esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
Recently, many researchers in the field of sentiment analysis have used a supervised machine learning algorithm as their clustering module and primary classification, such as the work in [8]. These methods classified and displayed user-created texts that contained the sentiment using n-gram features as well as the bag-of-words (BOW) technique, and they were occasionally combined [9]. The n-gram characteristics have been developed to address the shortcomings of the simple BOW model, such as the fact that it ignores grammatical structures and word order [10]. When using n-gram features, there is a significant disadvantage, particularly when n \(\geq 3\), in that the feature distance output is remarkably high dimensional. As a result, feature selection algorithms have been used extensively in recent studies [11] to overcome this disadvantage. Users' sentiments can be extracted from their text using a variety of algorithms, including SVM, Naive Bayes (NB), and artificial neural networks (ANN). Several methods, including that in [12], have demonstrated good performance in this area. When using supervised approaches, one of the challenges that can arise is that they are often slow and require a large amount of time throughout the training process. There have been numerous approaches based on the unsupervised lexicon that have been proposed to address these issues, including those in [8; 13]. These methods are quick, scalable, and simple to implement. On the other hand, they rely largely on vocabulary, which has resulted in them being less accurate when compared to their supervised counterparts [13; 14]. There have been very few researchers who have taken advantage of the advantages offered by both lexicon-based and supervised-based approaches and then merged them in a variety of ways, as cited by [14; 15]. For sentiment analysis, [PERSON] et al. [16] presented an approach that is divided into two parts at the entity level of tweets. Having a high recall rate is the first step in the process, which is achieved through the use of the supervised approach. The second phase is a high-precision lexical method that is built on the previous stage. According to [17], machine learning methods and lexicon-based sentiment analysis have been combined in a concept-based sentiment analysis model that has been presented. When compared to simply statistical methods, their method was more accurate and justified, and its ability to discern the polarity or strength of sentiment was superior to lexicon-based methods.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
###### Abstract  
Peer-to-peer (P2P) content distribution and file sharing systems aim to facilitate the dissemination of large files over unreliable networks. Network coding is a transmission technique that has captured the interest of researchers because of its ability to increase throughput and robustness of the network, and decrease the download time. In this survey paper, we extensively summarize, assess, compare, and classify the most recently used techniques to improve P2P content distribution systems performance using network coding. To the best of our knowledge, this survey is the first comprehensive survey that specifically focuses on the performance of network coding based P2P file sharing systems.  
content distribution networks; peer-to-peer computing; random linear network coding; file sharing; rarest-piece issue; information thoery
# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 2 Materials and Methods
### Detection Method Used in This Study
#### 2.2.3 FP Reduction  
A notable number of FPs emerged from the bleeding detection method, particularly within the bone, kidney, and vascular regions. This trend is consistent with previously reported findings [18]. To address this issue, we incorporated an FP reduction strategy based on multi-organ segmentation, as developed in [18].  
To prepare the CT data for organ segmentation, we first resampled the volumes to an isotropic resolution of 1.5 mm using trilinear interpolation. Each axial slice was padded with \(-\)1000 Hounsfield Units (HU) to fit a standardized matrix size of 512 \(\times\) 512 pixels. Voxel values were then linearly scaled from the native [\(-\)1000, 1000] HU window to a normalized range of [\(-\)1, 1]. A 2D U-Net [26], trained on the same anatomical structures as in our previous study [18], was employed to extract the bone, kidney, and vessel regions. Subsequently, post-processing was performed using multiple processes, such as region growing, morphological operations, and connected component analysis. The segmented result was resampled to align with the isotropic resolution employed in the bleeding detection.  
