esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Novel Frameless Slotted-ALOHA Protocol
### Protocol Description  
The slotted-ALOHA protocol divides the time axis into equal length slots and stipulates that all senders can only send packets synchronously at the beginning of each slot. The sender is used to send the packets of active users. The receiver is used to receive the packets sent by active users. Figure 1 shows the basic principle of the novel frameless slotted-ALOHA protocol. In this protocol, when a user has a newly arrived packet in a current slot, this packet will be sent at the beginning of the next slot. If only one packet is transmitted in a slot, this packet will be successfully received. If more than one packet is transmitted, these packets will conflict.  
As shown in Figure 1, one packet arrives in the first slot. The packet will be sent at the beginning of the second slot and without conflict. Three packets arrive in the third slot. These packets are sent at the beginning of the fourth slot. Obviously, these packets will collide when they are transmitted. In this case, the receiver temporarily buffers these conflicting packets and sends feedback to all senders involved in this conflict according to the feedback mechanism in Section 3.1. After that, replicas of conflicting packets are transmitted by the senders. The receiver will decode all the conflicting packets by SIC. In the fifth slot, a new packet arrives when the sender is retransmitting. Then, the newly arrived packet is discarded directly because the sender has no buffer. Usually, retransmission has a higher priority than the first transmission.  
In this work, we investigate the potential of applying signature codes to the frameless slotted-ALOHA framework. Our objective is to design a random-access solution based  
Figure 1: Novel frameless slotted-ALOHA protocol.  
on analogous principles. Note that we are required to briefly introduce the concept of the frameless slotted-ALOHA: the frame length is not a priori set, so new slots can be added until a sufficiently high fraction of users has been resolved [18]. Unlike frame ALOHA, where slots are organized in a frame; before the frame starts, each terminal randomly and independently selects a single slot within the frame to transmit its packet and receives feedback at the end of the frame.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 3 Methodology
### Accuracy Assessment  
For each tested method, the assessment of the classifier results was expressed as a function of the overall accuracy (OA), Kappa and the F-score by applying the generated model on the validation dataset (50%). The precision, recall, and F-score were also calculated for each class (irrigated/non-irrigated). In fact, several studies report the use of these indices to assess the accuracy of the classification [20; 31; 37; 61]. While OA can be easily interpreted as the percentage of correctly classified plots to the total number of plots, Kappa and F-score can be used to assess the statistical differences between classifications. The obtained accuracy indices were calculated for the obtained results of each method as proposed by [62; 63; 64]. Table 2 summarizes the equations used to calculate these accuracy indices.
# Traffic Simulation of Future Intelligent Vehicles in Duisburg City Inner Ring
## 2 Methodology
### Traffic Demand  
With the help of the Duisburg city officials and the WBD (Wirtschaftsbetriebe Duisburg in German), two kinds of data sources are provided: OD (Origin-Destination) matrix data and induction loops data. The OD matrix describes people's movement in a certain area; it is very valuable for modeling transport demand in rural and urban areas [25]. In principle, an OD matrix divides an area into smaller parts and describes the traffic volume among these parts. From an overall perspective, these small parts can be seen as points; the traffic volumes are described from point to point. Induction loop data are recorded by the induction loops installed below the roads. When a vehicle passes by, the metal vehicle cuts magnetic induction lines, and the current changes. Multiple induction loops on the same section can not only detect the number of vehicles but also their speed. The induction loops data are relatively more accurate in a specific road compared to other data sources, but sufficient induction loops layout and reasonable pre-processing of data are the prerequisites for ensuring this accuracy.  
Figure 2a shows the partition of the OD matrix of Duisburg's inner ring. There are 18 parts with roads in the inner ring and these parts are shown in five colors. Shown in Figure 2b is the induction loops distribution of this area. As the induction loops here are normally located in the intersections, all the intersections with traffic lights are marked with their intersection numbers. The blue numbers stand for fixed traffic light control intersections, and the red numbers represent dynamic traffic light control intersections. Unfortunately, not all the roads around each intersection have installed induction loops; the green arrows in the figure represent the in-city direction roads equipped with induction  
Figure 1: The location of Duisburg inner ring (Source: Here map).  
loops. In this figure, the bus stations and subway stations are also marked with H and U, respectively. There are, in total, 16 bus lines and 3 subway lines that pass through this area.  
