esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Node Feature Learning
#### 4.2.1 Learning POI Embeddings  
In LBSN data, the historical check-in records \(H_{u_{i}}=\left\{H_{t_{1}}^{u_{i}},H_{t_{2}}^{u_{i}},...\right\}\) of each user \(u_{i}\in U\) are represented as a user trajectory sequence \(T_{u_{i}}\), where the user check-in trajectory is shown in Figure 2. To reduce computational complexity and capture richer semantic patterns of movement, we divide the original trajectory \(T_{u_{i}}\) of each user \(u_{i}\) by day, thus obtaining a series of continuous sub-trajectories \(T_{u_{i}}=\left\{T_{1}^{u_{i}},T_{2}^{u_{i}},...\right\}\).  
Subsequently, we use a Skip-Gram-based model to learn the sub-trajectories of all users to obtain the embedding of POI \(v_{j}\in V\). The Skip-Gram model, commonly used for learning word embeddings, maps words to a low-dimensional vector space while preserving semantic relationships. In this paper, POIs are treated as \"words\", and user sub-trajectories are treated as \"sentences\". To incorporate more temporal information, the initial feature \(\Psi_{v_{j}}\) of POI \(v_{j}\) not only includes spatial features like geographic location \(l_{v_{j}}\) and venue category \(c_{v_{j}}\), but also considers the average visit frequency \(m\) of the location as a temporal feature. For a given POI \(v_{j}\in V\), we define its context as \(C\left(v_{j}\right)\), where \(C\left(v_{j}\right)=\left\{v_{j}-c,...,v_{j}+c\right\}\), and \(c\) represents the sliding window size. The embedding of POI \(v_{j}\)\(\Psi_{v_{j}}^{\prime}\) is learned by maximizing the probability of predicting its context, i.e., the probability of the co-occurrence of context POIs with POI \(v_{j}\) in the sub-trajectory \(T_{k}^{u_{i}}\) containing \(v_{j}\), as shown in Equation (1):  
\[max\prod_{v_{j}\in\mathbb{C}(v_{j})}\frac{exp(\Psi_{v_{j}}^{T}\Psi_{v_{j}}^{ \prime})}{\sum_{v=1}^{|\xi|}exp(\Psi_{v_{j}}^{T}\Psi_{v}^{\prime})} \tag{1}\]  
For each POI \(v_{j}\), the final embedding vector \(\Psi_{v_{j}}^{final}\) of POI \(v_{j}\) is obtained by averaging the learned embedding vectors \(\Psi_{v_{j}}^{\prime}\) across all sub-trajectories containing \(v_{j}\).
## 3 Image Sensor: Power Consumption Analysis
### Image Sensor: Power Consumption Profile  
After the timing profile of running mode operations, we will perform electric current measurements of both standby and running modes. We have also measured the current demanded by our image sensor node when it is in standby mode, being a constant current flow of 8 \(\upmu\)A. The camera module showed an average consumption of 63.6 mA at nominal operation, and the power required by radio transmitter and receiver is about 16 mA and 18 mA, respectively. The CC1110F32 SoC consumes an average of 5.6 mA when working in running mode (radio off).  
Now, we perform a set of electric current measurements (one measure every 2 ms) during the execution of each basic operation to calculate the average value. After that, for each basic operation we  
Figure 5: Timing profile of running mode operations.  
can determine the power consumption in mAh units, as the average duration of each operation and its demanded electric current are known.  
In Figure 6 we show the power consumption profile of running mode operations. As with the timing profile, we can observe that those operations not involved with slice data management have near constant power consumption for all image sizes. The slice management operations are the ones that most power demands, exponentially increasing the required power as the image size increases. In particular, the transmission of the image slices (T_Send_Slice) represents the 36%, 46%, 61% and 66% of the overall power consumption for \(80\times 64\), \(160\times 128\), \(320\times 240\), and \(640\times 480\) image sizes, respectively.  
This fact shows us that much of the energy consumed in the running mode will largely depend on the number of slices we have to send via radio to the central station. Thus, we think that the image compression system should play a fundamental role in reducing energy consumption, and as a consequence it would allow us to further extend the lifetime of our node. We will back to this issue in a later analysis.
