esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 3 System Model  
Figure 1 shows a G-cells MIMO-BC Interference channel model. There are \(K\) users per cell and each user is equipped with \(N\) (receiving) antennas. Each base station is equipped with \(M\) antennas. \(k_{\mathrm{g}}\) indicates the user \(k\) in cell g. Its degree of freedom is \(d_{k_{\mathrm{g}}},\forall\;k,\;\mathrm{g}\). In order to maximize the degree of freedom of the system, we may set \(d_{1_{\mathrm{g}}}=d_{2_{\mathrm{g}}}=\cdots=d_{k_{\mathrm{g}}}=d\). It is assumed that the channels between the transmit and receive pairs on the same frequency at the same time are flat fading and the channel coefficients are independent and identically distributed.  
Then, the received signal of user \(k\) in cell g is:  
\[\hat{\mathbf{S}}_{k_{\mathrm{g}}}=\mathbf{U}_{k_{\mathrm{g}}}^{H}\mathbf{H}_{k_{ \mathrm{g}},\mathrm{g}}\mathbf{V}_{k_{\mathrm{g}}}\mathbf{S}_{k_{\mathrm{g}}}+ \sum_{l=1,j\
eq k}^{K}\mathbf{U}_{k_{\mathrm{g}}}^{H}\mathbf{H}_{k_{\mathrm{g}}, \mathrm{g}}\mathbf{V}_{l_{\mathrm{g}}}\mathbf{S}_{l_{\mathrm{g}}}+\sum_{\mathrm{ j=1},j\
eq\mathrm{g}}^{G}\sum_{l=1}^{K}\mathbf{U}_{k_{\mathrm{g}}}^{H}\mathbf{H}_{k_{ \mathrm{g}},\mathrm{g}}\mathbf{V}_{l_{\mathrm{j}}}\mathbf{S}_{l_{\mathrm{j}}}+ \mathbf{U}_{k_{\mathrm{g}}}^{H}\mathbf{n}_{k_{\mathrm{g}}} \tag{1}\]  
Among them, \(\mathbf{S}_{l_{\mathrm{j}}}\in\mathbb{C}^{d\times 1}\) is the transmission signal of user \(l\) in cell j, which satisfies \(E\Big{\{}\mathbf{S}_{l_{\mathrm{j}}}^{H}\mathbf{S}_{l_{\mathrm{j}}}\Big{\}}=P\); \(\mathbf{V}_{l_{\mathrm{j}}}\in\mathbb{C}^{M\times d}\) is the precoding of user \(l_{\mathrm{j}}\), which satisfies \(trace\Big{\{}\mathbf{V}_{l_{\mathrm{j}}}\mathbf{V}_{l_{\mathrm{j}}}^{H}\Big{\}}=d ;\mathbf{U}_{k_{\mathrm{g}}}\in\mathbb{C}^{N\times d}\) is the interference suppression of user \(k_{\mathrm{g}}\) matrix; \(\mathbf{H}_{k_{\mathrm{g}},\mathrm{j}}\in\mathbb{C}^{N\times M}\) is a channel link from base station j to user \(k_{\mathrm{g}}\), which obeys a complex Gaussian distribution with a mean of 0 and a variance of 1. \(\mathbf{n}_{k_{\mathrm{g}}}\in\mathbb{C}^{N\times 1}\) is a Gaussian White Noise with a variance of \(\delta_{\mathrm{g}}^{2}\).  
Figure 1: The proposed system model for massive MIMO-BC with G-cells.  
From Equation (1), it can be seen that the ideal interference alignment should satisfy:  
\[\left\{\begin{array}{l}rank\Big{(}\mathbf{U}_{k_{\mathrm{g}}}^{H}\mathbf{H}_{k_{\mathrm{g }}\cdot\mathbf{S}}\mathbf{V}_{k_{\mathrm{g}}}\Big{)}=d,\ \forall\ k,\ \mathrm{g}\\ \mathbf{U}_{k_{\mathrm{g}}}^{H}\mathbf{H}_{k_{\mathrm{g}}\cdot\mathbf{S}}\mathbf{V}_{\mathrm{ Ig}}=0,\ \forall\ l\
eq k\\ \mathbf{U}_{k_{\mathrm{g}}}^{H}\mathbf{H}_{k_{\mathrm{g}}\cdot\mathbf{j}}\mathbf{V}_{\mathrm{ Ig}}=0,\ \forall\ j\
eq\mathrm{g}\end{array}\right. \tag{2}\]  
When there is a time delay and error in \(\mathbf{H}_{k_{\mathrm{g}}\cdot\mathrm{j}}(\forall\ k,\ \mathrm{g})\), Equation (2) will not hold, and a robust IA scheme needs to be designed.
