esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
###### Abstract  
The New Urban Agenda (Agenda 2030) adopted at the United Nations Conference related to Sustainable Urban Development (Habitat III) in the year 2016 has the goal of prompting cities to achieve the identified Sustainable Development Goals by the year 2030. In this context, cities can experiment strategies of circular economy for the optimization of resources, waste reduction, reuse, and recycling. The data generated by the components of an Internet of Things (IoT) ecosystem can contribute in two relevant ways to a smart city model: (1) by the generation of a circular economy and (2) by the creation of intelligence to improve the decision-making processes by citizens or city managers. In this context, it is in our interest to understand the most relevant axes of the research related to IoT, particularly those based on the LoRa technology. LoRa has attracted the interest of researchers because it is an open standard and contributes to the development of sustainable smart cities, since they are linked to the concepts of a circular economy. Additionally, the intention of this work is to identify the technological or practical barriers that hamper the development of solutions, find possible future trends that could exist in the context of smart cities and IoT, and understand how they could be exploited by the industry and academy.  
IoT; LoRa; LPWAN; smart city; circular economy +
Footnote †: journal: Applied Sciences
## 3 Orthogonal Matrix-Autoencoder-Based Encoding Method for Unordered Multi-Categorical Variables  
Among the existing methods for encoding categorical variables, part of the methods is not applicable to unordered multi-categorical variables with no size, order, or distancerequirements between categories, and the other part of the methods have large dimensionality after encoding. Therefore, this paper proposes the method O-AE based on orthogonal matrix-autoencoder for encoding and dimension reduction of unordered multi-categorical variables. O-AE for unordered multi-categorical variables encoding and dimensionality reduction process is shown in Figure 1.  
\[\begin{split} CV:[c_{i}]\overset{f_{O-AE}}{\to}\mathbf{Z}\in\mathbb{Z} \Leftrightarrow\langle CV\rightarrow\mathbf{A}\overset{QR}{\rightarrow}\mathbf{Q} \rightarrow\mathbf{X}\in\mathbb{X},\mathbf{X}\in\overset{encoder}{\rightarrow}\mathbf{Z }\in\mathbb{Z}\overset{decoder}{\rightarrow}\hat{\mathbf{X}}\in\mathbb{X}\rangle \end{split} \tag{8}\]  
The model is mainly divided into two parts, the original code generation part and the code reduction part.  
The original code generation part is \(CV\rightarrow\mathbf{A}\overset{QR}{\rightarrow}\mathbf{Q}\rightarrow\mathbf{X}\in \mathbb{X}\). The code dimensionality reduction part is \(\mathbf{X}\in\mathbb{X}\overset{encoder}{\rightarrow}\mathbf{Z}\in\mathbb{Z} \overset{decoder}{\rightarrow}\hat{\mathbf{X}}\in\mathbb{X}\).  
First, according to the number of categories \(N\) in the unordered multi-categorical variables, the \(N\)-order orthogonal matrix \(\mathbf{Q}\) is constructed, and the orthogonal matrix is mapped one by one with the original unordered multi-categorical variables according to the positional indexes into an orthogonal numerical coding matrix \(\mathbf{X}\) with the same data size, independent positions, and equal distances.  
Second, the orthogonal matrix of numerical code matrix \(\mathbf{X}\) is input to the autoencoder, and the autoencoder is trained step by step by compressed reconstruction coding through the encoder and decoder in the autoencoder; finally, the learned feature \(\mathbf{Z}\) is obtained.
## 4 Monitoring System Testing and Analysis
### System Simulation Experiment
#### 4.1.2 Experimental Results and Analysis  
In this section, the performance of the ODMT proposed in this paper is evaluated through simulation experiments and compared with the EGRPM [19] algorithm, MECA [20] algorithm, and EEDD [21] algorithm in the same sensor scale with a total time of Ts, to verify the effectiveness of the proposed ODMT. The simulation results show that the ODMT ensures a high acceptance rate of the network, which enables the sensor network to operate properly. Figure 5 shows the average energy consumption for 100 sensor nodes. As the number of burst data nodes increases, the acceptance rate of the experimental algorithm decreases from 99% to 98%, which is a small decrease. The acceptance rate of the EEDD algorithm decreases from 90% to 68% as the number of burst data nodes increases, which is a relatively large decrease. Compared with other algorithms, the acceptance rate of the algorithm in this paper is higher, and the reduction is negligible.  