In this work, both traffic demand data sources are used in generating traffic, and the generated traffic volumes are compared with actual induction loops data. Public transportation, such as bus and subway, is neither included in the OD matrix data source nor the induction loops data. Therefore, in our simulation, the emphasis is placed on passenger cars.
## 4 System Methodology
### Data Acquisition  
The dataset used in this study is publicly available and is known as the Bern-Barcelona database. It is recorded at the University of Bern, Department of Neurology, from five epilepsy patients that have longstanding pharmacoresistant temporal lobe epilepsy. All the patients were screened for epilepsy surgery, and before the surgery, the recording of EEG signals was conducted as one of the diagnostic procedures. The EEG recording was conducted intracranially using depth electrode and intracranial strips with an AD-TECH (Racine, WI, USA) device in a multi-channel scenario. The number of channels is more or less than 64 channels using the standard international 10-20 system with Fz and Pz as reference electrode locations. Figure 6 shows the EEG signal acquisition arrangement with seizure hotspots marked in a red colour. Focal and non-focal EEG signals were carefully inspected by two certified neurologists visually; the first detected ictal signal from the EEG channels are referred to as focal signals, while the signals from the remaining channels are termed as non-focal EEG signals [24]. Of the focal and non-focal EEG signals, referred to as  
Figure 4: An example of a separable problem in a 2-dimensional space.  
x and y, 3750 pairs were acquired with a sampling frequency of 512 Hz and then filtered with a bandpass filter with a cut-off frequency of 0.5-150 Hz, with each signal consisting of 10,240 samples. One focal EEG channel was chosen at random from any patient, and another focal EEG channel from the neighboring selected channel was also selected and denoted as x and y, respectively. The details of this dataset can be found in [49]. In this work, complete sets of 3750 with both focal and non-focal signals were used in our analysis. Examples of focal and non-focal EEG signals are shown in Figure 7.  
Figure 5: Block diagram of the proposed techniques, (**a**) classification of focal and non-focal EEG signals using DWT and hybrid features with SVM and 1D-CNN. (**b**) using WT-SST time-frequency matrix features with 2D-DCNN.
## 3 System Development: Methodologies and Analysis
### Flood Frequency Analysis
#### 3.2.1 Rainfall Analysis
\begin{array}{l}[(Area^{\prime}0^{\prime}\times Rain@Nafanua)+(Area^{\prime }2^{\prime}\times Rain@Afianalu)+(Area^{\prime}3^{\prime}\times Rain@LePue)+( Area^{\prime}4^{\prime}\times Rain@Tiavi)\\ +(Area^{\prime}5^{\prime}\times Rain@Tigitogiga)]+(Area^{\prime}6^{\prime} \times Rain@Maagiagi)/Totalareao\,fcatchment\end{array}  \tag{1}\]  
\[Lelatactchmentaveragerainfall=[(6.53\times Rain@Nafanua)+(8.39\times Rain@Afiamalu)+(5.31\times Rain@LePue) \tag{2}\]  
\[ \begin{array}{l}+(0.67\times Rain@Tiavi)+(0.17\times Rain@Tigitogiga)+(11.03 \times Rain@Maagiagi)/32.11\end{array}  \tag{3}\]  
\[ \begin{array}{l}AlaoaEastcatchmentaveragerainfall=[(0.93\times Rain@Nafanua)+(0.17 \times Rain@Afianalu)\\ +(5.27\times Rain@LePue)+(0.02\times Rain@Tiavi)+(0.17\times Rain@Tigitogiga)+( 10.48\times Rain@Maagiagi)]/17.04\end{array}  \tag{4}\]  
\[ \begin{array}{l}AlaoaWestcatchmentaveragerainfall=[(1.39\times Rain@Nafanua)+(7.94\times Rain@Afianalu)\\ +(0.05\times Rain@LePue)+(0.65\times Rain@Tiavi)]+(0.55\times Rain@Maagiagi)/10.57 \end{array}  \tag{5}\]  
If one or more rain gauges breaks down or becomes faulty, then the following assumptions are applied:  
* If Tiavi data are missing then use data from the next closest gauge (which in order of priority) Le Pue, Afiamalu, Togitogiga, Maagiagi, Alafua, or Nafanua;
* If Le Pue data are missing then use data from the next closest gauge (which in order of priority) Tiavi, Togitogiga, Afiamalu, Maagiagi, Nafanua, or Alafua;
* If Togitogiga data are missing then use data from the next closest gauge (which in order of priority) Le Pue, Tiavi, Maagiagi, Afiamalu, Nafanua, or Alafua;
* If Afiamalu data are missing then use data from the next closest gauge (which in order of priority) Maagiagi, Le Pue, Tavi, Alafua, Nafanua or Togitogiga; and
* If Nafanua data are missing then use data from the next closest gauge (which in order of priority) Alafua, Afiamalu, Maagiagi, Le Pue, Tavi or Togitogiga. Rainfall thresholds for design event durations were identified using rainfall intensity-duration-frequency (IDF) curves previously established at Afiamalu (Table 2) [22; 23; 32]. These were used to identify thresholds required to trigger early warnings for a given real-time nowcast or forecast event. For example, 1-h rainfall intensity of 156.8 mm observed at Afiamalu during the 2012 flood represents an event greater than a 100-year return period event (Table 2).