# Mechanical Properties of Cement Concrete with Waste Rubber Powder
## 3 Results and Discussion
### Compressive Strength  
The results depicted in Figure 19 revealed a noticeable impact of waste rubber powder quantity on the compressive strength of waste rubber powder cement concrete. Specifically, when the particle size remained constant, the compressive strength decreased as the waste rubber powder content increased. Additionally, particle size played a role in influencing the compressive strength, with larger particles causing more pronounced reductions in strength at equivalent contents.
## 3 Results
### Antioxidant Activity  
The results for antioxidant activity are expressed in Table 3. The results are expressed as ascorbic acid equivalents and trolox equivalents in ABTS and ORAC assays, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Total Phenolic Content (mg GAEs/g of Dry Extract)**} \\ \hline  & M1 & 15.80 \(\pm\) 1.54 \\ _P. ostreatus_ & M2A & 16.57 \(\pm\) 0.26 \\  & M2B & 16.65 \(\pm\) 1.01 \\ \hline  & M1 & 9.06 \(\pm\) 0.63 \\ _P. eryngii_ & M2A & 7.90 \(\pm\) 0.46 \\  & M2B & 9.25 \(\pm\) 0.28 \\ \hline  & M1 & 10.67 \(\pm\) 0.35 \\ _A cylindracea_ & M2A & 13.35 \(\pm\) 0.68 \\  & M2B & 12.79 \(\pm\) 0.82 \\ \hline \hline \end{tabular} Statistically significant differences were not detected among the total phenolic content of all extracts evaluated with \(p\) < 0.05. Extract M1—hot extraction (1 g of mushroom dry matter (DM); 20 mL of water; 90 \({}^{\circ}\)C, 700 rpm, 1 h); extract M2A—room-temperature extraction (1 g of mushroom DM; 20 mL of water); extract M2B—hot extraction of residue from M2A (1 g of mushroom DM; 20 mL of water; 90 \({}^{\circ}\)C, 700 rpm, 1 h).  
\end{table}
Table 2: The total phenolic content of extracts M1, M2A, and M2B from _P. ostreatus_, _P. eryngii_, and _A. cylindracea_. The total phenolic content is expressed as gallic acid equivalents. The results are presented as the mean \(\pm\) SD.  
The results for antioxidant activity showed that all extracts had antioxidant capacity. Extract M2A from _A. cylindracea_ showed the highest antioxidant activity through the ABTS method, while the _P. erygii_ M2A extract showed the lowest activity from the same methodology. The other extracts showed no significant differences among them. Regarding the ORAC methodology, the _A. cylindracea_ extracts showed the highest antioxidant activity among all mushrooms, with no significant differences among the three extracts. Extract M2B from _P. erygii_ followed the _A. cylindracea_ extracts' antioxidant activity, and no significant differences were observed between the remaining extracts. Although statistically significant differences were not detected in the total phenolic compounds (Table 2), _A. cylindracea_ showed a higher antioxidant capacity than _P. ostreatus_. Different studies have also reported good and effective antioxidant activity from aqueous extracts from these mushroom species; however, the values reported in these studies were not obtained using the same methodologies nor reported in the same units, making direct comparisons more challenging [37; 38; 39; 40]. The results obtained in the present study suggest that antioxidant activity cannot be explained by the total phenolic content alone. According to previous studies, besides phenolic compounds, mushrooms are rich in other molecules with antioxidant properties, namely \(\beta\)-glucans and lectins [1; 8].
## 2 System Model
### Scenario 2: Repeated Transmission through a Relay  
In the second scenario, as shown in Figure 4, the source transmits data to the destinations in the group, and the relay repeatedly sends the received data to the destinations if the transmission from the source to the relay is successful. If the relay fails to receive, there is no second transmission. The destination receives the data successfully if the source-to-destination transmission is successful, or both the source-to-relay and relay-to-destination transmissions are successful.  
