esa-sceva/satcom-chunk-collection huggingface dataset:
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**3 GPP** & Third-Generation Partnership Project \\
**4G** & Fourth generation of cellular networks \\
**5G** & Fifth generation of cellular networks \\
**6G** & Sixth generation of cellular networks \\
**ADAS** & Advanced Driver Assistance Systems \\
**AF** & Ambiguity Function \\
**AI** & Artificial Intelligence \\
**BER** & Bit Error Rate \\
**CRLB** & Cramer-Rao Lower Bound \\
**DFRC** & Dual-Functional Radar Communications \\
**DIR** & Data Information Rate \\
**ELAA** & Extremely Large Aperture Arrays \\
**ETSI** & European Telecommunications Standards Institute \\
**GNSS** & Global Navigation Satellite Systems \\
**IoT** & Internet of Things \\
**ICAS** & Integrated Communications and Sensing \\
**IM** & Index Modulation \\
**IRS** & Intelligent Reconfigurable Surfaces \\
**ISAC** & Integrated Sensing and Communications \\
**ISG** & Industry Specification Group \\
**JCAP** & Joint Communications and Positioning \\
**JCAS** & Joint Communications and Sensing \\
**JCR** & Joint Communications and Radar sensing \\
**JPAS** & Joint Positioning and Sensing \\
**JPSAC** & Joint positioning, sensing, and communications \\
**JSAC** & Joint Sensing and Communications \\
**KPIs** & Key Performance Indicators \\
**LEO** & Low Earth Orbit \\
**LFM** & Linear Frequency Modulation \\
**LIS** & Large Intelligent Surfaces \\
**LNA** & Low Noise Amplifier \\
**LOS** & Line of Sight \\
**MAE** & Mean Absolute Error \\
**MI** & Mutual Information \\
**ML** & Machine Learning \\
**MIMO** & Multiple Inputs-Multiple Outputts \\
**mMIMO** & massive Multiple Inputs-Multiple Outputts \\
**NTN** & Non Terrestrial Networks \\
**NLOS** & Non-Line of Sight \\
**NOMA** & Non-Orthogonal Multiple Access \\
**NSF** & National Science Foundation \\
**OFDM** & Orthogonal Frequency Division Multiplexing \\
**OTFS** & Orthogonal Time Frequency Space \\
**PAPR** & Peak to the Average Power Ratio \\
**PNT** & Positioning, navigation, and Timing \\
**RCC** & Radar Communications Coexistence \\
**RF** & Radio Frequency \\
**RIS** & Reconfigurable Intelligent Surfaces \\
**RMSE** & Root Mean Square Error \\
**SE** & Spectral Efficiency \\
**SINR** & Signal to Interference plus Noise Ratio \\
**SLAM** & Simultaneous Localization and Mapping \\
**SNR** & Signal to Noise Ratio \\
**SPEB** & Squared Position Error Bound \\
**TN** & Terrestrial Networks \\
**UAV** & Unmanned Aerial Vehicles \\
**UE** & User Equipment \\
**umMIMO** & ultra-massive Multiple Inputs-Multiple Outputts \\
**XLMIMO** & Extremely Large-scale Multiple Inputs-Multiple Outputts \\ \end{tabular}
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 3 Case Study for Vienna
### Subdivision Graph  
All benchmark instances share the same subdivision graph \(G=(V,E)\), which is based on the official geographical subdivision of Vienna into 250 sub-districts (officially called \"Zahlbezirke\", i.e., _counting districts_). Each sub-district \(i\) is given as a polygon \(P_{i}\) describing its perimeter as a sequence of latitude-longitude pairs [14].  
The graph \(G\) contains one vertex for each sub-district. An edge \(\{i,j\}\in E\) exists between any two vertices \(i,j\in V\) whose corresponding polygons \(P_{i}\) and \(P_{j}\) touch, i.e., if the minimum distance between them is zero. The cost \(c_{i}\) of each vertex \(i\in V\) is the area of its corresponding sub-district's polygon \(P_{i}\) in square meters. This allows us to use the budget \(B\) to specify the maximum area of the AoO that we want to find. The revenue parameter \(p_{ij}\) of edge \(\{i,j\}\in E\) represents the expected travel demand from sub-district \(i\) to \(j\). The derivation of these parameters from various data sets is described in detail in Section 3.2.
