esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution Framework  
Here, we introduce the CEVTI algorithm and then form its derivations. To consider a specific application of CEVTI, we apply it to the case of a coded multiuser system. In this multiuser scenario, we have multiple users whose channel coefficients must be calculated.  
First, given the output of filters at signaling interval \(l\)\(\mathbf{r}[l]\), channel coefficient vector \(\mathbf{a}\), and transmitted signal \(\mathbf{d}\), we obtain the joint probability of transmitted signals, channel coefficients, and output signal matrix, conditioned on the hyper-parameter of \(a\)\(\alpha\) and the hyper-parameter of \(d\)\(\theta\):  
\[p(\mathbf{d},\mathbf{a},\mathbf{r}|\alpha,\theta)\propto\prod_{i\in[N]}p( \mathbf{a}|\alpha)p(\mathbf{d}|\theta)p(\mathbf{r}\ \mathbf{l}\ \mathbf{a},\mathbf{d}) \tag{2}\]  
Please note that we assume channel coefficients, transmitted signals and output signals are independent with each other, for instance, \(p(\mathbf{d}|\theta)=\prod_{i\in[N]}p(d|\theta)\), and the joint probability of transmitted signals, channel coefficients, and output signal matrix can be extended using the chain rule in Bayesian network to simplify computation.  
According to Bayesian theory, the estimation of \(d\) that has minimum error rate is:  
\[\begin{split}\hat{d}_{i}&=\arg\max_{d_{i}}p(d_{i}| \mathbf{r},\alpha)\\ &\text{where }p(d_{i}|\mathbf{r},\alpha)=\sum_{i\in\mathcal{N}_{j} \setminus i}\int_{a}p(\mathbf{a},\mathbf{d}\ \mathbf{r},\alpha)\mathbf{d}\mathbf{a}\end{split} \tag{3}\]  
of which \(\mathcal{N}_{j}\) is the neighbor of base station antenna \(j\).  
We can directly see that computing \(p(d_{i}|\alpha,\theta)\) is trivial since \(p(a,d|r,\alpha)\) is unavailable. To solve this problem, we can borrow a concept from belief propagation (BP) and the mean-field method (MF) that uses a more factorable distribution to approximate the conditional probability in the above equation.
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 3 Results  
### Implementing the Optimization Method Using MATLAB  
The required inputs for a MATLAB computer program which implements the iterative method are found in Table 2, along with their units. The non-dimensional hub radius is found by dividing the hub radius by the propeller tip radius.  
The angle of attack input specifies the additional twist angle that is desired on the propeller. As can be seen in Equation (29), if the angle of attack is specified as zero, the radial twist angle distribution will be the same as the radial flow angle distribution.  
As the method is not analytical, there will inevitably always be errors. The main sources of error in the method are the assumptions of light-loading and incompressible flow, made throughout the method. Without greatly increasing the method complexity, there is little that can be done about this. However, the accuracy of the method can be increased by varying two of the inputs in Table 2. Firstly, the number of blade elements defines the blade element width. Using more blade elements will result in smaller blade element widths, resulting in a more complete and accurate output, as can be seen in Figure 7a,b. The displacement velocity ratio convergence level defines how close the calculated value of zeta needs to be to the original value for the program to classify the displacement velocity ratio as 'converged'.  
Using a smaller value for this input will therefore increase accuracy, at the inevitable expense of increased computation time, due to an increased number of iterations, as can be seen in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input** & **Unit** \\ \hline Diameter (\(D\)) & Inch \\ Velocity (\(V\)) & m/s \\ Shaft Power (\(P\)) & W \\ Propeller Speed & RPM \\ Air Density (\(\rho\)) & kgm\({}^{-3}\) \\ Number of Blades (\(B\)) & - \\ Airfoil \(C_{L}\) & - \\ Airfoil \(C_{D}\) & - \\ Non-Dimensional Hub Radius & - \\ Angle of Attack (\(\alpha\)) & Degree \\ Displacement velocity ratio convergence level & - \\ Number of blade elements & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inputs to MATLAB computer program which outputs optimal propeller geometry.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 4 Discussion
### Applications  
The retroreflected energy levels achieved by the most successful focusing retroreflector array configurations are similar to the reverberant energy levels expected in small reflective rooms. The large ideal reflector yielded several scenarios where mean retroreflected energy level was 5 dB or greater, which in broad terms is comparable to ST\({}_{V}\) of \(-8\) dB or greater (Figure 1). Similar results were achieved from the array of seven large parabolic trihedra, demonstrating that these high values are achievable through surface form. If such results are achievable in practical designs, applications could include:  
* Didactic rooms (e.g., classrooms) in which the talker's voice could be acoustically supported by focusing retroreflectors, allowing for comfortable speech without reverberant degradation of intelligibility;
* Multi-talker rooms (e.g., offices including activity-based workplaces, eating establishments) where it would be advantageous to return each talker's sound to them to ameliorate speaking power levels and to reduce unwanted transmission across the space; and Outdoor environments, where a wall, canopy or even sculpture-like objects could be shaped to substantially increase acoustic support.  
