esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobile Gait Training System Providing an Active Interaction
## 2 Proposed System  
The proposed system is a system that provides interactive training content in real-time according to the trainee's gait, for which the system consists of (1) Sensing part, (2) Moving part, and (3) Augmenting part, as shown in Figure 1. The proposed system operates as shown in Figure 2. The detailed explanations of the block diagram are described in the following subsections.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Attributes** & **Lokomat** [17] & **Walkbot** [18] & **G-EO** [19] & **Robowalk** [20] & **Rehawalk** [21] & **Proposed System1 [22] & **Proposed System2 (98\%)** \\ \hline Immersion & O & O & O & O & O & O \\ \hline Activation & X & X & X & X & X & O & O \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing and proposed systems.  
Figure 1: Proposed system: (**a**) concept of the system; (**b**) the photograph of the actual system.  
The system proposed in this paper is an extended version that improves the low recognition rate (80%\(\rightarrow\)98%) which was lacking in our previous research [22]. Since the previous system recorded an unstable recognition rate of 80%, it was intended to improve this. The system proposed in this study changed the recognition part from our existing system (Image processing method, Camera) to a precision measurement method (Triangulation Method, 2D-LIDAR (Light Detection and Ranging)).
# Soriasis and Seasonality: Exploring the Genetic and Epigenetic Interactions
## 5 Geoepidemiology of Psoriasis  
The seasons are different depending on the latitude. In the latitudes of tropical and subtropical climate zones, the weather is usually less dynamic and variable than in temperate zones. Those differences connected to exposure to different humidity, temperature, sunlight, etc. may also influence the severity and seasonality of psoriasis. Reports on the geoepidemiological profile of PsV are limited, with only 19% of the countries having epidemiological data on the disease [180]. Most studies were conducted in Europe, North America, and Australasia. In 2020, The Global Burden of Disease (GBD) analyzed the prevalence and impact of skin conditions [181]. Among the fifteen most common skin diseases, psoriasis was assigned the sequelae of rich and disfigurement. The authors also emphasized the concern about the pathogenic association between PsV and cardiovascular disorders. A study by GBD showed an increased number of psoriasis diagnoses between 1990 and 2017. In addition, the risk of psoriasis assessed in the above study increased with age, which is consistent with literature data. [PERSON] et al. [180] conducted a meta-analysis and systemic review of the worldwide epidemiology of psoriasis based on 159 studies. The studies differed in the prevalence of PsV regionally and nationally. Regionally, the occurrence of the disease in the overall population varied from 0.11% in East Asia to 1.58% (from 0.50% to 5.73%) in Australasia and 1.52% (from 0.87% to 2.74%) in Western Europe. Country-specific prevalence of psoriasis varied substantially. Considering the estimate for the overall population, Australia 1.88% (from 0.59% to 6.10%), Norway 1.86% (from 0.94% to 3.97%), Israel 1.81% (from 0.83% to 4.44%), and Denmark 1.79% (from 0.91% to 3.61%) had the highest estimates of the prevalence of PsV. The estimated prevalence of psoriasis in countries in East Asia is significantly lower. Taiwan, with a PsV prevalence of 0.05% (0.02% to 0.16%), seems to be the country with the lowest number of cases of psoriasis per 100,000 people worldwide. The prevalence was highest in high-income countries, which may also have influenced the study results due to better healthcare systems, disease awareness, and better data quality. Another interesting study conducted by [PERSON] et al. [182] described that the incidence of psoriasis in Chile depended on the latitude. The data revealed a gradual elevation of prevalence in the population living in the country's southern parts when compared to northern areas. Among many factors that could have impacted this phenomenon, the authors mention environmental ones.
## 5 Conclusions and Future Research Directions  
Five batch-acquisition Bayesian Optimization (BO) algorithms are investigated in this study with the objective of identifying the most suited approach for maximizing the profit of an Underground Pumped Hydro-Energy Storage (UPHES) operator. The profit is given by a simulator computing the expected profit according to a set of decisions. The latter evaluation of a decision vector lasts 10 s, and the optimization must be completed in 20 min (initial sampling excluded). These constraints place us in the context of black-box optimization with a time-consuming simulator, except that the simulator is time-consuming in regards to the time constraint. Therefore, unlike the classical BO assumption, learning and acquisition time are not negligible.  
