esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors
### Proposal of a New SSM
#### 3.1.2 Lateral Risk Assessment (Lane-Changing Behavior)  
The lateral risk assessment considers the critical vehicle during lane-changing, as well as the safe lane-changing distance and available lane-changing space.  
1 Determining Available Lane-Changing Space and Critical Vehicle  
Available lane-changing space is used to determine whether there is enough space on the target lane to perform a safe lane change and to identify the critical vehicle (leading or following vehicle).  
The calculation of front available space (\(S_{\text{front}}\)) is given by Equation (8):  
\[S_{\text{front}}=x_{\text{front}}\left(t\right)-x_{n}(t)-L_{n} \tag{8}\]  
The calculation of rear available space (\(S_{\text{rear}}\)) is given by Equation (9):  
\[S_{\text{rear}}=x_{n}(t)-x_{\text{rear}}\left(t\right)-L_{\text{rear}} \tag{9}\]  
where  
\(x_{\text{front}}\left(t\right),x_{\text{rear}}\left(t\right)\): The longitudinal positions of the front and rear vehicles on the target lane at time \(t\).  
\(L_{n}\), \(L_{\text{rear}}\) : The lengths of the subject vehicle and the rear vehicle on the target lane.  
Critical Vehicle Selection  
If \(S_{\text{front}}\ \leq S_{\text{rear}}\), select the front vehicle as the critical vehicle \(k\).  
Otherwise, select the rear vehicle as the critical vehicle \(k\).  
Determining the Lateral Safe Distance  
The Lateral Safe Distance \(D_{\text{safe}}\ (t)\) ensures that the vehicle has sufficient lateral space during a lane change to avoid collision with the critical vehicle. The calculation of \(D_{\text{safe}}\ (t)\) is given by Equation (10):  
\[D_{\text{safe}}\ (t)=\frac{W_{\text{lane}}\ -W_{n}}{2}-D_{\text{margin}} \tag{10}\]  
where  
\(W_{\text{lane}}\) : The width of the target lane which can be inferred from the dataset.  
\(W_{n}\): The width of the subject vehicle.  
\(D_{\text{margin}}\): Lateral safety margin, considering driving errors and vehicle deviations, usually a fixed value (0.2 m).  
Lateral Risk Assessment  
The calculation of \(R_{\text{lat}}\ (t)\) is given by Equation (11).  
\[R_{\text{lat}}\ (t)=\gamma\frac{\max(D_{\text{safe}}\ (t)-\Delta y_{k}(t),0)}{D_{ \text{safe}}\ (t)}+(1-\gamma)\frac{\max\Bigl{(}\Delta v_{y,nk}(t),0\Bigr{)}}{v_{y,n}(t)+\epsilon} \tag{11}\]  
where  
\(\Delta y_{k}(t)=|y_{n}(t)-y_{k}(t)|\): The lateral distance between the subject vehicle and the critical vehicle.  
\(y_{n}(t),y_{k}(t)\): The lateral positions of the subject vehicle and the critical vehicle.  
\(\Delta v_{y,nk}(t)=\Bigl{|}v_{y,n}(t)-v_{y,k}(t)\Bigr{|}\): The lateral speed difference between the subject vehicle and the critical vehicle.  
\(v_{y,n}(t),v_{y,k}(t)\): The lateral speeds of the subject vehicle and the critical vehicle which can be calculated by differentiating the \(y\) coordinate over time.  
\(\gamma\): Weight parameter ranging from \([0,1]\), balancing the influence of lateral spacing difference and speed difference.