Figure 5: Average energy consumption when there are 100 sensor nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Parameter** & **Set Value** \\ \hline The battery is fully charged & \(E_{full}\) & 2 J \\ \hline Cache space capacity & \(B_{full}\) & 25 KB \\ \hline Network length & L & 400 m \\ \hline Network width & W & 400 m \\ \hline Number of nodes & N & 150 \\ \hline Transmission radius & \(d_{tr}\) & 50 m \\ \hline Burst packet & \(b\) & 256 bits \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
The network survival time is defined as the time when the first sensor node dies in the transmission process, and for a network size of 100 and 200 nodes where each node has different initial energy, it is the time when there remains 20 fixed nodes in the network to collect the burst data and transmit the burst data. Figure 6 shows the network survival time when the number of sensor nodes is 100 or 200; ODMT and EGRPM [19], MECA [20], and EEDD [21] algorithms have similar network survival times, over 2000. However, in terms of acceptance rate, ODMT always had a high acceptance rate of more than 80%. The comparison shows that the algorithm of this paper can accurately represent the network acceptance capability of different sensors.  
The results of these experiments have been summarized in Table 4. The results presented in Table 4, confirm the efficiency of ODMT in achieving a high acceptance rate and at the same time, maintaining a competitive network survival time.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 3 The Proposed Method  
### Conventional Beamforming  
For any angle, \(\theta\), constructing an array weighting vector, \(\mathbf{a}(\theta)\), and multiplying it with the correlation value vector, the array output can be represented as follows:  
\[\begin{array}{l}Y(\theta)=\mathbf{a}^{H}(\theta)\mathbf{r}(t)\\ =A\sum_{m=0}^{M}\alpha_{m}\mathbf{a}^{H}(\theta)\mathbf{a}(\theta_{m})R_{c}( \Delta\tau_{m})e^{i2\phi_{m}}\\ =A\sum_{m=0}^{M}\alpha_{m}R_{c}(\Delta\tau_{m})e^{i2\phi_{m}}\sum_{n=0}^{N-1}e ^{2\pi m(d/\lambda)(\sin\theta-\sin\theta_{m})}\\ =\sum_{m=0}^{M}\widetilde{A}_{m}\frac{\sin(\pi N(d/\lambda)X_{m})}{N\sin(\pi( d/\lambda)X_{m})}\end{array} \tag{8}\]  
Figure 3: Array configuration of uniform linear array.  
where \(X_{m}=\sin\theta-\sin\theta_{m}\) and \(\widetilde{A}_{m}=A\alpha_{m}R_{c}(\Delta\tau_{m})e^{i\Delta\phi_{m}}\). \((\cdot)^{H}\) denotes the conjugate transpose. For the \(m\)th signal component, the peak of the beamforming energy spectrum occurs at point \(\sin\theta=\sin\theta_{m}\) with a maximum output energy of \(\left|\widetilde{A}_{m}\right|^{2}\). The position of the peak point will vary in accordance with the alteration in the incidence angle of the multipath signal.  
