esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Governing Equations and Numerical Implementations
### Viscous Flux Treatments
#### 3.4.2 Full Viscous Approach  
As the most accurate model, the full viscous approach comprises all the stream-wise (\(\xi\)), circumferential (\(\zeta\)), and normal (\(\eta\)) viscous flux terms. The resultant governing equation is as per Equation (15). Similar approaches have been employed in numerous previous studies on cavitation flows. [PERSON] et al. [28] applied the incompressible scheme to a flat-plate flow, a lid-driven cavity flow, and the impingement of a liquid drop on a solid wall. [PERSON] and [PERSON] [29] developed a pressure-based compressible solver and applied it to cavitation flow over hemispherical and blunt bodies. Based on the prediction results, some of the empirical constants of the standard k-\(\varepsilon\) turbulent model were modified to predict recirculation zones more accurately. [PERSON] and [PERSON] [19] also tested three-dimensional incompressible multi-phase solvers on natural and ventilated cavitation flows, over axisymmetric bodies with hemispherical and blunt head forms, by varying computational conditions, such as cavitation number and angle of attack.
## 5 Simulation Results and Analysis
### Waveform Error Analysis  
Normalized waveform error is a performance metric that can be used to describe how closely the desired waveforms match the synthesized waveforms. Moreover, it can also give convergence performance, as the faster it decreases with the increasing number of iterations, the better the performance of the algorithm. Mathematically, this metric, denoted here as \(\eta_{i}\), is defined as  
\[\eta_{i}=\frac{\left\|\mathbf{X}_{i}\right\|_{F}^{2}}{\left\|\mathbf{X}\right\| _{F}^{2}} \tag{38}\]  
where  
\[\mathbf{X}_{i}=\mathbf{A}^{H}(\Theta)\mathbf{S}_{i}. \tag{39}\]  
Figure 5: The transmit beampattern formed by the DFRC system with 32 antenna elements.  
Figure 7 shows the normalized waveform error plotted against the iterations.  
Figure 6: The transmit beampattern formed by the systems.  
Figure 7: Normalized waveform error.  
Table 3 provides a comparison of different methods for the normalized waveform error.  
Figure 8 shows how the CM constraint is met. The plots in the figure show the maximum and the minimum modulus samples of \(\mathbf{S}\), and the desired uni-modulus samples, at each iteration. As can be seen in the plots, the maximum and the minimum values settle at 1 at about the 10 th iteration.
## 2 Theoretical Background
### Transaction Cost Economics (TCE)
Transactions in highly uncertain environments are more prone to run into unanticipated contingencies that require renegotiation and monitoring than the transactions conducted in more stable environments [53; 54]. Therefore, market governance is more difficult in highly uncertain environments than in low-uncertainty environments. Second, small numbers bargaining captures the degree to which a decision maker has alternative sources of getting what he or she wants [53; 54]. Thus, fewer possible suppliers or collaborators will constrain the decision maker's possible strategic options. Finally, empirical evidence suggests that asset specificity is one of the most essential variables affecting the choice of governance mode [53; 54]. Asset specificity refers to the level of customization and transferability associated with the transaction [53; 55; 56; 57]. It comes in different forms; for example, human asset specificity, site specificity, or physical asset specificity. It can also differ in terms of degree from highly customized and idiosyncratic assets to elements that are highly standardized, and therefore, non-specific in nature. Consequently, low asset specificity indicates that transactions are relatively frequent, and therefore, transactions are most likely to be efficiently governed by markets. Empirical evidence suggests that high asset specificity leads to hierarchical governance [57]. When asset specificity is high, specific assets are costly to re-deploy in alternative uses, and it is, therefore, likely that the hierarchical form of governance will be selected [53]. With few exceptions, most of the TCE related research primarily has concentrated on examining vertical integration issues, interorganizational relations, and the validity of TCE's assumptions. However, the TCE has been applied to examine different economic theories of organizational boundariesin the truck industries [25; 27; 59] and agency theory [28; 59; 60]. To our knowledge, no empirical studies have used TCE to explain the differences between the organizational buying versus individual buying decisions when facing the varied sustainable technology attributes in the Chinese context. In addition to the organizational buying behavior theory, this study adopts the TCE perspective to provide additional reasons and assumptions behind the differences between carriers and owner-operators in choosing sustainable technology attributes.
