esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Descent Maneuver  
For the descent maneuver, the simulation result without the presence of the wind is shown in Figure 17. Both the change of pitch rate, \(\Delta q\), and \(\dot{L}_{\rm s}\) are regulated within \(\pm\)0.01 rad/s and \(\pm\)0.02, respectively. The aircraft descended from 158 to 150 m in around 40 s, while maintaining the airspeed within \(\pm\)0.2 m/s from trim airspeed. For the simulation results of aircraft descend with the presence of wind, as shown in Figure 18, the errors of \(\Delta q\), \(\dot{L}_{\rm s}\), airspeed, altitude, and altitude tracking settling time are almost the same as the aircraft climb maneuver.  
Figure 16: Simulation results of climbing with the presence of wind.  
Figure 17: Simulation results of descending without the presence of wind.
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 4 Proposed Search Patterns  
In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy to obtain the global solution of complex optimization problems. Since the proposed scheme is based on the response of the second-order systems, it can be considered as a physics-based algorithm. In our approach, the temporal response of second-order system is used to generate the trajectory from the position of \(\mathbf{x}_{i}^{k}=\left\{x_{i,1}^{k},\ldots,x_{i,d}^{k}\right\}\) to the location of \(\mathbf{b}=\left\{b_{1},\ldots,b_{d}\right\}\). With the use of such models, it is possible to produce more complex trajectories that allow a better examination of the search space. Under such conditions, we consider the three different responses of a second-order system to produce three distinct search patterns. They are the underdamped, critically damped and overdamped modeled by the expressions Equations (8)-(10), respectively:  
\[x_{i,j}^{k}=\left(1-\frac{e^{-\zeta\omega_{n}k}}{\sqrt{1-\zeta^{2}}}\sin \left(\omega_{n}\sqrt{1-\zeta^{2}}+\tan^{-1}\left(\frac{\sqrt{1-\zeta^{2}}}{ \zeta}\right)\right)\right)\left(b_{j}-x_{i,j}^{k}\right); \tag{8}\]  
\[x_{i,j}^{k}=\left(1-e^{-\omega_{n}k}(1+\omega_{n}k)\right)\left(b_{j}-x_{i,j} ^{k}\right); \tag{9}\]  
\[x_{i,j}^{k}=\left(1+e^{-(\zeta-\sqrt{\zeta^{2}-1})\omega_{n}k}\right)\left(b_{ j}-x_{i,j}^{k}\right); \tag{10}\]  
where \(i\) (\(\in[1,N]\)) corresponds to the search agent while \(j\) (\(\in[1,d]\)) symbolizes the decision variable or dimension. Since the behavior of each search pattern depends on the value of \(\zeta\), it is easy to combine elements to produce interesting trajectories. Figure 4 presents some examples of trajectories produced by using different values for \(\zeta\). In the Figure, it is assumed a two-dimensional case (\(d=2\)) where the initial position of the search agent \(\mathbf{x}_{i}^{k}\) is \((0.5,0.5)\) and the final location or the best location \((1,1)\). Figure 4 presents the case of \(x_{i,1}^{k}\leftarrow\zeta=0\) and \(x_{i,2}^{k}\leftarrow\zeta=1\). Figure 4b presents the case of \(x_{i,1}^{k}\leftarrow\zeta=0.1\) and \(x_{i,2}^{k}\leftarrow\zeta=0.5\). Figure 4c presents the case of \(x_{i,1}^{k}\leftarrow\zeta=1\) and \(x_{i,2}^{k}\leftarrow\zeta=1.67\). Finally, Figure 4d presents the case of \(x_{i,1}^{k}\leftarrow\zeta=0.5\) and \(x_{i,2}^{k}\leftarrow\zeta=1\). From the figures, it is clear that the second-order responses allow producing several complex trajectories, which include most of the other search patterns known in the literature. In all cases (a)-(d), the value of \(\omega_{n}\) has been set to 1.  
