esa-sceva/satcom-chunk-collection huggingface dataset:
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 2 System Model  
### Basic Concept  
The OTFS modulation block diagram is shown in Figure 1. At the transmitter, data symbols, \(x[k,l]\), of size \(N\times M\) are placed in the DD domain, and the data symbols are mapped to the TF domain symbols, \(X[n,m]\), by the inverse symplectic finite Fourier transform (ISFFT):  
\[X[n,m]=\frac{1}{\sqrt{MN}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}X[k,l]e^{-j2\pi(\frac {ml}{M}-\frac{nk}{N})} \tag{1}\]  
After that, \(X[n,m]\) is transformed from the TF domain to the time domain by the Heisenberg transform:  
\[s(t)=\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}X[n,m]e^{j2\pi m\Delta f(t-nT)}p_{tx}(t-nT) \tag{2}\]  
where \(p_{tx}(t)\) is the pulse shape at the transmitter, and \(\Delta f\) and \(T\) are the subcarrier interval and the sampling interval, respectively.  
From [3], the channel model in the DD domain can be expressed as  
\[h(\tau,\
u)=\sum_{i=1}^{P}h_{i}\delta(\tau-\tau_{i})\delta(\
u-\
u_{i}) \tag{3}\]  
where \(P\) is the number of paths, and \(h_{i},\tau_{i},\
u_{i}\) stand for channel path gain, delay, and Doppler shift, respectively. The time domain signal, \(r(t)\), is obtained at the receiver after passing through the channel, which can be written as  
\[r(t)=\int_{\
u}\int_{\tau}h(\tau,\
u)e^{j2\pi\
u(t-\tau)}s(t-\tau)d\tau d\
u+n (t) \tag{4}\]  
where \(n(t)\) denotes Gaussian noise.  
Figure 1: OTFS Modulation.  
The Wigner transform converts the received signal, \(r(t)\), into the TF domain signal, \(Y[n,m]\):  
\[A_{p_{rx,r}}(t,f)\triangleq\int p_{rx}^{*}(t^{\prime}-t)r(t)e^{-j2\pi f(t^{\prime }-t)}dt^{\prime} \tag{5}\]  
\[Y[n,m]=A_{p_{rx,r}}(t,f)\Big{|}_{t=nT,\ f=m\Delta f} \tag{6}\]  
where \(p_{rx}(t)\) represents the receive pulse shape, and \(\left(\bullet\right)^{*}\) represents the complex conjugate operation. Finally, the TF domain signal is converted to the DD domain signal by SFFT:  
\[y[k,l]=\frac{1}{\sqrt{MN}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}Y[n,m]e^{-j2\pi( \frac{\#}{N}-\frac{\#}{M})} \tag{7}\]  
From (1)-(7), we can obtain the input-output relationship formula in the DD domain:  
\[y[k,l]=\sum_{k^{\prime}=0}^{N-1}\sum_{l^{\prime}=0}^{M-1}x[k^{\prime},l^{ \prime}]h_{\omega}[(k-k^{\prime})_{N^{\prime}},(l-l^{\prime})_{M}]+n[k,l] \tag{8}\]  
where \(n[k,l]\) is the Gaussian noise term with power spectral density, \(N_{o}\), and \(h_{\omega}[k,l]\) denotes the equivalent channel matrix in the DD domain. It is worth mentioning that the pulse used in Equation (4) is the ideal pulse. However, in real systems, non-ideal pulses can lead to cases of fractional Doppler [24]. In this paper, we focus on the ideal pulse case, and Equation (8) is only applicable to that specific scenario.
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Analysis and Artificial Intelligence
#### 3.2.1 Data Analysis for the Determination of Management Zones  
Being able to determine spatial and temporal variability in crops is of paramount importance for PA, as mentioned in the introduction. Towards this direction, management zones are adopted to define subregions in a given field, which are characterized by similar soil, landscape, and yield-limiting factors [25]. In these subregions, farmers can employ homogeneous management practices in terms of irrigation, use of pesticides and fertilizers, planting density, tillage, soil sampling, etc. The use of management zones leads to increased efficiency and profitability and supports environmental sustainability.  