# Mitigation of Electro Magnetic Interference by Using C-Shaped Composite Cylindrical Device
## 3 Results and Discussion
### Monitoring of Electromagnetic Fields
The effective results showed the degaussing rate of \(-5.06\) dB when the electromagnetic field was shielded at the \(-7\) cm position, and that of \(+5.05\) dB when the enhanced electromagnetic field was shielded at the \(+7\) cm position beyond experimental errors, as shown in Figure 5 and Table 3. In Figures 2 and 5, it is worthy to mention the magnetic field distribution as a function of the distance was not symmetrical, because the single opening on the high-permeability material directed the emission of the ELF from line currents. The C-shaped permalloy tube did collect electromagnetic fields and emitted them from the opening space. As the electromagnetic field decayed rapidly with the distance, the reinforcement of permalloy was not obvious due to the low electromagnetic field as well. The C-shaped cylindrical device demonstrated here was only a one-dimensional device. It is easy to assemble C-shaped cylindrical devices in the three-dimensional space for more advanced applications. Figure 4: The measured electromagnetic fields of the high-permeability material. Figure 5: The measured electromagnetic fields in the C-shape composite device.
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 5 Results
### Path Loss
#### 5.2.1 Path Loss Variation  
We use Equation (1) to calculate the path loss from measured RSSI and SNR data. As seen in Figures 9 and 10, each SF presents values with different ranges, reflecting in the path loss values, particularly when the range of SNR values includes positive values above 10 dB and negative values below \(-5\) dB. Figure 11 illustrates that SF9 exhibits the highest path loss values, reaching a maximum of 142.3 dB, while SF8 shows a minimum of 80 dB. The obstruction caused by vegetation between the end node and the gateway contributed to a higher path loss value. Despite this obstruction, the SF9 values can cover a greater distance, encompassing the entire established route, due to having more airtime.  
Maximum values were 130 dB, 132.5 dB, and 142.3 dB; minimum values were 84 dB, 80 dB, and 85 dB for SF7, SF8, and SF9, respectively. The average values were 104.6 dB, 105.9 dB, and 107.9 dB for SF7, SF8 and SF9, respectively. The standard deviation values (\(\sigma\)) are 10.10, 10.43, and 11.12 for SF7, SF8, and SF9.
# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 5 Conclusions  
This paper presented a method for indoor building modeling starting from MLS; the core of the methodology is the transformation of the indoor reconstruction into the labeling problem of structural cells in a 2D floor plan. The input data of the presented method are point cloud and scanner trajectory. The method was designed to extract watertight 2D floor plans and reconstruct in a plausible way occlusion both in 2D floor plans and in 3D walls. The obtained model can also include semantic information enhancing in this way the capacity of the adopted methodology to classify recognized component such as walls, doors, ceilings, and windows. The method proved to be robust against noise and occlusions. In the experiments carried out, the final models presented accuracies comparable with the quality of the input point cloud.  
A major limitation to the full optimization of the method is due to the Manhattan-World domain assumption. Because of such an assumption, walls which build up the floor plan are assumed to be rectangular and intersect orthogonally. However, more complex room geometries such as the curved surfaces of triangular walls (as in the case of lofts) may occur in scanning indoor environments. In addition, also \"pending walls\" are assumed to rely on the Manhattan-World assumption. The methodology also relies on the assumption of horizontal and planar ceilings and floors. In order to reduce model assumptions, in our future work we are planning to reconstruct indoor scenes also taking into consideration cylindrical walls and spherical or freeform ceilings by using a hybrid indoor model representation combining mesh and geometrical primitives. Since the method is performed off-line, it cannot be used for real time localization  
Figure 15: Corridor dataset: in the middle of the corridor due to the presence of people it presents a highly cluttered area (**a**), point clouds colored with points-to-model unsigned distances for walls (**b**), and histogram of residuals between the reconstructed model and point cloud for walls (**c**).  
