esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 4 IoT and Satellite  
In most of the papers in the state-of-the-art about satellite networks' employment for IoT applications, IoT devices are installed in rural environments where no terrestrial communication is available such as deserts, mountains, oceans, etc. [35], where the only communication means is by satellite. In other environments, a satellite/IoT system may provide a cost-efficient solution compared to other terrestrial technologies. The work in [5] provided an overview of a possible satellite/IoT system architecture based on an LEO satellite constellation, which allows obtaining lower propagation delay and lower information loss than GEO satellites. The work in [36] focused on a technique used to collect data called the Satellite-Routed Sensor System (SRSS). In this technique, the satellite is the entity responsible for data collection from distributed IoT sensors and for data transmission to ground stations. A tsunami detection system was introduced in [37]. The aim of this system was to help in the early prediction of any possible tsunami. For that large number of sensor terminals consisting of buoys and sensors, the satellite is considered as an intermediate node that forwards the gathered data from sensor terminals in remote areas to base stations where the analysis operations take place. The wide coverage of the satellite allows the collection of data from all the sensor terminals deployed all over Japan.  
However, there are still many open challenges that require being solved before proposing a solution able to allow IoT communications properly through satellite networks, such as the choice of the best suitable IoT communication protocol, as pointed out in [38]. The authors in [39] investigated and proposed different models to reduce the impact of control and data message overhead when the IoT was supported over an integrated satellite-terrestrial network based on Information-Centric Networking (ICN). The employment of wireless and satellite systems to support M2M/IoT services in smart grid use cases was presented in [40]. The authors investigated the pros and cons of the possible exploitation of different wireless technologies, including satellite communications, to support the smart grid use case and, in particular, the support of M2M and Supervisory Control And Data Acquisition (SCADA) over satellite links. The work in [41] was a study of the use of the CoAP protocol over a random access satellite medium and aimed to test the reliability of the considered system and improve the congestion control. A similar study that focused on the configuration of both CoAP and MQTT in an integrated M2M satellite network was presented in [42].  
Energy consumption is another challenge under investigation to fulfill the energy efficiency of 5G KPI also in satellite networks considering IoT/M2M data traffic flows [43].
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
## 4 Results  
### Implementation  
**Experiment scenario and setup.** We recruited five volunteers to participate in the study, including four females and one male. We conducted the experiments in the laboratory, and informed the volunteers of the experimental details. For each volunteer, we collected 300 hand gestures, where there were 60 trials for each of the five types of hand gestures, including swipe left, swipe right, push forward, pull back, and draw a circle. For each trial, we told the volunteers the type of hand gesture, and they performed the hand gesture after the speaker started transmitting acoustic signals. In the experiments, the volunteers were asked to sit in front of the transceiver in the range from 0.5 m to 3 m, and the transceiver was placed at the same height as the hand.  
**Hardware.** As shown in Figure 5, we adopt a commercial speaker (JBL Jembe, London, UK, 6 Watt, 80 dB) to transmit acoustic chirp signals and a commercial microphone array (ReSpeaker 6-Mic Circular Array Seeed Studio, Inc., Shenzhen, China) as the receiver. The speaker and microphone array are connected to Raspberry Pi 3B, which is connected with the laptop (Dell Inspiron 7566 Dell Technologies, Round Rock, YX, USA) through the network. We use the laptop to control transmitting and receiving acoustic signals, and process the signals with MATLAB R2021a. For the acoustic chirp signal transmitted by the speaker, the parameters are the starting frequency \(f_{c}\) = 17 kHz, the frequency bandwidth \(B\) = 6 kHz, and the chirp period \(T\) = 0.08 s. The sampling rate of the microphone array is 48 kHz.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Human Mobility Modeling and Prediction
#### 2.3.1 Challenges and Opportunities
For example, [61] discusses the application potential of the GM in regional science and geography, and in spatial sciences in general (including trade theory) and models international trade flows by means of these models and gravitational gradient models, using Dutch export flows as a test case. Although the GM demonstrated good predictive capacity, affect trade costs, and their impacts on trade flows. It can also be applied in other kinds of applications. For example, the model can clearly explain the spatial dynamics of infectious-disease spread. [62] incorporates trip duration into the mechanism of disease spread, and based on this model, understand how fast and how far a pathogen might spread in a human population. Finally, SEM is another trend model for human activity analyses. It solves the simultaneous equilibria of plural regions and implies that utility levels are equated across between two regions. [63] indicates that standard spatial equilibrium models generally do a good job in describing the spatial distribution of economic activity and the factor prices of labour and land, but these models also important for urban activity location and travel choices in large city regions that anticipate major development or restructuring. For example, [64] consider the spatial relationship between urban green space (UGS) and human activity intensity (HAI) important for urban planning and management, especially for ecological construction of big cities. The spatial equilibrium revealed the spatial correlation between UGS and HAI from the perspective of the ecosystem service (ES), processing and analyzing the whole urban UGS and HAI. In its turn, [65] propose and calibrate a dynamic stochastic general equilibrium (DSGE) model to understand the effect of infectious disease outbreak on tourism. Infectious disease outbreaks, including coronavirus, greatly jeopardize the tourism industry, given its reliance on human mobility.
