17] studied the train driver recovery problem (TRDP), which is similar to the RSRP. They treat TDRP as a set partitioning problem and adopt a depth first search of the branch & bound tree to obtain integer solutions. They tested the method based on data from the Danish passenger railway. [PERSON] et al. [18] proposed a rolling stock management approach for the case of disturbed operations. The rolling stock decision is considered only for a period of time. After arranging the trains during the current period, the next period will be considered. Through this rolling horizon approach, they improve the solution efficiency. From the perspective of passenger service, [PERSON] et al. [19] proposed an integer programming model to determine which trains still operate after being disturbed. They tested the method on the Dutch railway. [PERSON] et al. [20] proposed a mixed integer linear programming rescheduling model including passenger demands. It is also suitable for train rescheduling problems with sudden changes in passenger demands. [PERSON] et al. [21] studied a mixed integer linear program model suitable for a busy corridor of the Dutch railway to obtain the short-turn scheme under disruption. The results show that the short-turn scheme is related to the disruption length and longer disruption length will cause more train cancellations. The research on the first scenario focused on rescheduling the train-set circulation plan under the existing timetable. Moreover, the research on the second scenario focused on adjusting the train-set circulation plan and timetable at the same time. The new timetable is different from the old timetable; therefore, the ideal train-set circulation plan corresponding to the two is also different. There are limitations in meeting the train-set demands of the new timetable by optimizing the train-set circulation plan on the old timetable. We refer to the idea in the second scenario to schedule a timetable and train-set circulation plan at the same time on the premise of meeting the needs of passengers.
# Carbon and Nitrogen Pools and Fluxes in Adjacent Mature Norway Spruce and European Beech Forests
## 3 Results
### Carbon and Nitrogen Stocks
#### Carbon and Nitrogen in Soil  
The soil carbon concentration was significantly (\(p\) < 0.001) lower in deeper mineral soil (10-40 cm) in the spruce stand compared to the beech stand. A higher C concentration in the beech stand was measured in the L horizon (\(p\) = 0.034) compared to the spruce stand. No significant differences were observed in the mass of fine soil throughout the soil profile in both forests, but due to the higher %C in the beech stand, a higher C pool was measured in beech forest soil (199 t\(\cdot\)ha\({}^{-1}\)) compared to spruce soil (114 t\(\cdot\)ha\({}^{-1}\)). In both forests, 33% of soil carbon was present in LFH horizons (Table 3).  
The soil nitrogen concentration was significantly higher in all horizons in the beech soil compared to spruce soil (\(p\) = 0.016 for the L horizon, \(p\) = 0.006 for the FH horizon, \(p\) = 0.01 for 0-10 cm and \(p\) < 0.001 for 10-40 cm). The total N pool accounted for 4460 kg\(\cdot\)ha\({}^{-1}\) in the spruce stand and 8550 kg\(\cdot\)ha\({}^{-1}\) in the beech stand. The nitrogen pool in the LFH horizon accounted for 34% of the total soil pool in the spruce stand and 37% in the beech stand. Lower soil C/N was measured in the beech stand, except for the mineral soil from 10 cm to 40 cm.
## 3 Results and Discussion
### Evaluation Indicators  
In this study, we use precision, F1 score, accuracy and recall as evaluation metrics to assess the effectiveness of different network models in detection tasks targeting chili pepper images, here Equations (1)-(4) are the formulas for F1, accuracy, precision and recall, respectively.  
\[F1=\frac{2 TP}{2 TP+FP+FN} \tag{1}\]  
\[Accuracy=\frac{\text{TP}+\text{TN}}{TP+FP+TN+FN} \tag{2}\]  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
where _TP_ is the number of positive samples predicted by the classifier and the true result is positive, i.e., the number of correctly identified positive samples; _FP_ is the number of negative samples predicted by the classifier and the true result is negative, i.e., the number of incorrectly predicted negative samples; _FN_ is the number of positive samples predicted by the classifier and the true result is positive, i.e., the number of underreported positive samples; and _TN_ is the number of negative samples correctly identified. Thus, Accuracy denotes the proportion of correctly classified samples to the total number of samples,  
Figure 9: YOLO-chili Structure.  
Precision denotes the proportion of samples with positive predictions that are actually positive samples, Recall denotes the proportion of the number of actual positive samples in samples with positive measurements to the proportion of positive samples in the full sample, and \(F1\) is a weighted average of Precision and Recall.
