esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To uncover the relationship between CO\({}_{2}\) concentrations in the mid-troposphere and at the surface, this study asks the questions: How are temporal scales of CO\({}_{2}\) in the mid-troposphere related to surface CO\({}_{2}\) fluxes in a complex, regional landscape, such as grasslands with woody encroachment in northeastern Kansas? And, to what extent can mid-tropospheric CO\({}_{2}\) concentrations be used as a proxy for ABL CO\({}_{2}\) behavior? The goals of this comparative study are to identify the dominant temporal scales of ABL-FT exchange of CO\({}_{2}\) in this region and to assess the utility of AIRS mid-tropospheric CO\({}_{2}\) measurements for illustrating local to regional source/sink dynamics. To examine whether and when AIRS CO\({}_{2}\) measurements in the mid-troposphere can be used as a proxy for concentrations at the land surface below, three EC towers were selected at sites with different land-cover types in northeastern Kansas. An information theory approach combining relative entropy and wavelet multi-resolution analysis was used to examine temporal dynamics between AIRS and EC time series of CO\({}_{2}\) concentrations [29]. Relative entropy, defined by [PERSON] [30] as an \"uncertainty deficit,\" can be a useful measure to improve our understanding of information flow, gain, and transfer in land-atmosphere interactions [29, 31]. In this study, the methodology combining wavelet multi-resolution analysis with relative entropy allows us to identify the temporal scales where there is the greatest similarity or the least divergence between the probability density functions of AIRS and EC time series of CO\({}_{2}\) concentrations in the mid-troposphere and at the surface.
# River Water Quality Monitoring Using LoRa-Based IoT
## 5 Results
### Monitoring a River (Real Scenario)  
The real scenario testing site for this prototype was on the left bank of the Jamor River, in Carnaxide, Oeiras council, at the following coordinates: [38.721951, \(-9.249049\)]; see Figure 32.  
The IoT Node was installed near the water line [36], close to the sensors, and the Gateway was installed near this location, on the nearby street, inside a car. Due to the topography of the location, there is a height difference between the two locations, which, although only 10 m away, influences the radio signal received in Gateway. The average Received Signal Strength Indication (RSSI) upon Gateway reception was \(-94\) dBm, with an Effective Isotropic Radiated Power (EIRP) of approximately 14 dBm at the IoT Node, and with an average Signal-to-Noise Ratio (SNR) of 7.33 dB.  
These tests were on 26 June 2024, and were conducted for almost 4 h in the morning, in the period between 9:25 am and 1:21 pm. In Table 2, a summary of the environmental conditions is shown.  
Figure 31: LoRa radio coverage test.  
Figure 30: IoT Node, power measurements.  
The data were sent every 30 s, recorded and then exported to Excel for processing, and the analysis of results is presented below.  
Figure 33a shows a graph with temperature variation, where a slight increase can be seen from 11:49, probably due to the influence of the sun. These values are normal for a location already close to the river mouth, as in this case.  
In Figure 33b, the pH variation shows an average value of 7.7, except at the beginning, perhaps due to the sensor adaptation period. The pH is within normal values for water (6.5-8.5).  
The average conductivity is 0.7 mS/cm and is below the maximum recommended value (2 mS/cm); the small variation is shown in Figure 33c.  
The turbidity throughout the measurement time always showed values equal to zero NTU, as the water was transparent, as seen in Figure 33d.  
For a comparison with the other water sources measured, see Table 3.  
And we can conclude, based on these parameters, that the water quality of the Jamor River is reasonable, but shows a slight presence of contaminants, due to the conductivity value of 0.7 mS/cm and some alkalinity due to the pH of 7.7.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Air temperature** & Around 25 \({}^{\circ}\)C \\ \hline
**Weather conditions** & Partly cloudy and light rain sometimes occurred \\ \hline
**Water depth** & Around one meter \\ \hline
**Water speed** & Very slow \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test site environmental conditions.  
Figure 32: River Jamor, test site: (**a**) openstreetmap location; (**b**) test site photo.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water** & **Temp.** & **pH** & **Conductivity** & **Turbidity** \\ \hline River Jamor & 21 & 7.7 & 0.7 & 0 \\ \hline Pub. Water supply (Cascais) & n.a. & 6.8 & 0.12 & 0 \\ \hline Bottled Luso water & n.a. & 6 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other water sources.
