esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The soybean-maize proteins are potential alternatives for applying food systems and the development of high-protein food, as they showed better solubility and general functionality compared to the pure soybean counterparts. Additionally, they were nutritionally more complete proteins. In solubility, the isolate and the protein hydrolysate of the soybean-maize blend had higher solubility (10% and 52%) than the soybean counterparts. Emulsifying activity indexes were also higher in the soybean-maize isolate and hydrolysate (303.9% and 22.7%), whereas the emulsion stability at 48 h was similar compared to the soybean samples. Foaming properties of the soybean-maize proteins were lower than soybean, but the stability was excellent, and the density was slightly higher (5%). Heat coagulation capacity of the soybean-maize isolate and hydrolysate was higher (36.3% and 1.2%) than the soybean proteins. The electrophoretic profile of soybean and soybean-maize proteins showed typical bands of lipoxygenase, \(\beta\)-congbycinin, and glycinin. In the case of a soybean-maize mixture, the electrophoretic profile showed three additional bands compared to the typical soybean pattern of 56, 55, and 52 kDa, which correspond to the globulins of maize germ. These proteins completed the isolates' amino acid content and hydrolysates, reaching the requirements established by the FAO/WHO [27] for 2-5-year-old infants, yielding a protein with better nutritional quality. This protein blend's functionality and nutritional value could be adequate for the development of products for children under five years of age, as breakfast drinks, for example, opening the potential to meet nutritional requirements. Additionally, new blends of cereals and legumes such as soybeans and quinoa can be explored to provide better protein alternatives for the consumer.  
Conceptualization, C.G.S.-H. and C.C.-H.; data curation, C.G.S.-H., S.O.S.-S. and C.C.-H.; funding acquisition, S.O.S.-S. and C.C.-H.; investigation, C.G.S.-H.; methodology, C.G.S.-H.; resources, S.O.S.-S. and C.C.-H.; supervision, C.C.-H.; writing--original draft, C.G.S.-H.; writing--review and editing, S.O.S.-S. and C.C.-H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research was supported by the Research Chair Funds 0020 IDP001 from Centro de Investigacion y Desarrollo de Proteinas (CIDPRO), Tecnologico de Monterrey-Campus Monterrey and [PERSON]'s postgraduate fellowships by Consejo Nacional de Ciencia y Tecnologia (CONACyT) and Tecnologico de Monterrey.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Methods
### The Application of Hidden Markov Model
#### 3.2.3 HMM-Based Approach to Risk Performance Reasoning  
The risk reasoning approach in an HMM framework can be achieved in several steps: data learning, modeling of HMM for risk reasoning, reasoning of risk performance, and performance evaluation of reasoning. The flowchart of the proposed model is shown in Figure 3.  
Firstly, the risk factors of the bauxite shipping process were identified based on the accident data of \"M.V Bulk Jupiter\", i.e., the Report of the Marine Safety Investigation into the Loss of a Bulk Carrier. The principal factors were obtained using principal factor analysis (PFA) from a dimensionality reduction of the risk factor set. The risk classification criteria were established after  
Figure 3: Flowchart of the risk performance reasoning for bauxite shipping process.  
Figure 2: HMM-based mapping of risk transfer for bauxite shipping process.  
scalar quantification of the principal factors. Based on the first 50 groups of operational data of a Handy bauxite carrier, the HMM was trained using the Baum-Welch algorithm. After the steps of feature extraction, dimensionality reduction using PFA, and scalar quantization, the last nine groups of operational data were input as test data into the HMM to obtain the log-likelihood probability, which was then used for similarity recognition and risk performance reasoning. Then, the reasoned risk performance of each factor was compared with the test data from the last nine groups to evaluate the performance of risk reasoning in terms of classification effectiveness, measurement accuracy, and reasoning sensitivity.
