esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 3 Results
### Object-Based Approaches  
In an object-based approach, pixels in an image are segmented into objects based on spectral similarity or external variables (e.g., land governance) to produce a map classification. Researchers commonly apply this approach to high-resolution imagery. [PERSON] (2016) and [PERSON] et al. (2017), for example, used Quickbird and Landsat imagery respectively, to perform a computer-assisted photointerpretation to manually classify each individual plot in their study area based on their similarity to predetermined patterns of land use. While the accuracy of these studies was unreported, [PERSON] (2016) required 700 h to complete this process, limiting its potential as a scalable, replicable method. Alternatively, [PERSON] et al. (2017) studied the effect the scale of an object has on the accuracy of the classification of SPOT imagery using two classifiers, Support Vector Machine (SVM) and Radial Basis Function (RBF), and boost-classifiers by building a linear combination of these classifiers (based on the AdaBoost algorithm proposed by [PERSON] (2017)). They concluded that coarser-scale objects had an accuracy of up to three percent greater and lower training times than the finer-scale objects and that the boost-classifiers improved accuracy over the RBF classifier by five percent. [PERSON] et al. (2017) compared the accuracy of a spectral pixel-based approach using a maximum likelihood classifier with an object-based support vector machine model to map coffee fields in Hawaii. The object-based method was the more accurate approach to map coffee, with a 76% producer accuracy and a 94% user accuracy for the object-based model compared to a 72% producer accuracy and a 69% user accuracy with the pixel-based model. [PERSON] (2017) performed a similar analysis comparing pixel-based methods to object-based methods. This study also found object-based analyses to be more successful than pixel-based methods, with a 3% overall accuracy improvement of the product and a 17% overall accuracy increase for the coffee class. Lastly, as in pixel-based analysis, ancillary data was also used to improve the accuracy of object-based classifications. By adding data such as elevation information from a DEM and a previously created national forest cover map to their coffee mapping methodology, [PERSON] et al. (2017) improved their overall classification accuracy from 50% to 87.6%.
## 2 Instrument Design
### Instrument Length  
As a minimum requirement, the \(z\) dimension of the MPA's magnetic chamber must be longer than two curvature radii associated with the fastest particles. Similarly, the \(x\) dimension must be deeper than one curvature radius (see Figure 1b). According to this requirement, the minimum dimensions of the instrument also depend on the magnetic-field strength, which defines the curvature radius. Keeping the remaining parameters constant, an increase of the magnetic-field strength increases the number of particles detected on each pixel, leading to a smaller statistical error in the measurement. However, it also increases the relative error in velocity measurements according to Equation (25). The MagEIS instrument provides us with a reasonable starting point for an achievable magnetic-field strength. The MagEIS properties are summarized in Table 1. We choose for MPA a starting value of 0.1 T. In Section 3, we study the influence of the magnetic field strength on the overall performance of our instrument.  
In the solar wind, the proton bulk speed ranges from roughly 300 (slow solar wind) to 800 km/s (fast solar wind) (see Table 1 in [24], Figure 2 in [25] and [PERSON] et al. [9]). The speed also depends on latitude and heliospheric distance [26]. Using Equation (4), we determine that the required length to measure protons with a speed of 800 km/s is 17 cm. However, the requirement to measure \(\alpha\)-particles as well makes necessary a longer instrument, since \(\alpha\)-particles have twice the charge of protons and four times their mass, leading to a larger hit distance in the sensor plane according to Equation (4). Therefore, MPA must be twice as long as estimated in the proton-only case in order to measure \(\alpha\)-particles with the same speed as the protons. We choose for a first design a length of 30 cm. This enables measurements of protons up to 1400 km/s and \(\alpha\)-particles up to 700 km/s. In contrast, the MagEIS instrument is approximately 7 cm long, i.e. smaller, and thus lighter than our design.
## 4 Experimental Results and Analysis  
To avoid occasionality, we put the proposed method into practice to test its performance under six different tampering conditions. In this experiment, we adopted the methods proposed by [PERSON] et al. [8], [PERSON] et al. [9] and [PERSON] et al. [7] for comparison purposes. Eight test images from the CVG-UGR image database [22] are displayed in Figure 6: all of which have a size of \(256\times 256\). We executed our experiments on MATLAB R2016a.  