## 5 Experiments
### Experiment 3--Reconstruction of Liver Tumor
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**User**} & \multicolumn{2}{c}{**Surface Reconstruction**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**std**} & \multirow{2}{*}{**rms**} & \multirow{2}{*}{**Hausdorff**} & \multirow{2}{*}{**Medial**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Area**} & \multirow{2}{*}{**Volume**} \\  & & & & & & & & **Distance** & & (\%) \\ \hline \multirow{4}{*}{User 1} & BPA & 3.45712 & 3.51744 & 4.93067 & 17.8971 & 2.35204 & \(-\)26.14 & \(-\)54.27 \\  & Power Crust & 2.54163 & 2.37519 & 3.47799 & 9.96287 & 1.76471 & \(-\)9.91 & \(-\)17.72 \\  & Poisson & 0.60799 & 4.58929 & 7.6063 & 2.420824 & 5.02371 & 26.70 & 30.68 \\  & VSR & 1.7113 & 1.8209 & 2.504 & 8.05269 & 0.974841 & \(-\)6.73 & \(-\)6.71 \\ \hline \multirow{4}{*}{User 2} & BPA & 4.48905 & 5.10272 & 6.79433 & 22.0837 & 2.09925 & \(-\)39.04 & \(-\)95.96 \\  & Power Crust & 3.2693 & 3.37088 & 4.69462 & 14.7158 & 1.9469 & \(-\)13.94 & \(-\)29.77 \\  & Poisson & 3.61952 & 4.32344 & 4.98086 & 15.594 & 2.48503 & 37.66 & 26.67 \\  & VSR & 1.99204 & 2.78775 & 3.42519 & 14.6415 & 0.946849 & \(-\)7.72 & \(-\)7.85 \\ \hline \multirow{4}{*}{User 3} & BPA & 4.40708 & 4.73458 & 4.6465 & 2.1115 & 2.62617 & \(-\)50.46 & \(-\)71.71 \\  & Power Crust & 2.72485 & 2.6747 & 3.27913 & 11.5233 & 1.95696 & \(-\)10.82 & \(-\)23.71 \\  & Poisson & 3.3612 & 2.8271 & 3.49114 & 13.3416 & 2.59396 & 47.83 & 22.53 \\  & VSR & 1.34403 & 1.71604 & 2.17905 & 9.10527 & 6.016535 & \(-\)571 & \(-\)5.89 \\ \hline \multirow{4}{*}{User 4} & BPA & 4.91483 & 4.93414 & 6.98522 & 2.0959 & 3.22823 & \(-\)92.22 & \(-\)46.76 \\  & Power Crust & 2.8246 & 4.99256 & 4.11397 & 12.8419 & 1.8294 & \(-\)13.05 & \(-\)20.83 \\ \cline{1-1}  & Poisson & 6.73954 & 5.39803 & 8.63311 & 24.1895 & 5.40374 & 80.34 & \(-\)26.53 \\ \cline{1-1}  & VSR & 2.06118 & 2.29825 & 3.3155 & 12.6043 & 0.963157 & \(-\)10.40 & \(-\)11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Difference between the reconstructed surface and the original surface (HUMAN KIDNEY; the contours were drawn by different users). Figure 12: _Cont._  
The results of this experiment conform to those of the previous reproducibility experiment, so we can conclude that:  
* The contours used greatly affect the quality of the reconstructed surfaces by all four methods. Visually, the contours drawn by User 3 produce the best approximation to the reconstructed surface from the contours drawn by User 1 using the BPA, Power Crust, and VSR methods. The contours drawn by User 4 produce the poorest approximation, especially when the Power Crust method is used; it fails to produce a surface. Figure 13 confirms this conclusion graphically.
