esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Biofeedback in Dynamic Biomechanical Gait Training for Chronic Ankle Instability
## 4 Discussion
### Kinematic and Kinetic  
In terms of kinematics, the results demonstrated increased dorsiflexion and version during walking and running after 6-week gait training. Dorsiflexion improvements were particularly evident during the early to mid-late stance and late swing phases of the gait cycle. Walking and running exhibited comparable enhancements, occurring during similar phases of the gait cycle.  
Human gait transitions from dorsiflexion to plantar flexion during preparation for the swing phase. Previous studies have identified dorsiflexion deficits in individuals with CAI during the early to mid-late stance phase compared with healthy individuals [15]. Reduced dorsiflexion angles have also been reported during the late stance phase of walking and running in patients [16]. Limited dorsiflexion is often linked to mechanical deficits arising from ankle injuries such as arthrokinematic restrictions. These restrictions cause anterior, internal, and superior displacement of the talus relative to the tibia [17; 18], leading to reduced dorsiflexion during gait [16; 19; 20]. Furthermore, patients with CAI frequently exhibit greater inversion angles throughout the gait cycle than healthy individuals. Research consistently indicates significantly increased inversion during both walking and running in individuals with CAI [14; 16].  
The findings of this study are consistent with those of previous studies. Subjects with CAI showed increased dorsiflexion, version, and abduction moments throughout the gait cycle during both walking and running. The observed increase in the pronated moment trend likely supports shock absorption during walking. Improvements were evident in the patient-reported outcomes, joint angles, and moments. Significant increases in dorsiflexion and aversion moments, along with the corresponding confidence intervals, were observed. These enhancements in ankle strength following the intervention are consistent with prior research reporting significant gains in ankle eversion strength [13; 21].  
Additionally, this study identified a significant improvement in the passive evasion range of motion among patients with CAI, potentially contributing to the observed increase in version strength. These findings suggest that improved ankle joint stability in individuals with CAI may result from achieving a \"close-packed position\" due to reduced plantar flexion and inversion [22].
## 3 Results and discussion
### Self-Snow-Melting Test
#### 3.1.3 Effect of Temperature  
With the asphalt mixture containing 4% of V-260, the quality of melted snow at different temperatures and snowfall intensities was studied, as shown in Figure 14.  
Figure 14: _Cont._  
Figure 13: The effect of different dosage of anti-icing filler on the melting quality of snow at different snowfall intensity and 5 \({}^{\circ}\)C.  
It can be observed that moderate snow was melted within 18, 13, 6 and 3 h at \(-10\), \(-5\), 0, and 5 \({}^{\circ}\)C, respectively; light snow was melted within 17, 5, 3 and 2 h at \(-10\), \(-5\), 0, and 5 \({}^{\circ}\)C, respectively; snow from a blizzard was melted within 13 and 5 h at 0 and 5 \({}^{\circ}\)C, respectively; and heavy snow was melted within 9 and 8 h at 0 and 5 \({}^{\circ}\)C, respectively. The melting rate rose with the increase of temperature. Under same snowfall intensity, anti-icing filler spent less time on melting the snows with same masses at a higher temperature. It is indicated that the snow-melting effect of anti-icing admixture is strongly dependent on temperature. The melting effect is weak at low temperatures.
# Sim-Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning
## 3 Sim-Real Model Performance
### Gripping by the Actual Robot  
In the actual machine, the external cameras attached to the system are utilized to supply the images of the setup and feedback. Thus, the motion optimized in the virtual space is reproduced in the real space. Table 7 shows the results of verification (10 times) for each arrangement of the target cylinder. It can be seen that in all cases, the gripping success rate was lower than the result derived in the virtual space (Table 6). In particular, in the case of grayscale and 10-gradation grayscale median filters, it was never possible to grasp the object. A possible reason for the failure of grasping in real space is that the difference between the virtual and real-space images could not be eliminated by reducing the image dimensionality or image processing alone.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image Processing** & **Left [\%]** & **Center [\%]** & **Right [\%]** & **Total [\%]** \\ \hline Grayscale & 100 & 100 & 100 & 100 \\ Binarization & 100 & 100 & 100 & 100 \\
10-gradation gray scale & 100 & 100 & 100 & 100 \\
4-gradation gray scale & 100 & 100 & 100 & 100 \\
10-gradation gray scale w/median filter & 100 & 100 & 100 & 100 \\
4-gradation gray scale w/median filter & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of grasping in the simulation environment.  
