esa-sceva/satcom-chunk-collection huggingface dataset:
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 2 Related Work
### Notations  
Table 1 provides notations used to describe the definition, and the proposed SSE scheme.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(D\) & \(D=\{D_{1},\ldots,D_{n}\}\), is a set of \(n\) documents. \\ \hline \(D_{i}\) & A document \(i\). \\ \hline \(S\) & \(S=\{S_{1},\ldots,S_{s}\}\), is a set of \(s\) servers. \\ \hline \(S_{x}\) & A server \(x\). \\ \hline \(C\) & \(C=\{\mathbf{c}_{1},\ldots\mathbf{c}_{s}\}\), is a set of lists of \(s\) encrypted blocks. \\ \hline \(id(x)\) & An identifier of an item \(x\). \\ \hline \(K\) & A set of cryptographic keys. \\ \hline \(I\) & A query index with a \(I[key]=value\) structure, in which given a \(key\), the \(value\) is returned. \\ \hline \(tok\) & A keyword token. \\ \hline \(\mathbf{t}\) & A temporary list to hold the identifiers of the encrypted blocks. \\ \hline \(F_{k_{f}}(.)\) & A keyed pseudorandom function with key \(k_{f}\). This is used to generate keyword token. \\ \hline \(\mathcal{E}\) & \(\mathcal{E}=(\mathtt{Gen},\mathtt{Enc},\mathtt{Dec})\) is a symmetric encryption scheme where \(\mathtt{Gen}\) is the key generation function, \(\mathtt{Enc}\) the encryption function and \(\mathtt{Dec}\) the decryption function. \\ \hline op & The document addition or removal operation of the update algorithm in a SSE scheme, \(\mathtt{op}\in\{\mathtt{Doc}+,\mathtt{Doc}-\}\). \\ \hline in & The information submitted to the update algorithm. in can be a document \(D_{i}\) for \(\mathtt{Doc}+\) or an identifier \(id(D_{i})\) for \(\mathtt{Doc}-\). \\ \hline \(\mathcal{L}^{l}\) & A leakage profile that states information leakage of the \(l\) phase in a SSE scheme (i.e., document size, index size), where \(l\in\{Setup,Search,Update\}\). \\ \hline \(\mathtt{negl}(\lambda)\) & A negligible function with security parameter \(\lambda\). \\ \hline \(\mathcal{S}\) & A simulator. \\ \hline \(\mathcal{A}\) & An adversary. \\ \hline \(B_{r}\left|B\right|\) & List of blocks, and the number of blocks in the list. \\ \hline \(b\) & Length (or size) of a block. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations used in the description of the SSE scheme.
# A New Line for Laser-Driven Light Ions Acceleration and Related TNSA Studies
## 6 Results: Ion Detection  
Concerning ion detection, a range of diagnostics was used in our experiments to measure ion acceleration, including radio-chromic films (GAF), CR39, Thomson Parabola, and Time of Flight (TOF) diamond detectors. Normally, Thomson Parabola, and TOF detectors were used simultaneously so that a cross-comparison of the signals obtained from the two devices was possible. This was done in view of a possible use of the diamond detector for on-line direct detection of accelerated ions during normal beamline operation. A detailed discussion of all these measurements with different detectors is given elsewhere [15; 19]. Here, we focus our attention on the interpretation of the TOF signal in particular. The plot of Figure 6a shows the TOF signal obtained with the diamond detector from the irradiation of a 5 \(\upmu\)m thick Mylar target and with a laser angle of incidence on the target of 30\({}^{\circ}\). The detector was placed at a distance of 30 cm and was filtered using a 12 \(\upmu\)m thick Al foil. The strong peak at \(-\)10 ns  
Figure 5: (**a**) Rear side optical imaging showing Optical Transition Radiation from fast electrons from laser irradiation of 10-\(\upmu\)m thick Al foil at the best focus position. The insert shows the raw spectrum of fast electrons obtained with a permanent magnet spectrometer. (**b**) Same image, but with the target displaced by two Rayleigh lengths (100 \(\upmu\)m).  
was originally attributed to a combination of X-rays and fast electrons reaching the detector soon after the interaction.  
