esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MIMO Antenna Configuration Analysis
### Orthogonal Placement  
In this case, the two-port MIMO antenna is designed so that the second element is placed perpendicular to the first element, as given in Figure 12. The hardware prototype is also fabricated to verify the suggested concept. The suggested two-port MIMO system in this case has an overall size of M\({}_{\text{X3}}\)\(\times\) M\({}_{\text{Y3}}\) = 20.5 mm \(\times\) 12 mm. The space between the two elements is fixed at G\({}_{3}\) = 3 mm to nullify mutual coupling between them.  
Figure 11: Comparison among gain and numerically calculated radiation efficiency of MIMO antenna with parallel and inverted structure element.  
Figure 12: Schematic of MIMO antenna for orthogonal elements.  
The scattering parameter of the suggested two-port MIMO antenna is given in Figure 13a. It can be observed that the antenna offers a bandwidth of 3 GHz ranging from 26-29 GHz. At these frequencies, the antenna offers return loss \(<\)\(-\)25 dB. The most important parameter of the MIMO antenna is transmission co-efficient, where the effect of one element over other is examined. The antenna offers S\({}_{12}\) and S\({}_{21}\)\(<\)\(-\)35 dB, as given in the figure. The similarity between predicated and tested results are also observed, which implies that the antenna is a good applicant for MIMO antenna application operating over a mm-wave band.  
The AR of the proposed antenna is analyzed to show that the antenna offers both LHCP and RHCP. The results of ARBW are given in Figure 13b, which shows that the antenna gives AR of 2.75 GHz ranging from 26.25-29 GHz and AR of 3 GHz ranging from 26.5-28.5 GHz, for RHCP and LHCP, respectively. The figure shows that there is no major difference between predicated and prototype tested results, which leads the proposed MIMO system as the best applicant for CP antenna applications operating over 28 GHz.  
Figure 14 depicts the frequency versus gain as well as radiation efficiency of the suggested two-port MIMO antenna. It can be noticed that the antenna offers gain > 7.6 dB at an operational band width of 26-29 GHz with a maximum gain value of 8.2 dBi at 28.5 GHz. The figure also expresses the radiation efficiency for the suggested antenna. The antenna offers the radiation efficiency > 97.5% at operational bandwidth with a maximum value of 98% at a resonance frequency of 28.5 GHz. The similarity between predicated and tested results is observed which makes the suggested design best for future high gain and high efficiency application antenna devices.
## 3 Proposed Yielding Protocol
### Yielding Messages  
The inter-vehicle communication standard in Japan, ARIB STD-T109 [10], supports neither unicast nor multicast. Messages are always broadcast. However, in the yielding process, a vehicle does not need to communicate with all the vehicles in the surroundings, but with a specific vehicle. Therefore, it is desirable to specify the message destination in the message so that vehicles not involved in the yielding process need not process messages unrelated to them. The former method employed one-to-one communication using destination ID and sender ID. Thus, if the same message was to be sent to multiple destinations, it was necessary to send as many messages as the destinations. In this paper, we have revised the message structure to allow one-to-many communication, as shown in Table 4. The yielding ID is used as a multicast address. It is a unique ID consisting of a unique vehicle ID and the timestamp identifying the time when the given request message was generated. The message body consists of the sender vehicle information, the yielding pattern, and the message type. A vehicle that has received a message identifies the sender by examining the sender vehicle information, which consists of information about the maker, the vehicle model, the vehicle color, the longitude and latitude of the vehicle's position and the vehicle's angle of direction. The yielding pattern indicates which yielding pattern applies. The message type indicates whether the message is a request, agreement, rejection, thanks, time-out or termination message.