## 4 Results
### Results on the Test-Set
For Recall, both the LSTM and Shallow Network achieve the best values, highlighting their greater ability to correctly identify anomalies in the data. The Random Forest ranks further behind, with values approximately 14% lower. Regarding F1-Score, which represents a trade-off between Precision and Recall, the LSTM achieves the best results, indicating an excellent balance between reducing false positives and correctly identifying anomalies. In particular, it achieves results 5.1% better than the Shallow Network and 5.5% better than the Random Forest, while the Single-Layer Perceptron confirms itself as the worst-performing model, achieving values 64.1% lower.
# Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance
## 3 Multi-Objective Optimization of Compensation Parameters
### Setting Constraints  
In order to reduce switching losses and improve IPT system efficiency, the system should work in soft-switching conditions; that is, the input impedance of the IPT system should be inductive. The input impedance angle \(\theta_{\text{in}}\) needs to be greater than 0. When the input impedance angle is less than 0, a large penalty coefficient \(M_{\text{pe}}\) is added to the objective function.  
Considering the physical boundaries and cost of passive electrical components, the compensation capacitors and compensation inductors are limited as follows:  
\[\begin{cases}L_{\text{min}}\leq L_{i}\leq L_{\text{max}}\\ C_{\text{min}}\leq C_{j}\leq C_{\text{max}}\end{cases} \tag{22}\]  
Meanwhile, voltage limits for capacitors and current limits of inductors for compensation networks should also be given to protect circuit safety, which are limited as follows:  
\[\begin{cases}U_{\text{C}}\leq U_{\text{Cmax}}\\ I_{\text{L}}\leq I_{\text{Lmax}}\end{cases} \tag{23}\]
## 2 Materials and Methods  
### Farm Survey Methods  
Strawberry growers from across New York State (US) were recruited to participate in the study. The cv. Jewel was selected for its excellent flavor and popularity amongst northeastern US growers [3; 25]. Fruit samples were collected at the peak of ripeness, once per year, from 22 farms over 3 years (2018-2020), although not all farms were sampled each year due to the travel distances involved and the short harvest season. Samples were visually assessed for ripeness and hand-picked. Samples were then placed into cardboard pulp containers (0.95 L), and the containers were inserted into 8 L freezer bags. Bags were kept in a closed, insulated cooler filled with ice packs and driven to Ithaca, NY where samples were cleaned of their calyx, rinsed with tap water to remove dirt and residues, and frozen to \(-\)10 \({}^{\circ}\)C within 24 h of harvest. Samples were then analyzed for SSC, titratable acidity (TA), and selected aromatic volatiles as described below. Harvest dates varied depending on the farm location.  
Weather data were obtained from the Network for Environmental and Weather Applications (NEWA, 2020) for 2018-2020 [26]. For each farm, the nearest weather station was located using map software. Growing degree days (GDD base 15 \({}^{\circ}\)C) and cumulative rainfall was calculated from weather station records ranging from 1 April to 30 June at the site closest to each farm. These dates were selected because they spanned the time from flower bud emergence to fruit ripening. Cumulative solar radiation, mean daily temperature, highest daily temperature, mean low temperature, and lowest minimum temperature were calculated for four discrete fruit developmental stages: April 1 until flowering (Stage 0, variable length), flowering until green fruit (Stage 1, 15 days), green fruit until pink (Stage 2, 10 days), and pink fruit until ripe fruit (Stage 3, 5 days). Dates for the developmental stage of each harvest were back-calculated from the date that the fruit was harvested, based on the assumption that fruits were ripe 30 days after flowering. Correlations were made between fruit quality variables and weather variables during each stage, including the temperature differential between average air temperature and minimum low temperature. Data were not collected on soil type or source of planting stock for farms in the sample.
