esa-sceva/satcom-chunk-collection huggingface dataset:
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 4 Experimental Evaluation
### Experimental Setup  
For the experimental part of our research, we utilized the datasets previously described: the dataset for vehicular noise (Section 3.3), the ground truth earthquake dataset (Section 3.4), as well as the two-fold synchronized dataset (Section 3.6). The former aims at providing information on vehicular noise, as perceived by the proposed low-cost sensory equipment. The second dataset aims to act as the ground truth point of reference, given its provenance from the European Integrated Data Archive in the National Observatory of Athens, and the fact that the sensory equipment used to collect these data is of very high accuracy. The latter dataset is the combined and synchronized dataset of seismic data from the low-cost seismic sensor in addition to seismic data from the professional seismic sensor, while both sensors were in very close proximity. All these datasets are also available ([[https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_senso_ssignals_contaminated_with_vehicular_noise/blob/main/Data_Availability](https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_senso_ssignals_contaminated_with_vehicular_noise/blob/main/Data_Availability)]([https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_senso_ssignals_contaminated_with_vehicular_noise/blob/main/Data_Availability](https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_senso_ssignals_contaminated_with_vehicular_noise/blob/main/Data_Availability)), accessed on 13 September 2023). For the training part of the process, as extensively discussed in Section 3.5, the TensorFlow and Keras frameworks were utilized to create the classification models. The creation of DNNs was based on the LSTM classification model (see Section 3.5 for more details) using five layers for training and validation. The hardware configuration used was a computer with an i7-9700k CPU, featuring 8 cores and 16 gigabytes of RAM, along with an MSI GTX 1660 Ti GPU, equipped with a 6-gigabyte memory card. In order to evaluate the results received, the metrics used herein were accuracy, precision, recall, F1 score, and the area under the curve (AUC) [39], as per the following equations:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
\[\text{Recall or Sensitivity}=\frac{TP}{TP+FN} \tag{3}\]  
\[\text{F1 score}=\frac{2*Precision*Recall}{Precision+Recall} \tag{4}\]  
where \(TN\), \(FN\), \(FP\), and \(TP\) are true negative, false negative, false positive, and true positive, respectively.
## 3 Structural Modeling  
In this study, the ISRS was computed for the auxiliary buildings of the APR 1400 nuclear power plant. An FE model was prepared for the auxiliary building using the commercial software ANSYS, as shown in Figure 3. Both walls and slabs were modeled using a shell element, SHELL1811 of which each node has six DOFs [25]. Basically, a four-node shell element. Mesh was generated automatically using the quadrilateral dominant method so that four-node elements were used mostly and triangular shell elements were included partially [26]. The average mesh size was 3 m, which was determined based on the FE model described in the design report of APR 1400 NPP [27]. The model used in this study comprised 23,569 elements and 1,166,116 DOFs. A fixed boundary condition was applied to constrain the six DOFs of all nodes at the ground level.  
Figure 2: Coupled model considering equipment–structure interaction: (**a**) Coupled system with full DOFs; (**b**) Decoupled system and corresponding mechanical impedance function; (**c**) Condensed coupled system.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 6 Conclusions  
In this paper, NODE is applied to the STLF in order to improve the forecasting accuracy. Two-month electricity load data collected from Queensland (2019-2021) are used to validate the effectiveness of the proposed NODE. We use different types of models to verify the effectiveness of NODE. Experimental results show that NODE distinctly decreases forecasting errors on different future steps. Therefore, the combination of NODE and LSTM or BiLSTM has practical significance and can be applied in electricity load forecasting.  
However, there are still some limitations of our work. The limitations of our work include the following:  
* Multivariate time series dataset is not adopted.
* Other neural networks that can be used to parameterize the derivative of NODE are not used.
* Long-term forecasting was not attempted.
* Other recurrent neural networks, such as GRU and basic RNN, were not attempted. In the future, based on remedying the above limitation, we will continue to investigate relevant aspects of NODE, including the following:
* Investigate more forms of NODE to balance the computation memory and forecasting accuracy.
* Apply the combination of NODE and LSTM-based models to multivariate time series forecasting.
* Explore NODE interpolation to fill in missing values of time series.  
**Author Contributions:** Conceptualization, S.H. and B.Y.; data curation, Q.L.; formal analysis, Q.L.; funding acquisition, Q.Z.; investigation, Q.L.; methodology, S.H. and B.Y.; project administration, Q.Z.; resources, J.S.; software, S.H.; supervision, B.Y.; validation, S.H., J.S., and B.Y.; visualization, S.H.; writing--original draft, S.H.; writing--review and editing, J.S. and B.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by State Grid Gansu Electric Power Research Institute under Grant No. SGSKY00 WYJS2100164, No. 52272220002W.  
