esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
###### Abstract  
Achieving less than 1 ms end-to-end communication latency, required for certain 5G services and use cases, is imposing severe technical challenges for the deployment of next-generation networks. To achieve such an ambitious goal, the service infrastructure and User Plane Function (UPF) placement at the network edge, is mandatory. However, this solution implies a substantial increase in deployment and operational costs. To cost-effectively solve this joint placement problem, this paper introduces a framework to jointly address the placement of edge nodes (ENs) and UPFs. Our framework proposal relies on Integer Linear Programming (ILP) and heuristic solutions. The main objective is to determine the ENs and UPFs' optimal number and locations to minimize overall costs while satisfying the service requirements. To this aim, several parameters and factors are considered, such as capacity, latency, costs and site restrictions. The proposed solutions are evaluated based on different metrics and the obtained results showcase over 20% cost savings for the service infrastructure deployment. Moreover, the gap between the UPF placement heuristic and the optimal solution is equal to only one UPF in the worst cases, and a computation time reduction of over 35% is achieved in all the use cases studied.  
5G; edge computing; user plane function; framework; ILP; optimization problem +
Footnote †: journal:  
_Article_  
A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G
## 2 Materials and Methods
### Signal Processing Applied to the Classification of Relaxation State
* h is the step size or distance between two consecutive points on the x-axis, represented as \(\mathrm{h}=\frac{\mathrm{b}-\mathrm{a}}{\mathrm{n}}\), where \(a\) is the lower limit of the interval, b is the upper limit of the interval, and n is the number of subintervals. Thresholding: In this phase, thresholds were established for state classification and calculated during the training stage. In this stage, various approximations of the area under the curve of the spectrum were obtained, and the mean was calculated from these values. Once the areas \(\mathrm{A_{1}},\ \mathrm{A_{2}},\ \dots,\ \mathrm{A_{n}}\), from the n samples were obtained, the mean \(\mathrm{\mu}\) was calculated:  
\[\mathrm{\mu}=\frac{1}{\mathrm{n}}\sum_{i=0}^{n-1}\mathrm{A_{i}} \tag{19}\]  
Next, the standard deviation \(\sigma\) of the area under the curve approximations was calculated:  
\[\sigma=\sqrt{\frac{1}{\mathrm{n}}\sum_{i=0}^{n-1}(\mathrm{A_{i}}-\mathrm{\mu} )^{2}} \tag{20}\]  
Three levels are defined based on the following thresholds:  
* Level 0 (active state): \(\mathrm{X}<\mathrm{\mu}-\sigma\)
* Level 1 (relaxed state 1): \(\mathrm{\mu}-\sigma\leq\mathrm{X}\leq\mathrm{\mu}+\sigma\)
* Level 2 (relaxed state 2): \(\mathrm{\mu}+\sigma<\mathrm{X}<\mathrm{\mu}+2\sigma\)
## 5 Discussion
### Relationship between LULC and Annual TN and TP Discharge Loads  
The high annual TN and TP discharge loads in the cultivated lands (Table 3; Figures 5 and 6) can be attributed to the fact that the use of agricultural fertilizers generates high levels of nitrogen [68; 69] and phosphorus [70]. Furthermore, tillage and other agricultural practices can promote erosion, resulting in higher losses of N [71; 72; 73] and P [74; 75]. Likewise, the high annual N and P discharge loads associated with urban land cover (Table 3; Figures 5 and 6) can be attributed to the alteration of the physical and hydrogeological properties of watersheds in urban areas and the development of nutrient sources in such areas. Septic tank drainage, yard fertilization, domestic pet wastes, and soil erosion in urban areas are widely connected to elevated N and P content [14; 16]. On the other hand, the annual TN and TP discharge loads are quite low in shrubland areas due to the limited input of nitrogen and phosphorus in such areas. The main contributor to nitrogen accumulation in watersheds with natural vegetation (such as shrublands) is the buildup of atmospheric nitrogen. The natural source of phosphorus is the weathering of specific geologic formations [76]. However, such natural sources of N and P are remarkably less common than anthropogenic sources such as fertilizer application and wastewater disposal [77]. Furthermore, in the absence of anthropogenic sourcing, N and P transfer from upland shrublands into waterways can be significantly reduced by microbial absorption assimilation [78; 79; 80] and soil-stream interaction processes [81; 82; 83].
