esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 5 Conclusions  
From the review, it can be concluded that the introduction of MMP into conventional optical methods significantly increases their capabilities for more accurate and reliable diagnostics of biological and bioorganic systems, including those related to agriculture. Existing MMP implementations are able to characterize organic and microbiological dispersions, surface structures and biological tissues by obtaining polarization contrast images and the microphysical properties (particle size distribution, fractal and anisotropic properties) from measured MMs. In addition, MMP has the potential to analyze the content of organic constituents in agricultural materials and products (milk and beverages, fodders, etc.) and monitoring soil properties and vegetation. From the MM, it is possible to derive material-specific parameters that can serve as indicators of the quality of agricultural products. Thus, we can consider MMP to be an effective diagnostic tool in biological and agricultural studies.  
Polarimetric optical elements allowing the measurement of the complete MM can be integrated into any standard optical spectrometer layout without rebuilding the signal registration system. The use of modern optical technologies, such as fiber optics, can further enhance the capabilities of MMP methods owing to flexible sensing system and noise reduction.  
Conceptualization, A.V.S., D.N.I. and S.V.G.; literature review D.N.I. and A.V.S.; writing, D.N.I. and A.V.S.; editing, A.V.S., Y.P.L. and S.V.G.; preparation of illustrations, D.N.I.; project administration, S.V.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Ministry of Science and Higher Education of the Russian Federation for large scientific projects in priority areas of scientific and technological development (grant number 075-15-2020-774).  
Not applicable.  
The authors declare no conflict of interest.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 3 Results
### Results of Forest Disturbance Mapping in Peru
The detailed confusion matrices, including plot numbers and applied area weights, can be found in the Tables A3-A8 for Peru and Tables A9-A11 for Gabon. We can calculate adjusted area estimates for each class based on the omission and commission errors and the respective area weights [79]. The above accuracy assessments clearly show that misclassifications in the forest/non-forest map contribute a major component to the overall error of the disturbance maps. When the FNF mask errors are removed, the overall accuracies increase significantly. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plot Size** & **Number of Disturbance Areas** & **S1 Only** & **Optical Only** & **Union** \\ \hline \(>\)1 ha & 51 & 0.686 & 0.647 & 0.784 \\
0.5–1 ha & 44 & 0.523 & 0.364 & 0.636 \\
0.2–0.5 ha & 101 & 0.248 & 0.277 & 0.416 \\ \(<\)0.2 ha & 166 & 0.157 & 0.175 & 0.295 \\ \hline all sizes & 362 & 0.301 & 0.293 & 0.530 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection accuracy per disturbance patch size for Gabon. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plot Size** & **Number of Disturbance Areas** & **S1 Only** & **Optical Only** & **Union** \\ \hline \(>\)1 ha & 34 & 0.882 & 0.853 & 0.971 \\
0.5–1 ha & 36 & 0.806 & 0.778 & 0.889 \\
0.2–0.5 ha & 43 & 0.698 & 0.674 & 0.884 \\ \(<\)0.2 ha & 35 & 0.486 & 0.657 & 0.800 \\ \hline all sizes & 148 & 0.716 & 0.736 & 0.885 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection accuracy per disturbance patch size for Peru.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 3 System Design
### Motion Detection  
After Wi-Senser selects the optimal subcarrier with the highest sensitivity to human head motion changes from 30 CSI subcarriers using the joint metric, it further utilizes the peak-finding algorithm to capture the true peak set caused by human head motion from the optimal subcarrier to achieve accurate detection of human head motion. The peak-finding algorithm is detailed in Algorithm 1. Specifically, for the processed CSI amplitude sequence \(C^{i}\), Wi-Senser first uses the sensitivity measure given by Equation (12) to calculate the sensitivity values of each subcarrier to head movement during sleep (lines 1-5 in Algorithm 1). Then, Wi-Senser selects the subcarrier with the highest sensitivity value as the optimal subcarrier (lines 6-10 in Algorithm 1). Next, for the selected optimal subcarrier, Wi-Senser sets a minimum peak height and uses a classical algorithm to find all local maxima as the candidate peak set (lines 11-12 in Algorithm 1). Finally, Wi-Senser filters out false peaks caused by large-scale body movements including getting out of bed and turning over (i.e., changing sleeping postures) from the candidate peak set based on a threshold-based approach to obtain the true peak set that records changes in head movement during sleep (lines 13-18 in Algorithm 1). The reason we can remove irrelevant large body movements is that the environmental changes involving large body movements (e.g., going to bed and turning over) result in much larger changes in CSI measurement than those of minute movements of human head [24].  