To ensure fairness and consistency, we conducted objective assessments of the experimental results as measured uniformly by PSNR and structural similarity (SSIM) [23]. The PSNR value reflects the differences in corresponding pixels between two images. The larger the PSNR value is, the better the objective quality of the result is. When the PSNR exceeds 35 dB, humans are unable to discern the difference between two images. However, the PSNR value does not completely reflect with the subjective effect. Therefore, we also introduce the SSIM value, which measures image quality from three aspects: brightness, contrast and structure. Similar to PSNR, the SSIM value is positively correlated with the objective quality.  
Figure 5: Embedding bits and the corresponding image watermark information.  
Figure 6: Test images: (**a**) Lena; (**b**) Goldhill; (**c**) Car1; (**d**) Car2; (**e**) Venice; (**f**) Clock; (**g**) Airplane; (**h**) Barbara.
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 3 System Model and Problem Description
### Channel Model  
For short bursts, a time-invariant channel was assumed throughout a burst when the UAV flies at medium and low speeds, and the channel in any two different bursts is independent. A typical communications scenario is shown in Figure 3. Considering the effects of topography, atmosphere, and other factors, the propagation shows the multipath fading problem. In a ground-to-air scenario for UAV-aided IoT communications, the UAV adopts the hover-and-fly method for data collection via the IoT uplink. In the communication between the UAV and the ground nodes, the multipath channel model can be described by a Ricean fading channel with the probability distribution of the Rician distribution:  
\[f(r)=\begin{cases}\dfrac{r}{\sigma^{2}}exp(-\dfrac{r^{2}+\alpha_{ LOS}^{2}}{2\sigma^{2}})I_{0}(\dfrac{r\alpha_{LOS}^{2}}{\sigma^{2}}),&0\leq r< \infty\\ 0,&r<0\end{cases} \tag{14}\]  
where \(r\) is the envelope of the fading signal, \(\alpha_{LOS}\) is the amplitude of the LOS component, \(\sigma^{2}\) represents the energy of the NLOS component, and \(I_{0}\) is a modified Bessel function of the first kind with order zero. Rician factor \(K_{Ricenn}\) is defined as the power ratio of the main signal to the multipath components, where \(K_{Ricenn}=\alpha_{LOS}^{2}/\sigma^{2}\). In the IoT-aided UAV communications scenario, this paper refers to [34] for the air-ground (AG) channel at 5060 MHz (C baud), which corresponds to a Ricean factor of 29 dB. Then, the two-ray Rician channel model, including the LOS and NOLS components, can be specified as  
\[c(\tau,t)=\alpha_{LOS}\delta(\tau-\tau_{1}(t))+\alpha_{2}e^{-j \phi(t)}\delta(\tau-\tau_{2}(t)). \tag{15}\]
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 3 Methodology
### Track Query Propagation  
To complete a full autoregressive tracker, we propose that the query propagation module is used to convert tracklet states into track queries for the next frame. This module comprehensively utilizes the disappearance time, confidence score, and appearance feature of a tracklet to better guide the track of the next frame. Figure 5 shows an example of this pipeline.  
For appearance information, we directly take the appearance features from the fuser output as the tracks' features at the current frame. This simple design is expressive enough  
Figure 5: An example of query propagation. The green target is lost in the latest frame, so its last appearance feature and confidence score are utilized to generate a new query with a different delta time embedding.  
since the tracks' appearance features have already been fused with the in-frame and cross-frame information in the previous modules. For confidence information, we use a similar embedding method as in score-guided multi-level query fuser (Section 3.4) to convert score values to embeddings. Additionally, to model the temporal uncertainty of the targets that are missing in previous frames, we use discrete delta-time embeddings to encode the frames passed since the last update of each tracking target. The appearance features and the confidence and time embeddings are concatenated and then passed through a simple MLP network to obtain track queries for the next frame.  
To obtain better position priors as reference points for deformable attention, we refer to the kinematic states stored in each tracklet to generate rough predictions as track position anchor inputs.