# Random Frequency Division Multiplexing
## 3 Random Frequency Division Multiplexing  
### Rfdm  
The proposed RFDM-based single-input-single-output (SISO) communication architecture is constructed as shown in Figure 1. A message bit sequence is digitally modulated as \(\mathbf{s}\in\mathbb{C}^{N\times 1}\) with the power constraint \(\frac{1}{N}\|s\|^{2}=1\); the elements \(s_{i},i=1,2,\ldots,N\) are individually taken from constellation set \(\mathcal{Q}\), e.g., quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM). Following serial-to-parallel (S/P) conversion and RFDM modulation, the obtained signal \(\mathbf{x}\) is  
\[\mathbf{x}=\mathbf{A}\mathbf{F}_{\mathbf{N}}^{\mathbf{H}}\mathbf{s}=\mathbf{U}\mathbf{s}, \tag{3}\]  
where \(\mathbf{F}_{\mathbf{N}}\) denotes the \(N\)-point normalized FFT matrix and \(\mathbf{A}\in\mathbb{C}^{M\times N}(M<N)\) denotes the sensing matrix of CS, in which we use Gaussian random matrices here. And \(\mathbf{U}=\mathbf{A}\mathbf{F}_{\mathbf{N}}^{\mathbf{H}}\in\mathbb{C}^{M \times N}\) denotes the random frequency (RF) transform. Then, a CP of a length at least equal to the maximum channel delay spread is added to \(\mathbf{x}\). After the transmit filter, the signal \(\mathbf{x}\) is sent out.  
The received signal \(r[n]\) at the \(n\)-th slot is given by  
\[r[n]=\sum_{p=0}^{P-1}x[n-p]g[n,p]+v[n], \tag{4}\]  
where  
\[g[n,p]=\sum_{i=1}^{L}h_{i}e^{i2\pi f_{i}(nT_{s}-pT_{i})}P_{rc}(pT_{s}-\tau_{i}), \tag{5}\]  
\(p=0,1,\ldots,P-1\), \(P\) is the channel tap, \(i=1,2,\ldots,L\), \(L\) is the number of multipaths, \(T_{s}\) is the system sampling interval, \(\{h_{i},f_{i},\tau_{i}\}\) represent the complex channel gain, the Doppler shift, and the delay at the \(i\)-th path. \(P_{rc}\) is the raised-cosine roll-off filter which can reduce signal bandwidth and intersymbol interference.
## 4 Results and Discussion
### Parametric Study on Hydrogen Purification Performance
#### 4.3.3 Effect of AD Time  
Figure 11 illustrates the changes in hydrogen purity and recovery with varying AD step times. Similar to the feed flow rate, extending the AD step time increases the total amount of mixtures processed during the entire PSA cycle, resulting in higher hydrogen recovery. However, as the AD time is further extended, the increase in recovery diminishes, suggesting that the processing capacity of the adsorption bed is limited and cannot fully meet the increasing demand for processed gas. As the PSA cycle progresses, the working capacity of the adsorbent decreases, which in turn reduces its adsorbed amount for impurity gases in subsequent AD steps. Consequently, prolonging the AD time may cause the impurity gases to reach saturation adsorption earlier, leading to an overflow of impurities from the outlet and a decrease in hydrogen purity. Furthermore, this trend highlights that with the continued extension of the AD time, the decrease in hydrogen purity becomes more pronounced.
# Impact of a Microbial Cocktail Used as a Starter Culture on Cocoa Fermentation and Chocolate Flavor
## 3 Discussion
### Chromatographic Analysis  
#### 4.3.1 Sugars, Alcohols, and Organic Acid Extraction and HPLC Analyses  
The carbohydrates, alcohols, and organic acids were extracted (from pulp and from the content inside the beans) and analyzed as described in previous work [3]. The analyses were determined by HPLC (Shimadzu, model LC-10 Ai, Shimadzu Corp., Kyoto, Japan) equipped with a dual detection system consisting of a Ultraviolet-Visible (UV-Vis) detector (SPD 10 Ai) and a refractive index detector (RID-10 Ai). The HPLC was operated at 50 \({}^{\circ}\)C for acids and detected via UV absorbance (210 nm), while the alcohols and carbohydrates were examined at 30 \({}^{\circ}\)C and detected via Refractive Index Detector (RID). The column used for separation was a Shimadzu ion exclusion column (Shim-pack SCR-101H, 7.9 mm \(\times\) 30 cm, Shimadzu, Kyoto, Japan) with a mobile phase of Perchloric acid (100 mM) at a flow rate of 0.6 mL/min. All samples were analyzed in triplicate.  