For the \(m\)th signal component, the peak of the beamforming energy spectrum occurs at point \(\sin\theta=\sin\theta_{m}\) with a maximum output energy of \(\left|\widetilde{A}_{m}\right|^{2}\). Therefore, the maximum value of the beamforming energy spectrum can be utilized for estimating the DOA parameter of the signal. Figure 4 presents normalized beam patterns using the conventional beamforming method under various numbers of array elements, where \(N\) indicates the number of array elements. It is evident from Figure 4 that the resolution of the beam pattern using the conventional beamforming method is heavily influenced by the physical size of the array; the greater the number of array elements, the narrower the main lobe width of the beam pattern. When the number of array elements is limited, traditional beamforming methods may not be adequate for DOA estimation. In this paper, we leverage the translational invariance property of the energy spectrum of the beam pattern to achieve high-precision DOA estimation through deconvolution techniques.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 6 Query Processing Module
### k-NN Query
: _In k-NN query, if the Mindist of a MBR is above the k-th minimum MinMaxdist value, this MBR must not contain the final result, and it should be pruned [17]._  
In order to enhance the readability, we use function _pruning_ (\(q_{k}\), \(\textit{tr}_{k}\), \(k\), \(\textit{sr}_{k}\)) and _distfrom_ (\(q_{k}\), \(k\), \(\textit{ors}_{k}\)), to represent the above pruning (steps 1 to 3) and distance calculation process (step 4), respectively. Algorithm 3 introduces the detailed steps. First of all, we estimate a spatial range \(\textit{sr}_{k}\) in line 1, and perform the subtasks in parallel through Endpoint from lines 3 to 6. Next, client collect the return results \(\textit{lps}_{k}\) of each tRegion; if the total number is smaller than \(k\), we expand the range of \(\textit{sr}_{k}\) and run subtasks repeatedly until the number is not less than \(k\) (lines 2 to 8). Finally, we choose the \(k\)-most-nearest distance and its corresponding trajectory points as the final results (lines 9 to 10). ```
Input: Trajectory number \(k\), Query time range \(\textit{tr}_{k}\), Query spatial location \(q_{k}\); Output: the \(k\)-nearest neighbor trajectory points \(\textit{kps}_{k}\) to \(q_{k}\);
1\(\textit{sr}_{k}\leftarrow\)estimateSpatialRange();
2 repeat
3 foreach tRegiondoin parallel
4\(\textit{ors}_{k}\leftarrow\)pruning (\(q_{k}\), \(\textit{tr}_{k}\), \(\textit{sr}_{k}\));
5\(\textit{lps}_{k}\leftarrow\)distfrom(\(q_{k}\), \(k\), \(\textit{ors}_{k}\));
6 end parallel
7\(\textit{sr}_{k}\leftarrow\)sr\({}_{k}\)expand();
8 until client.sum(\(\textit{lps}_{k}\)-size)\(\geq\)k
9\(\textit{kps}_{k}\leftarrow\)client.collect(\(\textit{lps}_{k}\)).top(\(k\));
10 return\(\textit{kps}_{k}\);
```  
**Algorithm 3**\(k\)-NN Query  
Incremental iterative: Different from spatiotemporal range query, it is difficult for \(k\)-NN query to ensure that a sufficient number of candidate trajectories are obtained with only one parallel processing, so numerous iterative processing for query is required. In the iterative process, the generated candidate trajectories and the enclosed areas do not need to participate in the next iteration, so it can be considered to reduce the computational scale by incremental iterative computation. In order to implement the incremental iterative model through Endpoint, and further reduce the communication and computing cost, we cache the Endpoint calculations by deploying an in-memory database locally (such as Redis [27]), which is helpful for obtaining parameters directly from the local to process the following iteration. Here, we cache the parameters \(\textit{cids}_{k}\) and \(\textit{gcells}_{k}\) for avoiding repeated processing in next iteration, where \(\textit{cids}_{k}\) are the MOIDs of the selected trajectories, and \(\textit{gcells}_{k}\) are the G-index grid cells which are enclosed by the previous \(\textit{sr}_{k}\).
## 5 Application to Rotating Machinery Fault Diagnosis
### Application to Rolling Bearing Fault Diagnosis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(p\) & \(\text{MOC}_{1}(t)\) & \(\text{MOC}_{2}(t)\) & \(\text{MOC}_{3}(t)\) & \(\text{MOC}_{4}(t)\) & \(\text{MOC}_{5}(t)\) & \(\text{MOC}_{6}(t)\) & \(\text{MOC}_{7}(t)\) & \(\text{MOC}_{8}(t)\) & Total \\ \hline
0 & 5 & 7 & 6 & 7 & - & - & - & - & 25 \\
0.5 & 5 & 6 & 6 & 8 & 8 & - & - & - & 33 \\
1 & 5 & 7 & 6 & 7 & 8 & 8 & - & - & 41 \\
2 & 5 & 7 & 6 & 6 & 8 & 7 & - & - & 39 \\
5 & 5 & 7 & 6 & 6 & 8 & 7 & 8 & - & 47 \\
10 & 5 & 7 & 7 & 6 & 7 & 7 & 7 & - & 46 \\
20 Figure 12: The decomposition results of the RS-LOD method on the rolling bearing outer-race fault signal (\(p=10\)). Figure 13: Enlarged view of the first MOC component \(\text{MOC}_{1}(t)\) and the second MOC component \(\text{MOC}_{2}(t)\) shown in Figure 12.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 2 Related Work
### CC Algorithm Based on the End-to-End Mode
#### 2.1.1 Loss-Based CC Algorithms  
Loss-based CC algorithms adjust the data transmission rate and window value based on the packet loss signal. The Reno algorithm is the earliest loss-based CC algorithm, which uses a slow start, congestion avoidance, fast retransmission, and fast recovery mechanism, which is the basis of many existing algorithms. NewReno is an improved version based on Reno, mainly improving the fast recovery algorithm; in the NewReno algorithm, only when all the lost packets are retransmitted and received does the algorithm acknowledge successful transfer. In NewReno, a recovery response judgment function is added, which enables TCP terminals to distinguish between multiple packets lost at one time and much congestion.  
