esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Installation of GNSS CORS and Corner Reflectors  
Following the application of the proposed methodology, a campaign for the installation of the GNSS CORS and corner reflectors begun. In total, twelve CRs and six CORS were installed in six AOI. In particular, the instrumentation of each location includes: (a) two triangular trihedral corner reflectors (one for each pass) of 1.5 m inner dimension, (b) an individually calibrated GNSS geodetic grade antenna (Trimble(r) GNSS-Ti v2 Choke Ring, Sunnyvale, CA, USA) equipped with radome, (c) a GNSS receiver (Trimble(r) Alloy, Sunnyvale, CA, USA), (d) a tiltmeter (Jewell D711-A-S, Manchester, NH, USA), (e) a weather station (VAISALA PTU307, Helsinki, Finland) and (f) a solar panel that provides power supply to gel batteries, which are housed inside of an stainless steel enclosure.  
Figure 19: The Line of Sight between GNSS antenna and the two CRs.  
Figure 18: The elevation profile between the GNSS antenna and the CR for the descending pass. The distance between them is 45 m, greater than the constraint of 30 m.  
The GNSS monument was implemented using the shallow-drilled braced monument (SDBM) type [55], which is composed of four solid stainless steel rods (304) anchored at a drilling depth of 2 m--using one of the highest-quality epoxy resins--to form a quad-pod. The GNSS antenna was mounted on an SCIGN mount, the highest standard for permanently mounting GNSS antennas [56], levelled and oriented to True North [57].  
The corner reflector's base was installed on top of a layer of reinforced concrete and was anchored with three stainless steel rods at a depth of 1 m, using the same epoxy resin fastening as the GNSS monumentation. The CRs were oriented and levelled towards Sentinel-1 satellite's pass (ascending and descending) line of sight, using a Brunton(r) Axis Transit compass (\(\pm\)0.5\({}^{\circ}\)), thus, increasing the likelihood of achieving the highest degree of RCS values. Finally, appropriate fencing was installed to mitigate animal disturbances and unauthorized access. An example of a collocation is shown in Figure 20.
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 3 Machine Learning for Time Series Prediction  
Predicting historical time sequences is an essential aspect of ML; it belongs to supervised learning approaches and is widely used in data science, applied in various domains. Several ML techniques, including regression, ANN, KNN, SVM, random forest, and XGBoost, can be used to predict time series. ML-based forecasting models have found wide application in time series projects required by various organizations to facilitate predictive allocation of time and resources.  
ANN can help historical series prediction by eliminating the instantaneous need for extensive feature technology processes, data scaling procedures, and the need for stationarity and differentiation of historical series data. RNN is suitable for supervised learning tasks when data are available in a temporal sequence. It can remember the historical information to estimate future time-series data. The RNN algorithm is trained based on the previous data of the historical series into the input level. The network's connectivity is adapted depending on the difference between the actual and expected outputs over the network. Before configuring the network, the operator must determine the network hidden layers to size and the training termination process.  
In prediction, the past information is used to predict what will follow, and the following information is predicted, relying on what happened. The temporal sequence addsa temporal dependency between historical information. This dependency is under limitations and is structured to provide an additional source of information. Historical time sequence prediction is a method of predicting information about a historical series. It expects the following information by analyzing the past information if the future information is similar to the historical information. It can be applied in several use cases, such as resource allocation, network traffic, weather forecasting, control engineering, statistics, signal processing, and business planning. These are just a few of the many possible applications for time series forecasting.  
In real-world time series--i.e., forecasting weather, air quality, and network traffic flow are scenarios based on IoT devices, such as detectors--abnormal time form, missing data, high noise, and complicated correlations are multivariate and current limitations of classical prediction techniques. Such methods usually depend on noise-free and perfect information for good performance: missing data, outliers, and other erroneous features are generally not supported. Time series prediction starts with a historical time sequence, and experts investigate the historical information and temporal decomposition models, such as tendencies, seasonal models, periodic models, and symmetry. Various sectors, such as commerce, utilize historical time sequences prediction to assess potential technological complexity and customer needs. Temporal series data models can have many variations and perform various random processes [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21].
