esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 4 Background on MAC Protocols
### Evaluation Metrics
#### 4.1.4 Energy Consumption  
From the point of view of MAC protocols, energy consumption is directly affected by the length of time in which data is being sent and received; to a lesser degree, energy consumption is also affected by the amount of processing required by the protocol. To evaluate the energy consumption, the length of time the transceiver is in transmission, reception, and idle modes should be compared. The consumption on each state depends specifically on the model of transmitter/receiver that is being used and the chosen MAC protocol. For example, in SigFox, the current consumption is 11 mA in reception mode and 125 mA in transmission mode [38]. The peak current consumption is about 32 mA and a range from 120 mA to 300 mA, in the cases of LoRa and NB-IoT, respectively [39].  
In general scenarios, the main energy limitation is found in the terminal nodes, since in most cases the receiving station has a virtually infinite energy source (e.g., a base station in a cellular network, a WiFi access point, etc.). In our study scenario, the case is different since CubeSats may also have energy limitations. Nevertheless, it is expected that energy limitation in the sensor nodes will be considerably higher than in the spacecraft.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 6 Conclusions  
This paper addressed the challenge of inter-cell interference in parallel multi-task Air-FL systems, where each cell independently trains a distinct model while simultaneously performing over-the-air aggregation. We analyzed how aggregation errors impact the local optimality gap and proposed a joint optimization framework for device transmit power and denoising factors aimed at minimizing the cumulative optimality gap across all cells. To characterize the trade-offs in multi-cell performance, we employed Pareto boundary theory and designed a centralized optimization scheme that serves as a performance upper bound. Building upon this, we introduced a distributed power control strategy based on IT, which decouples the globally coupled problem into locally solvable subproblems. This approach allows each cell to independently adjust its transmit power using only local CSI. To enhance computational efficiency, we derived closed-form solutions for the subproblems through Lagrangian duality theory and implemented a dynamic IT update mechanism to effectively approach the Pareto boundary. Simulation results demonstrated that the proposed distributed scheme outperforms benchmark methods in terms of training convergence speed, cross-cell performance balance, and test accuracy. Future work will extend our framework to larger-scale and more heterogeneous datasets such as CIFAR-10, which will enable validation under non-convex learning objectives and more realistic visual data distributions. This will further demonstrate the scalability and robustness of our distributed scheme.  
Conceptualization, C.T. and J.Y.; methodology, C.T. and J.Y.; software, C.T. and D.H.; validation, C.T., J.Y. and D.H.; formal analysis, C.T. and J.Y.; investigation, C.T. and J.Y.; resources, J.Y.; data curation, C.T. and D.H.; writing--original draft preparation, C.T. and J.Y.; writing--review and editing, C.T. and J.Y.; visualization, J.Y. and D.H.; supervision, J.Y.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are contained within the article.  
Figure 6: Learning performance on MNIST dataset versus communication rounds.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Middlewares
#### 4.2.2 Cloud-Based  
Cloud-based middleware for IoD service facilitates diverse applications to integrate their operations into the network architecture. The most commonly used robotics application middleware is the robot operating system (ROS) which utilizes the benefits of its elastic resources by offloading computation and processing to the cloud for efficient resource usage. This process is named cloud robotics [1]. The cloud-based middleware provides reliable communication between the ground network and UAV [35]. The cloud-based middleware delivers a very rapid response to the requested service. It further benefits the IoD to actively communicate with the other networks.
# A Survey on Bias in Deep NLP
## 2 Defining Bias
### Definition of Bias at Semantic Level  
Description logics [24] provides a complete set of elements for knowledge base structure, population and manipulation. It is, actually, the ontology formalization acquired by the Semantic Web and its high level ontological terminology OWL (Web Ontology Language) [25]. An OWL ontology has the following components: \(<C,P,I,L>\)_classes_\(C\), _properties_\(P\), _individuals_\(I\) and _literal values_\(L\). For the sake of simplicity, we will summarize saying that individuals are instances of classes, instances are interrelated by properties and literals are associated to individuals by properties. For example, [PERSON] is an individual which is of \"type\" Woman (belongs to class Woman). She works in a hospital (works-in would be a property). She has a job as a doctor (has-job would be another property). Woman is a class that can be defined through the expression has-gender \"female\" (this is called _class expression_ in OWL), where has-gender is a property and \"female\" is a literal value. This simple knowledge can be graphically plotted as in Figure 1.  
