esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Pipeline Transportation System Scheduling  
Scheduling in pipeline transportation systems can be broadly categorized into static and dynamic scheduling. Static scheduling establishes the task sequence and timing at the initial stage, resulting in a relatively straightforward process; however, due to its rigidity, it is often incapable of responding effectively to unexpected changes or anomalies. By contrast, dynamic scheduling adapts task sequences, transportation routes, and related plans in real time based on up-to-date information and specific conditions [15]. Research on scheduling methodologies generally falls into four main categories: mathematical programming, simulation optimization, heuristic algorithms, and machine learning. Among these, mathematical optimization techniques have been extensively studied in the literature [16; 17; 18], and the associated scheduling problems have been proven to be NP-hard [19]. Consequently, many studies have employed heuristic algorithms to improve the computational efficiency of mathematical programming approaches [20]. Simulation optimization, which models dynamic system environments and integrates dispatching rules for performance evaluation, has been widely applied in practical settings [21]. Additionally, machine learning techniques have been leveraged for predicting operation durations, identifying bottlenecks, and autonomously adjusting schedules.  
In the context of pipeline transportation, approaches based on mathematical programming and heuristic algorithms have been proposed, including the precise modeling of internal fluid flow to improve pipeline stability and efficiency [22]. The developed decision support system (DSS) framework aims to advance pipeline transportation optimization by improving throughput capacity, optimizing schedules to reduce total transportation time, and incorporating buffering mechanisms [23]. Additionally, an automated pipeline design system integrated within a CAD environment was introduced, employing network optimization to develop pipeline routing systems [24]. In crude oil scheduling, smart field (SF) technologies have been utilized to reduce oil switching frequency, thereby decreasing transportation costs [25]. Furthermore, a hybrid modeling approach combining mechanical and data-driven models was proposed to predict the operating parameters of oil and gas pipelines, facilitating optimized scheduling decisions [26]. [PERSON]. [27] proposed a novel scheduling framework utilizing Boolean satisfiability (SAT) and mixed integer programming (MIP) methods. Within this framework, schedulers must make real-time decisions based on partial information, lacking full knowledge of future tasks or constraints.  
This approach addresses the challenge of effectively allocating limited resources amid continuously changing and uncertain conditions.
# Departure from Flux-Gradient Relation in the Planetary Boundary Layer
## 2 Methodology
### Data Selection and Filtering  
The FINO3 dataset was selected based on the entire lidar measurement campaign, from September 2013 to October 2014. For the Hamburg site, we selected the first two months of measurements from 6 April 2011 to 2 June 2011 where the lidar was operating without breaks and properly aligned with the north.  
For both sites, the data were filtered according to the carrier-to-noise ratio (CNR), which can be used as a quality measure of the \(v_{r}\) estimation. Values with CNR higher than \(-29\) dB are considered valid in this work. Only wind profiles with valid data at all heights up to 500 m were selected. In addition to the CNR threshold, data were excluded when the radial velocity greatly exceeds the median value over a 30 min period, i.e., \(|v_{r}-\delta_{r}|>6\) m s\({}^{-1}\).  
From the observations, we computed 30-min statistics throughout this work. From a sample of 10,926 30-min periods at FINO3, we applied the above filters to obtain 76.2% of valid data (8323) up to 500 m. At the Hamburg site, 2719 30-min periods were filtered so we obtained 40% of valid full profiles (1089).  
At FINO3, high-frequency measurements from sonic anemometers were not available and so we could not directly compute the Obukhov length (\(L\)). Hence, we used the potential temperature difference as a proxy for atmospheric stability. The potential temperature difference is defined as \(\Delta\Theta=\Theta_{30m}-\Theta_{buoy}\), where \(\Theta_{30m}\) is the potential temperature using concurrent air temperature measurements at 30 m and \(\Theta_{buoy}\) is the water temperature measurement at 6-m depth from a nearby buoy. The criterion for stability classification reads simply as \(\Delta\Theta<0\) K for unstable conditions and \(\Delta\Theta>0\) K for stable conditions.  
