esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Random Modulated Power Converter on G3 Power Line Communication
## 3 Overview of G3-PLC  
Existing PLC technologies can be categorized, depending on the bandwidth, into: Ultra Narrowband PLC (UNB-PLC), Narrowband PLC (NB-PLC), and Broadband PLC (BPL). UNB-PLC refers to systems using very narrow bandwidth for data transmission in the frequency range below 3 kHz and is limited to data rates of a few hundreds of bps. NB-PLC refers to systems that work with medium data rates in frequency interval between 3 and 500 kHz. This frequency range includes the European CENELEC bands, the US FCC band, the Chinese band, and the Japanese ARIB band, as summarized in Table 1. Finally, BPL encompasses a large variety of systems that aim at high data rates, operating in the frequency range from 1 MHz up to 250 MHz [21; 22].  
G3-PLC belongs to the category of NB-PLC and is developed by the G3-PLC Alliance. G3-PLC exploits the Orthogonal Frequency Division Multiplexing (OFDM) modulation technique, which enables faster and more cost-efficient data transfer over existing power lines. G3-PLC can be used either on the low frequency CENELEC A band or in the high frequency band (150-500 kHz), covering the frequency bands reported in Table 1[23; 24].  
The G3-PLC selected for this activity works in the low frequency CENELEC A band, with specific frequency between 35 kHz and 91 kHz (see Table 2 for details).  
Figure 2: PSD of the switching signal with uniformly distributed random pulse position.
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.1 Kinematic Data  
Hand trajectory data (\(x\), \(y\), and \(z\) position) were filtered with a second-order two-way low-pass Butterworth filter with a cut-off frequency of 5 Hz.  
Sixteen hand joint angles were obtained from the CyberGlove data, as described in [21], from a reference posture defined as the hand flat on the table with fingers fully extended and closed: metacarpophalangeal flexion (MCP1 to MCP5, thumb to little finger), interphalangeal flexion of the thumb (IP1), proximal interphalangeal flexion of fingers (PIP2 to PIP5), flexion and abduction of the carpometacarpal joint of the thumb (CMC1), relative abduction between MCPs joints of adjacent fingers (index-middle, middle-ring, ring-little), and palmar arching. All hand joint angles were filtered with a second-order two-way low-pass Butterworth filter with a cut-off frequency of 5 Hz. Joint angles were amplitude-normalised using the mean active range of motion of healthy individuals, distinguishing flexion (+)/extension (-) and abduction (+)/adduction (-) from the reference posture [23]. To achieve this, angles corresponding to extension/adduction were divided by the absolute value of the maximum range of extension/adduction, while angles corresponding to flexion/abduction were divided by the absolute value of the maximum range of flexion/abduction. The 16 normalised joint angles were then combined into five parameters that describe the hand kinematic synergies [24]: _Thumb_F_ (average flexion of CMC, MCP, and IP joints), _MCPs_F_ (average MCP flexion of fingers 2-4), _PIPs_F_ (average PIP flexion of fingers 2-4), _Thumb_A_ (thumb abduction), and _Fingers_A_ (average fingers abduction). As a result of this process, these kinematic synergies were normalised to a scale ranging from \(-1\) (maximal extension/adduction) to 1 (maximal flexion/abduction) [24].  
Figure 1: Experimental setup showing a participant instrumented and performing the task. The RBBT and the VBBT are shown at the bottom left and bottom right, respectively.