## 3 Physical (PHY) Layer and Media Access Control (MAC) in IoT
### Transmission and Network Modes in IoT  
Transmission modes are selected with respect to the application constraints and requirements. In areas where deployment of devices is massive [24], it is recommended to use a low data rate and noise tolerant connectivity schemes. Similarly, a dense network uses low-power low-range communication. Sensors in combination with actuators use bi-directional data communication modes. Separate devices might use either transmitter or receiver, based on the application requirements. Similarly, mesh topology is generally employed for geographically scattered nodes, whereas for general home automation, industrial automation, etc., the central-coordinator-based star topology is preferred [25]. Z-Wave is used for geographically scattered IoT [26]. Its data rates are lower as compared to other IoT-enabling technologies, but its networking complexity is reduced because of its 232 fixed number of hops for data transfer and control in an IoT.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Component(s)** & **Area** \\ \hline Market [11] & Smart Home, Connected Homes, Smart Health, Smart Cities, Smart Grid and Energy distribution, Surveillance \(\ldots\) & Personal, Local, Global \\ \hline Data Acquisition [12] & Sensors and actuators: pressure, temperature and humidity sensing, GPS, measurements, smartphones \(\ldots\) & Personal, Local \\ \hline Interfacing [13] & Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), DECT/DECT-ULE, \(\ldots\) & Personal, Local \\ \hline Connectivity and Integration [13] & Data sensing, Economics and Stats, GIS \(\ldots\) & Local, Global \\ \hline Data Analytics [19] & Machine Learning, Statistical Analysis, Data mining, Big Data handlers \(\ldots\) & Local, Global, Security, computing, Management \\ \hline Applications and Software [20] & Clouds, Data centers, Local observatories, Software \(\ldots\) & Global, Selective/User defined \\  & Defined Networking (SDN), Modular cities connectivity, Service Oriented Architecture (SOA) \(\ldots\) & \\ \hline Core Services [21] & Energy, Health, Entertainment, Industry, Production, Transportation, Communication \(\ldots\) & Local, Global, sometimes personal \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components based on the consumer market.
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Parameters Used in Ensemble Model for Attack Detection and Noise Reduction
#### 5.3.5 K-NN  
Although the K-NN model can perform classification most intuitively, its computational complexity is very high due to the complexity of features and the number of entries in a given dataset. Therefore, we set a lower criterion of 3 than the default value of 5, which reasonably compromises computational performance. Also, these shallow criteria make the model more responsive to rapidly changing data patterns.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 2 Materials and Methods
### Characterization of Canopy-Level Photosynthetic Efficiency
#### 2.3.2 Calculation of Effective Quantum Yield and Electron Transport Rate  
For data derived from hyperspectral images (HSI), the potential maximum SIF value was estimated for each of four combinations of genotype and treatment. In each genotype-treatment combination, a regression model was used to fit SIF measurements over reciprocals of PAR, and the model's y-interception (the reciprocal of PAR equaled to zero) was treated as the maximum SIF value for all plants in that combination. An exponential model was used for the two control treatment combinations, whereas three different models were used for the two Diuron combinations due to a lack of obvious data distribution patterns (likely due to damage to the photosynthetic apparatus and resulting non-photochemical quenching). The three models for the Diuron combinations included a linear regression model, quadratic regression model, and average model that used the mean value of SIF measurements as the maximum SIF. After obtaining the maximum SIF value for each approach, the effective quantum yield of PSII (\(\phi_{PSII}\)) and photosynthetic ETR were calculated using Equations (3) and (4) for data points in each combination.  
\[\phi_{PSII}=\frac{SIF_{m}-SIF}{SIF_{m}}=1-\frac{SIF}{SIF_{m}} \tag{3}\]  
\[ETR=PAR\times\phi_{PSII}\times A_{leaf}\times R_{PSII}, \tag{4}\]  
where \(\phi_{PSII}\) was the effective quantum yield of PSII for a SIF value (\(SIF\)). \(SIF_{m}\) was the estimated maximum SIF value for a genotype-treatment combination. \(A_{leaf}\) was the leaf absorbance of incident light, and a typical value (0.84) for C3 species (e.g., cotton) was used in this study [50; 51]. \(R_{PSII}\) was the distribution ratio of absorbed energy between photosystem I (PSI) and photosystem II (PSII), which was assumed as an equal distribution (0.5) [52].  
