esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodologies
### Positive-Unlabeled Learning  
The objective of mineral exploration is to discover valuable ore bodies. Therefore, predicting new ore bodies in unknown areas based on the few ore bodies accumulated in geological data is highly significant. Positive-unlabeled learning (PUL) is a one-class classification algorithm in which the sample data types consist only of positive samples (P) and unlabeled samples (U). By analyzing the characteristics of known positive samples, the algorithm identifies the most probable samples in U that share the same data features as potential positive samples. Through self-sampling with replacement, an equal number of unlabeled samples can be selected for training alongside the positive samples. In mineral exploration, a small number of ore bodies can serve as positive samples, while a vast amount of non-ore body data can be used as unlabeled samples. The PUL algorithm offers significant advantages in mineral prediction during quantitative evaluation [37; 67]. In this study, an improved positive-unlabeled learning (PUL) algorithm called Bagging-based positive-unlabeled learning (BPUL) is adopted, which incorporates the concept of Bagging. By employing the Bagging method, we repeatedly and randomly selected equal amounts of unlabeled samples to serve as negative samples for constructing training sets. This approach allowed us to train a series of base learners. We then made predictions on the unlabeled samples that were not selected, recording and calculating their average scores to obtain the final optimal training model.  
The predefined number of iterations is set to \(N\). Upon completion of the iterative process, all scores for each sample are recorded. The final score \(S(x)\) for each sample in \(U\) is calculated by computing the average of its scores; thus,  
\[S(x)=\frac{\sum_{i=1}^{N}f(x,i)}{N}=E(f(x,i)),i=1,2,\cdots N \tag{4}\]  
The comparison of errors between the base learners and the BPUL algorithm can be achieved through expectations. For the labeled samples (\(Y\)) and each base learner \(f(x,i)\), the inequality \(E(Z^{2})\geq(E(Z))^{2}\) holds. Therefore,  
\[E((Y-f(x,i))^{2})=Y^{2}-2 YE(f(x,i))+E(f^{2}(x,i))\geq(Y-S(x))^{2} \tag{5}\]  
From the above equation, it is evident that the BPUL algorithm exhibits smaller errors compared to the base learners. By averaging the outputs of these base learners, the impact of unreliably selected negative samples is mitigated. Furthermore, various data-driven algorithms or models can be employed as base learners within the BPUL framework.
## Materials and Methods  
### The SnowSense Concept  
The overall aim of the demonstration project SnowSense was to build up an integrated service for run-off and hydropower assessment and forecast related to snow cover dynamics in remote areas. The idea of the service concept encloses the integration of in total three components for the provision of snow hydrological information. The service is based on the integration of (i) GNSS-based in situ measurements, (ii) EO monitoring and (iii) a hydrological model, to generate SWE, snowmelt and run-off products for remote areas in a temporal and spatial resolution, related to user's operation defaults. We chose one day as temporal and 1 km as spatial resolution. Furthermore, it is possible to provide estimations and forecasts on hydropower generation (Figure 1).  
Figure 1: The modular concept of SnowSense, integrating satellite technologies like Global Navigation Satellite System (GNSS), satellite communication and Earth Observation (EO) as the basis for a service.  
In the following subchapters, the applied Canadian test sites and the description, development and assimilation of the three basic components of the snow cover sub-system as well as the entire service are presented.
## 3 Results and Discussion
### The Validation of the Methods
#### 3.3.3 Accuracy and Precision  
The reliability of the method was evaluated by determining the blank spiking recovery. A known concentration of the mixed standard solution was added to ultrapure water such that the calculated concentrations after addition were at three levels, 50, 100, and 500 ng/L. Five parallel samples (\(n=5\)) were analyzed using the HS-SPME-GC-FPD method. The results are summarized in Table 5.  
