esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Subsequent Operations by Smart Contracts
The proposed method enforces that the mining of \(b^{[i+1,1]}\), \(b^{[i+1,2]}\), \(\ldots\), \(b^{[i+1,N_{i+1}]}\) begins only after the execution of all transactions registered as hash values in the prior segment \(b^{[i,1]}\), \(b^{[i,2]}\), \(\ldots\), \(b^{[i,N_{i}]}\) is completed. This ensures that the sequential integrity across \(\mathbb{PC}_{M}\) segments is preserved. To better understand what it means for a transaction to be fully executed, consider \(tx_{1}\) in (9), where user \(u\) sells digital asset \(D_{K}\) to \(v\). Suppose the hash of \(tx_{1}\) is included in the \(\mathbb{PC}_{M}\) segment \(b^{[i,1]}\), \(b^{[i,2]}\), \(\ldots\), \(b^{[i,N_{i}]}\), which ends at the \(\mathbb{RC}\) block \(B^{[n_{i+1}]}\). The execution of begins only after \(B^{[n_{i+1}]}\) is anchored to \(\mathbb{R}\mathbb{C}\) and the full content of \(tx_{1}\) is disclosed on \(\widehat{\mathbb{P}\mathbb{C}}_{M}\). This rule applies equally to all transactions registered as hash values in the segment, and only once all such executions are completed can the subsequent segment \(b^{[i+1,1]}\), \(b^{[i+1,2]}\),..., \(b^{[i+1,N_{i+1}]}\) be created. However, defining the execution of \(tx_{1}\) as complete only after all of \(tx_{2}\) in (11), \(tx_{3}\) in (12), \(TX_{4}\) in (13), \(tx_{5}\) in (14), \(tx_{6}\) in (15), and \(TX_{7}\) in (16) have been completed is not practical. This is because \(TX_{4}\) and \(TX_{7}\) are executed on the reference chain \(\mathbb{R}\mathbb{C}\), and the platform has no mechanism to enforce their timely execution. To address this, the framework requires \(TX_{4}\) to be pre-registered when \(tx_{1}\) is created, and it is expected to be completed before \(tx_{3}\) is triggered. As for \(TX_{7}\), there is no need to wait for its completion to consider \(tx_{1}\) finalized, since \(TX_{7}\) is executed by the smart contract \(sc_{M}\) and, by its deterministic nature, its successful execution is guaranteed once invoked. Accordingly, in this study, we define the execution of \(tx_{1}\) as complete once \(tx_{6}\) has been successfully processed. Before closing this section, we emphasize that transactions \(tx_{2}\), \(tx_{3}\), \(tx_{5}\), \(tx_{6}\), and \(TX_{7}\) are automatically derived from the registration of \(tx_{1}\). Therefore, they do not require separate registration. In fact, \(TX_{7}\) is not even recorded in \(\mathbb{R}\mathbb{C}\), as the reference chain does not store the outcomes of smart contract executions. Nonetheless, the proposed framework records all of these derived transactions in both \(\mathbb{P}\mathbb{C}_{M}\) and \(\widehat{\mathbb{P}\mathbb{C}}_{M}\), so that the results of the smart contract executions remain publicly verifiable without requiring users to re-execute the logic themselves.
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 1 Introduction  
Customer interactions are being transformed by the proliferation of task-oriented conversational systems and digital assistants in everyday routines. As a result, the ability to comprehend users and their requirements has become increasingly crucial for these services and devices. In the realm of online customer services, a plethora of business services offers these assistants to enhance the customer experience, particularly in providing prompt and accurate responses to queries. Although human workers possess a superior understanding of customer needs, they are constrained by inherent limitations in their abilities. Conversely, automated agents are cost-effective and easy to implement, can work uninterruptedly, are less prone to errors, and can even communicate in multiple languages. These features make them a viable solution for online customer service.  
However, it was reported that, while chatbots are gaining popularity, customers still prefer interacting with real-life assistants due to concerns about potential errors made by chatbots [1]. Consequently, the main challenge is equipping these services and agents withthe capability to comprehend customer needs and respond to their queries in a manner that mimics human behavior. The technology that corresponds to this field of knowledge is Natural Language Understanding (NLU), a subset of Artificial Intelligence (AI) that addresses challenges present in Natural Language Processing (NLP). NLU is responsible for comprehending human language, enabling computers to understand without a predefined syntax, such as programming languages. While both NLU and NLP can comprehend natural language, the former focuses on communicating with individuals and comprehending them.  