# Monitoring a Pre-Normative Multi-Family Housing Case-Study in a Mediterranean Climate
## 1 Introduction
In spite of a number of remarkable exceptions [16; 17; 18; 19], the majority of results are based on energy simulations under standard use and operative conditions [20]. In most cases, mainly related to social housing in the Mediterranean climate, the results turn out to be very far from reality [21]. The aim of this work is to present the monitoring campaign of a case-study dwelling in a multi-family building under Mediterranean weather conditions and to highlight it as a basic issue when studying the real energy behavior of the building as a previous step to its retrofitting intervention. Monitoring allows not only the in situ assessment of the building's hygothermal behavior in its current state and the establishment of its relationship to indoor comfort conditions, but also the calibration of the generated energy models [22; 23; 24]. The former becomes an indicator of its degree of obsolescence from an environmental and energy angle, while the latter makes it possible to evaluate retrofitting strategies in the case study. This study forms part of a research project based on the environmental and energy assessment of multi-familiar dwellings in the Mediterranean climate and on the proposal of retrofitting strategies concerning the openings. This paper aims to present and analyze the results of a monitoring campaign, undertaken for the duration of a year of a dwelling from a multi-family building, located in a neighborhood of national heritage, which was built in Seville in the 1950s. Data recording has been carried out in 30-min intervals, with data loggers that record air temperature, relative humidity, pressure, dew point, wind direction and speed. An annual database with environmental variables measured both indoors and in the adjacent surroundings of the building is obtained.
## 3 Results and Discussion
### Measurement Results
#### 3.2.2 On-Body Communication System  
The BSA's transmission characteristics for on-body monitoring activity were tested in two locations: the chest and the arm, in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios.  
Measurement Setup  
The measurement set up for the path loss is shown in Figure 22. The BSA was used as a receiver antenna and was mounted on the body phantom on a wooden tripod at a distance of at least 1.5 m (m) from the ground and 1.7 m from any wall. The BSA link budget was assessed in free space as well as in on-body models in both indoor and outdoor environments. The platform was rotated to perform measurements in various orientations. The measurement was performed at 2.45 GHz to evaluate the path loss over various distances. For the LOS and NLOS situations, measurements were taken at various distances (d) and positions between the gateway (A) and the receiving BSA (B). In both cases, the measuring environment was roughly 20 m wide. The transmission coefficient S\({}_{21}\) was measured via the LED/receiving signal strength programming code, which was measured between the BSA and the WiFi router. The spectrum analyzer (MS2720T) with zero spans was also connected to the BSA. The gateway (A) was mounted to the wall, while the BSA (B) was moved up to 40 m to assess the path loss (S\({}_{21}\)). It was determined that all the surrounding objects were in a stationary state and that the measurement was performed correctly. To clear the ripple effects due to the cables and the measurement equipment, a two-port calibration was performed. For on-body link budget calculation, we used a Blynk platform, using iOS and Android apps, to control BSA devices over the internet. We installed the Android Blynk App to display the output data of the BSA device at 2.4 GHz because WiFi routers operate at 2.4 to 2.48 GHz. The signal strength was tested via the LED/receiving signal strength using programming codes in the wireless sensor module to track the body location with a maximum range of 40 m (see Appendix B). Therefore, we uploaded the codes for LED/RSSI at the GitHub repository [65].
# Development of an Artificial 3D Liver Phantom for Analysis of Radiotherapeutic Effects In Vitro
## 3 Results  
### Microfluidic Chips Successfully Simulate the Hypoxic Condition of a Solid Tumor  
As previously mentioned, one key characteristic of HCC is the hypoxic environment generated within the solid tumor due to rapid cellular proliferation. To confirm hypoxic conditions of HepG2 cells in MCs, we measured \(\text{Hifl}\,\alpha\) expression in the nucleus, a common hypoxia marker [12], and compared it to cells cultured in standard 48-well plates. A translocation of \(\text{Hifl}\,\alpha\) from the cytoplasm to the nucleus is visible in Figure 2A,B. We found a significant increase in \(\text{Hifl}\,\alpha\) fluorescence intensity from 74.4% \(\pm\) 2.0% in well-plate-cultured cells to 81.8% \(\pm\) 1.9% in MC-cultured cells (\(p\) = 0.0395) (Figure 2C). This confirms that cells grown in MCs experience hypoxia and better simulate the realistic hypoxic conditions in the HCC tumor environment.