The BIC-TCP algorithm uses a binary search to determine the growth scale of the congestion window. First, it records a maximum point of the congestion window, which is the value of the congestion window when TCP packet loss occurred the last time. A minimum point is also recorded, which is the size of the window when no packet loss events occur in an RTT cycle. A binary search takes the middle point of the minimum and maximum values. When the congestion window grows to this middle value and no packet loss occurs, this indicates that the network can accommodate more packets. Then, this median value is set to the new minimum value and a search for the middle value between the new minimum and maximum values is conducted.  
The CUBIC algorithm is the next-generation version of the BIC-TCP algorithm. It greatly simplifies the BIC-TCP window-tuning algorithm by replacing the concave and convex parts of the BIC-TCP window growth with a cubic function, which retains the advantages of BIC-TCP (especially its stability and scalability) while simplifying the window control and enhancing its TCP friendliness.  
The CUBIC algorithm's window growth function can be expressed as  
\[\mathrm{W(t)}=\mathrm{C(t-K)}^{3}+W_{\mathrm{max}} \tag{1}\]  
where C is a parameter of CUBIC, t is the time elapsed since the window was last lowered and is an elastic value, and K is the time that the above function increases the current congestion window W to the Wmax elapsed without further packet loss. The K calculation formula is as follows:  
\[\mathrm{K}=\sqrt[3]{\frac{\mathrm{W_{max}*\beta}}{\mathrm{C}}} \tag{2}\]
## 4 Experiment and Result Analysis
### Datasets and Preprocessing  
The semantic segmentation parts of UOD-SS and UOD-Un were trained using the Lost and Found and the Cityscapes datasets, respectively, and the test set in the Lost and Found dataset was used as the test images. The Lost and Found dataset contains about 2100 images taken from various street scenes with pixel-level semantic annotations. The Cityscapes dataset has 5000 images of urban driving scenes, including many classes, and provides corresponding semantic annotations. Examples of the two datasets are shown in Figure 7. Before the algorithm was implemented, the image was resized from 2048 \(\times\) 1024 to 1024 \(\times\) 512, and pre-processing operations such as random cropping, color dithering, and random horizontal flipping were performed on the image.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
###### Abstract  
In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases. This paper presents a communication data optimization scheme to mitigate the decrease in throughput due to communication performance bottlenecks in distributed deep learning. To optimize communication, we propose two methods. The first is a layer dropping scheme to reduce communication data. The layer dropping scheme we propose compares the representative values of each hidden layer with a threshold value. Furthermore, to guarantee the training accuracy, we store the gradients that are not transmitted to the parameter server in the worker's local cache. When the value of gradients stored in the worker's local cache is greater than the threshold, the gradients stored in the worker's local cache are transmitted to the parameter server. The second is an efficient threshold selection method. Our threshold selection method computes the threshold by replacing the gradients with the L1 norm of each hidden layer. Our data optimization scheme reduces the communication time by about 81% and the total training time by about 70% in a 56 Gbit network environment.  