As shown in Table 5, the recoveries of DMSs at 100 and 500 ng/L were 97.22% and 99.07%, respectively, with relative standard deviations (RSD) lower than 5.94%. The recoveries of DMDS at concentrations of 50, 100, and 500 ng/L were 93.39%-99.34%, with RSD lower than 6.25%. The recoveries of DMIS at 50, 100, and 500 ng/L ranged from 91.17% to 99.25%, with RSD lower than 5.47%. The HS-SPME-GC-FPD method for the determination of DMS, DMDS, and DMIS in water established by this study is more reliable and stable and can be used for the determination of even trace DMSs in water.
# Radio Number for Friendship Communication Networks
## 1 Introduction
For more details about the mathematical models, the reader can refer to [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. As the number of wireless networks and services increase, this leads to many transmission stations that may be close to each other. Consequently, in most cases, there is at least one transmission station that will overlap with many other stations. This inhibits the ability of the receiver to decipher incoming signals. This concept is illustrated in Figure 1, which shows a typical situation in which the signal of transmission stations A and B overlap in the vicinity of transmission station C as in Figure 1a, while Figure 1b shows the modeling of this interference by path graph. Whenever the number of transmission stations increases, as Figure 2a, every station hopes to increase its coverage area, which leads to more physical overlapping and hence, more radio frequency interference. This situation can be modeled by the friendship graph as shown in Figure 2b. The objective of the present paper is to study the radio labeling of friendship networks \((F_{3,k},\text{ }F_{4,k},\text{ }F_{5,k}\), and \(F_{6,k})\). On the other hand, a mathematical model is proposed to find the upper bound of \(F_{3,k},\text{ }F_{4,k},\text{ }F_{5,k}\), and \(F_{6,k}\). A computational study is presented to prove the efficiency of our results compared to the previous results. Finally, the empirical study shows that the proposed results outperform the previous results according to the upper bound of the radio num  
Figure 1: Path graph for modeling frequency interference of stations A, B and C. (**a**) Physical frequency interference. (**b**) Path interference graph. The rest of this paper is organized as follows. Section 2 presents the upper bounds for the radio number of the above-mentioned friendship graphs. The integer linear programming model of radio labeling of such friendship graphs is presented in Section 3. In Section 4, we present an experimental study for comparing results obtained in Sections 2 and 3, and algorithms that solved the same problem from [3,4]. The conclusion of this paper and future work are presented in Section 5.
# A Nodal Analysis Based Monitoring of an Electric Submersible Pump Operation in Multiphase Flow
## 4 Materials and Methods
### Nodal Analysis Simulation
As shown in Figure 5a-d, PIPa falls when the ESP is installed as designed. However, the ESP performance falls as the gas fraction increases inside the pump; the TPR-ESP curve is thus shifted upward until the ESP shuts down. Therefore, the PIP increases, and the oil production rate (Q\({}_{\text{o}}\)) decreases, compared with the design parameters. Figure 4: Computation of the ESP (discharge pressure) operating threshold limit. (**a**) Design conditions. (**b**) Minimum and maximum normal operating threshold limits. Figure 3: ESP PIP operating threshold limits. (**a**) Design conditions. (**b**) Minimum and maximum normal operating threshold limits. Figure 2: (**a**) Operating BHP for a naturally flowing well. (**b**) Static BHP for a dead well. Using Equations (11) and (12), a conceptual graph showing IPR curve variation was drawn to reflect production well status (ESP shutdown, normal ESP operation, and abnormal ESP operation) at the ESP discharge node (point B of Figure 1). The ESP performance curve is added to the IPR curve to generate the IPR + ESP curve (Equation (11)); this adequately represents the inflow performance of the ESP. As shown in Figure 6a-d, when an ESP is normally installed (i.e., under the design conditions), normal ESP operation increases the \(\mathrm{P_{d}}\) and \(\mathrm{Q_{o}}\), compared with abnormal ESP operation or ESP shutdown. Reduced ESP performance shifts the IPR curve downward, thereby reducing \(\mathrm{Q_{o}}\) and \(\mathrm{P_{d}}\).