Figure 8: Learning curve in training using the sample image processing technique. (**a**) Grayscale operation. (**b**) Binarization operation.  
From the table, the success rate for each cylinder position was 15% for the left, 57% for the center, and 43% for the right. When placed in the center, only Motor 2 had to be operated to approach, but when placed on the left and right, multiple motors had to be operated, which increased the difficulty of the task. From the above, the results of attempts to implement a system on a real machine using the conventional method show that the system could not behave in the same way as in a virtual space. It can be said that the reality gap cannot be erased simply by image processing strategies alone.
## 3 Experimental Results
### Results of ATA Classification System  
Random Forest, J48 DT, Ada Boost, and SVM were used to evaluate the ATA scoring system. The findings revealed that J48 DT yielded the top accuracy rate with 96.2895%. Table 8 shows the success rates for all classifiers. While Table 9 presents the confusion matrix generated for the J48 DT classifier, Table 10 demonstrates the evaluation metrics for J48 DT.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Classifier**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \hline Random Forest & 98.3747 \\ \hline J48 DT & **98.5587** \\ \hline Ada Boost & 97.8534 \\ \hline SVM & 85.8632 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative results for the classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Class**} & **FP Rate** & **TP Rate** & **ROC Area** & **Recall** & **F-Measure** & **Precision** \\ \hline
0 & 0.003 & 0.999 & 0.997 & 0.999 & 0.997 & 0.995 \\ \hline
1 & 0.001 & 0.997 & 0.997 & 0.997 & 0.998 & 1 \\ \hline Weighted Avg. & 0.002 & 0.998 & 0.997 & 0.998 & 0.998 & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation metrics for J48 DT.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Classifier**} & **Accuracy (\%)** \\ \hline Random Forest & 95.1549 \\ \hline J48 DT & **96.2895** \\ \hline Ada Boost & 96.2282 \\ \hline SVM & 95.6762 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative results for the classifiers.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Class**} & **FP Rate** & **TP Rate** & **ROC Area** & **Recall** & **F-Measure** & **Precision** \\ \hline
0 & 0.003 & 0.999 & 0.997 & 0.999 & 0.998 \\ \hline
1 & 0.004 & 0.969 & 0.991 & 0.969 & 0.981 & 0.993 \\ \hline Weighted Average & 0.021 & 0.986 & 0.991 & 0.986 & 0.986 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metrics for J48 DT.
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 4 Proposed Construction
### System Model  
In our design, there are three parts: data owner (DO), data user (DU) and cloud server (CS). The cloud server stores and manages the data owner's ciphertext set and helps legitimate data users search for the corresponding data. The system model is shown in Figure 1. First, the data owner collects the public keys of all legitimate users to be used to compute the relevant data pp for the search token. In the second step, the data owner sends the encrypted EDB, ABF, and B to the cloud server for storage. The above is the initialization preparation. Next, if there is a user (legitimate or not), he/she can request the data about the search token from the data owner (Step 3). After that, the data owner sends pp to the data user (Step 4). In step 5, the data user uses the data pp to calculate the corresponding keyword search token. Here, only legitimate users can calculate the correct search token using their private key; otherwise, they will only obtain the wrong data. In the next step, the data user sends the search token to the cloud server to apply for the search. In the seventh step, the cloud server sends a collection of encrypted files from the search to the data user, and finally, in the eighth step, the data user decrypts the data in the res to obtain the plaintext.  
The scheme proposed in this paper consists of eight algorithms:  
\(Setup(\lambda)\to para\): Input the security parameter \(\lambda\) and generate the hash function, pseudo-random function, bilinear mapping, and other data required in the next step.  
\(KeyGen(H_{0},id,g)\to sk_{du_{i}},pk_{du_{i}}\): Each DU generates her/his own public/private keys using its own id.  