Figure 3: Scenario 1.
# Adaptive Imputation of Irregular Truncated Signals with Machine Learning
## 3 Materials and Methods
### Determining the Percentage of Missing Data in a Dataset  
To facilitate our adaptive data imputation framework, the system must be able to determine the percentage of missing data in a dataset, as different levels of missing data may necessitate different imputation methods being used to achieve the best results. To ensure our system could do this, we designed a simple method. The pseudocode for this function is given in Algorithm 3.  
```
0:df, columns, condition \(\triangleright\) DataFrame, columns to check, optional condition
1:ifcondition \(\
eq\) None then
2:df/Relevant \(\gets\) df[condition][columns]
3:else
4:df/Relevant \(\gets\) df[columns]
5:endif
6:totalCells \(\leftarrow\) np.product(df/Relevant.shape)
7:missingCells \(\leftarrow\) dfRelevant.isnull().sum().sum()
8:missingPercentage \(\leftarrow\) 0
9:iftotalCells > 0 then
10:missingPercentage \(\leftarrow\) (missingCells/totalCells) \(\times\) 100
11:endif
12:returnmissingPercentage
```  
**Algorithm 3**Calculate missing data percentage  
The logic of this function can be described as follows:  
* Since we have defined the MAR and MNAR mechanisms as occurring only when data is missing from particular features of the dataset, the function to calculate the percentage of missing data takes a parameter that acts as a filtering condition. If no parameter is provided in the function call, then the missing data scheme defaults to MCAR. If a parameter that specifies the missing data is MAR or MNAR is provided, then the function only considers the relevant rows and columns in the DataFrame for these conditions.
* The determination of the missing data percentage relies on the total number of cells, which is calculated as the product of the dimensions of the DataFrame, and the total number of missing values, determined by summing the count of NaN values across the selected columns.
* The actual calculation to determine the percentage of missing data is calculated by dividing the total number of missing cells by the total number of cells and multiplying by 100 to convert this ratio to a percentage.
* Also included in this function is a safeguard against dividing by zero.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Missing Data** & **Accuracy (\%)** \\ \hline MCAR & 100.00 \\ MAR & 88.85 \\ MNAR & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of the missing data classification system.
## 2 Methodology  
This work has adopted the F5G group timeline [2], which defines the time duration of each generation of fixed networks, while a layered approach is adopted in order to examine the first five generations from three different perspectives, namely the app/service perspective, the networking perspective and the physical layer perspective. These layers are dissimilar to the OSI-compliant layers. Our approach is illustrated in Figure 1. The network and physical layer can also be decoupled into two sub-parts: the backbone and access. We chose a layered approach for three reasons. First, a layered analysis reduces the overall complexity as it elegantly categorizes the vast number of existing standards and technologies into a small number of groups. Second, it facilitates engineering in modules, simplifying teaching and learning. Last but not least, it aims to define the main limitations and novelties of each generation from various viewpoints.  
Each of the three layers has specified targets in order to serve the final user seamlessly and efficiently. In particular, the physical layer aims to increase the transport capacity and transmission reach. Next, the network layer is responsible for providing increased connectivity and reduced latency in order to attain sufficient quality of service (QoS) and reliability. Finally, the app/service layer is targeting towards maximizing the user's quality of experience (QoE). Our procedure regarding the five generations is dialectical, as it follows an evolutionary manner; that is, the limitations of each generation are becoming the novelties of the next one. In this way, the burdens of each layer for each generation are becoming more distinguishable. This dialectical analysis reveals that each layer evolvedat a different pace, as the evolutionary drivers and the limitations are completely different among the three layers. For example, the app/service layer evolved rapidly after 2005 due to the introduction of video services, the access part of the network layer showed significant advances even from the first generation, while the backbone part of the network layer blossomed after 2000 due to the success of Synchronous Optical NETworking (SONET)/Synchronous Digital Hierarchy (SDH). Finally, the physical layer shows a more balanced evolution over the 50 years (1970-2020) and significant technological advances can be found in all five decades. As a consequence, in the app/service layer and in the network layer, the timeline of the F5G group is followed; however, in the physical layer, a different timeframe is considered in order to designate all the principal milestones, as shown in Figure 1. In the next three sections, we analyze the evolution of the fixed networks from three different perspectives.