# Aircraft Mode S Transponder Fingerprinting for Intrusion Detection
###### Abstract  
Nowadays, aircraft safety is based on different systems and four of them share the same data-link protocol: Secondary Surveillance Radar, Automatic Dependent Surveillance System, Traffic Collision Avoidance System, and Traffic Information System use the Mode S protocol to send and receive information. This protocol does not provide any kind of authentication, making some of these applications vulnerable to cyberattacks. In this paper, an intrusion detection mechanism based on transmitter Radio Frequency (RF) fingerprinting is proposed to distinguish between legitimate messages and fake ones. The proposed transmitter signature is described and an intrusion detection algorithm is developed and evaluated in case of different intrusion configurations, also with the use of real recorded data. The results show that it is possible to detect the presence of fake messages with a high probability of detection and very low probability of false alarm.  
SSR; radar; ADS-B; fingerprinting; ATC; safety; security; intrusion detection Article
# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 5 Results and Discussion
### Image-Level Detection Performance
#### 5.4.1 The General Test Result  
For an image, we first define the object-level detection metric as follows:  
* ObjDetection: the detector marks the location of the hidden object, and the IoU between the detection bounding box and the ground truth bounding box is more than 50%.
* ObjFalseAlarm: the detector marks the location of the hidden object, but there are no objects.  
Then we define image-level detection indicators as follows:  
* ImgDetection: if some or all of the hidden objects in a terahertz image are ObjDetection.
* ImgFalseAlarm: if there is any ObjFalseAlarm in a terahertz image.  
Finally, we obtain a image-level Detection Rate (DR) and False-Alarm Rate (FAR) as follows:  
\[DR=\frac{1}{n}\sum_{i=1}^{n}ImgDetection(i) \tag{5}\]  
\[FAR=\frac{1}{n}\sum_{i=1}^{n}ImgFalseAlarm(i) \tag{6}\]  
where \(i\) is the image index and \(n\) is the total quantity of images.  
The image-level detection result is shown in Table 6. The Detection Rate (DR) of RetinaNet is over 90% when its False-Alarm Rate (FAR) is 1.27%. Although YOLOv4 has the lowest FAR, its DR is lower than RetinaNet. FRCN-OHEM has the worst performance. RetinaNet has the best performance in detection of terahertz images, which makes it suitable for practical terahertz security inspection.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **On Arms (8)** & **On Body (69)** & **On Legs (70)** & **Parallel (95)** & **Perpendicular (52)** \\ \hline YOLOv4 & 0.2500 & 0.4783 & 0.6143 & 0.5789 & 0.4423 \\ FRCN-OHEM & 0.375 & 0.4638 & 0.5000 & 0.5158 & 0.4038 \\ RetinaNet & **0.5000** & **0.4928** & **0.7000** & **0.6105** & **0.5577** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Recall rate of detection in different parts of the human body. The detection results of 147 objects in the test set are counted. The results with underline are the worst, and the results with **bold** are the best.
## 1 Introduction
The spatial multiplicity versus the transmission distance accomplished in recent SDM- wavelength division multiplexing (WDM) transmission experiments is plotted in Figure 2[19], where the region with a spatial multiplicity over 30 is designated as the \"DSDM region\". The dots in Figure 2 represent the cases of multimode fiber (MMF), MCF, and coupled-core fibers (only three-core and six-core), as well as the latest multicore multimode fiber experiments [20]. Figure 1: The typical examples of optical fibers for space division multiplexing (SDM) technology: (a) multi-element fiber; (b) multicore fiber; (c) multi-mode fiber [4]. The outline of this review article is sketched as follows: Section 2 describes the principles of MIMO equalizers, the LMS and RLS techniques, and the space-time block-coding (STBC) assisted RLS algorithms, while Section 3 introduces the configuration of a typical multimode coherent transmission system transmission system with FMFs for high-order modulation formats. In Section 4, the performance analysis of various adaptive filtering schemes is presented for quadrature phase shift keying (QPSK), 16 quadrature amplitude modulation (16 QAM), and 64 QAM, correspondingly. Then, Section 5 explores the STBC-based enhancement amongst different modulation formats. In Section 6, we further investigate the bit error rate (BER) versus optical signal-to-noise ratio (OSNR) curves of mode-dependent loss equalization. Last but not least, Section 7 demonstrates the complexity optimization using the proposed single-stage architecture. Finally, Section 8 provides a summary of recent expressive experiments of SDM transmission using MIMO DSP for short-, medium- and long-haul distances.