```
Input: The processed CSI amplitude sequence: \(C^{i}=\left\{c^{i}_{1},\ldots,c^{i}_{n}\right\}(1\leq i\leq 30)\);  weight factor: \(\theta\); threshold used to discriminate large body movements: \(T_{max}\). Output: The true peak set: RealPeakSet
1:\(Q^{max}\gets 0\);
2:for\(i\)= 1:30 do
3:\(sd^{i}=\sqrt{\frac{\frac{\pi}{\rho_{i}}\left(\zeta^{i}-\zeta\right)^{2}}{n}}\);
4:\(kt^{i}=\frac{\frac{1}{\rho_{i}}\left(\zeta^{i}-\zeta\right)^{2}}{\left(\frac{ 1}{\rho_{i}}\left(\zeta^{i}-\zeta\right)^{2}\right)^{2}}-3\);
5:\(Q^{i}_{(1,n)}=(1-\theta)\times sd^{i}+\theta\times kt^{i}\); # Sensitivity calculation
6:if\(Q^{i}_{(1,n)}>Q^{max}\)then
7:\(Q^{max}\gets Q^{i}_{(1,n)}\); # Optimal subcarrier selection
8:endif
9:endfor
10:\(i\gets max\);
11:\(P_{height}=\alpha\times Mean\left(C^{i}\right)\); #Set a minimum peak height used to filter out non-movement interferences
12:\(LocalMaxSet\gets FindLocalMaxS\left(C^{i},P_{height}\right);/*LocalMaxSet=\{ \varphi_{k},1\leq k\leq K\}*/\)
13:for\(k\)=1:\(K\)do #Find the true peak set caused by head movements
14:if\(amplitude(\varphi_{k})<T_{max}\)then
15: add \(\varphi_{k}\) into \(RealPeakSet\);
16:endif
17:endfor
18:return\(RealPeakSet\).
```  
**Algorithm 1**Peak-finding algorithm
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.6 Response Indication Deferral (RID)  
This method is an extension of Virtual carrier sensing mechanism originally defined in legacy IEEE 802.11 (i.e., Network Allocation Vector (NAV)). The short header defined by IEEE 802.11 ah does not include the Duration/ID field that is required by the NAV. Both NAV and RID indicate countdown timers used to show the channel idle time. However, the two schemes differ in the procedure to set the counter (while NAV is set after the complete and correct reception of a frame, RID can be set after the complete header of the frame is received).
