esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Mechanical Performance Under WD Cycles
Its physical-chemical interactions likely form a pseudo-gel matrix that binds particles while allowing limited deformation under stress, thus maintaining post-peak energy absorption capacity [38; 44]. In the most adverse conditions tested, the undrained shear strength (\(S_{\text{u}}\)) of guard gum-treated samples remained above 29 kPa after 15 wetting-drying (WD) cycles under saturated conditions, outperforming cement-treated samples, which exhibited a comparable \(S_{\text{u}}\) of 27 kPa at the same stage. According to Eurocode 7 [46] and prevailing geotechnical design practices, a \(S_{\text{u}}\geq 25\) kPa is generally deemed sufficient for lightly loaded structures, such as road subgrades, embankments, and shallow foundations, on soft cohesive soils. Notably, guard gum-treated soils also exhibited the highest modulus of toughness (\(U_{\text{t}}\)) across all moisture conditions, indicating superior energy absorption capacity and ductility. These characteristics are particularly advantageous in applications subjected to repeated environmental loading, such as seasonal moisture fluctuations or rainfall cycles. The elevated toughness values suggest enhanced resistance to cracking and a reduced likelihood of brittle failure, aligning with long-term performance expectations for resilient and sustainable infrastructure [47; 48]. Moreover, X-ray computed tomography (CT) imaging revealed that guard gum-treated samples maintained superior microstructural integrity after 10 wetting-drying (WD) cycles, displaying significantly fewer and narrower cracks than both untreated and cement-stabilised counterparts. This evidence underscores the effectiveness of biopolymer stabilisation in enhancing resistance to moisture-induced degradation, thereby supporting its suitability for geotechnical applications demanding long-term mechanical and volumetric stability. To build upon these findings, future research should employ advanced quantitative image-processing techniques to systematically evaluate pore structure parameters and crack density across untreated, cement-treated, and guar gum-treated soils subjected to repeated WD cycling.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 2 Background
### Trajectory Generation Problem Definition  
The trajectory generation problem of a multi-rotor UAV can be defined as follows: Given a state space \(X\in\mathbb{R}^{n}\), a free state space \(X_{free}\), an initial state \(x_{init}\) and a goal region \(X_{goal}\in X_{free}\), find a path \(\sigma:[0:T]\to X_{free}\) such that \(\sigma(0)=x_{init}\), and \(\sigma(T)\in X_{goal}\) and \(c(\sigma^{*})=min_{\sigma\in\sum_{free}}c(\sigma)\).
## 4 Results and Discussion
### Rotor Blade Load and Aerodynamic Force  
The power capability of the radial-inflow turbine depends on rotor blade load and it can be further studied by rotor blade pressure distributions. Figure 15 presents the pressure distribution at the mid-span and its partially enlarged view, i.e., the rotor blade load along the streamwise location of different solidity and working fluid. As Figure 15a indicates, when solidity is higher than 11.94, the pressure below critical point appears. The trans-critical phenomenon arises at the trailing edge of the blade. In both Figure 15a,b, when solidity is 7.96, the pressure difference between suction side and pressure side is the largest. Hence, the blade can obtain the highest power capacity when the solidity is the lowest. Additionally, the capacity of the blade concentrates on the front and middle of the streamwise location, especially for solidity 15.92, which has little power output at the latter half of the blade. This phenomenon derives from the diminishing pitch of the blade along the flow direction. Moreover, for both SCO\({}_{2}\) and air, the solidity mainly affects the pressure distribution on the suction side and the pressure on the pressure side is of minor variation.  
The axial force of a single rotor blade with different solidity and working fluid are presented in Figure 16. The direction of the force is shown by the red arrow. The axial force of the rotor is not beneficial to the safe operation and requires high-level balance method of axial thrust as well as a reliable bearing. From Figure 16, we can conclude that with the solidity increasing, in a SCO\({}_{2}\) radial-inflow turbine, the axial force of a single rotor blade increases. However, with the working fluid air, the axial force first increases and when the solidity is greater than 13.93, it slightly decreases. In general, the axial thrust of the rotor increases considering the rotor blade number. The effect of rotor solidity on axial force is greater for SCO\({}_{2}\) turbine than it is for air turbine. The high density of SCO\({}_{2}\) working fluid causes much higher axial thrust. With the augmented axial force, the safe operation of the turbine is affected and the load of the bearing enlarges.  
