esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 3 Asran
### How to Distinguish Transient Link Instability from Buffer Overflow
#### 3.3.1. Dealing with Transient Link Instability  
Figure 2a shows the effectiveness of the \(ASRAN\) with CWND size fluctuations between original TCP-NewReno and \(ASRAN\) in transient link instability situation. The initial rise of CWND is the result of TCP slow-start. In addition, it takes the first segment loss which results in halving CWND value. At this moment, original TCP-NewReno updates its current ssthresh, \(original\_ssthresh\), as the halved CWND in order to enter congestion avoidance phase. For the first time, \(ASRAN\) also enters congestion avoidance phase because \(last\_max\_cwnd\) is not updated before. Assuming \(last\_max\_cwnd\) is 0 at this moment, maximum \(ssthresh\) value becomes \(original\_ssthresh\), resulting in congestion avoidance phase after all. Then \(last\_max\_cwnd\) is updated to CWND size before halved, which will be used for the next segment loss event. At the second segment loss event in Figure 2a, updated \(last\_max\_cwnd\) is larger than \(original\_ssthresh\) value. Because the size of CWND is less than the recalculated \(ssthresh\) value(\(recalculated\_ssthresh\) value in Algorithm 1), TCP flow quickly recovers CWND by using the slow-start algorithm rather than congestion avoidance. After the second segment loss, CWND rising curve of \(ASRAN\) seems like a linear curve. However, it is a quadratic curve that is increased by slow-start. This is due to the method of CWND size increases in Slow-Start algorithm. Slow-Start algorithm increases CWND size per ACK received. This method results in a quadratic increase in CWND size, unlike congestion avoidance which CWND size increases linearly. Since slow-start phase increases CWND size quadratically, the transmission speed of \(ASRAN\) recovers rapidly, resulting in better performance than original TCP-NewReno.
## 4 Results
### Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Plots** & **RCD** & **TN 1** & **MT 2** & **OE 3** & **CE 4** & \(p\) & \(r\) & \(F\) \\ \hline \multirow{4}{*}{HAIS set aggregation} & U1 & 61.58\% & 132 & 86 & 19 & 27 & 0.76106 & 0.81905 & 0.78899 \\  & U2 & 69.09\% & 186 & 112 & 58 & 16 & 0.87500 & 0.65882 & 0.75168 \\  & G1 & 93.25\% & 108 & 63 & 18 & 17 & 0.78750 & 0.77778 & 0.78261 \\  & G2 & 96.40\% & 261 & 142 & 74 & 44 & 0.76344 & 0.65741 & 0.70647 \\  & Overall & & 687 & 403 & 169 & 104 & 0.79675 & 0.72826 & 0.75743 \\ \hline \multirow{4}{*}{ITS set aggregation} & U1 & 61.58\% & 132 & 100 & 20 & 12 & 0.89286 & 0.83333 & 0.86207 \\  & U2 & 69.09\% & 186 & 133 & 47 & 6 & 0.95683 & 0.73889 & 0.83386 \\ \cline{1-1}  & G1 & 93.25\% & 108 & 65 & 24 & 7 & 0.90278 & 0.73034 & 0.80745 \\ \cline{1-1}  & G2 & 96.40\% & 261 & 183 & 60 & 16 & 0.91960 & 0.75309 & 0.82805 \\ \cline{1-1}  & Overall & & 687 & 481 & 151 & 41 & 0.91801 & 0.76391 & 0.83285 \\ \hline \hline \end{tabular}  
* The actual number of individual trees in the plot. 2 The number of correctly segmented individual trees. 3 The number of under-segmented individual trees. 4 The number of over-segmented individual trees.  