At Hamburg, sonic anemometer measurements at 50 m represent surface-layer conditions [12]. Therefore, we computed \(L\) using these measurements and defined stability regimes as \(|L|>500\) m representing neutral, \(0\) m \(\leq L\leq 500\) m stable, and \(-500\) m \(\leq L<0\) m unstable conditions.  
For FINO3, the wind direction reconstructed from the lidar and a wind vane at 100 m had an offset of -11.7\({}^{\circ}\)[13]. Thus, we apply this offset in our computations. At Hamburg, the lidar was aligned with the north for the selected period, so no offset was applied.
# Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology
## 3 Failure Physical Analysis for Signal Selection
### The Relevance Analysis between Failure Mechanism and Output Signal  
Due to the external load, the machining equipment will gradually be damaged during operation. The damaged machining equipment will have a certain influence on the output signal during the cutting process. The relationship between external load, output signal, and machining equipment damage is shown in the Figure 3. In the figure, \(Z(t)\) indicates the external load acting on the machining equipment over time, \(U(t)\) indicates the degree of damage of the machining equipment over time, and \(X(t)\) indicates the change of the output signal of the cutting process with time [49].  
Figure 2: Signal acquisition principle.  
\(Z(t)\), \(U(t)\) and \(X(t)\) are both quantities that change over time. And their relationship can be expressed as Equations (1) and (2).  
\[U(t)=\phi(Z(t)) \tag{1}\]  
\[X(t)=y(U(t)) \tag{2}\]  
It can be seen from the equation that the relationship between the output signal represented by \(X(t)\) and the degree of damage represented by \(U(t)\) depends on the mapping affected by the machining equipment structure and the failure mechanism. In addition, the physical properties of the machining equipment material itself are closely related to its damage. The damage is the characterisation of the microscopic physical process of the machining equipment material. And the process signal is a macroscopic reflection of the machining equipment state change process.  
For the common defects in the cutting process, the relationship between the failure mechanism and the process signal is summarised. Based on the summary results, the process signal that is most sensitive to a certain failure mode can be selected.
## 6 Conclusions  
The Chi-Chi earthquake is a complicated event that was caused by the reactivation of the Chelungpu fault whose geometry is quite complex and which occurs in a region of steep topography and dense vegetation. Five types of geodetic observations (GPS, levelling, benchmarks, InSAR and SPOT offsets) were combined to determine the fault slip distribution of the 1999 Chi-Chi earthquake. A methodology was established in order to precisely estimate the relative weights between the data sets, which is a critical step for the modelling. A wrong estimation will create an under or over-estimation of the slip distribution. For this purpose, the generalized ABIC formula was applied to estimate the relative weights between the observations and the constraint. The slip distribution is mainly determined by the GPS as presented in previous papers, however the incorporation of other geodetic observations constrains the model especially on the curved part of the fault. The preferred slip distribution including all the data sets and using the relative weights estimated by gABIC shows that the maximum slip was 12.6 m on the north curved part of the fault with a seismic moment of \(3.9\times 10^{20}\) N\(\cdot\)m, corresponding to a magnitude of \(M_{w}\) 7.6. The study of the moment and Coulomb Stress Changes show that most of the slip happened on the north part of the fault at shallow  