Geostatistical and clustering models, interpolation methods, and GIS are particularly useful for the calculation of the management zones. [PERSON] et al. underline the contribution of kriging interpolation methods in calculating plausible management zones for PA. These geostatistical methods help overcome several difficulties in identifying subfield areas. Such difficulties are often posed by a complex combination of factors, which can influence the effectiveness of specific inputs (e.g., fertilization, irrigation, irrigation, use of pesticides) [26]. [PERSON] et al. demonstrate a weighted multivariate spatial clustering model for the determination of irrigation management zones. The specific model is based on machine learning and spatial statistics. A comparison with other clustering techniques showed a significant advantage of this model regarding the separability of points and their spatial distribution [27]. [PERSON] et al. analyze a solution in which geostatistical models based on GIS are used for the predictive mapping of soil properties. This solution had important benefits over existing tedious, time-consuming, and expensive soil analysis methods. Six geostatistical interpolation methods were utilized for seven soil properties (total nitrogen, soil pH, soil organic carbon, available phosphorus, sand, clay, and cation exchange capacity), drastically improving the soil maps resolution by providing detailed information. The results can also be utilized by small farm holders to increase their production and reduce their irrigation requirements [28].  
Groundwater models are also of great importance for PA, as they simulate and predict aquifer conditions by utilizing various hydraulic parameters and input datasets. Meta-heuristic algorithms are used for determining optimal and near-optimal parameters in groundwater models. [PERSON] et al. compare two meta-heuristic algorithms, i.e., particle swarm optimization and pattern search. The resultsof this comparison indicated that the latter approach offered more accurate and efficient aquifer parameters calibration [29].
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
## 2 Numerical Model
### Flow Settings
The PIMPLE (Pressure Implicit Method for Pressure-Linked Equations) algorithm was used for pressure-velocity coupling to solve the momentum and continuity equations together in a segregated manner. The PIMPLE algorithm integrates the PISO (inner corrector) and SIMPLE (outer corrector) loops to achieve a fully converged steady-state solution within each time step. The estimated size of the time step \(\Delta t\) can be determined by the Courant number (\(Co=v\Delta t/\Delta x\)), where \(v\) is the velocity through the shared face between two neighboring cells and the distance between the \(\Delta x\) centers of these two cells. A Courant number value \(Co=0.5\) was selected, as in [17], giving a time-step requirement of 0.001s for the mesh size and flow velocity considered.
## 4 Parametric Excitation of the TSBD Due to Vertical Forcing
The areas of the \(\lambda\), \(\gamma\) diagram, which lead to stable solutions of the Mathieu equation, are shown shaded. One can see that the regions of instability decrease with increasing values of \(\lambda\). Figure 4: Ince–Strutt stability diagram for a parametric-excited dynamic system: (**a**) stability diagram for an undamped system, (**b**) detailed view of stability diagram for a damped system [37]. Figure 4a indicates that the stability diagram is symmetrical to the \(\lambda\)-axis and parametric resonance of undamped dynamic systems may occur with increasing \(\lambda\) for an infinite number of excitation frequencies \(\Omega_{v}\). For small values of \(\gamma\), i.e., small vertical excitation amplitudes \(v_{0}\), it is seen that instability occurs at the values \(\lambda=n^{2}/4\), where \(n=1\), \(2\),..., \(\infty\). The fact that parametric resonance occurs at an infinite number of sub- and superharmonic frequencies is a significant difference from the conventional resonance phenomenon, which occurs just for \(\lambda=1\). Figure 4b illustrates a detailed view of the stability diagram from \(\lambda=-0.5\div 1.7\) for a damped dynamic system. It is indicated that the presence of structural damping decreases the regions of instability significantly. The effects of the damping influence on the change of the stability regions and boundary curves have been comprehensively described by, e.g., [PERSON] [38] or [PERSON] [40]. From Figure 4, one can see, that the major areas of instability occur at \(\lambda=0.25\) in the Ince-Strutt stability diagram. Thus, the instability phenomenon of parametric resonance occurs with the highest probability in the case when the vertical excitation frequency is twice the natural frequency of the TSBD, i.e., \(\Omega_{v}=2\cdot\omega_{TSBD}\) (see Equation (19)). It can be shown that for a stable forced vibration of a damped dynamic system, with parametric resonance at the critical point \(\lambda=0.25\), the following cut-off value of the Lehr damping ratio is required [41; 42],  
\[\zeta_{TSBD}\ >\ \omega_{TSBD}^{2}\ \frac{v_{0}}{g}\ . \tag{20}\]  
In Equation (20), the parameters of the TSBD have already been used for the definition of the value of the required Lehr damping ratio. Assuming one wants to deliberately induce parametric resonance in the rolling body of the TSBD, the magnitude of the actual existing Lehr damping ratio must be correspondingly smaller than the value given in Equation (20).
