esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.4 Interoperability  
At the time of writing, all control, management, knowledge, application, and data planes have well-defined standard protocols and interfaces for communication with each other, as discussed in this survey. However, different vendors may use different data formats and different machine learning or knowledge-generating models or algorithms, knowledge representation formats, rule representation formats, etc., making it difficult to integrate KDN technologies from multiple vendors into one platform. This can lead to issues with data quality, compatibility, and performance among different KDN platforms from different vendors. Furthermore, there is a lack of approaches for performance evaluation of the knowledge plane. Due to that, it will be difficult to compare the performance of KDN solutions from different networks, making it difficult to evaluate which solutions are better. For example, consider two KDN networks that detect intrusion using a supervised learning approach and an unsupervised learning approach. It is difficult to compare the classification performance of the two approaches, as they are trained from data generated from two different KDN networks that may use different protocols, different feature selection approaches, different machine learning approaches, etc.
# Review of Indoor Positioning: Radio Wave Technology
## 3 Radio Signals-Based Positioning  
This section describes radio and non-radio-based systems for IPS, depicted in Figure 7. A GPS that receives signals from satellites is broadly used and very popular in outdoor localization applications, but it is ineffective for indoor localization due to the LoS transmission problem. Therefore, various wireless technologies such as infrared, optical (LED, laser), ultrasound, an IMU, vision, VLC, and the radio signals--including Wi-Fi, ZigBee, RFID, Bluetooth low energy (BLE), UWB, long-range radio (LoRa), sigfox, near field communication (NFC) and cellular networks--have been used in IPS. In addition, some of the works have been utilized in hybrid approaches for indoor positioning and tracking. This paper will discuss only the radio signal technologies. Table 2 provides the strengths and weaknesses of the radio technologies for indoor positioning systems. Figure 7: Categorization of indoor positioning technologies.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 3 Source Coding Protection
### Error Concealment  
Finally, we propose a simple Error Concealment (EC) technique that will hide the missing information to the user in such a way that the received video quality will not be seriously damaged. A well-known EC technique (defined as \"zero-MV concealment\" in [20]) exploits temporal redundancy in video sequences by applying a motion compensated concealment with the zero motion vector.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Encoding Mode** & **QP** & **Bit Rate (Mbps)** & **PSNR (dB)** \\ \hline AI & 40 & 1.13 & 31.07 \\ IPIP & 36 & 1.11 & 32.82 \\ IPPP & 33 & 1.11 & 34.13 \\ LP & 27 & 1.10 & 36.77 \\ LPI4 & 32 & 1.08 & 34.24 \\ IPv25 pctCTU & 32 & 1.08 & 33.62 \\ IPv25 pctTIL & 32 & 1.09 & 33.64 \\ IPvpattern & 32 & 1.07 & 33.63 \\ IPvx & 29 & 1.07 & 35.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected values for the QP parameter and bit rate and the PSNR values obtained for each encoding mode.  
Figure 3: (a) Rate/Distortion representation for bbd_25 sequence encoded with all the encoding modes; (b) Zoom in the range of 1.06 to 1.14 Mbps of Rate/Distortion representation for bbd_25 sequence.  
In this technique, the missing area of a frame is replaced by the co-located area of the previous frame. This technique has a particular case in which a whole frame is missing or corrupted. In this case, the last correctly decoded frame is duplicated in order to conceal the loss of the missing one. This technique is known as \"frame copy concealment\" in [21]. In order to add error concealment features to video streaming we have modified the HEVC reference software decoder to add the options which allow us to apply these EC techniques.
# Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System
## 8 Conclusions  
This paper addressed the application of an ANFIS as a blast-induced vibration predictor. Based on the research papers reviewed, this technique has not yet been employed for blast-induced ground vibration and frequency prediction. In order to confirm the superiority of the ANFIS, the ANFISBLAST prediction model was tested and compared with the three most widely used conventional predictors. The predictors were evaluated in terms of their SSE, RMSE, R-squared, and adjusted R-squared values. Since the ANFIS can detect patterns in training datasets and can be updated when new training datasets are presented, a better degree of accuracy can be achieved compared to other conventional techniques. Considering the complex relationship between the inputs and outputs, the achieved results were satisfactory. However, it should be noted that the ANFISBLAST model is not directly applicable to the vibration analysis on other tunnel construction sites. Further prediction methods could be investigated in future studies based on the data provided in this article, and the most robust and accurate model should be used to predict ground vibration levels.  
Conceptualization, P.J., S.L., and A.I.; methodology, P.J. and S.L.; formal analysis, P.J., S.L., and A.I.; writing--original draft preparation, P.J., S.L., and A.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Slovenian Research Agency, grant number P2-0268.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 2 Overall Observing System Configuration  
The overall integration of ocean phenomena observation systems includes the following parts (Figure 1): marine vehicles' onboard system (MVOS), onshore server and database storage system (OSDSS) and data processing and vehicle path planning system (DPVPPS). MVOS includes sample sensors (Conductivity Temperature Depth profiler (CTD)), vehicle sensors (sonar, 3-Axis Compass (TCM3)), propulsion system, storage to store sample information and vehicle dynamic state, and the onboard control part. Onboard control as the management center of the onboard systems collects sample data from CTD; on the other hand, this part controls the vehicle state according to commands from the leader AUV. The onboard control part establishes the connection with other vehicles (or the leader vehicle). Vehicles' sample information and dynamic information are stored temporarily on the SD card. Multi AUVs are operated in the pre-planning mode, or transmit information to each other through acoustic. Multiple AUVs can follow the leader AUV or work together to complete the observation mission. USV executes ocean surface observations and transfers information between AUG, AUV and OSDSS. AUG can connect and transmit data to OSDSS through satellites or temporarily store sample data on the USV. OSDSS is the center for data storage and data-sharing. Through OSDSS, we can downloaded the control command and trajectory from the vehicles path planning system, and then transmit to each marine vehicle through USV or satellites; on the other side, we can upload and storage the sample data and vehicle's motion state from the marine vehicles. The internet (WWW/FTP) is selected to connect DPVPPS and OSDSS.  
The design of MVOS varies with marine vehicles. In this paper, DPVPPS is described in detail. DPVPPS mainly includes the following modules: (A) decomposition and assignment of observation missions; (B) autonomous trajectory planning and optimization; (C) ocean sample data assimilation and ocean model simulation; (D) semi-physical simulation platforms of marine vehicles. For a specific  
Figure 1: System configuration.  
observation mission, we first decompose the mission and assign the mission to suitable vehicles; then choose favorable history data information from ocean data assimilation module to initialize the mission. Considering the sample data information, optimal criterion and vehicles' motion characteristics, vehicles' trajectories are then planned and optimized. After getting new sampling data, we fuse the sampled data and historic data to construct a NRT sample system and provide information for virtual display. The new sample data, data assimilation results and vehicles' present state are used to predict vehicle trajectory in the next steps. The continuous trajectory is discretized to waypoint form, and then the control law between each two waypoints is designed in marine vehicles' semi-physical simulation platforms. By using the virtual display, we can obtain general information, and monitor each module's status and provide assistance to each module in case of emergency.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 3 Methodology
