esa-sceva/satcom-chunk-collection huggingface dataset:
# Fluctuations of Tracks and Layers during Aluminium Laser Powder-Bed Fusion
## 1 Introduction
[20] developed an analytical model that considers the powder availability for different tracks within one layer and observed that tracks can be completely remelted and are partly not even visible anymore in the cross section. It was further shown that the track shapes can significantly vary due to different amounts of available powder material for each single track. The reason for the variations is assumed to originate from the loss of powder due to spattering [20] and denudation effects [21]. Two dynamic processes were observed that led to a loss of material for building the tracks. Spattering was observed, denoting melt pool ejections that reduce the amount of molten volume for the track. In addition, powder in the vicinity of the processing zone is blown away. Both spatters and powder loss lead to a loss or redistribution of powder and agglomerates on the powder bed. Agglomerations of spatter even lead to larger particles on the powder, where following tracks will be built, which can alter the processing. Denudation effects were observed in X-ray analyses [22] and fluid dynamic calculations [23], while the Bernoulli Effect was suggested to be the main driver for powder particle movement on the powder bed before the laser interaction [24]. Even though the LPBF process is working in practice, there is still limited knowledge about the process fundamentals and, in particular, the impact of the track dimensions based on the previously built tracks and layers and powder availability variations. A deeper knowledge about the influences of process behaviors on the track and layer development would help to increase the process efficiency/speed and decrease powder loss by applying only the energy needed to enable the melting of the powder material with a slight but sufficient melting the previously built structure. Given this scenario, the present work is aimed to investigate the impact of the actual powder availability on the track and layer formation in LPBF. Track and layer appearances were modelled at different hatch distances and compared to experimental results. Possible influencing factors on the varying powder availability are discussed.
## 4 System Model
### Simulation Setup  
Extensive simulations have been conducted with MATLAB 2018a simulator to evaluate the performance of the MRLAM routing scheme, which is compared with MP-OLSR and its extended version MP-OLSRv2 routing schemes, using different speed of node scenario. Random topologies with a maximum of 49 nodes are generated over a rectangular field in the area of 1000 m \(\times\) 1000 m. The nodes are placed in the middle of the simulation area and seven data sources are randomly chosen for the scenarios and all sources are transmitting their data to the destination node. The Constant Bit Rate (CBR) generates traffic into networks having a size of the CBR 512 bps from sources and the simulation runs in total for 200 times. 802.11a standard and modules of the wireless physical  
Figure 3: Route selection procedure of the Mobility, Residual energy and Link quality Aware Multipath (MRLAM) routing scheme.  
layer are utilized in the simulation in order to provide a high level of accuracy. The User Datagram Protocol (UDP) has been used as the transport layer protocol, which, in contrast to Transmission Control Protocol (TCP), provides a simple transmission model. Random Waypoint Mobility model is used as the mobility standard that enables node speed variation from 10 m/s to 60 m/s. The other performance evolution scenarios are presented in Table 2 as follows:
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 3 Low Complexity Equalizers for UP-OCDM under Doubly Selective Channels
### Band MMSE Block Equalizer
#### 3.2.1 Band MMSE Equalization Algorithm
In summary, by exploiting the band LDL\({}^{H}\) factorization algorithm (Algorithm 1) of \(\mathbf{R}\), the MMSE estimate of symbol vector, which is denoted as \(\mathbf{\hat{d}}_{MMSE-BLE}\) can be efficiently obtained in four steps:  