Figure 3: Trajectories produced by, (**a**) attraction, and (**b**) spiral models.
## 4 Results
### Micro-CT Analysis
White and brown sand proppants showed minimal changes in intragranular porosity, as expected for monolithic quartz grains. However, increases in intergranular porosity (+7.6% and +4.2%, respectively) suggest that surface-level chemical interactions are involved. White and brown sand proppants showed negligible variation in intragranular porosity, with values remaining close to 0.01% throughout. While the brown sand exhibited a 66.7% relative increase in intra-porosity, the absolute change (from 0.003% to 0.005%) is extremely small, thus not necessarily indicative of meaningful internal structural alteration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proppant** & **Porosity** & **Initial \(\Phi\) [\%]** & **Final \(\Phi\) [\%]** & \(\Delta\)\(\Phi\) \%** \\ \hline \multirow{2}{*}{ULD} & Intra-porosity & 0.23 & 0.16 & \(-\)30.40 \\ \cline{2-5}  & Inter-porosity & 36.22 & 34.48 & \(-\)4.80 \\ \hline \multirow{2}{*}{RCS} & Intra-porosity & 0.01 & 0.01 & 0.00 \\ \cline{2-5}  & Inter-porosity & 34.81 & 40.79 & +17.20 \\ \hline \multirow{2}{*}{White sand} & Intra-porosity & 0.01 & 0.01 & 0.00 \\ \cline{2-5}  & Inter-porosity & 33.64 & 36.20 & +7.60 \\ \hline \multirow{2}{*}{Brown sand} & Intra-porosity & 0.003 & 0.005 & +66.70 \\ \cline{2-5}  & Inter-porosity & 33.64 & 35.04 & +4.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Porosity values of proppant packs pre- and post-exposure to working fluids, indicating microstructural changes related to packing behavior, grain morphology, and resin redistribution. Figure 14: Micro-CT segmentation results for the RCS proppant before and after exposure to geothermal fluids at 130 \({}^{\circ}\)C for 25 days. (**a**) 2D pre-reaction image showing a smooth and undisturbed packing structure, (**b**) 3D pre-reaction rendering highlighting uniform particle distribution, (**c**) 2D post-reaction image with red circles indicating areas of resin redistribution and disrupted packing, and (**d**) 3D post-reaction rendering showing uneven coating surfaces and grain clustering, with red circles highlighting non-uniform resin distribution.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 5 Anomaly-Based Model
### Distinguishing Normal from Jamming Conditions  
Once we have selected the set of performance variables for the anomaly model, it is important to perform an analysis of such variables to determine if they can be useful for the detection of the jamming attack. Ideally, only those nodes affected by the jamming should present significant deviations in the variables used in the model; thus, other nodes that are not neighbors of the nodes under jamming would not have relevant changes in the behavior of the observed variables; however, in practice not all the variables may exhibit such tendency, therefore a majority voting rule for the detection algorithm will be later considered.  
We have studied attack-free and jamming scenarios with a jammer located at the center of the grid (above node 24). The simulated time is 100 s, each variable is sampled every second and the values are displayed in Figures 4-12, which show the results of the performance metrics for each node of the network. Blue points represent the values for normal condition and red ones are for the jamming conditions.  
For instance, Figure 4 shows the sampled values of the energy consumption for each node. Each node is tagged with an integer ranging from 0 to 49, this tagging corresponds to the network grid presented in Figure 3. We have obtained in total 200 sampled values in the simulations for each node, 100 values were obtained simulating normal conditions and 100 under the jamming attack. Such 200 resulting samples are drawn according to their values in Figure 4 in a vertical alignment above the tag for each node, Figures 5-12 were built following the same construction method. From this graphical representation can be seen where a significant deviation of the performance variables occurs, which can be related to the jamming attack. A larger separation of the blue points from the red points result in a better distinction of an attack from a normal behavior. Since the attack here considered was centered in the node with tag 24, then ideally those nodes nearby with tags 16, 17, 18, 23, 25, 30, 31 and 32 are also expected to be affected by the behavior of the variables of the model. In the following subsections, we will study how each variable behaves using Figures 4-12.  
