esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 3 Identity-Based Encryption Scheme
### Security Reduction
According to the Left over Hash lemma [29], \(-\mathbf{R}^{\mathrm{T}}\mathbf{v}^{*}\) obeys the discrete random distribution, so the \(\mathbf{R}^{\mathrm{T}}\mathbf{v}^{*}+\mathbf{e}^{\prime}\) also obeys the discrete random distribution. Therefore, the distribution of challenge ciphertext \(\mathbf{c}^{*}\) is indistinguishable from Game3, and is randomly selected by the challenger \(\mathcal{C}\) from \(\mathbb{Z}_{q}^{n^{\prime}}\). **Queries2** The adversary \(\mathcal{A}\) can continue to query the secret key in the same way as **Queries1**. **Guess** The adversary \(\mathcal{A}\) distinguishes whether the ciphertext is a random independent vector on \(\mathbb{Z}_{q}^{n^{\prime}}\) or a valid ciphertext of plaintext message \(\mathbf{b}^{*}\), and the challenger \(\mathcal{C}\) answers whether the samples in the LWE problem are from \(\mathcal{O}_{\mathbf{s}}\) or \(\mathcal{O}_{\mathbf{\hat{s}}}\) according to the guess results. In summary, when \(\mathcal{O}=\mathcal{O}_{\mathbf{s}}\), the view of the adversary \(\mathcal{A}\) is the same as Game2; when \(\mathcal{O}=\mathcal{O}_{\mathbf{\hat{s}}}\), adversary \(\mathcal{A}\) has the same view as Game3. Because the advantage of algorithm \(\mathcal{E}\) in solving the LWE problem is the same as that of adversary \(\mathcal{A}\) in distinguishing Game2 and Game3, and because there is no PPT algorithm that can effectively solve the LWE problem; thus, the scheme is IND-sID-CPA secure, and the proof is over. \(\square\)
## 2 Principles and Methods
### The Label Switching-Based ISTNs
#### 2.2.1 Principles of Label Switching
Currently, two typical protocols are specified by the Internet Engineering Task Force (IETF): (1) resource reservation protocol-traffic engineering (RSVP-TE) [15], which is a traffic engineering modification to the resource reservation protocol (RSVP) [16], and (2) constraint routing label distribution protocol [17], which is built based on LDP. The most significant distinction between the two protocols is how to share the control information inside the MPLS domain, namely: RSVP-TE employs a soft state method with update messages, while CR-LDP employs a hard state refreshing approach using TCP. For ease of implementation, we use CR-LDP and modify parts of its TCP function, and the algorithm of the optimized CR-LDP is revised in Algorithm 2. In contrast to the integrated services (IntServ), which use RSVP to provide end-to-end route reservations, differentiated services (DiffServ) are used in MPLS-based satellite networks to meet the QoS requirements. In the DiffServ method, all IP data are grouped into a behavior aggregate, which is tagged by a DiffServ code point (DSCP) inside each frame based on the same behavior request. ```
1:Initialize OSPF configure and periodically send out Hello message;
2:if message received then
3:switch type of received message do
4:case Hello:
5:if without its own Router ID and in Full status then
6: Switch to UNI_DIRECTION_SEND status and send uni-directional message;
7:endif
8:if with its own Router ID and in UNI_DIRECTION_SEND status then
9: Switch to Full status;
10:endif
11:if in UNI_DIRECTION_RECEIVE status then
12: Switch to Full status;
13:endif
14:case Database Description:
15: Compare with link-state database, consistent with standard OSPF;
16:case Link State Request:
17: Organize and send link-state update message, consistent with standard OSPF;
18:case Link State Update:
19:if This update belongs to the link plan then
20: Ignore this update message and restrain the flooding because the satellite update itself through the link plan;
21:endif
22:if This update does not belong to the link plan then
23: Update link-state database and send link-state acknowledgment message, consistent with standard OSPF;
24:endif
25:case Link State Acknowledgment:
26: Stop retransmitting link-state update message, consistent with standard OSPF;
27:case Unidirectional:
28: Switch to UNI_DIRECTION_RECEIVE status, organize link-state acknowledgment which includes uni-directional link and flood it;
29:case NonUnidirectional:
30: Switch to Down status, organize link-state acknowledgment which excludes uni-directional link and flood it;
31:endif
32:if Hello message not received before timer expires and in UNI_DIRECTION_SEND status then
33: Switch to Down status and send NonUnidirectional message;
