esa-sceva/satcom-chunk-collection huggingface dataset:
# Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques
## 6 Conclusions  
The density plays a vital role in the additively build part of powder-based additive manufacturing. The experimental and numerical analysis requires more time and expertise and incurs huge costs to find the optimum parameters. ML techniques are utilized for density prediction with higher accuracy. Therefore, in this work, four different ML techniques have been used and executed in R-studio to predict the density of additively manufactured SS316L. The data from previous results are divided into the training and testing dataset, and the prediction accuracy is evaluated with different error values. The study's findings revealed that the SVM method is comparatively better for density prediction than other methods. The results obtained from the SVM technique showed 96.01% accuracy in the predicted vs. actual value for the density. The second-best model was found to be the ANN, which provided the highest correlation values, i.e., 95.5%. However, there exists a high risk of getting over-fitted in the ANN model. The error values such as RMSE, MAE, and MAD were obtained and observed to be higher in the ANN model. The KNN gives limited performance for the density prediction with the R-squared value of 0.862. AM industry is still growing, and thus limited data set of process parameters available for analysis. Hence in the future large data set could be trained using ML techniques to predict the parameters of the building part. One of the limitations of the current study is that it deals with the small data. This limitation can be overcome by utilizing other techniques to help accurately analyze the ML models. In the future, the researchers may use the findings from the study to develop the different ML models for analyzing other mechanical properties such as tensile strength, hardness, fatigue strength, etc. Feature importance analysis of the identified ML model could be performed to realize the material science interpretation.  
Conceptualization, M.G.; methodology, M.G., A.D. and V.W.; validation, M.G., A.D. and V.W.; formal analysis, M.G.; data curation, M.G.; writing--original draft preparation, M.G.; writing--review and editing, P.S.; visualization, P.S., K.G., J.L. and J.K.; supervision, P.S., K.G.,J.L. and J.K.; project administration, P.S., K.G., J.L. and J.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Military University of Technology, grant number 22 889.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### Water Quality Controls
#### 3.3.4 Groundwater Quality Trends with Basin Elevation  
Groundwater concentrations generally increase closer to the lake as shown in Figure 11 for TDS and Figures S10 and S11 for specific ions and parameter values. (noting groundwater extraction is from \(c\). 20-50 m below ground elevations plotted). The TDS plot shown with reference to both lake levels and water quality criteria illustrates increased TDS upon lake approach. Whilst surface water TDS also generally increases down basin (down-stream), the maintained 1 to 2 orders of magnitude lower TDS compared to groundwater clearly demonstrates the benefit to underlying groundwater resource of receiving influent river water recharge. Only groundwater close to Lake Chilwa displays  
Figure 10: (**a**) Piper plot of observed Phalombe District groundwater data, and (**b**) spatial distribution of water types in groundwater. Conversion of EC to TDS may be made via: TDS = 0.602 EC + 59.78 (R\({}^{2}\) = 0.9814, n = 67) based on field EC and lab analysed sum of major ions.  
TDS approaching lake perimeter salinities. The Basin elevation trend in TDS quality of groundwater potentially abstracted shown in the earlier Figure 6 integrated data plot (plotted with reference to former lake high-level and terrace deposits and probable screened aquifer elevations) illustrates the control of former lake deposits and current lake proximity on TDS in water supplies, but also the heterogeneity in TDS that may occur also evident in Figure 11.  
Increasing trends in solute concentrations towards lower elevations occur for all the major ions, but with greater scatter than for TDS (Figures S10 and S11). Scatter is unsurprising given varying screen depths between wells, intra-well mixing of groundwater from different horizons screened within individual wells, and expected spatial variability in both concentration and hydrochemical process occurrence. Increasing concentration trends with declining elevation are evident for even minor contributing solutes such as sulphate and nitrate. Specific hydrogeochemical controls are evident from inspection. For instance, pH buffering to approximately 7.2 occurs from high elevations to the current lake shore; groundwater is increasingly likely to attain dolomite and calcite saturation around the paleo-lake limit and approach to current shoreline. We do not pursue further ground elevation-water quality relationships herein, but note their potential usefulness in basin groundwater systems such as Chilwa.