This peak is followed by the actual ion signal that, as shown by the blue arrow, starts at 4 ns, namely, 15 ns after the starting of the photo-peak. Taking into account the TOF distance and assuming a signal predominantly due to protons that have the highest charge-to-mass ratio, calculations yield a high energy cut-off of approximately 2 MeV. A similar measurement carried out placing a magnetic dipole with a 1T magnetic field in front of the diamond detector is shown in Figure 6b. This measurement shows that the peak at \(-10\) ns is strongly reduced, leaving a clean proton signal, indicating that the peak was due to the presence of fast electrons that are stopped by the strong magnetic field. Incidentally, the measurements of Figure 6 were obtained at the 30\({}^{\circ}\) angle of incidence, which is comparable to the maximum energy of 1.9 MeV obtained [15] on the 10 \(\upmu\)m Al target at a 15\({}^{\circ}\) angle of incidence, indicating that such a change in the angle of incidence does not lead to significant changes of the interaction conditions. This is consistent with the numerical simulation results of Figure 3 where, for similar values of laser intensity, no significant dependence on the angle of incidence was expected. A more systematic investigation is planned to confirm the observed behavior with the angle of incidence and target thickness.
## 4 Plant-Bacteria Interaction Regulates Plant Protein and Metabolites Production
### Bacteria-Mediated Plant Metabolite Biosynthesis for Salt Stress Mitigation (Indirect Mechanism)
#### 4.2.5 Sodium and Potassium Transport in Plant Cell  
Arabidopsis K\({}^{+}\) Transporter 1 (AKT1) is a potassium plasma membrane transporter that participates in root K\({}^{+}\) absorption at extracellular concentrations greater than 10 uM (Figure 3). The _AKT1_ gene was shown to be highly up-regulated in plants infected with _Burkholderia phytofirmans_ PsJN and subjected to 250 mM salt stress [64]. Recently _Kocuria rhizulia_ 14 asp inoculation decreased sodium uptake and increased potassium transport in roots of pea grown in 150 mM salt concentration [59]. In line with this, another study utilizing _Bacillus velezensis_ FMH2 demonstrated that it reduced sodium buildup in root and shoot and enhanced potassium uptake in tomato plants cultivated in 171 mM salt stress [45].  
Under saline circumstances, the toxicity of Na\({}^{+}\) might be reduced in plant cells by a variety of methods, including restricting Na\({}^{+}\) absorption, translocating Na\({}^{+}\) from the xylem stream to the root system, sequestering Na\({}^{+}\) in vacuoles, and exporting it out of cells. Na\({}^{+}\)/H\({}^{+}\) antiporters (_NHX1_ and _NHX7_) and ion balance regulators (_H\({}^{+}\)-PPase_ and _HKT1_) regulate Na\({}^{+}\) sequestration, export, and recirculation. [PERSON] et al. [61] discovered that _Serratia liquefaciens_ KM4 inoculation significantly reduced plant Na\({}^{+}\) and Cl\({}^{-}\) accumulation and increased the expression of _NHX1_, _H\({}^{+}\)-PPase_, and _HKT1_ genes in maize under control and saline conditions, implying that _Serratia liquefaciens_ KM4 helps in reducing toxic ion accumulation by sequestering them in plant vacuoles (Figure 3).
## 3 Background and Related Works
### HIP DEX Distribution Scheme
#### 4.1.2 New HIP DEX Secure Lightweight Association Establishment Phase
Indeed, the base station is responsible for the generation of the OTP IDs that are updated after a certain period and calculated by a dedicated algorithm. These OTP IDs are stored in a local database in the target base station;
3. The second step is about the verification if it is the first time to communicate with the CTTP. If it is the case, the mutual authentication using the OTP ID received, previously, from the base station is used to identify each other. Otherwise, there is no need to perform mutual authentication, and R1 is sent to the CTTP. To verify the knowledge of the CTTP as identified previously, we propose to memorize the IP address of the CTTP by the HIP-peers after the first successful mutual authentication. A new mutual authentication is performed when one of the following cases arises: (1) When the IoT devices receive the new OTP-ID; (2) If the network connection between the HIP-peers and the CTTP is discarded. The proposed SLA scheme has another advantage. Besides the energy efficiency, the security threats are restricted because we consider that the HIP-peers and the collaborator nodes are in the same IoT domain.