## 7 Conclusions and Future Work  
In this paper, we analyzed the energy balance consumption theoretically and proposed two energy balanced strategies: EBH and DIB for both underwater moored monitoring system and two-dimensional sparsely deployed UWA-SNs.  
To achieve balanced energy consumption of individual nodes, EBH alternately changes nodes's transmit mode between hop by hop and direct transmission based on nodes's residual energy. With pre-assigned battery power according to node's traffic loads, DIB can achieve balanced energy consumption.  
The simulation results show that both strategies can achieve balanced power consumption per node throughout the network while maximize the lifetime of networks in both shallow and deep water.  
In future work, we will apply our approaches to other network topologies, such as tree-like networks and so on.
## 6 The Evaluation of Unmatched Receiving of ACE-BOC
### Performance Evaluation
#### 6.2.4 Multipath Resisting Performance  
For ACE-BOC, as mentioned, different receiving strategies result in different shapes in cross-correlation function, which also influence the ranging performance under multipath. Figure 5 shows the average range error envelope Equation (22) of ACE-BOC(15,10,[1,1,3,3]) under three  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Central Frequency of** & **Bandwidth of Narrowband** & **Bandwidth of Bandlimited** \\
**Narrowband Interference** & **Interference** & **Interference** \\ \hline \hline \(f_{r}\pm 1\) MHz 1 & 10 kHz & 5 MHz \\ \hline \hline \end{tabular}  
* \(f_{r}\) stands for the receiving central frequency. For ACE-BOC, \(f_{r}\) is specified in Table 1. For MBOC, \(f_{r}\) is the central frequency of the double-sideband MBOC spectra, which is denoted as \(f_{0}\).  
\end{table}
Table 2: Interference parameters in the evaluation scenario. This configuration is effective in Sections 6.2.3 and 7.2.3.  
Figure 4: Anti-interference rate of ACE-BOC under three kinds of interference: narrowband interference, bandlimited interference and matched spectrum interference. Receiving strategies are IMR and BLR for component \(s_{\text{LQ}}\). Larger value means more vulnerability to such interference.  
receiving modes, with 52 MHz bandwidth when MDR is \(-5\) dB. The IMR and BLR receive the component \(s_{\text{LQ}}\). The receiver configuration can be seen in Table 1.  
It can be seen that in wideband receiving of 52 MHz, as shown in Figure 5, the multipath resistance performance ranks as matched \(>\) IMR \(>\) BLR when the relative delay of the multipath over the direct signal is larger than 0.32 chips. When the relative delay is smaller than 0.32 chips, however, the multipath resistance of matched receiving is even larger than IMR. It indicates that IMR works better at short-delay multipath scenarios.  