\end{table} 
Table 6: HAIS set aggregation vs. ITS set aggregation. Figure 10: Fixed bandwidth vs. Dynamic bandwidth. \begin{table} \begin{tabular}{c c c c c c c c c} \hline \hline
**Bandwidth** & **Plots** & **RCD** & **TN 1** & **MT 2** & **OE 3** & **CE 4** & \(p\) & \(r\) & \(F\) \\ \hline \multirow{4}{*}{Fixed bandwidth} & U1 & 61.58\% & 132 & 86 & 19 & 27 & 0.76106 & 0.81905 & 0.78899 \\  & U2 & 69.09\% & 186 & 112 & 58 & 16 & 0.87500 & 0.65882 & 0.75168 \\  & G1 & 93.25\% & 108 & 63 & 18 & 17 & 0.78750 & 0.77778 & 0.78261 \\  & G2 & 96.40\% & 261 & 142 & 74 & 44 & 0.76344 & 0.65741 & 0.70647 \\  & Overall & & 687 & 403 & 169 & 104 & 0.79675 & 0.72826 & 0.75743 \\ \hline \multirow{4}{*}{Dynamic bandwidth} & U1 & 61.58\% & 132 & 103 & 9 & 27 & 0.79231 & 0.91964 & 0.85124 \\  & U2 & 69.09\% & 186 & 153 & 18 & 16 & 0.90533 & 0.89474 & 0.90000 \\ \cline{1-1}  & G1 & 93.25\% & 108 & 70 & 15 & 11 & 0.86420 & 0.82353 & 0.84337 \\ \cline{1-1}  & G2 & 96.40\% & 261 & 187 & 55 & 16 & 0.91218 & 0.77273 & 0.84045 \\ \cline{1-1}  & Overall & & 687 & 513 & 97 & 70 & 0.87075 & 0.85266 & 0.885876 \\ \hline \hline \end{tabular}   
* The actual number of individual trees in the plot. 2 The number of correctly segmented individual trees. 3 The number of under-segmented individual trees. 4 The number of over-segmented individual trees.
# A Study on the Stress Field Around an Elliptical Wellbore with an Eccentric Casing in Deep Wells
## 3 Result Analysis and Discussion
### Stress Distribution of Casing Under Different Eccentricities
#### 3.1.4 Mises Equivalent Stress Variation Law  
Figure 10 depicts the contour plots of equivalent stress distribution in casing at different eccentricities. It can be concluded that an increase in eccentricity leads to an uneven distribution of equivalent stress in the casing. The high-stress regions shift towards the side with a thicker cement sheath, while the low-stress regions shift towards the side with a thinner cement sheath. Additionally, both the areas of high-stress regions and low-stress regions continuously increase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\epsilon\)**=0.0** & \(\epsilon\)**=0.3** & \(\epsilon\)**=0.6** & \(\epsilon\)**=0.9** \\ \hline \(\tau_{\text{Max}}/\text{MPa}\) & 15.47 & 47.94 & 78.98 & 96.39 \\ \(\tau_{\text{Min}}/\text{MPa}\) & 1.52 & 2.12 & 2.61 & 2.04 \\ \(K\) & 10.21 & 22.62 & 30.28 & 47.45 \\ \hline \hline \end{tabular}  
* Footnotes: \(\tau_{\text{Max}}\) is the maximum shear stress, \(\tau_{\text{Min}}\) is the minimum shear stress, and \(K\) is the stress non-uniformity coefficient.  
\end{table}
Table 6: Maximum shear stress and stress non-uniformity coefficient under different eccentricities.  
Figure 10: Equivalent stress cloud map of casing under different eccentricity ratios. Equivalent stress cloud map at eccentricities: (**a**) \(\epsilon=0.0\), (**b**) \(\epsilon=0.3\), (**c**) \(\epsilon=0.6\), and (**d**) \(\epsilon=0.9\).  
Figure 9: Distribution of shear stress on the outer wall of casing under different eccentricity ratios.  
Figure 11 shows the scatter plot of equivalent stress distribution on the outer surface of the casing under different eccentricities. By observing the curves, the following conclusions can be drawn: as the eccentricity increases, the stress distribution curve on the outer surface of the casing exhibits multiple extreme points. In summary, increasing eccentricity leads to fluctuations in the equivalent stress on the outer surface of the casing and the appearance of multiple extreme points. However, the maximum equivalent stress remains at the thickest part of the cement sheath. In addition, according to the data in Table 7, increasing eccentricity increases the numerical values of the maximum and minimum equivalent stresses and slightly increases the stress non-uniformity coefficient.
# Present and Future Marine Microwave Satellite Missions in China
## 3 Progress History of China's Marine Microwave Satellite Remote Sensing Technology
### Retrieval Technology for Marine Dynamic Environmental Elements
[54] conducted statistical analysis regarding the spectral gradient rates and polarization gradient rates of typical sea areas and determined the brightness temperature eigenvalue required for calculating sea ice concentrations. The calculation results were found to be consistent with the two state-of-the-art sea ice concentration products provided by the National Snow and Ice Data Center and the University of Bremen (Germany) [54]. Henan University of Technology [55] invented an algorithm that was subsequently patented: an improved ASI (the Arctic Sea Ice) sea ice concentration retrieval algorithm. The obtained sea ice concentration values were found to be significantly lower than the ASI algorithm results, with the sea ice area reduced by approximately 15%. The number of pixels with ice concentrations above 0.15 was approximately 28.6% lower than the obtained using the traditional ASI version 5.3 [56]. The results of the improved ASI algorithm are very different from those of the traditional ASI algorithm [56], which is closer to the results of NASA team [57]. The retrieval algorithm of marine environmental parameters designed according to the characteristics of China Marine microwave remote sensing satellite plays a very important role in the data processing system.