Figure 11: (**A**) Position of the chelungpu fault (in red) and the fault geometry in black. Dots are representing the aftershocks during the 6 months following the event. Larger dots: \(M_{w}\geq 5\), small ones \(3\leq M_{w}<5\). (**B**) A-B profile showing the aftershocks in depth (m). depths and that most the energy released by the event was transferred to the faults at the north of the area, which is now the most probable location of a future event. In the last decade, satellite images have dramatically improved especially with the Sentinel constellation and the evolution of atmospheric methods, as well as the densification of GPS networks. It is essential to take advantage of all these data, to take into account their quality and coverage by estimating precise relative weights and thus to establish methodology to precisely monitor the earth deformation. [PERSON] did the processing, performed the result analysis and wrote the manuscript. [PERSON] and [PERSON] provided suggestions on the processing and analysis and reviewed the manuscript. [PERSON], [PERSON] and [PERSON] provided information for gABIC and PSOKINV. [PERSON]-C.H. provided data resources. All authors have read and agreed to the published version of the manuscript. This research was funded by ESA-MOST DRAGON-4 project, grant number 32244. Part of the work was also supported by the UK NERC through the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET, Ref. : come30001) and the LICS project (Ref. NE/K010794/1). Some figures were made using the Generic Mapping Tools by Reference [39]. ERS data were provided by ESA and SPOT images by Airbus Defence and Space. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:
# Elemental Mercury Removal over CeO\({}_{2}\)/TiO\({}_{2}\) Catalyst Prepared by Sol-Gel Method
## 2 Materials and Methods  
### Catalyst Preparation  
Sol-gel is a widely used technology for producing metal oxides or other solid compounds from small organic or inorganic molecules. Sol is a colloidal suspension liquid and it could then turn into a gel-like material through gelatinification. The porous gel can be purified by a chemical method or high temperature treatment in order to obtain oxides with high purity. In this study, a series of novel powdery Ce/Ti catalysts was synthesized in order to remove the elemental mercury from the coal-fired boiler flue gas. The detailed operation procedures are shown in Figure 1. Butyl titanate (C\({}_{16}\)H\({}_{36}\)O\({}_{4}\)Ti) and cerium nitrate (Ce(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O) were employed as the precursors of Ti and Ce, respectively. A certain amount of butyl titanate was mixed with absolute ethanol to obtain Solution 1. Besides, cerium nitrate, absolute ethanol, nitric acid, and deionized water were mixed together to obtain Solution 2. Solution 1 was pumped into Solution 2 at the speed of 2 mL-min\({}^{-1}\) by a peristaltic pump. Then, the pale yellow sol could be obtained after a 3-h magnetic stirring. The pale yellow sol would be put into a drying oven at 105 \({}^{\circ}\)C for 48 h. Before being calcined in a 500 \({}^{\circ}\)C muffle furnace for 5 h, the sol had been grounded and sieved into 60-100 mesh. After all these procedures, the powdery CeO\({}_{2}\)/TiO\({}_{2}\) catalysts were eventually synthesized. As mentioned in the introduction part, the addition of vanadium could improve the performance of the catalyst. Considering this, vanadium triisopropyloxy oxide (C\({}_{9}\)H\({}_{21}\)O\({}_{4}\)V) was taken as the precursor of V, which would be mixed together with absolute ethanol, nitric acid, hydrochloric acid, and deionized water to obtain Solution 3. Solution 3 was slowly added to the pale yellow sol at 2 mL-min\({}^{-1}\) by using a peristaltic pump. After the same drying, grounding, sieving, and calcination processes, the powdery V\({}_{2}\)O\({}_{5}\)-CeO\({}_{2}\)/TiO\({}_{2}\) catalyst was obtained. The flow chart of the sample preparation is shown in Figure 1. Five CeO\({}_{2}\)/TiO\({}_{2}\) catalysts and one V\({}_{2}\)O\({}_{5}\)-CeO\({}_{2}\)/TiO\({}_{2}\) catalyst were synthesized in this study, including pure TiO\({}_{2}\), CeO.1 Ti, CeO.2 Ti, CeO.5 Ti, Ce1.0 Ti, and CeO.5 Ti-V. The number in the name of the catalyst represents the mass ratio of cerium dioxide to titanium dioxide. For example, CeO.1 Ti refers to the catalyst whose mass ratio of cerium dioxide to titanium dioxide is 0.1. The mass fraction of vanadium pentoxide is 0.5% in CeO.5 Ti-V. For each catalyst, 10 g sample was prepared by sol-gel method. The amounts of the precursors used in the preparation process for all samples were listed in Table 1.  
