esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
First, we did not run a psychometric validation of the Danish translation of the vitamin D knowledge, attitudes, and behaviors' questionnaire, considering that the pilot test was sufficient for our purposes. The questions on ethnicity and sociodemographic characteristics in the questionnaire were self-constructed. For the sociodemographic questions, usual Danish phrasing and response options were used, and we did not anticipate validity problems here. Ethnicity in the study was defined as self-reported ethnicity, and operationalized via categorization used by Statistics Denmark: Danes, Western immigrants and descendants (i.e., children of immigrants), and non-Western immigrants and descendants. Our initial intention was to categorize ethnicity based on our respondents' and their mothers' and fathers' country of origin, using self-reported ethnicity for sensitivity check. However, a large number of respondents did not provide information on their country of origin, therefore we used self-reported ethnicity for ethnicity categorization. The analyses of those who provided information on the country of origin showed that the majority of the respondents were children of people who immigrated from Turkey and Somalia, which reflected well the Statistics Denmark data [26]. Some individuals, being born and raised in Denmark and talking fluently Danish may consider themselves to be Danes, even if their parents came from abroad, which, according to the Statistics Denmark definition, would make them descendants. Thus, we may have missed some individuals who would be considered as non-Western descendants by Statistics Denmark. Another limitation of our study is its generalizability: we do not think that the respondents in the study represent all the non-Western ethnic minority women in Denmark. There are a few reasons to claim that. Firstly, the questionnaire was in Danish, so all who do not speak the language were automatically excluded. Our choice to distribute the questionnaire in Danish was based on the intention to reach for diversity of non-Western ethnic minority groups. Secondly, the questionnaire was distributed online, and only those who have e-mails and are members of Facebook groups could be reached. Furthermore, the majority of the questionnaires was distributed to the university students, which was reflected in educational background of the respondents. To raise representability of similar surveys in the future, other ways to reach out and recruit non-Western ethnic minorities (i.e., via nongovernmental organizations, religious or cultural communities) should be explored and utilized. Moreover, as non-Western immigrants and descendants is a culturally heterogeneous group, in the future studies in more homogenous with respect to nationality and religion samples, as well as in depth qualitative studies should be conducted to further explore the specifics of vitamin D knowledge, attitudes, and behaviors in non-Western minorities in the Nordic countries.
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 2 Millimeter-Wave Antenna Array for 5G Communication
### Millimeter-Wave Antenna Arrays for 5G Mobile Terminals
#### 2.1.2 Multi-Polarized Multilayer Phased-Array Antennas for 5G Mobile Terminals
The implemented  
Figure 7: **(a)** Two discrete 28-GHz mm-wave antenna elements, i.e., the horizontally polarized planar Yagi-Uda antenna (left), and the vertically polarized multi-plate antenna (right). **(b)** Final topology of vertically polarized multi-plate antenna. **(c)** 16-element phased-array configuration at the edge of the mobile terminal. **(d)** Photograph of the prototype testing in an anechoic chamber and close-up view of 5G mm-wave phased-array antenna (Redrawn from [46]). horizontally polarized and vertically polarized antenna array designs for the lateral edge as well as the multi-polarized antenna for the corner edges are shown in Figure 8b. The 10-dB impedance bandwidth is 25% with a port isolation of more than 20 dB. The peak realized gain of the array exceeds 11.8 dBi, with an overall efficiency of more than 80%.
## 5 Experiment Setup and Result Discussion
Besides that, a faster converging time to fixed solution is also obtained as shown in Table 4. However, compared with the 5.9 km baseline test, the results demonstrated some differences, e.g., the standard deviations of all the baseline components of GPS and BDS are slightly larger than that of the 5.9 km test which is possibly caused by the longer baseline. Another thing to notice is that the proportion of fixed solutions for either GPS or BDS in 9 km baseline test is higher than that of the 5.9 km baseline test. This is probably due to the smaller proportion of the converging time with a longer time span. However, there is still a gap between the fixed solution proportions of the combined method for the two short baseline tests to 100%, which indicates the limitation of the combined method--it cannot completely solve the problem of availability once and for all, some other measures should be investigated in future work. Figure 11: Number of available satellites and ratio _vs_ time of 9 km baseline experiment. \begin{table}
\begin{tabular}{l l l l} \hline
**Item** & **GPS** & **BDS** & **GPS + BDS** \\ \hline East standard deviation (fixed solution)/mm & 6.18 & 14.05 & 7.47 \\ North standard deviation (fixed solution)/mm & 9.80 & 14.12 & 6.40 \\ Up standard deviation (fixed solution)/mm & 22.52 & 25.17 & 12.55 \\ Proportion of fixed solution (ratio \(>\) 3) & 73.57\% & 44.25\% & 80.20\% \\ First time to fix/s & 1062 & 1734 & 467 \\ \hline \end{tabular}
\end{table} 
Table 4: Statistics of 9 km baseline experiment. Figure 10: ENU baseline result of 9 km baseline experiment (fixed epochs, ratio \(>\) 3).