Figure 4: Energy consumption of the nodes from Figure 3 with AODV (Ad hoc On Demand Distance Vector) under normal (blue) and jamming (red) conditions.  
Figure 5: Energy consumption of the nodes from Figure 3 with DSR (Dynamic Source Routing) under normal (blue) and jamming (red) conditions.  
Figure 6: Energy consumption of the nodes from Figure 3 with MPH (Multi-Parent Hierarchical) under normal (blue) and jamming (red) conditions.
## 4 Discussion
### Possible Applications of Food Processing By-Products as Dust Suppressants at Mine Sites  
#### 4.3.1 Technical Aspects  
The present study demonstrates that food processing by-products can considerably suppress dust emissions from sandy mine soils (Figure 6). However, the results also demonstrate that the tested biomaterials are more likely to be used only for temporary or short-term dust control practices due to their susceptibility to be leached from soil surfacesupon rainfall events and possible degradation in the environment. Factors that may contribute to the degradation of biomaterials include solar radiation, heat, abiotic oxidation, biotic reactions of microorganisms and saltating soil particles' impact force [1; 5; 43; 44; 45]. Repetitive application of the amendments to mine soils may counteract their diminishing dust control effectiveness over time.  
Considering their water-solubility, tested amendments could either be sprayed on substrate surfaces or incorporated into mine soils, which is usually applied on traffic areas [1]. This method involves higher single application costs but extends the service life of treatments [46; 47]. However, it appears challenging to exploit this durability benefit due to the amendments' susceptibility to leaching and biodegradability.  
The fact that the tested reagents could likely be applied using existing application equipment (e.g., water trucks with spray bars) already available at many mine sites is promising. The biomaterials readily dissolve in water and have not notably impaired the rheology of the aqueous solution. By contrast, thermal-gelling biopolymers, such as gellan gum or agar gum, require heated water to be dissolved or applied, respectively, and yield highly viscous solutions even at low concentrations [11; 12]. As [PERSON] et al. [41] already stated, any application equipment may need to be modified or developed considering the rheological properties of biopolymers.  
The tested biomaterials contain carbohydrates and proteins, which promote seed germination and plant growth and serve as sources of energy for soil bacteria [48; 49]. Therefore, an application of food processing by-products can provide nutrients to plants while simultaneously stabilizing the soil surface until the vegetation cover has been developed. Hence, the use of food processing by-products in mine closure and mine site rehabilitation activities might also support revegetation efforts of mined lands.
## 1 Introduction
A ubiquitous healthcare system generally comprises of three components:  
* A portable bio-signal data-gathering device, represented by wired/wireless connected sensors;
* A device for transmitting previously collected data by communicating with a remote server;
* A server used to investigate the patient's medical information. Despite several contributions to significant advances in the world of ubiquitous services, these studies did not provide a data analysis support that can help medical staff to interpret correctly the various changes in a patient's medical metrics. In addition to medical data classification, our approach fills this need by offering anomaly detection based on the learning model, as well as a probabilistic study that can illustrate in depth the physiological behavior of the patient by comparing the distributions at different critical points throughout the data-recording period. Our approach therefore meets two key needs:  
1. Provide a solution adapted to each patient on a case-by-case basis. This therefore implies a single-use learning model for any subject in this analysis and responds to the challenge of generalizing the interpretation of the various medical data in order to provide as accurate a diagnosis as possible (age, environment, habits, etc.) \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensors** & **Nomenclature** & **Technical Specificities** \\ \hline ECG & Electrocardiogram & - \\ EMG & Electromyography & 10 Mb/s \\ EEG & Electroencephalography & 5–10 m \\ B.P & Blood Pressure & \(\sim\)50 mW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Most used medical sensors and their technical information. 2. Once an anomaly is detected, another classification method has been introduced to assess the severity of the situation. This perfectly meets an immediate need that can regularize medical intervention in the event of a patient's critical situation. The remainder of this paper is organized as follows: following this introduction, Section 2 moves on to discuss the methodologies used to implement the proposed architecture. Section 3 carries out an analysis in which the results are interpreted and explained. Section 4 describes the significance of the findings based on the achieved results. Finally, Section 5 presents the conclusion and open areas for research, and proposes potential solutions to issues that are currently faced in this study.