(1) Performing FFT algorithm on the received signal \(\mathbf{r}\) to obtain \(\mathbf{r}_{F}\);(2) Constructing the banded matrices \(\mathbf{E}\) and \(\mathbf{R}\). Performing the LDL\({}^{H}\) factorization \(\mathbf{R}=\mathbf{LDL}^{H}\) to obtain the diagonal matrix \(\mathbf{D}\) and the triangular matrix \(\mathbf{L}\);  
(3) Solving the system \(\mathbf{R}\mathbf{y}=\mathbf{r}_{F}\) by solving firstly the lower-triangular system \(\mathbf{L}\mathbf{f}=\mathbf{r}_{F}\), secondly the diagonal system \(\mathbf{D}\mathbf{g}=\mathbf{f}\), and finally the upper-triangular system \(\mathbf{L}^{H}\mathbf{y}=\mathbf{g}\);  
(4) Obtaining the estimate of symbol vector by \(\mathbf{\hat{d}}_{MMSE-BLE}=\mathbf{F}^{H}\mathbf{E}^{H}\mathbf{y}\). ```
\(\mathbf{v}=\mathbf{0}_{N\times 1}\); \(\mathbf{L}=\mathbf{I}_{N}\); \(\mathbf{D}=\mathbf{I}_{N}\) for\(k=0\) to \(N\)do \(m=\max\{1,j-2Q\}\); \(n=\min\{j+2Q,N\}\) for\(i=m\) to \(j-1\)do \(v_{i}=\mathbf{L}_{j,i}^{*}\mathbf{R}_{i,j}\); endfor \(v(i)=\mathbf{R}_{i,i}-\mathbf{L}_{j,m;j-1}v_{m;j-1}\) \(\mathbf{L}_{j,j}-\mathbf{L}_{j,m;j-1}\) \(\mathbf{D}_{j,j}=v_{j}\) \(\mathbf{L}_{j+1:n,j}=\left(\mathbf{R}_{j+1:n,m;j}-\mathbf{L}_{j+1:n,m;j-1}v_{m;j-1}\right)/vj\) endfor
```  
**Algorithm 1** band LDL\({}^{H}\) factorization algorithm
## 3 Proposed Approach and Methodology
### System Architecture
#### 3.1.2 Multi-Agent Reinforcement Learning  
Figure 6 illustrates the centralized control algorithm, where there is no direct communication between agents. Our research has explored various junction types, starting with a single junction [24] and progressing to two [22] and now three, focusing on the behavior and dynamics of different traffic scenarios in these environments. Because the junctions are homogeneous, the similar experiences observed by each agent allow for training of a single neural network, which acts as a global agent to make decisions and determine the best actions. This approach has proven effective in managing both pedestrian and vehicle traffic at these intersections. However, large occupancy peaks in the queues can sometimes occur, leading to congestion and inefficient traffic flow.  
The global agent can dynamically direct local agents to reroute traffic, avoiding congestion zones and ensuring smoother flow. By monitoring traffic density and flow patterns, it adjusts signal phases across intersections to create synchronized green waves that enhance throughput and reduce stop-and-go behavior. For instance, it may shorten green time at one intersection to prevent overwhelming a downstream intersection [27; 28; 29].  
From our study across various scenarios, we gained insights into traffic queue behavior and identified lane capacity limits. To address the lack of direct communication between agents, we integrated this knowledge into the network by setting threshold values for queues. This allows the global system to manage traffic flow in critical sections by evaluating the volume that can be accommodated in each direction.  
Figure 6: A schematic of the algorithm employed using centralized MARL.  
Although the neural network is trained centrally, traffic signal agents at each intersection locally implement these rerouting strategies. Congestion thresholds are set to adapt and optimize traffic flow, ensuring efficient management even in high-demand conditions.
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 2 Literature Review
### Variables Selected in Baseball Research  
In the 1970s, [PERSON] began to write the book _Baseball Summary_ and proposed sabermetrics (also known as baseball statistics) in the late 1970s [18]. There is no clear distinction between common baseball statistics and sabermetrics. Common statistics include the numbers of home runs (HRs), runs batted in (RBIs), and stolen bases. Data such as the on-base percentage (OBP), on-base plus slugging (OPS), and earned run average (ERA) that need to be calculated are called sabermetrics.  