# Assessing Water Infiltration and Soil Water Repellency in Brazilian Atlantic Forest Soils
## 3 Results and Discussion
### Unsaturated versus Saturated Soil Hydraulic Conductivity  
The two types of infiltration tests, i.e., tension and ponding experiments, highlighted a clear increase of the hydraulic conductivity, especially at the remnant forest plots, when moving from near-saturated to saturated conditions (Figure 6). It is important to underscore that saturated and unsaturated conditions could be affected by the different soil texture. Our previous study [14] shows that soils with higher clay content (i.e., P2M, F6M, F6D) evidenced greater variations, by contrast, sandy soils (i.e., P1U, P1M, F5D) had lower variation. The mean values of the ratios between saturated, \(K_{\text{s}S}\), and near-saturated soil hydraulic conductivity, \(K_{\text{-20}}\), were 10.7, 21.5, and 118.3, for the pasture, restored forest and remnant forest, respectively. A similar trend was also detected when the \(K_{\text{-5}}\) values were considered, with the mean values of the ratios equal to 2.2, 5.6, and 23.7. Similar results were also obtained when \(K_{\text{s}B}\) values were considered, with the values of the ratios equal to 10.6, 17.5, 92.0, and 2.2, 4.6, 17.4, for the \(K_{\text{-20}}\) and \(K_{\text{5}}\) data, respectively. We also noticed a discrepancy between \(K_{\text{s}S}\) and \(K_{\text{0}}\) data, especially at the Forest site, because only under ponded conditions at the surface the macropores are activated [63]. The increase of the difference between saturated and unsaturated conditions can be explained by the activation of macroporosity at the forest plots [64]. Overall, the soil in the remnant forest is heterogeneous and characterized by a dominance of complex macropores. For example, a higher soil macroporosity and total porosity have been reported in the same forest soil by our previous work [14]. This soil macroporosity resulted from the better soil structure, which is caused by the high amount of biopores, roots, soil fauna activity and greater inputs of organic matter [52; 63; 64]. Moreover, soil variability at the scale of a few meters could have been less represented by the MDI, due to the small diameter of the infiltrometer (i.e., 4.5 cm).
# A Machine Learning Method for Modeling Wind Farm Fatigue Load
## 5 Conclusions  
This paper introduces a GP-based machine learning model to predict the damage equivalent load under yaw misalignment. This model considers the nonlinear relationship between fatigue load and yaw misalignment, which solves the problem of inaccurate prediction by LUT. The nonlinearity between the fatigue load and yaw misalignment strengthens with the increase of turbine depth (row number in the downstream direction); however, the sensitivity of the fatigue load to turbine depth is different. For example, in the three-turbine case, the damage of the blade root edgewise moment is very little affected by its yaw deflection, with a max-min range of 2% for the first row of turbines. However, as turbine depth increases, the max-min range increases to 32% (WT2) and 33% (WT3). The difference with the above load is that the blade root flapwise moment of the first row of turbines is influenced by 38%. As the turbine depth increases, the max-min range increases to 100% and 75%. Compared to the LUT algorithm, GP has a more accurate prediction of fatigue loads. For example, in the three-turbine case, the prediction accuracy of GP improved by 13.99% (\(RMSE\)) and 0.54% (\(MAE\)) at the blade root edgewise moment and 51.87% (\(RMSE\)) and 1.78% (\(MAE\)) at the blade root flapwise moment.  
In the future, the wake steering control strategy will be optimized by farm-level fatigue load prediction and farm power prediction, thus enabling the wake steering control to balance power generation and turbine lifetime. Further, wake steering control will be combined with independent pitch control to reduce wind farm loads.  
Figure 16: \(RMSE\) and \(MAE\) error of GP and LUT predictions on blade root edgewise moment.  
Figure 17: \(RMSE\) and \(MAE\) error of GP and LUT predictions on blade root flapwise moment.  
**Author Contributions:** Conceptualization, Y.M.; Funding acquisition, M.N.S.; Methodology, Y.M.; Supervision, A.H.; Writing--original draft, Y.M.; Writing--review & editing,Y.M., M.N.S. and A.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 3 Results
### Designs of the Input/Output Fuzzy Membership Functions  
From Figure 3, illustration of the sample input/output designs for the fuzzy inference systems variables and their respective membership functions plots are indicated.  