### Code Multipath  
The multipath effect on code observations was determined using the CMC combination of code and phase observations, which allow us to isolate the multipath impact on the accuracy of the pseudorange measurement from other measurement errors based on the principle that the impact of most measurement error sources is identical both on code and carrier-phase observations. The CMC combination equation can be formulated on the basis of simplified equations of code and carrier-phase observations:  
\[P^{i}_{a,f}=\rho^{i}_{a}+c(\delta t_{a}-\delta t^{i})+\delta O^{i}_{a}+\delta I ^{i}_{a,f}+\delta T^{i}_{a}+M^{i}_{a,f}+E_{a,f} \tag{1}\] \[L^{i}_{a,f}=\rho^{i}_{a}+\lambda_{f}N^{i}_{a,f}+c(\delta t_{a}- \delta t^{i})+\delta O^{i}_{a}-\delta I^{i}_{a,f}+\delta T^{i}_{a}+m^{i}_{a,f }+\epsilon_{a,f} \tag{2}\]  
where  
\( \begin{array}{ll}i_{a}:&\text{satellite $i$ and receiver $a$;}\\ f:&\text{observation/frequency of signal;}\\ P:&\text{code pseudorange;}\\ L:&\text{carrier-phase pseudorange in meters;}\\ \rho:&\text{geometric range;}\\ \lambda:&\text{wavelength;}\\ N:&\text{carrier-phase ambiguity;}\\ c:&\text{speed of light;}\\ \delta t_{a}:&\text{receiver $a$ clock offset;}\\ \delta t^{i}:&\text{satellite $i$ clock offset;}\\ \delta O:&\text{orbital error of a satellite;}\\ \delta I:&\text{ionospheric delay;}\\ \delta T:&\text{tropospehric delay;}\\ M:&\text{code multipath error;}\\ m:&\text{phase multipath error;}\\ E:&\text{code measurement noise;}\\ \epsilon:&\text{carrier phase measurement noise.}\\ \end{array} \)  
The subtraction of Equations (1) and (2) on both sides allow for the elimination of geometric distance as well as components related to clock, orbit, and troposphere errors, since their impact on both types of measurements is identical:  
\[P^{i}_{a,f}-L^{i}_{a,f}=-\lambda_{f}N^{i}_{a,f}+2\delta I^{i}_{a,f}+M^{i}_{a,f}+E_{a,f}-m^{i}_{a,f}-\epsilon_{a,f} \tag{3}\]Thus, the equation obtained contains only the doubled value of the ionosphere error (since this parameter affects both types of observations equally, albeit with opposite signs), the value of carrier-phase ambiguity, and the multipath errors for code and carrier-phase observations along with the corresponding values of measurement noise. Since the values of the multipath errors and measurement noise for carrier-phase observations are one/two orders of magnitude smaller than for code observations, the corresponding components can be neglected, obtaining the following CMC combination equation:  
\[\mathcal{CM}_{a,f}^{i}=M_{a,f}^{i}+E_{a,f}=P_{a,f}^{i}-L_{a,f}^{i}-2\delta I_{ a,f}^{i}+\lambda_{f}N_{a,f}^{i} \tag{4}\]  
Assuming an expected zero value of the combination, the ambiguity parameter can be eliminated by using the average of the observation for single satellite arc (see Equation (11)). In turn, the ionosphere component can be eliminated using observations at two frequencies.
# Brewing with 10% and 20% Malted Lentils--Trials on Laboratory and Pilot Scales
## Results and Discussion
### Pilot-Scale Brewing Trial
#### 3.2.5 pH  
The pH values of the wort and beer samples were a bit higher than recommended in the literature (pH 5.3-5.6 for wort and pH 4.2-4.6 for beer) [40; 46]. Wort produced with 100% barley malt showed the exact value given by the supplier, i.e., 5.92 \(\pm\) 0.02. Slightly higher pH values were observed for wort made with the addition of lentil malt, with pH values of 5.96 \(\pm\) 0.04 (BM + 10% LM) and 6.01 \(\pm\) 0.04 (BM + 20% LM). As an ideal wort pH is between 5.3 and 5.6, acidification of the mash or wort could be a solution. For example, a lower pH would enhance enzymatic activity in the mash and subsequently lead to a more pronounced bittermess and enhanced protein coagulation during boiling [46]. With fermentation, a drop in pH is expected and was also observed in this research. The observed decreases in pH were around 1.29-1.41, indicating good fermentation, and resulted in beer pH values of 4.51 \(\pm\) 0.10 (BM), 4.63 \(\pm\) 0.09 (BM + 10% LM) and 4.72 \(\pm\) 0.07 (BM + 20% LM), which are close to the recommended value of 4.2-4.6 [40; 46]. The pH was slightly higher in beer produced with 20% lentil malt, with a pH of 4.72, which could make it more vulnerable to microbial contamination. An additional pasteurization step after bottling of the beer would be recommended. Overall, the addition of lentil malt resulted in an increase in pH. This correlated with the lower extract values in these wort samples, which therefore have less sugars available for fermentation.