## 4 Performance Evaluation  
In this section, we evaluate the performance of our approach in the context of 3D video transmission, assessing the quality of three viewpoint videos (the received left and right views, along with the virtually generated center view) using the SSIM index. This evaluation demonstrates the performance improvement of the proposed network and channel coding method in comparison to the referenced methods. For this assessment, _Champagne Tower_ and _Pantomine_ MVD sequences are used, each with a resolution of \(1280\times 960\) and a frame rate of \(29.411\) fps [24]. Additionally, MPEG's View Synthesis Reference Software (VSRS) 4.1 is employed to generate the virtual center view [25]. The VSRS implements DIBR to perform view synthesis. Essentially, the reference software uses depth images to render new views, showcasing how DIBR is applied in practical scenarios. The overall experimental setup is illustrated in Figure 5.  
Figure 4: The iterative network and IL-FEC decoder for Node A.
## 3 Smart and Secure HIE Systems: SSHES
### Blockchain and Integration into HIE Systems
#### 3.3.4 Hybrid Blockchains in HIE Systems  
A hybrid blockchain melts the characteristics and capabilities of both public and private blockchains. It aims to leverage the benefits of both types while addressing their limitations. In a hybrid blockchain, certain aspects of the network are public, allowing for transparency and decentralization, while other aspects are private, providing control and privacy. In the last decade, several hybrid blockchains have been released, e.g., Hyperledger Fabric, Corda, etc. Integrating hybrid blockchain technology into health care informationexchange has the ability to revolutionize the industry by enhancing data interoperability, privacy and security. A hybrid blockchain takes benefits of both private and public blockchains, offering a flexible and scalable solution for health care organizations.
## 4 Discussion
### Uncertainty Assessments  
The spatial distribution pattern of U is shown in Figures 3, 4 and S2. On the one hand, the uncertainty is from the heterogeneity of the spatial characteristics. We found relatively high uncertainty in the southwestern area of Songmen Plain, the western Liaohe Plain, and northern and southern Sanjiang Plain, with fragmented fields, various land-use types, or dramatically changeable altitudes. As environmental characteristics greatly change in these areas, the correlations between SOC and covariates are complicated and unquantifiable, which leads to the lack of information on model fitness and a decrease in model stability [17]. On the other hand, the deviation in multi-source data products and in the resampling procedure may be propagated into the predictions [65].  
Additionally, predictions in the compacted layer were slightly higher than in the plough layer. That is because almost all covariates from satellites can only directly reflect topsoil information, while the downward trend of the correlations with depths getting deeper is nonlinear, which has negative effects on the accuracy and uncertainty of predictions.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 3 ESRA Routing Algorithm  
In a WSN, in order to improve the energy efficiency of the entire network and maintain a long network lifetime, it is usually necessary to adopt an energy-efficient routing strategy to reduce energy consumption. At the same time, the node load can not only reflect the traffic of the node, but also predict the residual energy of a node to some extent because of the transmission energy consumption generated by forwarding data. Therefore, so as to avoid the uneven energy distribution caused by too light or too heavy loads of the nodes, the load balancing of the nodes also should be considered.  
Firstly, our algorithm considers the two factors of energy consumption and hops to establish the link cost, and thus generates a minimum routing graph. Secondly, in order to balance the load of nodes, an edge-cutting strategy is implemented on the minimum routing graph. Among this process, a candidate parent node with the smallest load is set as next hop, and the links between source node and other candidate parent nodes are removed, thus the minimum routing tree is obtained so as to avoid the load unbalance caused by the influx of large amounts of data. The data flow is transmitted along the minimum routing tree, which will be dynamically adjusted by sensing the load of all sensor nodes and updating their residual energy in real time.
