esa-sceva/satcom-chunk-collection huggingface dataset:
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 6 Understanding Inter-Sensor Differences  
### SRAL Differences Due to Sensor Mis-pointing  
Inter-comparisons between S3A and S3B measurements generally reveal an excellent agreement for sea surface height, wind speed, significant wave height, and land ice.  
In this subsection, we present an example of the capability of tandem data to reveal minor error sources. It concerns the assessment of regional biases due to orbital effects in comparisons between SRAL-A and B. Comparisons between the two sensors reveal a very small bias in Northern latitudes (30\({}^{\circ}\) to 60\({}^{\circ}\) North) on ascending tracks, see Figure 4. This difference has been eventually traced back to a waveform mispointing of 0.003 deg\({}^{2}\) on SRAL-A. This anomaly has never been detected before.  
Figure 4: Mispointing angle differences between Sentinel-3 A and B for ascending (**left panel**) and descending passes (**right panel**).  
Figure 3: Buoy match-up results for significant wave height (SWH) Sentinel-3 and buoys. Labels identify variables plotted on the x-axis and y-axis. Histograms on diagonal relate to the variable identified on the x-axis. Scatterplots show data as grey dots, the least-square fit line in red, and the one-to-one line in blue. Each scatterplot includes the Pearson correlation coefficient (R; those that are significant at the 0.05 level are marked by *) and the number of data pairs (N).
## 5 Experiment and Performance
### Experiment in Real Underground Coal Mine  
The field test was carried out in the Shangwan coal mine, located in Erdos (Inner Mongolia, China). The test environment is shown in Figure 7, where there is a main tunnel with a depth of about 160 m, width of 5 m and height of 4 m, and there are multiple branch tunnels along the main tunnel. Due to the underground coal mine management rules, the scale of our testing network is restricted. In this case, only two mesh WSNs were deployed: WSN_1 and WSN_2. Each WSN contains a sink node, two routing nodes and three sensor nodes. Each sensor node has three sensors: MC112, LM35 and HM1, thus is capable of collecting three types of parameters of gas, temperature and humidity, as shown in Figure 3. Figure 7b depicts the testing environment and the deployment of the WSNs.  
In the field test, the parameters are almost the same as the parameters of the laboratory tests, except the working period is 3 min not 5 min. To avoid influencing the normal production of the coal mine, the field test only lasted for 1 h and mainly tested the data transmission reliability.  
Figure 7: **(a)** Testing scenario in an underground coal mine; **(b)** Deployment of WSNs.  
The test results are recorded in Table 5, where, the Number of periods means the times that the sensor nodes transmitting data frames to the sink node while the Number of received data means the data frames the sink node had successfully received. As shown in Table 5, the WSNs are working reliably.
## 2 Basic Concepts  
### Graph Representation of Kinematic Chains  
Graph theory is widely used in the conceptual design stage because it can clearly represent the connectivity between the links of a KC and helps the designer enumerate KCs systematically. A graph can be represented by changing the links of a KC into vertices and the joints into edges. For example, the 2-DOF 9-link 11-joint KC in Figure 1a can be expressed as a graph with nine vertices and eleven edges, as shown in Figure 1b, and the KC in Figure 1c can be expressed as a graph in Figure 1d. Thus, as shown in the examples, when a KC with a complex structure is represented as a graph, its structure and the connectivity among the links can be easily comprehended. As described above, there is a one-to-one correspondence between graphs and KCs. Therefore, based on graph representation advantages, designers can efficiently obtain KCs by enumerating graphs that satisfy desired conditions.
# Inner-Loop Controllers for Grid-Forming Converters
## 2 Materials and Methods
### Proposed Inner-Loop Voltage Controller with Current Limiter
#### 2.1.2 Voltage Controller Design
The full block diagram of the proposed inner-loop voltage controller with a current limiter is presented in Figure 9.  
Considering the parameters of the project presented in Table 1, \(\mathbf{Q}=\mathbf{I}_{5\times 5}\) and \(\mathbf{R}=1\) as the weighting matrices of DLQR, \(\mathbf{k}_{i}=\left[ \begin{array}{cc}5&0&1\end{array} \right]^{\mathsf{T}}\), \(k_{r}=8\), and \(k_{w}=8\), the gains obtained are \(\mathbf{k}_{1}=\left[ \begin{array}{cc}0.1085&0.1149&0.0106\end{array}  \right]^{\mathsf{T}}\) and \(\mathbf{k}_{2}=\left[ \begin{array}{cc}0.0251&-0.0259\end{array} \right]^{ \mathsf{T}}\). The Bode diagram that relates the reference \(v_{ref}\) and the output voltage \(v_{c}\) is presented in Figure 10. The Bode diagram that relates the input current \(I_{o}\) and the output voltage \(v_{c}\) is presented in Figure 11.  