Many websites record MLB baseball game information, and each website records slightly different statistics. The most popular websites include Baseball-Reference, FanGraphs, [PERSON]'s database, and Retrosheet. Related studies [8; 9; 19] have used competition files from Retrosheet and different feature selection methods to select the most relevant variables for input into the models for predicting the outcome of a match. [PERSON] and [PERSON] [20] used baseball data from FanGraphs, [PERSON]'s database, and mlb.com to predict the outcome of an MLB league championship. No studies have used data from Baseball-Reference to predict the outcome of a baseball game. The data recorded on the Baseball-Reference website are sufficient and detailed; these data include the number of pitches in the plate appearance (PA) (Pit) for batters, number of swinging strikes (StS) for pitchers, and game score (GSc) for starting pitchers (SPs). Therefore, in this study, the Baseball-Reference website was selected for the collection of baseball data.  
In 2018, [PERSON] and [PERSON] [21] reviewed the literature on outcome prediction for MLB matches. Most studies predicted pitchers' performance, which indicates the importance of pitchers in baseball games. [PERSON] [9] used the competition data of 30 teams in the MLB from 1930 to 2016 to construct a predictive game win-loss model. The research variables were hitting, pitching, and defensive data, all of which were team data. [PERSON] et al. [7] constructed a predictive game win-loss model for the Boston Red Sox and the New York Yankees in 2014, and only selected data from the SP were used as the input variables. Selecting different research variables provided different prediction results. Therefore, to compare the prediction accuracies, the present study divided the pitcher data into two data sets: data set 1 included the data on the SP only, and data set 2 included data on all pitchers.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Transport Layer
#### 3.2.2 Steam Control Transmission Protocol  
SCTP was initially designed for VoIP and telephony services and standardised in 2007; SCTP is a transport layer protocol developed to address limitations inherent in TCP and UDP [72]. It is distinguished by its multi-streaming and multi-homing capabilities, along with the capability to support multiple IP addresses per association. Consequently, these unique facets have propelled the expansion of SCTP into a wider array of applications, augmenting its significance and relevance in the IoT communication landscapes [73].
## 2 Materials and Methods
### Life Cycle Inventory Analysis
#### 2.2.2 Inventory Analysis of the RFID Tag  
The RFID tag used in milk cartons comprises several key components that are laminated together, with the main elements being a silicon chip for storing information and an aluminum antenna for capturing radio frequency waves. Detailed data on the materials and weights of these components were provided by Smartrac company, as shown in Supplementary Table S2. The antenna is made of aluminum, produced through a deposition process, and the chip is composed of silicon and a tetravalent metal semiconductor. Adhesive layers, such as acrylic and epoxy resins, bind these elements together, resulting in a total tag weight of approximately 0.2 g.  
For assessing the environmental impact of the RFID tag's production, a conservative approach was adopted:  
* Antenna impact: The environmental impact of the aluminum antenna was estimated by reducing the environmental footprint of a printed wiring board antenna by a factor of four. This approach likely results in an overestimation of the actual environmental burden of the tag antenna.
* Chip impact: The environmental impact of the silicon chip was calculated with a conservative approach, halving the environmental impact of integrated circuit production. This adjustment was made because the RFID chip is thinner than the typical wafer used in circuit production.  
Energy consumption during the assembly phase is estimated at 0.007 kWh per tag. This figure was used in the environmental assessment, which utilized the European electricity mix data for analysis. Several transportation steps were considered in the environmental assessment:  
1. The antenna and chip are manufactured in China and air-shipped approximately 6000 km to Smartrac's headquarters in Stuttgart, Germany, followed by a 200 km truck journey within Germany.
2. Other RFID tag components are sourced locally within a 200 km range from Stuttgart.
3. The completed RFID tags are shipped to retail sites in Italy via a 1000 km plane journey from Frankfurt to Milan, followed by a 200 km truck journey to deployment sites.  
These transport distances were factored into the environmental impact analysis, following standard assumptions from literature.  
Once attached to milk cartons, RFID tags typically end up in landfills as electronic waste since consumers dispose of the entire milk carton, including the tag, along with household waste. These cartons are generally not separated for recycling. The disposed cartons, along with RFID tags, are transported to landfills where they are buried. Due to their small size and quantity, RFID tags contribute minimally to the overall electronic waste stream, but their disposal was included in the environmental impact assessment to ensure a comprehensive analysis of their lifecycle.
