esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Therefore, detecting topological charges and identifying different OAM modes play a crucial role in the application of the OAM beams.  
Generally, vortex beams can be manipulated in a variety of ways, such as spiral phase plates [14; 15], glued hollow axicons [16], birefringent elements [17; 18], orbital-angular-momentum holography [19], microscopic ring resonators [20], spatiotemporal dynamics of plasmonic vortices [21], spatial light modulators (SLMs), ultrathin and planar metasurfaces [22; 23], peritrophic multiplexing metasurfaces [24], and azimuthal-quadratic diffraction optical elements [25]. OAM identification is challenging due to the missing phase information for traditional intensity detectors. The early OAM detection was proposed based on interference with the mirror or reference wavefront [26; 27], diffraction of specific apertures [28], and mutual projection of forked diffraction grating [6]. Nowadays, a variety of vortices have been successfully detected, such as single-photon vortices [27], supercontinuum femtosecond vortices, noninteger vortices [28], and entanglement vortices [4]. However, multiple repeated projection measurements and calculating the number of interference fringes are inefficient for detecting possible OAM modes, especially for higher-order OAM modes [29].  
In addition, the traditional vortex optical recognition system is often complicated and bulky, and the recognition method of the OAM is usually complex, which brings certain limitations to the practical application. A more straightforward system will lead to more excellent stability from an engineering point of view. In recent years, a variety of functional devices with excellent properties have emerged, such as metastructures [30], nanoparticles [31], graphene [32], plasmas [33], and multi-functional structures [34], with the rapid development of new technologies. In addition, with the same secondary phase form of a radial converging lens, a more convenient design has been proposed, namely, angular lens [35], which focuses different OAM modes to corresponding azimuth positions in the focal plane at a constant intensity. It can significantly reduce the optical system's complexity, improve the system's stability, and increase the identification range of the vortex topological charge.  
Based on the principle of symmetry transformations via quadratic phase [36], [PERSON] et al. used a spin-decoupling metasurface to realize the sorting detection of topological charges of multiple incidents of left and right vortices simultaneously [37]. However, composed of discrete unit cells, the spin-decoupled metasurface suffered from the problem of insufficient phase sampling, which reduced the detection range of the OAM. Here, an optimized streamlined metasurface (OSM) [38] was adopted to improve detection capabilities and achieve the same size to increase the detection range of the OAM, especially for higher-order OAMs. In addition, compared with the \(l_{0}\) = 40 conducted by the discrete metasurface (DM) at present, the detection range of the OAMs by the OSM in this paper was improved by about 6.5 times.
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 2 Dual-Window Superpixel Data Augmentation (DWS)
### Patch Subdivision  
Augmentation operations are commonly performed on the patch as a whole. The complete patch is flipped, rotated, etc. This paper hypothesizes that, in contrast, applying transformations over independent regions of the patch would produce better results. The patch subdivision operation in Figure 2 depicts the patch subdivision stage proposed in this paper. Patches are divided into two regions according to the distance from the central pixel. In the example, the inner region is set to \(15\times 15\) pixels. This subdivision makes it possible to apply transformations to the inner region and outer regions of the patch independently. Any transformation able to operate over a patch of data can be applied, regardless of its nature.  
The process of patch extraction and subdivision is shown in more detail in Figure 3. On the left, the borders of the different superpixels obtained after applying SLIC over the image are depicted, along with the central pixel of one of the superpixels. The black square centered on that same pixel represents a patch of the desired dimensions that will be extracted at that location. The patch, shown on the right, is then subdivided into two regions as part of this stage.  
Figure 1: Cropped section of the Salinas scene after SLIC superpixel segmentation.  
Figure 2: Patch preparation stages of the dual-window superpixel data augmentation (DWS) framework.
