esa-sceva/satcom-chunk-collection huggingface dataset:
# CMAG: A Mission to Study and Monitor the Inner Corona Magnetic Field
## 2 Mission Concept
### Mission Operations Baseline and Spacecraft Concept  
CMAG would be launched in a stacked configuration, piggybacking on the occulter spacecraft. It will be commissioned during the initial stages of the transfer to the Lagrangian point. After arrival, formation flying commissioning and calibration will take place. The CMAG operational phase will comprise three stages (see Figure 4): Science Mode (SM), Stand-By Mode (SBM), and Parking Mode (PM). During SM, CMAG will position the instrument aperture at the necessary location within the occulter shadow and perform station-keeping with high accuracy, using the highest precision elements in the AOCS and metrology chain, and a propulsion system. As the occulter limits the solar panel's power, the shadow time will be limited to the energy storage capacity. Preliminary estimates suggest a duration of approximately 2.5 h until battery depletion.  
SBM is needed to optimize mission resources when not performing fine position control. The spacecraft will exit the shadow region to charge the battery while remaining within the coarse metrology's operational range to ensure a swift transition back to fine positioning when needed. This mode also allows for propellant saving since the positioning requirements are much less stringent. The current recharging time estimation is seven hours. A 9.5 h SBM period will then allow for a 12 h observation cycle, making it easier to synchronize CMAG's observations with the ground segment. PM ensures safety against collision for extended periods, although the Lagrangian points already ensure safe and simple operations without collision risk. PM allows CMAG a long-term, passive relative orbit for commissioning, fault recovery, or maintenance. PM can be entered when commanded from the ground. SM and SBM will be fixed and defined by design, autonomously managed by the CMAG spacecraft without operator intervention. The proposed design baselines two observational cycles per day, representing the worst-case scenario, with each cycle providing 2.5 h of scientific operations.
## 3 Results and Discussion
### Preparation and Characterization of the Material
#### 3.1.5 Thermogravimetric Analysis  
All materials were evaluated with thermogravimetric analysis (Figure 4). The results are summarized in Table 2.  
For bare TiO\({}_{2}\), one stage in the range of 26-384 \({}^{\circ}\)C with 2.51% mass loss was noted, and the mass loss can be associated with the evaporation of surface water. For the composite material, the thermal decomposition of TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) reveals two stages. The first stage begins at 26 \({}^{\circ}\)C and ends at 187 \({}^{\circ}\)C with a mass loss of 2.24%, whereas the second stage appears in the range of 188-635 \({}^{\circ}\)C with a resulting mass loss of 1.45%. The first stage can be associated with the evaporation of water residue from the surface of TiO\({}_{2}\). The mass changes in the second stage result from the condensation of surface hydroxyl groups within TiO\({}_{2}\)[20], similarly to bare TiO\({}_{2}\), but in the composite material, this process is split between the two stages. Based on literature data, Fe\({}_{3}\)O\({}_{4}\) is known to not undergo any decomposition and only lose moisture [21]. While exposed to air, any mass changes coming from the oxidation process of Fe\({}_{3}\)O\({}_{4}\) into Fe\({}_{2}\)O\({}_{3}\) overlap with the changes associated with the evaporation of surface water.
# Energy Harvesting over Rician Fading Channel:
## 3 System Performance
### Achievable Throughput  
With the outage probability obtained from Theorem 1, the achievable throughput of the considered system can be given by \[\tau_{i}=(1-P_{out\_i}).\frac{R}{2}.(1-\alpha),i\in\{1,2\}. \tag{16}\]
## 3 System Design
### Multicast Member Join/Leave Procedure  
Multicast members including source and receiver are all in access networks. When a legitimate receiver wants to join a multicast group, it needs to send a join message with multicast group address (MSI) and matched token to its ASR. Then the ASR transfers this request to MMC to check if this receiver has subscribed this multicast group. If yes, the ASR then checks if there is already other group members in this access network. Only if there is no member joined such multicast group in the access network, ASR will begin to send join message with multicast address, AID and token to MMC. If it has members joined such multicast group, ASR will not send join request to MMC. Figure 5 shows the general member join/leave procedure.  