## 3 Results and Discussion
### Oxidation Status
The potential difference between control and treated samples was not observed, maybe due to the short storage period, where TVB-N production usually requires more storage days. [PERSON] and [PERSON] [31] found that the TVB-N of rainbow trout (_Oncorhynchus mylks_) fillets during frozen storage was reduced by the effect of sumac extract. They suggested that this sumac extract inhibited the decomposition of macromolecular components that occur via spoilage bacteria and endogenous enzymes. The results of this study show that elevation of the percentage of sumac treatment had no significant effect on the TVB-N values in carp fillets. Contrary to our results in this regard, [PERSON] et al. [29] found that the level of TVN formation in beef meat reduced with an increasing concentration of sumac extract through the refrigerated storage period by reducing the primary counts of the common spoilage bacteria. Free fatty acids are produced as a result of the enzymatic hydrolysis process of esterified lipids. A pro-oxidant effect of free fatty acids on lipid matter is possible through the catalytic impact of the carboxyl group on the formation of free radicals during the process of decomposition of hydroperoxides [33; 34]. Free fatty acids in the investigated samples at 24 h of storage increased with sumac application, significantly (\(p<0.05\)) in T2 and T3. Free fatty acids in T2 and T3 were not affected by the time factor during the storage period, as opposed to T0 and T1, which were significantly (\(p<0.05\)) reduced. The increase in free fatty acid formation is attributed to hydrolysis of phospholipids and triglycerides resulting from lipase and phospholipase processes [35]. In contrast to the finding of [PERSON] and [PERSON] [31], our results (T1, T2 and T3 on day 1 of storage) indicated that the elevation in sumac percentages accelerated lipid hydrolysis and thereby free fatty acid accumulation. However, the lipolysis effect of sumac was attributed to its containing lipases that are capable of hydrolysing the fat in meat [36].
## 3 Results and Discussions
### Connection to Cyclic Variations and Combustion Performance  
Figure 12 displays at the top the in-cylinder pressure and the burn durations of all 19 tested firing cycles. Four cycles were selected to display the cyclic variations in flow and flame behavior. These four cycles represent different combustion performance at the same operating condition. The peak pressure of cycle A is the highest because the first initial combustion (short combustion period). On the other hand, combustion pressure of cycle B is the lowest with long combustion period. In short, cycle A represents the best performing cycle, cycle B the worst performing cycles while cycles C and D are closely matching to the ensembled average. Also shown in Figure 12 is the instantaneous flow at the ignition timing of these four cycles and their flame kernel at TDC (15\({}^{\circ}\) CA aIT) and 10\({}^{\circ}\) CA aTDC (25\({}^{\circ}\) CA aIT) together with the flame centroids movement over the whole investigation period. The plot of the derived flame growth speeds for the 4 cycles is also shown. All cycles have the same main tumble direction (clockwise) and the flames in cycles A, B, and C develop and propagate with the tumble direction, which can be easily observed when the centroids are plotted. Cycle D, while having the same tumble direction, the tumble center is located very far toward the squish area side of the intake and that make the flow velocity very minimal near the spark plug. As a result, the flame propagates in quite a symmetrical fashion across the spark plug axis. The centroids of this flame actually move along the spark plug axis, interestingly. Clear pattern in the flame speed in correlation with burn duration is not well observed but it is hinted that the early higher flame speed of cycle A does correspond to a faster 10% burnt duration. The variations in flame development path does not seem to have any correlation with the burnt duration.  
Figure 11: Velocity of the in-cylinder mixture flow at many different points as the flame front approaches and passes through those selected locations.  
Upon further analysis of the flow, the turbulence intensity near the spark plug appears to have stronger correlation with the burnt durations, with the velocity of the flow on the spark plug side that is downstream of the tumble flow results in a correlation factor of 0.56. This seems to agree with previous research about flow across the spark plug [2].
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 4 Colloidal Semiconductor
### HgTe and HgSe Nanocistals  
In the infrared region, Hg nanocomposites have been widely explored to produce active materials in both planar and vertical PD. In particular, HgTe colloidal nanocrystals have been investigated in PD at telecom wavelength [141; 142; 143] and for solar applications [144; 145] for their photoconduction properties [146].  
In the NIR, typical applications are in communications technologies, biological imaging, and night imaging. Among possible colloidal nanomaterials to be used in NIR, HgTe NCs have reached enough maturity. In those materials, interband transitions assure IR absorption, see Figure 8 from [23].  
Figure 8: (**A–C**) Scheme for interband, intraband, and plasmonic transitions, respectively, in HgX NCs. (**D**) Absorption spectra HgX NCs (reprinted from [23]).  
For wavelengths up to 1.7 \(\upmu\)m, performances of InGaAs based PD are again higher. However, Hg based nanocomposites are promising as extending the range of wavelengths and reducing cost [147]. In fact, if in PD based on InGaAs is possible to achieve extremely low dark current at room temperature (<20 fA), in colloidal NCs based devices currently reported, such dark current are not so low. However, colloidal NCs based PDs have typically faster detection times, then they are usually employed to flame detection [148], right imaging [149] and biological imaging [150] for which detection is mandatory sub-ms sampling times.  
