esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper delves into the impact of the Tracking and Data Relay Satellite (TDRS) constellation orbit accuracy on Differenced One-Way Doppler (DOWD)-based user spacecraft orbit determination, specifically when the TDRS orbit is derived solely from Telemetry, Tracking, and Command (TT&C) range-only tracking. The study revealed that retiring the Bilateration Ranging Transponder System (BRTS) without fully comprehending the TT&C bias and its uncertainty could hinder achieving the required level of orbit precision for both TDRS satellites (<75 m) and user spacecraft (<300 m). If the TT&C range bias and its associated uncertainties are not accurately calibrated in a TT&C-based TDRS orbit, it could lead to an orbit error of up to 17 km in the TDRS, yielding a DOWD-based orbit error of up to 5 km for the user spacecraft. The research identifies a linear relationship between TDRS orbit error and user spacecraft orbit error, with several factors impacting the slope of this relationship, including the number of DOWD passes obtained, the TDRS's relative position during DOWD measurement acquisition, and dynamic errors in the user spacecraft orbit. Despite the imprecision in the orbits of the TDRS and user spacecraft, the Local Oscillator Frequency drift estimation remains accurate.  
Keywords:orbit determination; orbit error; FDF; space relay; Doppler; BRTS; TT&C; range bias
## 5 Experimental Results
### Tests in a Mobile Scenario
#### 5.4.1 Scenario Description  
We consider a single PU and four smartphones acting as SUs. The PU is placed in a given car, while the remaining smartphones (\(\mathrm{SU}_{1}\), \(\mathrm{SU}_{2}\), \(\mathrm{SU}_{3}\) and \(\mathrm{SU}_{4}\)) are positioned in distinct vehicles. The vehicles move assuming a platoon configuration led by the PU. For the whole duration of the experiment, the vehicle with \(\mathrm{SU}_{1}\) remains behind the car containing the PU, while the other vehicles periodically exchange their positions. During the test, the smartphones are held by the driver or positioned within the cockpit (e.g., over the seats or over the dashboard). The five vehicles involved in the tests complete 20 laps of the circuit shown in Figure 21, designed within the campus of the University of Parma. The circuit path length is \(1.72\) km, and the vehicles completed the test with an average speed of roughly \(40\) km/h (\(11.1\) m/s) and maintaining an inter-vehicle distance within the interval, [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30] m. (The average speeds and the inter-node distances are obtained by means of GPS.) Since the test has been conducted on a public road, sporadically, other cars have disturbed the platoon configuration. At every lap, the PU sends two distinct archives: one at the beginning of the lap and the second at the middle of the lap. Therefore, the results shown here have been obtained averaging over \(40\) transmission acts conducted in 20 laps of the circuit.  
Figure 20: CDF of received _DATA_ messages before _REQUEST_ messages in Scenario 2.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 1 Introduction
### Research Scope and Contributions  
The main contributions of this paper are summarized as follows.  
* Comparative analysis of MANET routing protocols in heterogeneous environments: We thoroughly review and compare the performance of four selected MANET routing protocols (AODV, OLSR, BATMAN, and DYMO) in a heterogeneous MANET. To this end, we analyze the impact of various node types on the QoS parameters, such as the end-to-end delays, throughput, and packet delivery ratios. We provide insights into the performance of the routing protocols in heterogeneous networks.
* Evaluation of the node types impacting the QoS parameters: This study highlights the significance of the node heterogeneity in MANETs and its effect on the system performance. Using the simulator, we measure the QoS metrics for heterogeneous nodes, thereby informing future implementations of MANETs in real-world applications.
* Guidance for next-generation wireless networks: The findings of this research offer valuable insights that can assist network researchers and engineers in developing next-generation wireless networks capable of accommodating heterogeneous networking constraints. By understanding the performance issues associated with various MANET routing protocols, this paper contributes to the ongoing advancement of efficient and robust wireless communication systems.