## 4 Discussion
#### Limitations  
While this study provides important insights into the potential for shortening the 3 MT using exponential functions, there are several limitations that should be considered when interpreting the findings.  
First, this study focused specifically on endurance-trained athletes (cyclists and triathletes) who regularly engage in high-intensity endurance exercise. This focus may limit the generalizability of our findings to athletes from other sports or individuals with different fitness backgrounds. For example, our results may not extend as effectively to power-dominant athletes, such as sprinters or baseball players, who rely more on anaerobic energy systems and tend to show a faster initial power decline because of the unique demands of their sport. Similarly, recreational athletes, who typically have lower baseline fitness, may respond differently. Endurance athletes, by contrast, often exhibit adaptations that allow for a more gradual decrease in power over time, reflecting the distinct physiological demands of their training.  
Second, the study did not investigate the long-term reliability of the shortened test. While the 90 s test was shown to provide accurate results in this single testing instance, it is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Element** & **Traditional CP Test** & **3 Min Test (3 MT)** & **Mono-Exponential** & **Bi-Exponential** \\ \hline Test Duration & 3–4 tests over 1–1.5 weeks & 3 min & 2 min & 1.5 min \\ Number of Trials & 3–4 & 1 & 1 & 1 \\ Recovery Period & 24–48 h between tests & None & None & None \\ Parameter Accuracy (CP) & High & High (5\% difference) & High (5\% difference) & High (5\% difference) \\ Parameter Accuracy (W\({}^{\prime}\)) & High & High (5\% difference) & High (5\% difference) & High (5\% difference) \\ Base of Repetition & Low & Low & Higher & Higher \\ Fatigue Level & High & Very High & Moderate & Moderate \\ Practically for Regular Use & Low because of time and & Moderate & High & \\  & fatigue & & & \\ Mental and Physical & High (due to multiple & & & \\ Exhaustion & exhaustive trials) & Very High & Moderate & Moderate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Critical power test comparison.  
unclear whether the test would produce consistent results if repeated over time or under different training or environmental conditions. Longitudinal studies are needed to assess the reliability of the shortened test across multiple testing sessions and over extended periods of training.  
Finally, while the shortened test reduces mental and physical fatigue, it still requires athletes to perform an all-out effort for 1.5-2 min, which some athletes may find uncomfortable. Although this is a significant improvement over the 3 MT, future research can explore the possibility of further reducing the test duration or implementing pacing strategies to make the test even more tolerable for athletes, especially for those who are less familiar with high-intensity exercise protocols.
# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 1 Introduction  
Economic and safety incentives are the primary motivators for autonomous vehicle (AV) deployments. The society of automotive engineers (SAE) categorized vehicles that can always operate without a human driver as level 5 automation [1]. Incentives include significant cost savings from eliminating the human driver, increased efficiency by operating at all hours, and enhanced safety by reducing accidents attributable to human factors such as intoxication and distracted driving [1]. Autonomous trucks can additionally benefit by eliminating driver-related costs like insurance, training, and onboard amenities for human comfort and entertainment. Despite these advantages, concerns about technology readiness and potential negative impacts on traffic safety and congestion have emerged. Such apprehensions have led to state-level legislative actions, including laws mandating a human backup driver, which counteract the core benefits of AVs [2].  
Currently, the United States lacks comprehensive federal regulation to uniformly address the safety, design, and operation of AVs. The failure of the 2018 U.S. Senate bill, AV START, due to safety concerns, has left a void filled by individual states introducing their own AV legislation [3]. This has resulted in a fragmented regulatory environment, posing challenges for AV manufacturers and stakeholders who must navigate a patchwork of state-specific laws [4]. Furthermore, existing regulations often do not adequately differentiatebetween the operation of autonomous trucks and other vehicle types, each with distinct sets of stakeholders.  