Figure 10: Bode diagram that relates the reference \(v_{ref}\) and the output voltage \(v_{c}\). Figure 9: Block diagram of the inner-loop voltage controller with current limiter and virtual impedance. 2.1.3. Thevenin Equivalent for Virtual Impedance Implementation  
For the Thevenin equivalent of the virtual impedance implementation, the block diagram in Figure 9 is considered. Since the continuous-time virtual impedance is non-causal, a pole must be introduced to make it causal. Any discretization method for solving ordinary differential equations can be applied; however, to simplify the state-space model, [PERSON]'s discretization method is chosen. By applying [PERSON]'s method with a sampling period \(T_{s}\), the discrete-time representation is obtained in (23) and (24) and is represented in state-space form in (25) and (26). \[x_{vk+1} =(1-a_{v}T_{s})x_{vk}+a_{v}T_{s}i_{Lk} \tag{23}\] \[v_{Lk} =(R_{v}-a_{v}L_{v})x_{vk}+a_{v}L_{v}i_{Lk}\] (24) \[x_{vk+1} =g_{vk}x_{vk}+h_{vl}i_{ok}\] (25) \[v_{Lk} =c_{vi}x_{vk}+d_{vi}i_{ok} \tag{26}\]
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 3 Performance Evaluation
### Sensor Network Lifetime: Average Lifetime and Scalability
\begin{split} K^{2}&=\frac{V\epsilon_{f_{\text{s}}}M^{2 }}{6\epsilon_{amp}d_{toBS}^{4}}\\ K_{opt}&=\sqrt{\frac{V\epsilon_{f_{\text{s}}}M^{2 }}{6\epsilon_{amp}d_{toBS}^{4}}}\end{split}  \tag{11}\]  
Therefore, the value of \(K_{opt}\) in Equation (11) is the optimal number of clusters for our energy model. Figure 14a evaluates the scalability of the algorithms when using a 500-node network, while Figure 14b reveals the scalability of the algorithms in terms of the optimal number of clusters as analytically derived above: an algorithm using less clusters is more scalable than one that requires many clusters. The experimental results are shown in Figure 14b using a number of sensor nodes ranging from 100-500, a network of length M = 100 and \(\epsilon_{f_{\text{s}}}=10\) pJ/bit/m\({}^{2}\). It is observed that the optimal number of clusters increases with the increase in the number of sensor nodes for all of the protocols. Compared to the DECSA and the MOCRN protocols, the SAC protocol revealed the least number of optimal clusters for the different number of sensor nodes considered. It is therefore more scalable than the two algorithms.
# Recent Advances in the Synthesis of Graphene-Based Nanomaterials for Controlled Drug Delivery
## 3 Drug Delivery with Graphene-Based Nanomaterials
### Graphene-Nanoparticle Nanohybrids
The as-prepared GN-CNT-Fe\({}_{3}\)O\({}_{4}\) nanohybrids exhibited superparamagnetic behavior (19.8 emu g\({}^{-1}\)), good biocompatibility for Chang liver cells, high loading capacity for 5-fluorouracilacil (0.27 mg mg\({}^{-1}\)), and possessed a pH-activated release profile [47]. [PERSON] et al., synthesized polymer-stabilized iron oxide-graphene (PIG) nanocomposites via a one-step solvothermal process [36]. The as-prepared PIG exhibited high colloidal stability and biocompatibility, and could efficiently bind with both hydrophilic and hydrophobic drugs. The drug loading efficiency of PIG is \(\sim\)87% for DOX and \(\sim\)91% for paclitaxel, respectively. [PERSON] and co-workers developed a multifunctional platform for synergistic therapy with controlled drug release, magnetic hyperthermia, and photothermal therapy [39], which used GQDs as caps and local photothermal generators and magnetic mesoporous silica nanoparticles (MMSN) as drug carriers and magnetic thermoseeds (Figure 4). The obtained MMSN/GQD could effectively produce heat under an AMF and/or near infrared irradiation. The combined chemo-magnetic hyperthermia therapy or chemo-photothermal therapy showed a higher efficiency than chemotherapy, magnetic hyperthermia, or photothermal therapy alone. Nanoparticle-modified GQDs could be also used as a potential drug delivery platform. For example, [PERSON] and coworkers functionalized fluorescent GQDs onto mesoporous silica  
Figure 4: Schematic illustration of the preparation of DOX- magnetic mesoporous silica nanoparticles (MMSN)/GQD nanohybrids and its controlled drug release and photothermal therapy mechanism. Reproduced with permission from [39]. Copyright [PERSON], 2017.  