In this case, the using of decorated NPs, i.e., coupling the absorption of the HgX NCs with plasmonic structures, show improvements in detectivity, as done in [151; 152; 153]. In particular, coupling HgTe with gold nanorods, the absorption is increased respect to thin layers of colloidal NCs, and a three time detectivity is reported respect the undecorated ones.  
However, the use of colloidal NCs has some challenges that need to be addressed to bring the IR NCs based PDs to industrial production. Main challenges are about materials properties, as well as the toxicity and the trial of large synthesis (>10 g). Moreover, the not well known band structure of new NCs implies difficulties to design device with optimized ohmic contacts and generally band alignments; the HgX NCs experience rapid oxidation in air, then either the NCs surface have to be chemically stabilized in air, or the material have to be processed in glow box and then encapsulated into a protective layer.
###### Abstract  
The quick and effective detection of humans trapped under debris is crucial in search and rescue operations. This study explores the use of antennas operating within the 150-650 MHz frequency range to identify human respiration and movement under building wreckage. A debris model consisting of construction materials was generated at the laboratory, and attenuation characteristics were observed to set ideal operating frequencies. Time-dependent transmission coefficient data were collected over 20 s and processed using short-time Fourier transform, wavelet transform, and empirical mode decomposition for time-frequency analysis. To enhance signal clarity, denoising techniques were applied before the radar signals were categorized into three classes: empty debris, human respiration, and human movement. Generative adversarial networks augmented environmental noise data to enrich training datasets comprising nine subsets. Deep learning models, including temporal convolutional networks, long short-term memory, and convolutional neural networks, were employed for classification. Hyperparameter optimization via random search further refined model performance. Results indicate that the convolutional neural networks using short-time Fourier transform data consistently achieved the highest classification accuracy across subsets. These findings demonstrate the potential of combining radar with deep learning for reliable human detection under debris, advancing rescue efforts in disaster scenarios.  
deep learning; remote sensing; search and rescue radar +
Footnote †: journal: Applied sciences  
Article
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 5 Performance Measurements
### Scenario 2: Earth-Moon Scenario  
Mobile networks of nodes in the space, air, ground, and sea domains may include links of large one-way light times. To evaluate one such case, a possible earth-lunar network was considered by modifying the propagation delays of the testbed. In this case, data need to be downloaded from a node in the lunar section to any of the two designated ground stations. Three links were configured as long-haul connections: g23-g26, g25-g27, and g22-g28, with a 1.3 s propagation delay for each communication direction. No random packet losses were introduced in this test. The propagation delays within each of the sections were selected to be in the range of 7-150 ms to represent different ground, satellite-satellite, and satellite-ground links, with satellites at MEO and LEO orbits.  
The link propagation delays that were in general longer that in the previous scenario directly impacted the response times of the flow. This effect is depicted in Figure 3a, where the average response times of either of the unicast routing transmissions were up to 10 times larger than the one obtained with the anycast CGR once congestion started forming. As with the first experiment, the lower response times were achieved by exploiting longer but  
Figure 2: Scenario 1: Homogeneous propagation delays and random packet drops. (**a**) Average response time; (**b**) average path length; (**c**) bundle throughput; (**d**) traffic split.  
less congested paths, as shown in Figure 3b. The g21-g30 sub-flow that was dynamically determined by CSG was transmitted over a longer path on average than the other sub-flow. As with the first scenario, a larger throughput was observed for medium to high traffic levels, as shown in Figure 3c. The average observed traffic split between the two group members as dynamically determined by the CSG anycast routing is depicted in Figure 3d with the g27 target achieving a slightly higher preference over the g30 target.
## 4 Diamond-Based 1D Metal Oxide Heterojunction Classes
### D N-ZnO/P-Diamond Heterojunction  
ZnO is a suitable unintentionally doped n-type semiconductor with the wide band gap of 3.7 eV and exciton binding energy as high as 60 meV, which is promising for a wide range of applications. Various forms of ZnO nanostructures, such as NWs [15], NRs [74], nanobelts [75] and nanosheets [76], have been commonly studied because of their favorable optoelectronic performance compared with the bulk material. In these nanomaterials mentioned above, 1D n-ZnO nanostructures are supposed to be the most efficient for optoelectronic diodes owing to their high-density surface trap states, fewer interference states, excellent carrier confinement and grain boundaries [77]. They are able to improve the optoelectronic performance of photodiode devices [6; 78].
