esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level
###### Abstract
As a matter of fact, what is actually important for optimizing the ADR is not the specific trajectory a node is taking but the mobility level, i.e., if it is fixed or mobile. Furthermore, specific optimizations can be performed in the ADR in the case of ED which are in \"deep indoor\" installations, such as energy or water meters. These nodes transmit their packets infrequently, but good reliability is needed so they need an ADR algorithm privileging the reliability of the data rate. We would like to remark that our work is quite peculiar and specific and--to our knowledge--it is the first of its kind. As a matter of fact, we are not aiming at localizing the LoRaWAN node, a problem addressed by many papers such as the recent one [16]. Our aim is to detect the level of mobility. Of course, the level of mobility can be derived from the positions of the node in subsequent time instants, but our method is much simpler and does not require precise localization, working only on the data present at the network server. The remainder of the paper is organized as follows. Section 2 gives an overview of the involved technologies. Section 3 introduces the machine learning algorithm to classify the node. In Section 4, we present the experimental environment. Finally, in Section 5 we analyze the results from our experiments and we draw the conclusions.
## 4 DRFM Performance Results
### Effects of DRFM Receiver Signal-to-Noise Ratio  
A significant concern in effectively recording a radar signal to the DRFM and replicating it is the presence of noise in the receiver corrupting the received waveform. To evaluate its effects, the scenario from the previous sub-section was modeled using only the I-Q DRFM under a variety of SNRs, from \(-10\) to 20 dB, with the noise represented as Additive White Gaussian Noise (AWGN).  
The effects can be seen for the LFM and PSK waveforms in Figures 11 and 12, respectively. The PSK example, with its lower processing gain, offers a slightly better visualization of the impact  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \(\mathbf{G_{p}}\) & \(\mathbf{\Delta t}\) & **SLL** \\ \hline LFM Complex & \(\approx\)0 dB & \(\approx\)0 \(\upmu\)s & 5.0 dB \\ LFM Envelope & \(-\)8.0 dB & 8.5 \(\upmu\)s & 1.0 dB \\ PSK Complex & \(\approx\)0 dB & \(\approx\)0 \(\upmu\)s & 11 dB \\ PSK Envelope & \(-\)4.0 & 6.2 \(\upmu\)s & 1.0 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complex and envelope DRFM comparison.  
Figure 10: Correlation results for a waveform with a 13-bit Barker code, with a pulse width of 10 \(\upmu\)s.  
of noise. At 20 dB of SNR, the PSK waveform is behaving as its autocorrelation would predict, with a main lobe and a gradually descending series of time-domain side-lobes. As the SNR is decreased, the side-lobe levels increase, effectively decreasing the dynamic range, and raising the noise floor. In both the LFM and PSK cases, there is little to no difference in signal processing gain across the various levels of AWGN. The results are summarized in Table 2.  
The surprisingly high amount of gain seen in all the examples in Figures 11 and 12 can be linked to the lack of straddle loss. As the system is sampled at a much higher rate than necessary in the jammer system, where instantaneous bandwidths will often exceed 1 GHz, straddle loss is minimized. At the same time, straddle loss was not modeled in order to isolate other factors.  
Figure 11: Correlation results for a linear frequency modulated wave with a pulse width of 10 \(\upmu\)s and a bandwidth of 5 MHz using a variety of SNR values in the DRFM receiver.  
Figure 12: Correlation results for a waveform with a 13-bit Barker code, with a pulse width of 10 \(\upmu\)s using a variety of SNR values in the DRFM receiver.  
Recognizing the effects of moderate SNR values in the EW receiver seem unconcerning, the issue is more problematic when a DRFM jammer often has a bandwidth of 1 GHz. Relative to the moderate bandwidths seen in threat emitters, typically less than 20 MHz, the SNR can be relatively low in the DRFM, even when receiving sufficient power for detection.