# An Interleaved DC/DC Converter with Soft-switching Characteristic and high Step-up Ratio
## 1 Introduction
Unfortunately, the added inductor is usually larger than the energy-stored inductor of the PWM-typed converter. It hinders reducing the size and improving the efficiency of the converter. Aiming to solve the above shortcomings and propose a better solution, this manuscript presents an interleaved converter. It is mainly composed of two boost converters connected in parallel. A coupled inductor consisting of a magnetic core and two windings is used instead of the two inductors of the boost converters. Although the boost converters are designed to operate at DCM, the current ripple is low due to interleaved operation. High circuit efficiency is achieved by operating all the semiconductors at either ZVS or ZCS. A 200 W 48 V/400 V prototype converter is built and measured to verify the feasibility of this proposed circuit.
# Portable and Highly Versatile Impedance Meter for Very Low Frequency Measurements
## 4 Conclusions  
In this paper, a platform for low frequency impedance measurements has been designed, implemented, and tested. In the proposed approach, we exploit a personal computer and a sound board as a powerful system for digital signal generation and analysis that, with the help of a low complexity and low-cost external hardware, allows to perform low frequency impedance measurements for various applications. For the implementation of the core system, we resort to public domain software, so that, together with the simple hardware, the system can be easily reproduced by researchers interested in EIS and tailored for targeting specific applications with minimal effort. To demonstrate the effectiveness of the proposed measurement system, we performed measurements both on conventional electronic components as DUTs and on a prototype of polypyrrole-based supercapacitor structure. The results have been compared with measurements performed by means of a standard LCR meter in the frequency range in which the explored frequency ranges for the two instruments overlap. The results showed that the instrument is highly versatile and accurate and can be used in all those applications that require low frequency impedance measurements, ranging from bioengineering to energy applications.  
Conceptualization, G.S. and A.A.; methodology, C.C. and G.S.; software, C.C.; validation, C.C., A.A., and G.S.; formal analysis, E.C. and G.G.; investigation, G.S.; data curation, C.C.; writing--original draft preparation, G.S. and C.C.; writing--review and editing, E.C. and G.G.; visualization, A.A.; supervision, C.C. and G.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the article. The data presented in this study can be requested from the authors. Conflicts of Interest: The authors declare no conflict of interest.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 3 and 5: Network Diffusion Algorithms and Performance Evaluation  
#### 4.2.1 Introduction  
Network diffusion algorithms are essential for understanding the spread of information, data, or malware across IoT and Space IoT configurations. This section explores three major categories of network diffusion algorithms: epidemic models, cascading models, and threshold models. These categories are based on the classification provided in the paper [119]. The key performance metrics used to assess the efficiency of these algorithms are further examined. Tables 5-7 present a structured classification of network diffusion algorithms based on their underlying models, application domains, and key performance characteristics (Abbreviation for mapping table can be shown in Tables 8 and 9). These tables focus on the following:  