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 5 Algorithm for Maximizing the Probabilities of Achieving Deadlines  
Maximizing the DAP of network traffic requires that the forwarding decisions for each packet at each node are made with optimal deadline-aware routing tables given the remaining TTD. To iteratively improve the routing tables and ultimately maximize the DAPs, we introduce an additional step in ADPAD that involves updating the routing tables in each round. The resulting algorithm is presented as the Algorithm for Maximizing the Probabilities of Achieving Deadlines (AMPAD).  
Empty routing tables are initialized in line 6. After finishing the loop in line 14, which calculates \(F_{u\to v_{i}}\) for all neighbors \(v_{i}\) of node \(u\), we carry out the following maximization in line 15, which may return a set with more than one optimal result:  
\[h_{u}(\tau)\in\operatorname*{arg\,max}_{v_{i}\in N(u)}F_{u\to v_{i}}(\tau). \tag{3}\]  
Here, \(h_{u}(\tau)\) (without the check mark) represents the best routing choice(s) that node \(u\) can make for the remaining TTD \(\tau\) based on its current knowledge about the network delays. In case the \(\operatorname*{arg\,max}\) operation provides more than one solution, \(h_{u}(\tau)\) must be chosen, for example, randomly from the returned set.  
Let us assume that node \(u\) adopts a new routing table according to Equation (3). Then, the update of \(u\)'s CDF \(F_{u}(\tau)\) in line 14 corresponds to the newly adopted routing table. By recursively applying this to each round, the routing tables improve toward maximizing the DAP.  
```
1:procedureInit
2:if this node is destination then
3:\(F_{s}(\tau)\gets 1\), \(\tau\geq 0\), 0 otherwise
4:else
5:\(F_{u}(\tau)\gets 0\)\(\forall\tau\)
6:\(h_{u}\leftarrow\emptyset\)
7:endif
8:endprocedure
9:procedureRound
10: Broadcast \(F_{u}\)
11: Receive CDFs \(F_{v_{i}}\) from all neighbors \(v_{i}\)
12:foreach \(v_{i}\in N(u)\)do
13:\(F_{u\to v_{i}}(\tau)\gets F_{v_{i}}(\tau)*f_{uv_{i}}(\tau)\)\(\forall\tau\)
14:endfor
15:\(h_{u}(\tau)\leftarrow\operatorname*{arg\,max}_{v_{i}\in N(u)}F_{u\to v_{i}}( \tau)\)\(\forall\tau\)
16:\(F_{u}(\tau)\gets F_{u\to h_{u}(\tau)}(\tau)\)\(\forall\tau\)
17:endprocedure
```  
**Algorithm 2** Algorithm for Maximizing the Probabilities of Achieving Deadlines (AMPAD)  
The routing tables in the AMPAD are interdependent between nodes. Changes to one table may affect the DAP of neighboring nodes and therefore their tables, and so on, rippling this way through the entire network. Therefore, maximizing the DAP also yields the set of DAP-optimum routing tables for the entire network and not just for individual nodes. The proofs in Appendix A regarding convergence in finite time are also applicable to the AMPAD. Furthermore, we provide proof in Appendix B that the solution obtained by the AMPAD is optimal. This proof can be read in direct continuation of the proofs of Appendix A.
## 2 Numerical Study: Physical Understanding of Wave Propagation Mechanism
### Numerical Parameters
#### 2.1.3 Solution Methods  
The laminar viscous model was used, and no turbulence model was applied. Since the first-order upwind scheme may tend to smooth the shocks, the second-order upwind flow discretisation was used. For the gradient spatial discretisation scheme, the least squares cell-based option was selected. The first-order implicit formulation was used as the transient formulation and is considered sufficient for most problems. Finally, the AUSM (Advection Upstream Splitting Method) was chosen as the convective flux type since it provides an exact resolution of contact and shock discontinuities and is recommended for such cases. The time-step of the transient simulation was set at \(5\times 10^{-10}\) s.
## 2 Thermal Energy Storage (TES)
### Sensible Heat Storage (SHS)  
In TES systems, thermal energy can be stored either as sensible heat or as latent heat (Figure 2). In case of sensible heat storage (SHS) systems, storing of energy is induced by utilization of the heat capacity gained by temperature increment of the material. The energy storage capacity of SHS systems depends on the specific heat capacity of the material, the quantity of the material and the temperature change gradient. Radiation, convection, or conduction are the parameters that affect and can raise the corresponding temperature [9,10].  
