esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Irradiation Experiments  
The PVDF membranes were cut into 47 mm diameter discs, or half discs with a diameter of 77 mm for tests with shielded and reused PVDF, respectively. Prior to irradiation experiments, the hydrophobic PVDF adsorbent was pre-wetted with ethanol for 2 min followed by washing with water for 2 and 5 min. Further, the PVDF adsorbent was placed in 78 mm soda-lime Petri dishes. 25 mL of diclofenac sodium salt solution (25 mg L\({}^{-1}\)) in deionized water (Milli-Q water filtration station, Millipore, Burlington, MA, USA) was added to petri dishes with and without PVDF. An 83 mm Petri dish was placed as a cover. The Petri dish was placed on a radial shaker. The sample was irradiated with UVA light (combined radiant flux density of 17 mW cm\({}^{-2}\), Heraeus Original Hanau Suncare OH M 21/25 R Slim25 W Tanning Tube from HN Sunlight GmbH, Hanau, Germany) for up to 18 h. Samples were taken afterwards to monitor the transformation process via UV-Vis spectroscopy, HPLC, TOC and GC-MS.  
For experiments with shielded (not irradiated) PVDF or reusability tests, the half discs were placed in an in-house manufactured 78 mm petri dish (see Figure 1). Glass mounts hold the PVDF discs in place. The covering petri-dish was half-shielded with an opaque reflective foil (see Figure 1) and placed in a way above the PVDF discs, so that the PVDF adsorbent was either shielded or irradiated.  
PVDF was also tested for re-use up to five times. The irradiation time was set to 30 min. In between the irradiation, the PVDF adsorbent was either directly reused, washed 2 times with water (each 25 mL) for 2 and 5 min or washed in ethanol (25 mL) for 2 min and twice in water (each 25 mL) for 2 and 5 min.  
To gain knowledge about the effect of adsorption of transformation products during photoreaction and afterwards a PVDF disc was added for 1 h after 18 h of UVA irradiation (without PVDF).
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 4 Interference Analysis of LTE-M Ground System
### Network Topology  
In this paper, the regular hexagonal macro cellular network is considered for the LTE FDD system topology structure. The macro cellular network topology structure is shown in Figure 9, where 1 represents the central cell, each cell includes three identical sectors and the cell base station adopts a \(65^{\circ}\) sector directional antenna. The simulation area simulates the layout of 19 regular hexagonal cells at two floors. The radius \(r\) of the cell is 250 m, the radius \(R\) of the macro cell is 433 m, and the inter-site distance (\(ISD\)) between the center base stations of the adjacent cell is 750 m. When simulating interference, wrap around the edge causes each cell to have the influence of at least two layers of peripheral cells.  
The LTE-M system model is not the same as the LTE FDD cellular network, and there is currently no standard protocol for it. Therefore, this paper establishes a linear topology model for LTE-M train operation system based on actual track size and train parameters. The given model is based on a linear train track. The train is located randomly on the linear track, and the vertical distance from LTE-M BS is no more than 500 m, and the LTE-M BS is set at a horizontal distance of 30 m from the track. The coverage radius of a single base station is 500 m. The system topology structure is shown in Figure 9.  
Offset \(D\) indicates the distance between the interferer system and the victim system. Figure 10 represents the offset between the two systems. When \(D=0\), it indicates the common location of the two systems, and when \(D=R=ISD/\sqrt{3}\), it indicates that the interfere system is at the cell edge of the victim system.