This research aims to provide a comprehensive analysis of the varying state regulations in the United States and their alignment or conflict with one another. Specifically, this research seeks to identify factors associated with the propensity of an authority to pass laws supporting AV operation without a human backup driver. By applying machine learning (ML) techniques to state data, this study offers insights into the complex relationships between different factors influencing AV policy decisions. This approach bridges the understanding of disparate regulatory frameworks and highlights factors that significantly impact legislative outcomes for AVs.  
The organization of the remainder of this paper is as follows. Section 2 presents a review of the literature on AV testing and deployment legislation. Section 3 describes the methodological workflow developed for this research. Section 4 discusses the analytical results and their implications for stakeholders, including investors and policymakers. Section 5 concludes the research and suggests directions for future work.
## 4 Results and Discussion
### Functioning of the Mirau Configuration
However, these parameters are specified for a profile length of \(1.25\,\mathrm{mm}\), which is significantly longer than the maximum edge length of the measurement field of \(324\,\mathrm{\SIUnitSymbolMicro m}\). Therefore, these parameters are not used in this work to characterize the roughness of the measured profile. Taking into account, that reference values for such roughness standards are usually obtained by tactile stylus instruments, the profile measured by the custom-built 20x Mirau CSI is compared to the same profile section sampled by the tactile stylus instrument Marsurf GD26 from Mahr, which is integrated in a multisensor measuring system [39]. As shown in Figure 8, the profile resulting from the CSI measurment agrees quite well to the tactile measurment result. However, a low-pass filtering of the profile obtained by the 20x Mirau occurs due to the relatively low NA of 0.28, resulting in slight deviations from the tactile reference profile.
## 6 Sea Level Monitoring  
At the national level, 18 out of 20 tide gauge stations operated by the General Directorate of Mapping (GDM) are integrated into the operations of KOERI-TSP. The integration of the entire GDM network to KOERI-RETMC is dependent on GDM's plans to upgrade the whole network with radar-type tide gauges, which is expected be finalized by 2022. Four Inexpensive Device or Sea Level (IDSL) stations [17] donated by EC-JRC have been installed in Bodrum, Bozcaada and Fethiye in the Aegean Sea and in Samsun in the Eastern Black Sea. Recent efforts focused on the installation of multihazard monitoring systems along the Marmara Coast, composed of accelerometer, tide gauge and meteorological sensors, which will be expanded by the observation capability of GNSS in the future. Three systems out of a proposed network consisting of 50 systems have already been installed along the northern coasts of the Marmara Sea. Sea level data are available to other TSPs and the IOC through an http protocol. Sea level data from INGV-TSP are available through a bilateral agreement and data from various providers are accessible through the IOC Sea Level Monitoring Facility [18] and JRC TAD Server [19]. All tide gauges available to KOERI-RETMC in its TSP operations are presented in Figure 1.
## 3 Engineering Case Study
### Determination of Representative Volume Element Size  
In numerical simulation studies, selection of REV dimensions critically impacts both the computational efficiency and simulation reliability [29]. This study developed a three-dimensional DFN model to investigate the size effects in fractured rock masses, with the objective of determining the optimal REV dimensions for the study area.  
A representative area of the 1542 m platform on the southern slope of the main mining area--distinguished by well-exposed strata and extensively developed joint systems (Figure 4a)--was selected for analysis. The slope point cloud data were acquired through Unmanned Aerial Vehicle oblique photogrammetry. We utilized an integrated analytical approach (including normal vector calculations, octree segmentation, Fuzzy C-Means clustering, and DBSCAN) to analyze the point cloud data [30]. Three dominant joint sets, comprising 488 individual joints, were identified (Figure 6).  