## 2 The Structure of Typical DC Microgrids and Traditional Control Strategies
### PI Double Closed-Loop Control
The primary goal of this HESS is to stabilize the DC bus voltage at a constant level. To achieve this, a low-pass filter (LPF) is integrated, which extracts high-frequency power components, serving as a reference input for the supercapacitor converter. To distribute power effectively among the n battery converters and maintain a stable DC bus voltage, droop control is implemented. Additionally, a bus voltage compensator is employed to mitigate any deviations in the DC bus voltage that may arise due to the application of droop control, ensuring optimal system performance. Aiming at solving problems of weak disturbance immunity and long adjustment time caused by PI control, active disturbance rejection control (ADRC) combined with improved MPC is proposed for HESS. Compared with traditional observers, the voltage expansion state observer of ADRC control is independent of the system model and parameters and consequently has strong disturbance immunity, and significantly reduces voltage overshoots during power fluctuations. Furthermore, the MPC-based inner current control loop accelerates current response speed, and the MPC is improved to achieve smaller switching losses. Based on ADRC, a compensatory approach for DC bus voltage deviations is presented to effectively increase accuracy and maintain constant DC bus voltage.
# _photonics_
## 4 Simulation Results  
### System Parameters  
To begin with, the wavelength of 1550 nm was selected. At this wavelength, we observed low atmospheric loss and decreased solar radiance [51; 52]. Through this study, only the satellite downlink scenario was examined, while only satellite elevation angle over 20\({}^{\circ}\) was considered. Considering the atmospheric parameters, the value of the refractive index structure parameter at the ground level was set to \(1.7\times 10^{-14}\)\((\text{m}^{-2/3})\), and the average wind speed was set to 10 m/s. The pointing error variance was set to 0.75 \(\upmu\)rad, and the pointing loss was calculated for an outage probability of 1%.  
Considering the satellites components, we assumed an aperture of 0.15 m [24] for all three transmitters that the satellites are equipped with, providing a small beam divergence of about 13 \(\upmu\)rad. On the receiver side, we assumed that every OGS is equipped with a different telescope aperture. Specifically, the OGSs of Skinakas, Helmos, and Cholomondas are equipped with a receiver telescope aperture of 1.3 m, 2.3 m and 0.75 m, respectively. To continue, in order to minimize the effects of strong daylight radiance, we assumed a narrow FOV of 100 \(\upmu\)rad [27] and a narrow band-pass filter of 0.2 nm with an insertion loss of 3 dB. Concerning the detectors, we assumed two superconducting nanowires single-photon detectors (SNSPDs), which offer high detection efficiencies and low timing jitter [53]. Specifically, in our analysis, we assumed SNSPDs with quantum efficiencies of 85% at 1550 nm, dark count rates of 300 counts per second (cps), timing jitter of 50 ps, dead time of 30 ns, and no after-pulsing effect. The detector's gate duration time was set to 1 ns. These values correspond to the typical performance of SNSPDs modules photon counters for single-photon detection at telecom wavelengths [53]. Finally, the Bob's receiver loss was set to 2.65 dB [54], Bob's interferometer visibility was set to 98%, and the polarization decoherence loss of the link was set to 0.3 dB [43].  
Considering the mean photon number values of the signal and decoy states, we proceeded with a numerical optimization in order to provide the possible highest key rates. Specifically, the quantum signal mean value was set to \(\mu=0.56\), the decoy mean value was set to \(\
u=0.11\), and the protocol efficiency was set to about \(q=2/5\) (signal:decoy:vacuum ratio = 4:1:16, and \(\frac{1}{2}\) due to the BB84 protocol; Appendix A) [35]. Finally, the bi-direction error correction efficiency \(f\) (\(e\)) was set to 1.22, corresponding to the CASCADE error correction algorithm [55].
## 2 EV Emulator Needs  
The EV battery needs a bidirectional power electronics system to charge and discharge its energy to the electric grid. However, current on-board EV chargers are only unidirectional and are not able to give energy to the grid. The main reason is that, as a rule of thumb, the unidirectional topologies can get higher power densities (W/m\({}^{3}\)) and more specific power (W/kg) than the bidirectional ones. Therefore, the V2G functionality is only available in DC standards, because in this case, the bidirectional power electronics converter is located in the EVSE, where the specific power index is not especially important.  