## 3 Methodology
### Mixed Data Model and Transfer Learning Model  
Although the proposed GoogLeNet-GAP model achieved good recognition accuracy on the real ID card character testing dataset (see Section 3.3), a certain degree of misclassification remained. To improve the performance of our model, we expanded the synthetic training set by including an augmented version of the data from a small part of the real ID card character images. We propose two new models: (1) A mixed data model and (2) a transfer learning model. The mixed data model was obtained by retraining the GoogLeNet-GAP model (see Figure 9) from scratch while mixing synthetic training data and augmentation data of partial samples from real ID card character images. In contrast, the transfer learning model used the pre-trained model (see Section 3.3) as the base model and performed fine-tune training with the augmented data of partial samples from the real ID card character images. The abovementioned two methods were validated through experiments in which both the proposed new models achieve substantial improvements in recognition accuracy. Moreover, the results show that the transfer learning model is more practical for practical applications. The model recognition performance can be gradually improved by periodically fine-tuning with newly-collected real data. Meanwhile, the training speed can dramatically increase because of the very small amount of new data. Section 4 presents the detailed experimental results and reveals the advantages of the transfer learning model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\# of Training Samples** & **MelnykNet-Res** & **GoogLeNet-GAP** \\
**per Character** & **Accuracy** & **Accuracy** \\ \hline
300 & 21.64\% & **51.40\%** \\
600 & 41.75\% & **90.62\%** \\
900 & 53.40\% & **95.71\%** \\
1500 & 68.95\% & **95.35\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the modified MelnykNet-Res model and GoogleLeNet-GAP model tested on real ID card data.  
Figure 10: Modified MelnykNet architecture: MelnykNet-Res model.
## 2 Materials and Methods  
### Reagents  
The standards used to analyze VOCs were purchased from Sigma-Aldrich (St. Louis, MO, USA) and are listed below, along with their corresponding CAS numbers and purity: ethanol (64-17-5, \(\geq\)99.9%), hexanal (66-25-1, \(\geq\)98.0%), (E)-2-hexanal (6728-26-3, \(\geq\)97.0%), (Z)-3-hexenyl acetate (3681-71-8, \(\geq\)98.0%), 1-hexanol (111-27-3, \(\geq\)99.9%), nonanal (124-19-6, \(\geq\)95.0%), 1-octhen-3-ol (3391-86-4, \(\geq\)98.0%), acetic acid (64-19-7, \(\geq\)99.8%), octane (111-65-9, \(\geq\)99.7%), ethyl acetate (141-78-6, \(\geq\)99.8%), ethyl propanoate (105-37-3, \(\geq\)99.7%), 3-methyl-1-butanol (123-51-3, \(\geq\)98.5%), (_E_)-2-heptenal (18829-55-5, \(\geq\)95%), 6-methyl-5-hepten-2-one (1010-93-0, \(\geq\)97.0%), (_E_)-2-4-hexadienal (142-83-6, \(\geq\)95.0%), propionic acid (79-09-4, \(\geq\)99.8%), (_E_)-2-decenal (3913-81-3, \(\geq\)95.0%), and pentanoic acid (109-52-4, \(\geq\)99.8%). 4-Methyl-2-pentanol (123-51-3, \(\geq\)95%) was used as internal standard (S), while a mixture of _n_-alkanes (C8-C20, \(\sim\)40 mg/L each, in _n_-hexane) was employed to calculate the linear retention indices (LRI).
## 3 Results and Discussion  
### Effect of Heavyweight Waste Glass on Compression Properties of Concrete  
Prior to the flexural test of the RC members, an experiment was conducted to evaluate the compression properties of the concrete specimens. The test results of the compressive strength and elastic modulus of the concrete specimens replaced heavyweight waste glass are presented in Figure 3. Likewise, results of previous studies [11; 12; 20; 21] indicated that the compressive strength increased with decreased water-binder ratio, and the use of mineral admixture, regardless of the heavyweight waste glass substitution ratio. However, when the fine aggregate was replaced by heavyweight waste glass in concrete, the compressive strength decreased, whereas it decreased by approximately 9.8-28% as compared to the 0% substitution case. Namely, when the heavyweight waste glass substitution ratio increased, the compressive strength decreased. This phenomenon is most likely due to the poorer adhesion between the smooth surface of the glass and the cement past [11; 22]. Furthermore, the elastic modulus decreased with increased of substitution ratio of heavyweight waste glass. As such, the elastic modulus was determined based on the compressive strength.  