## 4 Discussion
Additionally, an analysis was performed to investigate the existence of a correlation between the results of the individual HRTF recognition experiment and the results of the localization experiment. However, the findings revealed no statistically significant correlation. With the improvements implemented into the methodology of the HRTF recognition experiment in future work, the possibility of a correlation between the results of these two experiments could be revisited. Concerning the latency of the binaural audio system used in the localization experiment, it is expected that the shortening of the latency time to below 50 ms may further improve the localization accuracy when one's own individual HRTFs are used. In future research, upgrades made to the binaural system setup could lead to lower latency, thus providing a solid base for new experiments, the results of which would help verify this premise. Despite using a different method of measuring individual HRTF and a different localization methodology, the presented results agree with previous studies [35, 36, 37] that also showed statistically significant performance improvement when one's own individual HRTF is used. This finding goes in favor of confirming the claim that the use of one's own individual HRTF provides a more natural and immersive listening experience. One study, in particular, confirms improvements in sound source localization by using individual HRTF over generic, but the emphasis is put on the compelling performance of a generic HRTF set [38]. On the other hand, one study found no difference between using individual HRTF and generic HRTF in dynamic virtual environments [39], although their methodology and experimental design are considerably different from the ones presented in this research. The implications of these contradictory findings suggest that additional investigation might help to identify the most informative dynamic cues in order to enhance our comprehension of the variations between the experimental conditions, which may clarify the underlying factors contributing to the observed discrepancies.
## 1 Introduction
[17] proposed the application of electrodialytic remediation (EDR) to LIB residues. This technique is based on the use of ion-exchange membranes for the selective separation of ions from liquid matrices by means of an applied electric potential. The EDR has been successfully used for the remediation of wet solid matrices and aqueous suspensions (e.g., polluted soil, treated timber waste, fly ash, wastewater sludge and harbor sediments [18,19,20,21,22]). To the extent of our knowledge, there are no previous experimental studies evaluating the application of EDR to LIBs, apart from initial unpublished experiments carried out in our group. These previous experiments tested the recovery of Li and Co from LiCoO\({}_{2}\) particles, placed in the central section of a standard three-compartment ED cell, using an anion-exchange membrane at the anode compartment and a cation-exchange membrane (CEM) at the cathode compartment. In those experiments, some limiting drawbacks were found, such as the difficulty to attain a proper stirring of the suspension within the cell, a significant deposition of particles at the surface of the ion-exchange membranes and the formation of chloride gas at the anode. With the aim of tackling the aforementioned limitations, a novel experimental setup based on a hydrometallurgical-electrodialytic treatment is proposed in this paper. This experimental system basically consists of an electrodialytic cell using CEMs for the separation of the central compartment from both electrode compartments, while the dissolution of the LiCoO\({}_{2}\) particles is carried out outside the electrodialytic cell, hindering the undissolved particles to reach the membranes and avoiding fouling. In order to achieve the proof of concept, the experiments presented here were carried out on new LiCoO\({}_{2}\) particles.
## 2 Materials and Methods  
### Plant Collection and Preparation of Mango By-Product Extracts  
Three mango cultivars (Keitt, Sensation and Gomera-3) were collected from the fields of the mango collection (UTM 28R 364451.00 m E 3133070.93 m N; 114 m.a.s.l.) of the Instituto Canario de Investigaciones Agarias (Tenerife, Canary Islands, Spain) at the physiological stage of ripeness (mature-green) and allowed to ripen (full-ripeness or consumption stage) at 18 \({}^{\circ}\)C and 80-90% relative humidity. The plant material was identified, processed and characterized. The extracts were obtained using optimized methods to achieve the maximum extraction of polyphenols and a high antioxidant capacity, as described in previous works [14; 15; 16]. Briefly, the peel and seed were manually separated (\(31\pm 1\)% and 4.2 \(\pm\) 2.1% of the total fruit weight, respectively), cut into small pieces (\(0.5\times 1\) cm) and freeze-dried at \(-40\)\({}^{\circ}\)C in a vacuum (50 mPa) for 5 days. The dried mango peels and seeds were then ground to a fine powder (particle size between 355 and 500 \(\upmu\)m) and stored at \(-20\)\({}^{\circ}\)C until the extractions were performed. Microwave-assisted extraction (MAE) (ETHOS 1, Milestone SRL, Sorisole, Italy) was used to obtain the extracts. Two types of peel extractions were performed: ethanol-water (1:1, _v:v_) at 75 \({}^{\circ}\)C and a weight-to-solvent volume of 1:50 (_w:v_) (extraction A) or 1:10 (_w:v_) (extraction B). On the other hand, the mango seed extraction was performed with acetone-water (1:1, _v:v_) at 50 \({}^{\circ}\)C and a weight-to-solvent volume of 1:30 (_w:v_). The resulting nine by-product extracts included three extracts (peel: extraction A and B and seed) for each of the three mango cultivars (Keitt, Sensation and Gomera-3) [14; 15; 16].  