Figure 14: The prediction results and local observation for 7-step forecasting in 2019. (**A**) represents the whole prediction result. (**B–D**) describe the prediction result of different local observation interval.
# State-of-the-Art Internet of Things in Protected Agriculture
## 5 Disscussion
### Network Challenge  
In protected agriculture, the sensor and device need to continuously operate under the harsh and varied environment and their arrangement is sparse and irregular. Compared with the wired network with high wiring costs, the wireless network has the advantages of low cost, good networking flexibility and high scalability and therefore becomes the main application mode of the current monitoring system. Many actual deployments have displayed that common wireless communication technologies are affected by temperature, humidity andbuildings or other spatial barriers [136; 137; 138]. The growth of animals and plants changes also result in background noise [139] because of the multi-path propagation effects, which lows reliability of data transmission. It is easy to isolate a node in the original communication network due to low node deployment density and the above obstacles in protected agriculture. If this node is responsible for communication routing tasks of many devices and this node is damaged and isolated from the network, this will cause partial or even paralysis of most of the network. Most relevant theories and research needles on the effects of the crop growth, the thickness and material of wall, and the radio signal transmission loss and electromagnetic wave transmission between soil and air interface, etc. To determine the optimal position, height and network topology of sensor nodes under different environmental conditions. On the other hand, ad hoc intelligent wireless network technology is needed to reduce or avoid paralysis of large-area networks. The rise of LPWAN may solve e above paralysis problems of WLAN due to its long communication distance, which obviates the need for the dense and expensive deployments of relays and gateways altogether. However, the cost of erecting LPWAN base stations is high.  
The process of recording various agricultural products' status from the field to the table generates a large amount of data, which brings great challenges to wireless communication with a low transmission rate. At same times, it also brought network nodes many problems in energy distribution, data calculation, storage and communication. Therefore, there is an urgent need to develop high-capacity data and real-time broadband communication standards for technologies in complex agricultural application environments.
## 2 Three-Dimensional Visual Servoing Problem  
In the case of 3D visual servoing, the function to be minimized is expressed in the Cartesian coordinate system. The location (position and orientation) of the camera denoted as r is deduced from the image data. The main task of 3D visual servoing is to reach the desired situation denoted by \(r*\). The operating principle of 3D visual servoing is described in Figure 1. The advantage of a 3D measurement is to be able to specify the trajectory of the camera in the space where it is most easily described, which also makes it possible to ensure a feasible trajectory of the camera in space and especially in the presence of obstacles.  
Figure 1: Three-dimensional visual servoing—block diagram.  
One of the major problems in 3D visual servoing is determining the relative pose and orientation of the observed object or target. In a formal way, the pose estimation is achieved by matching image pixels with space points: 2D-3D correspondence. This estimation is carried out by using variables deduced from points, regions or curves.  
To obtain \"good\" pose estimation, different filters are generally used to estimate the translation and rotation parameters. It should be noted here that regardless of the pose reconstruction method used, the depth estimate is highly sensitive to measurement noise and errors caused by the calibration of the visual sensor. After convergence of the servo control and once the steady state is reached, we have a bias between the position of the robot and the object of interest to be reached. Another major problem with 3D visual servoing is that there is no control in the image plane. The target may leave the field of view during navigation. We propose to solve these two problems using the concept of differential flatness.
## 3 Methodology
### Databases  
**The Dutch Corpora:**_Corpus Gesproken Nederlands (CGN)_[56]: The CGN is a corpus containing Dutch speech spoken by native speakers from the Netherlands and Flanders. In this study, we only used the data recorded in the Netherlands. The corpus consists of monologue and multilogue speech spoken by speakers within the 18-65 age range. It has 15 different speech types, which include read speech, lecture recordings, broadcast data, spontaneous conversations, and telephonic speech. The unprocessed training data consist of the speech from all components summing to around 480 h spoken by 1187 female and 1710 male speakers. Two test sets were used: broadcast news (BN) and conversational telephone speech (CTS). The pre-processing procedures and test partitions are the same as in [57]. The Dutch models used in this study are trained on the adults' speech from the CGN corpus. _Jasmin-CGN corpus_[36]: Jasmin-CGN corpus is an extension of the CGN corpus. It consists of read speech and Human-Machine Interaction (HMI) speech spoken by Dutch children, teenagers and older adults, and teenagers and adult non-native speakers of Dutch. Here, all native and non-native children's and teenager's speech (Dutch Native Children: DC; Dutch Native Teenagers: DT; and Dutch Non-Native Teenagers: NnT) are used for testing only. **The German Corpora:**_Mozilla's Common Voice (CV)_: The German adults' speech dataset used in this study is obtained from Mozilla's Common Voice (CV) project [58]. It is a large open-source dataset consisting of read speech where speakers contribute speech by reading words from a screen. The dataset contains metadata, including the gender, age, and accent region of the speaker. In this work, around 600 h of standard-accented German from adult speakers is used. The training, validation, and test splits are those as used in [59]. The German models are trained on the adults' speech from the CV corpus. _kidsTALC dataset_: The German children's speech dataset used in this study is the kidsTALC dataset [38]. The kidsTALC dataset is specifically designed to support the development of speech-based technological solutions and contains 25 h of continuous speech from children aged 31/2-11 years. In this study, all children's speech from the kidsTALC validation set is used for testing our models (as the kidsTALC test set does not contain transcripts). **The Mandarin Corpus:** This dataset was released as part of the Children Speech Recognition Challenge (CSRC) organized as an event of the IEEE Spoken Language Technology 2021 workshop [37]. It consists of 3 sets of Mandarin speech data, i.e., Set A contains 341 h of adult read speech, Set C1 consists of 29 h of child read speech, and Set C2 of 30 h of child conversational speech. In this paper, we follow the data splits for training, validation, and test from [52]. For the Mandarin experiments, we train the Mandarin ASR with Set A of adults' speech and test on the test sets of Set A, Set C1, and Set C2.