Figure 1: A simple example in Web Ontology Language (OWL).  
Now it is time to borrow some terminology from fairness measures in machine learning and some elements from OWL.  
Definition 2: A stereotyped knowledge is represented by the tuple \(<C,P,I,L,p_{p},P_{s}>\) where \(C\) is the set of classes, \(P\) is the set of properties, \(I\) is the set of individuals, \(L\) the set of literals, \(p_{p}\in P\) is the protected property and \(P_{s}\subset P\) is the set of stereotyped properties. This expresses that groups of individuals resulting from different values of the protected property \(p_{p}\) could exhibit inequality in the distribution of values for stereotyped properties \(P_{s}\).  
In the example displayed in Figure 1, we could consider has-gender as the protected property \(p_{p}\) and \(P_{s}=\{\texttt{has-job}\}\) as the set of stereotyped properties, so the tuple would be \(<C,P,I,L,\texttt{has-gender},\{\texttt{has-job}\}>\). According to Definition 2, this means that values for property has-job could be not equally distributed over individuals of both classes defined by has-gender. For example, we may find that for individuals with has-gender \"female\", it is more frequent to observe has-job nursery than has-job doctor, while the situation is the inverse for the class with individuals holding has-gender \"male\". It is important to note that a _stereotyped knowledge_ is only defining a potential bias, i.e., a bias we are sensible to.
## 3 Experimental Results
maximum output power measured). Low-noise current sources were used to avoid spectral broadening of the laser linewidth due to current induced fluctuations. Technical noise is also another important factor that induces remarkable spectral distortion, especially at low frequency values, as it evolves as 1/f. As a consequence, the measured laser linewidth is often fitted with a pseudo-Voigt profile that accounts for the Lorentzian-like shape of the emitted signal and the Gaussian-like shape of the technical noise [22]. As can be observed, the increase in the laser's driving current induces a consistent linewidth narrowing down to a minimum measured value of \(\
u=259\) KHz for an injected driving current of \(\mathrm{I_{Laser}}=110\) mA. It is worth remarking that all measurements were performed under a negative optical feedback regime to reduce the laser linewidth spectral shape as much as possible. This regime, also called detuned loading, introduces an amplitude-phase coupling of the lasing field that enhances the stability of the laser frequency. To attain such regime, the laser optical frequency of the solitary mode should be slightly detuned (redshifted) with respect to the frequency-dependent mirror reflectivity spectrum, \(\mathrm{r_{eff}}(\omega)\)[42]. Nonetheless, this method often provides an underestimation of the laser linewidth as it is very difficult to completely isolate the device under test from residual noise sources in the setup. Thus, the value of 259 KHz should be considered as an upper bound for the optical linewidth. Alternatively, one can use a direct frequency noise measurement to finely measure the laser linewidth [43]. Despite that, the measured Lorentzian linewidth with the self-delayed heterodyne method is in agreement with previously reported widely tunable lasers containing similar extended Si cavities, proving the good spectral purity of the co-integrated device. Thus, we can discard a remarkable laser linewidth broadening effect caused by an eventual ASE feedback from the SOA booster into the widely tunable laser. The co-integrated widely tunable laser-SOA booster has a maximum power consumption of 420 mW when operating under the most power demanding conditions, i.e., with \(\mathrm{I_{laser}}=\mathrm{I_{SOA}}=110\) mA and with \(\mathrm{I_{heater1}}=\mathrm{I_{heater2}}=30\) mA. This value is reasonably low compared to other reported values on single III-V/Si devices [40; 44]. Once more, the origin of such low power consumption is attributed to the high compactness of our widely tunable laser-SOA booster device.
## 3 The Proposed Method
### HI Extraction and RUL Prediction Process  
#### 3.2.1 Construction Method of Degradation Labels  
After obtaining the vibration data of the bearings, it is necessary to divide them into training and testing sets. Since the obtained original data do not have corresponding labels, it is necessary to construct the degradation labels corresponding to the vibration data. There are mainly two conventional methods for constructing degradation labels: linear degradation and segmental smoothing, and Figure 4 shows the differences between the two methods.  