To summarize, receiving strategy selection for a multi-resolution signal like ACE-BOC is a balance among multiple performance aspects and complexities. BLR requires the least complexity and achieves advantageous acquisition performance with narrow receiving bandwidth which is no more than 28 MHz. When higher complexity in receiver is supported, including wider front-end bandwidth and more complicated local despreading waveform generator, IMR can trade the complexity to notable enhancement in accuracy than BLR. Further, if the most complex matched receiving can be implemented, it is possible to make full use of the wideband ACE-BOC signal, thus the optimum performance can be obtained. Details of the trade-off between performance and receiving complexity can be observed from the evaluation results, which provides a reference in receiving strategies selection.
## 3 Dataset  
In our experiments we used the Munich dataset used in [20] which contains squared blocks of \(48\times 48\) pixels including 13 Sentinel-2 bands (see some samples in Figure 6). Each 480-m block was mined from a large geographical area of interest (102 km \(\times\) 42 km) located north of Munich, Germany. In our experiments we used the split 0 of the dataset, containing 6534 blocks for the training set, 2016 blocks for the test set and 1944 blocks for the evaluation set.  
The ground truth is a 2D image containing the segmentation of the various crops present in each sample, where each pixel has an associated class label obtained from the two growing seasons 2016 and 2017. The segmentation data are not for each date of the time series but are associated with an entire year, so in each pixel, the label represents the harvest crop declared in that year. The 17 classes in the dataset are reported in Table 1. The dataset does not contain information on when a crop is present (sowing and plant growth) and when it is absent (harvest) during a year of observations. The dataset was divided into training, validation and test sets. The dataset is very unbalanced and the cardinality of the last two sets is shown in Table 1. The cardinality reported in the paper [20] does not correspond to ours despite having used the same splits, probably because we used a time series of different number of samples (we extracted 30 samples in each time series) or because some image augmentation was used in their paper. The original dataset can be downloaded from [21].  
Figure 5: Schematic representation of the last two layers of the neural model involved in the computation of the Class Activation Intervals. Starting from the aggregate features \([\hat{y}_{1},\dots,\hat{y}_{T}]\) it is possible to obtain the vector of activations \([y_{1},\dots,y_{C}]\) to determine the class associated with a pixel and vice versa, it is possible to obtain the Class Activation Intervals \([n_{1},\dots,n_{T}]\) starting from the values \([y_{1},\dots,y_{C}]\).  
Figure 6: (**A**)–(**D**) Four random samples of input-output pairs from the Munich dataset. The input on the left is shown as an RGB image, while the output shows the class labels as colors.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 3 TestBed Materials and Machine Learning Model
### Ultra-Wide Band (UIWB) Sensor for Movement and Breathing Monitoring  
The movement and breathing (respiration) dataset was collected using a UWB Sensor by \"Thunder Series\". This sensor operated in the UWB territories (7-9 GHz), which can be utilized to recognize the presence of an individual or creatures inside recognition range. Sensor \"Thunder Series\" gives the information by Bluetooth Low Energy (BLE) and UART interfaces. The sensor's most extreme working distance can be changed through the UART interface or utilizing infra-red (IR) [49]. Visualization can be done using the following software: RealTerm, BLE Terminal, and 'nRF' for smart phone. To ease integration of all data, we have adopted the RealTerm software for data capture, visualization and saving. The RealTerm software visualization of the moving and breathing values are shown in Figure 4. The movement value changes, depending on how fast the person can move. When there is no movement of person or only one person in isolation, breathing value is captured. When there is no person in the smart factory shop floor, the moving and respiration value will be zero (0).  
Figure 3: Visualizing the vibration data. Here, the sensor connect shows the 3-axis data of the vibration sensor for little vibration, moderate vibration, and intense vibration.  
Figure 2: Vibration sensor connection to the host computer using the universal serial bus (USB) gateway. Visualization and saving of data is achieved with the sensorconnect software.