# Solution for Active and Passive Earth Pressure on Rigid Retaining Walls with Narrow Backfill
###### Abstract  
For a retaining wall adjacent to rock or rigid structures, existing model test results indicate that the slip soil in the limit state can be approximated as a trapezoidal slip wedge. Based on the static equilibrium condition of the slip wedge, a calculation method for active and passive earth pressures is proposed that considers the effect of backfill width through extreme value analysis. As the backfill width increases, the trapezoidal slip wedge transitions to a triangular slip wedge, introducing a critical width to distinguish between finite and semi-infinite soil conditions. For cohesionless soils, the proposed method converges to Coulomb theory at the critical width; when the backfill is clay, the critical width exceeds the width of Coulomb's triangular slip wedge due to the stabilizing contribution of cohesion. Parameter analysis reveals that with increasing backfill width, the active earth pressure of cohesionless soil follows a non-linear upward trend, whereas the passive earth pressure decays exponentially. For clay, the active earth pressure initially increases with backfill width and then decreases, whereas the passive earth pressure first decays exponentially and then exhibits a slight increase. Variations in the friction angle significantly affect both active and passive earth pressures, while cohesion mainly influences active earth pressure, and wall-soil friction angle exerts a stronger impact on passive earth pressure. The effectiveness of the proposed method is verified by comparison with results from model tests and numerical simulations.  
retaining wall; earth pressure; finite soil; critical width; trapezoidal slip wedge +
Footnote †: journal: Applied sciences  
Article
## 1 Introduction
### Use on Bitumen Products in Construction Works
The proportions of HDPE, LLDPE, and LDPE in r-PE significantly influence compatibility with bitumen and PMB performance. r-PE with a melting temperature above 130 \({}^{\circ}\)C primarily consists of HDPE, which has low compatibility with bitumen. Incorporating this PE increases viscosity and heat resistance, but can cause phase instability and asphaltene precipitation at high concentrations. Conversely, r-PE with a melting temperature around 115 \({}^{\circ}\)C is mostly LDPE and shows high compatibility with bitumen, enhancing elasticity. r-PE with a melting temperature of about 125 \({}^{\circ}\)C is mainly LLDPE. When added in high concentrations, it forms a polymer network that stabilizes the bitumen across a temperature range of 20-110 \({}^{\circ}\)C, due to the high sorption capacity of r-LLDPE for bitumen components. Lower melting temperatures and crystallinity improve compatibility with bitumen, as more branched LDPE macromolecules increase solubility. At low r-PE concentrations (approximately 3% by mass), the internal structure and rheological properties of PMBs remain similar regardless of r-PE type, resembling the base bitumen's rheological behavior. Bitumens modified with r-HDPE have enhanced heat resistance. At higher r-PE concentrations (around 7% by mass), the type of r-PE significantly affects the PMB's internal structure and rheological properties. Only r-LLDPE forms a polymer network within bitumen, making it especially suitable for roofing PMB applications [19; 20; 21; 22]. Despite these advancements, there remains a need for a composite material that combines the best properties of these existing technologies while offering improved performance characteristics, particularly in terms of flexibility, tensile strength, and environmental resistance.