## 2 Materials and Methods
### Enrichment of Microbes  
Since oilfield samples were characterized by low microbial content, produced water samples (PW_N1-PW_N5) were inoculated into selective nutrient media to obtain sulfate-reducing enrichment cultures as the primary factor of biocorrosion (\(n\) = 5 for each sample). The composition of the modified liquid Postgate's medium for sulfate-reducing bacteria is as follows (per liter): 0.5 g of Na\({}_{2}\)SO\({}_{4}\); 0.3 g of KH\({}_{2}\)PO\({}_{4}\); 0.5 g of K\({}_{2}\)HPO\({}_{4}\); 0.2 g of (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\); 1.0 g of NH\({}_{4}\)Cl; 0.06 g of CaCl\({}_{2}\)\(\times\) 6 H\({}_{2}\)O; 0.1 g of MgSO\({}_{4}\)\(\times\) 7 H\({}_{2}\)O; 2.0 g of Na lactate; 1.0 g of yeast extract; 0.004 g of FeSO\({}_{4}\)\(\times\) 7 H\({}_{2}\)O; 0.3 g of Na citrate \(\times\) 2 H\({}_{2}\)O with a pH of 7.5. Additionally, 10 mL of sterile 10% Mohr's salt solution [(NH\({}_{4}\))\({}_{2}\)Fe(SO\({}_{4}\))\({}_{2}\)\(\times\) 6 H\({}_{2}\)O] and 0.05 mL of sterile 1% solution of Na\({}_{2}\)S \(\times\) 9 H\({}_{2}\)O were added to 1 L of the sterile Postgate's medium. A sterile ascorbic acid solution was also added to achieve a final concentration of 0.1 g L\({}^{-1}\)[33].  
Thioglycolate broth as a general-purpose medium for the cultivation of anaerobiles, and aerobes was also used for the enrichment of bacteria from deposits on pipe surfaces (\(n\) = 5 for each sample). Cultivation was performed according to standard protocols. The composition of the thioglycolate broth is as follows (per liter): 15.0 g of casein enzymatic hydrolysate; 5.0 g of yeast extract; 2.5 g of NaCl; 5.0 g of glucose; 0.5 g of Na thioglycolate; 0.75 g of L-cysteine hydrochloride; 1.0 g of Na\({}_{2}\)CO\({}_{3}\); and 0.75 g of agar.  
Culture media were distributed into test and control bottles and sterilized by autoclaving. Samples for the enrichment of sulfate-reducing cultures were added to serum bottles with Postgate's medium, purged with nitrogen, and clamped with sterile rubbers and aluminum caps. Experimental bottles and bottles with only nutrient media as a negative control were incubated at +32 \({}^{\circ}\)C in a thermostat RI 53 Red Line (Binder, Tuttlingen, Germany). A black precipitate formed in the bottles with Postgate's medium confirmed sulfate reduction and hydrogen sulfide production by microbes, while gas formation and turbidity of the medium were noted as positive results in the thioglycolate growth medium assay.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 5 Code Construction and Simulation Results
### Decoder  
The decoder uses an SRAM-PUF observation \(y^{n}\) and the previously stored helper data \(m_{t}^{n}\) to reconstruct the key \(\hat{s}\). First, the log-likelihood ratios (LLR) of the received code bits are calculated. Since the SRAM cells are independently distributed, we can calculate the LLR of each bit separately. The LLR of a code bit \(c\), after observing the corresponding helper data \(m_{t}\) and SRAM-PUF observation \(y\), is  
\[\text{LLR}(y,m_{t})=\log\frac{\pi_{t+1}(y+m_{t})}{\pi_{t+1}(y+t-m_{t})}. \tag{28}\]  
See the Appendix A.1 for a derivation of the above equation. Please note that there are \(2(t+1)\) possible combinations of \((y,m_{t})\). However, due to the symmetry properties of the LLR function (see Appendix A.1), we only need to store \(\frac{(t+2)}{2}\) LLRs in a look-up table. Finally, the LDPC decoder uses an iterative Soft-Decision decoder (belief propagation algorithm), combined with CRC for error detection, to reconstruct the 128 bit secret \(\hat{s}\) from the received LLRs.