In Figures 4-6 the triangular type membership function designed plots for NO\({}_{2}\), SO\({}_{2}\) and PM\({}_{2.5}\) are indicated. For instance, Figure 4 illustrates the fuzzy inference system for input variables for NO\({}_{2}\) (Low, Medium, and High) and its corresponding membership function plot. Additionally, the estimated KAQI as an output variable is illustrated in Figure 7.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Rule No.** & **NO\({}_{2}\) (ppb)** & **SO\({}_{2}\) (ppb)** & **PM\({}_{2.5}\) (\(\mu\)g/m\({}^{3}\))** & **KAQI** \\ \hline
1 & Low & Low & Low & Good \\
2 & Low & Low & Medium & Moderate \\
3 & Low & Medium & High & Unhealthy \\
4 & Low & Medium & Low & Moderate \\
5 & Low & Medium & Medium & Sensitive \\
6 & Low & Medium & High & Very Unhealthy \\
7 & Low & High & Low & Unhealthy \\
8 & Low & High & high & Very Unhealthy \\
9 & Medium & High & High & Very Unhealthy \\
10 & Medium & Low & Low & Moderate \\
11 & Medium & Low & Medium & Sensitive \\
12 & Medium & Low & High & Unhealthy \\
13 & Medium & Medium & Low & Moderate \\
14 & Medium & Medium & Medium & Moderate \\
15 & Medium & Medium & High & Very Unhealthy \\
16 & Medium & High & Low & Very Unhealthy \\
17 & Medium & High & Medium & Very Unhealthy \\
18 & Medium & High & High & Hazardous \\
19 & High & Low & Low & Unhealthy \\
20 & High & Low & Medium & Unhealthy \\
21 & High & Low & High & Hazardous \\
22 & High & Medium & Low & Unhealthy \\
23 & High & Medium & Medium & Very Unhealthy \\
24 & High & Medium & High & Hazardous \\
25 & High & High & Low & Hazardous \\
26 & High & High & Medium & Hazardous \\
27 & High & High & High & Hazardous \\ \hline \hline \end{tabular}
\end{table}
Table 4: FIS Rule base for NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\).  
Figure 3: Design of the Air Quality Index prediction model for NO\({}_{2}\), SO\({}_{2}\) and PM\({}_{2.5}\) vs. KAQI.
## 3 Results
### Critical Test Scenario Selection Example Based on an Existing Vehicle  
To perform the sample calculation, the first step is to select an autonomous vehicle or a vehicle equipped with an automated driving feature. The choice fell on a production vehicle with an L3 highway driving function and an automated parking feature. After completing the questionnaire, the responses shown in Table 2 were obtained. From these responses the highlighted were then used to create the vector necessary for filtering. Next, the WCS calculation was performed using Formula (1). As a result, 390 relevant test cases were identified from more than 1000 possible variations. Subsequently, using Formula (2) applying CS, the test runs were arranged in descending order based on the CS results. Following this, using Formulas (3) and (4) as described in Section 2.4, the MD and ED values were calculated. Based on these results, test runs that were too similar were excluded. During this process, we examined how the number of filtered cases evolved above 0 and 2 thresholds with a step size of 0.1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question** & **Answer** & \
\begin{tabular}{c} **Vector Parameter and** \\ **Value If Applicable** \\ \end{tabular} &
\begin{tabular}{c} **Weight If** \\ **Applicable** \\ \end{tabular} \\ \hline
**Purpose of the application** & \
\begin{tabular}{c} **Transporting of** \\ **passengers** \\ \end{tabular} &
\begin{tabular}{c} **M = 1** \\ **N = 0** \\ \end{tabular} & **0.5** \\ \hline Length of the vehicle in mm & 5000 & & \\ \hline Height of the vehicle in mm & 1500 & N/A & N/A \\ \hline Width of the vehicle in mm & 1850 & & \\ \hline
**Weight of the vehicle in kg (GVW)** & **2200** & \
\begin{tabular}{c} **Dimensions 1—\(\leq\)3.5t = 1** \\ **Dimensions 2—\(\leq\)5t = 0** \\ **Dimensions 3—\(\leq\)12t = 0** \\ **Dimensions 4—\(\geq\)12t = 0** \\ \end{tabular} &
\begin{tabular}{c} **0.25** \\ **0.25** \\ **0.25** \\ **0.25** \\ \end{tabular} \\ \hline
**Target operational environment** & \begin{tabular}{c} **MW: Closed roads with** \\ **physical separation and at** \\ **least two or more lanes in** \\ **the same direction of** \\ **travel, without junctions** \\ **(e.g., motorways,** \\ **expressways)** \\ \end{tabular} & \begin{tabular}{c} **MW = 1** \\ **RR = 0** \\ **UA = 0** \\ **(e.g., motorways,** \\ **expressways)** \\ \end{tabular} & \begin{tabular}{c} **0.33** \\ **RR = 0** \\ **UA = 0** \\ **(e.g., motorways,** \\ **expressways)** \\ \end{tabular} \\ \hline Does the vehicle handle transient between & \
\begin{tabular}{c} Empty \\ **N/A** \\ \end{tabular} &
\begin{tabular}{c} N/A \\ **N/A** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Processed answers of the questionnaire and the created vectors with weights. As shown in Figure 3, with the same threshold value, more test runs remain when using MD compared to ED. However, upon comparing the filtered cases, it is evident that
## 4 Vibration Data Analysis
### Peak Displacement Analysis  
Vibration is not just a physical phenomenon; it directly impacts people's quality of life. Significant vibration displacements can cause discomfort and disrupt residents' daily activities and work. Prolonged exposure to high-vibration environments may even have adverse health effects. By analyzing and controlling vibration displacements, the comfort and health of residents can be improved. Additionally, ground surface vibration displacements reflect the operating status of tracks and trains. Excessive vibration displacements may indicate issues with the track structure or train operation. Monitoring and analyzing these data allows for timely identification and resolution of problems, ensuring the safe and stable operation of the rail transit system.  