Table 1 shows the current status of the DC chargers standards. Among these standards, CHAdeMO [19] is the first and most used standard with V2G capability [20]. There have been five updates of the protocol in order to include the different necessities of the new EV and their uses. Consequently, an electric vehicle emulator compatible with this standard will cover most of V2G EVSE in the market.  
Accordingly, the EVE also needs a bi-directional power electronics system to test both charge and discharge. If the emulator takes energy from the same point of common coupling as the V2G charger, the electric consumption during the test is only the sum of EV emulator and V2G charger losses, which saves in general more than 90% of the test energy. Furthermore, if the power factor is close to 1, the test can be done in facilities with lower electric power supply, which also saves money and allows testing in several places. This is important for testing unidirectional chargers, especially old EVSE [21], since the Active Front End (AFE) can be a topology with no control of the reactive power consumed during the charging state. Therefore, an EVE with a four-quadrants AFE will be able to test different types of EVSE, ensuring low apparent power consumption during the complete test.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Standard** & **CHAdeMO** & **GB/T** & **CCS Type 1** & **CCS Type 2** & **Tesla** & **Chaoli** \\ \hline Maximum Voltage (V) & 1000 & 750 & 600 & 900 & 500 & 1500 \\ \hline Maximum Current (A) & 400 & 250 & 400 & 400 & 631 & 600 \\ \hline Maximum Power (kW) & 400 & 185 & 200 & 350 & 250 & 900 \\ \hline Communication Protocol & CAN & CAN & PLC & PLC & CAN & CAN \\ \hline V2X Function & Yes & No & No & No & Unknown & Yes \\ \hline Start year & 2009 & 2013 & 2014 & 2013 & 2012 & 2020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current status of the different DC chargers standards [20].  
The end user of EVE should be laboratories which need to check the integration of the EV in a specific electric grid; for instance to test stability, time response, compatibility, etc. However, it could also be interesting for EVSE manufacturers to check the behaviour of their developments and for maintenance purposes. Therefore, extra functionality to debug the correct behaviour of the EVSE will be desired.
###### Abstract  
Dynamic searchable symmetric encryption (DSSE) enables searches over encrypted data as well as data dynamics such as flexible data addition and deletion operations. A major security concern in DSSE is how to preserve forward and backward privacy, which are typically achieved by removing the linkability between the newly added data and previous queries, and between the deleted data and future queries, respectively. After information leakage types were formally defined for different levels of backward privacy (i.e., Type-I, II, III), many backward private DSSE schemes have been constructed under the definitions. However, we observed that the backward privacy can be violated by leveraging additional secondary leakage, which is typically leaked in specific constructions of schemes in spite of their theoretical guarantees. In this paper, in order to understand the security gap between the theoretical definitions and practical constructions, we conduct an in-depth analysis of the root cause for the secondary leakage, and demonstrate how it can be abused to violate Type-II backward privacy (e.g., the exposure of the deletion history) of DSSE constructions in practice. We then propose a novel Type-II backward private DSSE scheme based on Intel SGX, which is resilient to the secondary leakage abuse attack. According to the comparative analysis of our scheme with the state-of-the-art SGX-based DSSE schemes, Bunker-B (EuroSec'19) and SGX-SE1 (ACNS'20), our scheme shows higher efficiency in terms of the search latency with a negligible utility loss under the same security level (cf. Bunker-B) while showing similar efficiency with a higher security level (cf. SGX-SE1). Finally, we formally prove that our scheme guarantees Type-II backward privacy.  
dynamic searchable encryption; information leakages; forward security; backward security 2024 14 2287 10.3390/app14062287
## 2 Analytical Framework
### Mean Wind Speed  
To analyze the wind characteristics, an essential step is to convert the wind speeds measured at different station conditions (i.e., various exposures, heights, and averaging times) to the standard condition. The standardization of the mean wind speed in this study follows the three steps: (1) determine the exposure type of the observation station; (2) calculate the gradient wind speeds over the observation exposure; and (3) calculate the mean wind speed at the reference height (10 m high) over standard exposure (open flat terrain) by assuming the gradient wind speeds are equal at the gradient height over different exposures.