## 2 Methodology  
The framework of the approach proposed in this paper is shown in Figure 1.  
* Step 1 is to describe the system and the operational environment. The system description includes the description of the system functions and the system structure. The operational environment description provides the infrastructures and objects that the system should recognize, as well as the environmental and operational factors that may influence the recognition. This step provides the knowledge of the interactions inside and outside the control system for the analysis in the next step.
* Step 2 is the identification of SOTIF-related hazardous factors with extended STPA. Firstly, the hazards of this system are determined. Then, a control structure with the feedback part extended for intelligent environmental situation awareness is built. After that, the unsafe control actions (UCAs) are identified. In the last part of the extended STPA, a causal analysis is carried out on the UCAs with SOTIF-related scenario classification.
* Step 3 is the evaluation of the hazardous factors including hazards, UCAs, and causal scenarios. A complex network is constructed for the hazardous factors and quantitative analysis is carried out with some customized topological indexes.  
The main contributions of this paper are the extended control structure, the SOTIF-related scenario classification used in Step 2, and the hazardous factors network modeling and quantitative topological evaluating indexes used in Step 3. The details of these are shown as follows.
## 4 Case Study and Results Discussion
### Pre-Model Preparation  
The compound dataset comprised operational and maintenance details from 17 electrical transformers, corresponding to 93 data points available from May 2014 to June 2017 and 15 vital signals as shown in Table 1.  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Covariates** & **Measurement** & **Description** \\ \hline Date & h & Date of measurement in hours since 1 January 1985. \\ FT & h & Operating time in hours since latest replacement. \\ Interv & - & Intervention type (where \(0=\) Failure, \(1=\)Suspension). \\ ID & - & Transformer identifier. \\ Dda & MVA & Electrical demand measured in megavolt-ampere. \\ Tint & \({}^{\circ}\)C & Internal temperature of the transformer. \\ Text & \({}^{\circ}\)C & Ambient temperature. \\ DirT & \({}^{\circ}\)C & Difference between internal and ambient temperatures. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the dataset covariates.  
For the initial analysis, only four covariates were considered with the objective of comparing them to a preliminary solution provided by the electric company. The dataset is represented in Table 2, and the preliminary covariate weights are shown in Table 3. Due to the varied data composition for each power transformer and the crucial need to sort data by operating time for an accurate analysis, the training set comprised data from power transformers 1 to 13, representing 65% of the dataset. The remaining 35% represented the testing data using transformers 13 to 17. The second analysis utilized all the covariates mentioned in Table 1.  
Finally, for both analysis, the study horizon spanned 100,000 h, divided into 500 h intervals. A precision level (\(\Delta\)) of 100 h was chosen for the conditional reliability calculation to improve computational efficiency. In addition, a preventive-to-corrective cost ratio of 1:7 was selected.
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 5 Results
### Sick Leave Prediction
#### 5.2.6 Feature Ranking with Random Forests  
In Table 9, we outline the most important attributes for sick leave using Random Forests across three different prediction horizons: one-week, two-weeks, and one month. The attributes are ordered by their importance based on impurity reduction.  