## 2 Key Technologies for Analog-Radio-over-Fiber (A-RoF) Fronthaul Links
However, recently a few demonstrations also reported on high bit rates by using more cost effective directly modulated laser (DML)-based Txs [44]. (3) WDM techniques to extend the capacity of the fronthaul network by the transmission of multiple wavelength channels over a typical single mode fiber (SMF). The mature WDM technology, recognized already as a cost-efficient access solution for passive optical networks (PONs) [48; 49]. With the same rationale, it can be also exploited in flexible and wavelength-reconfigurable fronthaul architectures [29] in order to meet the high-capacity 100 Gb/s KPI target for hotspot use cases [5] while at the same time, allowing for a smooth migration from the multi-SMF space division multiplexing (SDM) networks to simplified and more cost-effective single-SMF deployments [49]. Similarly, driven by the recent progress in the development of multi-core fiber (MCF) SDM hardware [50], SDM fronthaul architectures based on MCF single-fiber deployments have been also proposed as a long term solution for the hotspot use case [51]. These solutions can provide an additional degree of freedom in multiplexing, however, several technical and economic aspects should be taken into account for a viable upgrade of a fronthaul from the mature WDM systems [52] to the possible future hybrid WDM/SDM solutions [53]. (4) PIC technologies that have been already proven beneficial for optical communication networks [54], can offer significant energy and cost savings also to the mobile fronthaul architectures. This can be achieved by employing cost-/energy-efficient and ultra-compact PIC-based optical TxRxs, to allow large-scale and dense 5G fronthaul topologies [55]. Moreover, PIC technology can provide a low-loss integrated platform for the ROADMs, allowing wavelength multiplexing and switching operations at the access networks with fast light-path reconfiguration times and low power consumption. However, both integrated devices have been yet investigated only for metro-rings and PONs [56]. Towards bringing 5G into realization in the near future, intense research and industrial efforts have indeed achieved remarkable progress on each of these distinct technologies. However, it still remains unclear what will be the optimum network platform that will manage to incorporate all innovation aspects within the pressing timeline of 5G [57; 58], while satisfying the ambitious 5G KPI requirements, for high data rates and low latencies. In this context, we present an analog 5G fronthaul architecture that manage to incorporate all these key-enabling technologies in order to provide broadband access services to overcrowded private hotspots.
# Programming Real-Time Sound in Python
## 3 Code Speedup
### Numba  
Numba is a just-in-time (JIT) compiler for vectorial computing in Python [31]. Using Numba, it is possible to speed up functions containing instructions that can be computed more efficiently at machine instead of application level, such as those applying to NumPy objects. In this case, the instructions are sent to the JIT compiler by adding the decorator @_jit_ before declaring a function.  
Figure 1: Real-time spectrum of the output signal and low-pass filter cutoff parameter control.  
At this point, the bytecode is translated in Low Level Virtual Machine Intermediate Representation (LLVM IR), an architecture-independent language that is similar to assembly. Numba provides two operating modes, that are assigned during the decoration: the _nopython_ mode (i.e., _@jit(nopython=True)_) compiles the code completely, whereas the _object_ mode tries to compile the loops while leaving to the interpreter the instructions outside them. The performance improvement resulting from the _object_ operating mode is limited and, in general, not sufficient for achieving the real time.  
Numba is easy to use; furthermore, it requires almost no changes of the Python code. On the other hand it translates only a fraction of the Python instruction set, even if its expressive power is increasing release after release. Most importantly, the functions compiled in _nopython_ mode cannot access Python objects at runtime; thus, data can be exchanged between such functions and a Python program only in form of input and return arguments. This limitation asks for refactoring the communication among functions and restructuring the code into simple functions, only containing critical instructions (i.e., arithmetic loops).  
Numba is instructed about static variables through the decorator, that must explicitly refer to the input and return arguments as Listing A4 shows. Without this information, the compiler in some cases could be unable to determine the variable scope and, consequently, force the _object_ mode. In the specific case a wrapper function named _wrap_numba_global()_ is needed to declare the global variable \(a\), and then pass it to the function _numba_global(a)_ as an argument. Otherwise, _numba_global_ cannot access its content since belonging to the runtime space. Listing A4 shows also the time profiling concerning the computation of the _sin()_ function applied to iterated variable after Numba refactoring. _wrap_numba_global()_ is called twice: in the first call the JIT compiler optimizes the code, hence slowing down the execution; from the second call on, the optimized cycle is computed in as fast as 7.45 ns.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Filter Module
#### 4.3.4 Internal Memories  
The memories utilized within the filter architecture employ four dual-port RAM to store the pixel data filtered vertically. A 4 \(\times\) 4 block of pixel data can be stored in each of the four segments of the 64-byte RAM (16 bytes or 128 bits). Figures 9 and 10 depict the transfer of the pixel data for the luma and the chroma Cb, Cr blocks from the RAM to the buffers. The amount of external memory access cycles is reduced by this method of data storage. It also avoids the utilization of transpose buffers. The first two memory regions are used exclusively during the chroma block filtering procedure, leaving the other regions unused.  
