esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Chirality Determined from Mueller Matrices
## 3 Theory
### Method II: Differential Decomposition  
Method I is not directly applicable if a sample has both circular and linear anisotropic effects, as linear effects may mix with circular effects complicating the interpretation of the Mueller-matrix elements. In method II, a differential decomposition is employed whereby the natural logarithm of a normalized transmission Mueller matrix is calculated. For a non-depolarizing sample, we obtain [2; 3; 5]  
\[\mathbf{L}=\ln\mathbf{M}=\left[\begin{array}{cccc}0&-L_{\rm D}&-L_{\rm D}^{ \prime}&C_{\rm D}\\ -L_{\rm D}&0&C_{\rm B}&-L_{\rm B}^{\prime}\\ -L_{\rm D}^{\prime}&-C_{\rm B}&0&L_{\rm B}\\ C_{\rm D}&L_{\rm B}^{\prime}&-L_{\rm B}&0\end{array}\right]. \tag{9}\]  
The birefringent and dichroic properties are obtained directly from the elements of \(\mathbf{L}\). For the result to be correct, the sample must be homogeneous along the optical path. The birefringence parameters are determined from the logarithm of exponential functions with arguments that are complex-valued. These functions are cyclic with a period of \(2\pi\). The parameters \(C_{\rm B}\), \(L_{\rm B}\) and \(L_{\rm B}^{\prime}\) are, thus, obtained with an uncertainty of \(N\cdot 2\pi\), where the order parameter \(N(0,\pm 1,\pm 2,\dots)\) is an integer that depends on the optical path length, and the sign is that of the birefringence parameter.  
The MATLAB algorithm delivers values confined to the range \(\pm\pi\). The data can be unfolded to the correct order by an offset adjustment of \(N\cdot 2\pi\), where \(N\) may be different in different spectral ranges as shown by [PERSON] et al. [15]. An offset adjustment will be demonstrated in the analysis of the \(\alpha\)-quartz sample in Section 4.2.  
If the sample thickness \(d\) is known, the elementary intrinsic materials properties are obtained from the elements of the matrix \(\mathbf{m}=\mathbf{L}/d\), e.g., \(\eta=L_{\rm B}/d=\frac{2\pi}{\lambda}(n_{x}-n_{y})\) and similar for the other elements of \(\mathbf{m}\) as specified in Table 1. If the sample is depolarizing, \(\mathbf{L}\) becomes asymmetric and can be written as a sum of two matrices  
\[\mathbf{L}=\mathbf{L}_{\rm m}+\mathbf{L}_{\rm u}, \tag{10}\]  
where \(\mathbf{L}_{\rm m}\) is Minkowski antisymmetric and provides the birefringence and dichroic properties of the sample, whereas \(\mathbf{L}_{\rm u}\) is Minkowski symmetric and provides the depolarization properties. Further details are given by [PERSON] [4].
# A New Generation of Fast and Low-Memory Smart Digital/Geometrical Beamforming MIMO Antenna
###### Abstract  
Smart multiple-input multiple-output (MIMO) antennas with advanced signal processing algorithms are necessary in future wireless networks, such as 6G and beyond, for accurate space division multiplexing and beamforming. Such a MIMO antenna will yield better network coverage and tracking. This paper presents a smart MIMO antenna configuration with a highly innovative beamforming technique using several nonlinear configurations of dipole arrays. Phase delay factors are optimized at the transmitter to form a single beam and then to steer the beam towards a particular direction. A number of phase shifters are added in order to obtain maximum directional gain. This configuration also significantly increases the power gain of the MIMO antenna at a low cost and with operational simplicity. The paper also demonstrates how the beam width and beamsteering can be effectively controlled. Wolfram Mathematica software was used to generate the three-dimensional radiated beam patterns of the transmitter antenna. There are two approaches to configure the receiver antenna. In the first approach, the received signal magnitude is maximized by aligning the contribution of all elements of the receiver antenna to the same phase. With this approach, the field gain of the proposed system is 25.52 (14.07 dBi). The signal processing gain at the receiver is 64 (18.06 dBi). Therefore, the overall power gain for this proposed new digital/geometrical smart MIMO system is 32.13 dBi. In the second approach, the receiver beam is directed towards the transmitter by optimizing the phase delay coefficients of the receiver. Here, the overall gain of the system is found to be 134.56 (21.28 dBi). Even though the system gain in the second approach is lower, it has the advantage of low interference at the receiver side.  