For FP reduction, we employed organ-overlap filtering, as shown in Figure 1. In this process, any predicted bounding box overlapping more than 30% with segmented organ regions was removed. In addition, predicted boxes with any dimension smaller than five voxels were discarded. Finally, non-maximum suppression (NMS) [27] with an Intersection over Union (IoU) threshold of 10% was applied to eliminate redundant bleeding candidates.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 5 Evaluation
### Results  
The results will be illustrated using the metrics presented in Section 5.1: delivery rate, overhead, delivery delay, and hop count. The two traces presented in Section 5.2 were used in order to demonstrate the performance of our opportunistic algorithms compared to the other algorithms tested. All simulations were performed using the MobEmu simulator [14] and the proposed festival mobility model. For each experiment, we performed five simulation runs with different random number generator seeds, and we show the average values in each chart. The rest of the simulation details are presented in Tables 2-4.
# Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
## 2 Mini-EUSO Experiment  
The main components of the Mini-EUSO telescope include two Fresnel lenses and a focal surface (FS). The lenses have a diameter of 25 cm with the focal distance of the optical system equal to 300 mm. The FS has a square shape with 2304 pixels. It is built of 36 multi-anode photomultiplier tubes (MAPMTs) Hamamatsu R11265-M64 each consisting of \(8\times 8\) pixels. All MAPMTs are grouped into nine so-called elementary cell (EC) units. Every EC unit has its own high-voltage system, which operates independently of the others providing necessary control of sensitivity of the respective MAPMTs. A 2-mm thick UV filter manufactured of the BG3 glass is located in front of each MAPMT. The size of one pixel equals 2.88 mm \(\times\) 2.88 mm. The point spread function (PSF) has a size of \(\sim\)1.2 pixels. Mini-EUSO has a wide field of view (FoV) of 44\({}^{\circ}\times\) 44\({}^{\circ}\) with spatial resolution (FoV of one pixel) equal to 6.3 km \(\times\) 6.3 km. From the orbit of the ISS, an area observed by the telescope exceeds 300 km \(\times\) 300 km. A detailed description of the instrument can be found in [12].  
Mini-EUSO collects data in three modes. The D1 mode has a time resolution of 2.5 \(\upmu\)s. This is called a D1 gate time unit (GTU). The D2 mode records data integrated over 128 D1 GTUs. Finally, the D3 mode operates with data integrated over 128 \(\times\) 128 D1 GTUs resulting in time resolution of 40.96 ms. To the contrary to the D1 and D2 modes, the D3 mode does not have a trigger, and its data can be considered as a series of videos with \"seasons\" corresponding to sessions of observations and \"episodes\" corresponding to night segments of the ISS orbit during a session. Each session takes around 12 h. With the orbital period of the ISS equal to 92.9 min, a typical session includes eight subsets of data taken during nocturnal segments of an orbit, with each of them taking slightly longer than 1/3 of the period. Every \"video\" has a resolution of \(48\times 48\) pixels and consists of \(T/40.96\)\(\upmu\)s frames, where \(T\) is the duration of one nocturnal segment. Observations are performed approximately twice per month through the UV-transparent window at the Zvezda module with the schedule coordinated with other experiments. Due to this, background illumination varies strongly from one session to another depending on the phase of the Moon and the season. The D3 mode allows for registering meteors and other slow phenomena taking place in the night atmosphere of Earth. In what follows, we use only data recorded during sessions 5-8 and 11-14 taken from 19 November 2019 to 1 April 2020. All artificial neural networks discussed below were trained using data of seven sessions and tested on the remaining session. This way, we checked all possible combinations of the eight sessions.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Classification Methods Used  
Experiments were carried out on product datasets taken from different stakeholders, which were fed to various classifiers like LR, RF, LR, SVM, and the MLP neural network. Each of these models will be elaborated upon in subsequent sections, providing a more in-depth understanding. The results obtained will be meticulously calculated and assessed based on measures such as Accuracy, Precision, Recall, F-measure, and the time taken to construct the model.