The compounds present in the nine by-product extracts were identified by the following acronyms: KPA (Keitt Peel, extraction A); KPB (Keitt Peel, extraction B); SPA (Sensation Peel, extraction A); SPB (Sensation Peel, extraction B); GPA (Gomera Peel, extraction A); GPB (Gomera Peel, extraction B); KSC (Keitt Seed, extraction C); SSC (Sensation Seed, extraction C) and GSC (Gomera Seed, extraction C).  
The characterization of the polyphenolic composition of the mango by-product extracts was previously conducted [16].
# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
###### Abstract  
Pupil tracking plays a crucial role in various applications, including human-computer interactions, biometric identification, and Autostereoscopic three-dimensional (3D) displays, such as augmented reality (AR) 3D head-up displays (HUDs). This study aims to explore and compare advancements in pupil-tracking techniques using event camera imaging. Event cameras, also known as neuromorphic cameras, offer unique benefits, such as high temporal resolution and low latency, making them well-suited for capturing fast eye movements. For our research, we selected fast classical machine-learning-based computer vision techniques to develop our remote pupil tracking using event camera images. Our proposed pupil tracker combines local binary-pattern-features-based eye-nose detection with the supervised-descent-method-based eye-nose alignment. We evaluate the performance of event-camera-based techniques in comparison to traditional frame-based approaches to assess their accuracy, robustness, and potential for real-time applications. Consequently, our event-camera-based pupil-tracking method achieved a detection accuracy of 98.1% and a tracking accuracy (pupil precision < 10 mm) of 80.9%. The findings of this study contribute to the field of pupil tracking by providing insights into the strengths and limitations of event camera imaging for accurate and efficient eye tracking.  
eye tracking; eye detection; event camera; dynamic vision sensor; system latency; Autostereoscopic 3D display; augmented reality; augmented reality 3D head-up display +
Footnote †: journal: Medical Image Processing
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 5 Formation of Mesh  
The nodes in the network are characterized as non-group (NM) members and group members. The former is not part of the mesh and represented in a black dot, as shown in Figure 3. The latter is divided into mesh relay (MR), intermediate receiver (IR) and terminal receiver (TR). The MR is the intermediate node between the core and a receiver and represented as a blue color. The TR is known as a terminal receiver, and the mesh finish on it, TR does not contribute to the packet forwarding and is represented in white color. Likewise, the IR acts as an intermediate node and a receiver and are represented as a red dot. The intermediate node between R48 and the core node is R42 node. At the start of the communication, only receivers are mesh members, but later on, the MR will also be a part of the mesh member because they occur between the TR and core node and communicate the data. In the PUMA and CAMP, the data are transmitted through flooding within the group. When the data are transmitted from a source to any mesh member, it is flooded immediately inside the mesh. As a result, the desired receiver within the group receives the data through flooding. The packet received twice by any group member will be rejected because the information about the repeated packet is already saved in the cache, which will further waste the resources of the group member. In Figure 3, R50 is the parent node of R38. The affiliation of the parent to child remains until it reaches the group member R38. When it reaches R38, then it immediately floods within the mesh of a group. Moreover, if the number of group members increases, then flooding is further worsening the performance.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Differences between Fusion Techniques