## 3 Methods
### S2 MP for SSM Estimation  
The estimation of the surface soil moisture (SSM) was accomplished using the S2 MP developed by [PERSON] et al. [47] using time series data from Sentinel-1 and Sentinel-2 and a neural network technique for estimating SSM within the upper 3-5 cm layer. The neural network employed sought the optimal fit between the reference and estimated SSM by incorporating the calibrated water cloud model (WCM) [90], along with the integral evaluation model for soil backscattering [91; 92; 93]. Within the WCM, the total backscattered coefficient was computed as the combined contribution of direct vegetation and soil, multiplied by an attenuation factor. This approach, which was used in this study, delivered satisfactory estimations of surface soil moisture (SSM) with a root mean square error (RMSE) of 5 vol.%. It required three inputs: the \(\sigma^{0}\) value in VV and VH polarizations, the SAR incidence angle (\(\theta\)), and an NDVI value. Consequently, the SSM estimations were obtained individually for each plot and each grid cell at every available SAR acquisition date within the eight temporal series of the four reference plots (Figure 4).  
Figure 4: Temporal evolution of surface soil moisture estimation (SSM), for both the ascending and descending SAR acquisition modes together at the plot scale (SSM\({}_{\rm P}\), yellow curve) and 10 km grid scale (SSM\({}_{\rm G}\), orange curve) and daily rainfall data (blue bars) throughout the maize-growing cycle for Montpellier: (a) P1 in 2017 and Tarbes: (b) P2 in 2018, (c) P3 in 2019, and (d) P4 in 2020. The green line with x-signs represents the normalized differential vegetation index (NDVI).
# Design and Additive Manufacturing of TPMS Heat Exchangers
## 2 Design and Modeling of TPMS HXs
### Design Examples of TPMS HX  
Two high-efficient cubic HXs (named HX70-555 and HX70-101010) were designed for laser AM. The dimensions of HX70-555 and HX70-101010 are shown in Figure 8. The parameters of these two types of cubic HX-70 are shown in Table 3. The material of HX-70 was aluminum. HX70-555 had a gyroid unit that was 5 mm in length in three directions, as shown in Figure 9. HX70-101010 had a gyroid unit with a length of 10 mm in three directions, as shown in Figure 10. Fluid A and fluid B indicates fluids (such as water) at different temperatures. The volume of the HX core was mainly used to simulate thermal transfer parameters including surface area, fluid field, heat transfer coefficient (HTC), pressure drop, and heat transfer capacity. Table 4 gives the simulation results under the assumption that the wall thickness of gyroid lattice is 0.3 mm and has a flow rate of 0.48 kg/s.  
The heat transfer coefficient (HTC) was used to compare the HX with different types of TPMSs. The overall heat transfer coefficient in the clean condition, _Uc_, was calculated as follows:  
\[Uc=Qt_{avg}/(LMTD\times A) \tag{1}\]  
where \(Qt_{avg}\) is total average heat transfer rate, calculated as the average of the hot stream heat transfer rate and the cold stream heat transfer rate, and _LMTD_ is the logarithmic mean temperature difference, which is defined as follows:  
\[LMTD=\frac{\Delta T1-\Delta T2}{In(\Delta T1/\Delta T2)} \tag{2}\]  
where  
\(\Delta T1=T_{hot,in}-T_{cold,in}\) and \(\Delta T1=T_{hot,out}-T_{cold,out}\) are parallel flow;  
\(\Delta T1=T_{hot,in}-T_{cold,out}\) and \(\Delta T2=T_{hot,out}-T_{cold,in}\) are counter flow;  
\(A\) is the effective surface area of the HX.  
As shown in Figure 11, the HX-70 unit was compared with the commercial 20 kW HX, BT3\(\times\)8-20. The two main work conditions of BT3\(\times\)8-20 are shown in Table 4. The HX-70 was three times smaller in volume, and at least five times lighter in weight than the BT3\(\times\)8-20, as shown in Table 5. Additionally, aluminum (Al) alloy is much cheaper than copper alloys or steels.  
The results of the commercial heat exchanger are from the documents downloaded from the official website of the manufacturer. Commercial heat exchanger BT3\(\times\)8-20, with a heat transfer capacity around 20 kW, met our requirements. At the same time, the BT3\(\times\)8-20 is a heat exchanger with high efficiency. Based on the parameters shown in Table 6, the performance of the HX-70 can be evaluated by comparing with the commercial heat exchanger.