## 2 Materials and Methods
### Feature Extraction  
The data after the ICA and ASR followed by ICA ASR or the ICA + ASR artifacts rejection were subjected to different processing steps in order to extract the characteristics specific to the experimental procedure. For each experiment, a different electrode montage was selected for analysis and adjusted to the type of effect being measured. A signal from parietal-occipital electrodes was chosen to measure changes in the alpha band (8-13 Hz) activity over visual cortex areas during the relaxed state with eyes open and closed (P3, PZ, P4, POz, O1, and O2) [47]. We expect an increase of alpha band power spectral density (PSD) for the eyes-closed condition compared with the eyes-open condition, as it is known to be one of the strongest human electroencephalographic responses ever discovered [36; 48; 49]. The electrodes above the left (O1) and right (O2) visual cortex, on the other hand, were chosen to record the response to stroboscopic stimulation [50]. The P300 component, whose propagation extends from the frontal cortex, through the central-parietal area to the occipital region, was chosen as an indicator of the response to stimuli in the oddball procedure [51].  
The ASR was calculated using clean_rawdata (EEGLab plugin) with parameter standard deviation cutoff set to the default value of 20 (data regions removed if they exceed 20 times the standard deviation). We decided to leave the default value of the standard deviation cutoff parameter after visual inspection of results. The ICA was applied to remove artifactual components based on ICLabel (EEGLab plugin). No-brain components were removed. The method of data analysis is shown in Figure 2.
## 6 Challenges and Future Research Directions
### Challenges  
The integration of SWIPT within mMIMO and NOMA systems presents a promising solution to enhance both spectrum and energy efficiency. However, the practical implementation of SWIPT in mMIMO-NOMA systems is fraught with several challenges that necessitate further investigation.
## 2 Related Work
### Topology Control Based  
The authors in [3; 5; 7; 20] proposed topology control-based solutions. TCEB and GARM schemes are proposed for controlling the topology of UWSNs in [7; 20], respectively. In addition, the [3] classifies different topological protocols. From [3], reliability and mobility is discussed. The work [3] focuses on single-hop and multi-hop while the work [7] only focus on next forwarder node. The challenges that discussed in [3; 5; 7; 20] are: high attenuation, mobility of sensor nodes, energy efficiency, low bandwidth, connectivity loss, high bit rate error, high deployment cost, complexities and optimal location of glider. Using dynamic topological strategy, work in [7] achieves energy efficiency and the work in [20] enhances both PDR and energy efficiency. In [5], mobility management is a major consideration using EEL and the concept of multi-hoping. In addition, the work [5] achieves better simulation results from compared ones.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 3 Solutions for Software Defined Metro and Access Networks
### Higher Layer Re-Configurability
The proposal is applicable also to PONs considering the passive network as a N + 1 ports distributed Ethernet switch: one port for OLT, N ports for ONUs [26]. On the other hand, [PERSON] and [PERSON] consider the proposal by [PERSON] and [PERSON] problematic for an OpenFlow controller to replace Dynamic Bandwidth Allocation (DBA) in GPON [27]. The idea of replacing DBA with a software-defined approach has been recently re-proposed by [PERSON] et al. [28] for Ethernet PON (EPON), i.e., the IEEE standard equivalent to GPON. Their proposal takes origin from the assumption that there is no single DBA algorithm able to fulfill different requirements, especially in the highly dynamic case described in the Section 2.1. While DBA algorithms are hard-coded in specific hardware, reprogrammable DBA module based on SDN can reduce traffic delay and increase throughput with better support to differentiated QoS [28]. Fixed access network sharing (FANS) is a re-configurable architectural approach, proposed by [PERSON] et al. [29] with the purpose of sharing access network resources between an infrastructure network provider (INP) and virtual network operators (VNOs), in order to allow fast and flexible service creation for the INP and better control and monitoring for the VNOs. According to this proposal, the physical access network is sliced into multiple virtual area networks. Two possible implementations of FANS have been presented: (i) a centralized management system, under the INP control, performing centralized functions and providing automated data from network elements to VNOs; (ii) a high-level abstraction layer based on an NFV which orchestrates virtual access nodes (vANs). Each vAN could manage interfaces belonging to different physical nodes by using a mapping between physical ports and virtual ports (INP port mapper).