The chemical compounds used as standards (purity N 99.8%), glucose, fructose, and citric acid, were purchased from Sigma-Aldrich (Saint Louis, MO, USA); acetic acid and ethanol were purchased from Merck (Darmstadt, Germany); and lactic acid was purchased from Fluka Analyticals (Seelze, Germany).  
3.2 Characterization of Volatile Compounds by Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry  
The volatile compounds from cocoa samples were extracted using the Headspace-Solid Phase Microextraction (HS-SPME) technique, as described in previous research [24], with modifications. Briefly, cocoa samples (2.0 g) from the beginning and end of fermentation (0 h and 144 h) and chocolate samples (2.0 g) were macerated using liquid nitrogen for headspace analysis. A divinylbenzene/carboux/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 mm SPME fiber (Supelco Co., Bellefonte, PA, USA.) was used to extract volatile constituents from the cocoa and chocolate headspace. The fiber was equilibrated for 15 min at 60 \({}^{\circ}\)C and then exposed to the samples (cocoa and chocolate) for 30 min at the same temperature.  
The compounds were analyzed using a Shimadzu GC model QP2010 equipped with a mass spectrometry and a capillary column of silica DB-FFAP (25 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 mm). The temperature program began with 5 min at 50 \({}^{\circ}\)C, followed by a gradient of 50 \({}^{\circ}\)C to 190 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min; the temperature was then maintained at 190 \({}^{\circ}\)C for 10 min. The injector and detector temperatures were maintained at 230 \({}^{\circ}\)C.  
The carrier gas (He) was used at a flow rate of 1.2 mL/min. Injections were performed by fiber exposition for 5 min. Volatile compounds were identified by comparing the mass spectra of the compounds in the samples with the database of the National Institute of Standards and Technology (NIST library, Gaithersburg, MD, USA) and the retention time with literature data using the n-Alkane index. All samples were examined in duplicate.
## 1 Introduction  
In recent years, more and more attention has been focused on the research and development of solid-state devices operating in the sub-terahertz (THz) and terahertz regimes [1; 2; 3]. Terahertz waves (100 GHz-10 THz) show great potential in the fields of communication, imaging, radar, spectroscopy, and security screening [4; 5]. The increase in solid-state devices is driving development of a wide range of new applications, which can be integrated conveniently with other electronic or optoelectronic devices. As a very important solid state oscillator, the Gunn diode has attracted more and more attention since its appearance in 1963 [6; 7]. The renewed planar Gunn diode, invented by [PERSON] et al., has shown great advantages over the vertical ones, such as low voltage, easy integration with MMIC, and easy modulation of frequency [8; 9; 10; 11; 12; 13]. The GaN hetero-structural planar Gunn diodes are one of the most promising devices in high power, high frequency, and high temperature applications [14; 15; 16]. Although in the field of microwave communication and power devices, GaN HEMTs (High Electron Mobility Transistors) are already commercially available, there are few publications about the GaN planar Gunn diode. More experimental and theoretical research is needed to promote the development of GaN-based planar Gunn diode.  
Achieving higher output power and terahertz oscillation frequency are two crucial issues for the GaN planar Gunn diode to attain the final fulfillment of its commercialized application. However, as reported in [17; 18; 19], planar Gunn diodes generate smaller RF (Radio Frequency) power than the vertical structure. Moreover, the fundamental oscillation for the GaN Gunn diodes reported so far is still far from the terahertz regime. Consensus between high frequency and high power is difficult to reach. By reducing the transit region length, operation frequency increases; however, the RF output power decreases. Furthermore, small-size devices bring many other new problems and a complex manufacturing process.  
In order to achieve the simultaneous enhancement of oscillation power and frequency in the planar Gunn diode, we propose the self-parallel Gunn diode (SPD) with well-designed trench-anode etching through the bottom of the 2 DEG channel. In Section 2, we give the structure of SPD and describe the simulation method. In Section 3, we present an explicit analysis on the physical mechanism and output characteristics on this novel diode. Conclusion is given in Section 4.