**Author Contributions:** Conceptualization, M.P. and L.D.-V.; Methodology, M.P.; Data curation, L.D.-V., M.S. coordinated and reviewed the manuscript.  
**Acknowledgments:** The second author would like to give thanks to the Xunta de Galicia for the financial support given through human resources grant no ED 481B 426 2016/079-0.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background
### Full-Duplex Jamming Practical Issues
#### 2.5.3 Decoding Strategies for [PERSON]  
If [PERSON] transmits a time-correlated jamming signal, it may be possible for [PERSON] to learn the signal, and then attempt to remove it prior to decoding. The use of signalling mechanisms for coordinated jamming (15) (as previously referred) can prevent [PERSON] from capturing a clean version of the jamming signal. The use of independent additive white Gaussian noise as the jamming source further prevents [PERSON] from being able to gain useful information that would allow here to make use of adaptive interference algorithms.  
In this work, we assume the jamming signal generated/transmitted by [PERSON] is a white Gaussian noise signal (with uncorrelated samples) in the transmission band. For this reason, any receiver in the vicinity of [PERSON] (including himself) must treat the jamming as an additional source of AWGN noise. Only [PERSON] is therefore capable of interference cancellation techniques. An optimal strategy by the eavesdropper is then to simply maximize the received signal-to-noise ratio (SNR) by way of matched filtering.
## 3 Experimental Results and Discussion
### Evaluation on the Single Unobserved Video Subsequence with Random Length Scenarios
#### 3.2.2 Evaluation Results When the Temporal Gap Is at the End of the Video Sequences  
In this evaluation scenario, we aim at evaluating the performance of the proposed approach in recognizing the four fall-related activities when the unobserved video subsequence is happening at the end of the video sequence. This is similar to the prediction scenario illustrated in Figure 1b. In particular, we have investigated the scenario in which the temporal gap is mainly affecting the video frames belonging to the last sub-activity and a subset of the second sub-activity in each of the four fall-related activities. Hence, our assumption is that the majority of the observed video frames belong to the first sub-activity. To achieve this goal, we have set the temporal gap interval to \([\gamma:T]\), where \(\gamma\) is a random integer in the range \((\frac{4T}{10},T)\). Table 5 provides the recognition results of the four fall-related activities expressed in terms of the precision, recall and F1-measure as an evaluation metric. In addition, Table 5 provides the average length of the temporal gaps introduced in the testing video sequences for each activity over the 10 repetitions of the LOVSO-CV procedure. The proposed approach achieved an average recognition accuracy of 73.3% in recognizing the four fall-related activities from partially-observed video sequences.  
Recognizing human activities from partially-observed video sequences with the unobserved video subsequence at the end of the video is considered challenging. The reason behind that is the absence of some key sub-activities that can distinguish different activities from each other. For example, when the sub-activity that represents the falling pose is unobserved, distinguishing between sitting and falling from sitting activities becomes challenging, especially when the duration of the falling from sitting pose sub-activity is short. Similarly, the absent of the fall pose increases the difficulty in distinguishing between the walking and falling from standing activities. This explain the reduction in the average recognition accuracy between this evaluation scenario and the previously described scenario in Section 3.2.1.