# Research on Inertial Force Attenuation Structure and Semi-Active Control of Regenerative Suspension
## 5 The Verification of HVRS Suspension Characteristics
### Semi-Active Control Test Verification  
Due to the significant changes in the suspension configuration compared to traditional suspensions after the addition of an HVRS, it is necessary to verify whether the skyhook control algorithm is effective for HVRS suspensions. The skyhook control algorithm was first proposed by [PERSON] et al. [31] and has been widely studied in academia and automotive industries as a means to improve ride comfort and road holding of vehicles with semi-active suspension.  
To achieve semi-active control of the HVRS energy-regenerative suspension, the sprung mass speed \(v_{s}\) and the damper speed \((v_{s}-v_{h})\) are selected as inputs for the skyhook control algorithm. The skyhook semi-active control algorithm for HVRS suspension is shown in Equation (27).  
\[c_{s}=\left\{\begin{array}{ll}c_{\rm sky}v_{s}\ /(v_{s}-v_{h})&c_{s}\in(c_{ \rm min},c_{\rm max}];&v_{\rm s}(v_{\rm s}-v_{\rm h})>0\\ c_{\rm min};&v_{\rm s}(v_{\rm s}-v_{\rm h})\leq 0\end{array}\right. \tag{27}\]  
Using the excitation signal of a vehicle traveling at a speed of 60 km/h on a C-level road surface as input, the semi-active control of HVRS suspension and the passive suspension state of the HVRS are compared and simulated, with a simulation duration of 20 s. The damping coefficient of passive suspension is constant. The time-domain data curves of the sprung mass acceleration, relative dynamic load, and dynamic deflection of two suspension configurations were obtained through experiments, as shown in Figure 16. For clearer comparison, only 5 s of data are shown in Figure 16. The comparison of root mean square values of suspension characteristic data is shown in Table 7.  
Through comparison, it can be seen that the semi-active control of HVRS suspension reduces the sprung mass acceleration by 5.98% compared to passive suspension, reduces the relative dynamic load by 7.23%, and increases the dynamic deflection by 2.56%. This indicates that the application of semi-active control algorithms in HVRS suspension can further improve performance while reducing the impact of equivalent inertial mass.  
Figure 16: (**a**) Comparison of sprung mass acceleration; (**b**) comparison of relative dynamic loads; (**c**) comparison of dynamic deflection.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Status** & \(\sigma_{\rm z_{s}}\)**/m/s\({}^{2}\)** & \(\sigma_{\rm F_{\mu}}\)/\(c_{s}\) & \(\sigma_{\rm s_{\mu}}\)/m** \\ \hline Passive HVRS & 1.8912 & 0.1936 & 0.0156 \\ Semi-active HVRS & 1.7781 & 0.1796 & 0.0160 \\ Changed & \(-5.98\%\) & \(-7.23\%\) & \(+2.56\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of HVRS suspension test results.
## 3 Experiments and Analysis  
### Experimental Setup  
The experiment was conducted on the first to fourth floors of two different buildings. For example, the fourth floor of one building is shown in Figure 2. A total of 1035 fixed APs operating on 2.4 and 5 GHz bands were deployed across the two buildings, covering an area of 717.12 square meters. The experimental area was divided into 490 grids, including the hallways of all the floors and the staircases, with 24 grids. Each grid represents \(1.2\times 1.2\) m\({}^{2}\) and was assigned a unique tag number. The fingerprint dataset contains 40,315 entries collected using WiFi scanners on five mobile phones. Each scanner reported its correct location to the database, which included information such as grid tag (location), timestamp, device model, medium access control address, and RSS values from each AP. The mobile phones were randomly placed on a small cart roughly near the center of each grid during the measurement. The measurements were conducted in daytime, for which moving people could be an obstacle between the APs and the receiving devices. Moreover, since most APs were located in the research labs and office rooms and the receiving devices were in the hallways, the inner walls were also obstacles to the WiFi signal propagation.  