nanoparticles (MSN) via acid-cleavable acetal bonds. The GQD@MSN nanohybrid not only can effectively load and release DOX, but can also track the drug delivery process in vitro [48]. [PERSON] et al., developed an aptamer-targeting photoresponsive drug delivery system by non-covalent assembly of Cy5.5-AS1411 aptamer conjugate on the surface of GO wrapped DOX-loaded MSN (MSN-DOX@GO-APT) for light-mediated drug release and aptamer-targeted cancer therapy [60]. The in vitro experimental results indicated that the synergism of chemo- and photothermal therapy in a single MSN-DOX@GO-APT platform has high performance to kill cancer cells than monotherapies. [PERSON] and coworkers developed a multifunctional drug delivery system by combining GO with low cytotoxic, high photothermal conversion efficient CuS nanoparticles. The as-prepared nanocomposite exhibited a high loading efficiency (900 mg\({}^{-1}\)) and pH-responsive DOX release under near infrared laser irradiation [40].
## 3 Results and Discussion
### Identification of Cu(II) Inhibition Extents in Anaerobic and Aerobic Stages
#### 3.4.1 Inhibition Causes of Phosphorus Metabolism  
Figure 4a shows no significant difference between the cases of Cu(II) initially present in the anaerobic and aerobic stages, regardless of phosphorus release or uptake. This revealed that deterioration of the ability for PAOs to take up phosphorus at the aerobic stage directly originated from the inhibition of aerobic phosphorus metabolism, and it was irrelevant to the inhibition of the previous anaerobic phosphorus metabolism. The inhibition percentages of phosphorus metabolisms were differentiated from different metabolic stages, as shown in Figure 4b. At 0.5 mgL\({}^{-1}\) of Cu(II), no inhibition of the anaerobic phosphorus release was found, whereas 36% of the aerobic phosphorus uptake was inhibited, among which, only 5% of inhibition came from the previous anaerobic stage and 31% resulted from the inhibition at the aerobic stage itself. At 1.0 mgL\({}^{-1}\) of Cu(II) only 5% of the anaerobic phosphorus release was inhibited, whereas 82% of the aerobic phosphorus uptake was inhibited because of the inhibition at the aerobic stage itself. At 2.0 mgL\({}^{-1}\) of Cu(II), 100% of the aerobic phosphorus uptake was inhibited because of the inhibition at the aerobic stage itself.
## 5 DRRF Extensions (for Non Real-time Traffic)
### Maximum Sustained Traffic Rate Extension  
Maximum sustained traffic rate, _rmax_, is typically used to control the peak rate. This rate excludes overheads, such as a MAC header and other subheaders [1, 2]. We modified DRRF to achieve this criterion as follows: We derive the maximum number of slots required to meet _rmax_. We call this number _rmax_slots_. The derivation is shown below (Equation 3):  
\[rmax\_slots(i)=\lfloor\frac{rmax(i)\times t\_frame}{MCS\_size(i)}\rfloor \tag{3}\]  
Here, again _t_frame_ is the frame size. _MCS_size(i)_ is the number of bytes per slot for the given MCS level of MS \(i\). Note that instead of the ceiling function in case of _rmin_slots_, here we use a floor function.  
Then, we apply the max-min fairness algorithm first without \(r_{max}\) constraint. Consequently, the number of fair share slots is derived. Due to the \(r_{max}\) constraint, some MSs may receive the fair share which is over the \(r_{max}\) limitation. We limit those fair shares to \(rmax\_slots\). Finally, in case there are some left-over slots due to the \(r_{max}\) limitation, we then apply another round of the max-min fairness algorithm to distribute the left-over slots fairly among the flows which have no \(r_{max}\) constraint.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 5 Proposed Scheme EIA
### Registration  
To protect user privacy, each vehicle must first register with the AMF to obtain its pseudonym before participating in the communication. Since the connection between RSUs and OBUs is wireless, it is inherently open and dynamic, rendering the channel insecure. Therefore, to register vehicles onto the 5G network, our scheme employs the 5G authentication and key agreement (5G-AKA) mechanism introduced by the 3 GPP standard to ensure compatibility with the 5G network. This creates a secure channel between the OBU and the AMF, through which the registration process is securely completed. A secure channel ensures the confidentiality and integrity of the data transmitted through it. The detailed process is described below and illustrated in Figure 2.  