# A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
## 4 Experimental Results and Analysis
### Comparison Experiment
Swarms of UAVs may have enhanced performance during performing some missions where having coordination among multiple UAVs may enable broader mission coverage and provide more efficient operating performance. Moreover, the proposed method will greatly help to improve the performance of UAV swarm systems including total energy, average end-to-end delay, packets delivery ratio, and throughput. According to the analysis, the application of the lightweight model provided by this paper may reduce the total energy demand and average end-to-end delay of the UAV swarm system, while increasing the packets delivery ratio and throughput of the system. This means that the UAV swarm system may be able to achieve higher data transmission efficiency at a lower cost, thus, better performing tasks.
## 4 Experimental Methodology
### Policy Performance Evaluation  
The derived policy parameters from inverse reinforcement learning (IRL) and standard reinforcement learning (RL) were deployed into the benchmark testing environment (FrozenLake-v1) for systematic navigation performance validation. A full parameter consistency protocol was implemented: environmental dynamics (including state transition mechanisms and reward computation architecture) strictly inherited configurations from Section 4.5 comparative RL experiments. Real-time trajectory tracking modules recorded agent kinematic characteristics, enabling quantitative comparative analysis of obstacle avoidance efficiency and path optimality between both policy types.
## 5 Conclusions  
We have retrieved 3D surface deformation components by integrating multi-stacked InSAR and MAI measurements of the deformation induced by recent magma intrusion at KILauea, Hawai'i. COSMO-SkyMed X-band SAR data from ascending and descending orbits was collected before and after the period of unrest from April to May 2015 to observe uplift associated with the intrusion of magma. From the 3D measurements, we clearly identified deformation patterns and the boundary of deformed regions, enabling us to identify the center of the deformation, which is located slightly to the south of the KILauea caldera. The maximum displacement induced by this volcanic event was approximately 8.2, \(-\)13.8, and 11.6 cm in the east, north, and up directions, respectively.  
In this study, model parameters of the magma reservoir have been estimated using 3D measurement inputs. Two different magma source models, the Okada and prolate spheroid models, were employed to determine the appropriate magma model for this minor event, and the best-fit model parameters were determined using ten thousand iterative simulations. Through comparison with the model parameters in previous studies, a similar position of the magma chamber was determined, but the uncertainties were markedly diminished through the modeling of the 3D measurement inputs. The source location based on the best-fit dislocation model is approximately \(-\)155.287 degrees longitude and 19.382 degrees latitude and lies at a depth of approximately 4.25 km beneath the surface. The source location of the best-fit spheroid model is approximately \(-\)155.288 degrees longitude and 19.388 degrees latitude. Although the depth of the dislocation model is quite similar to that in the previous investigation, the best-fit estimate of the spheroid model suggests a relatively shallow depth of the magma source, approximately 2.8 km beneath the surface. The source dimensions and depth are differ somewhat from the previous report, but they exist within a reasonable range.  
This minor volcanic event is characterized by complex magma behavior during a short time period. Thus, it is not easy to define the characteristics of the magma source, and a certain risk of oversimplification exists in the model parameter estimation. However, we expect that measuring precise 3D displacements and magma model determinations will provide guidance as a first attempt for this volcanic event. If we update the geometry and the conditions of the magma source using 3D-based modeling, the results will be helpful in better understanding of complex magmatic systems.
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 4 Experiments and Analysis
### Static Steering Test
An obvious problem can be observed in both Figures 9 and 11, that the synchronization error between the left and right wheel steering angle is not controlled and reduced well in both case 1 and case 2. This is because the adopted MS control strategy does not consider the interaction between rear wheels and external uncertainty disturbance. In particular, the error can reach 0.665 deg at the moment of the steering direction changes in case 1. Then, by comparing the results shown in Figures 10 and 12, it can be seen that the tracking performance in case 3 is basically the same as in case 2, since the same lower control method (LADRC) is used. By comparing the results in Figures 11 and 13, it can be seen that case 3, which adopts the  
Figure 11: The synchronization errors between left and right wheels in case 2. Figure 9: The synchronization errors between left and right wheels in case 1.  