To train the NLU models, deep neural networks optimized for large-scale utterances are typically used. These models typically use a supervised machine learning approach and require training with annotated data. However, the annotating process is often costly and time-consuming. It has been reported that up to 76% of enterprises in AI have annotated their own data [2]. However, insufficient data quality can result in project deployment failures.  
A solution for addressing this problem is proposed in this work by using a text representation model, based on transfer learning, to feed a clustering mechanism for the annotation of unlabeled data. For this purpose, both classical approaches and transformer-based models were examined for text representation. Furthermore, three clustering algorithms are evaluated. The goal is to propose a solution for annotating unlabeled data by feeding the embeddings of transformer-based models to clustering algorithms and training the models to identify the user's intent in the text. This article comprises five sections, outlining the state-of-the-art in Section 2. Section 3 presented the used materials and developed methods. Furthermore, Section 4 presents and discusses the attained results. The conclusions reached are shown in the final section.
# Analysis of AMI Communication Methods in Various Field Environments
## 5 Experimental Results
### Measurement Results of the Wireless Communications and Regression Analyses  
In this section, the RSSI measurement results of the wireless communication methods for the 18 sites with 48 MPs are summarized within the five environment areas. We then conduct the linear regression analyses of (10) to construct wireless models. In the following Figures 25-29, the solid lines imply measured RSSI values and the dotted lines imply measured baseline noise.  
We first summarize the experiment results of the overhead power lines. For the Overhead Residential Area, the measured results are illustrated in Figure 25. Both Wi-SUN and ZigBee modems provide similar ARs of \(a=-44\). However, the start point of the RSSI curve of Wi-SUN was better than the ZigBee case by 3.4 dB and the baseline noise of Wi-SUN was lower than the ZigBee case by 6.4 dB. Hence, the Wi-SUN modem had a better margin than the ZigBee case by 9.8 dB. For the Overhead Commercial Area, the measured results are illustrated in Figure 26. The Wi-SUN modem provided an AR of \(a=-27.7\) and the ZigBee modem provided an AR of \(a=-25.9\), which was better than the Wi-SUN case by 1.8 dB. However, the baseline noise of ZigBee was higher than the Wi-SUN case by 7.4 dB. At Site 2-2 of the Overhead Commercial Area, we can notice a higher baseline noise compared to other sites. We can guess that this site is noisy and thus the communication distance is relatively short. For the Overhead Rural Area, the measured results are illustrated in Figure 27. The Wi-SUN modem provides an AR of \(a=-31.5\), which is higher than that of the ZigBee modem by 7.4 dB. Furthermore, the baseline noise of Wi-SUN was lower than the ZigBee case by 5.0 dB. For the Overhead Factory Area, the measured results are illustrated in Figure 28. The Wi-SUN modem provides an AR  
Figure 23: Overhead Factory Area. (**a**) Received power. (**b**) Noise power.  
Figure 24: Underground Area. (**a**) Received power. (**b**) Noise power.  
of \(a=-30.7\), which is similar to the ZigBee modem case of \(a=-30.8\). However, the baseline noise of Wi-SUN was lower than the ZigBee case by 8.2 dB.  
For Underground Area, the measurement results for each MP are illustrated in Figure 29. We can notice that the communication characteristics vary greatly. Site 5-3 showed quite low RSSI values and Site 5-2 yielded RSSI values even lower than \(-100\) dBm. On the other hand, Site 4 yielded quite good communication quality. As a result, both AR and EPD show the worst values as \(a=-93.9\) and \(d_{0}=39.6\) m, respectively, among the five environment areas. This observation implies that the wireless communication based on underground environments cannot provide a stable data collection for AMI. For the ZigBee case, the data collection condition was similar to the Wi-SUN case.  
Figure 26: RSSI values for the Overhead Commercial Area. (**a**) Wi-SUN. (**b**) ZigBee.  
Figure 25: RSSI values for the Overhead Residential Area. (**a**) Wi-SUN. (**b**) ZigBee.