Figure 16: Axial force of a single rotor blade with different solidity and working fluid.  
Figure 15: Rotor blade load along streamwise location of different solidity: (**a**) SCO\({}_{2}\); (**b**) air.
## 3 Wind Estimation Algorithms  
### Method Based on Drive Train Model for Wind Estimation  
One method for estimating wind speed involves applying the estimated wind speed using a 3D look-up table. The input variables of the 3D look-up table are rotor speed, pitch angle, and rotor torque. Rotor speed and pitch angle, used as input variables, are based on feedback from operational data. Additionally, rotor torque can be obtained through two methods, the first of which involves dividing the measured generator torque by the gear ratio; the second method involves calculating and applying aerodynamic torque, using the formula shown in Equation (5), where \(J_{r}\) is the rotor inertia, \(N\) is the gear ratio, \(J_{g}\) is the generator inertia, \(T_{g}\) is the generator torque, \(B_{r}\) is the damping coefficient of the rotor shaft, \(B_{g}\) is the damping coefficient of the generator shaft, and \(\Omega_{r}\) is the rotor speed. For implementation of the simplified model without time derivative terms, \(J_{r}\) and \(J_{g}\) are assumed to be 0. Additionally, \(B_{r}\) and \(B_{g}\) are assumed to be very small and are considered as 0 in this study [3].  
\[T_{a}=\begin{pmatrix}J_{r}+N^{2}J_{g}\end{pmatrix}\frac{d\Omega_{r}}{dt}+NT_{ g}+\begin{pmatrix}B_{r}+N^{2}B_{g}\end{pmatrix}\Omega_{r} \tag{5}\]
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
###### Abstract  
Aiming at the problem that the fully homomorphic encryption scheme based on single identity cannot satisfy the homomorphic operation of ciphertext under different identities, as well as the inefficiency of trapdoor function and the complexity of sampling algorithm, an improved lattice MIBFHE scheme was proposed. Firstly, we combined MP12 trapdoor function with dual LWE algorithm to construct a new IBE scheme under the standard model, and prove that the scheme is IND-sID-CPA security under the selective identity. Secondly, we used the eigenvector method to eliminate the evaluation key, and transform the above efficient IBE scheme into a single identity IBFHE scheme to satisfy the homomorphic operation. Finally, we improved the ciphertext extension method of CM15 and constructed a new Link-mask system that supports the transformation of IBFHE scheme under the standard model, and then, converted the above IBFHE scheme into MIBFHE scheme based on this system. The comparative analysis results showed that the efficiency of this scheme is improved compared with similar schemes in the trapdoor generation and preimage sampling, and the dimension of lattice and ciphertext size are significantly shortened.  
lattice; full homomorphic encryption; multi-identity encryption; LWE problem +
Footnote †: journal: Applied sciences
## 3 Structure and Salient Characteristics of the BeSYC Software and Its GUI
The _main panel_ is divided into five sections:  
* The computational options to select the code and the number of parallel code runs if the execution of the calculation is requested;
* The code executables section to provide the path to the code's executable(s);
* The Terminal/Command prompt section used to select if the code runs have to be executed (\"Execute the calculations\" and the \"Run the default calculation\" check boxes), and, if so, if the terminal/command windows should appear on the screen or if the calculations have to be performed in background;
* The best-estimate methodology section where the execution of the BEPU methodology and the verbosity of the output files can be selected. In this panel, it is also possible to select _sensitivity_ and \(C_{ar}\) matrices taken from _csv_ files. * The best-estimate input deck section to specify if a best-estimate input deck has to be written and executed. In this section, is also possible to specify the creation of a new BeSYC input file containing the best-estimate standard deviation values. Figure 2: _Cont._  
The _responses panel_ presents only four main elements: the paths to the experimental data file and the default input deck, the calculation time (_starting time_) from which the BEPU methodology is applied, and a table that is used to select the experimental and computed responses to be used in the BEPU methodology. _Starting time_ is expressed in seconds, and it is used to skip the initial phase of a calculation that might be necessary to reach a steady state. The _parameters panel_ presents two main sections: a top section to select a \(C_{\alpha\alpha}\) matrix from a _csv_ file and a bottom section to define which parameters should be investigated together with their standard deviation and variation (in percentage) in the input decks. For both codes, each parameter can be selected, specifying the card and the word in which they appear, as described in the RELAP5-3D, MELCOR 1.8.6, and MELCOR 2.2 User's Manuals [15; 16; 17]. The parameters are defined into several independent tables contained in different panels (_Tab 1, Tab 2_, etc. ), each provided with two check boxes to state if the variations of the parameters specified in the current table should be written into a single input deck or not and, if yes, if they belong to a parameter evolving in time.