## 4 Experimental Results
### Ensemble Model Evaluation
#### 4.3.4 The IEMOCAP Dataset  
After adjusting the classification thresholds to achieve the desired accuracy, the model demonstrated an average accuracy of 85.3%, an average precision of 94.1%, an average re  
Figure 11: Ensemble model (**a**) loss curves and (**b**) accuracy curves for the SAVEE dataset.  
Figure 12: Ensemble model confusion matrix for SAVEE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Emotion** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **MCC** \\ \hline Anger & 96.2\% & 95.2\% & 97.3\% & 96.3\% & 92.9\% \\ Disgust & 100\% & 100\% & 99.4\% & 100\% & 90.5\% \\ Fear & 82.8\% & 81.0\% & 99.9\% & 89.3\% & 92.2\% \\ Happiness & 87.9\% & 86.4\% & 72.2\% & 77.6\% & 89.6\% \\ Neutrality & 106.2\% & 100\% & 100\% & 107.4\% & 89.4\% \\ Sadness & 100\% & 100\% & 100\% & 100\% & 97.5\% \\ Surprise & 99.2\% & 96.5\% & 93.0\% & 94.8\% & 92.3\% \\ Average & 96.5\% & 95.3\% & 95.6\% & 95.2\% & 93.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ensemble model performance on the SAVEE dataset.  
call of 61.4%, and an F1 score of 73.6% for all emotions in the IEMOCAP dataset. Table 6 presents the results. While the precision and recall values were adjusted to achieve the desired accuracy, the overall F1 score reflects the trade-off between precision and recall. Figure 13 shows the loss and accuracy curves for the IEMOCAP dataset, and the confusion matrix for the emotion classification model appears in Figure 14. The neutrality and surprise classes recorded the highest accuracies of 95.0%, whereas excitement recorded the highest precision at 99.0%. Neutrality and surprise produced the highest recall rates (92.0%), and surprise produced the highest F1 score (94.0%).
# Concept to Reality: An Integrated Approach to Testing Software User Interfaces
## 5 Implementation and Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SI.** & **Design Parameters** & **Keywords** & **Selected Users Comments** \\ \hline \multirow{2}{*}{01} & \multirow{2}{*}{Aesthetic Design} & (P) easy on the eye & (P) the overall design is easy on the eye \\  & & (N) navigation bar & (N) the navigation bar should be improved more \\  & & (N) form layout & (N) form layout should be more organized \\ \hline \multirow{2}{*}{02} & \multirow{2}{*}{Communication} & (P) contact us & (P) users can easily contact with the management team \\  & & (N) live chat & (N) no live chat option \\ \hline \multirow{2}{*}{03} & \multirow{2}{*}{Compatibility} & (P) cross-platform & (P) the system is not a cross-platform application \\  & & (N) offline access & (N) cannot be accessed offline \\ \hline \multirow{2}{*}{04} & \multirow{2}{*}{Cost} & (P) reasonable price & (P) room price is very reasonable \\  & & (N) no offers & (N) there are no offers \\ \hline \multirow{2}{*}{05} & \multirow{2}{*}{Performance} & (P) easy navigation & (P) easy to navigate \\  & & (N) no filtering & (N) users cannot filter their rooms according to their desire \\ \hline \multirow{2}{*}{06} & \multirow{2}{*}{Satisfaction} & (P) pop-up ads & (P) there are no pop-up ads \\  & & (N) room categories & (N) room categories are limited \\  & & (N) better pictures & (N) better pictures of rooms should be provided \\ \hline \multirow{2}{*}{07} & \multirow{2}{*}{Security} & (P) login & (P) login facility is provided \\  & & (N) authentication & (N) two-factor authentication should be implemented \\  & & (N) student verification & (N) users who are not students can also book rooms, \\  & & & so student verification is needed \\ \hline \multirow{2}{*}{08} & \multirow{2}{*}{Usefulness} & (P) usability & (P) easy to use \\  & & (N) details & (N) more details should be provided \\  & & (N) advanced facilities & (N) there should be more facilities for students \\ \hline \multirow{2}{*}{09} & \multirow{2}{*}{Responsiveness} & (P) booking response & (P) booking requests are responded to quickly \\  & & (P) helpline & (P) helpline feedback is available \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{Technical Glitch} & (P) image quality & (P) high-resolution and clear image \\  & & (P) 404 error & (P) 404 error message did not occur \\ \hline \end{tabular} Note: P denotes positive feedback, and N denotes negative feedback.  