# A Comparative Study of the Pullout Strength of Geostraps and Geogrids in Reinforced Soil
###### Abstract  
The sustainable development of geotechnical infrastructure necessitates using durable, efficient, and environmentally resilient reinforcement materials. This study investigates the pullout performance of geostraps to assess their potential as a sustainable alternative to conventional geosynthetics. This study focuses on the pullout performance of geostraps, flexible, polymeric reinforcement materials. There has not been a thorough study of their pullout resistance, which directly affects the stability and durability of reinforced soil structures. Pullout tests were conducted on sandy soil in a controlled environment. The experimental findings from the pullout test were then validated in a numerical model. The model was used to determine the pullout resistance of different grades of geostraps for comparative analysis. This helped to identify the possible application areas based on the pullout capacity of various grades. The results obtained for the geostraps were then compared with those in the established literature on geogrids. Initially, the pullout resistance of the M65 geostrap was up to 20% higher than that of a biaxial geogrid. This makes it a suitable option for reinforced earth applications. However, the maximum pullout resistance of geogrids was up to 8% higher than that of geostraps when subjected to a surcharge of 17 kN m\({}^{-2}\) in poorly graded sand. This study highlights the potential of geostraps as reinforcement materials, particularly in challenging environments where conventional geosynthetics may underperform. Future research may explore their behaviour with different soil types and other controlled environmental factors to establish their broader applicability and design charts.  
geosynthetics; unpaved roads; fibre reinforcement; geostrap; pullout strength; numerical modelling; field tests 2025
## 2 Materials and Methods
### Proposed Method  
The proposed method enhances driver drowsiness detection using computer vision by integrating face detection and efficient machine learning models. A comprehensive series of interconnected modules was utilized, specifically tailored to address the dynamic and unpredictable nature of drowsy driving indicators, employing traditional machine learning techniques and feature analysis for the robust, real-time identification of drowsiness.  
By transitioning to the practical application of these techniques, by performing training on the NTHU-DDD dataset and hyperparameter tuning, the system can analyze real-time webcam footage taken from a generic webcam with a resolution of 720 p and 30 fps. The footage from the webcam is then preprocessed by first extracting each frame, then performing face detection, facial landmark detection, landmark frontalization, calculation of PERCLOS and POM, and head and pose estimation in real time on a low computational device such as a Jetson Nano Developer Kit. These features are then fed into finely tuned models to enable the real-time assessment of drowsiness detection by a driver.  
Building on this foundation, the proposed method is suitable for simpler machine learning models, namely, SVM, Random Forest, and XGBoost. These models were chosen to reduce the complexity of the models, which in turn keeps the model smaller, similar to those implemented in Refs. [11; 12]. These models are adept at processing detailed data extracted from facial landmarks and head pose analyses. Utilizing features such as the degree of eye and mouth openings, as well as head pose angles and metrics such as PERCLOS and POM, the models effectively classified the state of driver drowsiness. The proposed method offers a balance between computational efficiency and accuracy for use in real-time applications on limited resources, such as the Jetson Nano.  