# An Image-Based Fall Detection System for the Elderly
## 4 Methodology
### System Architecture and Operating Flow  
As it is a complete surveillance system, IFADS is able to detect falls in the elderly by using images from surveillance cameras in public areas to offer them prompt treatment. In addition to falls during walking, we focus on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking.  
Figure 1 shows the architecture of IFADS. First, when the person appears on camera, the person is far from the chair. IFADS recognizes that the person is standing. If the change in the height of the person's bounding box is significant, it will further determine whether the person has fallen. In addition, if the person is beside the chair, since he/she may sit on the chair, IFADS uses the posture of the person to determine their status. When the person is trying to sit down, their status is in progress. In addition, if the person sits on the chair, their status is sitting. Since the person may fall while sitting down or standing up from the chair, IFADS checks if the person is in danger. Finally, if IFADS cannot detect the person, it will use the person's previous state to make a decision.  
We adopted the well-known human model [34; 35] to determine a body posture. As shown in [35], the upper portion of the body from the crodt to the head is four head lengths. Also, the lower portion of the body from the feet to the crodt is four head lengths. The thigh is two head lengths, the knee is one head length, and the calf is two head lengths. Based on the human model, we take the height changes while walking to determine the body posture. If the height decreases more than a half of a head length, the posture is regarded as the starting point of sitting down. In addition, if the person is falling down or kneeling down, the length below the navel (five head lengths) should reduce by over half. Therefore, the height will be less than 5.5/8 (= (3 + 2.5)/8) of the original height. Finally, for the case where the person suddenly disappears from the scope of the surveillance system and his/her height in the previous frame is less than the average height of standing up and kneeling down (i.e., 6.5/8 (=(8 + 5.5)/2)), he or she is regarded as falling down.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 2 Related Work
Secondly, they obtain the position of vehicles by using GPS technology and then use GPRS technology to transmit the data of mobile objects to provide a vehicle count to detect congestion. Importantly, the significant work proposed in [69] provides a tool to extract information from social networks, particularly Twitter, related to traffic congestion in certain areas. The compilation of \"tweets\" is then processed by an algorithm to determine whether a specific area is congested or not. Exploring a different way to detect vehicular traffic, this paper promotes the use of vehicle-to-vehicle (V2V), vehicle to infrastructure and infrastructure-to-infrastructure (I2I) communication to enable vehicles to share their positions, speeds, paths and travel times. With that data, a big data cluster can identify traffic congestion events and inform drivers so that they can make real-time decisions to avoid traffic congestion. Importantly, the big data cluster generates metrics that can lead to public policies for cities to improve the quality of life of its habitants from the environmental, health and economic points of view.
# Local Community Detection Based on Small Cliques
## 3 Local Community Detection Algorithms Based on Small Cliques
### Clique Based Community Expansion  
The basic idea of Clique Based Community Expansion is to start with the maximum clique in the subgraph induced by the neighbors of the seed node. For weighted graphs, we use the clique that has the maximum sum of all edge weights inside it and towards \(s\). This is an extension that can be applied to all algorithms we consider. For algorithms based on simple greedy addition of nodes, we simply first add all nodes of the clique, for PageRank-Nibble the PageRank approximation simply starts with the initial weight equally distributed to the nodes in the clique.  