Across all prediction horizons, past sick and vacation leave data remain the most important features to forecast future sick leave. The importance of 12-month historical data is evident, indicating that patterns and trends spanning over a year offer valuable insights into predicting future absence. As the prediction horizon extends from one week to one month, there is a slight shift towards considering more recent historical data (six months) alongside longer-term data (12 months). This suggests that both recent and long-term trends are essential for more accurate predictions over extended periods.
## 4 Results
### Evaluation of Temporal and Spatial Dynamics
These results are consistent with the findings of [27], who reported the higher overestimation of satellite products in the foothills of northern mountain range of Hindukush, Karakoram and Himalaya (HKH in Pakistan. Generally, the GPCC and TRMM products showed the better accuracy and consistency in terms of spatial distribution of statistical metrics with higher values of CC and lower values of ME and RMSD. Figure 8: Spatial statistical indicators for the assessment of (**A**) (GB) and (**B**) (SB) precipitation products against reference data. Overall, the extent of under/over estimation of precipitation by GB and SB products suggests the significance of data correction before their reliable utilization in climate and hydrological studies. For the correction of precipitation products, we analyzed the estimated mean error in different data products and introduce a correction factor of each GB and SB product for annual, winter, and summer monsoon season over the Punjab region. The regional correction factor of each product for annual, winter, and summer monsoon during the period of 1979-2017 and 2003-2017 for GB and SB precipitation products are summarized in Table 2. The correction factor needs to be multiplied with the respective data products in order to minimize the percentage of error over the target region. The corrected product data could be directly used in climate modelling and other relevant studies [27].
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 2 Materials and Methods
### 5G Fronthaul Network Architecture, Connectivity, and Intelligence Splits  
IoT connections are predicted to increase from 12 billion in 2019 to 24.6 billion by 2025 [34]. Most of these 12.6 billion new connections are expected to come from smart homes and smart buildings, with 3.3 billion and 2 billion new connections, respectively. In addition, it is anticipated that, between 2016 and 2021, the amount of mobile data traffic worldwide will increase sevenfold, at a CAGR of 47%, to a total of 49 exabytes per month [35]. Video streaming will account for most of this traffic as emerging use cases such as virtual reality machine-to-machine and augmented reality interaction emerge. Current 4G networks cannot easily handle sudden surges in traffic. Figure 2 shows that the number of IoT connections will grow from 12 billion in 2019 to approximately 25 billion in 2025.  
Figure 2: IoT (Internet of Things) connections added in 2025 against 2019.  
An increase in wireless traffic capacity of about 1000 times is what 5G networks aim to achieve [36]. This new capability will enable significant improvements in self-driving cars, smart grids, efficient disaster response, and other fields. In addition to having more capacity than 4G, 5G networks have 100 times higher data throughput, less than 1 millisecond latency, less energy usage, and more dependable connectivity [37]. Wireless networks will advance to meet the demands of 5G, growing in speed, intelligence, and complexity. Three key strategies are being used by wireless operators and telco equipment makers to become ready for the higher needs of 5G.  
While older IoT devices operate at frequencies below 6 GHz, data transfer at frequencies up to 300 GHz uses centimeter-wave (cm-wave) and millimeter-wave (mm-wave) technologies [38]. To aid in boosting network density, construct small-cell networks that offer faster speeds and a larger range of devices. A single antenna can better serve a larger range of clients and use cases by using beamforming and multiple-input multiple-output (MIMO) antennas with tens or hundreds of elements each [39].  
The devices and use cases that 5G networks will support will be diverse in terms of vertical, horizontal, and architectural diversity. However, as networks become less centralized and more complex, the need for fronthaul networks has increased and will continue to increase.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry  
The _Pioneer_ and _Mariner_ space programs were nearly simultaneous exploration programs with complementary program goals. Each achieved a number of \"firsts\" in space exploration. Because the earliest (attempted) launches carried _Pioneer_ spacecraft, the history of channel coding begins with the _Pioneer_ program. History emphasizes channel coding \"firsts\" and remarkable missions where channel coding played an enabling role.