Figure 1: Operation procedures of catalyst preparation by sol–gel method.
## 5 Results
### Passive Security Indicators in Benign Traffic  
Although the dataset captured only benign traffic, several indicators of potential security relevance were extracted and analyzed using Zeek. The weird log recorded various protocol inconsistencies, retransmissions, and unexpected connection terminations. These events were categorized by type and tracked over time, revealing patterns that could guide anomaly detection. Table 10 summarizes these event types.  
In addition, analysis of SSH sessions revealed a diversity of client and server banners, potentially useful for identifying vulnerable software versions. DNS query strings were evaluated for Shannon entropy, enabling future correlation with domain-generation algorithms (DGAs). Finally, log entries related to file transfers included MIME types and cryptographic hashes (MD5, SHA1, SHA256), which can serve as features for malware signature correlation.  
Additionally, we computed the Shannon entropy of DNS query strings extracted from dns.log. As shown in Table 11, the average entropy per query was 3.42 bits per character, with a standard deviation of 0.19. These values are consistent with legitimate DNS traffic, where domain names tend to follow human-readable or structured patterns. The maximum entropy observed (4.17) indicates the presence of some compact or less predictable domains, while the minimum value (1.5) corresponds to repetitive or simple names. Entropy analysis is a standard technique in anomaly detection, particularly for identifying the algorithmically generated domains (DGAs) used in botnets or malware communications. Although this dataset contains only benign traffic, this analysis illustrates the laboratory's ability to extract behavioral features relevant for future cybersecurity applications.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Event Type** & **Count** \\ \hline possible split routing & 132 \\ data before established & 132 \\ inappropriate FIN & 132 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Top categories of Zeek weird events.  
Figure 10: Distribution of flow durations grouped by hour. The green line marks the median.  
SSH session logs were also analyzed to extract passive security indicators from connection metadata. Additionally, the dataset includes information on SSH negotiation parameters. Figures 11-14 show the most frequently observed cryptographic algorithms and client versions. These features provide visibility of encryption practices and protocol usage, and can help identify legacy clients, weak ciphers, or unsupported negotiation algorithms, which are often used in cyberattacks.  
This analysis also reinforces the laboratory's ability to extract technical metadata from benign traffic for future comparative or forensic tasks.  
In addition to data capture, the laboratory implements a data processing pipeline that covers the entire process from data ingestion to visualization. Using open-source tools like Node-RED v4.0.9, InfluxDB v2.7.6, MongoDB v7.0.9, and Grafana v11.0.0, the system can manage the entire data lifecycle, without depending on external cloud services.
###### Abstract  
In massive multiple-input multiple-output (M-MIMO) systems, a detector based on maximum likelihood (ML) algorithm attains optimum performance, but it exhaustively searches all possible solutions, hence, it has a very high complexity and realization is denied. Linear detectors are an alternative solution because of low complexity and simplicity in implementation. Unfortunately, they culminate in a matrix inversion that increases the computational complexity in high loaded systems. Therefore, several iterative methods have been proposed to approximate or avoid the matrix inversion, such as the Neuamnn series (NS), Newton iterations (NI), successive overrelaxation (SOR), Gauss-Siedel (GS), Jacobi (JA), and Richardson (RI) methods. However, a detector based on iterative methods requires a pre-processing and initialization where good initialization impresses the convergence, the performance, and the complexity. Most of the existing iterative linear detectors are using a diagonal matrix (**D**) in initialization because the equalization matrix is almost diagonal. This paper studies the impact of utilizing a stair matrix (**S**) instead of **D** in initializing the linear M-MIMO uplink (UL) detector. A comparison between iterative linear M-MIMO UL detectors with **D** and **S** is presented in performance and computational complexity. Numerical Results show that utilization of **S** achieves the target performance within few iterations, and, hence, the computational complexity is reduced. A detector based on the GS and **S** achieved a satisfactory bit-error-rate (BER) with the lowest complexity.  
