esa-sceva/satcom-chunk-collection huggingface dataset:
# Educational 5G Edge Computing: Framework and Experimental Study
## 3 5G Smart Campus System Architecture
### Smart Education Dedicated Network Architecture
#### 3.2.2 Wide-Area Education Dedicated Network  
In addition to ensuring network access requirements within a single campus, the multi-campus wide-area education dedicated network (WEDN) also needs to establish high-speed dedicated channels for regional interconnection, school interconnection, and mobile device interconnection to further support the interconnection of networks, services, and data across multiple campuses. By deploying a multi-level education cloud environment and private network gateway, the wide-area education private network can leverage cloud-side collaboration to achieve data distribution at different levels and wide-area interconnection of multiple campuses. Moreover, the education edge cloud can be deployed close to the main campus of each school to serve network access control while handling the school's educational business needs. For the branch campuses, their education business and data can be dynamically connected over wide areas via the 5G private network. It should be noticed that the gateway keeps sensitive data within the campus, and the rest of the data is shared across multiple campuses over a wide area network, thus ensuring the data security to a certain extent.
# Unsupervised Classification of Human Activity with Hidden Semi-Markov Models
## 3 Results
### Model Selection  
Several model parameters were compared in order to select the optimal model, which would produce the best results, including the data features and the hyperparameters for the prior distribution. First, the effect of features was assessed by comparing the correlation coefficients of models trained with acceleration magnitude only, with axes acceleration only, with both magnitude and axes acceleration and with magnitude acceleration and axes angles. For this comparison, the durations' prior hyperparameters were set to \(\alpha=360\) and \(\beta=2\)--this is the case of the so-called \"medium informativeness\". Furthermore, these values corresponded to \(\lambda=\)180 s, which was the average duration of an activity according to the accelerometer data. Table 4 shows the correlation between time spent in activities estimated with our unsupervised model and with a supervised method for different feature sets [7]. Magnitude alone performed best and was therefore selected for testing the different priors.  
The effect of the prior parameters \(\alpha\) and \(\beta\) was examined by comparing the \"medium informative\" prior (\(\alpha=360\) and \(\beta=2\)) with a \"very informative\" (\(\alpha=1800\) and \(\beta=10\)) and a \"very uninformative\" prior (\(\alpha=18\)
# Evaluating 3D Printers Using Data Envelopment Analysis
## 4 Results and Discussion
#### Context-Dependent DEA  
To further investigate the applicability of the DEA approach in the problem of assessing the efficiency of 3D printers, context-dependent DEA was applied. As described earlier, context-dependent DEA classifies DMUs in successive efficient frontiers/groups and assigns at each DMU attractiveness and progress values that can help in further assessing the technical efficiency of a DMU and how this can be improved.  
The context-dependent evaluation of the extended sample of 3D printers resulted in the formation of eight efficient frontiers/groups, which are presented in the first column of Table 9, where the corresponding printers' attractiveness scores for the successive levels of analysis are also included. It can be observed that as the evaluation context level increases so does a printer's attractiveness score. It should also be noted that the relative ranking of printers according to the attractiveness score in each group, does not necessarily remains the same through all eight levels of analysis. Printer 32, for example, at the first group of printers E1, has higher attractiveness from printer 35 at the first six levels of analysis level, but this ranking is reversed at the last two levels. The relative ranking of printers in the second group E2, on the other hand, remains the same at all levels. It is, therefore, obvious that the evaluation context affects the ranking, as has been observed and in previous studies [62]. An examination of the attractiveness score of top-rated printers shows that the five of them that achieved high efficiency in previous analysis are also found in the first two efficient frontiers/groups, as expected. Among them, printer 31 is the most attractive choice at all levels of analysis. The remaining four top-rated printers are accordingly placed in the fourth (printers 37 and 34) and the fifth efficient frontiers (printers 24 and 36).  