## 5 Cyber-Physical Inverter-Based Microgrid Implementation and Experimental Results  
In this part, we implement the real-time simulation of the islanded inverter-based MG in OPAL-RT to assess the MG's frequency control performance as a cyber-physical system. After that, it was integrated with the communication network emulation implemented in ns3 to obtain the control signals from the centralized secondary controller. Table 2 shows the tested MG and its control system parameters, considering that the two DGs have the same parameters.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Docker Container Name** & **IP Address** & **Device** & \multicolumn{2}{c}{**Signals**} & \multicolumn{2}{c}{**Modbus Space**} & \multicolumn{1}{c}{**Position in Memory**} \\ \cline{3-6}  & & & **Name** & **Data Type** & **on the Server** & **Position in Memory** \\ \hline \multirow{4}{*}{Node 1} & \multirow{4}{*}{10.1.1.21} & \multirow{4}{*}{VSI-1} & Meas f1 & float & 0–1 & Output 0 \\ \cline{3-6}  & & & Meas v1 & float & 2–3 & Output 1 \\ \cline{3-6}  & & & Ctrl f & float & 4–5 & Input 0 \\ \cline{3-6}  & & & Ctrl v & float & 6–7 & Input 1 \\ \hline \multirow{4}{*}{Node 2} & \multirow{4}{*}{10.1.1.22} & \multirow{4}{*}{VSI-2} & Meas f2 & float & 0–1 & Output 2 \\ \cline{3-6}  & & & Meas v2 & float & 2–3 & Output 3 \\ \cline{3-6}  & & & Ctrl f & float & 4–5 & Input 2 \\ \cline{3-6}  & & & Ctrl v & float & 6–7 & Input 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mapping of data exchange.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Filter inductance & \(L_{f}\) & 3.5 mH \\ Filter capacitance & \(C_{f}\) & 50 \(\mu\)f \\ Coupling inductance & \(L_{c}\) & 0.35 mH \\ Coupling resistance & \(R_{c}\) & 0.05 \(\Omega\) \\ Inner voltage controller proportional gain & \(K_{pv}\) & 0.285 \\ The microgrid voltage & \(V_{rated}\) & 110 V \\ The reference frequency & \(F_{ref}\) & 60 Hz \\ Inner voltage controller integral gain & \(K_{ip}\) & 590 \\ Inner current controller proportional gain & \(K_{pc}\) & 55 \\ Inner current controller integral gain & \(K_{ic}\) & 1570 \\ Secondary controller proportional gain & \(K_{pf\_sec}\) & 0.08 \\ Secondary controller integral gain & \(K_{if\_sec}\) & 7 \\ The total connected load & \(P_{load\_total}\) & 13.9 kW \\ \hline \hline \end{tabular}
\end{table}
Table 2: The MG parameters and the control system’s gains.  
Figure 8: Data exchange between OPAL-RT and the central controller through ns3 (machine 2_ Linux OS).  
In this paper, we perform different experimental studies to investigate the MG's frequency control performance across various real-world scenarios. The experiments commenced with a real-time simulation of the proposed MG without the centralized secondary controller under different loading conditions. Subsequently, communication links were established between the secondary and primary control layers through the proposed ns3-based communication model. Finally, we assess the frequency stability of the MG under communication delays and the emulated DDoS attack that hacks the central controller.