* Diffusion Algorithm Type--Categorizes studies using Epidemic (EM), Cascade (CM), Threshold (TM), or Other (Oth) models. * Application Domain--Highlights where these models are applied, such as epidemic modeling, social networks, and wireless sensor networks. * Network Considerations--The impact of network topology, heterogeneity, dynamicity, and multi-layered structures on diffusion performance. These aspects are discussed in detail in Section 4.3. Figure 2: Distribution of network simulators: This chart represents the number of publications that utilized each simulator based on the included references in this paper. Ns-3 appears as the most widely used simulator, followed by Cooja (Contiki) and OMNeT++. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**n** & **Year** & **Top** & **Het** & **App** & **Wgt** & **Dep** & **Dyn** & **Lay** & **L** & **Dir** & **Algo** & **Cat** \\ \hline
[120] & 2013 & RT & Hm & BC & & & Dyn & SL & NL & & MHBC & CM \\ \hline
[10] & 2013 & & & SN & & & Both & Both & Both & Many & \\ \hline
[121] & 2013 & RW & Het & SN & Both & Int & Dyn & & Both & SIR-SIS-LTM & EM-TM \\ \hline
[122] & 2013 & SF-RT & Inhm & SN & & Int & Dyn & & Undir & SIHR & EM \\ \hline
[123] & 2013 & RT & Het & SN & & & ML & & SIR & EM \\ \hline
[124] & 2013 & & Het-Inhet & SN & & Int & & Undir & SIR & EM \\ \hline
[125] & 2013 & LS & & SN & & Both & & & LR & Oth \\ \hline
[126] & 2013 & LS & & SN & Wgt & Dyn & L & Dir & LRT & Oth \\ \hline
[127] & 2013 & & & & & & & & SI1I2S & EM \\ \hline
[128] & 2014 & & & & Int & Dyn & ML & & SIS-UAU & EM \\ \hline
[129] & 2014 & SW & & Gbl & Int & & & Undir & LTM & TM \\ \hline
[130] & 2014 & & & & Wgt & Int & Dyn & Both & & & Oth \\ \hline
[131] & 2014 & RT-SF & Both & PHE & & Int & Dyn & ML & & SIRV-SIR & EM \\ \hline
[132] & 2014 & NM & & & Both & Dyn & & Dir & RW & Oth \\ \hline
[133] & 2014 & RT-SF & Het & Virus & & & ML & Undir & SIIS
###### Abstract  
In this paper, discrete orthonormal Stockwell transform (DOST)-based vibration imaging is proposed as a preprocessing step for supporting load and rotational speed invariant scenarios for signals of various health conditions. For any health condition, features can easily be extracted from its generated health pattern. To automate the feature selection process, a convolutional neural network (CNN)-based transfer learning (TL) approach for diagnosis has also been introduced. Transfer learning allows an established model to use feature knowledge obtained under one set of working conditions through hidden layers to diagnose faults that occur under other working conditions. The network learns from the massive source dataset, and that knowledge is applied to the target data to identify faults. Using the bearing dataset of Case Western Reserve University, the proposed approach yields an average 99.8% classification accuracy and, specifically, 99.99% for healthy condition (HC), 99.95% for inner race fault (IRF), 99.96% for ball fault (BF), 99.68% for outer race fault for 12 o'clock sensor position (ORF@12), 99.93% for outer race fault for 3 o'clock sensor position (ORF@63), and 99.89% for outer race fault for 6 o'clock sensor position (ORF@66). In this paper, the proposed approach is compared with conventional artificial neural networks (ANNs), support vector machines (SVMs), hierarchical CNNs, and deep autoencoders. The proposed approach outperforms these conventional methods in the accuracy under all working conditions.  
Stockwell transform; vibration imaging; fault diagnosis; transfer learning; neural network; vibration signals +
Footnote †: journal: Applied Sciences
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 5 Discussion
### The Advantages of CEM  
We first analyzed the characteristics of the crop planting area in the GF-2 images and designed the structure of the CEM model based on those characteristics. By adopting two convolution kernels and utilizing the multi-path refinement structure, the network structure of the CEM model is well adapted to the characteristics of GF-2 images. This explains the higher precision obtained from the CEM model.  
The field surveys revealed that in the crop planting area, a GF-2 pixel covers an area containing about 350-450 winter wheat or rice plants and approximately 180 maize plants. Since the content of each pixel area showed no significant difference and the texture is relatively smooth, the multi-layer convolution hardly generated more effective features. It even introduces noise, resulting in poor segmentation effects, as presented in Tables 3 and 4, and Figures 6 and 7.