## 5 Permanent Displacement of the Slope  
### [PERSON]'s Method  
Some time ago, [PERSON] proposed an analysis method for dam slopes in which the stability of the slope under seismic action depends only on the permanent displacements it undergoes [27]. A pseudo-static method was subsequently used to derive an expression for the permanent displacement assuming the geotechnical object acts as a rigid body. [PERSON] thus found that the permanent displacement \(D\) only depends on the action of the  
Figure 12: Contour plots of the displacement in the \(Y\)-direction of a slope (\(\alpha=40^{\circ}\) and \(\beta=70^{\circ}\)) subjected to a sinusoidal wave. The plots show the displacements in the plane corresponding to \(x=10\) m at the end of the period of disturbance (\(t=5\) s) for different values of \(\gamma\): (**a**) \(0^{\circ}\), (**b**) \(5^{\circ}\), (**c**) \(10^{\circ}\), (**d**) \(15^{\circ}\), and (**e**) \(20^{\circ}\).  
seismic waves and safety factor of the slope under static forces. Based on this, an expression can be derived for \(D\) of the form,  
\[D=\int\limits_{0}^{T}\int\limits_{0}^{T}\left[a(t)-Ng\right]\,\mathrm{d}t^{2} \tag{7}\]  
where \(a_{t}\) is the acceleration history and \(Ng\) is the horizontal critical acceleration, calculated using:  
\[N=(SF-1)\tan\alpha \tag{8}\]  
where \(\alpha\) is the slope angle and \(SF\) is the safety factor of the slope under static conditions. [PERSON]'s method is simple and easy to use. Other researchers have subsequently modified and improved the method and applied it to evaluate the stability of slopes and landslides [28; 29].  
As [PERSON]'s method can only consider the acceleration history in a single direction, in this work (for the sake of convenience later), the E-W direction of the Wolong seismic waves was selected for analysis purposes. This corresponds to the slope direction of displacement (i.e., the \(Y\)-axis). Furthermore, the displacement data for monitoring point D6 on the slope were chosen for analysis. Nine models were analyzed in all (Table 2).  
The \(SF\) values in the table were obtained via limit equilibrium analysis. Figure 13 illustrates the process used to find the permanent displacement using [PERSON]'s method (taking model No. 2 as an example). The permanent displacements thus obtained for each model are also shown in Table 2. It can be seen that the permanent displacement of the slope \(D\) tends to increase as the values of \(\alpha\) and \(\beta\) increase. However, changing the angle \(\alpha\) exerts a stronger influence compared to changing \(\beta\) because the surface of the slope is a free surface. Thus, the slope's dip angle is inevitably the most important factor determining the displacement of individual points on the surface. In addition, the larger the joint
## 1 Introduction  
Infertility is defined as a failure to achieve a pregnancy within a year or more of regular unprotected sexual intercourse [1] due to several reasons, including male or female reproductive systems [2; 3; 4] and other related disease [5]. Infertility is a global issue, especially in high sociodemographic countries [6], and therefore the use of assisted reproductive technology (ART) by infertile couples has increased by 5% to 10% per year [3]. The most common infertility treatment is in vitro fertilization (IVF) as an alternative to human reproduction that has extensively evolved into the cultivation of human embryos used in embryological laboratories.  
IVF is a fertilization procedure in which a mature egg (oocyte) and sperm are combined in vitro (in glass) in a specialized laboratory. The fertilized egg (embryo) is allowed to grow in a protected environment up to seven days [7] before being transferred into the woman's uterus, hence increasing the chance that a pregnancy will occur. However, different IVF techniques may result in different types of embryo selection that are of questionable quality, particularly at the blastocyst phase of development.  
Several articles have been published that propose computer-based methods for IVF applications such as computer vision technology [8], image processing techniques [9; 10], and emerging technologies [11]; however, a scoping review with a specific focus on blastocyst-stage quality grading has yet to be conducted. This article aims to provide a state-of-the-artreview of the current techniques for blastocyst assessment, with a focus on intelligence-based approaches of distinguishing high-quality embryos based on recent advances in CMA and TL imaging of IVF technology. This review is not limited to human blastocysts, but also includes bovine and murine blastocysts for extensive comparisons. As the selection of good-quality embryos has undoubtedly improved the successful outcomes in IVF, intelligence-based technologies and methods are providing new insights into the traditional approach of morphometric assessment, especially at the blastocyst stage.