# Investigating the Effect of Motion Capture Suits on the Test-Retest Reliability of Gait Parameters
## 4 Discussion
### Markerless Motion Capture  
One of the key advantages of using a MOCAP suit is the increased speed at which data collection can be conducted. Markerless motion capture, which uses regular cameras as opposed to infrared cameras, is constantly improving with the use of machine learning and may provide a faster and cost-efficient alternative to traditional motion capture, rendering MOCAP suits irrelevant. Commercial products, such as Theia3D (Theia Markerless Inc., Kingston, ON, Canada), have been the subject of studies evaluating the accuracy and reliability of markerless motion capture compared to traditional motion capture. [PERSON] et al. (2023) compared the kinematics measured from the PiG model using markers and the Theia3D markerless inverse kinematics model [29]. The study [29] reported that root mean square difference (RMSD) values generally differed by less than 6\({}^{\circ}\). The markerless system did, however, show greater intertrial variability and was concluded to not yet be sufficiently accurate or reliable to pose as an alternative method to marker-based motion capture. In contrast, [PERSON] et al. (2021) [30] reported that a markerless Theia3D system gave similar values to a marker motion capture system with RMSD values lower than 5.5\({}^{\circ}\) for almost all segment angles, concluding that this is a viable alternative to traditional motion capture. However, this study [30] did not evaluate the test-retest reliability of either the marker or markerless system and, therefore, although these systems may be producing similar results, they may not be producing reliable results. As a result, it might be suggested that there is not yet evidence that markerless motion capture technologies match the established reliability of marker-based MOCAP techniques [29], although they present an exciting prospect for the future of minimally invasive MOCAP.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 4 Results and Discussion
### Discussion  
In summary, in these two case studies, the SAE model was able to detect the fault before the measured signals, showing obvious changes. The early detection of faults in pumps enables the operators to schedule maintenance at optimum time and thereafter increase the safety and reliability of the system, reduce energy consumption, service and maintenance costs. When compared with the PCA model, the SAE model can give a relatively lower FAR (around 0.1), higher FDR (over 0.9) and higher AUC value (over 0.98) in both cases. Especially when detecting the misalignment fault in case 2, the FAR for the PCA model is over 0.22 when FDR is above 0.8. In contrast, the SAE model kept its good performance with high FDR (over 0.9) and low FAR (lower than 0.1). The results indicate that a proposed fault detection scheme based on SAE is a good alternative for some conventional fault detection models in industrial fault detection systems, which are insensitive to incipient fault or have higher false alarm rates. Furthermore, the SAE performed better in isolating the relevant variables that are responsible for the fault. It can help to correctly isolate the abnormal variables of the detected fault, assisting operators and data analysts to trace back the root cause, making it easier for maintenance planning.
# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
### Definitions and Problem Statement  
Here, we provide the definitions and notations used in this paper. The following definitions are the general ones used in the field of process mining [1], with our added definition of a set of prefixes (SOP).  
* Definition 2.1.1 (Event). An _event_ is a tuple denoted \(e=(a,c,t,d_{1},d_{2},\ldots,d_{m})\), where \(a\) is the activity of the process associated with the event, \(a\in A\), while \(A\) is a finite set of process activities, \(c\) is the case identifier, \(c\in C\), \(C\) is a set of all case identifiers, \(t\) is the timestamp of the event, \(t\in T\), \(T\) is the time domain, \(d_{1},d_{2},\ldots,d_{m}\) is a list of other attributes of the event, \(d_{i}\in D_{i}\), and \(D_{i}\) is a set of all possible values of attribute- \(i\). The event universe is denoted \(\mathcal{E}\). At each event \(e\), we also define the mapping functions: \(\pi_{A}(e)=a\), \(\pi_{C}(e)=c\), \(\pi_{T}(e)=t\), \(\pi_{D_{i}}(e)=d_{i}\) where \(1\leq i\leq m\), \(m=|D|\).  
Definition 2 (Case). A _case_ is a finite sequence of events denoted \(\sigma=(e_{1},e_{2},\ldots,e_{n})\in\mathcal{E}^{*}\), where \(\mathcal{E}^{*}\) is the set of all possible sequences over \(\mathcal{E}\), \(|\sigma|=n\), \(\pi_{T}(\sigma_{i})\leq\pi_{T}(\sigma_{i+1})\), and \(\sigma(i)\) is the event in case \(\sigma\) at index \(i\). An ongoing case is a prefix case of length \(l(l\leq n)\), denoted \(\sigma^{l}=(e_{1},e_{2},\ldots,e_{l})\).
* Definition 2 (Event log). An _event log_ is a set of \(K\)-sized cases, denoted \(L=\{\sigma_{i}:i=1,\ldots,K\}\), such that each event appears at most once in the event log. So, we can write the \(i\)th case of length \(n_{i}\) as \(\sigma_{i}=\left(e_{1}^{1},e_{i}^{2},\ldots,e_{i}^{n_{i}}\right)\).