When a multicast source leaves the group, the multicast delivery tree will be suppressed if there are no other sources in this group.  
Figure 4: Registration procedure.  
In CCSM, the multicast receiver cannot send join/leave request to MMC directly for that they are located in different name spaces. To forward this join/leave request message, the ASR which multicast receiver attached, has to forward this request for multicast receiver. In this way, MMC maintains the multicast group membership information, and calculates SPT of the core network according to multicast members.
# Intelligent Tennis Robot Based on a Deep Neural Network
## 2 Tennis Ball Detection with Improved YOLOv3 Algorithm  
With the development of computer vision, a large number of excellent research results have been achieved in the field of object recognition. These results not only have certain theoretical significance, but also have extraordinary practical value, which can provide us with great convenience. In particular, various detection algorithms based on deep learning have replaced the traditional object recognition methods.  
Conventional object recognition methods include using a gradient vector histogram (HOG) [2] for pedestrians in a video, using various animal and vehicle detection methods in still images, face recognition using Haar features [3]. These types of traditional identification methods have low recognition rates. They usually take more time to complete the entire recognition process. Additionally, the generalization ability is poor. The emergence of a series of requirements, such as the diversification of recognition objects, diversity of recognition angles, and recognition of background complexity in identification tasks, has brought more challenges to the research of object recognition. Deep learning focuses on studying how to automatically extract multi-layer feature representations from data and uses a series of nonlinear transformations to extract features from raw data in a data-driven manner, avoiding the drawbacks of manual feature extraction. The convolutional neural network (CNN) [4] has been widely and successfully applied to image classification tasks, making it the gold standard for image classification.  
Methods based on the convolution neural network can be divided into two categories: one is based on region nomination and the other is based on regression. The regional nomination-based approach is represented by the R-CNN series: R-CNN [5], SPP-Net [6], fast R-CNN [7], and faster R-CNN [8]. Although various improvements have been made, it still adopts the step-by-step detection strategy of first extracting candidate frames and then classifying based on candidate frames. The FPS of the faster R-CNN only reaches seven frames per second (f/s), which is far from reaching real-time requirements. YOLO is a regression-based detection algorithm [9]. It first uses the regression method to directly predict the bounding box coordinates and classification of an object from an image. The detection speed reaches 45 f/s. However, due to the single scale and proportion of the candidate frame selection, the accuracy is reduced when the speed is increased. [PERSON] successively proposed the YOLOv2 [10] and YOLOv3 [11] detection algorithms, among which YOLOv3 has a better detection effect. It uses the depth residual network to extract image features and achieves multi-scale prediction, which is implemented on the COCO data set. Additionally, the mAP achieves an effect of 57.9%. In the target detection field, YOLOv3 can ensure accuracy and a detection rate of 51 ms, achieving better detection results.
## 5 Algorithm
### Q-Value and Rewards  
As is usual in routing, the RL problem is posed as distance minimization instead of reward maximization. Furthermore, as usual in these cases, we fix the discount factor \(\gamma=1\). The resulting Q-update function is shown in Equation (7).  
\[Q_{c}(d,x)\leftarrow(1-\alpha)Q_{c}(d,x)+\alpha(R+\min_{y\in N_{x}}Q_{x}(d,y)) \tag{7}\]  
The definition of the immediate cost \(R\) itself is flexible. For example, Table 2 presents the immediate cost of edges under several distance metrics. The _hop count_ metric minimizes the number of transmissions, while the _latency_ attempts to deliver the data as fast as possible. The _power consumption_ cost can be seen as a generalization of _hop count_, where the transmission cost is a function _Pwr_ of the distance between the nodes, the radio environment, message size, etc.  
Notice that the immediate cost \(R\) used to update a Q-value is computed on every encounter or ONM graph edge. As a result, the instances of evaluation of a state-action pair and the values of \(R\) are non-deterministic.  
In RL algorithms, the definition of when a message is considered delivered is under the control of the reward function, which is an arbitrary function. It can accommodate concepts such as multicast (deliver to a set of nodes), anycast (deliver to any of a set of nodes), or generalizations such as subscription-based delivery (deliver to nodes that request messages with specific properties).