## 2 Literature Review  
### Constitutive Equations  
According to [PERSON] (2016), the absorbed energy is an important result to extract when performing crashworthiness analysis of vehicle-object collisions. These results are needed to understand the magnitude of energy and find strategies to mitigate the impact force or the absorbed energy of the vehicle. This can be expressed using Equation (1).  
\[E_{a}=F\times d=(Ma)\times d \tag{1}\]  
where \(E_{a}\) is the absorbed energy, \(F\) is the impact force, and \(d\) is the maximum plastic deformation. The model also includes all forms of non-linearity (geometric, material, and contact), as is expected in real-world scenarios. The non-linear physical properties will significantly influence the outcome of the crashworthiness results. Material non-linearity, for instance, will not be constant for the entire history of the material's stress-strain curve, due to effects such as plasticity and damage. Therefore, the material's behavior must be expressed with a non-linear form of [PERSON]'s Second Law, shown in Equation (2).  
\[m\ddot{x}+k(x)x=F(t) \tag{2}\]  
where \(m\) is the total mass of the system; \(\ddot{x}\) is the instantaneous acceleration, which is a second-order derivative of the displacement \(x\); \(k(x)\) is the spring constant (if \(k\) does not vary with displacement) or the linear elastic properties of the system; and \(F(t)\) is the instantaneous force at every increment, with respect to time, \(t\). Similarly, compressive impacts are expected in such crashworthiness analysis. Therefore, geometric non-linearity must be considered, and its equation is expressed in Equation (3).  
\[m\ddot{x}+k\Big{(}x+ax^{2}\Big{)}=F(t) \tag{3}\]  
Figure 1: **(Left)** Traffic lights damaged beyond repair due to car accidents (source: [PERSON] (2016)). **(Right)** Corner impact of a sedan with a streetlamp (source: [PERSON] (2016)).  
where \(kax^{2}\) represents non-linearity terms of the elastic properties. When the gap between two bodies is close enough to initiate contact, non-linearity due to contact forces occurs. The contact force, according to [PERSON] [7], is also an implicit function of time where \(F(x(t))\) is denoted and \(F(x)\) is considered geometric. Through the non-linear function \(F(x)\), we can use Equation (4), as demonstrated by [PERSON] et al. [8], to integrate \(F(x)\) and output the total absorbed energy during the crashworthiness analysis.  
\[E_{a}=\int_{0}^{d}F(x)dx \tag{4}\]  
The absorbed energy of the material can then be extracted using Equation (5).  
\[SEA=\frac{E_{a}}{M} \tag{5}\]  
where \(M\) is the mass of the material used in the simulation. In this paper, the mass of the material is assigned to the car's outer shell and the street pole. These values are important to understand how much impact energy the materials can mitigate during the collision.
## 3 Results and Discussion
### PheoBSA Conjugate D In Vivo Biodistribution and Tumor Detection
(**B**) B-mode ultrasound (left) and photoacoustic images (right) of a representative TS/A tumor-bearing mouse before and 20 min after the intravenous injection of PheoBSA D (dose: 10 mg/kg). Figure 5: Photoacoustic signal measured by [PERSON] after 10, 20, and 30 min IV administration of PheoBSA D at a dose of 10 mg/kg in (**A**) TS/A tumor-bearing mice (\(n=2\)) and (**B**) PC3 tumor-bearing mice. (**C**) Representative images before (PRE) and 20 min post-injection in TS/A breast tumor-bearing mice and (**D**) PC3 prostate tumor-bearing mice. observed for the TS/A tumor model. This observation may be accounted for in terms of a larger vascularization of prostate cancer and consequently the major distribution of the blood pool agent. As a proof of concept for a DCE-PA imaging experiment that exploits the altered permeability of tumor vasculature, the photoacoustic signal was acquired every 72 s and the uptake of the melanin-based contrast agent PheoBSA D over time was assessed in the tumor region (Figure 6). This result demonstrated a slow but steady increase of the PA signal with a peak at about 15-25 min after the injection of PheoBSA D, that one may assume as the time needed for the extravasation and accumulation of the nano-sized agent in the tumor region. More quantitative analysis for DCE-PA imaging has also been proposed for taking into account the different compartments and laser fluence [56; 57]. In the last years, similar dynamic contrast enhancement photoacoustic 3D PA studies using ICG as contrast agent have been performed [58; 59; 60; 61]. However, ICG applications are limited by their rapid clearance, low photostability, low water solubility, and tendency to form aggregates. Herein we propose a melanin-based nanoparticle endowed with a longer circulation time compared to ICG-albumin adduct that allows for prolonged DCE experiments.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 7 Suppression Method of Photodarkening