34:endif
```  
**Algorithm 1** Optimized OSPF, Periodic Update of LSDB and Uni-directional Link Routing.
## 4 Results
### Device, Sensor and Body Location  
Regarding the type of devices used, 20 studies (91%) used a single type of device for data recording, while [83] employed two different devices and [82] used three types of devices. In total, 25 devices were used in the 22 studies. As shown in Figure 7a, the most commonly used device is the IMU (N = 11 of the 25 devices, 44%) [21, 66, 67, 71, 73, 74, 77, 78, 81, 82, 85], followed by the smartphone (N = 7, 28%). [68, 72, 75, 79, 80, 82, 84], pressure insoles were used  
Figure 5: Distribution of the number of articles according to the number of participants.  
Figure 6: Gender distribution in the selected papers.  
in 3 cases (12%) [65, 69, 70], while other devices such as tablets were used in 4 studies (16%) [76, 82, 83].  
In terms of sensor types ( Figure 7b), of the 55 total sensors used, the most used sensor is the triaxial accelerometer (N = 17, 31%), followed by the triaxial gyroscope (N = 11, 20%), the touch screen (N = 7, 13%), triaxial magnetometer (N = 6, 11%), VGRF sensors (N = 3, 5%), and barometer (N = 2, 4%). Other sensors (N = 9, 16%) include the goniometer, GPS or galvanic skin response.  
Figure 8a presents the distribution of the number of devices used. The most common option involves a single device (N = 14, 64%). The remaining studies used multiple sensors. Specifically, 6 papers (27%) [21, 66, 71, 74, 83, 85] used between 2 and 3 devices, while the 2 remaining papers (9%) [73, 82] used equal or more than 8 devices. Figure 8b presents the number of sensors used. The most popular choice is to use 2 sensors (N = 8, 36%). A single sensor was used in 3 articles [67, 76, 77] (14%), 3 sensors in 6 articles [21, 66, 67, 74, 80, 81] (27%), while 5 articles (23%) use more than 10 sensors.  
Figure 9 shows common positions of the sensor on the human body. The device was placed in front of the person (e.g., smartphone, tablet) or on the foot or in the lower back in 8 articles each (18%). In 5 cases (11%), the wearable device was placed in the pocket, in the hand in 4 papers (9%) and the wrist in 2 cases (5%). The rest of the studies positioned the sensors in other locations like the ankle or the waist. Half of the included articles used a single location, while the other half used more than one body position.  
Figure 8: Number of (**a**) devices and (**b**) sensors used.  
Figure 7: Distribution of types of (**a**) devices and (**b**) sensors used.
## 3 Results and Discussion
### Evaluation of Neuroprotective Potential of Aaptoline B  
The neuroprotective potential of aaptoline B **(1)** was investigated using a _C. elegans_ PD model. To test the effect of **1** on dopaminergic neuronal loss induced by MPP\({}^{+}\), we analyzed the fluorescent signals of the transgenic nematode BZ555 (Pdat-1::GFP), which expresses GFP in dopaminergic neurons. As shown in Figure 4A,B, the treatment of nematodes with MPP\({}^{+}\) for 96 h significantly decreased the viability of neurons to 46.3 \(\pm\) 5.6%. Compared to untreated nematodes, **1**-fed nematodes exhibited a notable increase in GFP signals of all eight dopaminergic neurons in a dose-dependent manner (Figure 4A,B). These findings indicate that **1** might have the ability to protect dopaminergic neurons against MPP\({}^{+}\)-induced neurotoxicity. To confirm the protective role of **1** in the MPP\({}^{+}\)-mediated dopaminergic neuronal damage, we examined whether **1** could modulate behaviors requiring dopamine function, such as food-sensing response and locomotion. Well-fed nematodes reduce their movement in the presence of food as compared to that afforded in its absence. This basal slowing response is known to be mediated solely by dopaminergic neural circuitry. In the current study, normal nematodes demonstrated a 73.5% reduction in locomory velocity when they came across the bacterial lawn (Figure 4C). When the nematodes were exposed to MPP\({}^{+}\), the slowing response was significantly decreased to 37.2%, and treatment with **1** efficiently restored this reduction in the slowing response induced by MPP\({}^{+}\) (Figure 4C). In addition, we observed the effect of **1** on the travel distance of nematodes under MPP\({}^{+}\) exposure conditions. Similar to the results from the food-sensing assay, the travel distance of nematodes was significantly decreased in the presence of MPP\({}^{+}\), while **1** supplementation restored these MPP\({}^{+}\)-induced defects in motor activities of nematodes in a dose-dependent  
Figure 3: Selected HMBC correlations (H\(\rightarrow\)C) of synthesized **1**.  