\end{table} 
Table 7: The highlighted positive and negative feedback from the users of the “Student Hostel” Website in terms of its services. latency reduction, real-world metaphor, integrity, track state, live chat, digital marketing, and user manual.
## 3 Conventional Positioning Solutions in 5G Networks
### Lessons Learned  
Based on the previously presented papers (summarized in Table 1), it can be said that there are several promising methods for positioning in 5G networks without utilizing machine learning algorithms. According to several simulation results, sub-meter accuracy could be achieved even if only 5G signals are used. Moreover, the same accuracy is obtainable in cases where the 5G signals are assisted with other technologies such as GNSS or VLC.  
It should be noted that in most cases, the positioning methods are tested in a fully or partially simulated environment, due to the lack of deployed 5G networks. Simulations tend to underestimate the effects of the physical environment because many of these effects are difficult to estimate and are highly dependent on the particular environment. The models differ greatly in several parameters, such as the number of LoS base stations, the type of simulated signal, and the size of the virtual space; therefore, comparing these  
Figure 5: Illustration of the hybrid GNSS-5G positioning [43].  
positioning solutions based only on their positioning error could be very misleading. To mitigate these problems, most of the works include sensitivity analysis to show the impact of increasing noise on the accuracy of the algorithm. To make simulation results easily comparable in the future, a common and consistent scale or viewpoint must be established. In addition, creating publicly available indoor and outdoor data sets and using them to evaluate the performance of the algorithms would further improve the comparability and reproducibility of the different positioning solutions.
## 2 Results
### Decay Control of VOCs Produced by BCN2 and BCN10 in Natural Postharvest Storage of Loquat Fruits
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{_Bastills methylotropiches_ **BCN2**} & \multicolumn{3}{c}{_Bastills thuringiensis_ **BCN10**} \\  & **RI * & \multicolumn{3}{c}{**Conc \({}^{\text{b}}\)**} & **RI * & \multicolumn{3}{c}{**Conc \({}^{\text{b}}\)**} \\ \hline \multirow{3}{*}{Alcohols} & 434 & Butan-1-ol & 0.17 \(\pm\) 0.02 & 430 & Butan-1-ol & 0.52 \(\pm\) 0.04 \\  & 506 & 3-Methylbutan-1-ol & 0.27 \(\pm\) 0.10 & 513 & 3-Methylbutan-1-ol & 0.56 \(\pm\) 0.08 \\  & 671 & Hexan-2-ol & 0.45 \(\pm\) 0.06 & 750 & Phenylmethanol & 0.05 \(\pm\) 0.00 \\  & 864 & Phenylethanol & 2.05 \(\pm\) 0.12 & 862 & Phenylethanol & 1.00 \(\pm\) 0.08 \\ \hline \multirow{3}{*}{Phenols} & 927 & 2,3,6-Timethylphenol & 0.37 \(\pm\) 0.05 & \multirow{3}{*}{412} & \multirow{3}{*}{Butan-2-one} & 0.25 \(\pm\) 0.05 \\  & 1256 & Benzothiazole-5,7-ol & 0.19 \(\pm\) 0.11 & \multirow{3}{*}{} & \multirow{3}{*}{} \\ \hline \multirow{3}{*}{Ketones} & 412 & Butan-2-one & 0.07 \(\pm\) 0.03 & 412 & Butan-2-one & 0.25 \(\pm\) 0.05 \\  & 465 & 3-Hydrolybutan-2-one & 1.43 \(\pm\) 0.07 & 459 & 3-Hydrolybutan-2-one & 1.80 \(\pm\) 0.07 \\  & 918 & Nonan-2-one & 0.71 \(\pm\) 0.12 & 403 & Butane-2-3-dione & 3.13 \(\pm\) 1.08 \\  & 1076 & Decan-2-one & 0.41 \(\pm\) 0.05 & 859 & Acetophenone & 0.95 \(\pm\) 0.03 \\  & 1198 & Undecan-2-one & 0.22 \(\pm\) 0.11 & 870 & 6-Methylheptan-2-one & 0.06 \(\pm\) 0.01 \\  & 1280 & Dodecan-2-one & 2.06 \(\pm\) 0.98 & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & 1333 & Tridecan-2-one & 0.23 \(\pm\) 0.08 & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & 1598 & Pentadecan-2-one & 0.19 \(\pm\) 0.11 & & \\ \hline \multirow{3}{*}{Hydrocarbons} & 419 & 2-Methylpropane & 0.39 \(\pm\) 0.07 & 417 & 2-Methylpropane & 3.13 \(\pm\) 0.08 \\  & 530 & Penta-1,3-dione & 0.44 \(\pm\) 0.06 & 533 & Penta-1,3-dione & 1.17 \(\pm\) 0.00 \\  & 751 & Toluene & 1.21 \(\pm\) 0.54 & 757 & 2-4-Dimamidobenzene & 0.45 \(\pm\) 0.19 \\  & 840 & Ethylbenzene & 0.98 \(\pm\) 0.23 & 868 & Oct-1-ene & 0.70 \(\pm\) 0.04 \\  & 941 & Propylbenzene & 1.48 \(\pm\) 0.08 & 1113 & 2-Methyllnphthalene & 3.05 \(\pm\) 0.51 \\  & 923 & Isopropylbenzene & 2.11 \(\pm\) 0.53 & 1265 & Dodecane & 0.79 \(\pm\) 0.07 \\  & 1407 & Tetradecane & 0.15 \(\pm\) 0.06 & 1412 & Tetradecane & 0.34 \(\pm\) 0.04 \\  & 1541 & Pentadecane & 5.98 \(\pm\) 0.30 & 1971 & Eicosane & 0.16 \(\pm\) 0.03 \\ \hline \multicolumn{3}{c}{Aldehydes} & \multicolumn{3}{c}{811} & \multicolumn{3}{c}{Benzaldehyde} & 4.01 \(\pm\) 0.17 \\ \hline Esters & 423 & Ethyl acetate & 0.78 \(\pm\) 0.19 & \multirow{3}{*}{} & \multirow{3}{*}{} \\ \hline Acids & 249 & Acetic acid & 1.54 \(\pm\) 0.01 & 241 & Acetic acid & 0.75 \(\pm\) 0.42 \\  & 477 & 2-Methylpropanoic acid & 0.06 \(\pm\) 0.02 & & \\ \hline \end{tabular}
\end{table} 
Table 2: Volatile compounds detected in the headspace from the Bacillus cultures inoculated in LB culture vial at 37\({}^{\circ}\)C for 12 h.
# Offloading of Atomic Tasks in Satellite Networks:
###### Abstract  
With the explosive growth of multimedia services and the continuous emergence of new space tasks, the spatial task scheduling timeliness problem is of great concern. The high computational cost of existing task scheduling methods is not suitable for the time-varying scenarios of space-based networks. This paper proposes a scheduling optimization method containing an atomic task offloading model based on maximum flow theory and a dynamic caching model. Firstly, the model calculates the task offloading upper limit in the satellite network based on the maximum flow theory to achieve the maximum volume of offloaded tasks to improve the resource utilization of idle satellites. Then, we design onboard task offloading and buffer optimization algorithms to reduce the request load of single-satellite atomic tasks. The method improves the overall computational performance and timeliness of the satellite network and reduces the waiting time of atomic tasks competing for resources. Finally, we analyze the time complexity of the proposed method and construct a simulation experiment scenario. The performance comparison results with various baseline models show that the proposed method has certain time complexity and task execution timeliness advantages.  