# PV-Powered Charging Station for Electric Vehicles: Power Management with Integrated V2G
## 3 Power Flow Management: G2V and V2G Algorithm  
According to users' wishes and the actual situation of EVs batteries, EVs can be divided into two types, only accept to charge, i.e., grid-to-vehicle (G2V), and only accept to discharge, i.e., V2G. The stored energy of the EV battery is \(E\), the maximum energy that EVs can discharge is \(E_{discharge}\), and the maximum energy that EVs can be charged is \(E_{charge}\), which can be calculated by Equations (2) and (3), respectively.  
\[\left\{\begin{array}{ll}E_{charge}=E\cdot(SOC_{EV\_final\_lim}-SOC_{EV\_in} )&\text{if} SOC_{EV\_in}<SOC_{EV\_final\_lim}\\ 0&\text{if} SOC_{EV\_in}=SOC_{EV\_final\_lim}\\ \end{array}\right. \tag{2}\]  
\[\left\{\begin{array}{ll}E_{discharge}=E\cdot(SOC_{EV\_in}-SOC_{EV\_final\_ lim})&\text{if} SOC_{EV\_in}>SOC_{EV\_final\_lim}\\ 0&\text{if} SOC_{EV\_in}=SOC_{EV\_final\_lim}\\ \end{array}\right. \tag{3}\]  
This section presents two algorithms for power flow management of G2V and V2G, which are demonstrated within an EV charging station to simultaneously support multiple EVs.
## 2 System Model
Then, the downlink channel matrix \(\mathbf{H}_{k,u,\ell}\) can be well approximated by  
\[\mathbf{H}_{k,u,\ell}\overset{M\rightarrow\infty}{=}\mathbf{U}_{k,u,\ell} \mathbf{G}_{k,u,\ell}\mathbf{V}^{H}, \tag{4}\]  
Figure 1: Coordinated multicast transmission for multi-cell massive MIMO. where \(\mathbf{V}\) is a deterministic matrix which depends on the array structure equipped at the BS. It is worth noting that the approximated channel model in (4) has been shown to be with very high accuracy in typical transmission scenarios and widely adopted in previous works [23; 31; 32]. In the following, we will adopt the massive MIMO channel model in (4).
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 5 Information Receiver for TDS-OFDM-NOMA  
The interference of the TDS-OFDM system has different features, as the OFDM data block is unknown, so it is difficult to get perfect symbols' detection over the UWA channel  
Figure 5: Architecture for the proposed energy harvesting receiver in IoUTs nodes based on TDS-OFDM communication system.  
as shown in Figure 4. In the TDS-OFDM, mitigation of the IBI represents a big challenge even with perfect channel knowledge. Removing the IBI of the TDS-OFDM data blocks into the training sequence is an uneasy task, as the receiver does not know the transmitted data of the OFDM blocks. On the contrary, the receiver knows the transmitted PN sequence; hence, by knowing the communication channel, removing the IBI of the PN sequence from the OFDM data block is an easy task.  
The channel estimation task was a hard job to be estimated based on the corrupted PN sequence; hence many schemes have been proposed to solve this issue. The DPN-OFDM [21], ZPN-OFDM [22], and channel estimation based on the free IBI region of the received PN sequence were proposed to solve the UWA channel estimation problem. The DPN-OFDM is used twice as the guard interval to avoid the IBI of the OFDM blocks on the received PN sequence. Unfortunately, duplicated guard intervals waste the spectral and energy efficiency of the communication system; these cannot be accepted in underwater communication, as the energy and recharging capability of the underwater node is unaccepted. ZPN-OFDM was proposed to solve the energy efficiency problem of the DPN-OFDM, but, unfortunately, it fails to solve the wasted spectral efficiency problem. Recently, the IBI-free region of the received PN sequence was proposed to be used for channel estimation and equalization of the UWA TDS-OFDM communication system. In the same line, we will use the IBI-free region for compressive-sensing-based channel estimation. The small IBI free region is existing in the received PN training sequence in practical applications due to the system margin design, and the worst case happens when the guard interval length is equal to or less than the UWA channel length. To deal with that, we assume that the channel length is always less than the guard interval and that there is a free IBI region in the received PN sequence.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Closed-Loop Optimization  
All the proposed architectures and the CMS framework promote closed-loop optimization by integrating system feedback and telemetry. This enables autonomous monitoring of deviations from the original plan and dynamic rescheduling when necessary. Such an approach significantly enhances system autonomy, eliminating the need for constant supervision, as the system can self-correct in response to newly available information. This paradigm directly addresses the challenges of uncertainty and adaptive scheduling by enabling rapid responses to unpredictable events and deviations. Furthermore, it ensures continuous adaptation by incorporating updated information, allowing the system to refine and optimize real-time schedules without a human operator's intervention.