manner (26.0%, 35.5%, and 38.2% at 5, 10, and 20 uM, respectively) (Figure 4D). Overall, our findings indicate that **1** could contribute to ameliorating dopaminergic neurodegeneration not only morphologically but also functionally.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 5 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**Diversity**} & \multicolumn{4}{c}{**Trade-Off**} \\ \cline{3-11}  & & **Recall@10** & **Recall@20** & **HR@10** & **HR@20** & **CC@10** & **CC@20** & **ILD@10** & **ILD@20** & **F1@10** & **F1@20** \\ \hline \multirow{9}{*}{10M} & CCF & 0.0451 & 0.0753 & 0.1423 & 0.2040 & 0.5124 & 0.6524 & 0.7452 & 0.7884 & 0.2227 & 0.3108 \\  & ACF & 0.0485 & 0.0784 & 0.1476 & 0.2074 & 0.5048 & 0.6432 & 0.7243 & 0.7543 & 0.2284 & 0.3137 \\  & DSPR & 0.0612 & 0.1025 & 0.1824 & 0.2216 & 0.5160 & 0.6445 & 0.7338 & 0.7856 & 0.2695 & 0.3298 \\  & TGCN & 0.0824 & 0.1246 & 0.2520 & 0.3152 & 0.5138 & 0.6580 & 0.7382 & 0.7821 & 0.3381 & 0.4262 \\  & LFGCF & 0.1050 & **0.1484** & 0.3035 & 0.3526 & 0.5027 & 0.6440 & 0.7336 & 0.7885 & 0.3785 & 0.4557 \\  & MMR & 0.0413 & 0.0645 & 0.1395 & 0.1875 & 0.5538 & 0.7064 & 0.7960 & 0.8108 & 0.2229 & 0.2963 \\  & DPP & 0.0438 & 0.0723 & 0.1412 & 0.1954 & 0.5672 & 0.6928 & 0.8072 & 0.8262 & 0.2261 & 0.3048 \\  & DGCN & 0.0845 & 0.1215 & 0.2476 & 0.3025 & 0.5524 & 0.7156 & 0.7852 & 0.8214 & 0.3419 & 0.4252 \\  & DTGCF & **0.1086** & 0.1452 & **0.3124 \({}^{*}\)** & **0.3580** & **0.5821 \({}^{*}\)** & **0.7340 \({}^{*}\)** & **0.8124 \({}^{*}\)** & **0.8350 \({}^{*}\)** & **0.4065 \({}^{*}\)** & **0.4813 \({}^{*}\)** \\ \hline \multirow{9}{*}{20M} & CCF & 0.0504 & 0.0768 & 0.1550 & 0.2114 & 0.5161 & 0.6258 & 0.7161 & 0.7525 & 0.2384 & 0.3160 \\  & ACF & 0.0523 & 0.0812 & 0.1632 & 0.2230 & 0.5026 & 0.6114 & 0.7120 & 0.7552 & 0.2464 & 0.3268 \\  & DSPR & 0.0689 & 0.1138 & 0.2035 & 0.2458 & 0.5214 & 0.6136 & 0.7212 & 0.7584 & 0.2927 & 0.3510 \\  & TGCN & 0.0933 & 0.1274 & 0.2664 & 0.3086 & 0.5126 & 0.6218 & 0.7268 & 0.7636 & 0.3506 & 0.4125 \\  & LFGCF & **0.1174** & 0.1362 & 0.3156 & 0.3552 & 0.5082 & 0.6134 & 0.7142 & 0.7650 & 0.3894 & 0.4499 \\  & MMR & 0.0431 & 0.0685 & 0.1468 & 0.1940 & 0.5528 & 0.6624 & 0.7848 & 0.8134 & 0.2320 & 0.3001 \\  & DPP & 0.0468 & 0.0786 & 0.1528 & 0.2016 & 0.5631 & 0.6748 & **0.8134\({}^{*}\)** & 0.8065 & 0.2404 & 0.3105 \\  & DGCN & 0.0912 & 0.1256 & 0.2610 & 0.2912 & 0.5648 & 0.6673 & 0.7768 & 0.7972 & 0.3570 & 0.4055 \\  & DTGCF & 0.1136 & **0.1421 \({}^{*}\)** & **0.3182** & **0.3624 \({}^{*}\)** & **0.5872 \({}^{*}\)** & **0.6882 \({}^{*}\)** & 0.8072 & **0.8264 \({}^{*}\)** & **0.4127 \({}^{*}\)** & **0.4748 \({}^{*}\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance comparison on two datasets in terms of accuracy, diversity, and trade-off metrics. The best result is in bold, and the second best one is marked with an underline. Statistical tests are conducted between the best result and the second best result. If significant differences are detected (\(p\)-value < 0.05), the corresponding best result is marked with a symbol \({}^{*}\).