For overlapping community detection, this was already considered by the Greedy Clique Expansion algorithm [8]. It first lists all maximal cliques in the graph and then expands a subset of them into communities. For local community detection, this was proposed by [9]. While they state that this can be combined with any greedy expansion scheme, they only evaluate it in combination with GCE M.  
Implementation Details  
Our Clique Based Community Expansion implementation first creates the subgraph induced by the neighbors \(N(s)\) of the seed node \(s\). Then we detect cliques using the simple ELS-bare algorithm of [PERSON] et al. [PERSON] et al. (2016), which runs in time \(\mathcal{O}(d^{2}n3^{(d/3)})\) on a graph with \(n\) nodes and degeneracy \(d\). While they also present a faster algorithm, this algorithm still achieves good running times in practice while being much simpler to implement [PERSON] et al. (2016). In our usage of the algorithm \(n=\text{deg}(s)\), since we only look in the neighborhood of a seed node \(s\). For unweighted graphs we simply select the largest clique, for weighted graphs we select the clique of maximum edge weight. For the weighted case, we simply iterate over all neighbors of the clique nodes and sum up the weights of intra-clique edges. As a graph with degeneracy \(d\) has at most \(d(n-\frac{d+1}{2})\) edges and the number of maximal cliques is at most \((n-d)3^{(d/3)}\) [PERSON] et al. (2016), this step needs at most \(\mathcal{O}(d\cdot n^{2}\cdot 3^{(d/3)})\) asymptotically. Therefore, the total running time of the initialization algorithm for unweighted graphs is the same as the running time for finding the cliques on the graph induced by the neighbors of the seed node, while for weighted graphs the clique selection step dominates the asymptotic worst case running time.
## 3 Network Protocol  
Each selected celestial body within the infrastructure design will have its own independent communication network, which will be interconnected with other networks of the same size (subnets), creating a unified network that reaches all selected celestial bodies. The entire network, including the communication subnets of the infrastructure, is modeled according to the Open Systems Interconnection (OSI) Model.
# Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus
###### Abstract  
In this work, hourly measurements of global solar irradiances obtained from pyranometers and sunshine duration data using either Kipp & Zonen CSD3 automatic sensors or Campbell-Stokes sunshine recorders were assessed through an extensive quality control procedure and statistical analysis on the measured and derived solar parameters for all the actinometric stations installed in various locations over the island of Cyprus, covering mainly the period 2019-2021. This information is useful for engineers concerning the solar energy capture systems and energy efficiency who can therefore take knowledge of the local radiation levels. Monthly mean hourly values of global radiation and sunshine duration are calculated and shown through isoline diagrams. During June or July, daily global irradiations ranged between 25 MJ/m\({}^{2}\) and 30 MJ/m\({}^{2}\), with the lowest occurring in the mountainous locations. On the other hand, in January or December, they ranged between 6.5 MJ/m\({}^{2}\) and 10.5 MJ/m\({}^{2}\). The total annual number of hours of sunshine duration ranged between 2500 and 3500, with the lowest values recorded at the mountainous sites. The clearness index and relative sunshine duration were used for the classification of the weather conditions over the island. Furthermore, the interrelationships between the said indices were used for the estimation of global radiation. This work has specifically contributed to the characterization and analysis of hourly and daily solar global radiation. Furthermore, the measurements on the ground level could be compared with satellite observations in order to improve the geographical distribution of global radiation, especially in areas where no measurements exist. The analysis could be also extended for the other shortwave radiation components (Direct, Diffuse and Photosynthetic Active Radiation (PAR)) in order to assess the solar radiation regime over the island.  