There are probably other potential applications for strong acoustic retroreflectors in architectural environments. Incidental acoustic retroreflectors have been used for robot navigation [73], and designed ones could conceivably contribute to human echolocation. There also may be noise control applications.
## 2 Results and Discussion
### Potential Medium Chain Fatty Acid (MCFA)-Derived Precursors of Ethyl Esters
#### 2.1.2 Methyl Esters of MCFAs as Transesterification Substrates  
The same concentrations of the methyl esters of nonanoic and dodecanoic acids as used for the free fatty acid experiments were added to MGJM to test the ability of these compounds to influence ethyl ester production. Methyl nonanoate stimulated ethyl nonanoate production when added at concentrations of 10 \(\upmu\)M and greater (Figure 1B). The addition of 1 mM methyl nonanoate to the model must resulted in ethyl nonanoate production of 2304 nM (429 ng\(\cdot\)L\({}^{-1}\)) and did not have the toxic effect observed when the free fatty acid was added to the must at this concentration. The addition of methyl dodecanoate produced a similar response to that of the dodecanoic acid additions, with no significant increase in ethyl dodecanoate at any concentration below 1 mM, but a significant increase of ethyl dodecanoate production at that level of addition (Figure 1B). These observations could again be related to either a toxicity response of the yeast above a certain threshold, a reflection of the size of the endogenous substrate pools for these compounds in relation to the amount added to the must, or a consequence of differential rates of transport into the yeast. The metabolism of methyl esters of fatty acids by yeast is seemingly unknown; however, methyl esters of a number of MCFAs have been found in grapes [33], and have been reported in model wines (e.g., [18]). Yeast-mediated transesterification, or alcoholysis, is a probable route of formation of ethyl esters from methyl esters, although the concentrations of these compounds in the fruit are likely to be too low to greatly influence ethyl ester concentrations in wine based on the observations in this experiment.
# A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors
###### Abstract  
One of the most important applications of remote imaging systems in agriculture, with the greatest impact on global sustainability, is the determination of optimal crop irrigation. The methodology proposed by the Food and Agriculture Organization (FAO) is based on estimating crop evapotranspiration (ETc), which is done by computing the reference crop evapotranspiration (ETo) multiplied by a crop coefficient (Kc). Some previous works proposed methods to compute Kc using remote crop images. The present research aims at complementing these systems, estimating ETo with the use of soil moisture sensors. A crop of kikuyu grass (_Pennisetum clandestinum_) was used as the reference crop. Four frequency-domain reflectometry sensors were installed, gathering moisture information during the study period from May 2015 to September 2016. Different machine learning regression algorithms were analyzed for the estimation of ETo using moisture and climatic data. The values were compared with respect to the ETo computed in an agroclimatic station using the Penman-Monteith method. The best method was the randomizable filtered classifier technique, based on the K* algorithm. This model achieved a correlation coefficient, \(R\), of 0.9936, with a root-mean-squared error of 0.183 mm/day and 6.52% mean relative error; the second-best model used artificial neural networks, with an R of 0.9470 and 11% relative error. Thus, this new methodology allows obtaining accurate and cost-efficient prediction models for ETo, as well as for the water balance of the crops.  
reference evapotranspiration; moisture sensors; machine learning regression; frequency-domain reflectometry; randomizable filtered classifier +
Footnote †: journal: Applied Sciences
# Surface Modification of Glass Textile for the Reinforcement of a Cement-Based Composite: A Review
## 3 Durability of Glass Fibers in Cement-Based Composites  
When glass fibers were first used for the production of GRC, the main problem was the low alkali resistance of E-glass fibers, which were commonly employed in polymer-based composites. The rapid deterioration process of glass fibers in cementitious matrices can be attributed to the breakdown of the Si-O-Si bonds, that make up the fiber backbone, by the OH\({}^{-}\) ions that are highly concentrated in the alkaline pore solution.  