The study reveals that in this context, as well as on benchmark problems than on the UPHES management problem, resorting to large batch sizes and large parallelization quickly becomes excessively time-consuming, worsening the performance of any BO algorithm considered in this work. The best compromise lies between batch sizes of 4 and 8. Best performances are achieved by the TrUst Region BO algorithm (TuRBO) with \(n_{batch}=8\) in case of benchmark function, whereas \(q\)-EGO with multi-infill criteria sampling (mic-\(q\)-EGO) and \(n_{batch}=4\) performs best in the UPHES context. Parallel BO algorithms manage to find consistently good solutions to the UPHES management problem and appear as a viable way to approach time-constrained applications despite the high overhead incurred by surrogate management. However, making the most of parallel computing in this context remains challenging.  
Finally, some general leads are discussed to overcome the scalability issues. Using fast-to-fit surrogate models, multiple complementary criteria and space partitioning (or space reduction) might considerably improve the performance of surrogate-guided optimization in the context of UPHES optimization.  
Conceptualization, M.G.; methodology, M.G.; software, M.G.; validation, M.G., J.G. and N.M.; formal analysis, M.G.; investigation, M.G.; resources, J.-F.T. and F.V.; data curation, M.G. and J.-F.T.; writing--original draft preparation, M.G.; writing--review and editing, M.G., J.G. and J.-F.T.; visualization, M.G.; supervision, J.G., N.M., D.T. and F.V.; project administration, D.T. and F.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Results and Discussion  
### Sensitivity of Parameters  
The sensitivity of neural network performance to some main structure parameters is investigated in this study. Table 1 summarizes the optimal parameters of our neural network. In this table, Kernels, Blocks, Hidden numbers, and LSTM layers mean the quantity of kernels in each convolutional layer, blocks in the channel-attention-Net-based encoder, hidden neurons in the LSTM unit, and LSTM layers, respectively. Figure 12a demonstrates the BER performance at various data rates. At lower-rate scenarios, we can see that the enhancement brought by more blocks is not significant. However, when the signal is modulated in a higher-rate at 40 to 46 kbaud/s, more blocks in the proposed encoder are necessary to generate a higher-level descriptor. Additionally, more convolutional kernels are conducive to improve the identification ability of the convolutional layer to the input map, as shown in Figure 12c, since there will be more feature extractors. Furthermore, it can be concluded from Figure 12b that there will be a more desirable performance when increasing the complexity of the LSTM-based equalizer, but we should consider that it can compromise the computational complexity and system performance.  
Figure 12: BER performance under different parameters of neural network structures. In addition to the parameters investigated in each experiment, the other parameters are set as shown in Table 1. (**a**) Sensitivity of neural network performance to the quantity of blocks in encoder; (**b**) Under 46 kbaud/s, sensitivity of neural network performance to the quantity of hidden neurons and layers of LSTM-based equalizer; (**c**) Sensitivity of neural network performance to the quantity of convolutional kernels in encoder.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 3 PUTPOSH Model
### The Queuing Model  
A preemptive resume priority (PRP) M/M/1 queuing model is used to differentiate the usage of the spectrum behavior of PUs and SUs (see Figure 2).  
Figure 2: Cognitive radio user behavior on the arrival of primary user (PU).  
The characteristics of the proposed queuing model are as follows:  
1. Two kinds of users can be served by each channel, i.e., high priority PUs and low priority CRUs.
2. The PUs has preemptive priority to interrupt the communication of CRUs. Once the CRU is interrupted, it leaves the existing channel and starts sensing further available channel to continue its transmission.
3. A CRU may experience several interruptions during its transmission.
4. In the case of multiple users with similar priority; access to the requested channel is served on the basis of first come first serve.  
In Figure 3, the presented PUTPOSH approach contains two channels and two queues for PU and CRUs. The PU reserved its position in a high priority queue while the CRU is located in a lower priority queue. When the transmission is interrupted by PU, the CRU may wait and stay with the ongoing operations stage or may transfer its communication to one of the new channels. The outstanding transmission of the CRU is placed at the head of the lower priority queue. The remaining transmission of CRU is positioned at the tail of the lower precedence queue [10; 11; 12]. In any case, the channel is available for transmission and CRU will continue its communication.
## 3 DBSCAN Clustering Method  
Clustering is a commonly used unsupervised technique in machine learning, data mining, and pattern recognition. For clustering, the classes of objects are non-predetermined and the basic task is to locate a group of objects which are similar between them and are dissimilar to the objects belonging to other clusters. Each clustering method may need to identify groups where member objects are different. Each of the clustering methods has  
Figure 1: (**a**) Input time-domain signal, (**b**) SSWT modulus, (**c**) the ridges extracted from SSWT, (**d**) signal reconstruction in intrinsic mode functions.  
its own classification form, depending on the procedure of the method. In the study, after performing a literature survey and numeric tests over affinity propagation, mean shift, and DBSCAN clustering methods [27; 33; 34], the authors adopted a DBSCAN to detect the number of TF ridges in the TF spectrum obtained by SSWT since it is not necessary to identify the number of harmonic and interharmonic frequency clusters in advance. It is effective to estimate the frequency components with time-varying magnitudes of a measured waveform.  