# An Experimental Study of the Leakage Mechanism in Static Seals
## 3 Experimental Design for Sealing Surface Leak-Rate Measurement
### Experimental Implementation  
The experimental work in this study was mainly dedicated to two purposes: to study the effect of sealing surface characteristics on leakage, analyze the leakage mechanism of sealing interfaces, and verify the leak rate prediction model; and to learn the effect of external factors of the sealing device on the leak rate, and apply the leak rate prediction model to optimize the design parameters of the sealing device.
## 4 Results and Discussion
### Estimation of Potential Soil Erosion  
Rainfall erosivity, slope length and steepness, soil erodibility, land cover management, and soil conservation represent the main erosion contributors. Figures 3-7 illustrate the results of the modeling of these factors. The modeling of rainfall erosivity reveals that the higher the rainfall intensity, the higher its erosion potential. The soil erodibility parameter, which indicates both the soil's vulnerability to erosion and the rate of runoff, demonstrate that the least transport-resistant soils that generate large runoff had the highest values for K parameter. Modeling the slope's length and gradient reveal that the steeper the slope, the larger the runoff velocities and, thus, the greater the erosion. Similarly, slope improves the conservation support practice parameter, and thus erosion. The cover and management component that indicates how various LULC types affect soil loss rates [48] reveal that farmland and bare land have the greatest values, while shrub land and urban land use have the lowest. Although all five RUSLE parameters influence the ultimate soil erosion rate, these parameters have a different impact on the erosion rate in the current study. The analyses in this study reveal that such parameters vary in different magnitude. For example, P-parameter, LS-parameter, C-parameter, K-parameter, and R-parameter vary by a factor of 85, 50, 43, 17, and 3.3, respectively (Figures 3-7), indicating that these parameters affect erosion rates in the same order. As a result of the final soil loss model, Figure 8 shows the annual erosion map of the study area, which helps to identify the areas that are prone to soil erosion. The annual global average erosion rate is 2.2 tons per hectare [93]. The annual  
Figure 7: Spatial distribution of P factor in the study area.  
erosion rates in this study in tons per hectare were extremely high (more than 50); very high (50 to 16.5); high (16.5 to 2.2); medium (2.2 to 1); and low (1 to 0), representing 16, 4, 13, 7, and 60 % of the basin area, respectively. The high soil loss rates are linked to areas of high rainfall levels, loamy soil domination, elevated terrains/plateau margins with a steep side slope, and high cultivation activities. Clearly, the upstream parts of the basin represent areas of high soil erosion potential. Thus, the current study's results may aid in decision-making for improved land conservation and soil management. Public agencies should take the necessary measures to mitigate the influence of these influencing parameters.
# Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
## 2 Directional Changes: An Introduction
### \(\text{DC}+\text{GA}\)  
In 2017, [PERSON] and [PERSON] (2017) proposed a DC-based trading strategy named \"DC + GA\". DC + GA runs multiple DC summaries concurrently (using multiple thresholds). For each threshold, DC + GA calculates the average time length of each DC and OS event for every DC trend during a training (in-sample) period. DC + GA employs two variables to express the average ratio of the OS event length over the DC event length. These two variables are \(r_{u}\) and \(r_{d}\), where \(r_{u}\) is the average ratio of the upwards OS event, and \(r_{d}\) is the average ratio of the downwards OS event. Thus, DC + GA analyses uptends and downtrends separately. The objective is to be able to anticipate the end of an uptrend, or downtrend, (approximately) and as a result make trading decisions (buy or sell) once an OS event had reached the average ratio of \(r_{u}\) or \(r_{d}\). Theoretically, DC + GA initiates a trade when the length of an OS event exceeds \(r_{u}\) or \(r_{d}\).  