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 3 Results and Discussion
### Validation of OMI AAOD with AERONET AAOD
compared the inversion between the OMI and AERONET AOD at 27 stations over East and Central Asia and yielded a satisfactory level of agreement, with R about 0.77, especially with more accurate retrievals in South Korea and East and North China [51]. OMI SSA retrievals were quite consistent with the AERONET SSA retrieved on the basis of 269 ground-based stations at a global scope, with around 69% retrievals concordance under an absolute difference of \(\pm\)0.05 [52]. As for AAOD, [PERSON] et al. evaluated the OMI retrievals in Karachi, Pakistan with AERONET AAOD, and the results revealed a higher positive correlation (R\({}^{2}\) = 0.78) [53]. There was a moderately positive correlation between the AERONET AAOD and OMI AAOD in a study conducted in Saudi Arabia (R\({}^{2}\) = 0.39) [26]. In Figure 3, 4452 daily 500 nm AAOD values for the time period of 2005-2018 were compared between the satellite-based OMI and ground-based AERONET at the above four sites. In general, the AERONET AAOD was higher than the OMI AAOD in all seasons and months. The seasonal distribution of AERONET AAOD exhibited the highest AAOD\({}_{500}\) values in spring (0.042 \(\pm\) 0.028) and winter (0.041 \(\pm\) 0.034), followed by autumn (0.031 \(\pm\) 0.025) (Figure 3a). The summer displayed the lowest AAOD value (0.026 \(\pm\) 0.025). OMI AAOD\({}_{500\text{nm}}\) retrievals revealed a similar temporal pattern in the order of winter (0.027 \(\pm\) 0.033) \(\sim\) spring (0.025 \(\pm\) 0.020) \(>\) autumn (0.018 \(\pm\) 0.028) \(\sim\) summer (0.019 \(\pm\) 0.013). As shown in Figure 3b, the lowest AAOD values were in the months of July (0.017 \(\pm\) 0.010) for AERONET and September (0.014 \(\pm\) 0.010) for the OMI, respectively. [PERSON] carried out validation of the OMI AAOD\({}_{500}\) with AERONET measurement, and also found lower AAOD values compared to the AERONET-AAOD with a distinct annual cycle [25]. On the whole, although the OMI underestimated the AAOD value, the scatter plots portrayed well the correlation between the OMI AAOD data and AERONET, and the OMI was highly credible and applicable over the studied region. Figure 3: Seasonal (**a**) and monthly (**b**) distribution of AAOD\({}_{500}\) derived from AERONET and OMI instruments at 4 sites (Xianghe, Taihu, Hongkong Polytechnic Univ, and Sacol) during 2005–2018. The bottom and top whiskers represent the 10 th and 90 th percentiles, the lower and upper boundaries of the central box represent the 25 th and 75 th percentiles, the black solid circle represents the arithmetic mean, and the center line of the box represents the median, respectively.
## 4 Security and Privacy of IoNT
### Security and Privacy Attacks
#### 4.4.2 Disclosure Attacks  
The following depicts the disclosure attacks that target the IoNT:  
* Eavesdropping: The attacker spies on the communication between two IoNT nodes in the network [1; 38; 40];
* Jamming attack: The attacker disrupts communication between other nodes by creating noise in the communication channel [1; 38; 40];
* Sybil attack: Geographical routing protocols are disturbed by a malicious node claiming multiple identities [38; 39; 40];
* Man-in-the-Middle attack: This attack grants the attacker the opportunity to exploit a potential weakness, view and listen to data, and then, thereafter, replay and modify the data that is being conveyed in a covert manner [38; 39; 40].