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 3 The State-of-the-Art in Intelligent Transportation Systems: An Overview
In [36], the authors suggested a hierarchical SDN-based architecture for vehicular networks and accordingly developed a communication protocol to address the lack of connection/coordination from the centralized SDN controller. Evaluation of the same was carried out on a real urban mobility scenario. In [37], an edge-up SDN-based design has been envisaged for vehicular networks in contrast to the traditional cloud-down design typically conceived for mobile ad hoc networks. Emphasis has been particularly placed on the latency control in order to support a diverse range of vehicular applications. In [38], recent research advances of SDN-based vehicular networks have been investigated, and key requirements for ensuring an efficient network resource management were outlined. A taxonomy was also presented in terms of the salient characteristics of software-defined vehicular networks, i.e., radio access technologies, applications and services, network architectural components, opportunities, system components and operational modes. In [39], an architecture supporting the cohesion of both SDN and named data networking (NDN) has been presented to fetch the requisite content within the vehicular networks. It thus assigns a name to the content (instead of the device, i.e., vehicle or infrastructure), and a pull-based communication approach is then used to retrieve the requisite content, as and when desired. In [40], a collaborative vehicular edge computing architecture has been envisaged for facilitating collaboration between the edge computing anchors to ensure scalable and efficacious vehicular applications and services. An abridged (self-contained) summary of the research challenges surrounding next-generation ITS architectures is depicted in Figure 3.  
Figure 3: Research challenges surrounding next-generation ITS architectures. Although a number of architectures have been recently proposed in the research literature for ensuring an enhanced network resource management in VANETs, most of them did not account for the unique VANET-associated features and characteristics in their designs, i.e., frequent changes in network topology owing to the highly dynamic behaviour of vehicles in the data plane, extremely large and distributed network, stringent delay constraints, the need for efficient and smooth handovers, etc. Moreover, a number of these architectures primarily rely on accumulating the centralized intelligence in a _single_ centralized SDN controller, which undoubtedly provides a global view of the entire underlying network, but may also become a _single point of network failure_ in case of an unfortunate event. Therefore, localized intelligence in addition to centralized intelligence is extremely indispensable for realizing the true potential of SDN-based HetVNets.
## 5 Emerging Paradigms and Future Directions
### Federated Physics Learning  
Privacy-preserving distributed learning frameworks address critical barriers in multi-institutional collaborations. The Federated PINN (Fed-PINN) architecture employs homomorphic encryption and Shamir's secret sharing to aggregate gradients securely:  
\[\mathcal{L}_{\text{enc}}=\text{HE}\Bigg{(}\sum_{i=1}^{N}\mathcal{L}_{i}\Bigg{)} \tag{13}\]where \(\mathcal{L}_{\text{enc}}\) represents the encoded loss, which is the result of the encoding process, HE denotes the encoding operation or a function applied to the sum of the losses, \(\mathcal{L}_{i}\) represents the individual loss associated with each term in the sum. Ensuring zero patient data leakage in multi-center medical trials. In a cardiac modeling study across five hospitals, Fed-PINN achieved FFRt prediction errors within 8% of centralized training while maintaining differential privacy budgets (\(\epsilon<0.1\)) [17]. However, encryption overheads inflate training times by 5-10x, and hardware heterogeneity complicates gradient synchronization.  
Adaptive compression techniques, such as top-\(k\) gradient sparsification [39], reduce communication costs by 70% without sacrificing accuracy. Meanwhile, federated meta-learning frameworks could enable cross-domain knowledge transfer--for instance, leveraging aerodynamics data to bootstrap scarce geothermal reservoir models.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 3 Results and Discussion  
### Spectral Analysis  
Mean absorbance and second derivative spectra of the ROI extracted for the three groups of almonds (sweet, bitter and mixtures) analysed using HSI are shown in Figure 1, and as can be seen in this figure, the different groups of samples displayed similar spectral features. The main absorbance band region can be seen at around 1150-1200 nm, related to the second overtone of -CH stretching, associated with unsaturated fatty acids [23]. Furthermore, near to 1430 nm, it can be seen another important band, which corresponds to the first overtone of the -NH stretching [24].  