At the end of the 1960s, [PERSON] developed glass fibers with a particular composition, which mainly differs from E-glass fiber for the addition of ZrO\({}_{2}\), observing high chemical stability in alkaline solutions. The higher stability of glass at high pH value is attributed to very stable hydrated ZrO\({}_{2}\) that forms on the glass surface, offering a barrier for the diffusion of OH\({}^{-}\) ions [37]. The incorporation of \(\sim\)16% ZrO\({}_{2}\) in the glass composition is currently the most efficient way to enhance the resistance of glass fibers in alkaline environments. However, the ZrO\({}_{2}\) layer formed on the glass surface can only slow down the diffusion of the OH\({}^{-}\) ions into the glass network, but can not prevent them to react with the SiO\({}_{2}\), so that in their service life AR-glass fibers do not have complete resistance to alkali attack. Moreover, studies conducted on the durability of GRC and TRC show that the corrosion of the fibers due to chemical attack is not the only degradation mechanism in glass fiber reinforced cementitious matrices. Different studies report that the degradation of glass fibers, with consequent strength loss, plays a minor role in the embrittlement of GRC composite over time [27; 38; 39]. According to [PERSON] et al., the microstructural changes, that occur over time in the matrix around the fibers, lead to the enhancement of the bond between fiber and matrix and to the stiffening of the interfacial transition zone (ITZ), resulting in a reduction of the mechanical properties of the composite [40]. These results were confirmed by [PERSON] et al., who investigated the durability of fibre-matrix interfaces in textile-reinforced concrete using different matrix compositions, concluding that losses in the mechanical performance of the composite are the result of both the formation of new solid products on and among the filaments, mainly brittle Portlandite, and the reduction of the filaments tensile strength, caused by defects growth [41]. Since both chemical attack and densification of the matrix adjacent to the filaments are phenomena that take place at the fiber-matrix interface, the modification of the fibers surface, in order to improve their resistance to alkaline corrosion and reduce the formation of a brittle matrix between the filaments, is commonly adopted to preserve the performance of the composite.
# Secure Remote Storage of Logs with Search Capabilities
## 4 Proposed Solution  
The proposed solution aims to enable remote storage of client encrypted log records, while still permitting search operations to be performed by the remote storage server without having access to the clear log records. The system is physically divided between the client side, where the log records generation occur and the consequent search operations are originated, and the cloud side, where the searchable encrypted log records are stored and retrieved. The client side contains the business applications from which the logs are forwarded to the Secure Logging Service (SLS). These logs, after conversion to searchable encrypted ones, are transferred over to the cloud side. The Secure Logging-as-a-Service (SLaS) application will securely store them on the cloud side. The search operations are also originated on the client side, using the SLS, sent over to the SLaS application, which returns the encrypted matching results to the client side.  
The proposed solution is envisioned as a part of a data pipeline, as shown in Figure 2. This pipeline is initialized by harvesting the log records produced by the client applications. Typically, these applications write their operational logs to file; thus, the harvesting of such logs is performed through file watchers. These file watchers serve as plugins for such applications that watch for changes of the log files, raising events every time a new log line is added. The events are then handled by the SLS, that receives clear text log records, transforms them into encrypted searchable data and sends them to the remote storage server. Although the transferred events include log records in clear text, the communication between the file watchers and the SLS is performed through encrypted channels, for which only the two involved parties possess the correct decryption keys and are able to see the information in transit. We assume that all communications between the involved parties are also performed over SSL/TLS connections.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Name** & **Integrity** & **Authenticity** & **Simple Search** & **Boolean Search** & **Nested Search** & **Field Search** \\ \hline [PERSON] & Yes & No & No & No & No & No \\ [PERSON] & Yes & No & No & No & No & No \\ Accorsi & Yes & Yes & No & No & No & No \\ Ma & Yes & Yes & No & No & No & No \\ Ray & Yes & Yes & No & No & No & No \\ Zawoad & Yes & No & Yes & No & No & No \\ Waters & Yes & No & Yes & No & No & No \\ Ohtaki & No & No & Yes & Partial & No & No \\ Sabbaghi & No & Yes & Yes & Partial & No & No \\ Zhao & No & Yes & Yes & Partial & No & No \\ \hline Ours & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with related work.  