###### Abstract  
Novel growth media formulations for improved rhamnoid production from _Pseudomonas aeruginosa_ PAO1 were evaluated on four carbohydrate sources: glucose, glycerol, soy hull hydrolysate (SHH), and mimicking soy hull hydrolysate (MSH) along with bioenhancers and other media components. This study is aimed at understanding the effect of different types of human neuroendocrine bioenhancers on growth performance and rhamnoid titer generation of the _Pseudomonas aeruginosa_ PAO1 in a growth media containing sustainable crude biomass carbohydrates. Optimization of the media factors for improved rhamnoidity titers with _Pseudomonas aeruginosa_ PAO1 was performed through a high-throughput response surface study for the best growth rate for concentrations of carbohydrates; bioenhancers, norepinephrine (NE) and dopamine (DP); and iron (Fe). In the high-throughput study, the microbial growth rates for all sugar types ranged between 0.2 and 0.5 log numbers in OD (optical density, indicating the concentration of bacterial cells within a liquid culture, as determined by a spectrophotometer) h\({}^{-1}\) at 600 nm, with glucose providing the highest growth rate in the best response surface media combination at 2.5% glucose concentration, 160 \(\upmu\)M norepinephrine, 66 \(\upmu\)M dopamine, 0.03% Fe concentration. The effect of this media on growth and rhamnoid production was further verified in 100 mL shake flasks. The highest OD and rhamnoid titers were achieved for glucose- and glycerol-based media at 2.78 g/L and 2.72 g/L, respectively, whereas significantly lower titers at 1.98 g/L and 1.72 g/L were observed for SHH- and MSH-based media, respectively. No significant growth enhancement effects by the bioenhancers norepinephrine and dopamine were observed at the concentrations evaluated.  
Keywords:_Pseudomonas aeruginosa_ PAO1; crude biomass hydrolysates; rhamnoids; bioenhancers; growth media
## 4 Numerical Simulation Studies
From Figure 9, it is seen that the tracking path generated by the proposed system has the smallest discrepancy compared to the prescribed path, and it has the fastest convergence rate to the prescribed path among the four control methods. Therefore, it is concluded that the suggested method is highly effective for a 3-DOF parallel manipulator in tracking control. The effects of uncertainties and external disturbances have been completely compensated by using the suggested method. Figure 8: The control input signals: (**a**) SMC, (**b**) S-SMC, (**c**) NFTSMC, and (**d**) S-FOTSMC. Figure 9: The desired trajectory and real trajectory of the end-effector when the parallel manipulator tracks a linear trajectory. **Remark 3**. : _Through simulation studies comparison among those of SMC, S-SMC, NFTSMC, S-FOTSMC, the experimental results performance comparison could be expected to show the effectiveness viability of our proposed scheme. In the next work, the authors will apply the proposed control method to the practical Robot. system compare them with other state-of-the-art control algorithms to demonstrate the effectiveness of this control method._
###### Abstract  
This paper studies learning frameworks for energy-efficient data communications in an energy-harvesting cognitive radio network in which secondary users (SUs) harvest energy from solar power while opportunistically accessing a licensed channel for data transmission. The SUs perform spectrum sensing individually, and send local decisions about the presence of the primary user (PU) on the channel to a fusion center (FC). We first design a new cooperative spectrum-sensing technique based on a convolutional neural network in which the FC uses historical sensing data to train the network for classification problem. The system is assumed to operate in a time-slotted manner. At the beginning of each time slot, the FC uses the current local decisions as input for the trained network to decide whether the PU is active or not in that time slot. In addition, legitimate transmissions can be vulnerable to a hidden eavesdropper, which always passively listens to the communication. Therefore, we further propose a transfer learning actor-critic algorithm for an SU to decide its operation mode to increase the security level under the constraint of limited energy. In this approach, the SU directly interacts with the environment to learn its dynamics (i.e., an arrival of harvested energy); then, the SU can either stay idle to save energy or transmit to the FC secured data that are encrypted using a suitable private-key encryption method to maximize the long-term effective security level of the network. We finally present numerical simulation results under various configurations to evaluate our proposed schemes.  
actor-critic; cognitive radio network; data encryption; energy efficiency; spectrum sensing +
Footnote †: journal: Applied Sciences
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Interference Management  
This paper also highlights the challenges in interference management for D2D communications in the 5G environment for future studies.