## 8 Experiments
### Error Analysis
We were able to observe several errors of this kind in the results. The inflective nature of Slovenian (and other inflected languages) gives more opportunities to the existence of non-standard forms identical to the standard forms of the same word in a different grammatical category. This can be seen in the unwarranted conversion in Figure 1, where the word _uspel_ (eng. to succeed in first person singular) was converted to _uspeli_ (eng. to succeed in first person plural). In Figure 2, we see the concatenation of two words in the normalization, resulting in two errors detected by the evaluation tool: a wrong conversion and a deleted word error (W-del). In Figure 3, we see that the word \(s\) (eng. with) was an inserted word error (W-Ins), and the word _kolektorji_ (eng. collectors) was deleted. Inserted and deleted words are highlighted in bright red in the HTML outputs and are mostly observed with transformer models, while statistical and LSTM models favor one-to-one conversions. Figure 1: Example HTML output with the most common errors (missing conversion marked in red, wrong conversion marked in green, and unwarranted conversion marked in blue). The manual inspection of the errors from the HTML output gives us a better understanding of the errors that occur, the words that are typically incorrectly converted, and the possible causes for the errors. Transformer models are known for their self-attention mechanism, which enables them to catch longer dependencies in sequence-to-sequence tasks. This results in better performance in more complex tasks where the output sequence is drastically different than the input sequence, e.g., translating into another language. We speculate that this mechanism creates deleted and inserted words in text normalization. When using character-based models, this can be seen as inserted or deleted characters or, like in Figure 2, as a deleted space, resulting in the merging of two or more words. On the other side, statistical and LSTM models tend to take into account only shorter dependencies while generally having a lower performance. These differences in the behavior of the three architectures might explain the results we obtained. The longer context taken into account by transformer models can be beneficial in cases where the literal form might correspond to different standard forms. For example, the Slovenian phrase \"veilk pomen\" can have two different meanings: \"means a lot\" or \"great importance\". Table 15 shows two example segments where this phrase is used and has to be normalized differently. To determine the correct normalization, a longer dependency is needed.
## 4 Results
### Training Analysis of the Proposed TPHO  
As shown in Table 5, three TPHO-based algorithms are compared to verify the performance of the specific designs for the proposed TPHO. TPHO-MRC is the proposed method, in which the reward function is defined as Equations (16)-(18), a proposed encoder-decoder network is used to generate the task sequence, and the MRC rule is used to allocate the satellite. In TPHO-EFT, the heuristic rule MRC is replaced by EFT. TPHO-Profit defines the increment in total profit as the reward function. TPHO-Linear employs multiple layers of fully connected networks to make decisions in the task sequence.  
Figure 6 shows the total profit during the training process of the four algorithms in 12 training scenarios with different numbers of AEOSs and tasks. The total profit increases in the early stage and finally oscillates around a certain value, demonstrating that the network can learn to improve its policy and converge to a stable policy. The slope of the training curve indicates the learning efficiency of the network, and the height of the training curve  
Figure 6 shows that TPHO-MRC provides the best solution among the four algorithms after training and shows good learning efficiency in all training scenarios. The training curves also indicate that the total profit of TPHO-Profit converges to a lower value. Using EFT as the resource allocation rule, TPHO-EFT performs worse than other algorithms in scenarios with four AEOSs. TPHO-Linear obtains worse results than TPHO-MRC in scenarios 3-1200, 4-900, and 4-1200. This proves that the proposed encoder-decoder network, MRC rule, and designed reward function can improve the optimization performance of TPHO.  
Figure 6: Training performance of the four algorithms. (**a**) Number of AEOSs and tasks: 2-300. (**b**) Number of AEOSs and tasks: 2-600. (**c**) Number of AEOSs and tasks: 2-900. (**d**) Number of AEOSs and tasks: 2-1200. (**e**) Number of AEOSs and tasks: 3-300. (**f**) Number of AEOSs and tasks: 3-600. (**g**) Number of AEOSs and tasks: 3-900. (**h**) Number of AEOSs and tasks: 3-1200. (**i**) Number of AEOSs and tasks: 4-300. (**j**) Number of AEOSs and tasks: 4-600. (**k**) Number of AEOSs and tasks: 4-900. (**l**) Number of AEOSs and tasks: 4-1200.