In Table 10 the printers' progress values are presented. An examination of progress and attractiveness values of printers in group E2 reveals that printer 38 is the most efficient unit of the group. It should be noted that the specific printer obtained the second-best score at the initial input-oriented DEA, as can also be seen in Figure 2. As noted, however, in the study of [63], this is not necessarily the case. Printer 17, for example, is the sole member of the last context level, which could very well mean that it is the worst choice, despite the fact that it did not attain the worst efficiency score, printer 16 did. An examination of progress scores for both printers also shows that at some context levels (levels 4, 5, and 7) printer 16 requires more effort to improve than printer 17. Finally, it should be noted that the efficiency scores for printers belonging in the E2 group are significantly lower from the corresponding scores for the rest of the printers (see Figure 2). This assessment is validated by the fact that E2 printers exhibit the lowest progress scores at the first context level.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
###### Abstract
Moreover, the importance of enhancing the equivalent inductance, capacitance ratio, and optimizing serial-parallel configurations for better RF performance is emphasized. In the fourth section, the article explores the application of RF MEMS switches in reconfigurable antennas, highlighting their enormous potential in future satellite communication systems. The existing reviews of RF MEMS switches tend to concentrate on general aspects such as reliability [19], driving voltage [20], and performance parameters [21]. Through a comprehensive analysis of existing research, this paper highlights the significance of RF MEMS switches in enhancing the performance and reliability of satellite communication systems, reducing costs and power consumption, and discusses their potential and challenges in practical applications. Overall, this paper aims to provide an in-depth analysis of the application of RF MEMS switches in the field of satellite communications, revealing their technological advantages and challenges, as well as their key role in future satellite communication systems. To our knowledge, this is the first comprehensive review to holistically examine the improvement of RF MEMS switches through various strategies for better suitability in space applications, offering a reference for the development or improvement of the next generation of RF MEMS switches, particularly in the field of satellite communications.
## 4 Results and Discussion
### Validation
The mean absolute errors obtained from the ANN model and the [PERSON]'s method are 0.165 and 0.266, respectively, for the shear-failure columns, whereas those obtained from the ANN model and the [PERSON]'s method are 0.188 and 0.316, respectively, for the flexural-failure columns. Figure 8: Comparison of experimental results of the ultimate drift and predicted results from the ANN model. Figure 9: Comparison results of the ultimate drift between the experimental, ANN model and explicit methods. Since there are currently no explicit formula for the estimation of capping drift corresponding to the maximum strength, Figure 10 only shows the comparison of the experimental results of the capping drift of the column and the predicted results from the ANN model, with the mean absolute error of 0.0794 and 0.1071, goodness of fit of 0.8001 and 0.8345, for the training set and the test and validation set, respectively. Nevertheless, it can still be seen that the ANN-based model for the capping drift estimation is reasonable and relatively accurate. Similarly, Figure 11 shows a comparison of the yielding drift of column between experimental results and predicted results from the ANN model. The mean absolute error for the training set and the test-validation set is 0.1081 and 0.0752, and the corresponding goodness of fit is 0.8548 and 0.8638, respectively. Generally, the yielding drift of RC columns always appears earlier with low nonlinearity than the ultimate drift. Therefore, the proposed ANN-based model can get a better prediction on the yielding drifts than the ultimate drift due to the nonlinearity difference between these two physical parameters. Figure 6:0 Comparison of experimental results of the yielding drifts and predicted results from the ANN model. Figure 6:1 Comparison of experimental results of the capping drift and predicted results from the ANN model. For the estimation of the yielding drift, [PERSON] et al. [40] proposed a statistical formula based on the results of 963 tests, which has been widely used in engineering [29]. Figure 6:2  
where \(\phi_{y}\) is the yielding curvature of the section, \(\alpha_{sl}\) is the slip coefficient (equals 1 if slippage of longitudinal steel is possible, or 0 if it is not), and \(\varepsilon_{y}\) is the yield strain of longitudinal steel. Figure 12 presents the comparison results of the yielding drift estimation of RC columns from the test set between the proposed ANN model and [PERSON] method. It shows that the ANN model performs better than the empirical formula, with the mean absolute error of 0.2327 and 0.4572, respectively. Therefore, the developed ANN-based model can provide a more reliable and accurate prediction of the critical parameters for the LP model.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 4 Proposed Spe-Cks Scheme
### Keyword Conversion Method  
The keyword conversion method is similar with the one proposed by [PERSON] et al. [10]. Let \(\phi\) be a random string, \(\phi\in\{0,1\}^{*}\). Let a hash function \(H_{1}\) be \(\{0,1\}^{*}\to Z_{q}^{*}\), which can map a keyword \(w\in\{0,1\}^{*}\) to a integer. Let \(q\) be a large prime which is larger than the size of the dictionary. So, \(H_{1}\) can be collision-resistance, which means that, if \(i\
eq j\), then \(H_{1}(w_{i}|\phi)\
eq H_{1}(w_{j}|\phi)\), where \(w_{i}\) and \(w_{j}\) are two distinct keywords and \(w_{i}|\phi\) means a concatenation operation over \(w_{i}\) and \(\phi\). The details of this method is described as follows.  