## 2 Rheological Model and Numerical Algorithm
Because the fundamental assumption of the VOF method is that each fluid is immiscible, there is no difference in terms of the VOF equations between subaerial or submerged slumps. For both the subaerial and submerged (underwater) slumps, the VOF method is briefly described here [50; 55; 56]. Two stability conditions of \(dt\) need to be satisfied while solving Navier-Stokes equations:  
\[dt_{c}<C_{r}\frac{dl}{Max(|\mu|)} \tag{11}\]  
\[dt_{\mu}<V_{\mu}\frac{(dl)^{2}}{Max\left(\mu_{eff}\right)} \tag{12}\]  
where \(dt_{c}\) is the time step restricted by the advection term, \(C_{r}\) is Courant number, which is defined as \(C_{r}=Max(|\mu|)dt/dl\), \(dl\) is the measure of the cell size, \(dt_{\mu}\) is the time step restricted by the diffusion term, \(V_{\mu}\) is the viscous number, which is defined as \(V_{\mu}=Max\left(\mu_{eff}\right)dt/(dl)^{2}\). When the viscosity is large in the un-yield zone [50; 57; 58], the time-step \(dt_{\mu}\) is very small, which might lead to the numerical divergence or even a crash in the solution procedure [59]. However, this small time-step restriction can be relaxed by adopting the implicit scheme. The viscous implicitness \(\theta\) is used to calculate the velocity \(\mathbf{u}\) at the time level \(n\), and \(\mathbf{u}^{\partial}~{}=~{}(1-\theta)\mathbf{u}^{n}~{}+~{}\partial\mathbf{u}^{n+1}\). In this study, \(\theta\) is given as unity, which implies a fully implicit treatment, and \(dt_{\mu}\) is no longer restricted by Equation (12). The algorithm of Splash3D solves equations of conservation of mass and momentum for any number of immiscible, incompressible fluids, and tracks the interfaces between them. Except for adding a water body to the computational domain in Part II to simulate slump-type landslide tsunamis, no additional numerical adjustment is required from Part I to Part II. Therefore, the findings in Part I can be applied to the underwater modeling in Part II. To simplify the complexness from the slump, water, and air, the slump-type landslide in the dry-land area, FGT66, is adopted in Part I for a better understanding of the model characteristics. The same numerical model will be applied to study the FGT66 in Part I and slump-type landslide tsunamis in Part II. To summarize the numerical method, this paper adopts Splash3D model to solve the conservation and Navier-Stokes equations for any number of immiscible, incompressible fluids. The LES turbulence model with Smagorinsky closure is used to add the effect from turbulence flow field. The VOF method is adopted to track the interfaces between each fluid including slumps. The new contributions from this study in terms of the numerical modeling are incorporating the BM, CBM, and MBM into the Splash3D model with the implicit scheme to solve the slump material with a large viscosity number.
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 2 EET Mechanisms
### Inward EET from Cathodes to EAMs  
Electrotrophic EAMs utilize the cathode as the electron donor via inward EET pathways [40], which are also divided into direct node and indirect modes. Some EAMs, including _S. oneidensis_ MR-1 and _G. sulfurreducens_ PAC, exhibit bidirectional EET features.  
Figure 1: EET mechanism between EAMs and electrodes: the direct process through outer-membrane Cyts, vesicles, and nanowires, and the indirect process via electron shuttles and extracellular enzymes (with permission from [25]).  
_S. oneidensis_ MR-1 was reported to use a reverse Mtr pathway to ingest electrons from the cathodes, and the electrons were injected into the menaquinone pool [41]. However, it is controversial whether _G. sulfurudecens_ also uses the reversed outward EET pathway to take up electrons from extracellular electron donors. The similar formal potential of acetate oxidation and nitrate reduction of mature electrode biofilms, mainly composed of _Geobacter_ sp., seems to support the standpoint of reversal EET [42]. In contrast, _G. sulfurudecens_ was found to express much lower levels of genes encoding outer-membrane Cyts and conductive nanowires when receiving electrons from the electrode compared to exporting electrons [43; 44], which suggested that the inward EET was not a simple reversal for the outward EET. In addition, flavins or artificial electron shuttles could significantly boost the rate at which they acquire electrons from the cathode [45; 46], which indicates the existence of indirect EET. Notably, the transfer of electrons from the cathodes to the bacterial cells has been extensively reported in many strains, such as acetogenic bacterium _Sporomusa_ [47], Fe(II)-oxidizing bacterium _Rhodopsseudomonas palustris_ [48], and sulfur-oxidizing bacterium _Thicolava electrotropha_ [49]. However, the specific inward EET mechanism of such electrotrophs is still far from clear. For more details on the EET mechanism, refer to previous reports [2; 50; 51; 52].