The displacement can be calculated from the acceleration data using Equations (3) and (4), as follows:  
\[v(t_{i+1})=v(t_{i})+\frac{a(t_{i})+a(t_{i+1})}{2}\Delta t, \tag{3}\]  
\[x(t_{i+1})=x(t_{i})+\frac{v(t_{i})+v(t_{i+1})}{2}\Delta t, \tag{4}\]  
where \(a(t_{i})\) represents the acceleration at the \(i\)-th time interval, \(a(t_{i+1})\) represents the acceleration at the \(i\) + 1-th time interval, \(v(t_{i})\) represents the velocity at the \(i\)-th time interval, \(v(t_{i+1})\) represents the velocity at the \(i\) + 1-th time interval, \(x(t_{i})\) represents the displacement at the \(i\)-th time interval, \(x(t_{i+1})\) represents the displacement at the \(i\) + 1-th time interval, \(\Delta t\) is the time interval, and the time interval of this instrument is 0.002 s.  
According to Figure 7, the displacement in the Z direction peaks at measuring point P4 and reaches its nadir at P2. In the X direction, it bottoms out at measuring point P5 and peaks at P6; in the Y direction, it dips at P5 and P6. The overall displacement reaches its maximum at measuring point P1 and its minimum at P4.  
The occurrence of peak displacement at certain measurement points can be attributed to several factors. The interaction between the tunnel structure and the surrounding soil plays a crucial role in determining the displacement pattern. The stiffness of the tunnel lining is relatively high, allowing it to effectively reflect ground vibration waves, which leads to amplification of displacement at certain locations. The propagation of vibration waves through the soil is influenced by the material properties of the soil and the geometry of the tunnel. On one hand, the presence of adjacent tunnels has a significant impact on the reflection and refraction of vibration waves. On the other hand, the track slab is located at the center of the tunnel, and the middle part of the circular lining is larger than the track slab (as shown in Figure 2). When vibration waves pass through the circular lining, they inevitably spread into the surrounding medium. This diffusion reduces the transmission of vibration waves directly above the tunnel while further amplifying vibrations at surrounding measurement points.
## 2 Analysis of the State of the Art
### Context  
Context is mostly defined by the definition by [PERSON] [42] as follows:  
**Definition 5**.: _Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves._  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Solution** & **Domain** & \multicolumn{3}{c}{**Ontologies**} & \multicolumn{6}{c}{**User characteristics**} \\ \cline{3-11}  & & & **A** & **C** & **Ex** & **I** & **E** & **P** & **S** & **L** \\ \hline
2002, [PERSON] _et al._[5] & Inclusive design & & & ✓ & ✓ & & & & & \\
2003, [PERSON] _et al._[20] & Automatic profiling & ✓ & & & & ✓ & & & & \\
2003, [PERSON] _et al._[21] & KMS & ✓ & ✓ & & & ✓ & & ✓ & & \\
2005, [PERSON] and [PERSON] [22] & Tangible interfaces & ✓ & & & & ✓ & & ✓ & & ✓ \\
2005, [PERSON] [26] & Multimedia adaptation & & & & & & ✓ & & & \\
2007, [PERSON] _et al._[27] & Product design demands & & ✓ & ✓ & & & & & & \\
2007, [PERSON] _et al._[29] & User profiling & ✓ & ✓ & & ✓ & ✓ & & ✓ & & \\
2007, [PERSON] _et al._[30] & Ubiquitous applications & ✓ & ✓ & & & & ✓ & ✓ & ✓ & \\
2008, [PERSON] _et al._[32] & Adaptive user interfaces & & & & ✓ & & & & & \\
2012, [PERSON] _et al._[34] & Adaptive applications & & & & & & & & ✓ & \\
2012, [PERSON] _et al._[36] & Mobile environments & ✓ & ✓ & ✓ & & ✓ & & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work for the user modeling approaches. Under the user characteristics heading Activities or behavior, Capabilities, Experience, Interests, Emotions, Personal, Stress and Location information are presented.  