A statistical analysis of the joint geometric parameters (Figure 7) revealed the following: Joint Set 1 intersects the slope surface obliquely and exhibits the longest average trace length (2.44 m) among the three joint sets. Joint Set 2 displays steeply inclined, anti-dip characteristics, with an average dip direction of 187.66\({}^{\circ}\) and dip angle of 75.64\({}^{\circ}\), along with the largest joint spacing (average: 1.56 m). Joint Set 3 presents a gently inclined, dip-slope structure characterized by an average dip direction of 54.11\({}^{\circ}\) and dip angle of 40.85\({}^{\circ}\), while demonstrating both the shortest trace length (average: 1.07 m) and minimum spacing (average: 0.69 m).  
Based on the statistical analysis of joint parameters, this study employed 3 DEC discrete element software to construct numerical models of jointed rock masses with systematically varied dimensions, ranging from 2 m \(\times\) 2 m \(\times\) 2 m to 16 m \(\times\) 16 m \(\times\) 16 m in 2 m increments (Figure 8). The Coulomb slip criterion governed the joint surfaces, while the Mohr-Coulomb model characterized the constitutive behavior of the rock blocks.  
Figure 6: Structural planes fitted from point cloud data.  
Figure 7: Statistics of joint geometric parameters (lower hemisphere projection).  
The parameters were assigned based on the physical and mechanical properties of the predominant lithology (slate) in the study area (Table 1).  
Uniaxial compression tests (Figure 9) revealed that when the sample dimensions exceeded 4 m, both the equivalent compressive strength and elastic modulus exhibited a pronounced decreasing trend with increasing size. The mechanical parameter variations demonstrated significant convergence at 10 m. Considering the 14 m bench height in the study area, a 14 m \(\times\) 14 m \(\times\) 14 m configuration was identified as the optimal REV, ensuring high geological-structural representativeness while maintaining an optimal balance between computational efficiency and accuracy.
# Material Flow Optimization as a Tool for Improving Logistics Processes in the Company
## 6 Discussion  
Based on the analysis of the current state of material flow at the company in Veself nad Luznici production facility, measures have been proposed to optimize its efficiency. The objective of this discussion is to compare two proposed solutions and evaluate their benefits in terms of effectiveness, cost reduction, and implementation complexity. Variant 1 focuses on analyzing the transportation costs associated with transferring SF to the Fruta Warehouse, located two kilometers from the manufacturing company. A significant portion of expenses arises from SF transport to Fruta, where the maturation process occurs before the goods are returned to the factory for final packaging into FG. An alternative solution to reduce transportation costs involves acquiring an in-house fleet of transport units, thereby reducing dependence on an external operator. This approach does not directly optimize the material flow, but rather reduces transportation costs, which constitute a significant portion of the overall expenses when allocated across individual transport operations. Implementing this strategy would result in cost savings of EUR 166,974.63 on SF transport. Figure 7 illustrates the transportation costs associated with SF. This transport optimization is reflected in the overall material flow costs, reducing the cost per transported meter and achieving savings of EUR 0.03 per meter. As a result, total variable costs decreased from the original EUR 1,158,039.09 to EUR 857,806.72, generating savings of EUR 300,232.36. From an investment return perspective, this investment proves to be cost-effective, yielding a 74% return. In terms of payback period, the investment will recover within 208 days of implementation, thereby meeting the company's investment condition of a return within two years. Variant 2 focuses on replacing external operators for FG transportation with in-house transport and analyzing costs related to FG transport to distribution warehouses, which are located 31 and 34 km from the manufacturing company. A significant share of the total material flow costs is attributed to FG transport from the factory to distribution warehouses, from which products are shipped to customers. One of the possible ways to reduce transport costs is to establish an in-house fleet, thereby eliminating the need for external operators. Like Variant 1, this approach does not directly optimize material flow but focuses on reducing costs related to material movement, which, when allocated to individual transport operations, account for a significant portion of total expenses. The costs associated with acquiring the fleet through long-term leasing, its operation, and staffing four drivers amount to EUR 201,710.96.