## 10 Social Impact and Clinical Applications of Robotic Prostheses with 3D Printing
### Clinical Applications and Customization of Prosthetics  
The adaptability of 3D printing technology has facilitated the production of prosthetics tailored to specific patient needs in ways not previously possible. Three-dimensional scanners capture precise anatomical data, enabling engineers to design prosthetics that fit each individual's body with precision. This high level of customization enhances comfort and minimizes the risk of complications like skin irritations or sores often associated with poorly fitted traditional prostheses [4].  
Example and Impact: The ability to create patient-specific devices quickly has made 3D printing invaluable in rehabilitation settings. For example, transitional prosthetics printed in-house can help patients gradually adapt to more advanced devices, improving outcomes and reducing complication rates.  
3D Printing Advantage: The customization afforded by 3D printing not only improves the fit and comfort of prostheses but also enables rapid adjustments and improvements, offering patients devices that evolve with their rehabilitation needs.
# Multi Ray Model for Near-Ground Millimeter Wave Radar
## 1 Introduction  
Wireless communication systems and radars are developing towards the utilization of millimeter wavelengths above 30 GHz. One of the most important issues at these frequencies is maintaining a proper link with minimal interference and clutter. Millimeter waves (MMW) face disruptive effects including atmospheric attenuation due to weather conditions, fog and rain [1], and losses while passing through objects. In this article, we will focus on the interference caused by the transmitted signal itself due to multi-scattered reflections from the target, as in MMW radars. Such interference results in constructive and destructive summations of the reflected signals, which are space-frequency dependent. The well-known two-ray model is practical for describing this phenomenon in wireless communication links [2; 3; 4], where the transmitter and the receiver are located at different sites. However, it fails to address other scenarios, such as when the link is at millimeter wavelengths [5; 6], operating particularly in urban areas [7; 8], or where multiple reflections from objects and buildings are involved. Multipath also occurs in radar links, even if the transmitter and receiver are located in the same place. In addition to the line-of-sight (LOS) wave reflected from the target, scattering due to the ground results in additional paths that interfere with the direct LOS received wave.  
Nowadays, short-range millimeter wave radars operating at 77 GHz are used commercially in anti-collision systems in vehicles [9]. The radar is positioned at the front of the vehicle and gives an alert when the distances to obstacles are too short. In this mode of operation, the radar height above ground is such that not only the direct line of sight (LOS) path exists, but ground-reflected rays are also involved. It is interesting to note that this problem has been addressed with regard to millimeter wave communication links between vehicles, and was investigated using the two-ray model [10; 11].  
In this article, the emergence of multipath signals is investigated while the millimeter wave radar is operating close to the ground. The effect of multiple rays scattered from the target and reflected backto the receiver is analyzed. Several scenarios are studied, revealing different fading of the received signals. The theory is examined using an experimental setup operating in the W band.  
Our analysis is based on a multi-ray model, in which waves scattered by the target and reflected by the ground simultaneously arrive at the radar receiver. In MMW radars, where directional antennas are employed, quasi-optical ray analysis is found to be sufficient to describe the multipath phenomena. The resulting model enables one to identify and differ between possible multi-path scenarios, which are also demonstrated experimentally.
# Recent Advances in Efficient Dynamic Graph Processing
## 3 Computational Complexity Model
This measure encompasses not only the direct output changes \(\Delta O\), but also includes all data that must be examined by any dynamic algorithm solving the given graph problem to identify these changes. A dynamic algorithm is _semi-bounded_ if its computational complexity remains polynomial in three key parameters: \(|\mathsf{AFF}^{\forall}|\), \(|Q|\), and \(|\Delta G|\). These parameters represent the necessary computations any dynamic algorithm must perform to solve a specific problem. **Theory analysis. ** The complexity analysis of dynamic algorithms often depends on the number of updates in dynamic graphs. The three models are defined in relation to the size of either \(\Delta(G)\)(the number of updates applied to graph \(G\)) or \(|\mathsf{CHANGED}|\)(a set that includes \(\Delta(G)\)). The difference between AFF and \(\mathsf{AFF}^{\forall}\) is that the relative boundedness is defined in terms of the affected area AFF, relative to a _specific_ algorithm \(\mathcal{T}\), while \(|\mathsf{AFF}^{\forall}|\) is for _all_ dynamic algorithms for query \(\mathcal{Q}\). Therefore, we have \(|\mathsf{AFF}^{\forall}|>|\mathsf{AFF}|\). Consequently, the tightness of the above three computational complexity models can be sorted as boundedness > relative boundedness > semi-boundedness. As the boundedness of algorithms is demanding, only few dynamic algorithms can be totally bounded, such like shortest path problems with single-source or all pairs under positive lengths [128; 132]. In addition, graph simulation is known as semi-bounded [131]. Ref. [130] proves that regular path queries and strongly connected components computation are relative bounded.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 5 Results
### Ranking of Flights and Further Research  
The existence of a formula for PDR gives the possibility of validating the evolution over time of predicted delay rates and of focusing on some extreme flights with the biggest PDR to test and analyze in detail. It can also create a business case for managing some actions associated with a high PDR to avoid losses and inform flight customers about possible delays. The current model gives the possibility of calculating the PDR directly, at the time of departure.  