## 2 Proposed Methodology
### The Purpose of New Indicators
#### 2.1.1 Data Preparation  
_(i) Required Data._ The calculation of the three indicators necessitates both spatial and temporal high-resolution images following regular monitoring. These images may be orthophotographs or very high-resolution satellite images. These images have to be georeferenced in a metric coordinate reference system such as UTM or any other national metric coordinate reference system.  
_(ii) Digitizing of the Active Channels._ It is important to follow some basic rules when digitizing the active channels, which should be manually digitized as lines. The lines should be digitized at the middle of the active channel as it appears on the georeferenced images. In the case of large rivers (>30 m), it may be better to digitize rivers as surface and then calculate the centerline of this surface using GIS tools (such as <<geometric attributes>> in QGIS). The digitizing has to be completed from upstream to downstream. In the case the river is divided into multiple channels, such as braided rivers, it is better to digitize each channel individually. At this point, it is possible to digitize the active channels at different dates if the data are available. With this temporal dimension, it will be possible to follow the planform fluvial dynamics through time and the evolution of the hydrological risk. After the digitizing of the active channels, the lines should be converted to points. Indeed, the three indicators we develop require working with points and not directly with lines. We propose to extract a point for each meter along the active channel. For example, the  
Figure 4: The three proposed indicators. Indicator 1 in a perspective of fluvial dynamics study is the distance from active channel to floodplain margins (**A**); Indicator 1 in a perspective of risk management is the distance from active channel to the edges of the stake (**B**); Indicator 2 is the distance from the stake to the active channel (**C**); Indicator 3 is the diachronic distance (**D**).  
plugin 'interpolate point on line' in QGIS allows us to extract regularly spaced points along a polyline in a very simple way.  
_(iii) Digitizing of the Floodplain Items._ It is possible to digitize any items of interest (e.g., structures; riparian forest; urbanized, recreation or agricultural areas). In the case we are interested in fluvial dynamics, we may digitize the boundaries of the floodplain. These boundaries should only be the margins both of the right and left banks of the floodplain. In the case we are interested in risk management, we can digitize the infrastructure we want to protect. This infrastructure may be digitized as a point, a line or a polygon. However, if the infrastructure is first digitized as a line or a polygon, it should then be converted to points using the QGIS tool 'polygon to line', for instance. Then, regular points should be extracted from this line. We propose to extract a point every meter along the line or at the edge of the polygon of the infrastructure.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Time Reversal Technique  
Time reversal technique is a commonly used sound source localization method in complex structures. It is closely related to artificial neural network computing. The steps of using time reversal technology for sound source localization are as follows: (1) collecting a training dataset by mechanically impacting multiple points on the structure and measuring the corresponding impulse response function (IRF) through one or more surface mount sensors; (2) recording the actual impact event signal on the sensor; and (3) identifying the IRF in the training dataset, which provides the maximum correlation with the IRF generated by the actual impact event, in order to determine the corresponding mechanical impact point.  
The advantage of time reversal technology is that it can be used for positioning in complex structures, but the disadvantage is that the positioning process is complex and requires high computational requirements.  
[PERSON] et al. [60] and [PERSON] et al. [61] proposed a method for sound source localization in inhomogeneous structures called the time inversion technique. [PERSON] and [PERSON] [62] also proposed a similar method for source localization by inverting the impact response function and avoiding wave mechanics analysis.  