The DBSCAN that recognizes the clusters is the density of points in each cluster, which is considerably higher than outside of those clusters [27; 28]. Data with low density is considered to be noise. Figure 2 illustrates the results of DBSCAN clustering, where the points can be easily detected as clusters of points, with the noise points not belonging to any clusters.  
There are two parameters, _min_pts_ and _eps_ (i.e., \(\epsilon\)), defined for the DBSCAN algorithm, where _min_pts_ indicates the minimum number of neighbors of the core point, and _eps_ indicates the distance from any core. A higher _min_pts_ or lower _eps_ indicates a higher density necessary to form a cluster. As shown in Figure 3, given a sample P, it is classified as the core point if at least _min_pts_ points are within the distance _eps_, and then any point that is not the core point and is out of the distance _eps_ from any core point is considered to be noise data.  
Next, the group range is extended outward to find data points that satisfy the density-reachable and density-connected conditions of the group, and allocated these data to the group. The points that do not belong to any group are considered to be noise. This process continues until the density-connected cluster is completely found. As illustrated in Figure 4, if point \(A\) can reach point \(C\) by going through point \(B\), then it can be considered to be a reachable point. Point \(F\) to point \(D\) are reachable points and point \(F\) to point \(H\) are reachable points as well. Thus, point \(D\) to point \(H\) is density-connected.  
Figure 3: Core point, border point, and noise point.  
Figure 2: Results of DBSCAN clustering.
## 3 Proposed Anonymization Method, Iterative _k_-Anonymous Clustering Method (I\(k\)-ACM)  
Proposed \(k\)-anonymization method, iterative \(k\)-Anonymization (I\(k\)-ACM), is based on the k-Anonymous Clustering Method given in [9]. I\(k\)-ACM basically produces a common \(k\)-anonymous data that meets each requirements of sinks by the help of encryption operation in addition to generalization operation. The main aim is to meet the privacy requirements with the minimum information loss. Sensor nodes directly transmit their sensed data to their group head sensors. After anonymization of data by using encryption and generalization at group head node, anonymized output is sent to each sink. Each sink decrypts the related encrypted parts and obtains data with required level of privacy.  
I\(k\)-ACM is based on hierarchical bottom-up clustering idea. Quasi-identifier attributes of records are extracted from event data and they are fed as input vectors to iterative hierarchical clustering process. The basic idea is to partition the input vectors into clusters where each cluster has at least \(k\) vectors. During the clustering, each cluster generates a representative vector which contains common generalized values or encrypted versions of attributes of all vectors belonging to that cluster. Vectors of clusters are all replaced with this representative vector in the anonymous output. Since an appropriate distance function is used during clustering and appropriate generalizations, this replacement is expected to create minimum information loss. \(k\)-anonymization work takes place in group head sensors.  
Subsection 3.1 explains how collected information is represented in our proposed method. In Subsection 3.2, distance metric which is used in the clustering process is described. Subsection 3.3 briefs how a common output is formed for meeting the needs of each sink. Subsection 3.4 gives the details of bottom-up clustering process which is the core of the proposed method. Subsection 3.5 investigates the computational complexity of method. Subsection 3.6 explains the details about the usage of proposed method in a WSN topology so that energy consumption is minimized.
# Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
## 1 Introduction
### Experimental Equipment and Method  
Before commencing the experiment, the soil sample was thoroughly mixed to ensure homogeneity and then placed in sealed containers for 48 to 96 h to achieve compaction and drainage to the designated moisture content. This process involved meticulous control over the height and mass of soil added each time. As depicted in Figure 1, the prepared soil samples were then positioned in a thermostat for 6 h to attain a uniform predetermined temperature, ensuring even temperature distribution across the samples. Concurrently, temperature sensors were installed within the soil sample, at the surface of the cold end, and on the bottom plate to consistently verify that the temperature conditions conformed to the experimental design requirements. Subsequently, under the established experimental  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample No.**} & \multirow{2}{*}{**Liquid Limit, \%**} & \multirow{2}{*}{**Plastic Limit, \%**} & **Density of Soil** & **Initial Weight** & **Wet Soil** & **Dry Soil** \\  & & & **Particles, g/cm\({}^{3}\)** & **Water Content, \%** & **Density, g/cm\({}^{3}\)** & **Density, g/cm\({}^{3}\)** \\ \hline a & & & & 56.1 & 1.64 & 0.973 \\ \hline b & 62.8 & 36.6 & 2.7 & 50.2 & 1.71 & 1.153 \\ \hline c & & & & 49.3 & 1.69 & 1.140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of the samples.  
conditions (outlined in Table 2), closed unidirectional freezing experiments were conducted over 3-4 days. These experiments were designed to investigate the freezing and water migration processes and their characteristics at different initial moisture levels under varying temperature gradients.  