Meanwhile, the compressive strength and elastic modulus of the 45 FA20 and 45 BFS50 case specimens demonstrated better results than the 45 OPC case at 91 days. The mineral admixture could be attributed to the pozzolanic reaction at a later ages.  
In general, the compressive strength of normal concrete (OPC) increases with age. Particularly, the influence of water content on concrete strength is obvious; however, sealed curing was executed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Diameter** & **Elastic Modulus (GPa)** & **Yield Stress (MPa)** \\ \hline D 13 & 200 & 430 \\ D 10 & 200 & 480 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical properties of reinforcement used in RC member.  
Figure 2: Schemed applied cyclic loading history.  
for RC members in this study. Owing to the difference between previous research [20] and the cuing condition of this study, therefore, the increment in the compressive strength of the OPC case was little after 28 days curing period.
## 3 RECAS Drilling Parameter Detection and Control System  
The drilling parameter detection and control system monitors the sensor parameters and analyzes the operational status of each subsystem within the sonde. It can perform automatic control according to preset programs or manual instructions to execute specific actions (Figure 5).  
Figure 4: Structural diagram of RECAS prototype.  
Initially, it was planned to use an AC power supply [20,25]. However, this method required the installation of multiple transformers within the sonde to convert high-voltage AC power into low-voltage DC power for the control system, which resulted in a significant increase in the overall sonde weight. To tackle this problem, a high-voltage DC power supply scheme was employed in the design of the RECAS working prototype. Moreover, the control cabin and motor cabin of the inner winch were integrated into the same chamber, effectively reducing the sonde weight from 450 to 330 kg.  
The downhole control system enables the collaborative operation of each subsystem for data interaction through the use of dual mainboards. The system covers functions such as heating, winching, sensor acquisition, and control. Considering the quantity and variety of sensors used, a distributed measurement-and-control architecture was adopted, along with the design of a comprehensive communication protocol. To ensure efficient data transmission, the downhole control system collects all the data and segments them into multiple data packets, then sends these packets to the surface computer at regular 3-s intervals.
# Adaptive Data Synchronization Algorithm for IoT-Oriented Low-Power Wide-Area Networks
## 3 Remote File Synchronization in IoT LPWANs  
### Motivation and Goal  
In the previous section we discussed how the remote file synchronization was mainly designed and employed in distributed systems (computer-based). On the contrary, only a few works have addressed this issue specifically for the IoT context and low-power communications.  
We then analyzed the protocols that are efficiently conceived for low-power consumption, sporadic interaction among devices and reduced data traffic, and we identified the fundamental aspects to be taken into consideration in the design. Multiple-round synchronization techniques may not be efficient in a low-power scenario because they require devices to be active for a rather long time period, also increasing the amount of bidirectional information exchanged between the parties. On the other hand single-round approaches, such as the early _rsync_ algorithm, [23] allow the communication to be simpler but the data traffic optimization could not always achieve maximum efficiency due to the limited adaptability of the protocols.  
Aiming to address these issues, we propose a remote file synchronization algorithm that uses double signatures to perform file scanning and update just as _rsync_ does but following an adaptive approach. In particular, the presented solution provides the dynamic adaptation of signatures and chunk size considered for the matching procedure. By doing so, the efficiency of single-round synchronization is improved, optimizing the data traffic as well.  
We can describe briefly our proposal for remote file synchronization. Based on the same procedure used in _rsync_ to find matching information between the file at the two parties, we introduce the following novel features the improve the performance of file synchronization:  
* The adaptive tuning of the chunk dimension based on the distribution and type of modifications presented by the latest file version with respect to the previous one. By doing so, the file processing procedure and the amount of generated traffic are optimized.
* The dynamic selection between a signature-based and signature-free synchronization procedure, driven by the adaptation of the algorithm parameters, to exploit, when possible, the data compression provided by the signatures or, otherwise, avoid the use of signatures when it is not convenient due to the computational cost.  