Currently, this can be formulated as a new question. Can an arrival delay > 60 be predicted before the airplane departs, based on information available before the time of departure?  
To answer this question, the second model is built. The full documentation of that model is not presented, but some interesting topics are pointed out.  
The predictive power is small--20.4%. It can be interpreted as follows: taking any two flights, one delayed and one non-delayed, there is a 60.20% ((Gini + 1)/2) chance our model can predict which of them will be delayed, so only slightly better than the 50% that can be achieved by a random model. This means that in this case, we do not have enough information to predict the event.  
Before diving into the variables used in the second model, it is important to highlight that its objective is to determine the likelihood of an arrival delay exceeding 60 min based solely on pre-departure information. Despite its limited predictive power, the model sheds  
\begin{table}
\begin{tabular}{c c c c} \hline
**Period** & **ALL** & **DR** & **PDR** \\ \hline
1980 & 114,143 & 3.11 & 3.05 \\
1990 & 518,581 & 3.67 & 3.58 \\
2000 & 644,153 & 5.54 & 5.34 \\
2010 & 588,782 & 5.62 & 5.85 \\
2020 & 190,164 & 5.57 & 5.82 \\ \hline \end{tabular} Source: Authors’ own elaboration.  
\end{table}
Table 14: Test of calibration.  
light on key factors that influence delays, providing a foundation for further investigation and improvement. Below is the list of variables included in the model:  
* Month--the month of the planned departure time.
* Dest_size--the number of flights per destination for all available flights.
* Origin_WDMH_size--the number of flights per weekday, month, and hour of planned departure from the origination for all available flights; this is a more advanced variable connected with heavy aircraft traffic issues.
* Reporting_Airline--the airline of airplane.
* CRSElapsedTime--the planned length of the flight.  
It should be underlined that there are three new variables: Origin_WDMH_size, Reporting_Airline, and CRSElapsedTime.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 3 Proposed Protocol
### Cluster Formation Phase
#### 3.2.3 Cluster Head Selection  
The CH node in our suggested approach is the one that is the closest to every other node in the cluster. It is reducing the number of hops lowers communication latency. As seen in Figure 2, the CH selection procedure continues to iterate until the clusters merge. This convergence is best attained if no modification is seen after two iterations [20]. When a first potential node enters a highway and forms its own cluster, as was covered in the previous subsection, the CH is chosen. The modified k-medoids algorithm determines the median of each cluster when the clusters are filled with nodes. Therefore, this median, also known as the medoid, computed using Equation (5), is elected as the new CH for a cluster [33]. Thus,  
\[M_{c}=\begin{cases}\left[\frac{n+1}{2}\right]^{th}&\text{if nodes }=\text{ odd}\\ \left[\frac{n}{2}+1\right]^{th}&\text{if nodes }=\text{ even}\end{cases} \tag{5}\]  
where \(Mc\) represents the set of CHs and n is the number of nodes in a cluster.  
The suggested k-medoids approach in Algorithm 3 provides phases for choosing a CH in a particular cluster. \(K\) and \(N\) are the inputs for Algorithm 3. This algorithm's output includes \(Mc\). The algorithm's complexity is on average \(\Theta(\text{n})\). The algorithm's outcome is an \(Mc\) set of the cluster heads each time.