Figure 2: Flow of data within the pipeline.
## 3 Materials and Methods
### Real-Life Scene Analysis Using the Pre-Trained Models
#### 3.3.2 Scene Encoding and Decoding Process Using LSTM Architecture  
LSTM (Long-Short Term Memory) neural and probabilistic framework is used for solving complex problems such as language modeling and machine translation. The pre-trained model of the Show and Tell model uses the Long Short-Term Memory (LSTM) network, which is trained as a language model conditioned on the image encoding. We also used an Inception-v3 image recognition model [30] that was pre-trained on the ILSVRC-2012-CLS image classification dataset. Figure 6 shows the architecture of the model.  
In the scene encoding and decoding process, the architecture of LSTM removes the last layer from the loaded model for internal information representation, as a conventional architecture of the model is used to predict a classification for an image. Our model is less focused on classifying images, but we are interested in an image's internal representation right before a learning context is  
Figure 6: The architecture of the LSTM (Long-Short Term Memory)-based Show and Tell model.  
generated. Therefore, we used the output of the last layer's output to represent the learning contexts. In our architecture, Inception-v3 is used as this image recognition model is one of the most popular convolutional neural network models for recognizing objects from natural images. Google develops the architecture of Inception-v3, which is the 3 rd version in a series of deep learning convolutional architectures. The architecture [30] of the Inception-v3 is made up of symmetric and asymmetric building blocks, including convolutions, average pooling, max pooling, concats, dropouts, and fully connected layers. In addition, loss is computed via softmax.
## 3 Results
### Relationship of Explanatory Variables Based on Lifestyle and Type of Sport Practiced  
Using the univariate ANOVA test, we analyzed if there were statistically significant differences based on lifestyle and the type of sport practiced (Table 4):  
Fat mass: The statistical significance (\(p<0.10\)) had a small effect (1.6%). Using [PERSON]'s post hoc test, a statistical significance was detected (\(p<0.05\)), which reflected a higher value in the control group compared to the rest of the groups. Muscle mass: Highly significant differences were observed (\(p<0.001\)) with a moderate-high effect size (9.4%). Using [PERSON]'s post hoc test, it was detected that the upper and lower body group (\(p<0.01\)) presented higher values than the rest of the groups. Furthermore, no differences were observed between the control group and the group that mainly used the lower body (\(p>0.05\)). Bone mass: No statistically significant results were observed when comparing the groups with each other (\(p>0.05\)), despite the mean values being generally higher among people in the control group. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Upper and Lower Body** & **Mainly Lower Body** & \multicolumn{2}{c}{**Sedentary**} & \multicolumn{2}{c}{**M-ANOVA Test**} \\ \hline  & **N** & **M and SD** & **N** & **M and SD** & **F** & \(p\)**-Value** & **R\({}^{2}\)** \\ \hline F-Fat mass & 135 & \(-0.02\pm 1.01\) & 216 & \(-0.04\pm 1.00\) & 24 & \(0.48\pm 0.87\) & 2.97 & 0.053 & 0.016 \\ F-Muscle mass & 135 & \(-0.37\pm 0.82\) & 216 & \(0.26\pm 1.03\) & 24 & \(-0.28\pm 0.88\) & 19.36 & -0.001 \({}^{*}\) & 0.094 \\ F-Bone mass & 135 & \(-0.04\pm 0.89\) & 216 & \(0.01\pm 1.09\) & 24 & \(0.16\pm 0.73\) & 0.46 & 0.629 & 0.002 \\ F-Skinfolds & 135 & \(-0.01\pm 0.97\) & 216 & \(-0.05\pm 1.03\) & 24 & \(0.51\pm 0.73\) & 3.44 & 0.033 \({}^{*}\) & 0.021 \\ F-Robustness & 135 & \(-0.22\pm 0.86\) & 216 & \(0.14\pm 1.07\) & 24 & \(-0.02\pm 0.88\) & 5.35 & 0.005 \({}^{*}\) & 0.028 \\ Cormic index & 194 & 51.53 \(\pm\) 2.37 & 307 & 52.48 \(\pm\) 2.12 & 73 & \(52.4\pm 2.82\) & 11.05 & -0.001 \({}^{*}\) & 0.037 \\ Relative Index of the & 194 & 94.52 \(\pm\) 9.65 & 307 & 90.88 \(\pm\) 8.15 & 73 & 91.0 \(\pm\) 11.04 & 10.12 & -0.001 \({}^{*}\) & 0.034 \\ \hline \hline \end{tabular} N = sample size; M = arithmetic mean; and SD = standard deviation. \({}^{*}\) = significant.  