## 5 Choice of neural network architectures  
For each technique, we have considered feed-forward neural networks having, besides the input layer, a number (1 to 3) of hidden layers with tan-sigmoid transfer functions and an output layer with linear transfer functions [11]. To select the most suitable architecture, we have used a growing technique adding 1 neuron in the first hidden layer at each training session, until a maximum of 20 neurons for each hidden layer. We have considered a maximum of 3 hidden layers, choosing among the possible combinations the architecture with the lower Root Mean Square (RMS) error computed comparing the network outputs of the test session with the corresponding ECMWF profiles, where the test session employs the profiles not used in the training phase (10 and 35 observations from desert and vegetation databases, respectively).  
The best neural network topologies in terms of performance for the dry refractivity, wet refractivity and dry pressure retrieval are reported in Table 1 and in Table 2 for desert and vegetation area, respectively. The optimal number of training epochs is greater for the early stopping with full profiles (less than 20 epochs), decreasing to about a half applying the PCA and the cross validation.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multicolumn{6}{|c|}{**Desert zone**} \\ \hline \multicolumn{6}{|c|}{**EARLY STOPPING - PCA**} \\ \hline  & **Input** & **HL1** & **HL2** & **HL3** & **Output** \\ \hline
**N Dry** & 15 & 11 & 2 & - & 11 \\ \hline
**N Wet** & **15** & 15 & 15 & 9 & 14 \\ \hline
**P Dry** & **15** & 6 & 6 & 5 & 8 \\ \hline \multicolumn{6}{|c|}{**EARLY STOPPING - Full Profile**} \\ \hline
**N Dry** & 56 & 10 & 10 & 2 & 56 \\ \hline
**N Wet** & 56 & 9 & 8 & - & 56 \\ \hline
**P Dry** & **56** & 19 & 15 & - & 56 \\ \hline \multicolumn{6}{|c|}{**CROSS VALIDATION (25\%) - PCA**} \\ \hline
**N Dry** & **15** & **20** & **20** & 18 & 11 \\ \hline
**N Wet** & 15 & 15 & 15 & 13 & 14 \\ \hline
**P Dry** & 15 & 19 & 8 & - & 8 \\ \hline \multicolumn{6}{|c|}{**CROSS VALIDATION (12.5\%) - PCA**} \\ \hline
**N Dry** & **15** & 7 & 4 & - & 11 \\ \hline
**N Wet** & **15** & 16 & 13 & - & 14 \\ \hline
**P Dry** & 15 & 6 & 6 & 3 & 8 \\ \hline \end{tabular}
\end{table}
Table 1: Best neural network topologies over desert zone obtained for each neural network approach: input, HL1, HL2, HL3 and output columns report the number of neurons for the input, hidden layer 1, hidden layer 2, hidden layer 3 and output, respectively. Each approach employs 3 neural networks, named N Dry (for dry refractivity estimation), N Wet (for wet refractivity estimation) and P Dry (for dry pressure estimation).
## 2 Data and Methods
### Methods
#### 2.2.1 Data Integration and Standardization  
In data integration, to enable all data to have the same resolution, long time-series LST, ET, GPP, PET, DHI-cum, DHI-min, and DHI-sea data were averaged on time-scales. Mammal richness, bird richness, and amphibian richness were added together to generate a variate \"Allclasses\" as the surrogate for all three animal classes.  
In data standardization, remote sensing metrics were normalized (0-1) using the feature scaling method, because absolute values had large differences in magnitude. All species richness data and remote sensing metrics were summed to a 0.1\({}^{\circ}\) spatial resolution separately using the nearest-neighbor interpolation method, because the lowest spatial resolution of the datasets was 10 km (species richness data). In order to remove the impact of non-value area, we excluded the areas which had non-value pixels of species richness data or remote sensing metrics.
# Applying Game Theory to Teach the PERT Scheduling Method
## 2 Literature Review
### Game Theory as a Teaching Tool  
Regarding active learning techniques, such as simulations and role-playing, these have been successfully applied in engineering education and project management training [40]. Simulations, for example, allow students to engage in realistic scenarios to practice decision-making and resource management in a controlled environment [55]. Role-playing, on the other hand, encourages collaboration and creativity by assigning students specific roles within a team to tackle complex problems [56].  