Keywords:distributed deep learning; multi-GPU; data parallelism; communication optimization +
Footnote †: journal: Applied Sciences
# Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes
###### Abstract  
Gene electrotransfer (GET) is a physical method of gene delivery to various tissues utilizing pulsed electric fields to transiently permeabilized cell membranes to allow for genetic material transfer and expression. Optimal pulsing parameters dictate gene transfer efficiency and cell survival, which are critical for the wide adaptation of GET as a gene therapy technique. Tissue heterogeneity complicates the delivery process, requiring the extensive optimization of pulsing protocols currently empirically optimized. These experiments are time-consuming and resource-intensive, requiring large numbers of animals for in vivo optimization. Advances in machine learning (ML) and computing power, data analysis, and model generation using ML techniques, such as neural networks, enable predictive modeling for GET. ML models have been used previously to predict ablation performance in irreversible electroporation procedures and single-cell electroporation platforms. In this work, we present ML predictive models that could be used to optimize pulsing parameters based on already completed experiments. The models were trained on 132 data points from 19 papers with the Matlab Statistics and Machine Learning Toolbox. An artificial neural network (ANN) was generated that could predict binary treatment outcomes with an accuracy of 71.8%. Support vector machines (SVMs) using selected features based on \(\chi^{2}\) tests were also explored. All models used a maximum of 24 features as input, spread across target species, needle configuration, pulsing parameters, and plasmid parameters. Pulse voltage and pulse width dominated as the critical parameters, followed by field strength, dose, and electrode with the greatest impact on GET efficiency. This study elucidates areas where predictive ML algorithms may ideally inform GET study design to accelerate optimization and improve efficiencies upon the further training of these models.  
pulsed electric fields; gene electrotransfer; machine learning; artificial neural networks; treatment optimization algorithm +
Footnote †: journal: applied sciences
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 2 Related Work
In general, authentication approaches that are based on usernames and passwords or tokens prove only the presence of the identity, and not the user. Thus, if such credentials or tokens are stolen, loaned, misused, or forged, there will be no proof of the user's legitimacy. The same issue exists with biometric-based authentication techniques. If an illegitimate user is using the end-user's device before the expiry of the access session, all applications and services that are accessed via this device could be accessed by unauthorized persons since there is no technique for verifying if the current user is authorized one. Much research has been conducted into enhancing traditional authentication techniques with contextual information. Table 1 provides a comparison of some relevant related works regarding utilized information, advantages, and limitations. The authors in [9] provide an authentication approach that involves GPS location, time and the tasks performed on the operating system. However, their approach considers location based on a GPS signal that will not be available when accessing services indoors. Based on the assumption that mobile users tend to use their apps in different locations at different times, location-based authentication is described in [29]. Assuming that users perform similar tasks at certain times during weekdays, a user profile approach that gathers behavioral information, such as text messages, calls and geographic location, is proposed in [30]. For observed actions, such as habitual or good events, this approach assigns a score. The study in [31] proposes an anomaly-based detection system based on monitoring users' actions, such as sending SMS messages or making calls. Additionally, other studies, such as [29; 30; 31], consider text messages and calling behavior for abnormal usage detection. As the number of mobile apps increases, SMS and calling functions are being ignored and replaced with apps that achieve the same purpose. In addition, unauthorized users who access mobile devices will, most probably, tend to operate inconspicuously, hence these functions provide insufficient evidence of the intended user. An approach for recognizing users based on wearable clothing colors as supportive identification attributes is proposed in [32]. While this approach may support the authentication process, it is
## 1 Introduction
(2019) [22] put forward a hybrid optimization method based on SW-PSO and the BP neural network to improve engine fuel efficiency and to reduce emissions. From the perspective of cloud computing, the PSO algorithm can be utilized to solve task scheduling problems [23]. In the application of PSO on bridges, the researchers mainly focus on the model-updating algorithm and the parameter value optimization of the control system but not on other analyses [24; 25; 26]. However, for suspension bridges, the main cable curve is an iterative process, and the PSO method might provide the basis of a new idea for its calculation. In this paper, the nonlinear solution equation of the main cable of a double-tower three-span steel-box girder suspension bridge is established using the theory of the segmented catenary method, and the accuracy and convergence speed of the improved PSO (IPSO) method are verified. Section 2 introduces the segmented catenary method. Then, Section 3 presents the IPSO method and conducted the test. Section 4 takes the practical suspension bridge as a background and compares the calculation results and the convergence problem based on the IPSO method, the N-R iteration method, and FEM. Finally, the conclusions are given in Section 5.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 3 MLE Discriminator
### Cost Function of MLE
It can be seen that the cost function reaches the maximum value when the Doppler frequency and phase errors are zero. It is a periodic function of the phase and has symbol flipping when the phase changes \(\pi\). This is easy to demonstrate by (9). Therefore, the frequency and phase can be obtained by searching the maximum or minimum points of the cost function.