\end{table} 
Table 4: M-ANOVA test. Differences in the factors and explanatory variables between the groups established according to the type of sport and depending on the training used.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 2 Background on Neutral Host Concept and Enabling Technologies
### Virtualized Multiradio Access Network  
The Cloud Radio Access Network (C-RAN) concept [30] defines the splitting and virtualization of cellular building blocks by placing Layer 1 (L1) functions close to the antennas and virtualized functions from upper layers integrated with NFV and SDN (i.e., Software Defined Radio (SDR)) architectures. In this context, projects such as Open-RAN [31], O-RAN Alliance [32], and NG-RAN architecture [33] are focusing on the splitting and virtualization of radio functions to integrate with SDN and NFV platforms for slicing.  
Targeted for 5G framework and strictly coupled with edge architectures, the virtualized RAN (vRAN) approach empowers the neutral host scenario with the capability of sharing the radio access part, by slicing its resources in multiple tenants, each one operated by a different Mobile Virtual Network Operator (MVNO). Moreover, given the high flexibility of the neutral host framework, different sharing and architecture models (i.e., Multioperator Radio Access Network (MORAN) and Multioperator Core Network (MOCN)) can be realized, thus providing a wide range of deployment solutions.  
For neutral host and 5G deployments, network slicing between Multiradio Access Technologies (Multi-RAT) will be essential. Hence, efforts from industry and academia are focusing on the integration of Long Term Evolution (LTE) and 5G New Radio (5 GNR) technologies with various slicing approaches [34] (e.g., assigning shares of the available airtime to different clients) from different wireless technologies (e.g., Li-Fi, Wi-Fi 6).  
To integrate multiple RAN technologies in the envisioned network slicing, a RAN controller is essential to slice, allocate, and monitor radio resources (in similar ways that the Virtual Infrastructure Manager (VIM) does with computing resources). A recently proposed SODALITE framework integrates back-haul traffic from Wi-Fi and 4G/5G in dense small cell networks [35]. Then, two well-known RAN controller platforms are the flexRAN [36] built on open-source LTE stack with OpenAirInterface [37] and EmPOWER [38] which adds Wi-Fi and Long Range (LoRa) radio technologies to LTE. Our neutral host framework goes beyond a novel RAN controller supporting LTE and Wi-Fi by adding capabilities to multiple RAN controllers from multiple vendors and radio technologies [39] (cf. Section 3).
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Choice of devices  
In [35], a study of the different standards for the devices used in the medical field is presented. Derived from this, the following entities are proposed:  
* Medical server (A): this entity must have the computational capacity to perform the most complex cryptographic operations. It is proposed that this entity is a desktop or laptop computer with an x86 processor. It is assumed that the patient's physician controls this entity. The entity must read the data from the other entities and generate instructions or commands to be executed by the corresponding actuator.
* Coordinator node (B): This entity has limited resources compared to the server. It will be with the patient at all times. The entity can be a smart cell phone, an embedded system, or a device with an ARM processor. It must perform the cryptographic operations necessary to fulfill the purpose of the protocol. This entity will be in constant communication with the sensors, actuators, and server.
* Sensors (C): They will be placed on the patient's body taking specific measurements. They must be able to take the measurements and send them to B. The transmission is assumed to be over a secure channel.
* Actuators (D): They will be on the patient's body waiting for a command. They must have the capability to perform their normal and inexpensive operations.  
Inspired by the work presented in [12], the architecture seen in Figure 1 is proposed. The architecture shows the position of entities A, B, C, and D, and their interactions. The direction of the communication indicates whether the information is encrypted or signature-protected.  
Figure 1: Proposed medical scenario WBAN architecture.