# Borophene Is a Promising 2D Allotropic Material for Biomedical Devices
## 1 Introduction  
The recent development of cutting-edge nanotechnologies has allowed researchers to achieve significant steps forward in the study of new materials that are applicable to medical biotechnologies. Recently, growing attention has been paid to an interesting 2D material, graphene. After the discovery of this smart and versatile material, scientific researchers have incorporated it into nanomaterials and nanotechnologies [1] with potential applications for innovative devices and medical disposables.  
Generally, 2D materials have remarkable physical properties that are applicable to electronic devices, while recently they have found applications in several new technologies in the field of photonics, energy conversion, and nano-engineering [2].  
Recently, the synthesis of thin 2D boron sheets, developed on Ag(111) substrates, has resulted in the creation of a 2D triangular structure [3] called borophene (BO).  
Unlike other allotrope materials, borophene has marked anisotropic properties; for instance, Young's modulus, measured along the smoothest surface of a borophene sheet, is higher than graphene [4]. Some researchers working on the thermic conductivity of borophene have shown that it can be explained because of its anisotropy; nevertheless, compared to other similar materials, thethermal properties of borophene are lower, due to the dispersive effect of its phonons. The Poisson's ratio and the thermal expansion coefficients are also low [3,4].  
On the other hand, the anisotropic optical properties reported on borophene sheets are quite interesting, as they hold high optical transparency [5].  
Therefore, borophene certainly has peculiar properties, and can be considered a 2D allotropic material, with several aspects that are applicable to medical devices.  
In this topical review, we will describe the main properties and the main applications of borophene, with a specific interest towards the biomedical field. In particular, we will describe the main properties of borophene, taking into consideration its molecular and crystal structures. We will further report how structures of borophene can have an impact on its ability to work in optical circuits, on electronic devices, and in biomedical sensors. We will carefully describe the main studies concerning the most important applications of borophene in the medical field, and try to express our perspective on the future challenges that could be overcome as a result of to this promising material. Borophene certainly has the potential for new healthcare applications, which should be of interest to materials scientists.
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 2 Methods for Observing and Capturing Geomagnetic Anomalies
### Extraction of Geomagnetic Anomaly Signals Caused by Earthquakes
When the fluctuation in the geomagnetic field increases, \(\Delta F\) shows a gradual increase or decrease, and \(\Delta F^{2}\times T\) follows changes. In one whole day, the total signal-to-noise ratio of the accumulated geomagnetic anomaly energy obtained by cumulative summation:  
\[SNR=\sum_{k=1}^{N}\frac{E(k)}{N(k)} \tag{5}\]  
The stronger the \(\Delta F\), the significantly stronger the \(SNR\). Therefore, observing the cumulative geomagnetic energy can effectively eliminate the surrounding magnetic interference and capture the weak geomagnetic anomalies before earthquakes. Our new algorithm for capturing impending geomagnetic anomalies is depicted as follows. Assume that geomagnetic field anomalies occur a time window (\(t^{\prime}\), \(t^{\prime}+T\)) set at the beginning of a certain time \(t^{\prime}\). Calculate the cumulative geomagnetic anomaly energy \(E(t^{\prime},\ t^{\prime}+T)\) according to the below formula:  
\[E\big{(}t^{\prime},\ t^{\prime}+T\big{)}=\sum_{m=1}^{M}\int_{0}^{T}\Delta F^{2 }(t)dt \tag{6}\]  
where \(M\) indicates the number of geomagnetic monitoring stations. So, we can derive the geomagnetic anomaly gradient (Equation (7)) for the earthquake prediction analysis. \[G=\frac{1}{M}\frac{d}{dt}E\big{(}t^{\prime},t^{\prime}+T\big{)} \tag{7}\]
# Vehicle Lane Change Models--A Historical Review
## 3 The Development Trend of Future Lane Change Models  
Lane changing is a very complex traffic behavior, involving the driving dynamics of the lane, adjacent lanes, and target lanes. Lane change behavior is an important part of the lane change decision model and has an important impact on traffic flow characteristics and traffic safety. Therefore, researchers have developed a variety of lane change behavior models, including the single-type and mixed lane change models. The single-type lane change model considers incomplete information, easily loses details, and has a mediocre detection performance when dealing with complex scenes. The mixed lane change model combines several different algorithm models, comprehensively considers the factors and characteristics of lane change behavior, and combines the advantages and characteristics of different lane change models to describe the movement trajectory of lane change behavior more accurately, such as in the following exmples. (1) Decision trees have a more intuitive decision flow, but do not capture nonlinear relationships. The neural network