Step 1: First, \(OBU_{i}\) sends its registration request and registration information to the AMF through the abovementioned secure channel. The information includes the distinct vehicle identity, denoted by the unique vehicle identifier \(RID_{i}\), essentially the chassis number of the vehicle. The chassis number is more accurately called the \"vehicle identification number\", commonly known as the frame number, which is the unique certificate for each vehicle. Concurrently, the registration process involves \(OBU_{i}\) generating a random number \(x_{i}\) within the group \(Z_{q}^{*}\). Subsequently, the registration request, specifically \(\{RID_{i},X_{i}\}\), where \(X_{i}=x_{i}P\), is sent to the AMF.  
Step 2: After the AMF receives the registration request \(RID_{i},X_{i}\), it verifies the legality and uniqueness of the identity and selects \(r_{i}\in Z_{q}^{*}\) and then calculates \(R_{i}=r_{i}P\), \(H_{i}=h_{1}(ID_{i}||X_{i}||R_{i})\), and \(y_{i}=(r_{i}+sH_{i})\), \(Y_{i}=R_{i}+P_{pub}H_{i}\). At the same time, the  
Figure 2: Registration.  
pseudonym of the OBU \(ID_{i}=RID_{i}\bigoplus h_{1}(s||R_{i})\) is generated. After completing these calculations, it sends \(\{y_{i},R_{i},Y_{i},ID_{i}\}\) to the \(OBU_{i}\) through the secure channel.  
Step 3: After obtaining \(\{y_{i},R_{i},Y_{i},ID_{i}\}\), \(OBU_{i}\) checks whether \(y_{i}P=R_{i}+P_{pub}H_{i}\) is held. If not, abort it. Otherwise, \(OBU_{i}\) will store the secret private key \((x_{i}+y_{i})\) and publish the public key \((X_{i}+Y_{i})\). Then, the registration of \(OBU_{i}\) is completed.  
Step 4: The registration steps for \(OBU_{j}\) are the same as those for \(OBU_{i}\). \(OBU_{j}\) will store the secret private key \((x_{j}+y_{j})\) and publish the public key \((X_{j}+Y_{j})\). Then, the registration of \(OBU_{j}\) is completed.
# Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information
## 4 Results and Discussion
This model was applied to flood events arising due to extreme rainfall events, and specifically, the SND component is applicable only in case of floods arising with respect to channel pixel slope  
Figure 8: Mapping CSI and FA of proposed model output compared with Sentinel-derived flood inundation map for Kerala flood event of August 2018.  
values. The model cannot be applied in its current form to other forms of flooding, including pluvial or coastal flooding, which can be possible only by considering other relevant factors. As such, there are several limitations to the validation of the proposed model. These include the choice of Sentinel-1A SAR data as the only other reliable source that can provide the spatial extent of a flood inundation. However, the Sentinel satellite overpass over the study regions was mostly during the receding phase of both the flood events; hence the flood peak extent could have probably been missed. Furthermore, the FA and CSI values are also affected by the amount of observational overlap between CYGNSS and Sentinel-1 datasets, as they do not have the same coverage extent and daily repeat rates. The presence of vegetation in both study regions greatly affects the accuracy in deriving the flood extent from SAR images, while rain and wind also affect the omission of inundation retrievals from single SAR imagery. In addition, the presence of smooth surfaces in the reference scene, including roads and rooftops, cause commission errors [36]. In the future, by adopting a probabilistic framework [37], through which it will be possible to generate a probabilistic flood inundation map by exploiting the histogram of the combined model output and separating the non-water pixels, which will result in better mapping capabilities of the inundation uncertainty for a region. With the availability of additional improved GNSS-R SNR datasets, from other navigation satellite constellations, there are active navigation signals that are already available, which are sensitive to the presence of inland surface water that can have downstream applications [38], including inundation mapping in the case of flood events. Those include satellites operated by different space agencies around the world, such as the Global Navigation Satellite System (GLONASS) of Russia), European Space Agency's GALILEO, and the Navigation with Indian Constellation (NavIC), launched by the Indian Space Research Organisation (ISRO).