MIMO systems; smart antennas; phase shifters; beamforming; beamsteering 2023121733103390/12/12/33
# Using Ground-Based Passive Reflectors for Improving UAV Landing
## 3 Algorithm Synthesis
### Principal Solution of the Navigation Problem in Onboard Radar
\tag{4}\]  
Each element of the matrix is the cosine of the angle formed by the tangent line to the circle and the appropriate axis of the reference system. The values of the elements of the cosine matrix were determined by the coordinates of the reflectors and the current coordinates of the UAV:  
\[\cos\alpha_{n,k} = \frac{X_{n}-\hat{x}_{k}}{R_{n,k}} \tag{5}\] \[\cos\beta_{n,k} = \frac{Y_{n}-\hat{y}_{k}}{R_{n,k}}\] (6) \[\cos\gamma_{n,k} = \frac{Z_{n}-\hat{z}_{k}}{R_{n,k}} \tag{7}\]  
The cosine matrix \(\mathbf{\hat{H}}\big{(}\mathbf{\hat{X}}_{k}\big{)}\) can also be presented in the form of partial derivatives of the position lines (circles) by the appropriate coordinates:  
\[\mathbf{\hat{H}}(\mathbf{\hat{x}}_{k})= \begin{bmatrix}\frac{\partial R_{1}( \mathbf{\hat{x}}_{k})}{\partial x}&\frac{\partial R_{1}(\mathbf{\hat{x}}_{k} )}{\partial y}&\frac{\partial R_{1}(\mathbf{\hat{x}}_{k})}{\partial z}\\ \frac{\partial R_{2}(\mathbf{\hat{x}}_{k})}{\partial x}&\frac{\partial R_{2} (\mathbf{\hat{x}}_{k})}{\partial y}&\frac{\partial R_{2}(\mathbf{\hat{x}}_{k })}{\partial z}\\ \frac{\partial R_{3}(\mathbf{\hat{x}}_{k})}{\partial x}&\frac{\partial R_{3} (\mathbf{\hat{x}}_{k})}{\partial y}&\frac{\partial R_{3}(\mathbf{\hat{x}}_{k })}{\partial z}\\ \frac{\partial R_{4}(\mathbf{\hat{x}}_{k})}{\partial x}&\frac{\partial R_{4} (\mathbf{\hat{x}}_{k})}{\partial y}&\frac{\partial R_{4}(\mathbf{\hat{x}}_{k })}{\partial z}\end{bmatrix} . \tag{8}\]  
Figure 7: Disposition of the corner reflectors and the UAV. There are two main factors that determine the errors of the positioning in TDOA and AOA systems. The first one is the error measuring the distance between the airborne radar and the reflectors \(\varepsilon_{R_{n},k}\). The second is the geometric dilution of precision (GDOP) that represents the expected precision loss in the positioning systems, given the special factors of the mutual disposition of the reflectors and the radar. The value of the GDOP can be estimated [52; 53] based on the cosine matrix using:  
\[GDOP_{k}=\left(tr\bigg{[}\left(\mathbf{\tilde{H}}^{T}\mathbf{\tilde{H}} \right)^{-1}\bigg{]}\right)^{-\frac{1}{2}} \tag{9}\]  
where \(tr[*]\) stands for the matrix trace. The potentially achievable minimum for the standard deviation of the error \(n\), the estimation of the distance between the reflector and the radar are determined by the spectral width \(\Delta f_{CK}\) of the probing pulse and the signal-to-noise [54] ratio \(Q\):  
\[\varepsilon_{R}=\frac{c}{2\Delta f_{CK}\sqrt{Q}} \tag{10}\]  
The real values of the range measurement precision were determined by the properties of the ground, resulting in a multi-ray signal propagation and, therefore, the noise in the correlator output signal, hardware errors, etc.