## 1 Introduction
On the other hand, a new method has recently been developed to assess changes in ionospheric absorption using amplitude data collected by European Digisondes (DPS-4D) [18]. The amplitude changes recorded at the three sites (Juliusruh, Pruhonice and San Vito) ranged from 68% to 1430% (these variations can be primarily associated with increased absorption in the D-layer). The amplitude variations at 4 MHz--reflecting cumulative absorption variations in the E- and D-layers--ranged from 18% to 565% at the same stations, which is slightly smaller than the changes observed at 2.5 MHz. However, the amplitude method has two main limitations: (1) it is not applicable at higher frequencies (\(>\)3 MHz) during dawn and dusk periods, and during negative ionospheric storms caused by geomagnetic disturbances when the value of the foF2 drops below 4 MHz; (2) no amplitude data is detected in the lower frequency band (2.5-3.5 MHz) following more intense flare events (greater than M3 class) due to partial or complete radio fade-outs, resulting in a minimum frequency (fmin) increasing above 4 MHz. There are multiple ways to model the ionospheric absorption. One of the better-known models is the so-called D-RAP (NOAA D-region Absorption Prediction) model, which uses the 0.1-0.8 nm solar soft X-Ray flux observed by the NOAA GOES satellites. In the papers by [PERSON] et al. [19; 20], a semi-empirical, data-based method to further refine the absorption prediction is introduced. Based on riometer data and taking into account the solar zenith angle of the site of observation, [PERSON] et al. [19; 20] was able to model the impact of a flare with a given X-Ray intensity on the ionosphere on different spatial and temporal scales for a higher frequency (30 MHz). Furthermore, there are physics-based absorption models, which solve dispersion equations to evaluate the enhanced ionization by solving radiative transport equations [21; 22; 23]. These models can be more accurate than the semi-empirical ones; however, their run time is much longer, which can be a problem in their use for operational purposes [19]. In this study, we compare the results of the amplitude method with absorption changes measured by the Finnish Riometer Network and assessed using the NOAA D-RAP model during 13 solar flare events that took place from 4 to 10 September 2017. The paper is structured as follows: next, we will describe the used measurement methods and data, and detail the applied model. The comparison of the ionosonde and riometer data with the D-RAP model for the same locations will be detailed in Section 3. Then, besides the discussion of the results, the main conclusions will be summarized in Section 4.
## 1 Introduction
As stated before, one of the most important weaknesses of limit analysis is that no direct information is given by the theoretical fundamentals of the analysis about the dissipation during the deformation process. It must be stressed that one of the hypotheses at the basis of the limit design is that the strain can develop completely until the load reaches its limit, namely, the permanent strain must be limited within the admissible amount. Only the knowledge of the load history allows precisely evaluating the amount of dissipated energy until the limit is attained. Hence, many works estimate the ultimate dissipation and displacements during plastic loading of structures at the incoming plastic collapse or to ratcheting [10; 11; 12], starting from Ponter that proposes a limit on the dissipated work and residual displacement [13; 14]. In the present work, a procedure has been presented through [PERSON]'s theorem [6] of the shakedown. The procedure has used the \"strain-based residual\" stress that constitutes a basis of the vector space of the self-equilibrated stresses. Such residual stress fulfills [PERSON]'s lower bound theorem and has been used to calculate upper bounds of the global and local dissipated work at the ultimate load level and pointwise permanent strain as well. The method has proposed, in the framework of discontinuous FEM, a linear optimization strategy that gives an upper bound of local dissipation and, finally, an estimate of local permanent displacement. In the paper, the numerical procedure has been applied to an academic example of a one-dimensional structure. Concerning a one-dimensional example, the numerical procedure was also compared to the experimental results obtained by the authors in previous work [15]. The experiment has concerned an aluminum beam over three supports, loaded by randomly variable one-point forces applied at the middle of each span. The structure has been loaded up to the shakedown limit and subsequently to ratcheting. The displacement has been measured during the loading process. The experimental results have been compared with the calculated ones, and a good agreement has been shown between theoretical and experimental approaches.