Their definition enables developers to easily enumerate those elements which take part in the context for a certain application domain. The author also defines that _\"if a piece of information can be used to characterize the situation of a participant in an interaction, then that information is context\"_. By a proposed example it is seen how different pieces of information are analyzed to determine if they belong to what [PERSON] states context is. These definitions are based on research experience. This section shows how modeling and defining context has evolved in the past 15 years.  
Context management allows us to identify the conditions of the environment. This way, developers are able to adapt services or applications for the user taking into account these conditions. To do this first there is the need of gathering context information. Next, this information has to be somehow processed and, finally, it will be used to personalize and contextualize the current situation.
# Balanced Parallel Exploration of Orthogonal Regions
## 5 Analysis
### Perimeter Scan
#### 5.1.5 Perimeter Traversal
All agents perform the perimeter traversals, so the difference in work appears when crosses are performed, as these are done by a single agent. Agents swap after completing a Cross if another agent has less work to allow them to perform the next Cross. Let us assume that an agent could exceed \(C_{\max}\) work compared to other agents. For two agents \(i\) and \(j\) with respective works \(W_{i}\) and \(W_{j}\), if \(W_{i}>W_{j}+C_{\max}\) then \(W_{i}>W_{j}\) before the Cross was taken, as agent \(i\) could gain at most \(C_{\max}\) new work. This contradicts the algorithm as the agent with the lower work would perform a Cross. Hence, all agents are bounded by a difference in work of \(C_{\max}\). **Lemma 6** (Perimeter Work). : _If \(N_{i}\geq 4C_{\max}k\), the perimeter work of each individual agent is \(O(P_{i}+N_{i}/k)\)._  
Proof. : From Lemma 4, we have that the total work is \(O(kP_{i}+N_{i})\) and Lemma 5 proves the max difference between any two agents is \(O(C_{\max})\). As agents are within \(C_{\max}\) work of one another, each agent must have work at most \((kP_{i}+4N_{i})/k+C_{\max}=P_{i}+4N_{i}/k+C_{\max}\). With the assumption that \(N_{i}\geq 4C_{\max}k\), this can be simplified to \(P_{i}+4N_{i}/k+(1/(4k))N_{i}\) which is \(O(P_{i}+N_{i}/k)\). **Lemma 7** (Perimeter Time). : _With total number of Corners \(\Gamma\) the total perimeter time is at most \(P+6\Gamma C_{\max}\)._  
Proof. : Each agent follows along the perimeter moving one position each time step, given \(P\) perimeter points, takes \(P\) time to visit each perimeter position. At every _Corner_ we have at most 3 directions that a room can be generated with a maximum of \(C_{\max}\) distance to traverse. An agent must traverse the closure in both directions, thus \(6\Gamma C_{\max}\) to traverse possible room closures for all _Corners_\(\Gamma\). This creates an upper bound on the total time to finish the perimeter scan portion \(P+6\Gamma C_{\max}\).
## 4 CILS Framework: Enhancing Cross-Cultural Language Learning
### Automation and Optimization of Language Learning  
#### 4.3.1 Application of Natural Language Processing  
Applying natural language processing (NLP) within the CILS significantly enhances the automation and optimization of language learning. NLP technologies enable the system to understand, interpret, and generate human language in a meaningful and effective way for learners. This capability is fundamental to creating a responsive language learning environment where linguistic nuances and cultural contexts are accurately captured and conveyed [33].  
In the CILS, NLP develops sophisticated language models to assess learner inputs--from written texts to spoken words--and provide immediate, contextually appropriate feedback. For instance, NLP algorithms analyze sentence structure, vocabulary usage, and grammar to offer corrections and suggestions that improve the learner's language proficiency. Additionally, these algorithms can detect subtleties in tone and style, providing feedback that helps learners adjust their language use according to different cultural norms and communication settings.  
NLP enables the creation of interactive dialogues that simulate real-life interactions. Learners can engage in conversations with AI-powered characters that respond in real time, adjusting their language complexity and cultural references to suit the learner's level and learning goals. This interaction is limited to linguistic practice and extends to cultural education, where learners can explore various cultural scenarios through language use, enhancing their linguistic skills and cultural understanding.