Finally, it is worth highlighting that, in general, file synchronization algorithms are implemented at the application layer of the OSI model, where it is safe to assume error-free and safe data communications. Data encoding, encryption, framing, and related issues are handled by other protocols at lower layers, therefore the addressing of these aspects goes beyond the scope of this work.
## 3 Risks of Extended Ground Times  
The urgency for minimized GT is given as GTs directly influence the time of departure, which is a key element within the air transport network. Each additional unplanned or unnecessary minute affects the system and induces risks to various stakeholders that are closely related to each other (partnership [9]). In terms of aviation, a stakeholder is either involved in or impacted by aircraft or airport operations having its characteristics, structures, responsibilities, and objectives [10]. This multi-stakeholder system takes in passengers and airport neighbours. However, stakeholders for GTs only addresses those parties that are actively engaged in the management of the aviation system. Therefore, airlines, airports, GH companies, and ANSPs are the main stakeholders:  
* _Legal authorities_: Policies of legal authorities that mainly provide guidelines and regulations aim at a safe and secure execution of air transport operations. However, in terms of reduced GTs, their influence is negligible.
* _ANSPs_: ANSPs are responsible for air traffic control (ATC) and particularly envision safety in the aviation sector, ensuring efficient management of air traffic by keeping sufficient separation between aircraft in the air and on the ground at any time [11]. GH companies are mainly responsible for the provision of turnaround activities including ramp handling and fuelling; their central interest is the fast and reliable provision of GH services [12].
* _Airports_: The business spectrum of airports is much larger. Airports are multifunctional entities that provide the basic infrastructure for the provision of commercial passenger flights. This includes the terminal buildings, runways, taxiways, and the apron. These essential facilities allow aircraft to take off and land, service aircraft while they are on the ground, and handle passenger movement on land and airside. The airport operator often supports and provides traditional GH services [13]. Furthermore, during the last decades, airports developed into commercial enterprises including non-aeronautical business activities. Their goal is to be profitable by focusing on a rising passenger inflow [10].
* _Airlines_: Similar applies to airlines. Airlines aim at a high passenger demand, high aircraft utilization (max. number of flights per aircraft per day), and short gate utilization so that the number of flights that an aircraft can perform per day is at its maximum [3]. The core business of airlines is to secure the execution of published flight schedules to transport passengers from A to B [14]. Generally, a wide range of airline business models differs among others in revenue, labor, and connectivity  
Figure 1: Ground time as part of one flight operation.  
systems [15]. However, airlines are not differentiated by their underlying business model as their high-level interests in terms of GTs are the same.  
Concerning stakeholders in equal measure, the following risks arouse from extended GTs which justify the demand for sustainable improvement of ground operational efficiency.
# Application of Neural Networks to Explore Manufacturing Sales Prediction
## 3 Methodology
### Back-Propagation Network Algorithm
If we define \(\delta_{j}\) as the error of the \(j\) output processing unit in an output layer, then:  
\[\delta_{j}=(T_{J}-Y_{J})\cdot f^{\prime}(net_{j}), \tag{7}\]  
Thus, the correction for \(W_{ij}\) is:  
\[\Delta W_{ij}=-\eta\frac{\partial E}{\partial W_{ij}}=\eta\cdot(T_{j}-Y_{j}) \cdot f^{\prime}(net_{j})\cdot X_{i}=\eta\cdot\delta_{j}\cdot X_{i}, \tag{8}\]  
and the correction to the threshold for the output unit is:  
\[\Delta W_{ij}=-\eta\frac{\partial E}{\partial\theta_{j}}=-\eta\cdot\delta_{j}. \tag{9}\]  
Backward correction, i.e., Equations (7)-(9), are repeated layer by layer to obtain the weighting value between each layer and threshold for each neuron, network output values and target values were gradually reduced. The error made the establishment of network models reachable. Generally, the momentum coefficient or inertia factor, \(\alpha\), is added to Equations (8) and (9) to correct the previous weighting value, reducing oscillation during convergence and making training smoother, i.e. :  
\[\Delta W_{ij}(n)=\eta\delta_{j}X_{i}+\alpha\Delta W_{ij}(n-1), \tag{10}\]  