Moreover, in the second derivative spectra (Figure 1b), it can also be appreciated another peak at around 1128 nm that might be attributed to C-H links of aromatic compounds [23]. Although the spectral pattern is similar for the three groups, the wavelengths that appeared to have the greatest weight to differentiate between the sweet and non-sweet almonds are those around 1128, 1200 and 1350-1400 nm.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 4 GIM Derivation from Multi-Source Data in Modip Latitude
### Comparison of Gim with CODE  
The GIMs obtained from CODE were derived by an SH function but used ground-based GNSS data and geomagnetic latitude [7]. The difference maps between GACI with CODE are shown in Figure 10, where the black line represents the true magnetic equator. The GIMs based on global basis expansions, such as the SH expansion, are more likely to be affected by the unbalanced amount of GNSS observations. The uneven IPP distribution in space distorts the inverted SH coefficients and thus is expected to affect the performance [14]. From Figure 4, the uneven distribution of IGS receivers will lead to the lack of ground-based observation data in the oceanic areas, especially in the Southern Hemisphere. Compared with the GIMs from CODE, the main difference occurs near the position of the EIA, where the TEC values and the difference between modip and geomagnetic latitude are the largest, ranging from 19.42 to \(-\)25.75 TECU. The difference at 20:00 UT is larger than that at 8:00 UT, which can be attributed to the proximity of the EIA and SAMA. At oceanic regions in the Southern Hemisphere, where the ground-based observations are limited, the GIMs from GACI have a larger values about 5 TECU generally, which can result from the addition of multi-source data.  
To have a clear picture of the TEC changes, especially in the areas of the EIA, SAMA and the Pacific Ocean, which lack observation data, three averaged latitudinal profiles at 90\({}^{\circ}\) E, 45\({}^{\circ}\) W and 150\({}^{\circ}\) W and three averaged VTEC longitudinal profiles at 20\({}^{\circ}\) N, 30\({}^{\circ}\) S and 60\({}^{\circ}\) S were extracted, the results of which are plotted in Figure 11. In addition, the profiles from GIMs of the IRI-2016 model were computed at the same epoch longitude. Similar to the conclusion we obtained earlier, the differences among GACI and CODE are more obvious in the EA and middle- and high-latitude areas of the Southern Hemisphere, where the number of observations is limited. The mean difference among GACI and CODE is less  
Figure 9: GIMs calculated with GACI (DOY 266, 2020).  
than 1.88 TECU, but has an obvious discrepancy up to 14.35 TECU in the oceanic areas of the Southern Hemisphere. GIMs obtained from the IRI-2016 of an empirical ionosphere model has a lower precision compared with GIMs from GACI and CODE, where the maximum difference value can reach about 40 TECU. However, the addition of data from the IRI model is still needed to provide ionospheric information for areas where the number of observations is limited to decrease the residual by making data evenly distributed and reduce the occurrence possibility of the unreasonable values.
###### Abstract  
This paper proposes a feedback-controlled adaptive method for detecting signals in diffusion-based molecular communication (MC) systems. Signal detection via a receiver nanomachine is a critical challenge for the exchange of information in MC systems. Incorrect estimations or small errors in signal detection can lead to high data detection errors. Existing methods for improving detection performance require high time costs or computational complexity. This paper proposes a simple and practical method that enables receiver nanomachines to automatically estimate signal detection times according to the measured molecular concentrations and weighted feedback errors. The proposed method adjusts the detection time even when the initial parameter values of the system are unknown to the receiver nanomachines. Simulations were performed to evaluate the bit error rate performance of the proposed and existing methods in terms of different data rates, transmission distances, and estimation error lengths under different initial conditions. The simulation results reveal that the implementation of the proposed method is simpler and demonstrates superior performance compared with that of existing methods.  
molecular communication; signal detection; diffusion +
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