The authors formed two parameters, namely \(b_{1}\) and \(b_{2}\), which define a range of time within the OS period, where trading is allowed. For instance, if a trader expects the OS event to last for 2 h (this expectation is based on the calculus of \(r_{u}\) and \(r_{d}\)), and assuming that the range of [\(b_{1}\), \(b_{2}\)] is [0.9, 1.0], then this means that DC + GA is going to trade (buy or sell) at the last 10% of the 1 h duration, i.e., in the last 6 min.  
Recall that DC + GA runs multiple DC summaries simultaneously (using multiple thresholds) for a given currency pair. Let \(N_{theta}\) be the number of employed DC thresholds. The user/trader should chose the values of the \(N_{theta}\) thresholds. DC + GA assigns a weight to each DC threshold. For a given price observation, each threshold provides a recommendation (buy, sell or hold). At a given time, the \(N_{theta}\) thresholds provide \(N_{theta}\) recommendations. These \(N_{theta}\) recommendations are combined to produce the final decision: buy, sell or hold. DC + GA employs a Genetic Algorithm (GA) approach to find the best combination of these \(N_{theta}\) recommendations. The GA module of DC + GA employs a fitness function which aims to minimize the maximum drawdown (\(MDD\)) and maximize returns at the same time.  
To evaluate the performance of DC + GA, the authors considered five currency pairs sampled with a 10-min interval over one year. The authors concluded that DC + GA \"_... could not consistently return profitable strategies and thus their mean returns were negative_\". The authors have adopted the buy and hold approach as a benchmark. They reported that the proposed trading strategy \"_... return a similar average return with BH\"_. We should finally note that the reported \(MDD\) of DC + GA is less than 0.15% (measured on daily basis) in all considered cases (Table 11 in [11]). We consider this value as an attractive level of the drawdown risk.
## 3 Results and Discussion
### The EYB Eyes Image Results  
#### 3.1.1 The Curvature-Based Method Results  
This section evaluates the EYB dataset, focusing on the eye images using 1-_NN_, _SVM_-1, and _SVM_-2. Table 3 shows the accuracy results from using the original images (no combination) and the extracted curvature features. The extracted curvatures were the Gaussian curvature (_K_), mean curvature (_H_), max principal curvature (_X_), and min principal curvature (_N_) from Equations (10)-(12) in Section 2.1. From Table 3, we observed that the extracted curvatures had not improved the performance results. The best accuracy was 66.36%, obtained from _SVM_-1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbr.** & **Method** \\ \hline \(K\) & Gaussian curvature \\ \(H\) & mean curvature \\ \(X\) & max principal curvature \\ \(N\) & min principal curvature \\ \(SS+K/H/X/N\) & scale space with curvature (Gaussian, mean, max principal, and min principal) \\ _O-L_ & octave level in scale space \\ _A\#_ & approximation coefficient in DWT from \# (decomposition level) \\ _Hr\#_ & horizontal coefficient in DWT from \# (decomposition level) \\ _V\#_ & vertical coefficient in DWT from \# (decomposition level) \\ _D\#_ & diagonal coefficient in DWT from \# (decomposition level) \\ _Sym2_ & Symlet wavelet with 2 vanishing moments \\ _Db2_ & Daubechies wavelet with 2 vanishing moments \\ _Bior2.2_ & biorthogonal wavelet with 2 vanishing moments for synthesis and analysis \\
1-_NN_ & k-nearest neighbor with k = 1 \\ _SVM_-1 & support vector machine with linear kernel \\ _SVM_-2 & support vector machine with polynomial kernel \\ \hline \hline \end{tabular}
\end{table}
Table 2: The list of abbreviations in this research.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Base**} & \multicolumn{4}{c}{**Curvature-Based**} \\ \cline{3-6}  & & \(K\) & \(H\) & \(X\) & \(N\) \\ \hline
1-_NN_ & 58.92 & 15.88 & 32.58 & 47.42 & 51.36 \\ _SVM_-1 & 66.36 & 31.80 & 51.61 & 59.91 & 58.99 \\ _SVM_-2 & 44.70 & 25.81 & 30.41 & 34.56 & 22.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy (%) results from the EYB dataset for the eye images and the extracted curvatures.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
###### Abstract  
Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydroologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov-Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies.  
flood characterization; HEC-RAS; hydraulic simulation; Indus River; Landsat; MODIS 2021  
Aticle  
Aticle