## 3 Results and Discussion  
### Qualitative Phytochemical Screening  
The results of qualitative phytochemical screening of the Calendula officinalis water extract are shown in Table 1. The preliminary phytochemical test results indicated the presence of alkaloids, saponins, flavonoids, glycosides and terpenoids in the tested extract.  
The results agree with the literature data [23; 24] and indicate that marigold can be considered a potential raw material for the extraction of bioactive components. Calendula officinalis is an important medicinal plant with diverse phytochemicals and biological activities, such as antioxidant, anti-inflammatory, and antimicrobial. Among the phytochemicals in marigold petals, triterpenes, flavonoids, phenolic acids, quinones and coumarins, and carotenoids are reported to play the most important role [25]. These compounds are responsible for antimicrobial activity, and their mechanisms of action are diverse, including inhibition of nucleic acid synthesis, interference with cytoplasmic membrane function, and slowing of microbial metabolism [26].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Phytochemicals** & **Alkaloids** & **Saponins** & **Tannins** & **Flavonoids** & **Terpenoids** & **Glycosides** & **Reducing** \\  & & & & & & & **Sugars** \\ \hline Test result & + & + & \(-\) & + & + & + & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phytochemical screening of the Calendula officinalis flower water extract.
## 4 Discussion
### Promising Acoustic Voice and Speech Features
#### 4.1.3 Cepstral Peak Prominence  
The effect size for average CPP, a standard indicator of vocal quality, in sustained vowels was comparatively small, at approximately 0.2. Inspecting the distribution of CPP values revealed that the average CPP at admission was below the normative threshold identified in [48]. Notably, those norms were computed on younger, comparatively healthy speakers relative to the HF population here, and future work is needed to identify CPP norms for older adults. Regardless, speakers whose CPP was low at admission were much more likely to show increases in CPP with treatment. Of speakers with CPP below the normative value at admission, 76% (13 of 17) had increased CPP at discharge. In contrast, of speakers with admission CPP above the normative value, only 47% (14 of 30) had increased CPP at discharge. The difference between the CPP changes in the low-CPP and high-CPP groups was nearly significant (paired \(t\)-test; \(p\) = 0.055). Future work could investigate other characteristics common to this low-CPP group and potentially identify speakers whose voices are likely to respond to changes in HF status.  
Interestingly, CPP tended to decrease for continuous speech tasks. This finding is unexpected, especially in light of the increases in CPP for sustained vowels. Increases in the usage of creaky voice, which tended to occur for continuous speech but not sustained vowels, may contribute to the decline in CPP at discharge relative to admission.
## 2 Generate Initial Solutions to PFSP Using a Deep Reinforcement Learning Method
### Policy Network Architecture
#### 2.2.1 Job Encoder  
For the job encoder, two linear transformations are used to embed each job processing time vector \(x_{i}=\left\{x_{i}^{c},x_{i}^{p}\right\}\), where \(x_{i}^{c}=\left\{r_{i},d_{i},o_{i}^{s}\right\}\) and \(x_{i}^{p}=\left\{o_{i}^{1},..,o_{i}^{m}\right\}\). The \(x_{i}^{c}\) vector includes constraint attributes for each job such as release date, due date, and total processing time, which can optimize the schedule from a macroscopic point of view; and the \(x_{i}^{p}\) consists of the processing time of each operation of the job, which helps to fine-tune the scheduling sequence. Therefore, two linear transformations are used to embed \(x_{i}^{c}\) and \(x_{i}^{p}\), then the two embedded vectors \(\widetilde{x}_{i}^{c}\) and \(\widetilde{x}_{i}^{p}\) are concatenated as a higher dimensional vector \(\widetilde{x}_{i}\in\mathbb{R}^{d}\), which can be of dimension 128, 256, etc. The weights of the two linear combinations are shared among all jobs. The feature vector dimension of the job is extended to