## 3 Performance Analysis
### Ber
#### 3.1.1 Derivation of \(\mathrm{P_{ci}}\) and \(\mathrm{P_{cm}}\)
The random variables \(\left|Z_{nq}\right|\) and \(\left|Z_{nq}\right|\) follow identical folded normal distribution [21; 32]. Thus, the probability density function (PDF) of \(\left|Z_{nq}\right|\) and the cumulative distribution function of \(\left|Z_{nq}\right|\) are formulated as  
\[f_{\left|Z_{nq}\right|}(r)=\frac{1}{\sqrt{2\pi\sigma_{1}^{2}}}\left[exp\left(- \frac{\left(r-\mu_{1}\right)^{2}}{2\sigma_{1}^{2}}\right)+exp\left(-\frac{ \left(r+\mu_{1}\right)^{2}}{2\sigma_{1}^{2}}\right)\right] \tag{25}\]\[F_{|Z_{nq}|}(r)=e\!r\!\!\left(\frac{r}{\sqrt{2\sigma_{2}^{2}}}\right) \tag{26}\]  
Then, the mean and the variance of \(\left|Z_{n\hat{q}}\right|\) can be given as  
\[\mu_{|Z_{nq}|}=\sqrt{\frac{2}{\pi}}\sigma_{1}\text{exp}\left(-\frac{{{\mu_{1}}^{ 2}}}{2\sigma_{1}^{2}}\right)-\mu_{1}\text{erf}\!\left(-\sqrt{\frac{{{\mu_{1}}^{ 2}}}{2\sigma_{1}^{2}}}\right) \tag{27}\]  
\[\sigma_{|Z_{nq}|}^{2}={{\mu_{1}}^{2}}+\sigma_{1}^{2}-\mu_{|Z_{nq}|}^{2} \tag{28}\]  
It is assumed that \(Y_{1}=\max\{\left|Z_{nq}\right|\}\) and \(q=1,2,\ldots,Q-1\). The conditional error probability of the Walsh code detection is calculated by  
\[P_{ed}(e|\gamma_{rd})=\int_{0}^{\infty}{[1-\text{Pr}\{Y_{1}\leq x \}]f_{|Z_{nq}|}(x)dx}=\int_{0}^{\infty}{[1-\prod\limits_{q=1}^{Q-1}{\text{Pr} \{Z_{nq}\leq x\}}]f_{|Z_{nq}|}(x)}\] \[=\frac{1}{\sqrt{2\pi\sigma_{|Z_{nq}|}^{2}}}\int_{0}^{\infty}{ \left\{1-\left[\text{erf}\!\left(\frac{x}{\sqrt{2\sigma_{2}^{2}}}\right) \right]^{Q-1}\right\}}\cdot\left\{\text{exp}\!\left[-\frac{\left(x+\mu_{|Z_{nq }|}\right)^{2}}{2\sigma_{|Z_{nq}|}^{2}}\right]\right. \tag{29}\] \[+\text{exp}[-\frac{\left(x-\mu_{|Z_{nq}|}\right)^{2}}{2\sigma_{| Z_{nq}|}^{2}}]\}dx\]  
It is assumed that \(L_{v},v\in\{sr,sd,rd\}\) channel coefficients are independent and identically distributed (i.i.d) Rayleigh-fading channels. Simultaneously, it is assumed that the average power gains of the \(L_{v}\) are equal. The PDF of \(\gamma_{r},v\in\{sr,sd,rd\}\) can be expressed as [21; 32]  
\[f(\gamma_{r})=\frac{\gamma_{v}^{L_{v}-1}}{(L_{v}-1)\gamma_{r}^{L_{v}}}\text{ exp}\!\left(-\frac{\gamma_{v}}{\overline{\gamma}}\right) \tag{30}\]  
where the average SNR is \(\overline{\gamma}=\frac{E_{s}}{N_{0x}}\mathbb{E}\Big{[}\alpha_{v,L}^{2}\Big{]},l\in\{1,2,\ldots,L_{v}\}\) with \(\sum_{l=1}^{L_{v}}E[\alpha_{v,L}^{2}]=1\). Therefore, the average error probability of the Walsh code detection can be given as  
\[P_{ed}=\int_{0}^{\infty}{P_{ed}(e|\gamma_{rd})f(\gamma_{rd})d\gamma_{rd}} \tag{31}\]
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
###### Abstract  
In recent years, 5G photonic technology has gradually gained increased attention from scholars. However, studies on 5G photonic technology have mostly focused on technological discussions or market development research, but have failed to identify the relevant key technologies from a comprehensive perspective. In particular, 5G photonic technology is an interdisciplinary technology that could create considerable potential business opportunities in the future, therefore, identifying related key technologies is crucial. Accordingly, the patents of 5G photonic technologies were used as the basis for analysis in this study, and a patent technology network for such technologies was constructed using network analysis. The results showed that the key technologies of 5G photonic technology are mainly related to optics, nanostructures, semiconductors, and