**Keywords:** Massive MIMO; Neumann series; Newton iteration; successive overrelaxation; [PERSON]; [PERSON]; [PERSON]; diagonal matrix; stair matrix +
Footnote †: journal:
## 1 Introduction
For a fundamental investigation of the capability of DAS in conjunction with a suitable sensor application geometry to detect and localize threatening pipe conditions in an undamaged pipe, as summarized under scenario 1, the present study was carried out in two sets of experiments. In the first set of experiments presented in this paper, we compare the fully distributed and quasi-distributed sensor configuration with respect to their capability to localize transient acoustic events occurring on a pipe. As a showcase application mechanical impacts on the outside of the pipe wall are analyzed. Different procedures for data evaluation and automatic impact detection and localization are introduced. Consequently, localization accuracy and precision are analyzed for different sensor configurations, data evaluation procedures and experimental settings of the DAS system. In the second part an example for monitoring critical conditions occurring inside the pipe, i.e., propagation of pressure waves, is presented. Data evaluation procedures introduced in the first part are employed to detect and localized such events. Additionally, we focus on separation of different acoustic signatures occurring at the same time and capturing of non-linear propagation.
## 2 Related Work
Subsequently, it constructs radio-disjoint paths to prevent collision and interference in consideration of the radio range of the sensor node based on the calculated number of paths. The guard band exists between these paths, and it could also be exploited for packet transmission. The odd-numbered packet is transmitted through the constructed paths, and the even-numbered packet is transmitted through the guard band. That is, there is a guard band between the radio-disjoint paths for the transmitting packet, and when transmitting a packet through the guard band, the paths are exploited as a guard band. In this process, intermediate nodes select the next node evenly in the process of transmitting the packet in consideration of the single-hop transmission success ratio, energy consumption and the remaining energy of the neighbor node. Thus, it could prevent specific nodes from being exploited repeatedly. Through this process, RMR could achieve an end-to-end transmission success ratio through multipath transmission while increasing network lifetime.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Analysis of Results
#### 3.3.1 Analysis of the Results of the Single-Trial Group
Overall, the results confirm the feasibility and accuracy of extracting point cloud heights for individual maize plants at the field scale. However, certain environmental challenges, such as varying light intensities or weather conditions, may still require adjustments to preprocessing or data acquisition techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Associated Points** & **Equation for Estimating Plant Height** & **R2** & **RMSE** \\ \hline a & 59 & 1.023x + 73.71 & 0.9989 & 0.0163 \\ b & 42 & 0.9183x + 56.87 & 0.9909 & 0.0283 \\ c & 41 & 1.031x + 49.57 & 0.9984 & 0.0148 \\ d & 38 & 1.017x + 89.23 & 0.9952 & 0.0226 \\ e & 39 & 0.9845 x + 93.56 & 0.9880 & 0.0290 \\ \hline \hline \end{tabular} Note. x corresponds to the manually measured plant height.  
\end{table} 
Table 5: Linear statistics for the five groups of tests.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 5 Conclusions
### Practical Implications  
From a practical implementation perspective, our methodology offers a balanced integration approach that leverages the strengths of individual regional forecasts without requiring substantial modifications to existing forecasting systems. The computational efficiency of our approach--with processing times averaging 15.37 s--further enhances its viability for operational deployment in dispatch centers, where rapid decision-making is essential.  
The performance improvements demonstrated in this study have significant implications for grid reliability, economic operation, and renewable energy integration. More accurate grid-level forecasting enables better generation scheduling, reduced reserve requirements, and more confident accommodation of variable renewable resources. These benefits directly translate into operational cost savings and enhanced grid stability.