The degenerate label construction equation of Figure 4a can be expressed as  
\[y_{i}=\frac{B-t_{i}}{B} \tag{17}\]  
Figure 3: The structure of MSCAE.  
where \(B\) denotes the total life of the bearing, \(t_{i}\) denotes the current time the bearing has been in operation, and \(y_{i}\) denotes the current degradation level of the bearing. The degenerate label construction equation of Figure 4b can be expressed as:  
\[y_{i}=\left\{\begin{array}{ccc}1&if&t_{i}\leq t_{h}\\ 1-\frac{t_{i}-t_{h}}{B-t_{h}}&&else\end{array}\right. \tag{18}\]  
where \(t_{h}\) indicates the degradation starting threshold; when \(t_{i}\) is less than or equal to \(t_{h}\), it is determined that the bearing has not started to degrade and the degradation label is always 1. When \(t_{h}\) exceeds the threshold, it starts to show a linear degradation trend.  
The first two methods of describing bearing degradation trends shown in Figure 4 have their own limitations compared to the true degradation of the bearing. The true bearing degradation does not exhibit a linear characteristic but rather degrades faster and faster as the operating time of the bearing increases. Therefore, the linear degradation method does not satisfy this operating characteristic. For the segmented smoothing method, the threshold \(t_{h}\) at the degradation onset moment is usually determined artificially. However, \(t_{h}\) is usually not the same for different operating conditions, and there are different bearings \(t_{h}\) for the same operating conditions, which limits the application of the segmented smoothing method. The literature [31] proposed a method for constructing a degenerate  
Figure 4: Three degradation label construction methods. (**a**) Linear degradation method. (**b**) Piecewise smoothing method. (**c**) Quadratic function method.  
trend based on a quadratic function, as shown in Figure 4c, and the specific expression can be expressed as:  
\[y_{i}=1-\frac{t_{i}^{2}}{B^{2}} \tag{19}\]  
The quadratic function-based method overcomes the shortcomings of the above two methods, and its constructed labels are more satisfying to the real degradation of the bearings. With the increase of the operation time, the degradation of the bearings is gradually accelerated, which is shown in Figure 4c as the slope of the curve gradually increases. Moreover, it is a convenient and effective method without artificially specifying the degradation threshold, so this paper adopts the quadratic function method to construct the degradation labels of bearings.
## 2 Detection Algorithms
### Learned Thresholded Subspace Projection (LTSP)
#### 2.6.4 Computational Considerations  
By using only the first singular mode and avoiding full subspace estimation, LTSP significantly reduces computational burden compared to MUSIC or full-rank SVD techniques. Its structure--comprising rank-1 SVD, diagonal averaging, and FFT--is highly compatiblewith high-level synthesis (HLS) and fixed-point hardware pipelines. This makes LTSP well-suited for deployment in real-time radar systems.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 2 Material Characteristic
### Sheet Resistance
#### 2.2.2 Mesh Structure  
The structure of the metal mesh makes it possible to make optically transparent conductive films for antennas. The principle of the mesh technique is to create holes in a structure to let light pass. Transparency and sheet resistance change with the surface variation of these holes. Figure 3 represents the honeycomb structure chosen in this work. In general, the sheet resistance of the structure of the network is calculated by the equation [29]:  
\[R_{s(mesh)}=\frac{q}{e}\times R_{s} \tag{2}\]  
where \(R_{s}=0.047\,\Omega/sq\) is the sheet resistance of the copper film (calculated by Equation (1) with \(\sigma=5.8\times 10^{7}\) S/m and \(t=370\) nm), \(q\) is the pitch value, and \(e\) is the metal strip.  
Since the honeycomb structure was used, the leaf resistance was calculated in the \(OX\) and \(OY\) axes, compared to the direction of the current propagation in the meshed antenna. The resistors along each axis are given by the following relations:  
\[R_{s(mesh)}=\frac{q_{x}}{e}\times R_{s}\,\text{and}\,\,R_{s(mesh)}=\frac{q_{ y}}{e}\times R_{s} \tag{3}\]  
The values of the chosen parameters are: \(q_{x}=0.32\,\text{mm}\), \(q_{y}=0.5\,\text{mm}\), \(e=0.05\,\text{mm}\), and \(R_{s}=0.047\,\Omega/sq\) (Equation (1)). Consequently, a value of approximately \(R_{s}=0.2\,\Omega/sq\) was obtained for the mesh. Note that this value is greater than the resistance of the copper film, but the use of these techniques increases the transparency of the material [25; 29].  