## 2 Methods
### Case Study  
The case study investigated is in Balaguer, a city with approximately 15,000 residents in the province of Lleida, Catalonia, Spain. Figure 1 provides an aerial view of the district case study.  
The entire district spans about 8825 m\({}^{2}\) and comprises various public buildings with diverse functions. From an energy standpoint, all these structures stand to benefit from efficiency measures due to common issues such as insufficient insulation, predominantly single-pane windows, and the prevalence of low-efficiency energy systems and lighting.  
Figure 2 reports some pictures of the main facades of the buildings and some brief descriptions of the buildings within the district which are as follows:  
* Angel Guimera Public School (1800 m\({}^{2}\)): Primarily operational during the day with limited afternoon occupancy and a month of inactivity in August. Envelope materials include solid brick and plaster, with single-glazed windows. Gas heating is utilized, and cooling is provided by single split systems in most areas, with some zones remaining unconditioned.
* Xalet Montiu (461.44 m\({}^{2}\)): A historic building showcasing modernist architecture, operating mainly from 9:00 am to 8:00 pm. Envelope features solid brick and plaster, along with single-glazed windows. Various split air conditioner systems complement radiator-based heating, with cooling based on split single systems.  
* Educative Service of La Noguera (1093 m\({}^{2}\)): Mainly active during the day with limited afternoon occupancy and a month of inactivity in August. Envelope comprises solid brick and plaster, with double-glazed windows. Gas burner and radiators facilitate heating, while multi-split air systems are employed for cooling.
* Museum of La Noguera (1496 m\({}^{2}\)): The building operates at different hours during the week. Envelope materials include brick, polystyrene, clay, plaster, and double-glazed windows. Gas is used for heating, and air systems distribute it, with chiller-based cooling systems of low efficiency.
* Library Margarida de Montferrat (3975 m\({}^{2}\)): It operates with variable schedules during the week. Envelope features brick and plaster, along with double-glazed windows. Heating relies on radiators gas-based, while split air conditioning systems provide cooling for the entire area of the building.
## 3 Results
### Effects of Low Temperature on the GPP/SIF Ratios for Different PFTs
#### 3.3.2 Tower-Based Dataset  
Considering the tower-based observations, we can see that SIF tracked the GPP well during the over-wintering period for both the XTS (Figure 14a,c) and Niwot Ridge sites  
Figure 12: Relationship between the GPP/SIF ratio and temperature for groups A and B of TeC3 CRA, C4 GRA, and C3 GRO (2018–2020). The slopes and determination coefficients of linear regressions are listed in each panel. The color bar represents the fraction of point density, normalized by the maximum point density.  
Figure 13: Distribution of the GPP/SIF ratios for all data with temperature above 10 \({}^{\circ}\)C (green) and below 10 \({}^{\circ}\)C (blue).  
(Figure 14b,d). The GPP/SIF ratio in winter wheat (C3 CRO) increased with temperature (Figure 14e); however, the GPP/SIF ratio in BoENF only increased with temperature when the temperature was below 10 \({}^{\circ}\)C. When the temperature was above 10 \({}^{\circ}\)C, the GPP/SIF ratio in BoENF decreased with temperature (Figure 14f). Therefore, the response of the GPP/SIF ratio to high temperatures might differ for different PFTs, which was different from the satellite results. This result needs to be further examined and verified. Nevertheless, the GPP/SIF ratio decreased under low-temperature conditions (below 10 \({}^{\circ}\)C) for both C3 CRO and BoENF, consistent with the satellite results. The lower seasonal changes in the canopy structure and chlorophyll content in BoENF provide reliable evidence that the GPP/SIF ratio is associated with the changes in physiological factors influenced by a low temperature. The results also demonstrate that low temperatures affect the SIF-GPP relationships and highlight the importance of incorporating the effects of temperature into SIF-based GPP estimation.
## 3 Improved Firefly Algorithm-Wavelet Support Vector Machine
### WSVM Parameter Optimization Based on Improved Firefly Algorithm  
The fitness function is a particular type of objective function that is used to evaluate the individual performance in the IFA. In this paper, error rate of recognition is adopted as the fitness function, which is inversely proportional to the performance of individual, namely brightness. The formulation of the function is described by the following expression:  
\[Fitness=1-\frac{T_{1}+T_{2}\cdots+T_{n}}{Sum}\times 100\% \tag{29}\]  
where \(T_{i}(i=1,\ 2,\ 3,\ \cdots,\ n)\) is the amount of the correctly recognized images of class \(i\); \(Sum\) is the amount of the images of sample set.  