## 6 Use-Case Examples
### Specialties of V2X Positioning  
Of all the use cases related to 5G positioning, V2X scenarios appear most frequently in scientific publications and reports. This is mainly due to the increased public expectations for autonomous vehicles on public roads. In general, 5G V2X requirements (in terms of throughput, delay, and other QoS metrics) are set in 3 GPP recommendation TS22.186 [105] for various use cases. These include trajectory sharing and coordinated driving, vehicle platooning, and remote driving, among others, which may or may not require position data retrieval and sharing. [PERSON] et al. [106] have summarized the key features and roadmap of the new 5G wireless system that will help meet these requirements. Similar to other authors, they emphasize the importance of sensor fusion, multi-connectivity, and the various security aspects of 5G V2X communications. They also devote a short chapter to precise positioning and describe the main features of NR-V2X that contribute to more precise positioning compared to LTE-V2X.  
According to [PERSON] et al., positioning for V2X applications is supported by five properties of 5G (and later 6G): high carrier frequencies, large bandwidths, large antenna arrays, device-to-device communications, and ultra-dense networking [107]. They reinforce the general view that accurate positioning will rely on a combination of sensors. In the highlighted promising research directions, gaps identified include tracking algorithms, information fusion of device-centric and network-centric positioning, accuracy, and reliability.  
[PERSON] and her co-authors focus on how 5G and beyond positioning can support vehicle safety applications [108]. They emphasize that joint communication strategies, such as integrated mobile communication, positioning, and radar (in short: JRC ), will be key to safety-critical vehicular applications. One of their concluding remarks is that during the definition and standardization of 6G, the communication parameters that affect JRC performance should be watched closely. Another important suggestion is that the waveform design (such as OFDM) should consider radar performance requirements for vehicular safety scenarios.  
In their paper, [PERSON] et al. analyze various solutions for long-term and high-precision positioning for new V2X mobile radios [109]. They propose a novel selection-positioning method to dynamically switch between GNSS and downlink TDOA measurements based on the locations of V2X UEs and the accuracy of the collected measurements.
## 2 System Model
### Cell Selection, User Association, and Handover of GUEs in AG-HetNet  
Using the familiarity with the system model defined so far, the ICIC interference mitigation techniques defined across all 3 GPP Releases, and the design for reduced power CSF and USF specified in Figure 4, we define the SIR experienced by a \(n\)th arbitrary GUE scheduled in USF or CSF of the base stations of interest (MOI/UOI) by following an approach similar to that given in [4; 5] and Table 3. Let \(\Gamma^{\text{mbs}}_{\text{usf}}\), \(\Gamma^{\text{mbs}}_{\text{csf}}\). \(\Gamma^{\text{uabs}}_{\text{usf}}\), and \(\Gamma^{\text{uabs}}_{\text{csf}}\) be the SIRs for the GUE scheduled in the CSF or USF radio subframes of the base station of interest (MOI/UOI). In Table 3, we define \(\mathbf{I_{agg}}\) as the aggregate interference experienced at scheduled GUEs from all base stations except the base stations of interest (MOI or UOI).  
The process of cell selection uses the SIR definition given in Table 3 for each base-stations of interest (MOI and UOI) and the positive-biased CRE \(\tau_{\text{uabs}}\) at UABSs. The positive-biased CRE \(\tau_{\text{uabs}}\) at UABSs is applied to increase the SIR coverage. Consequently, during a cell selection, the GUE always prefers capping on base stations of interest (i.e., MOI or UOI), which guarantees the most suitable SIR. After cell selection, an MBS-GUE (MUE) and UABS-GUE (UUE) would be scheduled in either USF or CSF radio subframes based on the scheduling threshold of \(\rho_{\text{uabs}}\) at UABS and \(\rho_{\text{mbs}}\)) at MBS. This strategy of cell selection and GUE scheduling in USF or CSF subframes of base stations of interest (MOI or UOI) is similar to that of [4; 5] and is summarized in Figure 5.  