## 8 Comparative Summary of Pre-Correlation RFF Methods in Existing Literature  
Table 1 gives a concise survey of main RFF-related studies in the recent literature, by specifying the wireless system under investigation, as well as the main algorithms used for feature detection and classification in those RFF approaches. As seen in Table 1 most of the research work dedicated to RFF has to date been for non-GNSS signals. Moreover, as clearly seen from the last column in Table 1, RFF in the aviation context has been receiving more and more attention in the last two years, e.g., focusing on automatic dependent surveillance-broadcast (ADS-B) surveillance signals and on UAV transmitters and controllers. Table 1 shows that a wide variety of classifiers have to date been investigated in the literature in the context of RFF: from a discrete wavelet transform (DWT) and continuous wavelet transform (CWT) to various neural networks, such as convolutional neural networks (CNN), probabilistic neural networks (PNN) and other machine learning algorithms, such as support vector machines (SVM), subclass discriminant analysis (SDA), multiple discriminant analysis (MDA), or permutation-entropy (PE)-based approaches.  
Unlike the typical narrowband terrestrial signals typically studied to date with RFF techniques (see Table 1), the GNSS signals are wideband and continuously transmitted, and hence do not exhibit strong transients to be used as differentiating factors. This means that, for GNSS signals, one should go deeper into the transmitter hardware char  
Figure 20: The confusion matrix of a 1 versus 1 scenario under 100 dB-Hz CNR.  
acteristics and detect the possibly differentiating features between spoofers and genuine GNSS transmitters.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
###### Abstract  
Underwater Sensor Networks (UWSNs) utilise acoustic waves with comparatively lower loss and longer range than those of electromagnetic waves. However, energy remains a challenging issue in addition to long latency, high bit error rate, and limited bandwidth. Thus, collision and retransmission should be efficiently handled at Medium Access Control (MAC) layer in order to reduce the energy cost and also to improve the throughput and fairness across the network. In this paper, we propose a new reservation-based distributed MAC protocol called ED-MAC, which employs a duty cycle mechanism to address the spatial-temporal uncertainty and the hidden node problem to effectively avoid collisions and retransmissions. ED-MAC is a conflict-free protocol, where each sensor schedules itself independently using local information. Hence, ED-MAC can guarantee conflict-free transmissions and receptions of data packets. Compared with other conflict-free MAC protocols, ED-MAC is distributed and more reliable, i.e., it schedules according to the priority of sensor nodes which based on their depth in the network. We then evaluate design choices and protocol performance through extensive simulation to study the load effects and network scalability in each protocol. The results show that ED-MAC outperforms the contention-based MAC protocols and achieves a significant improvement in terms of successful delivery ratio, throughput, energy consumption, and fairness under varying offered traffic and number of nodes.  
underwater sensor networks; media access control; collision-free MAC protocols; duty cycle mechanism; depth-based scheduling +
Footnote †: This paper is an extended version of our paper published in 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Milan, Italy, 4-7 July 2017.
## 4 LEO Satellite Mega-Constellation Network Caching Policy
### Caching Based on IoT Data Lifetime  
The concepts of \"IoT content lifetime\", and \"IoT content freshness\" need to be defined first, because the IoT data are typically transient and small. Then, the caching policy based on RF is described.  
IoT content lifetime (CLT) is defined as the time length between when data is generated by a content producer and when it is no longer valid, i.e., expires [57]. In the packet of ICN, there is a signature information domain containing information such as publisher ID, key locator, stale time, timestamp, etc. [57]. A satellite node learns when the data are generated through a content producer by checking the timestamp.  
IoT content freshness f is denoted by [58]:  
\[f=T_{c}-T_{g}, \tag{9}\]  
where \(T_{c}\) is the current time, and \(T_{g}\) is the time when the data are generated.  
The data freshness is zero when \(T_{c}=T_{g}\). When \(f=CLT\), the content should be discarded.  
The same content may have different freshness requirements for different applications. Content may only be sent back to an application if the freshness value of the content is less than the freshness requirement of the application.  
In this study, the IoT devices, e.g., the radars, file servers, and IoT sensors, etc., are the content producers, and the content consumers are the UT.  