\(Initial(W_{ind},para)\to(ABF,EDB,B,NE)\): data owner initializes the related data. Encrypts (keyword, file set) pairs and sends the encrypted data to cloud server.  
\(Trapdoor(w,para,h,s)\to st_{w}\): DU runs this algorithm, enters its private key (the data are sent from the DO into the algorithm), calculates the search token, and sends it to the cloud server.  
\(Search(st_{w},para,EDB,ABF)\to res\): The data user sends the keyword search token to the cloud server, and the server runs the algorithm to send the corresponding encrypted file set to the data user.  
\(Update(Doc,para,EDB,ABF)\): The algorithm is used to update the data. This algorithm is run by the DO to encrypt the newly stored keywords or files and put them in the corresponding location.  
\(UpdateST(W,para,EDB,ABF)\to EDB,ABF\): This algorithm is run by the DO, which updates all keyword search tokens at the end of each update, and then sends all the updated data to the server.  
\(Dec(res,w,H_{2})\to tal\): The data user decrypts the collection from the server to obtain the required file.
## 4 Conclusions  
Evaluation of creep and drying shrinkage prediction models for SCC was conducted using forty SCC mixtures containing different types of coarse aggregate, NMSA, levels of filling ability and types of SCM/filler. Five prediction models were compared using measured data and the following conclusions were made:  
1. The AASHTO LRFD model provided better prediction for creep coefficient of SCC with lower scattering of data when different types of SCM/filler were used. On the other hand, using limestone powder increased measured creep strains more than predicted and required the use of a modification factor (greater than 1.0) in all models.
2. The AASHTO LRFD and ACI 209 models provided similar predictions of creep coefficient for SCC, while CEB-FIP MC90-99, B3 and GL2000 models overestimated the creep coefficient significantly.
3. Regardless of the type of SCM/filler, CEB-FIP MC90-99 and GL2000 models provided similar prediction of creep coefficient and drying shrinkage of SCC.
4. All models provided lower prediction of drying shrinkage of SCC. However, GL2000, CEB-FIP MC90-99, and ACI 209 models provided better prediction than AASHTO LRFD and B3 models.
5. Modification factors were needed for all drying shrinkage prediction models to account for the higher drying shrinkage of SCC mixtures. The values of these factors depend on the type of SCM/filler used.  
Conceptualization, M.A. and G.M.; methodology, M.A. and G.M.; validation, M.A. and G.M.; formal analysis, M.A.; investigation, M.A. and G.M.; resources, G.M.; data curation, M.A.; writing--original draft preparation, M.A.; writing--review and editing, G.M.; supervision, G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by National Cooperative Highway Research Program (NCHRP), Project Number 18-16.  
Not applicable.  
Data Availability Statement: Data can be found in the Appendices of the NCHRP Report 819.  
The authors would like to acknowledge the financial support of National Cooperative Highway Research Program (NCHRP) for the NCHRP 18-16 Project. The research was conducted in collaboration with [PERSON], [PERSON], and [PERSON] at Iowa State University (ISU).  
The authors declare no conflict of interest.  