## 6 IEC's Market Drivers
### Healthcare  
Health facilities, based on IEC solutions, became an important area to adopt 5G technologies [37; 105; 106]. For instance, some of the medical services as tele-surgeries require extremely low latency, uninterrupted communication links and collaboration between surgeons located in different locations. As another example, monitoring patients remotely in places away from medical facilities enables doctors to interact with them effectively. Based on these situations and potential usage scenarios, IEC's role in the health and social aid industries becomes more apparent. Previous research investigated the cooperation between IEC and IoT in the healthcare areas [107; 108]. IEC and 5G technologies also enable critical medical services including examining large numbers of patients in a short time and with less effort. Therefore, doctors dispense with a long time to analyze these tests without eliminating the presence of a doctor. Besides, IEC enters the medical paths of doctors in the future and it could be an auxiliary tool for the doctors. In addition, IEC technologies have the ability to predict diseases through an application that analyzes data, predicts infections and diseases that enable doctors to take the critical decision and diagnose the diseases [109; 110].
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## Appendix A Simplification from Equations (25) to (27)  
From Equation (25), we can see that the minimization of the BER is equivalent to the minimization of Equation (23), which can be shown to be equivalent to minimizing the weighted difference between the hybrid and the fully digital equalizers, and whose optimality condition is \(\mathbf{W}_{res,J}\mathbf{W}_{a}=0\), with [25]  
\[\mathbf{W}_{res,I}=\mathbf{\Omega}_{d}\overline{\mathbf{W}}_{dJ}(\mathbf{W}_{a})^{H}\mathbb{E} [\mathbf{y}_{I}\mathbf{y}_{I}^{H}]-\mathbf{\Omega}_{d}\mathbf{H}_{I}^{H}, \tag{28}\]  
where \(\overline{\mathbf{W}}_{d,I}\) is the non-normalized digital equalizer, that is, the digital equalizer without considering the power constraint of Equation (26), and  
\[\mathbf{\Omega}_{p}=N\text{diag}\Bigg{[}\sum_{l=0}^{NM-1}\Big{[}\overline{\mathbf{W} }_{dJ}(\mathbf{W}_{a})^{H}\mathbf{H}_{I}\Big{]}\Bigg{]}^{-1}, \tag{29}\]  
\[\mathbb{E}[\mathbf{y}_{I}\mathbf{y}_{I}^{H}]=\sigma_{d}^{2}\mathbf{H}_{I}\mathbf{H}_{I }^{H}+\sigma_{n}^{2}\mathbf{I}_{N_{\text{z}}}. \tag{30}\]  
By applying the properties of the Frobenius norm to Equation (23), we obtain  
MSE \[\begin{array}{l}=\sum\limits_{l=0}^{NM-1}\text{tr}\Big{\{}(\mathbf{W}_{dJ}(\mathbf{ W}_{a})^{H}\mathbf{H}_{I}-\mathbf{I}_{U})(\mathbf{W}_{dJ}(\mathbf{W}_{a})^{H}\mathbf{H}_{I}- \mathbf{I}_{U})^{H}\sigma_{d}^{2}+\mathbf{W}_{dJ}(\mathbf{W}_{a})^{H}\big{(}\mathbf{W}_{a} \big{)}^{H}\sigma_{n}^{2}\Big{\}}\\ =\sum\limits_{l=0}^{NM-1}\text{tr}\Big{\{}(\mathbf{W}_{dJ}(\mathbf{W}_{a})^{H}\Big{(} \sigma_{d}^{2}\mathbf{H}_{I}\mathbf{H}_{I}^{H}+\sigma_{n}^{2}\mathbf{I}_{N_{ \text{z}}}\Big{)}(\mathbf{W}_{a})^{H}\big{(}\mathbf{W}_{dJ}\big{)}^{H}\\ \quad-(\mathbf{W}_{dJ}(\mathbf{W}_{a})^{H}\mathbf{H}_{I}\sigma_{d}^{2}-(\mathbf{H}_{I })^{H}(\mathbf{W}_{a})^{H}\Big{(}\mathbf{W}_{dJ}\Big{)}^{H}\sigma_{d}^{2}+\sigma_{d}^ {2}\mathbf{I}_{U}\Big{\}}.\end{array}\] (31)  
From Equations (30) and (31), we can prove that  
\[\text{MSE}=\overline{\text{MSE}}+\sigma_{d}^{2}\sum_{l=0}^{NM-1}\text{tr} \Big{\{}\mathbf{I}-\overline{\mathbf{W}}_{fdJ}\overline{\mathbf{W}}_{fdJ}^{H}\Big{\}}, \tag{32}\]  
with  
\[\overline{\text{MSE}}=\sigma_{d}^{2}\sum_{l=0}^{NM-1}\|\Big{(}\mathbf{W}_{dJ}(\mathbf{ W}_{a})^{H}-\overline{\mathbf{W}}_{fdJ}\Big{)}\mathbb{E}[\mathbf{y}\mathbf{y}_{I}^{H}]^{1/2} \|_{F}^{2}, \tag{33}\]where \(\overline{\mathbf{W}}_{fdJ}\) denotes the non-normalized fully digital equalizer. Because minimizing MSE is equivalent to minimizing \(\overline{\text{MSE}}\), because \(\overline{\text{MSE}}\) is equal to MSE, up to a constant, for an MRC approach, we can define  
\[\sigma_{d}^{-2}\overline{\text{MSE}}^{(MRC)}=\sum_{l=0}^{NM-1}\left\|\mathbf{W }_{dJ}(\mathbf{W}_{a})^{H}-\mathbf{H}_{l}^{H}\right\|_{F}^{2}, \tag{10}\]  
and we obtain the optimization problem in Equation (27).