and:  
\[\Delta\theta_{ij}(n)=-\eta\delta_{j}+\alpha\Delta\theta_{j}(n-1). \tag{11}\]  
There are many data splitting methods reported and used in literatures. These methods can be roughly categorized into three different types [42; 43; 44]: (1) Cross-validation, (2) randomly selecting a proportion of samples and retaining these as a validation set and then using the remaining samples for training, and (3) the Kennard-Stone algorithm. In this study, we choose the random method for the ease of application, and are applied on 7/8 portion of training data. The remaining 1/8 portion is the test data. The ANN is trained by selecting a training set of samples to repeat the learning steps multiple times until errors converge. However, training loops should not be too long to avoid over-training the ANN, ensuring new input samples produce correct outputs.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 4 Simulation Setup and Result Analysis
### Simulation Results and Discussion  
For performance comparison, we have compared three other routing protocols with our proposed protocol. These protocols are GSR [38], E-GyTAR [2] and TFOR [3]. The performance metrics used for comparison include packet delivery ratio, end-to-end delay, and routing overhead. Packet delivery ratio is defined as the percentage of packets that are successfully delivered to their destination vehicles. End-to-end delay is defined as the average delay incurred by the packet from its source to its destination. Routing overhead is the ratio of total control packets produced to the total data packets received at the destinations during the complete simulation [2,3,31].
## 5 Numerical Results  
In this section we present results for the throughput and the packet loss rate, using the formulation derived in Section 3, while considering the model given in Section 2. Moreover, we also investigate the performance of the proposed traffic load distribution algorithm introduced in Section 4.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 5 Conclusions  
A cost model is developed for virtualization infrastructure for the next-generation virtualized 5G core based on network slice demand and is applied to a smart factory as a case study. The costs for different deployment scenarios are analyzed, with the highest cost savings in a fully centralized scenario, followed by a distributed scenario with a local user plane and a centralized control plane, followed by the most expensive fully local scenario. Cost savings increase with the number of factories and the growth of the number of devices. A large factory with a large number of devices has less to gain in terms of relative cost savings than a small factory by centralizing resources in a service provider's central cloud and thus might prefer a fully local deployment, enjoying scale benefits of its own. While a specified smart factory case is evaluated, a sensitivity analysis is performed by varying the expected growth of devices and the number of slice customers to be provisioned. Additionally, the difference between full-isolation and no-isolation cases is evaluated for the above scenarios, clearly depicting each scenario's different cost of isolation. The results indicate that the model can be applied to different markets, customer sizes, number of customers, and deployment scenarios. Furthermore, the cost breakdown in terms of cost for every slice type and per device offers insights for stakeholders about the required investments for every slice type and device. While a large factory can prefer a completely isolated local network for its reliability instead of cost savings, a large network operator or slice provider serving multiple factories should still consider cost savings as an important driver.  
Conceptualization, J.S.W., H.H., K.K., H.F., S.Y. and M.M.-B.; data curation, J.S.W. and S.Y.; formal analysis, J.S.W., H.H. and K.K.; methodology, J.S.W., H.H., K.K., H.F., S.Y. and M.M.-B.; writing--original draft, J.S.W.; writing--review and editing, J.S.W., H.H., K.K., H.F., S.Y. and M.M.-B. All authors have read and agreed to the published version of the manuscript.  
This research was supported and funded by Business Finland in the 5G VIIMA project (grant no. 6430/31/2018).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 12: TCO comparison with different overbooking ratios and deployment scenarios.
## 5 Case Study  
This section examines the implementation of the proposed scheme in smart agriculture, aiming to advance sustainable and eco-friendly agricultural practices. This initiative is expected to enhance agricultural production efficiency and minimize resource wastage.