(1) For an index keyword set \(W=\{w_{1},w_{2},\ldots,w_{n}\}\), the following function is given.  
\[f(x) =(x-H_{1}(w_{1}|\phi))(x-H_{1}(w_{2}|\phi))\ldots(x-H_{1}(w_{n}| \phi))\] \[=a_{n}x^{n}+a_{n-1}x^{n-1}+\ldots+a_{0}x^{0}\]  
The coefficients of the \(f(x)\) can be built as an index vector \(\vec{a}=\{a_{0},a_{1},\ldots,a_{n}\}\).  
(2) For an query keyword set \(Q=\{q_{1},q_{2},\ldots,q_{m}\}\), a vector \(\vec{x}=\{x_{0},x_{1},\ldots,x_{n}\}\) can be obtained, where \(x_{i}=H_{1}(q_{1}|\phi)^{i}+H_{1}(q_{2}|\phi)^{i}+\ldots+H_{1}(q_{m}|\phi)^{i}\) and \(i\in[0,n]\).  
Note that if there is \(Q\subseteq W\), it is easy to find that \(\vec{a}\cdot\vec{x}=0\). This property can be used for the keywords search. The concrete SPE-CKS scheme is given in the next subsection.
# Predictive Modeling and Experimental Analysis of Cyclic Shear Behavior in Sand-Fly Ash Mixtures
## 3 Experimental Study  
To investigate the cyclic shear behavior of TS alone, and TS-fly ash mixtures at varying contents (0%, 2.5%, 5%, 10%, 15%, and 20%), a series of tests were conducted using the constant normal load (CNL) cyclic direct shear testing machine. CNL and constant volume (CV) conditions can be regarded as the extreme conditions, framing the real paths followed by the interface. This geotechnical method is specifically designed to simulate real-world cyclic loading conditions that soils experience in dynamic environments, such as seismic zones or areas with heavy traffic. Unlike conventional direct shear tests, which maintain constant normal stress, the CNL test keeps a steady normal load while subjecting the specimen to cyclic shear, closely replicating the pressures from overburden and superimposed loads. The tests were carried out using the Geocomp Shear Trac II apparatus, a fully automated system capable of performing consolidation, static, and cyclic direct shear tests. This advanced setup features a computer-controlled unit, a micro-stepper motor for precise vertical load application, and a high-performance servo motor for applying horizontal cyclic loads. The CNL apparatus allowed for a controlled cyclic rate of up to 5 Hz, with typical test frequencies ranging from 0.033 to 2 Hz and a maximum vertical displacement of 25.4 mm. The cyclic tests, conducted in a strain-controlled manner, applied a displacement of \(\pm\)3 mm at a loading rate of 2 mm/min. For each specimen, five cycles of cyclic shearing were applied under a 100 kPa vertical load. The materials used in the experiments and the direct shear testing equipment with the tested specimens are shown in Figure 3. The research will carry forward the studies completed so far in this specific field by following the test conditions established previously in order to ensure consistency [31, 36, 37, 38]. The specimens tested in the experimental works were 63.5 mm in diameter, and 25.4 mm in height. Data from these tests provide critical insights into shear stiffness and permanent deformation, which help to assess potential problems like liquefaction, fatigue, and cumulative deformation in soil foundations under cyclic loading, which are essential for the design of resilient infrastructure.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Chemical Composition**} \\ \hline SiO\({}_{2}\) (\%) & 50.50 \\ \hline Al\({}_{2}\)O\({}_{3}\) (\%) & 23.70 \\ \hline Fe\({}_{2}\)O\({}_{3}\) (\%) & 5.80 \\ \hline CaO (\%) & 9.30 \\ \hline MgO (\%) & 2.60 \\ \hline SO\({}_{3}\) (\%) & 1.40 \\ \hline Na\({}_{2}\)O (\%) & 0.34 \\ \hline Loss of ignition (\%) & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis report of physical and chemical characteristics of fly ash [35].  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Sieve Size (mm)**} & **Retained (\%)** \\ \hline