## 2 Observation Data and Data Processing Methods  
### SD Profile Geomagnetic Survey  
To examine the geophysical characteristics of faults and sedimentary structures in this area, comprehensive geophysical profiles perpendicular to the strike of these faults and sedimentary structures are designed. Based on the analysis of geological data and field investigation results, a comprehensive geophysical profile with a nearly N--S trend was laid from Suhbaatar to Dalandzadgad in Mongolia, which passes Ulaanbaatar, with a total length of approximately 900 km, as shown in Figure 1.  
A total of 160 geomagnetic observation points were arranged along the SD profile, with the codes from north to south being M001 to M160 and the distance between adjacent points being 5-10 km. When selecting the points, the site environment was examined, and the total geomagnetic intensity gradient was measured. The magnetic field gradient within 10 m of each point was less than 5 nT/m, which basically excludes the surface anomalies at the sites. The observation data reflect the magnetic field values of rocks above the top of the Curie threshold in the lithosphere and exclude the influence of shallow anomalies as much as possible. Additionally, 117 sampling points were selected in the profile, and 5 samples were collected from each point, for a total of 585 rock samples, including 235 samples of sedimentary rocks, 45 samples of metamorphic rocks, and 305 samples of igneous rocks [32].  
Geomagnetic measurements were made from July to August 2011. The measurement instrument was a G-856 proton precession magnetometer [33] ([[http://www.magnetometer](http://www.magnetometer)]([http://www.magnetometer](http://www.magnetometer)).  
Figure 1: Geomagnetic stations of the SD profile, seismic activity, and fault distribution in Mongolia. F1 is the Erhun River fault, F2 is the Khangai fault, F3 is the Hanbogdor fault and F4 is the Wenduerhile fault.  
cn/?m=home&c=View&a=index&aid=143, accessed on 22 August 2022), which measures the total magnetic intensity F with a resolution of 0.1 nT and an absolute accuracy of 0.5 nT. Its dynamic range is 20,000 to 100,000 nT, and the sampling rate can be set to 5, 10, or 60 s. At each point, the measurement position of the instrument is 1.5 m above the ground, the sampling rate is at 5 s, and 30 values of F were observed by the proton precession magnetometer. An average value is calculated from every 10 geomagnetic total intensities to obtain 3 average values. During the geomagnetic survey, the Global Positioning System (GPS) was used to measure the longitude, latitude, and elevation of the geomagnetic survey points.
## 3 Results and Discussion
### Amplitude Demodulation Analysis
#### 3.3.1 Without Modulating Frequencies  
The results presented in this section are the ones obtained with the excitations called \"Exc-1-2 kHz-0\", \"Exc-10-11 kHz-0\", and \"Exc-20-21 kHz-0\" in Table 1. Without any modulating frequencies and only the pseudorandom excitation, after an amplitude demodulation analysis, the only frequency expected to be found is the rotating frequency (\(f_{t}\)) for those cases under rotation in case there is a misalignment in the shaft.  
Figure 7 shows this amplitude demodulation analysis for the sensors installed in the rotating frame. It is observed that, for the accelerometer in the shaft (ASH), the rotating frequency is clearly seen for both cases in air and in water, and with greater amplitude in the case of the high-frequency band. The accelerometer in the disk (ADISK) also sees this rotating frequency, but only in the case of the water, where probably the misalignment of the rotating structures is higher. However, the AE sensor located in the shaft (ESH) does not capture this rotating frequency for any of the excitation bands tested. This is another proof that this sensor is not detecting the structural response of the shaft, but it is detecting only the excitation by the transmission of acoustic waves through the structure.  
The results for the stationary sensors are shown in Figure 8. The accelerometer located in the bearing (ABEAR) presents similar results than the one located in the shaft (ASH in Figure 7): it detects the rotating frequency and its harmonics, especially in the high-frequency band (20-21 kHz). However, the rest of sensors from the stationary frame also detect the rotating frequency of the motor (20.5 Hz), its harmonics, and its subharmonics. This is because the vibration of the motor at its rotating frequency is also transmitted through the air and through the support, which is the same piece as the rest of the casing. Therefore, stationary sensors, especially the microphone and the AE sensors in the casing, in this case, are also affected by the motor vibration and not only by the vibration of the rotating part. This is a particular situation in this test rig, but it has to be considered for the analysis of the results. Even so, the accelerometer located in the casing (ATT) is able to detect the rotating frequency of the disk in the case of water for the excitation of 10-11 kHz, where the transmission is better through the liquid, as commented in Section 3.1.  
Figure 7: Amplitude demodulation for the sensors on the rotating frame and the excitation without any modulating frequencies. Rotating case (\(f_{t}\) = 4.1 Hz).