can capture more complex features and relationships by learning a large number of sample data, to improve the accuracy and generalizability when predicting the driver's lane change decision. (2) The information-processing model mainly records the driver's perception and judgement of information in the traffic environment, and it can process and understand important traffic information, but the model is unable to obtain nonlinear relations. In this case, the combination of machine learning models can improve the predictive power of the model. Machine learning models can mine patterns and associations by learning instance data, to improve the ability to make lane change decisions. The results obtained with the information-processing model can be used as inputs to the machine learning model to extract higher-level behavioral features. (3) A cognitive mapping model (such as the driver's mental model) is mainly used to model the driver's cognitive processes during a lane change in combination with the behavior planning model. The cognitive mapping model contains the driver's intention, goal, and decision basis in the process of a lane change, but the cognitive model cannot be accurately translated into specific behavioral strategies. The behavior planning model can generate specific behavior strategies according to the state and goal to support the execution of the lane change behavior. Therefore, different types of lane change models can be combined to form a mixed model that overcomes the limitations of the single lane change model. The mixed model can comprehensively consider the factors and characteristics of the lane change behavior, to improve the lane change. In the future, the mixed lane change model will be one of the trends in our research and development, aiming to reduce traffic accidents between a car and neighboring vehicles during lane changes.
## 5 Conclusions and Discussion  
### Conclusions  
This study comprehensively analyzes pedestrian avoidance behavior on school roads using three predictive models (BP neural network, GA-BP neural network, and XGBoost). The key findings are as follows:  
1. Model Performance Comparison  
Among the three models, the GA-BP model outperforms the others in terms of both prediction accuracy and stability. Integrating genetic algorithms with the BP neural network enables faster convergence and more accurate predictions for pedestrian avoidance behavior. Although XGBoost demonstrates strong initial performance and lower algorithmic error due to its normalized loss functions, it ultimately shows a higher misclassification cost when evaluated under cost-sensitive criteria. This indicates a greater likelihood of high-risk errors, particularly false negatives, than the GA-BP model. While robust in simpler scenarios, the conventional BP model exhibits limitations under complex conditions. Therefore, considering the conventional performance metrics and the cost-sensitive analysis, the GA-BP model is the most suitable for predicting pedestrian avoidance behavior on school roads.  
2. Key Features Analysis  
Traffic volume and vehicle speed are the most influential features in predicting pedestrian avoidance behavior. Specifically, STV and OTV are the most significant predictors. These features directly influence pedestrians' perception of traffic density, impacting their decision-making when crossing the road. The SV and OV also play crucial roles, as higher vehicle speeds tend to lead to more cautious pedestrian behavior.  
3. Other Feature Impacts  
Other features also influence pedestrian behavior, though to a lesser extent. For example, SS, SM, OS, and OM, representing the types of vehicles encountered in the same and opposite directions, respectively, show some effect. However, it is less significant compared to traffic volume and speed. The vehicle type--a car or an electric bicycle--may also affect pedestrians' decisions, with electric bicycles generally perceived as less risky. Additionally, features such as O and S reflect the relative movement between pedestrians and vehicles, influencing pedestrians' avoidance behavior based on the movement and speed of the vehicles. Feature P has a notable impact, as group behavior tends to make pedestrians more cautious and likely to avoid dense traffic areas or wait for changes in traffic signals. However, W has a relatively small impact on avoidance behavior, as wider roads may offer more crossing space but are less significant when traffic flow and speed are high.
# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
## Problem Description and Modeling  
### Problem Description  
Considering airspace constraints and the low-altitude operation conditions of the drone itself, this paper predicts drone demand and the corresponding costs of delivering express packages from distribution points to various demand points for two urban air traffic networks constructed using road and building data. In order to make the scheduling results meet the requirements of the logistics development trend, the following problems need to be solved:  
1. Forecast the delivery cost required to complete all delivery tasks under the optimal situation;
2. In the case of the minimum distribution cost, predict the ideal delivery route of the distribution point under the optimal situation.