The UT sends interests to retrieve content. This paper denotes the IoT-requested content at time \(t\) by \(\mathit{Req}(t)=\left\{\mathit{req}_{1}(t),\mathit{req}_{2}(t),\mathit{req}_ {3}(t),\ldots,\mathit{req}_{n}(t),\ldots,\mathit{req}_{N}(t)\right\}\). Each request is composed of \(\mathit{req}_{n}(t)=\left\langle c_{l},f_{c_{l}},t\right\rangle\), where \(c_{l}\in C\), \(C=\left\{c_{1},c_{2},c_{3},\ldots,c_{l},\ldots,c_{L}\right\}\) is the set of contents, \(f_{c_{l}}\) is the freshness of contents \(c_{l}\), and \(t\) is the time at which the request arrives.  
All the satellite nodes have caching capability to cache content. \(s(i,t)\in\left\{0,1\right\}\) is used to denote the caching status of the satellite node \(i\) at time \(t\). If the content \(c_{l}\) is cached at the satellite node \(i\) at the time \(t\), \(s(i,t)=1\); otherwise,\(s(i,t)=0\). Furthermore, \(R(\mathit{Req}(t),\sum_{i=1}^{|I|}s(i,t))=\left\{r_{1}(t),r_{2}(t),r_{3}(t), \ldots,r_{n}(t),\ldots,r_{N}(t)\right\}\) is used to indicate how many requests in the requests set \(\mathit{Req}(t)\) that can be obtained from the satellite node \(i\), where \(i\in I\), \(I\) is the satellite nodes set, \(|I|\) is the total number of satellite nodes. \(r_{n}(t)\in\left\{0,1\right\}\) means whether the \(n^{th}\) request can be served from the satellite nodes at a time \(t\). By applying a caching policy CARF, \(s(i,t)\overset{CARF}{\rightarrow}s(i,t+\Delta t)\), the new caching status of the satellite node \(i\) at time \(t+\Delta t\) can be achieved.  
Figure 9: RF algorithm.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 6 Therapeutic Targeting of CSCs
### Targeting Cell Surface Markers  
One of the possible strategies for HNSCC stem cell eradication is targeting cell-surface markers. As we already mentioned, several cell-surface markers have been found to be specific for HNSCC stem cells: CD10, CD24, CD29, CD44, CD44/CD27, CD133 [126], CD90, CD98 [127], and CD147 [128]. However, not all of these markers have been demonstrated as good targets for therapy. Nevertheless, CD133 is one of the most studied markers for HNSCC CSCs. CD133+ OSCC cells show enhanced resistance to chemotherapeutic drugs [77], indicating that CD133 is a good target for cancer stem cell suppression. [PERSON] et al. developed 10 years ago a biological drug targeting EpCam and CD133 which was effective in inhibiting multiple carcinoma lines in vitro and caused regression in HNSCC tumors in vivo [129]. [PERSON] et al. showed that the treatment of HNSCC cells with a newly synthesized flavonoid derivative, 2-(3-hydroxyphenyl)-5-methylnaphthyridin-4-one (CSC-3436), upregulated E-cadherin and downregulated N-cadherin, vimentin, and CD133 while also reducing EMT and stemness [130]. [PERSON] et al. showed that targeting CD133 by shRNA reduces side population (SP) cells and in vivo tumorigenicity [131].
## 4 Experiment and Result
### Evaluation of Results  
#### 4.4.1 Accuracy Assessment  
In the subsection of quantitative evaluation of the accuracy of cropland extraction, the main indicators we use are IoU and overall accuracy (OA). \(Area(Segy\cap Seggt)\) refers to the intersection area of the plane space between the segmented cropland target and the manually marked cropland, and \(Area(Segy\cup Seggt)\) refers to the union area of the plane space between the segmented cropland target and the manually marked cropland. The _IoU_ index can well-reflect the positioning accuracy of the detected edge.  
\[\text{IoU}=\frac{Area(Segy\cap Seggt)}{Area(Segy\cup Seggt)} \tag{6}\]  
\[\text{OA}=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
where \(TP\) is the number of pixels that are correctly classified as cropland, \(FP\) is the number of pixels that are incorrectly classified as cropland, \(FN\) is the number of pixels that are incorrectly classified as other categories, and \(TN\) is the number of pixels correctly classified as other categories.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 5 Simulation and Result  
### Simulation Setup  
The experiments were conducted under Ubuntu 20.04 LTS, and the hardware configuration included an Intel Core i5-13600K processor (14 cores, 20 threads, 3.5 GHz) and 32 GB DDR4 memory. This experiment is based on the open-source ICARUS model, using Python 3.7.16 and NetworkX 2.5.1. We use the S1 shell of the Starlink constellation for our LSN analysis. Table 2 shows our LSN setup parameters and values. In order to keep pace with the original model [21], we used the standardized bandwidth mentioned in the paper. The ISL bandwidth is 20 Gbps and the GSL bandwidth is 4 Gbps.  