## 7 Conclusions and Future Work  
This paper describes a WSN tool to increase safety in urban and industrial fire fighting. It uses a WSN with static and mobile nodes carried by firefighter. However, it assumes that there is no preexisting WSN in the building, which improves applicability but imposes constraints such as lack of previous information and need for high adaptability and low delays. Once deployed by firefighter, static nodes provide a communication backbone with the Base Station through dynamic multi-hop routes. All nodes are equipped with fire/smoke sensors. Measurements from static and mobile nodes are transmitted to the Base Station for monitoring and registering. This paper focuses on the following functionalities: fire monitoring, firefighter monitoring and dynamic escape path computation.  
The proposed system includes a robust localization method that employs RSSI-range models dynamically trained to cope with the peculiarities of the environment. The training and application stages of the method are applied simultaneously, resulting in high adaptability. The proposed localization method exhibited significant accuracy and robustness in the indoors experiments carried out. The system also includes an escape guiding method. The Base Station uses optimization graph-based tools to periodically compute minimal cost paths using updated fire measurements. The new guiding information is broadcasted only if it represents a significant improvement. The method is efficient and can be used simultaneously by as many firefighters as necessary. All these functionalities were integrated in a prototype that was satisfactorily tested in experiments in close to operational conditions.  
The proposed system showed promising results in the tests carried out. Of course, further testing and evaluation are necessary before its use in the real application. The codification of local guiding information, which can simplify interpretation by firefighter and minimize potential errors, and the development of security mechanisms are also necessary before its use in the real application. In the proposed method, each static node knows its own location. The iterative cascade-like localization of the whole WSN using few initial anchor nodes with known location is also under research. In addition, the real application should require enclosures to protect nodes from the environment. The prototype we develop used a standard plastic box but the design of the enclosure considering different cost-performance solutions is currently under work. Expensive solutions are more effective from a protection point of view. We are currently working on the evaluation of the most suitable enclosure from a cost-robustness tradeoff perspective.  
Figure 16: Number of FireSense packets received per node ID.
## 3 Results and Discussion
### Additional Characterizations  
In order to define the chemical composition changes, Fourier transform infrared spectroscopy results of electrospun collagen/PEO non-crosslinked and 18 h crosslinked are showed in Figure 5a. In contrast with the non-crosslinked sample, the absorption peak of N-H stretching bond around 3300 cm\({}^{-1}\) showed a decrease in the 18 h crosslinked sample for the consumption of amidogens in collagen by the crosslinking treatment. Meanwhile, the characteristic absorption peaks that reflected the conformation of protein polypeptide for collagen were observed at 1650 cm\({}^{-1}\) (amide I), 1516 cm\({}^{-1}\) (amide II), and 1246 cm\({}^{-1}\) (amide III) in both non-crosslinked and crosslinked samples without considerable differences in the location of these characteristic peak. However, the absorption peak of -C=\(N\)- stretching vibration generated by GA crosslinking was not observed in the range of 1640-1690 cm\({}^{-1}\), which may be due to the overlap with strong absorption of the amide I band (1650 cm\({}^{-1}\)) [24].  
DSC was carried out to assess the effect of the crosslinking on the thermal stability of the collagen matrices. During the heating process, the triple-helix structure of collagen will be ruptured. Therefore, the characteristic endothermic peaks have often been termed as denaturation temperature (T\({}_{\text{D}}\)), which is indeed affected by the degree of crosslinking [41]. As shown in Figure 5b, compared with only one characteristic endothermic peak of the untreated sample, a couple of peaks were found before and after crosslinking of the samples. The DSC of electrospun PEO membrane was carried out to determine whether the weaker endothermic peak near 51 \({}^{\circ}\)C was attributed to the PEO and the left one for collagen. As shown in Figure S3, a single endothermic peak near similar temperature was observed in electrospun PEO membrane. The T\({}_{\text{D}}\) of the non-crosslinked electrospun collagen was about 10.8 \({}^{\circ}\)C lower than that of the untreated collagen foam. This phenomenon was associated with the electrospinning process, which has been explained to result in relatively increased segmental mobility of the fibrous polymers [42]. Compared with the non-crosslinked collagen membrane, T\({}_{\text{D}}\) of the 18 h crosslinked increased to nearly 9.5 \({}^{\circ}\)C. This was probably due to the interchain crosslinking within molecules of crosslinked collagen [43]. DSC results indicated the crosslinking treatment enhanced the thermal stability of the electrospun collagen fibers appreciably.  