material analysis, indicating an interdisciplinary feature instead of focusing only on one specific technological field. Additionally, the relevant technologies that have seen active development in recent years are mainly related to optical elements and semiconductor devices. Finally, a patentee analysis demonstrated that information technology companies were the key players in the development of 5G photonic technologies, and the semiconductor industry will have a crucial role in the development of such technologies. In this study, a patent technology network model was constructed to explore the development trend of 5G photonic technologies, thereby providing a reference for the government to promote these emerging technologies.  
Keywords:fifth-generation mobile communication system; photonic technology; patent; key technology; network analysis +
Footnote †: journal: Applied Physics Letters
## 2 Power Quality in DC Microgrids
#### IEC TR 63282 Technical Report  
In IEC TR 63282 [20], in active DC systems, voltages are divided into different levels for temporary and continuous operation. Between zero and maximum, the voltages are divided into six different stages: \(U_{1}\)\(\ldots\)\(U_{6}\) and, in the middle, \(U_{n}\) for continuous and steady-state operation, as shown in Figure 2. The interval between two voltages is called the voltage band. Voltage bands are useful for describing voltage limits without going into the detail of time limits. \(U_{i}\) corresponds to the upper limit of the band \(B_{i}\) (\(i=1,2,3,4,5,6\)).  
* B1: Blackout band --In this voltage band, only small pits up to zero are allowed.
* B2: Emergency band--This is the band in which the voltage can drop below the normal operating band due to a high overload. (Emergency devices and infrastructure-relevant devices can operate in this voltage band).
* B3: Normal operating band--This is the normal operating band (between \(U_{2}\) and \(U_{3}\); Figure 2).  
Figure 2: Voltage bands of a DC system.  
* B4: Operating band of switching, switching and protection devices--In this band, the voltage can exceed or rise due to a sudden change in current. Voltage pulse protection devices do not operate in this band.
* B5: Operating band for overvoltage protection devices--This band is dedicated to the operation of overvoltage protection devices. Above B5, semiconductors can be destroyed by an overvoltage of very short duration.
* B6: Overvoltage trip band--In this band, voltages are not tolerated by the equipment and are likely to cause malfunctions.
* B7: Forbidden band--In this band, permanent damage to the equipment is very likely.  
There are certain events that can cause the DC voltage to momentarily or temporarily deviate from the normal operating band or fluctuate. Irregular operation or the tripping of a station can result in a sharp voltage drop, a high voltage rise or fluctuations, all of which are DC power quality problems. The frequency and magnitude of the events leading to these DC voltage disturbances must be limited. As an example of a time-domain voltage acceptability curve for DC voltage, Figure 3 shows the relationship between voltage bands and power quality that must be considered in DC systems.  
IEC TR 63282 also specifies the recommended voltages for LVDC distribution. For voltage levels between lines for unipolar systems, or between line and midpoint for bipolar systems, Table 2 is presented.  
For voltage levels between lines for two-pole systems, see Table 3.