[PERSON] et al. [63] proposed an impact imaging method based on the time inversion technique and validated it for an aircraft wing box with bolt holes and reinforcements. [PERSON] et al. [64] used a passive transducer and a time inversion technique for detecting the impact source location in reverberant complex composite structures in real time.  
[PERSON] et al. [65] proposed a new impact imaging technique using the time inversion concept with surface mounted piezoelectric transducers and scanning laser Doppler vibrometer to pinpoint the location of impact events in a complex structure.  
[PERSON] et al. [66] utilized the virtual time reversal (VTR) technique to locate a continuous acoustic source in a cylindrical shell structure and an imaging technique to extract the wavelet transform coefficients of the AE signals at a specific frequency to construct a transient abrupt feature of the signals for further processing. They also provided an algorithm to determine the shortest path between the AE source and a sensor.
## 2 Results and Discussion
### Total Phenolic Amounts and Targeted Phenolic Analysis
Most of the previous studies reported strong correlation between total phenols and antioxidant capacity [23, 37, 39, 40]. HPLC-DAD was used for targeted analysis of the polar compounds in _Oblica_, _Lastovka_ and _Levantinka_ VOOs. The most abundant secoiridoids of the samples (Table 5) were dialdehyde form of elenolic acid linked to hydroxytyrosol or tyrosol (\(p\)-HPEA) respectively assigned as 3,4-DHPEA-EDA, \(p\)-HPEA-EDA and 3,4-HPEA-EA. Statistically significant differences among phenolic levels were observed for the oils. _Levantinka_ and _Oblica_ VOOs were characterized statistically by highest amount of \(p\)-HPEA-EDA. _Lastovka_ VOOs contained the highest amount of 3,4-HPEA-EA. 3,4-DHPEA-EDA, the dialdehyde form of decarboxymethylelenolic acid linked to hydroxytyrosol, was statistically different in all tested oils with the highest value in _Oblica_ oil (85.0 mg/kg) and lowest value in _Lastovka_ oil (34.4 mg/kg). Flavones, such as apigenin and luteolin, showed in total the highest value in _Oblica_ oil, while the lowest value was measured in _Levantinka_ oil. Lignans, such as pinoresinol, were estimated only in _Oblica_ oil. These results are similar to those reported by several authors for other monovarietal VOOs [31, 40, 41]. \begin{table}
\begin{tabular}{c c c} \hline
**VOO Sample** & **Total Phenols (mg GAE/kg)** & **DPPH (mmol TEAC/kg)** \\ \hline _Oblica_ & \(212.21\pm 7.71\)a & \(0.91\pm 0.09\)a \\ _Lastovka_ & \(206.09\pm 13.81\)a & \(0.78\pm 0.19\)a \\ _Levantinka_ & \(144.60\pm 2.59\)b & \(0.55\pm 0.05\)b \\ \hline \end{tabular} All values are expressed as mean of triplicate determinations \(\pm\) SD; the mean values within each column labeled with different letters are significantly different (Tukey’s test, \(p<0.05\)).  
\end{table} 
Table 4: Total phenolic amounts and DPPH assay for antioxidant activities of the hydrophilic fractions of _Oblica_, _Lastovka_ and _Levantinka_ VOOs.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 2 Fundamentals of Array Processing
### The Beamforming Principle  
Beamforming is nothing but the linear combination of the signal samples coming from each of the antennas in the receiver array, with the aim of concentrating the array beam toward a specific DoA and filtering out any other contribution coming from a different DoA. This is performed by applying a set of spatial filtering coefficients or _weights_ to the signal in (1) perceived by the array. This is  
\[y(n)=\mathbf{w}^{H}\mathbf{x}(n) \tag{4}\]  
where \(\mathbf{w}\in\mathbb{C}^{L\times 1}\) is the weight vector, and \(y(n)\) is the signal at the beamformer output. Note that \(y(n)\) is a scalar magnitude, and it can thus be understood as the output of an equivalent _smart_ single antenna. The problem, then, boils down to determining the coefficients in \(\mathbf{w}\) that satisfy a given optimization criterion. This is where the beamforming techniques come into play, as explained next.