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 2 Unlicensed Medium Access Mechanisms
### WiFi  
According to the 802.11 WiFi standards (including its latest versions ad and ax), the primary channel access mechanism used for unlicensed band is the Distributed Coordination Function or DCF [17] based on CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance). WiFi DCF uses aCCA sensing based mechanism similar to LBT (Figure 2) with two related functions: Carrier Sense (CCA-CS) to detect and decode a WiFi preambles and Energy Detect (CCA-ED) for non-WiFi energy in the operating channel and back off data transmission. Although they operate simultaneously, different thresholds are set for each function.  
When the channel is empty for a DIFS (DCF Inter Frame Space) period duration, the WiFi station sends the whole frame. In the case of finding the channel busy, a random timer (with backoff count) starts. Such timer only decreases its value when free channel has been sensed during the slot period. Once it expires, the WiFi stations must transmit. Every time a frame is correctly decoded at the receiver, an ACK is sent after SIFS (Short Inter Frame Space). Thus, when such confirmation (ACK) is not received, the backoff period is increased in an exponential way.  
In summary, although the mechanism of access to the LBT and CSMA/CA contention channel are very similar. In WiFi, the detection of a collision is done by means of a confirmation frame (ACK).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Priority Class (p)** & **CWmin** & **CWmax** & **MCOT** \\ \hline
1 & 3 & 7 & 2 ms \\
2 & 7 & 15 & 3 ms \\
3 & 15 & 63 & 8 ms \\
4 & 15 & 1023 & 8 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: 3 GPP LBT CAT4 priority classes.  
Figure 2: Comparison of WiFi and LBT access mechanisms for the unlicensed band.  
Conversely, in 3 GPP standards, there is no such frame, so collision detection is based on the feedback from the hybrid automatic repeat request (HARQ). Thus, according to the standard, if 80% of the HARQ feedbacks from the most recent MCOT are NACKs, a retransmission is required [5].
## 4 Conclusions  
I-V characteristics on TMBS diodes were performed using a graphene nanowall (GNW) as Schottky barrier material. We have shown that this carbon-based material gives an excellent result in leakage current performance at high temperature, surpassing the conventional TMBS diode with TiSi\({}_{2}\) as the barrier layer. From the measurement results, GNW has the potential of replacing TiSi\({}_{2}\) in lowering leakage, in addition to giving higher breakdown voltage and having the ability to withstand much higher temperature at 473 K without having a thermal runaway on the device tested.  
From the forward I-V data at V \(>\) 3 kT/q, three plots can be generated: \(ln(I)\) vs. \(V\), d(\(V\))/d(\(ln\)) vs. \(J\), and H(\(I\)) vs. \(J\). The second and third plots gave linear lines, while the first plot gave a curve requiring linear fitting, which may give error in choosing the data to include in the linear fit. The Schottky barrier parameters, \(\phi_{B}\), \(n\), and \(R_{s}\), for GNW at 298 K derived using [PERSON]'s method, were 0.703 eV, 1.64, and 35 ohm respectively. The major current transport mechanism at a temperature in the range of 298-373 K for the GNW-TMBS diode is thermionic emission. Future work will include contact resistance optimization between GNW to silicon and metal to reduce the resistance effect on \(\phi_{B}\) and study of the GNW Shi density towards Schottky diode device performance.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/8/1587/s1](http://www.mdpi.com/2076-3417/9/8/1587/s1)]([http://www.mdpi.com/2076-3417/9/8/1587/s1](http://www.mdpi.com/2076-3417/9/8/1587/s1)), Figure S1. 5 samples of GNW-TMBS diodes tested at different temperatures, Table S1. Leakage current at different temperatures measured at 60 V for GNW-TMBS and TiSi-TMBS.  