In the case of data fusion in agriculture, techniques, such as fuzzy logic [18], random forest [78; 82], support vector machines [83; 92; 113], k-nearest neighbors [90], and shallow neural networks [24; 69; 85] have been employed, often showing advantages when digital images were involved, but without ever dominating other strategies. This began to change with the inception of deep neural networks. Deep learning is a branch of machine learning in which the models have deep architectures; that is, the neural networks have many layers with well-defined purposes [134]. They are particularly well suited to deal with digital images and, with the exception of satellite images, this type of technique is quickly becoming the standard for image data fusion [39]. The downside of this success is that deep learning is frequently to unsuitable datasets. Deep learning models are known to require a large number of samples to properly capture the data distributions of the classes to be considered [1]. The minimum number of samples depends on several factors, but in general, the larger the variabilities associated with the problem, the more samples are needed. This is bad news for agricultural applications because the number of factorsthat introduce variabilities in image datasets captured in the field is very large [1]. When the dataset lacks variability and training and test samples come from the same dataset (which is almost always the case), accuracies tend to be unrealistically high, and the trained model will almost certainly suffer from overfitting and will tend to fail when fed with new samples. Image augmentation is often used to mitigate this problem, but in many cases this process is not applied correctly, thus aggravating the overfitting problem [4]. The literature has strong evidence that deep learning is indeed the best approach when dealing with all types of digital images [21; 22; 39], as long some of the pitfalls associated with this type of approach are carefully avoided. Decision rules [74], majority rules [46; 117; 121], and model output averaging [30] are all relatively simple techniques applied in high-level fusion with the purpose of combining the information extracted by different models. Because model selection tends to have a greater impact on the fusion effectiveness, the combination of the model outputs is usually carried out using one of these standard approaches.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems
#### 2.2.3 Arnold Maps  
The Arnold map, which was introduced by [PERSON] in 1968 [21], is a nonlinear chaotic transformation that maps a unit square into itself, exhibiting chaotic behavior; this is desirable in image encryption for producing pseudorandom sequences. The Arnold map can be represented by the following formula:  
\[\Gamma(x_{n+1},y_{n+1})=(2x_{n}+y_{n},x_{n}+y_{n})\mod N, \tag{3}\]  
where \((x_{n},y_{n})\) and \((x_{n+1},y_{n+1})\) are the coordinates of a point in the unit square before and after the transformation, respectively. N is the size of the unit square.  
[PERSON] et al. [13] proposed an encryption algorithm based on the Arnold map. The proposed algorithm divides an image into small blocks with the same width and height, and each of the small blocks is permuted using the Arnold map. It is worth noting that the proposed algorithm does not diffuse the pixel's values of the image, resulting in the cipher image having the same histogram information as the original plaintext image.  
In [14], [PERSON] et al. proposed a medical image encryption algorithm based on an enhanced Arnold map and AES. Their proposed algorithm consists of three steps. It starts by sharpening the edges of the plaintext image to preserve the details. Then the sharpened plaintext image pixels are chaotically shuffled for \(i\) number of iterations based on a modified Arnold map. Finally, the permuted image is encrypted using AES-128 encryption in CBC mode.  
It should be noted that the Arnold map is designed to work only on square images where the height and width are equal, and it is not suitable for use on rectangular images without first resizing the image to make it square before encryption. Furthermore, the Arnold map is known to exhibit periodic behavior, meaning that after a certain number of permutation iterations, the image pixels will return to their original position.