In addition to simple machine learning models, SVM and Random Forest are examples of models used in Ref. [12]. Moreover, XGBoost was used because the data derived from the implemented feature extraction are numerical, unlike other studies where image data are inputted into the model. This research uses numerical values derived from frontalized facial landmarks, pairwise differences of landmarks, and metrics such as PERCLOS, POM, and head pose. The use of these data allows for a more flexible machine-learning approach for drowsiness detection.  
Furthermore, the detected features were first fed into multiple models for each facial area. Features related to the eye are fed into a model to detect the status of the eyes according to whether the eyes are sleepy or not. Features related to the mouth are fed into another model to detect the status of the mouth--if the mouth is yawning, still, or talking--and another model is fed features related to the head to detect whether the head is still nodding or looking to the side. From the results of these three trained models, the predictions will then be fed into another model to perform the classification of drowsy or non-drowsy. This is shown in the flowchart in Figure 2.
## 4 Experiment
### Generation of Position Fingerprints  
#### 4.1.1 Environment  
In this study, the experiment was conducted in a university research room (about 7.8 m \(\times\) 7.9 m \(\times\) 2.8 m). Obstacles included desks, lockers, etc. No one other than the experimenter was present during the measurement. Four BLE beacons \(\left\{\text{BLE}^{id}|id=0,1,2,3\right\}\) were placed at different locations on the ceiling. The height of the measurement terminal was fixed at 0.65 m when measuring RSSI values. The experiment environment is shown in Figure 8a. Specifications about the experiment devices are in Table 1.
# Mechanical Faults Analysis in Switched Reluctance Motor
## 2 Materials and Methods
### Electric Motor Faults and Diagnostic Methods  
#### 2.2.1 Introduction to Diagnostics  
Diagnostics was initially applied in medicine and used in technical applications afterward. Therefore, in the field of technology, the term diagnosis can be understood as an analysis of the temporary technical condition of machines or an assessment of the ability to operate the object. Its most important tasks are as follows:  
* Detection--detection of the occurrence of an emerging failure;
* Location--determination of the location, a defective part, or link of the failure that arises;
* Specification--determination of the cause of the failure;
* Forecast--determining the remaining period of possible operation to plan the appropriate and favorable date of repair and ensure the necessary logistics activities.  
The examined object is the SRM, and as was mentioned, it is primarily operated in locations of military or industrial importance, where the replacement or repair of the mechanism can be complicated, and such an activity must be carefully planned. The type and degree of fault will determine the downtime length and the repair's complexity.  
An appropriate test plan and measuring tools are necessary to identify any fault in a given electric motor. For this purpose, various physical quantities of the motor can be analyzed during its operation. Usually, these are vibrations (acceleration), phase currents, or the voltage at the motor terminals. Sometimes, the temperature measurement or one of the magnetic quantities (field strength, flux, or other) is met.  
In addition to experimenting on the stand, it is also recommended to simulate it using an analytical or having it based on the finite element method (FEM) model. It allows the study of the object's behavior almost cost-free in ideal conditions. Indeed, this is only sometimes possible, e.g., when experimenting with coupled mechanical, electrical, temperature, and other test-related phenomena that are impossible to include in the computer model.
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 2 Methods
### Monitoring System Overview
#### 2.2.1 Multi-Sensor Device  
Multi-sensor device was designed for measuring the selected physical and chemical parameters of the beehive air. The sensors of the following quantities were mounted in the device: temperature and relative humidity--SHT25 (Sensirion AG, Stafa, Switzerland), particulate matter--SPS30 (Sensirion AG, Stafa, Switzerland), carbon dioxide--SCD30 (Sensirion AG, Stafa, Switzerland), total volatile organic compounds (TVOC) with ionization potentials < 10.6 eV--PIDAH2 (Alphasense, Skyfall, United Kingdom), TVOC--SGP41 (Sensirion AG, Stafa, Switzerland), NOx--SGP41. The measurement characteristics of the applied sensors are provided in Table 1.  