We normalized the dataset to ensure that each feature was given equal weight, eliminated the influence of outliers, and accelerated gradient descent for optimal solution convergence. The normalized dataset follows a standard normal distribution with a mean of 0 and a standard deviation of 1. The RSS value of non-positioning APs was arbitrarily set to \(-1000\) dBm. Figure 3 shows examples of RSS values obtained from grids 4425 and 4426. The collected data were randomly split into training, validation, and test sets in a 70:15:15 ratio. We used the Python library _scikit-learn_ to implement Algorithm 1.
# Invasive Prenatal Diagnostics: A Cornerstone of Perinatal Management
## 2 Techniques of Invasive Diagnostics
### Amniocentesis
#### 2.1.2 Intra-Amniotic Infections  
Intra-amniotic infection (IAI) is proven to be one of the factors leading to preterm delivery (PTD) [98; 99; 100]. Moreover, there is strong evidence indicating a higher frequency of neonatal brain injury in foetuses exposed to chorioamnionitis [101; 102]. Therefore, it appears crucial to eradicate microorganisms in order to prolong pregnancy and improve outcomes in patients with threatened preterm delivery and confirmed IAI. Numerous studies have shown that intraamniotic microbial invasion can be successfully treated with specific antibiotic regimens based on ceftaxone, clarithromycin, and metronidazoleade [103; 104; 105]. However, the evidence is limited, and it does not directly demonstrate improvement in perinatal outcomes since the neonatal death rate and the incidence of bronchopulmonary dysplasia, periventricular leukomalacia, intraventricular haemorrhage, and necrotising enterocolitis were not improved [104]. In 2001, [PERSON] et al. [106] proposed the following phenotypes of intrauterine inflammation: intra-amniotic inflammation with confirmed microbial invasion (MI) and sterile intra-amniotic inflammation without MI. A study conducted by [PERSON] et al. [104] showed adverse effects of antibiotic therapy among patients with threatened preterm birth and microbe-negative amniotic fluid. At the same time, the authors emphasised the importance of IAI evaluation in management in PTD. Currently, the most credible method of diagnosing IAI is a direct bacterial culture or PCR analysis. However, in recent years, many biomarkers of intra-amniotic inflammation have proven to be valuable. In 2011, [PERSON] et al. [107] first showed that matrix metalloproteinase-9 (MMP-9) and interleukin-6 (IL-6) obtained from amniotic fluid had a higher predictive value of IAI than maternal serum C-reactive protein (CRP). In 2015, [PERSON] et al. [108] presented evidence that rapid tests of MMP-8 in amniotic fluid can identify 42% of spontaneous PTD among asymptomatic pregnant women. In 2017, [PERSON] et al. [109] analysed the concentration of proinflammatory biomarkers derived from activated and degranulated neutrophils. The authors concluded that the concentration of neutrophil-based amniotic fluid biomarkers, such as MMP-8, MMP-9, IL-6, myeloperoxidase (MPO), elafin, and tissue inhibitor of matrix metalloproteinase-1 (TIMP-1), were associated with IAI and MI. However, the number of inflammatory mediators was also reported simultaneously in maternal serum during IAI [110; 111; 112; 113; 114]. Therefore, amniocentiss is not routinely offered to patients with PTD.
## 4 Experiment and Results
### Comparative Experiments
#### 4.1.1 Comparative Experiments on PH\({}^{2}\)
When the lesion occupies a larger proportion of the image, the performance differences among the models become more pronounced. As exemplified in Figure 11a, U-Net tends to exhibit under-segmentation, failing to fully capture the extent of the lesion area. Although LCAUnet and SCSONet demonstrate improvements over U-Net in segmenting the primary regions, LCAUnet produces excessively smoothed boundaries, leading to a loss of fine-grained details, while SCSONet struggles to accurately preserve irregular boundary structures when compared to the proposed MA-DenseUNet. Overall, these visual results substantiate that the proposed MA-DenseUNet can more effectively capture intricate lesion features and suppress noise-induced artifacts, thereby generating segmentation maps that more closely approximate the ground truth annotations.