# Progress of Organic/Inorganic Luminescent Materials for Optical Wireless Communication Systems
## 2 Luminescent Materials for VLC Systems
### Organic Molecules-Organic-Light Emitting Diodes (OLEDs) in VLC
This assumption is true in the case of OPDs containing active layers, such as bathocuproine (BCP) doped with 3,4,9,10-perylenetetracarboxylic bis-benzimidazole (PTCBI), which provides high values of bandwidths (\(\sim\)430 MHz) [58]. Other examples of OPDs are based on active layers containing donor species, as is the case for donor-\(\pi\)-acceptor selenophene (composed by heterocyclic units) devices with acceptor molecules, such as fullerene [59]. Through the fabrication of bulk and planar donor-acceptor heterojunctions to facilitate the carrier transport and increase as much as possible the photoresponsivity in the OPD, maximum values of frequency bandwidth and real-time data rate were estimated to be 1.4 MHz and 150 Mbps, respectively. The principal inconvenient factor for a majority of organic devices is related to their low bandwidth (<1 MHz); thus, most of recent studies focused their efforts on the design of OVLCs with higher data transference rates [23]. For example, an OVLC based on organic semiconductors, such as polypara-phenyl vinylene copolymer, reported by [PERSON] and coworkers [17] afforded a bandwidth of more than 200 MHz, although this bandwidth was limited by the other components in the system (the excitation source and the detector). Furthermore, they achieved a 1.68 Gbps data rate, which was an excellent result for white-light VLC link. Other OLED devices were able to achieve bandwidths of \(\sim\)245 MHz with data rates beyond 1 Gbps, as shown in Figure 2c,d, respectively, making them excellent organic materials for VLC applications [32]. At this point, these types of OLEDs can br fabricated by adding a conventional luminescent emitter based on 2,5,8,11-tetra-tert-butylperylene (TBPe) and a fast emission molecule (FEM), such as 4,4\({}^{\prime}\)-bis [4-(diphenylamino)styryl]biphenyl (BDAVBi). Moreover, different generations (G) of device structure (1, 2 and 3) and the sizes (denoted as S, M and L) of the active areas were compared, with the best performance achieved for G3-FEM-OLEDs. It is important to recognize that OVLC has a set of limitations, since there is a shortage of commercially available organic devices, and these systemsrecently emerged as promising materials in this area. In this way, it is deductible that these materials were not prepared at industrial quantities.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 5 Discussion and Conclusions
* (28) Maximum Permissible Levels of Interference in a Satellite Network (GSO/FSS; Non-GSO/FSS; Non-GSO/MSS Feeder Links) in the Fixed-Satellite Service Caused by Other Codirectional FSS Networks below 30 GHz. 2002. Available online: [[https://www.itu.int/dms_pubrec/itu-r/rec/s/R-REC-S.1528-0-200106-](https://www.itu.int/dms_pubrec/itu-r/rec/s/R-REC-S.1528-0-200106-)]([https://www.itu.int/dms_pubrec/itu-r/rec/s/R-REC-S.1528-0-200106-](https://www.itu.int/dms_pubrec/itu-r/rec/s/R-REC-S.1528-0-200106-))! !PDF-E.pdf (accessed on 1 December 2022). **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
## 6 Simulation Results and Discussion
### Average Throughput  
In this subsection, we presented a comparison graph of the average throughput of the MUE and FUE under FUE-assisted, MUE-assisted, range based power control technique and the proposed active power control technique in Figure 5. The graph indicates that the proposed power control provides a balanced trade-off between the performance of the femto user and the macro user.  
Figure 4: Downlink Throughput CDF of the Femtocell User.  
Figure 3: Downlink Throughput CDF of the Macrocell User.
## 4 Experimental Results and Discussion
### Focal loss Implementation  
Focal loss is a modified version of Cross-Entropy (CE) loss. It is widely used in object classification problems. CE loss can be used for n number of object classes. For \(n=2\), CE loss is named as Binary Cross-Entropy (BCE) loss, comprising of only two labels for the two object classes. In addition, a network's accuracy can also be determined by the Log-Average Miss Rate (LAMR) evaluation metrics. It is computed by averaging the miss rate on the false positive per image (FPPI) ranging from \(10^{-2}\) to \(10^{0}\).  
Experiments prove that the optimal values of alpha and gamma suggested in [10] for object detection do not remain valid for the Traffic sign dataset. Table 3 states average precision values for different values of gamma and alpha. For CE loss (\(\gamma=0\)), it failed quickly since the network was diverging during training, even for the range \(0<\gamma<1\). Hence, \(\gamma\) was initiated to 1 to find an optimal value for \(\alpha\). The optimal values of \(\alpha\) range from 0.5 to 0.75 for the traffic sign dataset as deduced from Table 3. The maximum achieved mAP for \(\alpha=0.75\), when \(\gamma=1\), with 0.01 mean LAMR of all three super-classes, while for \(\alpha=0.5\) there is a slight decrease in mAP by 0.71% with mean LAMR of 0.04.  