combine each operation in the job, allowing the network to learn combinatorial relationships between multiple operations of a job. Since \(x_{i}^{c}\) and \(x_{i}^{p}\) are of different orders of magnitude, they are normalized separately to avoid unbalanced feature values. The job encoder is an LSTM, the vector \(\widetilde{x}_{i}\) of the job \(x_{i}\) selected at the current time step will be encoded by the job encoder. The hidden state of the LSTM output needs to be fed into the decoder and then into the job encoder at the next time step. More specifically, the job encoder encodes the currently known scheduling sequence (not finished) and outputs a hidden vector \(\widetilde{x}_{i}^{h}\) for the current time step. \(\widetilde{x}_{i}^{h}\) is input to the decoder to find out which job should be selected next. Then, the vector \(\widetilde{x}_{j}\) of the selected job \(j\) is input to the encoder along with \(\widetilde{x}_{i}^{h}\) to obtain \(\widetilde{x}_{j}^{h}\) of the next time step. This is a cyclic process until the complete sequence is obtained.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 3 System Model and Problem Description
### Channel Model  
#### 3.3.1 IoT Device-UAV Channel  
The coordinates of the UAVs are denoted as \((X_{m},Y_{m},H_{m})\), and the coordinates of the IoT device are represented as \((X_{i},Y_{i},0)\); then, the horizontal distance is indicated as  
\[d_{im}=\sqrt{(X_{m}-X_{i})^{2}+(Y_{m}-Y_{i})^{2}} \tag{1}\]  
It is assumed that each small affected area is covered by a drone and is within the service area of just one drone. If the IoT device \(i\) sends a task to the drone \(m\), the IoT device has to be inside the drone's coverage [5].  
\[d_{im}\leq d_{max},\forall i\in\mathcal{N},m\in\mathcal{M} \tag{2}\]  
where \(d_{max}\) dictates the maximum coverage radius of the UAV.  
The transmission from the IoT device to the drone is assumed to take place over a wireless channel [29]; to prevent significant co-channel interference, IoT devices offload their computing tasks to drones in the form of frequency division multiple access (FDMA) [30]. When an IoT device and a drone communicate, the drone flies at a low altitude, the channel is considered line-of-sight (LoS) [31] and the small fading effect of the channel is negligible [32]. The uplink data rate \(r\) can be expressed as  
\[r_{im}=\omega_{u}\log_{2}\left(1+\frac{p_{i}g_{im}}{\sigma^{2}}\right) \tag{3}\]  
where \(\omega_{u}\) denotes the channel bandwidth of the IoT device to the UAV, \(g_{i,m}\) is the channel gain of the uplink, \(\sigma^{2}\) is the additive white Gaussian noise (AWGN) power and \(p_{i}\) is the transmission power of the channel [33].  
\[g_{im}=\frac{g_{o}}{d_{im}^{2}+H_{m}^{2}} \tag{4}\]  
where \(g_{o}\) denotes the reference channel gain and \(d_{i,m}^{2}+H_{m}^{2}\) denotes the Squared Euclidean Distance from the IoT device to the UAV.
# Dynamic SDN Controller Load Balancing
## 4 DCC Problem Formulation
### Clusters' Load Differences  
As we mentioned in Section 3, the first aspect of the high-level load balancing is to achieve balanced clusters (in this paper we assume that all controllers have the same processing capabilities, therefore balanced clusters is the overall optimal allocation). For this purpose, the gaps between their loads must be narrowed. A cluster load is defined as the sum of the controllers' average loads included in it, as follows:  
\[\theta\left(t\right)_{i}=\sum_{j=1}^{M}l\left(t\right)_{j}Y\left(t\right)_{ji} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Semantics** \\ \hline \(C_{j}\) & \(j\)th controller \\ \(G_{i}\) & \(i\)th cluster \\ P & the number of requests a controller can handle per second \\ \(d_{ij}\) & Minimal hop distance between \(C_{i}\) and \(C_{j}\) \\ \(Y\left(t\right)_{ji}\) & \(Y\left(t\right)_{ji}=1\) if \(j\)th controller is in cluster i in time slot t, else \(Y\left(t\right)_{ji}=1\) \\ \(l\left(t\right)_{j}\) & Controller load—Average flow request of \(j\)th controller per second in time slot t \\ SC & Super Controller—collects controllers’ loads from masters and re-clustering \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key notations.  