Figure 8: Mapping of pixel blocks to buffers: (**a**) pixel blocks; (**b**) buffers.  
Figure 9: Mapping of internal memory to buffer before horizontal filtering for luma block.
# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 3 Thinger.io Overview
### Storage Management  
One of the features in this platform is related with the storage management, which is called \"Data Buckets\". A bucket is a cloud resource for storing time series data. A data bucket is a time series storage where devices can push information when required. Each data point is automatically timestamped in the cloud at reception time, as IoT devices do not handle a real-time clock (RTC) by themselves. This information is stored in the cloud in secure, efficient, and scalable solutions (mainly DynamoDB from Amazon Web Services). The information stored in a bucket can be both displayed on a dashboard inside the console interface or exported in scalable storage (like Amazon S3) in ARFF, JSON, or CSV for its offline analysis, as illustrated in Figure 7.  
Figure 6: Example of how a resource definition modelled in the device, like turning on/off a digital pin, is automatically converted to an REST API endpoint that can be executed directly from the console or any other third party application.  
The storage of information in a scalable cloud infrastructure like Thinger.io can be divided in the following steps:  
1. Model data resources on the device. A device can expose a set of resources, i.e., temperature or humidity. The resources are not tied to any data structure, as they are managed as non-structured documents, like in JSON format. Modelling such resources is covered in Section 4.
2. Connect the device to Thinger.io infrastructure. The device exposes the available resources, that are immediately available both for feeding real-time dashboards, or storing them in data buckets.
3. Configure a Bucket in Console. Once the device is connected to the platform, it is possible to define a new data bucket that is fed by a device resource. The user normally selects here the sampling interval required. In this moment, the device will start to stream the information at the required frequency.  
In this approach, the Thinger.io cloud infrastructure automatically subscribes to the device resources at the required interval, as long as there are sinks for this information (a dashboard, or a data bucket). In the case of storing the information in data buckets, the platform supports the use of different document storages, like DynamoDB or MongoDB. Both alternatives provide highly scalable solutions for storing data in the Cloud, as they natively support sharing. Moreover, the Thinger.io infrastructure automatically generates OLAP (On-Line Analytical Processing) cubes for providing metrics like average, sum, count, max, and min, aggregated by different time periods. All of this information can be used then for the mentioned analytics dashboards, or for exporting for data analysis. Thinger.io relies on scalable cloud storage for this part, as it supports export to Amazon S3, which can contain files up to 5 TB.
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 4 Trusted Encrypted Validator Module (TEVM) Based on Token Authentication  
As can be seen in Figure 2, a server named Token-based Authentication Service (TBAS), which is responsible for generating tokens for legitimate devices, was added to the SCADA system architecture. In addition, a Trusted Encrypted Validator module (TEVM) machine was added to the TBAS system to encrypt, decode, and verify the device's legality. As the major medium applied program of the SCADA server, the Control Server (CS) was in charge of controlling IoT devices, and the secure connection among CS, TEVM, and TBAS was achieved through encryption by TLS 1.3. This paper proposed a solution named the encrypted authentication mechanism, which could address security problems in two ways; the first encrypts Modbus TCP through Transport Layer Security 1.3, meaning hackers could not access or modify the content, and the second verifies CS and IoT devices through tokens to stop hackers from paralyzing the SCADA system through DoS attacks.  
Figure 1: Energy management system in southern Taiwan.  
Figure 2: The proposed architecture.