solar global radiation; sunshine duration; quality control; clearness index; statistical analysis; isoline diagrams; classification of sky conditions +
Footnote †: journal: _Sciences_
## 2 Mathematical Model and Inversion Method
### Coordinate Transformation
#### 2.3.1 Transformation of Geographical Coordinates  
The geographical coordinate of the observation data \(\left(\varphi,\theta,r\right)_{S}\) is transformed from the spherical coordinate system to the cone coordinate system. The transformation is conducted in three steps:  
(1) In the spherical coordinate reference frame, Equation (8) is used to transform the geographical coordinate \(\left(\varphi,\theta,r\right)_{S}\) to the earth's core space rectangular coordinate system \(\left(X,Y,Z\right)_{S}\);  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]_{S}=\left[\begin{array}{c}\cos\theta\cdot\cos\varphi\\ \cos\theta\cdot\sin\varphi\\ \sin\theta\end{array}\right]_{S}. \tag{8}\]  
(2) Equation (9) is utilized to transform \(\left(X,Y,Z\right)_{S}\) to the cone space rectangular coordinate system \(\left(X,Y,Z\right)_{C}\);  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]_{C}=R_{y}(\theta_{0})\cdot R_{z}(\varphi_{0})\cdot\left[ \begin{array}{c}X\\ Y\\ Z\end{array}\right]_{S}. \tag{9}\]  
In Equation (9), \(R_{y}(\theta_{0})=\left[\begin{array}{ccc}\cos\theta_{0}&0&-\sin\theta_{0} \\ 0&1&0\\ \sin\theta_{0}&0&\cos\theta_{0}\end{array}\right]\)\(R_{z}(\varphi_{0})=\left[\begin{array}{ccc}\cos\varphi_{0}&\sin\varphi_{0}&0\\ -\sin\varphi_{0}&\cos\varphi_{0}&0\\ 0&0&1\end{array}\right]\), and \(\left(\varphi_{0},\theta_{0}\right)\) refers to the longitude and geocentric colatitudes of the cone pole;  
(3) In the cone coordinate reference frame, Equation (10) is used to transform \(\left(X,Y,Z\right)_{C}\) to the cone geographical coordinate system \(\left(\varphi,\theta,r\right)_{C}\):  
\[\left[\begin{array}{c}\varphi\\ \theta\end{array}\right]_{C}=\left[\begin{array}{c}\arctan\frac{Y}{Y}\\ \arcsin\frac{Z}{r}\end{array}\right]_{C}. \tag{10}\]
## 6 Performance Evaluation
### Simulation Results and Analysis  
Because of the mobility of the actuator node, the distance between the sensor and the actuator varies during runtime according to Fig. 6. The RF power of the sensor is assumed to be 0 dBm. Accordingly, the packet loss rates with respect to different distances will be randomly extracted from the data set reported in Fig. 4. For instance, when the distance is 10m from \(t=16\) to 20s, the packet loss rates will take random values out of {0.7160, 0.2716, 0.6790, 0.913  
The performance of the control application is compared with respect to two different WSAN design methods: 1) traditional design method without packet loss handling mechanism on the event-triggered actuator; and 2) the application-level design methodology proposed in this paper. Some relevant parameters are set as follows: \(\tilde{K}_{p}=0.3\,,\ \ \tilde{K}_{t}=0.2\,,\ \ \tilde{K}_{D}=0.5\,,\) and \(m=3\).  
Fig. 7 shows the control performance in terms of the IAE associated with different design methods. The cumulated IAE value of the system designed using our method is only 19.4% that of the system using traditional design method. The rapid increase in IAE from time \(t=16\)s to 20s implies that the system becomes unstable during this period of time. This can also be seen from Fig. 8 (the upper part),  
Figure 6: Variable distance in simulations.  
Figure 7: Control performance in terms of IAE.  
where the measured/sensed system output is depicted. The instability is mainly caused by the large packet loss rates at the distance of 10m, which has been shown in Fig. 4.  
In contrast, the system remains stable all the time, when the design method proposed in this paper is employed. This is justified by the quite slow increase in IAE throughout the simulation, see Fig. 7. As also shown in Fig. 8 (the lower part), the performance of the control application is satisfactory even when the system may encounter considerably severe packet loss. Since no nodes are allowed to work beyond its radio range (i.e., 30m in this case) in practice, the above results demonstrate that the method proposed in this work is effective in mobile control applications where the communication distance may change over time.