Once the \(n\)th arbitrary GUE is assigned to the base stations of interest (i.e., MOI or UOI) and scheduled in the USF or CSF radio subframe, using the SIR definitions, the Shannon capacity of the GUE scheduled in the CSF and USF subframes is defined by \(\Gamma^{\text{mbs}}_{\text{usf}}\), \(\Gamma^{\text{mbs}}_{\text{csf}}\), \(C^{\text{uabs}}_{\text{usf}}\), and \(C^{\text{uabs}}_{\text{csf}}\). In Table 3, \(N^{\text{uue}}_{\text{usf}}\), \(N^{\text{uue}}_{\text{csf}}\), \(N^{\text{mue}}_{\text{usf}}\), and \(N^{\text{mue}}_{\text{csf}}\) are the number of MUEs and UUEs scheduled in the USF or CSF subframes of the UABS and MBS, respectively.  
Figure 4: Illustration of LTE-Advanced frame structures for time-domain ICIC techniques, i.e., almost blank subframes (ABS) with \(\alpha=0\) is the 3 GPP Release-10 eICIC, \(0<\alpha<1\) is the reduced power, and ABS (RP-ABS) is the 3 GPP Release-11 FeICIC.  
Figure 5: Cell selection, association, and handover of GUEs in coordinate and uncoordinated radio subframes for all base stations in the AG-HetNet.
# Recent Advances in Efficient Dynamic Graph Processing
## 6 Future Work
### Efficient Approaches for Streaming Graphs  
Streaming graph algorithms are an emerging and important area of research, especially as real-world graphs (e.g., social networks, communication networks, and sensor networks)increasingly involve dynamic changes such as the arrival of new vertices and edges over time. While there has been significant progress in dynamic graph algorithms, streaming graph algorithms are still relatively underexplored. By focusing on problems like community detection, path planning, and clustering, and leveraging approximate approaches to balance efficiency and accuracy, researchers can develop scalable and practical solutions for dynamic graphs. Future work should also explore theoretical foundations, adaptive algorithms, and integration with real-world systems to further advance this field.
## 2 Background and Related Technologies  
### Leo  
Low Earth orbit (LEO) satellites operate at an altitude of up to 2000 km above Earth and play a critical role in providing global coverage and low transmission latency. These characteristics make LEO satellites highly suitable for applications such as high-speed Internet provision, remote area connectivity, Earth observation, and environmental monitoring. Specifically, their ability to support high-density data transmission and real-time communication makes them ideal for services requiring low-latency connections. Compared to traditional Geostationary Orbit (GEO) satellites, LEO constellations offer more flexible and scalable network configurations.  
In response to the growing demand for global connectivity, LEO satellite networks have been rapidly expanding, driven by large-scale constellation projects from companies such as Starlink and OneWeb [5]. However, despite their scalability, the operation of LEO constellations presents several technical challenges. For example, the large number of satellites increases network maintenance costs, and the rapid movement of the satellites results in frequent handovers. To address these challenges, advanced technologies such as automated network management systems, efficient handover mechanisms, and satellite-to-satellite laser links have been actively developed. These innovations not only improve overall performance but also pave the way for more reliable and sustainable LEO network operations.
# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 3 Dataset Construction  
In this section, we construct a labeled TCS environmental pattern dataset for the training and validation of the deep learning models. First, remote sensing images and DEM data of TCSs are collected from three areas with different topographic conditions in China. Next, the collected data are preprocessed to generate usable training and testing data.