## 1 Introduction and Background  
### Problem Overview  
In recent decades, various forms of computer networks, along with other developments in technology, enabled almost any kind of information imaginable to be produced and distributed in unforeseen amounts. This _almost ubiquitous_ availability of vast amounts of information at our fingertips enriched our lives, and continues to do so. However, despite very impressive developments, we have not yet perfected the art of transmitting this much information: we have to deal with bandwidth limitations. Moreover, by 2013, growth of global mobile data traffic is projected to rise by sixty-six times (projection relative to 2008, as estimated by International Telecommunication Union in April 2010), creating discussions on a possible \"bandwidth crunch\" [1,2]. Having limited bandwidth leads to long download times for large amounts of data and can have an impact on decision making, task fulfillment, performance and various usability aspects, such as effectiveness, efficiency, and satisfaction (e.g. [3, 4, 5, 6, 7, 8, 9]). Thus, we need to filter information. The word filtering in this context means having to selectively use information both for designing visual displays and for deciding what to transmit when. Therefore, filtering task is both a technical challenge and a cognitive one--_i.e._, we work on algorithms that will manage the data intelligently, but at the same time we need to understand what our minds can process to customize the technology and the design accordingly. If we can _match_ the cognitive limitations with bandwidth limitations; we may find a \"sweet spot\" where we can handle the data just right and possibly improve both human and machine performance considerably.
## 6 Experimental Results and Analysis
### Influence of V-Rib Attack Angles (\(\beta\))  
#### 6.2.1 Heat Transfer Rate Results  
As demonstrated in Figure 10, the heat transfer (\(Nu\)) enhances with the Reynolds number (\(Re\)). The increased \(Nu\) for heat transfer improvement exceeds that of a plain tube, which suggests that the heat exchange tube into which twisted tapes are installed demonstrates greater sensitivity to air velocity. A continuous V-rib twisted tape (CVRT) generates stronger longitudinal vortices and more air mixing, which leads to marked heat transfer augmentation. At a constant \(Re\), the heat transfer rate decreases as the attack angle (\(\beta\)) increases. A twisted tape with \(\beta\) = 15\({}^{\circ}\) yields the most increase in heat transfer, with a  
Figure 9: Comparison of predicted and experimental \(Nu\) and \(f\) values of plain tubes. (**a**) \(Nu\). (**b**) \(f\).  
Nusselt number exceeding that of a TT, and CVRTs with attack angles (\(\beta\)) of 30\({}^{\circ}\) and 45\({}^{\circ}\) by 29.1%, 4.6%, and 7.1%, respectively. Figure 10 demonstrates that the attack angle affects the thermal augmentation of the twisted tape at elevated Reynolds numbers. This occurs since at greater Reynolds numbers, the air velocity and turbulence intensity within tubes containing CVRT inserts increase, resulting in improved heat transfer relative to a plain tube and a TT.
###### Abstract  
The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network's lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching-learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26's full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.  
Internet of Things; WSN; clustering; route selection; metaheuristics; fitness function; network lifetime; energy efficiency +
_Sustainability_
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 4 Comparison between Transformations
### Nonparametric Repeated Measure Factorial Test  
Treatment effects in the presence of factors can be examined by employing the nonparametric repeated measure factorial test [31; 32] for a pool of time series of different characteristics. Thus, the effect of data transformation (treatment) can be examined by using this test under different characteristics of a time series.  
Let us assume that we have \(K\) time series in the pool with series code \(A_{k},\ k=1,\ldots,K\) and for each of the series \(RMSE_{h}\) for \(h=1,3,6,12\) are computed. If the interest lies on exploring the effect of transformation for the skewness property of time series, we essentially perform the test for treatment effect (transformation) for categories of skewness properties of these time series. There are three factor levels of the factor Skewness, namely Skew Negative, Skew Positive, and Skew Symmetric. Similarly, we will have two levels for the factor Normality (Yes = normal; No = not normal) and two levels for the factor Stationarity (Yes = stationary; No = nonstationary). To test the effect of transformation (No transformation, Standardisation, and Logarithmic transformation), we follow the procedures described below.  
First, we learn some basic characteristics of a time series such as normality, stationarity, skewness, and frequency. For example, the frequency of a time series can be learnt by examining the time of measurement: hourly, daily, weekly, monthly, or annually. We also classify time series into different categories via a series of statistical tests such as the Jarque-Bera test for normality [33], the KPSS test for stationarity [34], and the D'Agostino test for skewness [35].  
Secondly, the nonparametric repeated measure factorial test [31, 32] is used to test the effect of the transformation on RMSFE, across different categories where categories are defined based on Frequency, Normality, Skewness, and Stationarity.
## 5 System Architecture
### Data Link Layer  
Two users interact by alternately transmitting and receiving a joint waveform containing communications data and navigation reference sequences. Transmissions are scheduled every 100 ms, and receptions must be processed before the next transmission. This cycle is depicted in Figure 4.  