Notations  
Compliance function for drying creep at concrete age \(t\) when loading and drying starts at ages \(t_{o}\) and \(t_{c}\), respectively, B3 model  
Compliance function for basic creep at concrete age \(t\) when loading starts at age \(t_{o}\), B3 model  
Mean modulus of elasticity of concrete when loading starts at age \(t_{o}\), psi  
Average thickness of a member, in., ACI 209R-92 model  
Member shape and size constant, days, ACI 209R-92 model  
Relative humidity expressed as a decimal  
Factor for the effect of the volume-to-surface ratio, AASHTO 2007  
Factor for the effect of concrete strength, AASHTO 2007
###### Abstract
In the literature [20], an initial low-cost yet adequately capacitated constellation was established, and the system capacity requirements were increased by launching subsequent satellites and reconstructing existing constellations. Furthermore, a hybrid configuration constellation comprising circular and elliptical orbits was employed for the design of satellite broadcast constellations. Initially, the LEO constellation is being utilized to fulfill a portion of the overall market demand, followed by the utilization of an elliptical satellite constellation to address requirements for high density and capacity growth [21]. In this study, an LEO independent hybrid navigation constellation was designed based on the NSGA-II algorithm. The performance degradation of MEO satellites was taken into account when the LEO constellation was deployed in stages, and the mission requirements of different deployment stages of LEO constellations are herein given under the scenario of degradation of MEO constellation performance. Due to the orbital characteristics of an LEO constellation, studies have shown that a hybrid configuration demonstrates better performance than a single configuration when achieving global coverage [16]. We herein propose a two-layer LEO hybrid navigation constellation configuration. By using the NSGA-II algorithm for a global optimization of the PDOP and the total number of satellites as objective functions, our proposed constellation outperforms or equals four GNSS navigation constellations. Addressing concerns about degraded service due to decommissioning or malfunctioning satellites within MEO navigational constellations, we conducted an in-depth study analyzing how both the quantity and mode of MEO satellite failures impact overall system functionality. Based on these findings, we present a phased deployment strategy for LEO navigation constellations considering MEO performance degradation. The first stage focuses on compensating for MEO satellite failures, followed by regional/navigation enhancement in the Asia-Pacific region during the second stage. Finally, global independent navigation is achieved in the third stage, with performance comparable to the BDS-3 constellation, ensuring smooth transition between stages. This paper is organized as follows: Section 2 describes the problem and the methodology, the results and analysis are presented in Section 3; and the conclusions and discussion are presented in the last section.
## 5 Spatial Polarization Correlations  
The array spatial resolution capability is primarily related to the signal propagation peculiarities. This lies in the normalized inner product of each array manifold vector. For spatial and polarimetric dimensions problems, the spatial polarization correlation  
Figure 2: Two overlapping subarrays.  
coefficient between the signal sources \(l\) and \(k\) is defined by the extended array manifold \(\widetilde{\mathbf{a}}(\phi)\).  
\[\begin{split}\beta_{l,k}&=\frac{1}{M}\widetilde{ \mathbf{a}}^{\mathrm{H}}(\phi_{k})\widetilde{\mathbf{a}}(\phi_{l})\\ &=\frac{1}{M}\bigg{(}c_{k1}^{*}c_{l1}\Big{(}\mathbf{a}^{[v]}( \phi_{k})\Big{)}^{\mathrm{H}}\mathbf{a}^{[v]}(\phi_{l})\ +c_{k2}^{*}c_{l2}\Big{(}\mathbf{a}^{[h]}(\phi_{k})\Big{)}^{\mathrm{H}}\mathbf{a }^{[h]}(\phi_{l})\bigg{)}\\ &=c_{k1}^{*}c_{l1}\rho_{l,k}^{[v]}+c_{k2}^{*}c_{l2}\rho_{l,k}^{[h] }\,\end{split} \tag{48}\]  
where \(\beta_{l,k}^{[i]}=(1/M)\left(\mathbf{a}^{[i]}(\phi_{k})\right)^{\mathrm{H}} \mathbf{a}^{[i]}(\phi_{l})\) represents the spatial coefficient.  
For the identical array manifolds between vertical and horizontal polarization, \(\mathbf{a}^{[v]}(\phi)=\mathbf{a}^{[h]}(\phi)\) and \(\beta_{l,k}^{[v]}=\rho_{l,k}^{[h]}\). The spatial polarization relation coefficient is the product of the individual spatial coefficient and polarimetric coefficient:  
\[\beta_{l,k}=\beta_{l,k}^{[v]}\rho_{l,k}\, \tag{49}\]  
with  
\[\rho_{l,k}=\mathbf{c}_{k}^{\mathrm{H}}\mathbf{c}_{l}=\cos(\gamma_{l})\cos( \gamma_{k})+\sin(\gamma_{l})\sin(\gamma_{k})e^{i(\eta_{l}-\eta_{k})}, \tag{50}\]  
expressing the polarimetric coefficient. Particularly, \(\eta_{\eta_{l}}=\eta_{\eta_{k}}=0\) (linear polarization) (50) simplifies to  
\[\rho_{l,k}=\cos(\gamma_{l}-\gamma_{k}). \tag{51}\]  
Since \(\big{|}\rho_{l,k}\big{|}\leq 1\), the equality is satisfied only when two signal polarization states are the same. The spatial polarization correlation coefficient is smaller than the single spatial coefficient. The introduced algorithm enhances signal recognition via dropping the correlation coefficient value with the polarization diversity. Namely, the signal recognition is difficult to obtain via a monopolar spatial array manifold, \(\mathbf{a}^{[v]}(\phi)\) or \(\mathbf{a}^{[h]}(\phi)\). However, the signals can be relatively easily identified by the extended spatial polarization array manifold, \(\widetilde{\mathbf{a}}(\phi)\). This enhancement is more pronounced when there is a large spatial correlation coefficient and small spatial polarization correlation coefficient.