Game Theory offers distinct advantages compared to these methods, particularly its ability to model strategic interactions and incorporate probabilistic decision-making [57]. These features align closely with the characteristics of the PERT method, which involves uncertainty and dependency among activities. While simulations and role-playing are valuable for experiential learning, Game Theory solidly supports the development ofanalytical and strategic thinking skills, making it an effective tool for teaching scheduling techniques like PERT.  
In learning, winning or losing focuses on how an individual can acquire knowledge and how this knowledge is applied in performing his or her activities. In other words, \"gaining\" new knowledge or \"losing\" opportunities to learn something new. In this sense, Game Theory has been used mainly to teach economists, market strategists, social and political scientists, and to some extent by practitioners of mathematical optimization [58].  
Game Theory has been found helpful in teaching undergraduate and graduate students in engineering, concluding that it improves students' knowledge by providing them with new tools and techniques to meet challenges in industry with novel and practical approaches. A working knowledge of Game Theory can also improve an engineer's ability to formulate conceptual problems. In addition to learning the mathematics involved, students are exposed to applications of these concepts in several engineering disciplines, such as electrical and computer engineering, through cutting-edge research [59].  
On the other hand, while the application of innovative methodologies to teaching scheduling methods has been explored previously [39], the specific use of Game Theory as a pedagogical tool is underrepresented within the spectrum of teaching project scheduling methods. In this sense, the present study emphasizes the integration of Game Theory within a structured learning environment to bridge the gap between theoretical concepts and practical applications--an area where traditional pedagogies often fall short. Unlike existing approaches, the methodology presented here demonstrates how Game Theory can enhance decision-making, strategic thinking, and analytical skills in a probabilistic context, aligning closely with the inherent nature of the PERT method [60].
## 4 Results and Discussion
### Liquid Velocity for Different Media with Varying \(B_{0}\)
#### 4.1.2 Medium 2  
Conversely, the normalized velocity \(w_{l}/W_{0}\) showed the dependency of the magnetic flux density already at the initial stage (\(t=10\) s), where for \(B_{0}=1\) T, the ratio \(w_{l}/W_{0}\) decreased by 4.2% with respect to the no-field condition. For longer times, the \(w_{l}/W_{0}\) value in Medium 2 decreased approximately by the same amount as in Medium 1 for the no-field condition. However, comparing at equal times, the normalized velocity decreased faster compared to Medium 1 with increasing \(B_{0}\). For instance, at \(t=450\) s, the \(w_{l}/W_{0}\) value in Medium 2 dropped by 11.4% for \(B_{0}=0.25\) T, 38.3% for \(B_{0}=0.5\) T, and 85.5% for \(B_{0}=1\) T (absolute differences). In Medium 1, the \(w_{l}/W_{0}\) ratio varied less than 5%, even for \(B_{0}=1\) T at \(t=450\) s. This situation was repeated for 900 s and \(t=1800\) s. In Medium 2, the decrease in \(w_{l}/W_{0}\) was much more pronounced for increasing \(B_{0}\) values compared to the reduction of the \(w_{l}/W_{0}\) value in Medium 1.
# Solar Sail Transfer Trajectory Design for Comet 29P/Schwassmann-Wachmann 1 Rendezvous
###### Abstract  
The goal of this paper is to analyze the optimal transfer towards the periodic comet 29P/Schwassmann-Wachmann 1 of a solar sail-based spacecraft. This periodic and active comet is an interesting and still unexplored small body that has been regarded as an object of the Centaurs group. In this work, a classical (heliocentric) orbit-to-orbit transfer is studied from an optimal viewpoint, by finding the spacecraft trajectories that minimize the flight time for a given value of the solar sail characteristic acceleration, that is, the typical performance parameter of a photonic sail. In particular, the optimal Earth-comet transfer is studied both in a typical three-dimensional mission scenario and with a simplified two-dimensional approach, whose aim is to rapidly obtain an accurate estimation of the minimum flight time with a reduced computation cost. The numerical simulations illustrate the mission performance, in terms of the characteristics of the rapid transfer trajectory, as a function of the typical propulsive parameter and the solar sail thrust model.  
solar sail; comet 29P/Schwassmann-Wachmann 1; optimal heliocentric transfer; preliminary trajectory design +
Footnote †: journal: Applied