Figure 3: The geometry of the honeycomb mesh.
###### Abstract  
Forest three-dimensional (3-D) structure, in the vertical dimension, consists of at least two components, including overstory and a forest background matrix (i.e., shrubs, grass, and bare earth). Quantitatively characterizing the proportions of forest sunlit (i.e., sunlit overstory and forest background) and shaded (i.e., shaded overstory and forest background) components is a crucial step in simulating the spatial variations of bidirectional reflectance distribution function (BRDF) of a forest canopy. By developing a Voxel-based sorest sunlit and shaded (VFSS) approach driven by aerial laser scanning data (ALS), we investigated the spatial variations of the forest sunlit and shaded components in a heterogeneous urban forest park (Washington Park Arboretum) with abundant tree species and a homogeneous natural forest area (Panther Creek). Meanwhile, we validated the forest canopy directional reflectance at both solar principal and perpendicular planes at the plot level. Moreover, we explored the effects of ALS data characteristics and forest stand conditions on the estimation accuracy of forest sunlit and shaded components. Our results show that (1) ALS data effectively stratify overstory and forest background with the accuracy decreasing from 87% to 65% as forest densities increase; (2) the root mean square errors (RMSEs) between the modeled- and ALS-based proportions of forest sunlit and shaded components range from 5.8% to 11.1% affected by forest densities; and (3) the scan angles and flight directions have apparent effects on the estimation accuracy of forest sunlit and shaded components. This work provides a solid foundation to investigate the spatial variations of directional forest canopy reflectance with a high spatial resolution of 1 m.  
**Keywords:** ALS; forest stratification; sunlit and shaded components; BRDF +
Footnote †: journal: Remote Sensing, 2020, 12, 1071; doi:10.3390/rs12071071
## 4 Performance Evaluation
### QoS Parameters
#### 4.2.2 Average End-to-End Delay  
In scenarios of 40 and 45 vehicles, the average end-to-end reaches a low value than other scenarios for the three protocols that have approximately the same values as shown in Figure 13. A high number of vehicles allows GeoQoE-Vanet to find the best paths to the destination with acceptable end-to-end delay. In scenarios of 10, 20 and 30 vehicles, GeoQoE-Vanet has an average end-to-end delay value greater than the other protocols. This is the result of following long paths to avoid congested vehicles and to decrease the frame loss.  
In all scenarios, GPSR has the smallest mean value of end-to-end delay since it always selects the closest vehicles to the destination which does not ensure that the data reach the destination. Similarly, based only on distance, GPSR-2P uses two paths when sending data and thus increases the end-to-end delay without guarantee that the data reaches the destination.  
Therefore, the GeoQoE-Vanet protocol offers better end-user QoE than GPSR and GPSR-2P in all scenarios. The simulation also shows that GPSR performs better than GPSR-2P. This is justified by the duplicate video data packets being dropped by the receiver vehicle and are not used in the final video reconstruction.  
All the three protocols suffer from major quality degradation in sparse environments, but GeoQoE-Vanet still provides better results compared to GPSR and GPSR-2P. In light of these results, further improvements of the proposed protocol are required to better adapt to sparse environments. The store and forward mechanism may be a promising candidate to explore. The estimation of a vehicle's QoE in GeoQoE-Vanet is based on delay, jitter and packet loss ratio. To enhance this estimation, many other factors may be included. The effects of these new factors on the end-user QoE of the received video must be thoroughly studied. Furthermore, many other QoE's formulas exist in the litterature and are interesting to use in testing GeoQoE-Vanet for a more accurate evaluation. In this  
Figure 12: Frame loss percentage of all scenarios.  
paper, GeoQoE-Vanet has been tested in an urban scenario. It is important to consider, in the future, a more in-depth evaluation in a highway scenario subject to different constraints and challenges.