### The Capture of Electrons and Holes Involved in the Generation of Defect  
[PERSON] et al. [24; 98; 99] compared and tested the additional loss of PD of Yb/Al and Yb/Al/Ce fiber by co-doped Yb-doped fiber with cerium (Ce\({}^{3+}\)/Ce\({}^{4+}\)). The additional losses of PD of Yb/Al and Yb/Al/Ce fiber were compared and tested. It was found that the introduction of cerium ions could improve the anti-PD performance of Yb-doped fiber. As shown in Section 2, the interaction between high-energy photons and matrix leads to the ionization of electron-hole pairs in optical fibers; Secondly, when electron-hole pairs are captured by defects in optical fibers, color centers will be formed. The two valence states of cerium ions in the co-doped cerium ion fiber can capture electrons or holes involved in defect generation and convert them into another valence state, thus inhibiting the formation of color center, then the PD effect is reduced. Ce\({}^{4+}\) is widely used as a decolorizer in the glass industry due to its strong oxidation, so it can reduce the generation of defects [100]. The researchers reported that the PD loss of Yb\({}^{3+}\)-doped fiber co-doped with cerium will be significantly reduced. In this case, because the energy levels of Ce\({}^{3+}\) and Ce\({}^{4+}\) are far from those of Yb\({}^{3+}\), this energy transfer will not have a great impact on the laser itself.
## 5 Examples of Forward-Inverse Modeling
### Atmospheric Input & Output
#### 5.3.4 Summary of Glint Issues
First, one does an \"atmospheric correction\" (assuming the water to be black at NIR wavelengths) to determined the water-leaving radiance. 2. Second, one retrieves the desired aquatic parameters from the water-leaving radiance. In the visible, up to 90% of the radiance measured by a satellite sensor typically comes from the atmosphere implying that:  
* Atmospheric correction becomes a very challenging task unless the near-infrared (NIR) _black-pixel approximation (BPA)_ is valid. * Estimation of diffuse transmittance is also important, but difficult because it depends on the angular distribution of the radiance just beneath the water surface. Also, accurate characterization of the atmosphere is important because:
* a small uncertainty in the atmospheric correction may lead to a big error in the inferred aquatic parameters, and
* aerosol optical properties vary considerably in space and time.
# Sum Rate Optimization Scheme of UAV-Assisted NOMA under Hardware Impairments
## 1 Introduction  
### Background  
Future sixth-generation (6G) communication requires a higher performance index, such as hyper-scale connectivity and ultra-high message transmission rates [1]. To meet the performance requirements, non-orthogonal multiple access (NOMA) as a promising candidate technology has aroused wide attention due to its characteristics for improving spectrum efficiency and system capacity [2, 3]. The dominant advantage of NOMA is to allow the multi-user sharing of the same time, frequency, and code domain resources by overlapping power domains at transmitters. At the receivers, and according to the receipt, power signals can decode the superimposed information with successive interference cancellation (SIC) technology [4].  
Meanwhile, unmanned aerial vehicle (UAV) communication is a new developing technology that can help NOMA obtain a preferable performance because of its high maneuverability and low overhead. UAVs provide a wider coverage range and larger throughput as flying relays for wireless communications [5, 6]. The authors in [7] studied the optimum altitude of UAV by considering the total power loss, outage probability, and bit error rate. Ref. [8] proposed an iterative method to obtain the locally optimal throughput and analyzed the algorithm's complexity by considering a moving relay system assisted by a UAV with a finite buffer. The authors first derived the closed-form expression oftransmitted power and then proposed a fractional programming-based suboptimal algorithm in [9]. The authors in [10] used a weighted K-means approach for drone deployment and proposed a Q-learning algorithm to optimize content placement. Ref. [11] focused on a multi-UAV-assisted wireless network, solving cache placement, trajectory, and power allocation problems in an iterative method to maximize the minimum throughput for UAV service users. The authors optimized the location of the optimal UAV base station under the coverage probability constraint by using the Gray Wolf optimization algorithm in [12].