task offload; task cache; maximum flow; satellite network; atomic task +
Footnote †: journal: Applied  
1
Footnote 1: School of Cyber Science and Engineering, College of Information Science and Engineering,  
Xinjang University, Urumqi 830046, China; [EMAIL_ADDRESS]  
2
Footnote 1: School of Computer Science and Engineering, Central South University, Changsha 410075, China; [EMAIL_ADDRESS]
###### Abstract
However, in the work in Reference [26], a neural network was used, obtaining an accuracy of 94% for the chromosomes, increasing to 64 intensity values in a similar way to Reference [37] but using a k-fold cross validation as a accuracy measurement with k = 3. In Reference [33], an accuracy of 90% using a gray-level-based similarity measure was achieved, but this time with k = 5. All these three approaches use Q-band chromosome images. In the existing literature, it can be observed that they use different measurements to evaluate the performance of their methods and that a standard measurement does not exist [9]. In this research, a k-fold cross validation with k = 10 is used as a accuracy measurement, with a 90% of acceptability according to the state of the art. In this paper, a two-stage automatic G-band chromosomes images classifier is proposed. The classifier was selected among 51 machine learning algorithms. Since the available dataset is composed of 24 karyograms segmented using a semi-automatic tool specially developed for this task, it is not possible to use advanced techniques such as Deep Learning, since they require a much bigger dataset than the available one. For this reason, only classical machine learning algorithms were evaluated. To evaluate the 51 classifiers, the image database reported in Reference [26] was used, which was acquired using the Q-banding technique. These images were acquired in a well controlled environment, that favors the classification task. In the other hand, the G-band images obtained at the CHT were not homogeneous, making it difficult to define a classification model with high accuracy. In addition, the Q-banding dataset has at least five times more images that the G-banding dataset. The rest of the paper is organized as follows. In Section 2, materials and methodology are described. In Section 3, results and discussion are presented. Finally, in Section 4, a conclusion is outlined.
## 1 Introduction
Figure 1: Illustration of an IoT network model with different devices and data message types. Also, the obligation of several routing creation processes to construct on-demand routes to P2P traffic can provoke a high control message overhead. Thus, the main objective of this work is to create an enhancement for LOADng to allow the protocol to better discover and maintain routes for traffic directed to the Internet in IoT networks formed by devices with different capacities. The proposed approach, LOADng-IoT, is composed of three improvements that are able to boost the process of route discovery, reduce the overhead of control messages, and improve the network's quality-of-service (QoS). In summary, the proposal allows the nodes to find Internet-connected nodes without the prior definition of a gateway. This behavior allows nodes without an Internet connection to forward their data packets to external Internet services with much greater reliability and lower latency. Also, the proposed solution presents a cache system for routes to Internet nodes to reduce the control message overhead required in the process of route discovery. Finally, the solution introduces a new error code that should be used to avoid the loss of data packets to the Internet. Thus, the main contributions of the proposal presented in this work (LOADng-IoT) are as follows:  
* LOADng-IoT improves network QoS and reliability by increasing the packet delivery ratio and the reduction of end-to-end latency for the different message types exchanged by nodes in both dense and sparse IoT scenarios. * It reduces the number of control messages required to construct routes among nodes, contributing to a more efficient network with lower overhead. * LOADng-IoT reduces the amount of energy required to both build paths and route data messages, making the network more power efficient. * It dispenses with the use of predefined Internet gateways since the Internet-connected nodes are sought on demand and can change according to their connection availability. This feature also removes the existence of a single point of failure (SPOF) for the connection of IoT devices with external Internet services. * It presents a flexible solution, whereby parts of the proposal can be adopted according to the hardware capacities of the nodes. The remainder of this document is organized as follows. Section 2 presents the most important related works on the topic and Section 3 presents an overview of LOADng to provide a clear understanding of the proposed solution. Section 4 gives a detailed description of the proposed LOADng-IoT, while Section 5 describes the used scenario for the performance evaluation, the obtained results, and corresponding discussion concerning the performance of the studied protocols. Finally, Section 6 concludes the paper and offers suggestions for future work.