## 3 Experimental Validation and Analysis  
### The Experiment of Shaking Table  
To validate the predictive performance of the proposed prediction model, deformation simulation experiments were conducted using a shake table at Beijing University of Civil Engineering and Architecture in March 2024. The experiment employed an IPMV GNSS/accelerometer integrated receiver, with a GNSS sampling rate of 5 Hz and an accelerometer sampling rate of 100 Hz. A calibration method was used to align the accelerometer axis with the X-axis of the vibration table. The specific structure of the vibration platform and the installation position of the measurement simulation deformation sensor are shown in Figure 5. Additionally, a control platform was used to set the sampling rate and vibration amplitude.  
The raw acceleration data generated by the vibration is depicted in Figure 6, illustrating the system's capability to accurately capture crucial moments of instantaneous acceleration increase. The graph distinctly shows the instances of acceleration spikes throughout the experiment. This graphical representation underscores the precision and effectiveness of the monitoring system in capturing dynamic changes in acceleration.
## 3 Position-Information Feature Added Two-Stream CNN with Pedestrian Detector and Tracker  
The proposed algorithm, the position-information feature added two-stream CNN with detection and tracking, is introduced in this section. The system flow configuration is presented in Figure 2. The inputs are consist of first frame for detector and \(N\) frames for tracker. Total \(N+1\) frames of video extract the dense SIFT flow image and position-information image. Then RGB, dense SIFT flow and position-information image are learned by position-information feature added two-stream CNN as an input. When pedestrian changes the behavior in the medium term, the input frames are chasing new behavior well before N frames, because the pedestrian must change the behavior with preparation action in a few frame. If the \(N\) is large, the proposed method cannot chase the new action when the pedestrian changes the action in the medium term. Additionally, if the \(N\) is small, the performance of pedestrian behavior detection would be low. To maximize the performance and the robustness to changes, the value of \(N\) is chosen as 10.  
In the first section, the pedestrian-action dataset was applied to binary descriptor dense SIFT flow-based two-stream CNN to improve the performance of action-attribute recognition. As pedestrian actions comprise a set of motions over time, there is a limit to recognizing pedestrian actions with a simple current motion scene. The two-stream CNN is an algorithm that analyzes a set of flow images that indicate how pedestrian motion changes according to the time sequence [22]. In this paper, instead of optical flow, which is a flow image used in the conventional two-stream CNN, a binary descriptor dense SIFT flow, which is robust in complex motion and light changes, is used [25]. In the second section, the  
Figure 1: Comparison between optical flow and binary descriptor dense SIFT flow: (**a**) Serial original RGB images, (**b**) \(x\)- and \(y\)-direction optical flow images, and (**c**) \(x\)- and \(y\)-direction binary descriptor dense SIFT flow images.  
Figure 2: Position-information feature added two-stream CNN structure in a time sequence.  
position-information feature added two-stream CNN, which improved the conventional two-stream CNN, is used to increase the performance of cross-attribute recognition.  
The cross attributes of a pedestrian are independent of the action attributes, such as the movement speed of the pedestrian, but are dependent on where the pedestrian is located [35]. Thus, the position-information feature of the pedestrian is expressed as a position-information feature image, and the position-information stream is fused with the conventional two-stream CNN. In the last section, we used the detector and tracker algorithm without the ground-truth boundary box to check whether the proposed algorithm applies to a moving vehicle in an actual outdoor environment. As most existing pedestrian behavior recognition studies operate algorithms based on ground-truth boundary boxes, there are limitations to their application in real-world vehicle environments.
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 4 Experimental Setup
### Performance Metrics
#### 4.3.4 End-to-End Delay  
The end-to-end delay represents the time taken for a packet to be delivered from the source node to the destination node. We measured the end-to-end delay only when the packet was successfully delivered. The end-to-end delay metric is defined as the time period between when the packet is sent from the source node and when the packet arrives at the destination node as follows:  
\(T_{a}\) = Time the packet arrived at the destination node  
\(T_{s}\) = Time the packet was sent by the source node  
\[E2E=T_{a}-T_{s} \tag{7}\]