Figure 2: \(Y\) matrix examples before and after re-clustering.  
where \(i\) is the cluster number and \(M\) is the number of controllers in the cluster.  
To measure how far a cluster load is from other clusters' loads, we derive the global cluster's load average:  
\[Avg=\frac{\sum_{j}^{M}l\left(t\right)_{j}}{K} \tag{4}\]  
where, \(k\) is the number of clusters.  
Then, we define the distance of a cluster's load from the global average's load (denoted above \(Avg\)) as:  
\[\vartheta\left(t\right)_{i}=\left|\theta\left(t\right)_{i}-Avg\right| \tag{5}\]  
In a second step, we define a metric that measures the total load difference between clusters' load as follows:  
\[\varsigma\left(t\right)=\frac{\sum_{i=1}^{k}\vartheta\left(t\right)_{i}}{K} \tag{6}\]
## 8 Conclusions  
In this paper, we have presented a novel single-antenna RFID localization system based on the LANDMARC method, which reduces the number of neighbors to improve the traditional positioning mechanism. By having only one nearest neighbor, it defaults the actual tag location to a point near the reference tag and allows the system to keep its high accuracy. This is especially useful for workspaces where the exact location is not needed, such as warehouses. Optimization based on beam patterns was used, along with their refresh rate. This study makes use of a single antenna for RFID localization and establishes an adaptive equilibrium point between range and accuracy whilst using a reasonable error,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Sing-Ant** & **ItemTest** \\ \hline Environmental bias & No & Yes \\ \hline Location update & Per cluster/sector cycle & Per beam change \\ \hline Accuracy rate & Constant & Varies per entry \\ \hline Variable refresh rate & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 6: System comparison of ItemTest and Sing-Ant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RFID Localization Method** & \begin{tabular}{c} **3D Position** \\ **tioning** \\ \end{tabular} & \begin{tabular}{c} **Fixed** \\ **Antenna** \\ **Position** \\ \end{tabular} & \begin{tabular}{c} **Single** \\ **Antenna** \\ **Exclusively** \\ \end{tabular} & \
\begin{tabular}{c} **High** \\ **Accuracy of** \\ **Positioning** \\ \end{tabular} &
\begin{tabular}{c} **Effective** \\ **Radius** \\ \end{tabular} \\ \hline LANDMARC & ✔ & & & ✔ & 2–3 m \\ ANTSpin & ✔ & & ✔ & 0.6 m \\
3 DLRA & ✔ & ✔ & & ✔ & 0.53 m \\ Zigbee & ✔ & ✔ & & ✔ & 10 m \\ Deviation Correction & ✔ & ✔ & & ✔ & 1.5 m \\ WIMEC-LANDMARC & ✔ & ✔ & ✔ & 2.5 m \\ Sing-Ant & ✔ & ✔ & ✔ & \textgreater{}5 m \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model comparison.  
which can be applied in indoor positioning systems. Our future research includes depth estimation and fully 3D positioning with a single antenna in RFID systems.  
Conceptualization, P.T. and T.H.T.; methodology, P.T.; data curation, P.T.; writing--original draft preparation, P.T. and T.H.T.; writing--review and editing, Z.X.; supervision and project administration, H.X.; funding acquisition, P.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province of China (No. 2020 SJA2389), in part by the Scientific Research Fund of Tongda College of Nanjing University of Posts and Telecommunications (No. XX205 XS20001), in part by the Scientific and Technological Support Project of Jiangsu Province (No. BE2019740) and the University-Industry Collaborative Education Program (No. 202102210003).  
Not applicable.  
The tag event data will be posted on GitHub alongside the Sing-Ant code.  
The authors declare no conflict of interest.