## 3 Performance Analysis for On-PCB Conditions
### PCB 95 mm \(\times\) 42 mm  
This section compares the same antenna booster with the FPC antenna on a 95 mm \(\times\) 42 mm PCB (Figure 8). The matching network for the antenna booster is the same as the previous case.  
As with the big PCB, for each height, three different positions of the FPC antennas are tested (top, middle, and bottom) (Figure 9). The best result in terms of total efficiency is  
Figure 8: (a): 12 mm \(\times\) 3 mm \(\times\) 2.4 mm (height) antenna booster on a 95 mm \(\times\) 42 mm PCB (the ground plane is 84 mm \(\times\) 42 mm). (b): the matching network for multiband operation comprising SMD lumped components.  
Figure 7: Measured total efficiency for the antenna booster and FPC solutions in Figure 6.  
then selected (Figure 10 and Table 4). The antenna booster surpasses the FPC antenna in the low-frequency region (698-960 MHz). For example, for the FPC antenna at 2.4 mm height, which is the same as the antenna booster height, the measured total efficiency is only 2.8%. In contrast, it is 28.1% for the antenna booster representing 10 dB of more efficiency. Even for the height of 20 mm, the total efficiency is still lower than the antenna booster being 12.1% for the FPC antenna. In this case, the antenna booster is 3.6 dB more efficient. The antenna booster again outperforms in the high-frequency region (1710-2690 MHz). The FPC antenna must be placed at 20 mm to achieve a similar efficiency to the antenna booster. These results show again that not only efficiency of the antenna booster is much above the FPC antenna but also, to achieve compact IoT solutions, the antenna booster is preferred.  
The same trends are obtained for this small PCB: the total efficiency of the antenna booster is always better at both frequency regions. The antenna booster is 15.3 dB more efficient at the low-frequency region for the same height as the antenna booster. Even for the 20 mm height of the FPC antenna, the antenna booster is 3.8 dB better in the low-frequency region. As far as the high-frequency region is concerned, the antenna booster provides superior total efficiency too.
# Energy Consumption Model for Drilling Processes Based on Cutting Force
## 3 Energy Consumption Model Calibration Experiments
### Energy Consumption Model Calibration
\begin{table}
\begin{tabular}{l c c c c c} \hline _No._ & \(n\) (**rpm**) & \(f\) (**mm/min**) & _Ptotal_ (**W**) & _Pidle_ (**W**) & _Pcutting_ (**W**) \\ \hline
1 & 400 & 32 & 1625.0 & 1290.0 & 262.4 \\
2 & 400 & 44 & 1679.0 & 1290.0 & 313.4 \\
3 & 400 & 56 & 1744.6 & 1290.0 & 364.4 \\
4 & 400 & 68 & 1781.4 & 1290.0 & 415.6 \\
5 & 550 & 44 & 1750.0 & 1340.6 & 360.9 \\
6 & 550 & 60.5 & 1837.4 & 1340.6 & 430.9 \\
7 & 550 & 77 & 1937.0 & 1340.6 & 501.1 \\
8 & 550 & 93.5 & 2001.8 & 1340.6 & 571.5 \\
9 & 700 & 56 & 1906.1 & 1404.7 & 459.3 \\
10 & 700 & 77 & 1988.8 & 1404.7 & 548.4 \\
11 & 700 & 98 & 2117.4 & 1404.7 & 637.8 \\
12 & 700 & 119 & 2217.9 & 1404.7 & 727.4 \\
13 & 850 & 68 & 2067.9 & 1482.3 & 557.7 \\
14 & 850 & 93.5 & 2217.5 & 1482.3 & 665.9 \\
15 & 850 & 119 & 2354.8 & 1482.3 & 774.4 \\
16 & 850 & 144.5 & 2474.4 & 1482.3 & 883.2 \\ \hline \end{tabular}
\end{table} 
Table 2: The measured total power (\(P_{\textit{total}}\)) and the calculated cutting power (\(P_{\textit{cutting}}\)) in Experiment-I. Figure 6: The auxiliary power at different cutting powers. In the next section, the experimental data under different processing parameters are compared and analyzed between the proposed model and the existing model.