Studies show that optimizing process parameters and post-treatments like heat treatment can improve the fracture resistance and durability of these components [9]. Components made from AlSi10 Mg via L-PBF tend to have a unique microstructure that affects their mechanical performance. Heat treatments, such as annealing, can significantly alter this microstructure, influencing hardness, ductility, and fracture mechanisms. For example, an annealed AlSi10 Mg part may exhibit different failure modes compared to a non-treated one, with improved fracture toughness in some cases [10]. This alloy has been widely used in prototype vehicle parts due to its good balance of strength and lightweight. However, real-world component failures are often related to the presence of residual stresses, which are introduced during the L-PBF process. Optimizing the laser parameters and applying appropriate post-processing strategies can mitigate these issues [11]. The presence of adverse residual stresses has a special impact on the fatigue phenomena. Fatigue is a process of progressive material degradation that occurs when a material is subjected to dynamic loadings. This degradation consists of the initiation and propagation of one or more cracks in parts subjected to dynamic stresses leading to their failure. Residual stresses interact with applied stresses by modifying the loading cycle. It can be said that residual stresses act as mean stress. For this reason, they interact with the mean stress of the applied stress cycle, thereby modifying the effective stress ratio, which can be detrimental to the fatigue life [12]. The main objective of this work is to prove, using a real functional part, the benefits of a heat treatment previously studied by the authors [9] on fatigue behavior. This treatment is similar to the concept of a traditional TS, also known as stress relief heat treatment, for aluminum alloys. The chosen functional part was a bicycle crank arm. Typically, the cranks are made from aluminum alloy and are subjected to different types of cyclic combined stress as tensile, torsional, and bending [13]. In this work, the analysis was conducted in two phases: first, the development of an improved crank design with various modifications aimed at reducing the weight produced by L-PBF from a conventional crank design. The second phase was to analyze the fatigue life in two different conditions: a crank arm subjected to stress-relieved heat treatment and an as-build condition.
## 4 Results and Discussion
### The Results of the ADI Mitigation Models
#### 4.2.6 The Results of the HGB Classifier-Based ADI Mitigation System

Table 30: The Bit Error Rates of the HGB classifier-based mitigation systems with different learning rates. the learning rate increases. Similarly, the SNR improves with higher learning rates, with Model 1 showing an increase in the SNR from \(-11.40\) dB to \(-9.90\) dB, demonstrating a noticeable enhancement in signal quality. The extended configuration approach generally results in a lower BER and a higher SNR, with Model 2 and Model 3 showing improved performance in Scenario Two, where the SNR reaches \(-10.30\) dB at a learning rate of \(0.032\). These findings suggest that higher learning rates, especially in extended configurations, lead to more effective ADI mitigation, with improved signal clarity and error reduction.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
###### Abstract  
The multiplicity of targets of the 5G and further future technologies, set by the modern societies and industry, lacks the establishment of design methods for the highly multidisciplinary application of wireless platforms for small cells. Constraints are set by the overall energy concept, structural safety and sustainability. Various Smart poles and Light poles exist but it is challenging to define the design drivers especially for a composite load-carrying structure. In this study, the design drivers of a composite 5G smart pole are determined and the connecting design between finite element modelling (FEM), signal penetration and computational fluid dynamics (CFD) for thermal analysis are reported as an interdisciplinary process. The results emphasize the significant effects of thermal loading on the material selection. The physical architecture, including various cutouts, is manipulated by the needs of the mmW radios, structural safety and the societal preferences of sustainable city planning, i.e., heat management and aesthetic reasons. Finally, the paint thickness and paint type must be optimized due to radome-integrated radios. In the future, sustainability regulations and realized business models will define the cost-structure and the response by customers.  
Keywords:tubular composites; finite element analysis; computational fluid dynamics; wireless communication; signal attenuation +
Footnote †: journal: Applied sciences
# Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities
## 7 Knowledge Extraction
### Sentiment Analysis  
Sentiment analysis computes the polarity (positive versus negative) of targets, such as documents, sentences, topics, and named entities. Therefore, it is useful to determine how targets are perceived by the public or to identify conflicting targets, i.e., targets with a high standard deviation of the sentiment value, which indicates that the coverage of these targets is framed differently in the analyzed outlets.  