While keeping alpha constant and increasing the value of hyper-parameter gamma, the mAP decreases as shown in Table 3; hence it can be stated that for traffic sign dataset 0.75 and 1 are optimal values of hyper-parameters alpha and gamma, respectively. By using the obtained hyper-parameter values of alpha and gamma, the network accuracy declined by 0.68%. Therefore, we concluded that the proposed tweaked network with regressive anchor box selection technique outperforms the focal loss adjusted network in the small size traffic sign detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mAP** & **Inference Time** \\ \hline SSD + FPN + ITA [20] & 80.30\% & - \\ Faster RCNN-Mobilenets [19] & 84.50\% & 0.13 s \\ Mask RCNN [1] & 96.16\% & 0.32 s \\ Ours & 93.09\% & 0.04 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art methods for GTSDB dataset.  
Figure 13: Detection results on GTSDB dataset; (**a–c**) Small size traffic sign recognition, (**d**) Blurred traffic sign recognition.
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
###### Abstract  
In complex electromagnetic environments, wireless communication system reliability can be compromised by various types of jamming. To improve reliability in the presence of malicious jamming, this paper introduces an anti-jamming power control algorithm for wireless communication systems, grounded in model predictive control (MPC) principles. The algorithm models the Vescommunication system as a linear control system, using the current signal-to-jamming-and-noise ratio (SINR) to predict future system states and transmission power over a defined time horizon. Only the first element of the optimal control sequence is then applied to manage system power. Simulation results indicate that, compared to traditional adaptive power control algorithms, the proposed algorithm responds more swiftly to jamming variations, significantly enhancing communication reliability in high-jamming environments.  
wireless communication; anti-jamming; model predictive control (MPC); power control Article
## 2 Materials and Methods
### Data Classification  
To directly address the **second objective** in this study, which was to establish effective data classification methods with UAS imagery to quantify land cover from both before and after planned disturbances, the georeferenced orthomosaics were used to engage in geographic object-based image analysis (GEOBIA). Object-based classification (OBC) was used because the image objects were comprised of many pixels due to the high-resolution imagery [19; 24]. OBC relies on image segmentation prior to classifying land covers, which groups pixels into objects with similar spectral and spatial characteristics to simplify texture and color variances [67]. Next, the user collects multiple sample polygons for each land cover type. These are then combined with the segmented image to train the classification algorithm. An example of the progression from orthomosaic imagery, through segmentation, and the resulting land cover maps can be seen in Figure 6.  
A widely studied component of OBC is the classification algorithm. Numerous studies have suggested using support vector machines (SVMs) over other popular algorithms, such as random forest, maximum likelihood, and ISO clusters [19; 68; 69; 70; 71; 72; 73; 74]. Additionally, few studies have examined the efficacy of an SVM classification algorithm in ArcGIS Pro for land cover change surrounding disturbances and instead have focused on software packages, such as eCognition, AgiSoft, and others [53; 54; 65; 68; 75; 76].  
Furthermore, determining the accuracy of the resulting land cover classification is done by plotting points randomly on top of the classified image and comparing what class each point falls within to what the actual land cover of each point is. [PERSON], [PERSON], and [PERSON] [37] validated this procedure by comparing the proportions of land covers in their classified dataset to proportions collected through field samples. After achieving an overall classification accuracy of 95% when compared to field measurements, the authors determined that standalone visual interpretation of their high spatial resolution  
Figure 6: Geographic object-based image analysis (GEOBIA) process. (**a**) PWA original image; (**b**) PWA segmented image; (**c**) PWA classified image; (**d**) Volz original image; (**e**) Volz segmented image; (**f**) Volz classified image.  
orthomosaic image was sufficient for performing an accuracy assessment of land cover classifications produced with that imagery.  