hierarchical synchronization control strategy proposed in this paper, has better synchronization control accuracy. The maximum synchronization error angle is less than 0.186 deg. Table 2 is the root mean square comparison table under three cases according to Equations (20)-(22). In order to show the differences more intuitively, Table 2 is drawn into a histogram in Figure 14. From Figure 14, we can get the following conclusion: (1) comparing case 1 and case 2, it can be seen that in the case of without hierarchical synchronization control strategy, the synchronization error can be reduced to a certain extent by improving the tracking control accuracy. However, it is still unable to achieve a better suppression effect on the external uncertain disturbance; (2) comparing case 2 and case 3, on the basis of the LADRC tracking angle controller, the hierarchical synchronization control strategy based on the dynamic model of the virtual rear axle steering mechanism is introduced to further reduce the synchronization error of ARIS system, and slightly reduce the tracking error at the same time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Index** & **Case 1** & **Case 2** & **Case 3** \\ \hline _max_\(e_{t}\) [_deg_] & 0.665 & 0.485 & 0.483 \\ _mean absolute_\(e_{t}\) [_deg_] & 0.202 & 0.137 & 0.136 \\ _max_\(e_{s}\) [_deg_] & 0.514 & 0.210 & 0.186 \\ _mean absolute_\(e_{t}\) [_deg_] & 0.149 & 0.054 & 0.039 \\ _RSME\({}_{t}\)_ & 0.249 & 0.187 & 0.166 \\ _RSME\({}_{s}\)_ & 0.194 & 0.095 & 0.049 \\ _RSME\({}_{c}\)_ & 0.216 & 0.106 & 0.096 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Control performance in three cases. Figure 12: The steering angle of rear axle wheel profiles and tracking errors in case 3. (**a**) The steering angle of left rear wheel profiles; (**b**) the tracking error of left rear wheel profiles; (**c**) the steering angle of right rear wheel profiles; (**d**) the tracking error of right rear wheel profiles. Figure 13: The synchronization errors between left and right wheels in case 3.
## 3 Analysis of Optical OFDM for Underwater Communications
### Modulation Index Optimization  
The modulation index, \(\beta\), in DCO-, ACO-, and U-OFDM can be optimized to improve the SINR. Since \(\beta\) controls the scale of the OFDM signals, a larger \(\beta\) can provide a higher signal power. However,  
Figure 8: Block diagram of the unipolar (U)-OFDM transmitter [21].  
the larger scale of the OFDM signal can introduce more clipping distortion, which is caused by the high peak to average power ratio of the OFDM signal and the peak transmitted power constraint of the LED. Therefore, the modulation index needs to be optimized to trade off the signal power and the clipping distortion. To achieve a high data rate, the modulation constellation size can be adaptively adjusted based on the SINR for a given \(\beta\).
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Defect Detection Techniques Based on Ultrasonic Waves
#### 2.3.1 Conventional Ultrasonic Techniques  
If these techniques are employed, the railroad is scanned by means of ultrasonic beams: defects are detected by measuring the reflected or scattered energy. The presence of a defect, along with its location and severity, is inferred from the amplitude of the resulting reflections and their arrival times [23]. The employed ultrasonic transducers are contained within a liquid-filled tire; this allows for improving signal quality by reducing the acoustic mismatch between rail steel and air. For the same reason, water is sprayed between the wheel containing the ultrasonic probes and rails [4]. Although ultrasonic testing is capable of inspecting the whole railhead [38], it has the following limitations: (a) limited car speed in ultrasonic inspection [16] (in practice, the achievable test speeds can be as low as 15 km/h [4] and, consequently, inspection has to be accomplished outside the operation period of commercial trains [1]); (b) _shallow crack shadowing_[13; 16] (small shallow cracks can shade more severe and deeper cracks by reflecting ultrasonic beams [1]); (c) _false defect detection_[1] (this slows down inspection operations).  