Figure 4: Ultimate tensile stress (**a**), ultimate tensile strain (**b**) and elastic modulus (**c**) of nanofibrous membranes varied as function of degree of crosslinking (Significant differences were marked by *** for \(p<0.001\); *** for \(p<0.01\) compared to the 30.8% crosslinked sample).
# Performance Analysis of Soft-Switching FSO/THz-RF Dual-Hop AF-NOMA Link Based on Cognitive Radio
## 4 Numerical Results
Specifically, around the smoothing point at \(I=35\) dBm, User2 achieves an OP of \(4.4482\times 10^{-4}\) in Case1, \(4.7948\times 10^{-5}\) in Case2, and \(9.3066\times 10^{-6}\) in Case3. In Figure 7, we maintain a constant SN power, denoted as \(P_{S}=P_{Peak}\). We compare the OP at different \(I\) and contrast them with the scenario without CR. As the \(I\) increases, it signifies a higher tolerance to interference, leading to improved link performance. Thus, we observe a consistent performance in the low-power region in all cases. The key distinction  
Figure 5: A comparison of \(P_{Peak}\) and \(I\) on OP under different \(P_{S}\). (**a**,**b**) correspond to \(P_{S}=10\) dBm, (**c**,**d**) correspond to \(P_{S}=20\) dBm, and (**e**,**f**) correspond to \(P_{S}=30\) dBm. In (**a**,**c**,**e**), User1 is represented, while in (**b**,**d**,**f**), User2 is represented. Figure 6: Impact of different turbulence and pointing errors on link performance. lies in the performance that is constrained by both \(P_{Peak}\) and \(I\) with CR, whereas without CR, it is solely limited by \(P_{Peak}\). The results reveal that when \(I=35\) dBm and the SN power ranges from 0 to 50 dBm, there is minimal disparity in performance between the with-CR and without-CR scenarios. Encouragingly, even as \(I\) continues to rise, the performance discrepancy remains insignificant.
# Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes
## 3 Results and Discussion  
### Simulation-Driven Heating Structure Design  
The metallic wires of the heating structure cause the damping of the emitted radar waves. However, the damping can be kept below 1.3 dB when wires of 0.3 mm diameter are routed parallel and spaced more than 4 mm apart [16]. Based on this design input, transient thermal simulations were conducted using SolidWorks Flow (version 2022) to analyze the heating power required for the de-icing of the radome. Natural convection, including radiation, was modeled using external simulation settings, specifically by defining a fluid box around the sensor with atmospheric pressure conditions and air entering at a defined atmospheric temperature. Infrared emissivity was simplified and set to 0.9 for all surfaces, as is typical for painted polymer surfaces. As a requirement, it was specified that the radome must be heated from \(-\)40 \({}^{\circ}\)C to 5 \({}^{\circ}\)C within 3 min. However, the localized overheating of the wire or the radome polymer material must be avoided.  
To simplify the electronic driver design, the voltage was considered to be between 8 and 16 V, and the maximum current was restricted to below 3 A. Therefore, a reasonable design to obtain 20 W of heating power using a 10 V driving voltage is a resistance of 5 \(\Omega\) for the heating coil. Joule heating was simulated by applying the respective 10 V to the end surface of the wire. By adjusting the duty cycle, the heating power could be conveniently varied between 0 W and 20 W. Transient thermal simulation and experimental validation are depicted in Figure 3, showing the heating from room temperature until steady-state conditions were reached. It was demonstrated that heating from \(-\)40 \({}^{\circ}\)C to  
Figure 2: (**a**) Predrying and sintering profile of Cu complex ink. (**b**) Predrying and sintering profile of Cu paste.  
5 \({}^{\circ}\)C could be achieved in 3 min using 4 W input power for a radome heater. Validation measurements and simulations were performed under worst-case conditions, i.e., without activating the heat load of the electronic components. The radome in the simulation was from a different radar sensor and approximately twice as large as the one investigated for the AM technologies. Therefore, for heating the smaller radome, 2-3 W of heating power would be sufficient, allowing for an even larger resistance of 30 \(\Omega\) for the heating coil.