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Verification of the CFD Simulation Results  
To verify the reliability of the AFT simulation results, this study calculates the normalized root mean square error (\(nRMSE\)) between \(Q_{\text{s}}\) and \(Q_{\text{m}}\) via Equation (3) [42]:  
\[nRMSE=\frac{\sqrt{\frac{\sum_{i=1}^{m}(Q_{\text{m}}-Q_{\text{s}})^{2}}{m}}}{ \overline{Q_{m}}}\times 100\% \tag{3}\]  
where \(\overline{Q_{m}}\) represents the average of the measured outlet flow rates and \(m\) is the number of measured values. The interpretation of the \(nRMSE\) is as follows: \(nRMSE\leq 10\%\): high simulation accuracy; \(10\%\)\(\circ nRMSE\leq 20\%\): moderate simulation accuracy; \(20\%\)\(\circ nRMSE\)\(\leq 30\%\): Average simulation accuracy; and \(nRMSE\)\(>30\%\): poor simulation accuracy.
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
## 4 The Experimental Results  
In order to demonstrate the practicality of the proposed CL CS-MAC scheme, we designed experiments based on software-defined radio (SDR) [18]. All experiments were conducted in an indoor environment where the surrounding objects were stationary. Nodes were in the same range as one another and transmitted with the same power level. In the experiments, a universal software radio peripheral (USRP) connected to a laptop running GNU Radio acted as a node. The transmission frequency was 2.45 GHz with GMSK modulation. The transmission power of the node was changed from \(-48\) to \(-28\) dBm. The noise power was \(-60\) dBm. We randomly chose \(K\) nodes to send requests for accessing the channel.  
The signal transmission process is illustrated in Figure 2. Some modulation technology can be selected in the GNU radio. In this paper, GMSK is selected in the experiment. Active sensor nodes send their data to the destination. To obtain enough measurement values, the destination is equipped with \(M\) antennas so that every signal of the sensor node has \(M\) degrees of freedom.
## 4 Methodology
### Gesture Definition
#### 4.1.3 Drone Control Features  
The drone control module was implemented following an agile development process; starting from a set of basic features, continuous testing and user feedback led to the refinement of the user requirements. Initially, the _hand gesture drone control_ was implemented and three additional features, _hand gesture camera control_, _periscope_ mode, and high-level _voice control_ commands were added.  
The primary task was to accurately navigate the drone using the selected palm gestures. It should also be noted that when no gesture is performed or when the user hand is not visible, no command is sent, and the drone stops. Inside the AR application, visual cues were displayed providing feedback to facilitate the navigation task. Drone navigation is performed using the user's right hand by default, however, this can be a parameter.  
In the hand gesture camera control, the unassigned user hand, by default the left hand, is used for controlling the view direction of the drone's camera. This feature is controlling the camera gimbal, which is able to perform 2 DoF movements: rotation up/down (pitch) and rotation right/left (yaw). The gesture vocabulary used for camera control is similar to the vocabulary for drone navigation, however, there are less defined gestures since the camera's possible movements are fewer; by tilting the open palm left or right, the camera  
Figure 6: The palm gesture vocabulary used for drone navigation.  
turns to the corresponding direction in the vertical axis while, by tilting the palm up or down, the camera rotates in the corresponding direction in the horizontal axis.  
Periscope mode is an additional form of gesture control, which does not involve the use of hands but it is based on the user's head direction. When Periscope mode is enabled, the drone viewing direction follows the user's head direction in real time. For example, when the user rotates his head to look 90\({}^{\circ}\) east, the drone rotates to the exact same direction. Periscope is an auxiliary feature allowing for a quick and intuitive inspection of the surrounding environment that has collected very positive reviews from the end-users.  
Finally, voice commands are used to perform high-level commands such as resetting the camera direction. Voice commands are completely optional, as all the commands can be alternatively performed by buttons and controls available in the virtual application menu, however, they offer a multi-modal tool of interaction and at the same time allow the user to use their hands for controlling the drone or perform an other task and at the same time dictate the desired command using speech.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Impulsive Noise Reduction
#### 3.3.4 PSD Signal Reconstruction  
In this block, the new coefficients are used as an input parameter. With them, a wavelet reconstruction is done (again using 'Haar'). The result of the reconstruction is the signal \(R^{\prime}_{i}(k)\), which is the PSD with the attenuation of the different noise variants on a linear scale. This processing can be seen in Figure 10.  