The ultrasonic testing vehicle \"us1-Galileo\" employed by the Italian railway infrastructure manager, _Rete Ferroviaria Italiana_ (RFI), is shown in Figure 9.
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 2 Materials and Methods  
### Receiver Antenna Phase Center Corrections (PCCs)  
Consistent with the convention of IGS regarding the ANTEX definition of GNSS receiver antenna corrections [12], as illustrated in Figure 1, the total PCC value is decomposed into the phase center offset (PCO) vector and the frequency- and direction-dependent phase center variation (PCV) [10; 11; 12; 17; 18; 19; 20; 27; 28; 29; 30; 32]. The well-known receiver antenna PCC function that connects PCC, PCO, and PCV is:  
\[PCC\left(\alpha^{i},z^{i}\right)=-\mathrm{e}^{\mathrm{T}}\left(\alpha^{i},z^{i }\right)\cdot\mathrm{PCO}+PCV\left(\alpha^{i},z^{i}\right)+r \tag{1}\]  
where \(\mathrm{e}\left(\alpha^{i},z^{i}\right)\) is the line-of-sight unit vector, \(\alpha^{i}\) and \(z^{i}\) are the azimuth and zenith angle of the satellite in the antenna frame (AF), and \(r\) is a constant component that is uniform in all directions, arising from the relative character of GNSS observations [11; 17; 32]. The PCO vector components and tabulated PCV values are stored in an ANTEX file.  
The PCCs, i.e., the PCO and PCVs, are defined within a three-dimensional (3D), left-handed coordinate system fixed to the receiver antenna (antenna frame--AF). The origin of the system is the antenna reference point (ARP), with the \(z\)-axis aligned along the antenna vertical symmetry axis. The \(x\)-axis is perpendicular to the \(z\)-axis and aligned with the antenna's north reference point (NRP), while the system's \(y\)-axis is defined as perpendicular to both the \(x\)- and \(z\)-axes, completing the left-handed system. In the field, during a survey, the \(x\)-axis is north oriented, and the \(xy\)-coordinate plane is aligned with the local horizon [17; 32; 33; 34; 35].  
Because PCCs can be converted to any PCO vector and PCV set, as can be seen from Equation (1), the standard approach to address this rank deficiency is to fix the PCV at zenith to zero, commonly referred to as the zero-zenith constraint, given by [5; 17; 32; 36]:  
\[PCV\left(\alpha^{i},z^{i}=0^{\circ}\right)=0. \tag{2}\]  
Therefore, when comparing different calibration results, the analysis must be conducted exclusively at the PCC level. Independent analysis at the PCO/PCV level is not meaningful.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 5 Experimental Results
### Concurrent Wake-Up Protocol  
To verify the protocol described in Section 4.1, we enhanced the T-ROME protocol [6] by the concurrent wake-up scheme described in Section 4.1 and implemented it on three wireless nodes to verify its performance. The nodes were placed similarly to the scenario depicted in Figure 1. Node-2 could receive wake-up messages from Node-1, but was not able to receive them from Node-3. Node-2 had data to be sent to Node-3.  
Figure 22 visualizes the states (send or receive) of the three nodes. Node-2 started to send a wake-up call (WUC) to Node-3, which did not respond as it was not in wake-up range. Then, Node-2 woke up Node-1 and sent it a concurrent wake-up request, including the address of Node-3. Node-1 acknowledged (ACK) the reception of the concurrent wake-up request (REQ), and based on this acknowledgment, both nodes (1 and 2) sent a concurrent wake-up call to Node-3. Finally, Node-3 verified the wake-up address that was included in the wake-up call and started the handshaking procedure, as introduced in [32], followed by data communication from Node-2 to Node-3.  
Figure 21: Number of successfully-received wake ups over distance between sender and receiver for single and concurrent wake-ups.