The core component of the experimental setup is the thermostat, which maintains both the ambient temperature and the temperature at the upper boundary of the soil sample at consistent levels. A freezer plate at the lower end of the sample ensures it is kept at a sub-zero temperature to facilitate the freezing process. Four temperature sensors are strategically placed to monitor the cold, warm, and ambient temperatures, with the results displayed on a computer. The overall design of the apparatus is relatively straightforward, making it easy to operate.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 6 Conclusions  
This paper describes the design and development status of a compact CP-SAR system compatible with a 150-kg class satellite. Small satellites introduce a new realm of options for their low costs and reduced manufacturing time of the spacecraft platforms, becoming very attractive alternatives to traditional remote sensing missions. The L-band CP-SAR microsat is a technology demonstrator and compact science mission for disaster mitigation using L-band CP-SAR developed by Chiba University and LAPAN. The key technology for a compact design is mainly based on the SAR antenna; the antenna is of deployable type, with total mass less than 15 kg and carefully designed to fit piggy-back volume and weight constraints. The deployable reflector is a center-fed parabolic mesh with diameter of 3.6 m.  
In this work, mission specification and overall design parameters are presented, justifying the use of the deployable mesh parabolic reflector in the current mission. Simulation and measurements of antenna radiation are presented, with contributions of different antenna elements to the final radiation pattern. Antenna surface estimation was realized using a laser distance meter along ribs and scanning the reflector surface with a 3D scanning device, resulting in surface accuracy values of 1.92 mm and 3.86 mm RMS, below the required 4.22 mm. Near-field antenna measurements also confirmed good agreement with simulated results of the parabolic mesh reflector with a backfire helix antenna as feed. Few parameters must be improved to comply with mission specifications: peak directivity, by increasing the number of loops of the helical antenna, and sidelobe levels, by modifying the current tower struts design to reduce the scatterings.  
Additional future work should concentrate in prototyping/testing the feed assembly, and further investigations on improving the overall antenna efficiency.  
Conceptualization, J.T.S.S. and K.N.U.; methodology, K.N.U., T.V. and C.E.S.; software, K.N.U.; validation, K.N.U., T.V. and C.E.S.; formal analysis, K.N.U.; investigation, K.N.U.; resources, J.T.S.S.; data curation, K.N.U.; writing--original draft preparation, K.N.U.; writing--review and editing, K.N.U. and T.V.; visualization, K.N.U.; supervision, J.T.S.S.; project administration, J.T.S.S.; funding acquisition, J.T.S.S.  
This research was funded by the Ministry of Education, Culture, Sports, Science and Technology, Japan, under Grant 2101, and Chiba University Strategic Priority Research Promotion Program FY2016-FY2018.  
This work was supported by collaborator [PERSON] from Kyoto University, and the research group colleagues [PERSON], [PERSON], [PERSON]. The authors offer deep appreciation for their kind support.  
The authors declare no conflict of interest.
## 5 System Engineering Analyses
### Link Budget
#### 5.2.1 Downlink Budget  
Table 8 displays the calculated parameters required for conducting a downlink budget analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**AIS Data Budget** & \\ \hline
**Telemetry data budget** & \\ \hline
**EPS (10 bits of resolution for each** & 120 bits \\ measurement)** & \\ \hline
**ADCS (10 bits of resolution for each** & 120 bits \\ measurement)** & \\ \hline
**GPS** & 24 bits \\ \hline
**Thermal (10 bits of resolution for each** & 120 bits \\ measurement)** & \\ \hline
**Orbital Period** & 5063.45 s \\ \hline
**Communication time interval** & 5 s \\ \hline
**Total telemetry budget** & 48.6 Kbyte \\ \hline
**Data rate download of communication radio** & 9600 bps \\ \hline
**Time to download 1 frame of telemetry** & 40.5 s \\ \hline
**Total time to download AIS data frame +** & 100.5 s \\ telemetry frame & \\ \hline
**Total time to download fire detection data** & 41.17 s \\ frame + telemetry frame & \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline
**EIRP (dBW)** & \(-3.1\) \\ \hline
**Rcvd. Frequency (GHz)** & 0.27 \\ \hline
**Rcvd. Iso. Power (dBW)** & \(-149.81\) \\ \hline
**Flux Density (dBW/m\({}^{2}\))** & \(-139.73\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Downlink budget to BUAA ground station.