The Swiss Media Criticism portal uses a context-aware sentiment analysis approach to determine the text sentiment, which considers the text's context prior to evaluating its sentiment. Therefore, it combines a static sentiment lexicon that contains sentiment terms \(t_{i}\) that either indicate a positive or negative sentiment (e.g., good and excellent versus bad and horrible) with a contextualized sentiment lexicon, which determines the sentiment value for ambiguous sentiment terms (\(t_{i}\in T_{ambig}\)) based on the text's context \(C_{i}\), expressed as a set of non-sentiment terms within the text. The term expensive, for instance, is considered negative in conjunction with context terms such as overpriced, while the context terms high value and quality might indicate a positive usage of this term.  
Aggregating the sentiment value of all terms \(t_{i}\) within a sentence with context \(C_{i}\) yields the total text's sentiment (Equation (3)).  
\[s_{\text{sentence}} = \sum_{i=1}^{n}\mathcal{N}(t_{i})\cdot s^{\prime\prime}(t_{i},C_{ i}) \tag{3}\]  
The sentiment lexicon \(s^{\prime\prime}(t_{i},C_{i})\) yields the contextualized sentiment value for ambiguous sentiment terms \(t_{i}\) and falls back to the static sentiment lexicon \(s(t_{i})\) for unambiguous sentiment terms or if no context terms are available.  
\[s^{\prime\prime}(t_{i},C_{i}) = \begin{cases}s^{\prime}(t_{i},C_{i})&\text{if $t_{i}\in T_{ \text{ambig}}$ and $C_{i}\
eq\emptyset$}\\ s(t_{i})&\text{otherwise}\end{cases} \tag{4}\]  
Figure 3: Named entity linking with Recognyze.  
A function \(\mathcal{N}(t_{i})\) considers negations by inverting the term sentiment if \(t_{i}\) has been negated.  
\[\mathcal{N}(t_{i}) = \begin{cases}-1&\text{if $t_{i}$ is negated}\\ +1&\text{otherwise}\end{cases} \tag{5}\]  
The system also propagates the sentence sentiment to topics, sources, and entities, i.e., it allows assessment of whether a certain entity occurs frequently in a positive or negative context or how a certain new media site frames a particular topic.
# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
## 3 System Illustration  
Figure 1 shows the differences between the general hierarchical localization method and DLALoc method. Dark blue and cyan flow is the method proposed by [5]. In this method, the last step of estimating a query image pose is based on the P3P method [39]. Using RANSAC to perform robust pose estimation inside a P3P method is the most time-consuming step in the whole pipeline of this visual localization method. In order to release the burden of this step, we proposed DLALoc, which is illustrated as dark blue and yellow flow. Same as [5], DLALoc is still structure-based and the first step is to retrieve the most relevant database images. Then, the 2D-2D feature correspondences are calculated through some feature matching strategies. Different from [5], we do not obtain these 2D-2D correspondences directly from these feature matching strategies, which contain many outliers. Instead, we use a pretrained MLP that models the 3D structure of the scene to filter outliers of 2D feature correspondences, thereby establishing more robust 2D-3D matching pairs in the next step. The filtration of outliers is based on optimal relative pose estimation of two images (i.e., a query image and a database image) with RANSAC. We find the maximum 2D correspondence set whose reprojection error on another image according to epipolar constraint is under the threshold. Moreover, by using the mesh to represent  
the scene, we could obtain the 2D-3D matching pairs from the rendered depth map, and these 2D-3D matching pairs are more precise than 5 fm model. Thus, we take advantage of this property, and propose to use the scale consistency to a step further help us to filter out the outliers. To be specific, the different depth information of five 2D-3D matching pairs between the depth map and output from the MLP can give a preknowledge of whether the relative pose has been accurately calculated or the input to calculate the relative pose contains outliers. In this way, we can skip the inner validation step of some wrong solutions to perform a more efficient RANSAC and obtain robust 2D correspondences in a short time.  
As illustrated above, in DLALoc method, because the 2D information can be handled more efficiently, we split the RANSAC in the P3P method into two steps. In the first step, we used RANSAC to filter 2D outliers. In the second step, RANSAC is used for robust pose estimation whose inputs contain less noise. The first RANSAC step was much more efficient than the second step. Therefore, the entire visual localization algorithm can be greatly improved in terms of efficiency and accuracy.