## 4 Data Pre-Processing
### Data Pre-Processing  
To effectively utilize raw data in ML algorithms, they should be transformed into a suitable format, and data pre-processing plays a crucial role in this regard. Data pre-processing involves the selection of variables to eliminate irrelevant features, thereby enhancing the performance of ML algorithms and reducing computational complexity. In this study, the data were subjected to four key stages to ensure the suitability of the classification techniques.  
First, the categorical variables were simplified. For example, in the marine accident column, a One-Hot Encoding process was applied, where occurrences of marine accidents were assigned a value of 1, and instances where no marine accidents occurred were assigned a value of 0.  
Second, columns deemed simple strings or those with low relevance were excluded during the data collection stage.  
Third, if special characters such as dashes or underscores were inserted into the numerical variables in the raw data, they were all removed to reflect only pure numbers.  
Finally, to address the issue of class imbalance, a synthetic minority oversampling technique (SMOTE) was applied using a model [34]. Further details regarding SMOTE are provided in Section 4.4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Number** \\ \hline MTSA-related accidents & 47 \\ \hline MTSA-unrelated accidents (exclude) & 599 \\ \hline Total & 646 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of MTSA-related/unrelated accidents.  
Figure 8: Related accidents.  
In addition, to better evaluate the proposed ML algorithm model, a subset of the original Data U set was utilized. This subset consisted of 10 instances representing MTSA-related accident occurrences (label 1) of a total of 47 instances, and 10 instances representing no MTSA-related accidents (label 0). These instances were randomly sampled and used for ML model evaluation.
# Development and Finite Element Analysis of a Novel Bent Bone Plate
## 3 FEA Results and Discussion
### Result Summary
#### 3.3.3 First Principal Strain (1)
Nevertheless, there is another consideration that makes a certain bone plate superior to the other, such as the form and the degree of the deformation. After comparing the flat plates, the developed flat plate is considered to be better in terms of total displacement, von Mises stress, and first principal strain. The result explained above shows that the developed plate has less overall deformation, while also being stronger and more stable than the traditional flat rectangular plate. Furthermore, the overall result shows that the bone plate bent at 10\({}^{\circ}\) in the middle part shows a superior quality, mainly in the maximum value of all aspects, while the average value can still be improved. It could be concluded that the bone plate with a 10\({}^{\circ}\) bending angle would be the best candidate for clinical use, in harmony with the essential properties of a bone plate that should not be easily broken, in order to ensure a speedy recovery. The highest stress acting on the plate is only 2.88 MPa, averaging at 0.271 MPa, which is less than the stress limit for Ti-6 Al-4V before its plastic deformation. The maximum displacement is also only 29.6 nm and average of 4.61 nm, with a maximum first principal strain of 23.6 \(\times\) 10\({}^{-6}\). Therefore, the bone plate implant bent at 10\({}^{\circ}\) in the middle would be a better solution for patient use, particularly in the left femoral bone, instead of the traditional flat implant. The FEA result, however, is still not fully independent and a physical mechanical test should also be conducted to ensure the reliability of this implant design. Figure 29: First principal strain (1) of each plate.