Figure 1: Classification of thermal energy storage types and materials.  
Figure 2: Sensible and latent heat.  
The materials used for SHS are either in the liquid phase or the solid phase. The utilized liquid phase materials are water, molten salts, and oils. Water as an SHS material is very efficient for applications in temperatures below 100 \({}^{\circ}\)C, due to its high specific heat capacity, abundance, and low cost. Molten salts also have desired properties, and they are suitable for application above 100 \({}^{\circ}\)C. On the other hand, oils have low specific heat capacity and thermal conductivity, thus their performance is limited. Solid-phase materials used for SHS can be metals or nonmetals. Metals such as aluminum, copper, iron, and metal alloys have high thermal conductivity and specific heat capacity, though their high cost limits their application. Nonmetallic materials that can be used for SHS are rocks, concrete, marbles, bricks, granite, etc., but the disadvantage of all of these materials lies in their low thermal conductivity and specific heat capacity [6,8].
# Far Off-Resonance Laser Frequency Stabilization Technology
## 2 Far Off-Resonance Laser Frequency Stabilization by Pound-Drever-Hall Technique
[PERSON]'s group at the East China Normal University is committed to the research on narrow-linewidth lasers and has made some progress [43; 44; 45]. PDH technology is not only widely used in the field of narrow-linewidth lasers, but also in various types of quantum optical experimental systems [46; 47]. The PDH technology offers the advantages such as flexible frequency stabilization range, high frequency stabilization accuracy, narrow linewidth, and high stability. However, vibration isolation, special structural design, and mounting configurations of reference cavities for less sensitivity to environmental vibration, the increase in the length of the reference cavity, glass or single crystal silicon with ULE, cryogenic cooling, and vacuum chambers with high temperature stability to reduce the thermal noise limit increase the cost and difficulty of the experiment. Figure 2: Schematic of a vertical vibration-insensitive optical cavity. (Figure reproduced from [33]).
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 4 Experimental Results  
### Testbed  
As shown in Figure 5, the testbed consists of two identical BeagleBone Black [41] boards. Each board is equipped with a TI AM3358 SoC [42] with a single-core ARM Cortex-A8 CPU [43] running at 1 GHz and 512 MB of SDRAM. Also of interest for this discussion, each board has a 10/100M Ethernet interface, which has been used for Modbus connectivity. To this purpose, the interfaces have been configured to operate in 100 BaseTX full-duplex mode at 100 Mb/s. No switches were needed to connect the two interfaces because they support automatic MDI/MDIX negotiation.  
To avoid interfering with the experiments, software upload and board management have been performed via an independent WiFi interface attached to their USB port. Moreover, data transfer through this interface took place only before and after the experiments. The boards boot from an SD card and run a version of the Linux operating system customized by the vendor, based on Debian 11.11 and kernel version 5.10.168-ti-r79. No further kernel or system software customization has been performed for the experiments described here. The UDP and TCP/IP protocol stack is also standard. As shown in the figure, four test programs were developed for the experiments:  
1. Two slave programs, based on the FreeMODBUS [28] Modbus slave library. Both programs respond to the RH and WM requests described in Section 3.2. The internal processing time is minimized by discarding incoming data and generating dummy data upon request.
2. Two master programs, based on the Modbus Master[29] Modbus master library. They issue \(n=\) 10,000 sequential RH and WM transactions to the corresponding slave and measure their round-trip time.  
One master/slave pair uses the standard Linux/TCP port distributed with the Modbus master and slave libraries, which implements the Modbus-TCP protocol. The other pair uses a custom Linux/UDP port developed as specified in Section 3 to implement the Modbus-UDP protocol. The round-trip time was measured by means of the Linux CLOCK_MONOTONIC clock, with a resolution of 1 us.  
In addition to conducting the Modbus-UDP performance evaluation presented in Sections 4.3 and 4.4, the broadcast-based master election protocol proposed in [13] and  
Figure 5: Hardware and software testbed used for performance evaluation.  
originally conceived for Modbus-RTU and Modbus-CAN was tested with Modbus-UDP to confirm that the latter can properly handle broadcast traffic. The results were positive since the protocol passed its test suite.