For PAM fluorometry data, the maximal fluorescence intensity and PAR were obtained, so \(\phi_{PSII}\) was calculated using Equation (5) [53], while ETR was still calculated using Equation (4).  
\[\phi_{PSII}=\frac{F_{m}^{{}^{\prime}}-F_{s}^{{}^{\prime}}}{F_{m}^{{}^{\prime} }}=1-\frac{F_{s}^{{}^{\prime}}}{F_{m}^{{}^{\prime}}}, \tag{5}\]  
where \(\phi_{PSII}\) was the effective quantum yield of PSII. \(F_{s}^{{}^{\prime}}\) and \(F_{m}^{{}^{\prime}}\) were the steady state and maximal fluorescence intensities under actinic light.
# Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
## 2 Related Work  
Graph Representation learning is related to a large spectrum of works, from kernel algorithms to graph neural networks.  
Kernels methods. Graph kernels have become a well established and a widely used technique for learning graph representations [4; 5]. They use handcrafted similarity measures between every pair of graphs. Some are restricted to discrete labels [6; 7], while others can handle continuous attributes [8; 9; 10]. The main drawback of kernel methods is the computational burden of building and storing the kernel matrix, which has quadratic complexity unless using approximation techniques.  
Infomax Principle hypothesizes that good representations maximize mutual information between the input data and its embedding. Deep Graph Infomax [11] and GraphSAGE [12] rely on negative sampling to build an estimator of Mutual Information (MI). It is used to produce node embeddings for solving a classification task. InfoGraph [13] uses the same estimator to produce embeddings for entire attributed graphs.  
Graph2 Vec [2] uses the same MI estimator in combination with Wesfeiler-Lehman procedure (see Section 3) to produce graph embeddings. It was originally inspired by languages models (especially Word2 Vec) and considers node embedding as the vocabulary used to \"write\" a graph.  
Graph coarsening. The aim of graph coarsening is to produce a sequence of graphs of decreasing sizes; it can be done by node pooling, as with [PERSON]'s algorithm [3], or by node decimation, like for example Kron reduction [14; 15]. Coarsening can be combined with edge sparsification [15; 16], so as to reduce the density of coarsened graphs. In another context, DiffPool [17] performs graph coarsening using clustering but it learns the pooling function specific to each task in a supervised manner. MinCutPool [18] also relies on clustering by optimizing a relaxed version of minCut objective, and provides high quality partitioning of the graph.  
Graph Neural Networks (GNNs). Developed after the renewed interest in Neural Networks (NNs), they are known to offer interesting graph embedding methods [1], in addition to solving several graph-related task, see [19] (and references therein) for a survey, and specifically the popular Chebyshev GNN [20], GCN [21] and GAT [22]. Stacking them to increase the receptive field may raise scalability issues [23].  
Still, it has been shown that some easy problems on graphs (diameter approximation, shortest paths, cycle detection, s-t cut) cannot be solved by a GNN of insufficient depth [24]. By combining GNNs with coarsening, e.g., [3], and node pooling, e.g., [14; 17; 25], those impossibility results no longer apply. This combination thus helps in designing a method encompassing all structural properties of graphs.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 1 Introduction
### TMA Features  
As shown above, the periodic modulation of a TMA is a nonlinear operation that generates sideband signals radiated at frequencies shifted from the carrier frequency at multiples of the time-modulation rate \(f_{0}\). The time parameters which govern the TMA (specifically, the switch-on durations \(\tau_{n}\) and the switch-on delays \(\delta_{n}\)[7; 8]) can be optimized to:  
1. exploit the fundamental pattern only --at the carrier frequency \(f_{c}\)-- with the aim of achieving ultra-low side-lobe level (SLL) while minimizing the SR, or
2. profitably exploit the harmonic patterns, endowing the TMA with smart antenna capabilities.  
Hence, and according to the second point, by adapting the harmonic patterns to the wireless channel, and by combining its corresponding outputs, TMA are able to perform adaptive beamforming with the benefit of using a single RF front-end. In this regard, the fact of avoiding the utilization of as many RF chains as the number of multipath components to be exploited, offers a series of key advantages:  
* Hardware simplicity, with the subsequent impact on the size and the cost of the system.
* Power consumption, as it has been remarked and properly quantified in recent papers like (Table I, [2]) and (Tables I and II, [3]), which have also proposed hybrid analog-digital architectures for beamforming. Indeed, in a fully digital implementation of an linear beamforming network (LBFN), the number of required RF chains, \(L\), must be equal to the number of antenna elements \(N\). In practice, however, a relationship \(N\gg L\) is often preferable due to a number of reasons, the power consumption per RF being the front-end one of the most important ones.
* Nonexistence of issues related to synchronization, phase coherence or coupling between different RF chains.  
In summary, the application of periodic pulses at the antenna level has the effect of converting space diversity into frequency diversity and, thus, a single RF front-end is enough to exploit such a space diversity.