## 4 Envelope Model Order Reduction
\begin{array}{l}\frac{dI_{M}(t)}{dt}=\\ \frac{C(t)L(t)\omega_{s}^{2}(t)\left(\omega_{s}(t)-\frac{1}{C(t)L(t)\omega_{s}^{2 }(t)}\right)\tan(\eta_{i}(t))-C(t)L(t)\omega_{s}^{2}(t)\frac{R(t)}{L(t)}}{\frac {1}{t}(C)\left(C(t)L(t)\omega_{s}^{2}(t)+1\right)\sqrt{1+\tan^{2}(\varphi_{i}( t))}}+\\ +\frac{C(t)L(t)\omega_{s}^{2}(t)\left(\frac{\pi_{in}(t)}{\pi L(t)}\right)\frac{ 1}{\pi L(t)}}{\frac{1}{P(t)}\left(C(t)L(t)\omega_{s}^{2}(t)+1\right)\sqrt{1+ \tan^{2}(\varphi_{i}(t))}}-\\ -\frac{\tan(\varphi_{i}(t))\left[C(t)L(t)\omega_{s}^{2}(t)\left(\omega_{s}(t)- \frac{1}{C(t)L(t)\omega_{s}^{2}(t)}\right)+C(t)L(t)\omega_{s}^{2}(t)\frac{R(t )}{L(t)}\tan(\varphi_{i}(t))\right]}{\pi_{in}^{1}\left(C(t)L(t)\omega_{s}^{2}(t )+1\right)\sqrt{1+\tan^{2}(\varphi_{i}(t))}}.\end{array}  \tag{68}\]  
Meanwhile, the following relation holds according to (28) and (63):  
\[i^{s}(t)=\sqrt{\frac{I_{M}^{2}(t)}{1+\tan^{2}(\varphi_{i}(t))}}. \tag{69}\]  
Substituting (69) into (66) and (68) and rearranging yields a modified reduced (third-order) nonlinear envelope model of the system, where subscript \"\(\prime\)\" is added to the state variables for the sake of differentiation from the full-order model counterparts:  
\[ \begin{array}{l}\frac{d}{dt}\tan(\varphi_{ir}(t))=\\ -\frac{C(t)L(t)\omega_{s}^{2}(t)\left(\omega_{s}(t)-\frac{1}{C(t)L(t)\omega_{ s}^{2}(t)}\right)\left(1+\tan^{2}(\varphi_{ir}(t))\right)}{C(t)L(t)\omega_{s}^{2}(t)+1} \\ -\frac{\sqrt{\frac{1+\tan^{2}(\varphi_{ir}(t))}{I_{M}^{2}(t)}}{\frac{1}{I_{M} ^{2}(t)}}\frac{\pi_{in}(t)}{\pi L(t)}\left(C(t)L(t)\omega_{s}^{2}(t)\tan( \varphi_{ir}(t))\right.}\\ \left.\frac{dI_{M}(t)}{dt}=\\ \frac{C(t)L(t)\omega_{s}^{2}(t)\left(\omega_{s}(t)-\frac{1}{C(t)L(t)\omega_{ s}^{2}(t)}\right)\tan(\varphi_{ir}(t))-C(t)L(t)\omega_{s}^{2}(t)\frac{R(t)}{L(t)}} \\ \left.\frac{C(t)L(t)\omega_{s}^{2}(t)\left(\frac{\pi_{in}(t)}{\pi L(t)}\right) \sqrt{1+\tan^{2}(\varphi_{ir}(t))}}{\left(C(t)L(t)\omega_{s}^{2}(t)+1\right) \left(1+\tan^{2}(\varphi_{ir}(t))\right)}-\\ -\frac{\tan(\varphi_{ir}(t))C(t)L(t)\omega_{s}^{2}(t)\left(\omega_{s}(t)- \frac{1}{C(t)L(t)\omega_{s}^{2}(t)}\right)}{\left(C(t)L(t)\omega_{s}^{2}(t)+1 \right)\frac{(1+\tan^{2}(\varphi_{ir}(t)))}{I_{M}^{2}(t)}}\\ -\frac{\tan^{2}(\varphi_{ir}(t))C(t)L(t)\omega_{s}^{2}(t)\frac{R(t)}{L(t)}}{ \left(C(t)L(t)\omega_{s}^{2}(t)+1\right)\frac{(1+\tan^{2}(\varphi_{ir}(t)))}{I _{M}^{2}(t)}},\end{array} \right.\end{array} \tag{71}\]  
\[ \begin{array}{l}\overline{v}_{in,r}(t)C_{in}\frac{d\overline{v}_{in,r}(t)}{dt }=R(t)\left(\frac{I_{Mr}(t)}{\sqrt{2}}\right)^{2},\end{array} \begin{array}{l} \overline{v}_{inr}(0)=V_{in0} \tag{72}\]  
with matching general initial conditions. The resulting block diagram is shown in Figure 6.  
Figure 6: Modified reduced-order envelope model of the system. Figure 7: Results of Simulation 1. Figure 8: Results of Simulation 2. Figure 9: Results of Simulation 3. Figure 10: Zoomed-in steady-state converter’s voltages and AC-side current. Figure 11: Results of Simulation 4. Figure 12: Results of Simulation 5.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 4 Experimental Setup and Results
### Pros and Cons of Employed Methods  
#### 4.7.1 Advantages  
1. This study embraces a diverse array of machine learning algorithms, providing a comprehensive evaluation of their efficacy in sentiment classification.
2. Emphasis is placed on the advantageous use of pre-trained word embeddings, particularly fastText, for enhancing model performance.
3. Valuable insights are offered into the practical applications of sentiment analysis within the Turkish stock market, holding potential significance for decision-makers.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 3 Filter Performance Measurement
### PLC Signal Filtering  
In some use cases such as the filter, by separating two areas of the PLC system (Figure 2) the filter must block the PLC signal.  
In the absence of an FRA or VNA there are two pragmatic ways of testing the PLC filters:  
* Use PLC modems connected before and after the filter and measure the frame error rate, as shown in Figure 11;
* Configure a PLC modem in transmit mode and measure the signal level with a spectrum analyzer before and after the filter, as shown in Figure 12.