## 4 Experiment and Result
### Evaluation of Results
#### 4.4.3 Regional Cropland Extraction
It can be seen from Figure 13d that the extracted results fit well with the actual cropland boundary, which can provide data support for agricultural growth and yield estimation in the future. Figure 12: The strategy of smooth overlap of moving sliding windows. Figure 13: Large-area cropland extraction result in Qingyun County, Dezhou City, Shandong Province. (**a**) The cropland result extracted from the mosaic image of Qingyun County, (**b**) the true-color image within the blue box in (**a**), (**c**) the cropland result of (**b**), and (**d**) the vector result of the cropland boundary. According to the extraction results, we can quickly calculate that the area of cropland extracted was 333,900 mu. Compared with the area data of 362,600 mu published by the Qingyun County Natural Resources Bureau [57], the error of the area extracted by our method was 7.9%.
# Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
## 3 Classes of Algorithms
### Heuristic Algorithms
Using an approach that supposedly selects the best communication paths by adjusting the network assignments and weights, the article in [16] proposes an algorithm based on the change in the service requirements and network conditions. The process implies the iterative evaluation of the possible network statuses and then accordingly adjusting the parameters and configurations to obtain the best balance between resource consumption, cost, and QoS. When applying heuristic algorithms in a networking context, particularly within SDN, the key consideration is how to adapt these general optimization strategies to the specific requirements and constraints of network systems. For example, the implementation of these algorithms must take into account real-time dynamics, scalability issues, and the need for distributed processing. Moreover, in the SDN paradigm, where the control plane is decoupled from the data plane, these algorithms can be implemented in the controller to dynamically adjust network configurations, such as routing paths, switch rules, and load balancing, in response to changing network states. Table 2 represents a summary of the heuristic algorithms discussed in the articles above while Table 3 summarizes the datasets and tools used. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Main Focus** & **Algorithms/ Techniques** & **Context/ Environment** & **Experimentation/ Comparison Parameters** \\ \hline \multirow{3}{*}{[11]} & Particle Swarm Optimization & \multirow{3}{*}{Particle Swarm Optimization} & \multirow{3}{*}{Software Defined Network} & \multirow{3}{*}{Controller Placement Problem} \\  & Algorithm for Controller & & & \\ \cline{1-1}  & Placement in SDN & & & \\ \hline \multirow{3}{*}{[12]} & SDN Routing Strategy based on Genetic Algorithm and Particle Swarm Optimization & Genetic Algorithm and Particle Swarm Optimization & & \\ \cline{1-1}  & Improved Ant Colony Algorithm & Improved Ant Colony & & \\ \cline{1-1}  & in Load Balancing of SDNs & & & \\ \hline \multirow{3}{*}{[14]} & Best Path Finding in SDN using Firefly Algorithm & \multirow{3}{*}{Firefly Algorithm} & \multirow{3}{*}{Software-Defined Networks} & \multirow{3}{*}{Path Finding} \\  & & & & \\ \cline{1-1}  & Cost-effective Tabu Search & & & \\ \cline{1-1}  & Algorithm for Controller Placement in SDN & & & \\ \cline{1-1}  & Simulated Annealing-Based & & & \\ \cline{1-1}  & Multilink Selection Algorithm in SDN-Enabled Avionic Networks & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A brief review of the articles addressing heuristic algorithms.