* Definition 2 (Set of prefixes). A _set of prefixes_ is a converted event log of \(N\)-size, where \(N=\sum_{i=1}^{K}|\sigma_{i}|\), which contains the prefixes and other essential properties of all events as tuples, denoted \(SOP=\{p_{k}:k=1,\ldots,N\}\), with \(p=(rt,et,pre_{act},pre_{att},t,c)\), where \(rt\) is the remaining time of a prefix case, \(rt\in\mathbb{R}^{0+}\), \(et\) is the elapsed time of a prefix case, \(et\in\mathbb{R}^{0+}\), \(pre_{act}\) is the tuple of a prefix of activities, i.e., \((a_{1},a_{2},\ldots,a_{l})\), \(1\leq|\sigma|\), \(pre_{att}\) is the tuple of a prefix of attributes, i.e., \(\left(d_{1,1},d_{1,2},\ldots,d_{|D|}\right)\), \(1\leq|\sigma|\), \(t\) is the timestamp of the last event in the prefix case, while \(c\) is the case identifier of the prefix case, \(t\in T\), \(c\in C\). We also define the mapping functions \(\varphi_{C}(p_{k})\) and \(\varphi_{T}(p_{k})\) that return \(c\) and \(t\) respectively at the \(k\)th \(p\).  
The problem we are dealing with in this paper is a supervised learning problem, namely the estimation of the remaining time \(rt\) of an ongoing business process instance. The objective is to minimize the risk function; we chose the Mean Absolute Error (MAE) over the test set.
## 2 Background and Related Work  
### NDN Structure and Operation  
NDN is a receiver-driven architecture using a pull-based communication model. NDN uses only two types of packets: the Interest packet emitted by a consumer to request a certain piece of data content, and the Data packet as an answer to such an interest, which contains the requested data content. Both packets contain the name prefix (or merely the name) of the data content. Routers (also referred to as nodes) use this content name to route the Interest towards the content provider (hereafter referred to as producer or server). Data packets are transmitted along the reverse paths towards their requesting consumers. Each node in the network has three main components: the Forwarding Information Base (FIB), the Pending Interest Table (PIT), and the Content Store (CS), as shown in Figure 1.  
To request content, a consumer issues an Interest packet bearing the content name. The Interest is forwarded based on this name from one router to the next according to the underlying FIBs until it either reaches a router having a cached copy of the requested content or ultimately reaches the producer. At the reception of an Interest, a router first looks up its CS, as depicted in Figure 1. Here, we distinguish two cases. If the router has a cached copy of the requested content in its CS, it sends it back to each of the interfaces registered in the PIT that have requested the content, and then deletes those entries. However, if there is no cached copy in its CS, the router merely forwards the Interest packet to the next hop router according to its FIB, and appends the incoming face of this Interest into the PIT.  
As also illustrated in Figure 1, upon the reception of a data packet, a router has first to decide whether or not to cache a copy in its CS, and then forwards back the data packet to all the faces indicated by the corresponding entry in its PIT and eliminates this entry. The decision of whether or not to cache the arriving content depends on the caching strategy in use by the router. The caching strategy may also call a replacement strategy that decides which content to evict from the CS to create a place for the incoming data content.
## 2 Materials and Methods
### Methods
#### 2.2.2 Data Flow: From Objects to Representations and Classification  
Figure 3 shows a full data flow from raw images to predictions for the case of two remote sensing image sources. After sensor- and application-specific preprocessing and pixel-wise feature engineering of the images \(A_{i}\), we extract for each object these resulting pixels from each type of image. We associate with each object an unordered pixel set per image type from within the geometric boundaries of the object shape. In the following, we represent these data from two data sources of different resolutions and extents using a shared representation of a multidimensional probability distribution per parcel.  
From the object-wise pixel sets we form density estimates \(p(\mathbf{x})\) for each object separately using a selected density estimation method, and then evaluate the density along a regular grid \(G\) to form the representation \(\mathbf{f}\). For practical computation, this representation is formatted as a vector, which we normalize for additional robustness so that the \(\ell_{2}\) norm is one, but this normalization is not a critical part of the pipeline. This vector then becomes the representation for the supervised learning algorithm. For Bayesian density estimators, we can also consider an alternative representation that also captures the uncertainty of the estimate, explained later after describing the Logistic Gaussian Process Density Estimation (LGPDE) method.  
Since our main focus is on the representation itself, we use standard classifiers readily available as a program library and in frequent use in the research field: the scikit-learn library's implementation of the Random Forest (RF), Support Vector Machine classifier (SVC) and a shallow feed-forward neural network (Multi-Layer Perceptron; MLP).