We first construct the LSN constellations by building the ISL topology from the given parameters, generating uniform grid points on the Earth's surface, and loading weighted,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Satellite & 1584 \\ Orbits & 72 \\ Satellites per orbit & 22 \\ Elevation & 550 km \\ Inclination & 53\({}^{\circ}\) \\ Satellite-to-satellite Bandwidth & 20 Gbps \\ Satellite-to-ground Bandwidth & 4 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: LSN setup.  
normalized GDP data. Ground-satellite coverage is computed at the minimum elevation angle, after which risk weights are assigned to all links. Then, source-destination pairs are randomly sampled according to grid weights. For each candidate path, bandwidth allocation is attempted: if every link on the path has sufficient residual capacity, the allocation is applied; otherwise, the path is discarded. We use an empty network without any benign traffic background in the network because it will be more of a resource burden for the attacker. Then, we execute our routing algorithm on these prepared topologies and finally execute the attack mechanism.
## 3 Results and Discussion
### Point-of-Care Testing with Standard Operating Procedure and Evaluation of REASSURED Criteria
\begin{table}
\begin{tabular}{l l} \hline
**REASSURED Criteria** & **Is this Criterion Met?** \\ \hline Real-time quality control & Doctor Vida enables real-time quality control via displaying fluorescence data in app \\ Ease of specimen collection & Using swab or small brush to collect specimen \\ Affordable by those in need & Reagent costs: EUR \(\sim\)0.50 Instrument cost: EUR 330 \\ Sensitive (few false negatives) & No false negatives were detected in both assays \\ Specific (few false positives) & High specificity for both assays [18; 19] \\ User-friendly & See SOP (Figure S3), initial POC testing revealed high user-friendliness \\  & Both assays can be conducted in less than 60 min: \\  & DNA extraction: \(<\)10 min \\ Rapid and robust & LAMP assay: 30 min \\  & Robust: Storage capacity is high at temperatures around 20 \({}^{\circ}\)C. See Figures 1 and 2 for detailed description. \\ Equipment-free & Test kit: Brush or swab for specimen collection; tube containing extraction buffer; reaction \\  & tube with lyophilized reaction mix; Doctor Vida Pocket test; and smartphone \\ Delivered to those who need it & Pending, future application of food controlling at institutions and industry realistic. \\ \hline \end{tabular}
\end{table} 
Table 3: Overview of REASSURED criteria and evaluation of the proposed standard operating procedure.
# The Corrosion Failure Mechanism of a Peak Load Boiler in a District Heating System
###### Abstract  
The peak load boiler (PLB) is a heat production facility that uses SA178 Gr. A and SA516 Gr. 70 low-carbon steels as tube and plate materials, respectively. Recently, failures were frequently observed near plugged tubes due to water leakage, raising concerns about corrosion mechanisms and their impact on tube durability. This work investigates the corrosion failure mechanisms using a combination of endoscopy, ultrasound inspection, oxide scale analysis (X-ray diffraction), chemical analysis (ion chromatography and inductively coupled plasma mass spectrometry), and computational fluid dynamics simulations. The undamaged tube near the leaked tube exhibited oxide scale levels comparable to those directly affected. Surface examinations revealed gas-side pits indicative of localized corrosion, while oxide scales were predominantly composed of iron oxides formed under humid conditions and sodium compounds derived from boiler water. Analysis of the leaked water revealed its mixture with combustion gases, forming an acidic, chloride-rich environment that significantly accelerates corrosion. Computational fluid dynamics simulations demonstrated that leaked water vapor facilitated the condensation of acidic ions near affected tubes, promoting dew point corrosion. These phenomena, driven by localized condensation and chemical concentration at the dew point temperature, exacerbate material degradation, emphasizing the importance of targeted prevention strategies.  
Keywords:district heating system; peak load boiler; dew point corrosion; fluid dynamics simulation