Conceptualization, S.K.W.S. and M.R.M.H.; formal analysis, R.M.S., S.K.W.S., M.R.M.H. and M.H.O.; funding acquisition, M.LS.; investigation, R.M.S., S.K.W.S., and M.H.O.; methodology, S.K.W.S. and M.R.M.H.; project administration, M.I.S.; Resources, R.M.S., S.K.W.S., and M.H.O.; supervision, M.I.S.; validation, R.M.S., S.K.W.S., M.R.M.H., M.H.O., and M.A.S.M.H.; writing--original draft, R.M.S.; writing--review and editing, R.M.S., S.K.W.S., M.R.M.H., and M.A.S.M.H.  
This research received no external funding.  
Authors would like to acknowledge [PERSON], [PERSON], and [PERSON] for assistance in test setup and availability of test tools for device testing.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Frequency Domain Methods of Calculation
### Superposition of Axle Pulses from Passing Trains
#### 3.5.2 Randomly Varying Track Support  
A soft or stiff track support has an influence on the load distribution of the track. A soft track support yields a wide load distribution, whereas a stiff track support yields a narrow load distribution. The distribution of the load along the track simultaneously means a distribution of the load in time. The impulses from the static axle loads are sharper for a stiff track support and smoother for a soft track support. The random variation of the track support stiffness is now included in the model in a simplified manner by modifying the width of the impulse. The width of the impulse is expressed in the filter function (17) of the track. A soft track support shifts the filter frequency \(f_{0}\) to a lower value. The random track support stiffness is realised in the calculation by a random support stiffness and hence a random filter frequency of the track. The stiffness for each excitation point is calculated as:  
\[k_{S}(y_{j})=k_{0}(1+q\ \text{rand}(j)) \tag{24}\]  
where \(k_{0}\) is the average value and \(q\) is the percentage of the variation, and rand(\(j\)) are equally distributed random numbers between \(-1\) and \(+1\). A total of 100 realisations of the stiffness distributions were analysed and the average of the response is presented.  
The other calculation parameters are the distance of the track points \(dy\) = 0.6 m, the number of the track points \(n\) = 168, and the length of the track \(L\) = 100 m. As the homogeneous soil has only a weak attenuation with distance, the train passage does not start with a zero amplitude. To avoid any boundary effect, a Hanning window  
\[h_{H}(y)=\cos^{2}\Bigl{(}\frac{\pi y}{L}\Bigr{)} \tag{25}\]  
is used.
# Augmented Reality for PCB Component Identification and Localization
## 5 Results
### Experiment 2  
Expanding on Experiment 1, which examined differences in component localization times between the HoloLens(tm) and traditional methods, Experiment 2 was designed to evaluate the accuracy of PCB component identification using Microsoft(tm) HoloLens(tm) 2, with a focus on its sensitivity to varying viewing angles.  
Participants (_n_ = 10) were equipped with the HoloLens(tm) 2 and tasked with identifying components on a printed circuit board (PCB). They were non-domain users with no prior experience in electronics manufacturing or PCB handling. All participants were students, some of whom had limited prior exposure to augmented reality. Each session began with a briefing on the experiment's objectives, followed by a training session to familiarize them with the HoloLens(tm) 2 and the AR application used.  
Participants were asked to identify the same set of twelve PCB components at three predefined viewing angles--90\({}^{\circ}\), 60\({}^{\circ}\), and 45\({}^{\circ}\)--in a fixed sequence. For each participant, all twelve components were identified at one angle before proceeding to the next. This design ensured systematic and consistent progression through the task and avoided angle changes between individual components, reducing potential variability and cognitive load.  
Accuracy in this study was assessed by calculating the Euclidean distances between the augmented positions and indicated positions relative to the reference positions, as described in the Section 4. The results were visualized using scatter plots and box plots. Additionally, the average time participants took to confirm the stable augmentation or rendering of each component was recorded and analyzed.  
Definition of Key Metrics:  
For clarity, the following terms are used distinctly in this study:  
* _Accuracy_ denotes the spatial precision of component localization, measured by the Euclidean distance between augmented/indicated positions and the reference position.
* _Correctness_ refers to a binary classification of whether the user-indicated position falls within the target component's footprint.
* _Confidence_ reflects the average proportion of correct identifications made by participants per component size category and angle.  
These definitions support consistent evaluation of user interaction and system performance.