45 mm & 23.3 \\ \hline
90 mm & 9.93 \\ \hline
200 mm & 2.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical analysis of fly ash [35].
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sampling**} & \multicolumn{4}{c}{**Base Model**} & \multicolumn{4}{c}{**Proposed Model**} \\ \cline{2-9}  & **Div-s** & \(k\) & **max Div-1** & **average Div-1** & **Div-s** & \(k\) & **max Div-1** & **average Div-1** \\ \hline N1 & 0.015 & 38.8 & 0.192 & 0.191 & **0.017** & **133.0** & **0.197** & **0.196** \\ U20 & 0.090 & 3859.6 & 0.359 & 0.307 & 0.128 & 3825.75 & 0.387 & 0.317 \\ U30 & 0.116 & 4355.8 & 0.391 & 0.319 & 0.201 & 4431.75 & 0.457 & 0.347 \\ U40 & 0.133 & 4550.2 & 0.410 & 0.326 & 0.274 & 4721.75 & 0.516 & 0.373 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of the diversity of the base model and the proposed model. The result of the proposed method is more diverse than the base model is with N1 sampling. range, the lower the accuracy and the higher the diversity. For reference, we computed the Div-1 of the MS COCO data set, which consisted of captions written by humans, and the value was 0.53. Figure 4 shows an analysis of the relationship between the diversity of content the image contains and the diversity of the captions generated. Assuming the images will tend to contain a variety of content if they include a variety of captions, the Div-1 value of the ground truth captions was used to represent the richness of the content. Div-1s were obtained for the ground truth images and the images were divided into five groups according to their Div-1 values. The divisions were as follows with Div-1 values between (0.0-0.2], (0.2-0.4], (0.4-0.6], (0.6-0.8], and (0.8-1.0]. Moreover, the caption was generated by the model for an image corresponding to each section. Then, the maximum Div-1 value was obtained by constructing five caption combinations per image in the same way as the experiment in Table 2, and the average Div-1 of the images was obtained within the interval. To reduce the random sampling error, the same experiment was repeated five times and the average value is shown. The value was plotted on the vertical axis in Figure 4. The results of the base model are represented in dashed lines and the results of the proposed model are shown in solid lines. As the image content diversity increased, the Div-1 value of the proposed model increased more rapidly than with the base model. In the case of the base model, the diversity of the generated caption increased as the diversity of the ground truth caption increased, but the incremental rate was low. Therefore, a large gap developed between the Div-1s of the generated and the ground-truth captions for the rich images. Our method modeled the image region feature into a latent space. When decoding, the latent variable was calculated in conjunction with the image and gave a weighting effect to various parts of the image. Therefore, it was more affected by the image content than was the base model using only the sentence features. It is expected that, with this system, if an image contains a greater variety of content, it will generate a greater variety of captions.
# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology
## 3 Microgrid Modeling and Design
### Microgrid Control  
Figure 12 demonstrates the detailed architecture of the PV-ESS microgrid equipped with a centralized controller used for power management. The connected loads are classified into two groups: group1 includes the high priority loads that must be permanently connected to the microgrid, while the other group includes fewer priority loads that can be islanded from the microgrid and reconnected to the utility grid through SSTS. The controller runs an algorithm that requires a set of consents and variables such as: number of loads (\(N\)), the threshold voltage (\(V_{\mu C-min}=v_{dd-min}\)) that can be extracted from voltage-power characteristics (\(V\)-\(P\)) of the microgrid, the real-time monitored microgrid voltage at the AC-bus (\(V_{\mu C}\)), and a power margin (\(\Delta P\)) used to adjust the amount of load shedding, as described in Algorithm 3. At regular intervals, the algorithm checks the power demands (\(P_{D}\)) and compares them with the power produced by the renewable resources (\(P_{DER}\)). If the demands exceed the microgrid capacity and the microgrid voltage falls below the predetermined threshold voltage, the algorithm classifies the loads and calculates the number and location of loads that must be removed from the microgrid. Upon receiving the transfer signal from the microgrid controller, the selected loads are momentary and seamlessly transferred to the utility grid. The LoRa communication network is used to communicate between the transfer switches, power smart-meters, and grid controllers. When the operating conditions of the microgrid get improved or the demand is reduced, the transferred load(s) can be restored to the microgrid.  
Figure 11: (**a**) Prototype design of smart power meter based on ZigBee communication protocol, (**b**) display dashboard, and (**c**) appliances remote control using Blynk IoT platform.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### United States Satellites  
In 2009, NASA launched the Orbiting Carbon Observatory (OCO), the first satellite dedicated to high-precision, global CO\({}_{2}\) measurements, but it failed that year. In 2014, OCO-2 successfully launched, equipped with a single payload that integrates three high-resolution grating spectrometers. OCO-2 features a spatial resolution of 1.29 km \(\times\) 2.25 km and revisits locations every 16 days. Its data products include: the primary class contains all the collected soundings; the secondary class has atmospheric CO\({}_{2}\) column concentration, surface albedo, and aerosol content; the tertiary product consists of the global CO\({}_{2}\) concentration maps; the quaternary product is the global CO\({}_{2}\) source/sink carbon fluxes.  
In 2019, OCO-3 was launched aboard the International Space Station (ISS). It offers enhanced CO\({}_{2}\) and Solar-Induced Fluorescence (SIF) observations with a broader range and better capability for local and point source measurements [22].
# Generation of Orbital Angular Momentum Modes Using Fiber Systems
###### Abstract  
Orbital angular momentum (OAM) beams, characterized by the helical phase wavefront, have received significant interest in various areas of study. There are many methods to generate OAM beams, which can be roughly divided into two types: spatial methods and fiber methods. As a natural shaper of OAM beams, the fibers exhibit unique merits, namely, miniaturization and a low insertion loss. In this paper, we review the recent advances in fiber OAM mode generation systems, in both the interior and exterior of the beams. We introduce the basic concepts of fiber modes and the generation and detection theories of OAM modes. In addition, fiber systems based on different nuclear devices are introduced, including the long-period fiber grating, the mode-selective coupler, microstructural optical fiber, and the photonic lantern. Finally, the key challenges and prospects for fiber OAM mode systems are discussed.  
Keywords:orbital angular momentum; long period fiber grating; mode selective coupler; photonics lantern; microstructure optical fiber +
Footnote †: journal: Applied Physics Letters
## 3 Case Study--The Partly Deserted Village of Qazbagi (Iraqi Kurdistan)
The resulting descriptive system in the form of a personal geodatabase thus enabled the creation not only of interpretive plans of the village, but also of chronological topographic plans of the built-up part of the settlement area, reflecting developments and dynamic changes from the 1970s to the present. The main goal was, of course, to capture changes in the settlement structure of a nucleated village.