## 3 User-side Constraints
### ISP Involvement  
In today's Internet model, the role of ISPs is limited to that of a data carrier, through fast packet delivery (at the core) and access provisioning (at the edge). Although some form of intelligence has been deployed in the network, mostly to better support content delivery, the deployment and maintenance of these intelligent nodes is still controlled by specialized service providers. The coordination between network providers (_i.e._, the ISPs) and CP/CDN providers has never been elaborated. Furthermore, it is reported in the literature that a cooperative model with more visibility (e.g., accessing topology of the ISP) would improve delivery performance and quality [26].  
Since the Edge Cloud, including the surrogates, is placed in the edge networks, the active involvement of ISPs is needed for the success of our proposed evolution of service deployment. ISPs could see their new roles (see Section 4.5 for further discussion) through emerged business model by providing a Cloud support to the CPs. It ensures that the likelihood of ISPs' active involvement is increased, which is a precondition of the deployment of any future Internet architecture.
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 5 Performance Evaluation
### Empirical Study
#### 5.1.1 Experimental Setup  
Figure 4 shows the topology of the empirical experiment. To construct the network with UAV, an Odroid device was mounted on every UAVs. Odroid device is a single board computer with heterogeneous multi-processing, and support Ubuntu OS [24]. Imagining that Operating_UAVs give feedback to Ground Control System (GCS) through an access network, Routing_UAV 1 acted like an Access Point (AP) to create a subnet which enables Operating_UAVs to access the network through Routing_UAV 1. The UAV network, which includes the Routing_UAVs, communicates in the 5 GHz bandwidth so as not to affect the network communication environment of the Operating_UAVs using the 2.4 GHz bandwidth Wi-Fi. In addition, the UAV network is based on the commonly used Destination-Sequenced Distance Vector (DSDV) routing protocol [25]. All Operating_UAVs act as TCP senders through Wi-Fi in the 2.4 GHz bandwidth. Operating_UAV 1 transmits data through default algorithm: TCP-CUBIC, and Operating_UAV 2 transmits data through TCP equipped with \(ASRAN\). Since TCP related implementation is held on the device kernel, \(ASRAN\) was implemented into the kernel of Ubuntu. In the kernel layer, there is a function to calculate the _ssthresh_ of TCP after segment loss. \(ASRAN\) compares the previously stored \(last\_max\_CWND\) with newly calculated _ssthresh_ and returns a larger value. To form a wireless multi-hop network, two Routing_UAVs acted like wireless intermediary nodes that forward data from Operating_UAVs to GCS. Role of the GCS is a receiver of TCP data transmitted by the Operating_UAVs in the experiment configuration. GCS is a laptop with Linux OS which acted like a gateway to an ad hoc network and assigned IP addresses to Routing_UAV to form a UAV network topology.  
With this multi-hop environment, Operating_UAVs are connected to Routing_UAV 1 with Wi-Fi. As aforementioned, Operating_UAV 2 uses original kernel, and Operating_UAV 1 uses modified kernel on which \(ASRAN\) is implemented. After establishing connection between Operating_UAVs and Routing_UAV 1, Operating_UAVs simultaneously transmit TCP data to GCS.
## 3 Digital Star Chart Generation
### Starlist Pre-Processing
#### 3.1.2 Star Partitioning  
Star partitioning is a method of further subdividing all the stars in the SAO (Smithsonian Astrophysical Observatory) catalogues into a smaller range of sky zones according to the right ascension and declination, which reduces the search range of the star chart and avoids searching for all the star point information in the chart in order to subsequently speed up the search for navigational stars. In this paper, the dynamic star simulator star map display algorithm is designed with a field of view angle of \(\Phi 20^{\circ}\), which is divided according to the partition interval B = \(12^{\circ}\), taking into account the effect of attitude offset and the factor of reducing the number of target star subzones [14]. The right ascension varies from \(0^{\circ}\) to \(360^{\circ}\) and is divided into 30 zones; the declination varies from \(-90^{\circ}\) to \(90^{\circ}\) and is divided into 15 zones, totaling 450 target subzones. The 450 target subzones are numbered according to the order of high to low declination and small to large declination, and the star zoning map is shown in Figure 2.