The listed quantities were chosen to be measured for several reasons. Beehive air temperature and relative humidity are the essential parameters pointing to the thermoregulatory capacity of the honey bee colony. This capability is one of the indicators of the colony's well-being. However, thermal conditions in the beehive are also influenced by other factors, such as the beehive construction materials, apiary location, and the ambient environment. In particular, urban heat islands may be a different challenge for bee colony thermoregulation activity, compared with rural conditions. Temperature and humidity sensors are very cheap, and the measurement of these quantities is relatively easy. It is also the most popular measurement in apiculture; therefore, considerable reference is available for comparison and discussion. The concentration of carbon dioxide in beehive air is related to honey bees' metabolism, health, and activity. It is also dependent on the colony development stage and size. This parameter is not so frequently measured in the beehives. CO2 sensors are a bit more expensive compared with temperature and relative humidity ones. Due to the small amount of reference data the CO2 concentration variation in the beehive is not well understood yet. In urban air, the concentration of CO2 is generally higher than in rural areas. Still, based on our observation, the urban background level remains well below the CO2 concentrations we observed inside beehives. Honey bee colony-related factors are predominant in shaping the temporal variation of this compound in beehive air. Examples of continuous monitoring of volatile organic compounds in the beehive atmosphere are extremely scarce. These compounds are usually measured in samples collected from the honey bees or the components of their environment in the framework of research studies aimed at detecting and quantifying some target compounds. Advanced analytical techniques and lab-based instruments are used for this purpose. Our goal is
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 5 Solutions of the CPS Communication Hub
### Information Sharing Services  
The CPS/M2M system needs to deliver information from one to one (1-1), but also from one to many (1-N). Therefore, the CPS hub relies on the publish/subscribe paradigm in the information sharing. For example, XMPP technology has such a publish/subscribe mechanism, which is based on the concept of specific entity called \"pubsub\" node [10; 11; 12]. Such a \"pubsub\" node acts in a way as the publish/subscribe service established to work in the XMPP server. After that, the publisher can publish information into the \"pubsub\" node and a subscriber can subscribe to information from it. A subscriber can subscribe to information and receive notifications, if the subscriber is authorized to access the published information. However, the referred XMPP-based publish/subscribe means have proved to be quite complicated. Therefore, the information sharing service of the CPS hub relies on slightly more simple solutions for the publish/subscribe capabilities of MQTT [8; 9].  
It is essential to have some common identities for the information, so that the entities know what information the other entities are publishing. MQTT provides a possible way for such an identification by defining the concept of \"topic\" name, which could be applied as the name for the shared information. However, MQTT seems not to have the proper capabilities to define the owners and their physical resources in a simple way. Therefore, the CPS hub applies XMPP and its identities (JID) approach for this purpose. The selected parameters for the MQTT-based publish/subscribe services are clarified as follows:  
\[\text{Username: }<\text{owner>@<home.domain>} \tag{2}\]  
Figure 14: XMPP buddy subscription.  
(2)  
where  
* Username, < _owner>@<home.domain_> is the same as the XMPP identifier, JID;
* <_physical resource_> refers to a physical resource, which can typically be, for example, an embedded device, gateway, or server;
* <_logical resource_> refers to specific information content \"topic\", or logical resource tree of the information;
* <_sub logical resource_> refers to the child information elements of the logical resource.  
The basic operation of the MQTT-based publish/subscribe services is visualized in Figure 15. First, the MQTT clients of the resources need to connect to the MQTT broker of the CPS hub using the defined username and password for authentication. After that, a MQTT client can subscribe to the selected topic, which is published by another MQTT client.  
However, the delivery of the information (\"Published\" in Figure 15) cannot happen like in the basic operation. This is because it is not known whether the owner allows delivery of the information to the subscriber in the current situation with the agreements and security policy of the owner. Instead, the trust checking services need to be executed at the latest before the delivery of the information to the subscriber.