The procedure of IFA-WSVM model construction is illustrated in the flow diagram shown in Figure 1. Equally important, the process of optimizing WSVM parameters with IFA is also described as follows:  
1. Initialize the improved firefly algorithm, and set up the parameters, including the number of fireflies \(K\), initial and final randomization parameters \(\alpha_{0}\) and \(\alpha_{\infty}\), maximum iterations \(t_{\text{max}}\), attractiveness \(\beta_{0}\), basic attractiveness \(\beta_{b}\) and light absorption coefficient \(\gamma\).
2. Generate the initial locations of fireflies at random as \(x_{i}(i=1,\ 2,\ \cdots,\ K)\), every firefly is composed of the WSVM parameters \(C\) and \(\tau\).
3. Calculate the fitness value of every firefly to determine or update its brightness.
4. Rank the fireflies by their brightness and regard the firefly with minimum brightness as the global-best point.
5. Evaluate the brightness of every current firefly and compare the brightness of any two fireflies \(i\) and \(j\). If \(I_{i}>I_{j}\), calculate the distance \(r_{ij}\) to obtain the improved attractiveness \(\widetilde{\beta}(r_{ij})\). Then, move firefly \(i\) towards \(j\) in all dimensions using Equation (28). The randomization parameter \(\alpha(t+1)\) is updated by Equation (26).
6. Check termination condition. If the maximum iteration limit is reached, stop the optimization process and output the values of optimal parameters \(C\) and \(\tau\). Otherwise, return to Step 3.
# Highway Speed Prediction Using Gated Recurrent Unit Neural Networks
## 1 Introduction  
The emergence of advanced location-aware technology has led to the generation of various mobility data. The massive amount of movement data could completely reshape our understanding of urban planning, transport management, and human mobility, because movement analytics enables us to reveal valuable and hidden patterns from big spatiotemporal data [1; 2; 3; 4; 5]. In particular, accurate traffic forecasting, including traffic flow and speed, is an essential factor for smart cities and transportation management [6]. Highway traffic speed prediction plays a crucial role in route guidance, congestion avoidance, and carbon dioxide emission reduction. Therefore, this paper aims to predict the future traffic speeds of highway road sections.  
However, much of the research up to now has limitations in performing accurate traffic speed prediction. Traffic data are non-stationary and nonlinear, which results in the ineffectiveness of previous approaches based on statistical models.  
The paper addresses two key challenges that arise in the design of traffic speed prediction models. First, this study uses the data-driven approach of deep learning. In particular, we use a neural network for time-series problems such as gated recurrent units (GRUs) because deep learning approaches are robust to nonlinear problems. Furthermore, this study determines the optimal hyperparameters for the network. We compared the number of hidden layers and investigated a couple of factors to make a better model. The proposed methodology is compared with the state-of-the-art alternative methods.  
In addition, this study utilizes the dynamic tachometer graph (DTG) data to predict the average speed on highway sections. The DTG records vehicles' operational information,such as speed. It is compulsory to install a DTG recorder for buses, trucks, and taxis in the Republic of Korea. Remarkably, the experiment area--the Gyeongbu Expressway--has a bus-only lane. We divided the analysis of buses and other cars, which enabled a better understanding of the traffic flows in the experimental area.  
The remainder of this paper is organized as follows. Section 2 begins by examining the strengths and limitations of previous research on traffic speed prediction in more detail. Section 3 presents the experimental data and methodology. The experimental evaluation and comparisons of the GRU method with other methods are presented in Section 4. Finally, the paper concludes with a summary and suggestions for future work in Section 5.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 3 Challenges of RRM in Cognitive HTS Systems
### Cost Efficiency  
With the tremendous demands on data traffic, there is a tendency to move the feeder link from the Ka band to higher frequency bands. Although there are larger available bandwidths as the frequency increases, the inherent hardware constraints of transceivers operating in high frequency bands make it impractical to build a complete radio frequency (RF) chain for each antenna element. This motivated the hybrid digital/analog (HDA) beamforming structure, proposed in mmWave communications, where a reduced-dimensional baseband digital beamforming is concatenated with a phase shifter network to reduce the necessary number of RF chains. In this regard, the RRM framework should be able to support cost efficient HDA precoding, in order to be valid for satellite systems spanning a large range of frequency bands.