Taking the previous example, the result of applying the wavelet reconstruction submodule is shown in Figure 11 on a logarithmic scale. In this figure, it is possible to distinguish the changes between the original PSD and the PSD modified by the noise inhibition module.  
Another example is shown in Figure 12. This same difference is observed here using two RTL-SDR devices with \(F_{\mathrm{CT}}=90.8\) MHz, a band exclusively used for broadcast radio [43]. Furthermore, it is possible to analyze a larger frequency range based on the number of connected SDR devices.  
Figure 10: PSD reconstruction after impulsive denoising.  
Figure 9: Original and modified coefficients: (**a**) approximation; (**b**) detailed view.  
In the examples shown in Figures 11 and 12, there are different PSDs that show two challenges. In Figure 11, an abundance of high-frequency noise with very sudden changes is shown, and Figure 12 exhibits an abundance of impulsive noise; in both cases, the noise inhibitor works correctly.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 7 Limitations and Future Works  
However, it should be emphasized that the recommendations in this paper are only applicable to similar geological conditions and the cases using the EPB shield tunneling technology. The following concerns and prospective study topics will be examined in the future:  
Compilation of a wider database of case studies in similar geological conditions--the database in Figures 4 and 5 is only a fraction of the experience with tunnel construction of Changzhou Metro Lines No.1 and No.2. The authors intend to expand this database as new case studies become available;  
Subsurface ground movements--because the analysis conducted in this paper was based solely on data from ground surface settlements, analyzing subsurface ground movements is a suitable next step in the future. This might provide a better understanding of the variations in the ground loss that occurs when deformations travel from the tunnel to the surface. On that basis, more precise advice on the tunneling impact on subsurface infrastructure could be offered.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 4 Energy Management Model Mathematical Formulation
### ILP Problem Formulation: OptTsNS
#### Constraints  
The following constraints are linked to the proposed adaptive energy management model.  
\[C1:P_{A}[t]\geq 0 \tag{9}\] \[C2:(P_{A}[t]-P_{D}[t])\geq 0\] (10) \[C3:\sum_{i=1}^{L}\sum_{j=1}^{M}\sum_{k=1}^{N}\left(p_{i}^{k,j} \times q_{i}^{k,j}\right)\times x_{ijk}[t]\leq P_{A}[t],\ x_{ijk}\in\{0,1\}\] (11) \[C4:t_{k,j}^{i}\geq 0\] (12) \[C5:\{t_{k,j}^{i}-u_{i}^{j,k}\}\geq 0\] (13) \[C6:W\geq\max\{t_{k,j}^{i}+d_{k,j}^{i}+u_{i}^{k,j}\} \tag{14}\]
* _Domain constraints:_ The energy supply by the MNO for service processing is assured by \(C1\). Instead, \(C2\) guarantees a non-negative difference between the power demanded and power provisioning. In Constraints \(C1\), \(C2\), and \(C3\), the power variables are specified at time slot \(t\), because the energy provisioning and consumption may vary at each time slot. Thus, the objective of the proposed model is the minimization of power consumption (from non-renewable sources) during all time slots within a finite time horizon \(W\).
* _Capacity Constraint:_ In the mobile communication system, the maximum energy capacity is limited by \(C3\), in which the decision variable \(x_{ijk}\) stands for the allocation of energy resources for the processing of the service \(S_{k,j}^{i}\), as shown in Equation (15). \[x_{ijk}[t]=\left\{\begin{array}{ll}1&\quad\text{if the service $k$ belonging to the network slice $j$ with priority}\\ &\quad\text{level $j$ ($S_{k,j}^{i}$) is processed at time slot $t$},\\ 0&\quad\text{otherwise}.\end{array}\right.\] (15) The correspondence between the service, its priority level, and the network slice to which it belongs is validated by Equation (16). \[p_{k,j}^{i}=\left\{\begin{array}{ll}\text{Power demanded by service $k$ with $\text{if service $k$ with priority level $j$ belonging}}\\ \text{priority $j$ and within the network slice $i$ to a network slice $i$ exists},\\ 0&\quad\text{otherwise}.\end{array}\right.\] (16)
* _Time constraints:_\(C4\) and \(C5\) ensure a non-negative starting time for the entire system (\(t=0\)). \(C6\) guarantees a finite time horizon for the analysis of services. The linear condition of the objective function, the constraints, and the decision variable deliver the linear nature to the problem.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 5 Results and Analysis
### Scenario 2: Load Variation Effect  
The results for this scenario are presented in Figure 13, which shows the estimated error of the fault distance calculated by Equation (32), and the actual distance to the fault.  