# Two-Dimensional Modelling Approach for Electrokinetic Water Transport in Unsaturated Kaolinite
## 2 Materials and Methods
### Mathematical Model
#### 2.1.2 Electric Charge Transport  
Assuming that the application of an external electric potential gradient does not produce any phenomenon that generates a charge accumulation in the soil [46] and that the system initially starts from an electrically balanced condition, the electric charge balance can be expressed as:  
\[\
abla\cdot\mathbf{i}=0 \tag{11}\]  
where \(\mathbf{i}\) is the current density, which is calculated as:  
\[\mathbf{i}=-\sigma_{\rm a}\
abla\psi \tag{12}\]where \(\sigma_{\rm a}\) is the apparent electrical conductivity of the partially saturated soil. The influence of the porosity and degree of saturation has been considered by estimating the apparent electrical conductivity by an expression based on [PERSON]'s second law [47; 48]:  
\[\sigma_{\rm a}=\frac{1}{a_{\rm A}\phi^{-m_{\rm A}}Sr^{-B}} \tag{13}\]  
where \(a_{\rm A}\) and \(m_{\rm A}\) are [PERSON]'s constants that depend on the nature of the soil and \(B\) is the saturation index [48].  
The energy consumption in the electrokinetic treatment, \(E\), is calculated according to the following time integral:  
\[E=\int I\Delta\psi_{\rm el}H_{\rm R}dt \tag{14}\]  
where \(\Delta\psi_{\rm el}\) is the electric potential difference between electrodes and \(I\) corresponds to the integrated value:  
\[\rm I=\oint\mathbf{i}\cdot\mathbf{n}\;ds \tag{15}\]  
which is analogous to the closed line integral of Equation (9) for the calculation \(\overline{\rm q}_{\rm R}\).
# AC Microgrids Protection: A Digital Coordinated Adaptive Scheme
## 2 Proposed Protection Schemes
### PCC Protection  
As mentioned before, due to operational situations, an MG can be operated in both the grid connected and islanded modes. However, to improve the power quality of loads in faulty conditions or abnormal events of the up-stream grid, the MG can be disconnected through an intentional or unplanned manner. Isolation detection procedure is mainly performed by PCC relays. Conventional OCRs, which are solely sensitive to the fault current amplitude, are not a suitable fault detector. To deal with this problem, the comprehensive fault detection scheme for the PCC relay shown in Figure 1 is presented. The PCC relay involves the following three main relays.
## 7 Discussion
The slightest deviation of the selected plane would result in discrepancies from the real anatomy. Although this method is simple and uses the patient's actual data as a template, it involves a high degree of user interaction and relies heavily on symmetrical plane computation. On the other hand, the cavity-filled approach is more automated, requiring minimal user intervention. It is faster, easier to use, more straightforward, and more accurate as it requires fewer repetitive tasks. This results in greater control and reliability of the implant performance, thus making it a more efficient reconstruction technique. The current research also demonstrates that the cavity-filled approach would give rise to a more robust implant that would satisfy aesthetic requirements. Contrary to this, the approximation of the midsagittal planeusing manual landmark points is done in mirror reconstruction, which contributes to deviation and causes implant placement problems. It also reinforces issues in subsequent post-operative recovery and movement of the mandible. The outcomes of the sample \(t\)-comparison, as well as one-way ANOVA analysis, of the two reconstruction techniques rejected the null hypothesis of the study. This underlined that the two methods, i.e., mirror and cavity-filled, varied substantially in terms of implant accuracy. It was also noted that the RMS error and AD estimates for the mirror reconstruction technique were greater than for the cavity-filled process. The current study, therefore, compared two reconstruction techniques for accurate mandible restoration, following tumor resection. It also focused on a procedure for estimating the accuracy of the reconstructed mandible models.
## 3 Results and Discussion
### Fractional Zener Model
For the second column of Figure 8 (Figure 8b,e,h), we set \(\alpha=\beta=0.5\), \(\psi=1\) and varied \(\tilde{a}\) along with \(\gamma\). The new phenomenon observed can be seen in Figure 8e,h. That is, the resonant behavior changes to monotonically increasing and finally to constant with increasing fractional order \(\gamma\). The similar trend is observed in case of varying \(\psi\) as shown in Figure 8c,f,i (\(\alpha=\beta=\gamma=0.5\)).