# HMM Adaptation for Improving a Human Activity Recognition System
## 2 Background
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Sensor** & **Target** & **Classes** & **Users** & **Time** & **User Adaptation** & **Performance** \\ \hline [PERSON] and [PERSON] [11] & Five bi-axial accelerometers, located at the hip, wrist, arm, ankle and thigh & Activity recognition and segmentation & 7 activities & 13 & 29 min & NO & Error: 1.6\% \\ \hline [PERSON] and [PERSON] [22] & Accelerometer in a LC smartphone & Activity recognition & 7 activities & 3 & 339 min & NO & Error: 15.0\% \\ \hline [PERSON] et al. [23] & Accelerometer in a Nokia n95 & Transportation mode recognition & 5 modes & 16 & 1200 min & NO & Precision and Recall \textgreater{}93\% \\ \hline [PERSON] et al. [24] & Tri-axial accelerometer (DMA/260) & Activity recognition & 6 activities & 13 & \textasci{}-100 min & NO & Error: 2.8\% \\ \hline [PERSON] et al. [25] & Simulated data & Detecting physical activity & \
\begin{tabular}{c} 2 sedentary \\ behavior vs. \\ physical activity \\ \end{tabular} & - & 1000 days & NO & Error: 18.8\% \\ \hline [PERSON] et al. [26] & Three MTs 3-DOF (Degree of Freedom) inertial trackers & Activity recognition and segmentation & 12 activities & 6 & \textasci{}-100 min & NO & Error: 9.6\% \\ \hline [PERSON] et al. [17] & Accelerometer in a Samsung Galaxy S2 & Activity recognition and segmentation & 6 activities & 30 & 220 min & NO &
\begin{tabular}{c} Error: 3.5\% (without sequence model) \\ \end{tabular} \\ \hline This paper & Accelerometer in a Samsung Galaxy S2 & Activity recognition and segmentation & 6 activities & 30 & 220 min & YES & Error: 2.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of previous HAR works based on HMMs. According to Table 1, this work is the first one (known by the authors) that proposes a user adaptation technique for improving the performance of a HMMs-based HAR system. In the literature there are some references proposing other user adaptation techniques: a semi-supervised method [27] or a Multi-Classifier Adaptive-Training (MCAT) algorithm [28]. The MCAT algorithm consists of using a meta-classifier for combining several pattern recognition methods. This meta-classifier is trained with user-dependent adaptation data to improve the results for a specific user.
## 5 Conclusions  
When comparing acceleration and velocity-based HVSR RotD50 datasets, both the local model and work by others show acceleration-based HVSR to have relatively less scatter when estimating \(V_{\text{S30}}\). A local model that includes the application of a minimum \(f_{\text{p}}\) but no \(A_{\text{p}}\) limits results in a model with the lowest uncertainty. When comparing the local dataset with models developed by others, the model designed for Central and Eastern North America performed better than those models designed for Japan; Iran; California, United States; and international compilations. Models that included \(A_{\text{p}}\) did not perform as well overall. However, it should be noted that the data comparison was made on a much smaller dataset.  
Conceptualization, C.-k.L. and E.Y.; methodology, E.Y.; software, C.-k.L. and E.Y.; validation, C.-k.L. and E.Y.; formal analysis, C.-k.L. and E.Y.; investigation, C.-k.L. and E.Y.; resources, C.-k.L.; data curation, C.-k.L.; writing--original draft preparation, C.-k.L. and E.Y.; writing--review and editing, E.Y.; visualization, C.-k.L. and E.Y.; supervision, E.Y.; project administration, E.Y.; funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2024 Research Fund of the KEPCO International Nuclear Graduate School (KINGS), Republic of Korea.  
Not applicable.  
Data Availability Statement:Data available in a publicly accessible repository that does not issue DOIs: [[https://data.kigam.re.kr/quake/](https://data.kigam.re.kr/quake/)]([https://data.kigam.re.kr/quake/](https://data.kigam.re.kr/quake/)) (accessed on 21 December 2024).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **HVSR Dataset** & \(f_{\text{p}}\) & \(A_{\text{p}}\) & \(f_{\text{p}}\) and \(A_{\text{p}}\) \\ \hline \multirow{2}{*}{GA14J} & Acceleration & 0.14 & 0.28 & 0.17 \\  & Velocity & 0.15 & 0.30 & 0.18 \\ \hline \multirow{2}{*}{GA14G} & Acceleration & 0.15 & 0.24 & 0.17 \\  & Velocity & 0.16 & 0.25 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSEs on log \(V_{\text{S30}}\) residuals for GA14J and GA14G models when compared with local acceleration and velocity datasets.