For this scenario, a small difference of the method's performance is observed, especially from the substation to the middle of the feeder. When IEDs information is used, the method presents a performance with errors below 0.2%. However, when IEDs information is not available, the proposed method presents a better performance. The errors are below 0.05% for both models of DER (INIDER and IIDER). On the contrary, for faults located after 30 kms, and using IED information, the method presented better performance than using the DER model. However, the maximum error for these cases is of about 0.8%. Additionally, the test results show a small correlation between the method's performance and the load value. This behavior occurs because in this work, no load compensation strategy was implemented. Nevertheless, a compensation strategy can be used as proposed previously [34].  
For the nominal-high load condition, the behavior of the method is of substitution. In other words, the method tends to estimate a distance lower than the real distance to the fault. In contrast, for scenarios where the load decreases, the method tends to overestimate the distance to the fault. This is explained because as the load increases, the method observes a lower impedance of the system and estimates a lower distance to the fault. Nevertheless, test results show very low error values. This is because the use of Thevenin models, decreases the impact of individual model parameter errors.  
Figure 13: Performance curve for Scenario 2: (**a**) using the IEDs information, (**b**) using INIDER model instead of IEDs information, and (**c**) using the Inverter-interfaced DER model in limiting current mode instead of IEDs information.
# Evaluating Earthworms' Potential for Remediating Soils Contaminated with Olive Mill Waste Sediments
## 3 Results and Discussion  
### Avoidance Behaviour Response Test  
We found no individual dead after 48 h of incubation in the ABR test, and they randomly distributed between both sections, as suggested by the similar percentages of earthworms found in the control treatment (53.3% \(\pm\) 5.8% in one section and 46.7% \(\pm\) 5.8% in the opposite section). However, the addition of OMW sediment to soil at the doses of 40% and 80% \(w\)/\(w\) caused a significant avoidance response (\(F_{4A72}=20.7\), \(p=0.003\)) with respect to the control treatment (Figure 2A). The ABR test is a common screening test for assessing soil pollution [38], and it is generally accepted that avoidance response is judged as significant when more than 80% of earthworms are found in the compartment containing the control soil. This is a threshold value that defines the soil habitat limit, as suggested by [PERSON] et al. [39]. Many studies have used the ABR test to evaluate the potential toxicity of soils contaminated by metals [40], pesticides [41; 42], cyanotoxins [43], veterinary pharmaceuticals [44], microplastics [45] or soil additives such as biochar [46; 47]. Although the ABR test guidelines recommend the use of the epigeic earthworms _Eisenia fetida_ or _E. Andrei_[19], soil-dwelling earthworm species such as _L. terrestris_ are now adopted in standardized methods such as the ABR test [48]. Moreover, some studies suggest that _L. terrestris_ is more sensitive to organic pollutants such as pesticides than the common and recommended species _E. fetida_[49].  
Figure 2: **(A)** Avoidance behaviour response of _Lumbricus terrestris_ to olive mill waste (OMW)-amended soils. Statistical differences between treatments indicated by different letters (\(p<0.05\), [PERSON]–Howell post-hoc test). **(B)** Mean body weight of earthworms exposed for 30 d to OMW-amended soils. Different letters indicate significant differences between treatments within a sampling time (\(p<0.05\)), whereas asterisks denote differences between sampling times (\(p<0.05\)).  
In this study, we used the ABR test as a screening assay to determine the amount of OMW sediment that, added to soil, does not result repellent to earthworms. Current results pinpoint to concentrations of OMW sediment \(\leq\) 20% _w/w_ (dry mass) as viable to remediate OMW-amended soils using the earthworm _L. terrestris_.