The current CHP2 implementation uses four antennas on each platform. This number is not fundamental and can be changed arbitraily in different implementations. In this testbed, we only use one antenna to transmit the communications payload, but a full implementation will use all available antennas to enable proper MIMO capabilities. Different navigation sequences are transmitted at different times from different antenna, as depicted in Figure 5. This creates the necessary spatial diversity to translate the ranging estimates into position and orientation estimates
## 2 DPS Modeling of LEO-SIN
### Performance Analysis
#### 3.3.1 The Time-Domain Envelope and PDF Analysis  
First, we select the Rayleigh model as the reference model, the PDF of the Rayleigh theoretical model is,  
\[f_{|u|}(r)=\frac{r}{\sigma^{2}}e^{(-\frac{r^{2}}{2\sigma^{2}})},\ r\geq 0. \tag{27}\]  
According to [PERSON]'s divergence (J-div) model, the similarity between the theoretical PDF and simulated PDF can be analyzed by the parameter of distance \(D_{J}\),  
\[D_{J}(P||Q)=\sum_{i}(P(i)-Q(i))\ln\frac{P(i)}{Q(i)} \tag{28}\]  
where, \(D_{J}\) denotes J-div, \(P(i)\) denotes discrete PDF of theoretical Rayleigh model, and \(Q(i)\) is a discrete PDF of the simulated model. Since the real part and imaginary part of the signals in RMEDS-SP satisfy the zero-mean Gaussian random process when the number of sinusoids tends to infinity, the PDF of RMEDS-SP simulated Rayleigh model would also obey Rayleigh distribution, then we have,  
\[Q(i)=\frac{i}{\sigma}e^{(-\frac{\sigma^{2}}{2\sigma^{2}})}. \tag{29}\]  
According to Equation (29) and the theory of infinite sine, \(\frac{P(i)}{Q(i)}=1\), it can be deduced that \(D_{J}(P||Q)\) is equal to 0, that is, the model conforms to the Rayleigh process.  
To characterize the fluctuations of simulation results caused by random phase and AOA better, we design a new first-order PDF of error based on the J-div model [28], that is,  
\[\overline{E}_{J}(P||Q)=\frac{1}{N_{s}}\sum_{n=1}^{N_{u}}\frac{1}{N_{e}}\sum_{ i=1}^{N_{e}}(P_{n}(i)-Q_{n}(i))\ln\frac{P_{n}(i)}{Q_{n}(i)} \tag{30}\]  
where, \(\overline{E}_{J}\) denotes the similarity between the average simulated PDF error and theoretical PDF error, \(N_{s}\) denotes the number of simulation times, and \(N_{e}\) is the number of inter-sample points for calculating the PDF of DPS in the simulations. Figure 8 shows the results of PDF of different models.  
As Figure 8 shows, the PDF of the signal's envelope model by RMEDS-SP is more similar to the theoretical PDF of Rayleigh distribution than those of MEDS-SP. The results substitute into Equation (30), we can get the modeling error of each model, as Table 1 shows.  
It can be seen from Table 1, when the number of sinusoids and the number of simulations time are the same, the similarity \(\overline{E}_{J}(P||Q)\) by MEDS, RMEDS, MEDS-SP, and RMEDS-SP are 0.007036, 0.006969, 0.007172, and 0.006807. That shows that the modeling error by the proposed RMEDS-SP has better performance than other models.
# Information Propagation and Bionic Evolution Control of the _Sebar_ Model in a Swarm System
## 6 Experiences
### Stability Analysis and Parameters Influence
As can be seen in Figure 5e, the speed of information dissemination is accelerated with the increase in \(\langle k\rangle\). However, when \(\langle k\rangle\geq 5\) exceeds a certain threshold, the convergence rate gradually slows down. The final proportion of state \(B\) also increases with \(\langle k\rangle\), but the degree of change is relatively small. Compared with other parameters, the impact of \(\langle k\rangle\) on the final proportion of individuals in state \(B\) is greater than that of \(p_{3}\).
## 5 Applications
### Standardization and Performances Assessment
With improving data analysis methods for heterogeneous structures, and with further advances in the development of experimental set-up components, we foresee TD-DOS to address pressing needs like depth sensitivity across various domains of highly scattering media (e.g., optical tomography of deep tissue).
## 3 Methodology and Data
### Network Mixing Patterns  
#### 3.1.1 The Evolution of Urban Agglomerations  
Drawing on the Flow Space Theory of intercity relationships, urban agglomeration emerges from resource exchanges between cities. These resource exchanges generate both attractive and repulsive forces, thereby shaping intercity relationships. The structural dimension is manifested in the hierarchical organization of urban networks, where cities occupy different positions within the core-periphery structure, while the economic dimension is reflected in the heterogeneous distribution of socioeconomic attributes across cities. This study aims to investigate how cities form urban agglomerations through their structural features and socioeconomic attributes. Network mixing patterns describe the tendency of nodes to connect with other nodes based on certain attributes. Following [PERSON]'s framework, mixing patterns reveal how nodes with similar or different characteristics tend to connect, providing insights into network formation and evolution. Based on these theoretical foundations, this study hypothesizes two distinct mixing patterns in urban agglomerations, as shown in Figure 2.  