While these findings provided a good foundation for performing GEOBIA, the specific parameters for this application required further experimentation to produce quality land cover classifications for image datasets surrounding both the prescribed burn and selective timber harvest NDBM treatments.
## 2 Materials and Methods
### Sentinel-1 SAR Interferometry
#### 2.3.2 Standard Repeat-Pass Interferometry (Single Pair)  
For the DEM generation in Site A (Seogwipo) and earthquake-induced ground deformation in Site B (Pohang), a standard repeat-pass interferometry was conducted by using SNAP (Sentinel-1 Toolbox) developed and freely distributed by ESA. Figure 5 presents the processing chain for the single pair standard repeat-pass InSAR.  
Figure 5: Standard repeat-pass interferometry processing workflow implemented in SNAP for digital elevation model (DEM) generation in Site A and ground deformation analysis in Site B.  
First, coregistration of two SAR images employing the Enhanced Spectral Diversity (ESD) method was performed after accurate orbit data implementation to create the interferogram of AOI and after subtraction of flat-earth phase. During the interferogram formation, coherence was also estimated. The output is then debarusted to smoothly stitch all burst data into a single image. For the DEM generation, topographic phase was not removed as it is the contributor of interest to be estimated. Moreover, multilooking was not performed as it reduces the spatial resolution of the resulting DEM product. For ground deformation estimation, the topographic phase was removed using precise orbit data and Shuttle Radar Topography Mission (SRTM) DEM 1 arcsec data. Phase filtering was then performed to increase the SNR. Phase unwrapping was performed using the SNAPHU program. Thereafter, the unwrapped phase was converted into either elevation or displacement. The converted product which was still in the radar geometry was then geocoded into the World Geodetic System 1984 (WGS84) and exported for post analysis and visualization in Google Earth and QGIS.
###### Abstract  
This paper proposes a novel method of training and applying a neural network to act as an adaptive decoder for a modulation scheme used in optical camera communication (OCC). We present a brief discussion on trending artificial intelligence applications, the contemporary ways of applying them in a wireless communication field, such as visible light communication (VLC), optical wireless communication (OWC) and OCC, and its potential contribution in the development of this research area. Furthermore, we proposed an OCC vehicular system architecture with artificial intelligence (AI) functionalities, where dimmable spatial 8-phase shift keying (DS8-PSK) is employed as one out of two modulation schemes to form a hybrid waveform. Further demonstration of simulating the blurring process on a transmitter image, as well as our proposed method of using a neural network as a decoder for DS8-PSK, is provided in detail. Finally, experimental results are given to prove the effectiveness and efficiency of the proposed method over an investigating channel condition.  
artificial intelligence (AI); neural network-based decoder; optical wireless communication (OWC); optical camera communication (OCC); dimmable spatial 8-phase shift keying (DS8-PSK); blur image processing; vehicular communication +
Footnote †: journal: Applied  
_Article_  
A Novel Neural Network-Based Method for Decoding and Detecting of the DS8-PSK Scheme in an OCC System
## 3 APD Output Models  
APD efficiently converts optical signals into electrical currents through photon-triggered carrier multiplication, making them compact and energy-efficient detectors for deep space optical uplinks. The McIntyre model [48] fundamentally describes the stochastic detection process of APD through exact conditional probabilities. Subsequent studies developed simplified Webb [33] and Gaussian [35] approximations to facilitate the analysis of communication systems, trading precision for computational tractability. Comparative studies [49] reveal that the Webb approximation better preserves detection statistics despite increased mathematical complexity. This section presents a comparative analysis of both approaches, structured as follows: First, we establish the foundational APD detection principles and noise characteristics. Second, we derive the Webb-approximated probability distribution for the APD output. Third, we formulate the Gaussian-approximated APD output model. Finally, we compare the two models and conclude that the Webb model better characterizes the gain dynamics of APD with higher precision, while the Gaussian approximation is primarily applicable in noise-dominated scenarios.