# Policy-Compliant Maximum Network Flows
## 3 Graph Model and Basic Algorithms
### Flow Networks and Flows  
We now introduce flow networks and flows, following the approach taken by [PERSON] et al. [4]. These definitions might differ from other approaches in several aspects, e.g., the fact that the capacity function \(c\) and the flow \(f\) are total functions, allowing more concise and elegant definitions of constraints. The rationale behind this way of defining the context is included in [PERSON] et al. [4] and we refer interested readers to study the mathematical discussions in that book. We thus define a flow network \(G=(V,E)\) as a directed graph together with a capacity function \(c:V\times V\to\mathbb{R}\) such that each edge \((u,v)\in E\) has a nonnegative capacity \(c(u,v)\geq 0\). Note that \(c\) is a total function such that, if \((u,v)\
otin E\), we have \(c(u,v)=0\). Moreover, we require that if \(E\) contains a certain edge \((u,v)\), then there is no reverse edge \((v,u)\) present in \(E\). We also disallow loops such that \(\forall u:(u,u)\
otin E\). Two specific nodes in the graph are now chosen out of \(V\), one vertex being the source \(s\) and one vertex being the sink \(t\). We now want to send a flow from \(s\) to \(t\), a flow being a total function \(f:V\times V\to\mathbb{R}\) subject to the following constraints:  
* A capacity constraint which expresses that flow can never exceed capacity: \[\forall u,v\in V:0\leq f(u,v)\leq c(u,v)\] (1)
* A flow conservation constraint which expresses that for all nodes (except source and sink) the incoming flow equals the outgoing flow: \[\forall u\in V-\{s,t\}:\sum_{v\in V}f(v,u)=\sum_{v\in V}f(u,v)\] (2) Note that \(f(u,v)=0\) if \((u,v)\
otin E\).  
The value \(|f|\) of a flow is defined as the flow leaving the source minus the flow arriving at the source: \(|f|=\sum_{v\in V}f(s,v)-\sum_{v\in V}f(v,s)\). Equivalently, we can define the value of the flow just as well as \(|f|=\sum_{v\in V}f(v,t)-\sum_{v\in V}f(t,v)\). Note that one can define the value of the flow using a formula containing only \(s\) but not \(t\), or a formula containing only \(t\) but not \(s\); one never needs both \(s\) and \(t\) in one formula at the same time, see the book by [PERSON] et al. [4]. Also note that we allow \(\sum_{v\in V}f(v,s)\) as well as \(\sum_{v\in V}f(t,v)\) to be strictly larger than \(0\). The classic problem, given a flow network \(G\), is to maximize the flow \(|f|\) from \(s\) to \(t\) under the constraints above. (The \(|.|\)-notation denotes flow value, not absolute value or cardinality.)
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 2 Related Knowledge
### Rhombus Pattern Based PEE
#### 2.3.3 Extraction and Recovery Procedures  
The extraction procedure is the inverse of the embedding procedure. Since the \"\(\bullet\)\" pixels do not change, the receiver has the same predicted pixel value \(p^{\prime}_{i,j}\) of \"\(\times\)\" pixels as the image owner. Given the modified pixel value \(P_{i,j}\) and the modified prediction error \(e^{\prime}_{i,j}\), Equation (14) is used to calculate which additional data have been embedded.  
\[e^{\prime}_{i,j}=P_{i,j}-p^{\prime}_{i,j}  
Finally, the original pixel value \(p_{i,j}\) is recovered as  
\[p_{i,j}=p^{\prime}_{i,j}+e_{i,j} \tag{17}\]