# Shape Optimum Design by Basis Vector Method Considering Partial Shape Dependence
## 4 Basis Vector Method and Partial Shape Dependency Relation
### Proportional Relationship of Distance between Arbitrary Three Points  
Consider the \(x\) coordinates of three arbitrary nodes \(a\), \(b\), \(c\) inside the structure as an example. Here, between the three nodes \(a\), \(b\), \(c\) in the original shape and each basic shape, it is assumed that there is a proportional relationship concerning the same proportional coefficient, respectively.  
\[\frac{x_{a0}-x_{c0}}{x_{c0}-x_{b0}}=k\;\frac{x_{a1}-x_{c1}}{x_{c1}-x_{b1}}=k\; \frac{x_{a2}-x_{c2}}{x_{c2}-x_{b2}}=k \tag{19}\]  
These formulas are rewritten to obtain Equation (20),  
\[\begin{array}{l}x_{c0}=\frac{x_{a0}+x_{b0}}{1+k}\;(\text{Original shape})\\ x_{c1}=\frac{x_{b1}+x_{b1}}{1+k}\;(\text{Base shape 1})\\ x_{c2}=\frac{x_{a2}+x_{b2}}{1+k}\;(\text{Base shape 2})\end{array} \tag{20}\]  
Also, the \(x\) coordinate of the three nodes \(a\), \(b\), and \(c\) in the newly transformed shape are as follows:  
\[\begin{array}{l}x_{a}=w_{0}x_{a0}+w_{1}x_{a1}+w_{2}x_{a2}\\ x_{b}=w_{0}x_{b0}+w_{1}x_{b1}+w_{2}x_{b2}\\ x_{c}=w_{0}x_{c0}+w_{1}x_{c1}+w_{2}x_{c2}\end{array} \tag{21}\]  
Substituting Equation (20) into the third equation of Equation (21) and rearranging it, Equation (22) is obtained.  
\[x_{c}=\frac{1}{1+k}[(w_{0}x_{a0}+w_{1}x_{a1}+w_{2}x_{a2})+k(w_{0}x_{b0}+w_{1}x _{b1}+w_{b2})] \tag{22}\]  
Substituting the first and the second equations of Equation (21) into Equation (22), Equation (23) is obtained.  
\[x_{c}=\frac{x_{a}+kx_{b}}{1+k},\;\frac{x_{a}-x_{c}}{x_{c}-x_{b}}=k \tag{23}\]  
This expression is consistent with Equation (20). That is, if the same proportional relationship is set between the original shape and the node coordinates in each basic shape, it shows that the same proportional relationship is always maintained regardless of the weighting coefficient between the nodes in the newly transformed shape.  
During the actual shape optimization, if some shapes and their proportional relationship are set in the same way using Equation (23), then even if it is transformed into the combination of arbitrary weight coefficients in the process of shape optimization, the proportional relationship will always remain unchanged.
# Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission
### System Design  
Figure 3 is an integration network for spaceborne SAR and data transmission based on FBMC. The satellite system includes a sequence controller, an OQAM modulator, an algorithm design, an FBMC modulator, imaging processing, a matched filtering of the SAR signals and data transmission spaceborne transmitter, and an echo SAR spaceborne receiver. The terminal system includes an FBMC demodulator and an image reconstruction from the data transmission receiver. The design scheme for the integrated waveform of the spaceborne SAR in spotlight mode and data transmission is as follows:  
Figure 3: Integration network for spaceborne SAR and data transmission.  
Figure 2: SAR echo reception and data transmission STAR sequence.  
Step 1: The ground user submits a SAR imaging task request to the satellite via a mobile terminal.  
Step 2: Upon receiving the user request, the sequence controller assigns a specific time slot for the SAR imaging task and configures the FBMC signal transmission plan based on the current task queue and resource availability.  
Step 3: The satellite transmits SAR pulses, modulated by FBMC, toward the imaging target. The target reflects the SAR signals, generating echo signals that travel back through the echo channel.  
Step 4: During the echo window period, the phased array ceases the transmission of SAR pulses, and the SAR satellite receives the echo signals using matched filtering.  
Step 5: The SAR image data are extracted through image processing, followed by compression, encoding, and OQAM modulation, converting the information into data transmission signals intended for the ground terminal.  
Step 6: The data transmission signal and the SAR signal for the next cycle undergo algorithmic power allocation and subcarrier channel assignment. They are then modulated using FBMC and transmitted through the wireless channel to the ground terminal.  
Step 7: The mobile terminal receives the data transmission signals through its receiving antenna, demodulates them using FBMC, and reconstructs the image to obtain the required SAR image, thus completing the imaging task.