## 5 Simulation
### Varying Packet Generation Rate
#### 5.2.3 Route Discovery Frequency and Routing Overhead  
In this case we calculated average route discovery frequency of the three protocols per second. This is the number of times a protocol has to discover route paths to the destination. The results shown here are intra-cluster-based discovery and does not include hotline assisted gateways. _i.e._, The source and destination nodes are in same group of nodes. As shown in Figure 16, AOMDV has minimum number of route discovery requests and WEAMR performs better than AODV.  
Figure 16: Average Route Discovery frequency with increasing number of packets.  
Figure 17: Average Routing overhead with increasing number of packets.  
Figure 17 shows results for routing overhead of overall communication. In this simulation WEAMR shows less overhead due to less number of nodes involved in routing as hotline assisted routing saves a lot of hops in the transmission.
## 4 Parameter Tuning Results  
In this section, we discuss the effects of tuning the hyperparameter on each of the models presented in Section 3. Each model is trained using the same dataset and given a range of parameter tuning values to be evaluated. The hyperparameters discussed in this section are the k values for KNN, cp for CART, ntree and mtry for RF and sigma and cost for SVM.  
(a) KNN--Accuracy versus the number of neighbours  
The KNN classifier uses the class properties of its k closest neighbours to categorise the predictors. Therefore, the k value is a critical factor in the KNN algorithm's performance and serves as a primary tuning parameter. In this study, we tested a range of k values from 1 to 50 to determine the best value for the KNN classifier parameter. This value range was chosen because it was not excessively large enough to cause considerable computation time. Then, the effects of varying the k values (or the number of neighbours) on the KNN model's accuracy can be observed. Therefore, the link between the number of neighbours and the accuracy of the KNN model can be observed by manipulating different k values. As seen in Figure 6, an initial 100% accuracy was reported between k values 1 and 20. However, the accuracy dropped considerably to \(\sim\)90% between the k values 20 and 30. Then, an uneven plateau was detected. The link between the size of neighbours and the classification error rate is not uniform, as shown by these imbalanced binary class issues. The results indicate that the model becomes too specific and fails to generalise well when the k values are set too low. Furthermore, the model tends to be highly sensitive to noise, resulting in an overfitting model. We can conclude from Figure 6 that if the k values chosen are too large, the model becomes too generalised and fails to predict the data points accurately in  
Figure 5: The effect of the SVM parameter setting on the hyperplane.  
both the training and test sets, resulting in underfitting. However, small datasets may not always benefit from a high k value. A high k value is not always the best choice for a large dataset because its results are not always better than the results of a low k value [40].
## 5 Discussion
### Modeling of Fluid Circulation Processes  
A conceptual model (Figure 10) of fluid circulation in the STS region is proposed, constructed based on regional topography and geological settings (Figure 1). Geothermal waters are primarily recharged by atmospheric precipitation from the Tianshan Mountains. As these waters percolate through the subsurface, dissolution of reservoir mineral enriches the geothermal fluids with various chemical components. The migration of deep fluids is governed by the spatial configuration and activity of fault systems, re gional stress fields, and rock permeability, all of which are influenced by the uplift of the STS Mountains and the subsidence of the Tarim Basin. Seismological and geophysical investigations [54; 55; 72; 73; 74] have identified a mid-crustal low-velocity, high-conductivity anomaly beneath the Tianshan orogen, suggesting the upward migration of mantle-derived fluids ascend through faults and discharge at the surface as thermal springs, indicating that mantle materials are transported to the crust via fault-facilitated pathways and ultimately released as geothermal discharges. Meteoric water infiltrates and circulates within deep reservoirs characterized by high geothermal gradients. It subsequently ascends along fault networks and mixes with shallow, colder groundwater within near-surface faults systems. This vertical fluid migration facilitates geothermal energy releases, drives extensive water-rock interactions, and alter both chemical and isotopic composition of the circulating fluids. The interplay between deep fluid upwelling and shallow mixing processes governs geothermal activity in the southern Tianshan region. This model provides insights into the regional tectonic-hydrothermal framework and serves as a foundation for hydrogeological and geothermal resource assessments. Continued multidisciplinary investigations are essential for evaluating the feasibility of geothermal exploitation and improving the predictive capacity for seismically induced hydrogeochemical responses.