As shown in Figure 2. First, the centripetal mixing pattern suggests that cities are more likely to establish connections with those having complementary functions regardless of their comprehensive strength, facilitating the formation of specialized divisions of labor and functional complementarity within the urban system. Second, the balanced mixing pattern indicates that cities tend to establish connections with others possessing similar comprehensive strength, reflecting their preference for comparable partners in resource exchange and cooperation. This study aims to explore the presence and operation of these two mixing patterns across different urban networks, examining whether both patterns exist and function distinctively in various urban networks, or whether a single pattern predominates. By recognizing these mixing patterns, this research contributes to identifying the formation mechanisms of intercity relationships and understanding of urban network evolution.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 1 Introduction
To the best of our knowledge, this work is the first attempt to estimate the E2E delay distribution using the collected network topology information and the Lyapunov central limit theorem so that it allows different one-hop delay distributions for groups. * The simulations were conducted to evaluate the proposed algorithm and compare with other related works. The evaluation results show that the proposed DDS scheme leads to better performance than existing algorithms with regard to the achieved DSR and energy consumption in a variety of network scenarios, including different delay bounds, required DSR, and event rates. The rest of this paper is organized as follows. Section 2 introduces related work. Section 3 presents the network model and defines a maximization problem that aims to find the maximal duty cycle interval while satisfying the given delay bound requirement. Then, we describe the delay-constrained duty-cycle scheduling algorithm in detail in Section 4. Evaluation of the performance and a comparison with existing algorithms are shown in Section 5. Finally, this paper is concluded in Section 6.
# Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
## 2 Materials and Methods
### Gaussian Distribution Model Conceptualization
#### 2.2.1 Data Normalization Operation  
Initially, each parameter that integrates the data set collected from aquaculture facilities was normalized between 0 and 1. This normalization operation is given mathematically by  
\[P(z_{k})_{nom}=\frac{P(z_{k})-\min\left(P\big{(}z_{j}\big{)}\right)}{\max\left( P\big{(}z_{j}\big{)}\right)-\min\left(P\big{(}z_{j}\big{)}\right)}. \tag{1}\]  
Here, \(P\big{(}z_{j}\big{)}\) represents a specific monitored parameter, where \(z_{j}:j=1,\ldots,k\) is the position vector.
# Modeling and NVH Analysis of a Full Engine Dynamic Model with Valve Train System
## 3 Modeling and Analysis
### Valve Train System Model
However, in uncoupled approaches, the components are generally simplified as rigid bodies connected by springs with constant stiffness, although the stiffness of the rocker arm varies continuously during the valve event [18]. In order to accurately simulate the rocker stiffness, the condensed rocker FEM model is adopted as shown in Figure 15. The rotational and translational DoFs of three nodes of the pivot point, the roller center, and the center of contact circle are retained. A typical HLA, as shown in Figure 16, is used to maintain zero valve clearance, containing a plunger, outer housing, supply chamber, check valve, return spring, and high-pressure chamber (HPC). The lubricant flowing into the HPC is controlled by the check valve at the bottom of the plunger. The check valve is normally closed. After a certain amount of lubricant is lost, the check valve opens to refill the HPC. Due to the extension of the plunger, the valve clearance decreases. When the engine valve enters the lift phase, the check valve is closed, and the pressure in the oil chamber keeps the plunger in place and maintains an effective length to support the rocker [19]. Figure 14: The topology of the SVT system. Figure 15: FEM model of the rocker. The lubricant pressure in the HPC is governed by the first order differential equation:  
\[\frac{dP(t)}{dt}=\frac{\beta}{V(t)}\bigg{(}-\frac{dV(t)}{dt}+Q_{cv}(t)+Q_{l}(t) \bigg{)}, \tag{14}\]  
where \(\beta\) is the bulk modulus, \(V\) is the HPC volume, \(Q_{cv}\) is the volumetric flow through the check valve, and \(Q_{l}\) is the leakage through the clearance between the plunger and the outer housing. The volumetric flow through the check valve can be obtained by:  
\[Q_{cv}=\pm C_{d}S\sqrt{\frac{2\big{|}\Delta p\big{|}}{\rho}}, \tag{15}\]  
where \(C_{d}\) is the discharge coefficient, \(S\) is cross-sectional area of the lubricant flow, \(\rho\) is the lubricant density, and \(\Delta p\) is the pressure difference between the HPC and the supply chamber. The leakage through the clearance between the plunger and the outer housing can be obtained by:  
\[Q_{l}=\pm\frac{\pi d\delta^{3}}{12\mu}\frac{\big{|}\Delta p\big{|}}{L}, \tag{16}\]  
where \(d\) is the plunger diameter, \(\delta\) is the radial clearance between the plunger and the outer housing, \(\mu\) is the dynamic viscosity of the lubricant, and \(L\) is the leakage length.