esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Scope  
Energy efficiency schemes have a significant role in developing an efficient IoT. In this paper, we have proposed an optimized solution to the problem of arranging objects in the implementation of an energy efficient and scalable IoT. Firstly, we gave the framework for the deployment of the IoT which has scalability features and makes it more extensible. Then, based on the framework, an optimization scheme which is constrained by the loads on wireless links and energy expenditure support the deployment of an energy efficient IoT. Lastly the ME-CBCCP algorithm has been proposed based on the clustering topology as a solution to the optimization problem. With numerical experiments, it is validated that the proposed scheme is better than traditional WSN schemes in terms of time, network lifetime and scalability. It is found that that new scheme consumes six times less energy than LEACH, five times less energy than SEP, four times less energy than genetic HCR, three times less energy than ERP, three times less energy than MODLEACH, two times less energy than T-DEEC and 1.4 times less energy than EESAA which further validate our work. It takes 23.22% less time than LEACH, 29% less time than SEP, 152 times less than ERP, 229 times than genetic HCR, 15% less time than EESAA, and 2 times less time than MODLEACH. In the future we will also try to exploit the benefits of heterogeneity and we will also propose improvements of end to end delay, data compression techniques, packet delivery ratios and throughput parameters, to achieve a more efficient green IoT.
# A Numerical Model to Estimate the Soil Thermal Conductivity Using Field Experimental Data
## 3 Results and Discussions
Figure 6: Relationship between the soil conductivity (obtained by Equation (2)—\(M4\) and Equation (3)—\(M5\)) and the soil water content (\(\theta\)): (a) the mean and standard deviation of daily \(\lambda\) value and grouped by \(\theta\) ranges (values without standard deviation represent only one data in dataset); (b) \(\lambda\) estimated for soil water content intervals; (c) \(\varepsilon\) from \(M5\) vs. \(\theta\); (d) \(\lambda\) values as in (b) and \(\lambda\) estimated by Johansen, [PERSON], and Tong Models. The \(\varepsilon\) values of Equation (3) using this latter methodology are shown in Figure 6c. A nonlinear variation of \(\varepsilon\) in relation to the soil water content is observed, with values between \(-3\) Wm\({}^{-2}\) and \(-1.5\) Wm\({}^{-2}\) and an average value of \(-2.1\) Wm\({}^{-2}\). Figure 6d shows the relationships between soil thermal conductivity and soil water content\(-\lambda(\theta)\), obtained using \(M4\), \(M5\) (both estimating \(\lambda\) for soil water content intervals), Johansen Model [35], Lu-Ren Model [36] and Tong Model [6] (see Appendix A). For Tong Model, the empirical parameters were calibrated using \(\lambda(\theta)\), for \(M4\) and \(M5\). The values of these parameters are presented in Table 2 together with the original values for sandy loam soil type. The results show that the variation of \(\lambda(\theta)\) depends on the method applied, consequently influencing the estimation parameters describing empirical equations for \(\lambda(\theta)\). The Tong Model calibrated using \(M5\) presented the best statistic index (Table 2).
# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 3 The Proposed AEDCN Methods
### Data Preprocessing  
To construct two-channel representations of music audio, a series of preprocessing steps are applied. Here is an overview of the steps:  
1. Gain Normalization: The gain of the audio data is normalized to \(-15\) dB. This ensures that the audio is brought to a consistent loudness level, which can help in reducing the impact of volume variations in the subsequent processing.
2. Stereo to Mono Conversion: If the input audio is in stereo format, it is converted to mono by taking the mean of the left and right channels. This step merges the two channels into a single channel, simplifying the subsequent processing.
3. Down Sampling: The audio is downsampled from the original sampling rate of 44,100 Hz to 22,050 Hz. Downsampling reduces the sample rate while retaining important information, helping to reduce computational complexity without significant loss of relevant audio characteristics.
4. Normalization: All audio signals are normalized by dividing the time-domain signal with its maximum value. This normalization step scales the audio data to a range between \(-1\) and \(1\), ensuring consistent amplitude levels across different audio samples.
5. Short-Time Fourier Transform (STFT): The time-domain signal is transformed into a time-frequency representation using the Short-Time Fourier Transform (STFT). This involves dividing the signal into short overlapping frames and applying the Fourier Transform to each frame. A window size of 1024 samples is used, with a hop size of 512 samples. STFT provides insights into the spectral content of the audio signal over time.
6. Mel-Scale Conversion: The linear frequency scale obtained from the STFT spectrogram is converted to the mel-scale. This conversion is based on human auditory perception and provides a more perceptually meaningful representation of the audio. A mel-frequency scale with 128 frequency bins is used, as suggested by previous studies on music annotation.
7. Magnitude Compression: The magnitude of the mel-frequency spectrogram is compressed using a natural logarithm. This compression helps to emphasize lower magnitudes and reduces the dynamic range of the spectrogram. It can enhance the representation of subtle audio features and ensure that important information is not overshadowed by dominant components.
8. Besides mel-frequency spectrogram, we also use tempogram [22] as concatenated features to construct a two-channel representation. Tempogram is capable of capturing the rhythm structure and repetitive patterns in audio signals by calculating the rhythm patterns present in the audio signal. In instrument recognition tasks, each instrument possesses its own distinctive rhythm patterns during performance, and tempogram proves useful in distinguishing the disparities among different audio clips of instruments. The final data representation is a concatenated feature of mel-frequency spectrogram and tempogram in two channels, and every channel has a 128 dimensional features.
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 3 PSR Protocol
### PS-AF Scheme  
For the MA phase, the description of the signal transmissions from the end nodes T\({}_{1}\) and T\({}_{2}\) to the relay node R can be done as in the PS-DF scheme, i.e., Equations (2)-(4). In the BC phase, the relay node amplifies and forwards the information signal as:  
\[\mathbf{z}_{\text{r}}=\frac{\widetilde{\mathbf{y}}_{\text{r}}}{\|\widetilde{ \mathbf{y}}_{\text{r}}\|} \tag{25}\]  
where \(\|\widetilde{\mathbf{y}}_{\text{r}}\|=\sqrt{(1-\rho)\Big{(}P_{1}\|\mathbf{h}_{ 1}\|^{2}+P_{2}\|\mathbf{h}_{2}\|^{2}+M\sigma_{\text{a}}^{2}\Big{)}+M\sigma_{ \text{c}}^{2}}\) and the sampled received (baseband) signal at the end node T\({}_{i}\)\((i=1,2)\) is given by:  
\[\begin{array}{ll}y_{i}&=&\sqrt{P_{\text{r}}}\mathbf{h}_{i}^{\text{T}}\frac{ \widetilde{\mathbf{y}}_{i}}{\|\widetilde{\mathbf{y}}_{i}\|}+n_{i}^{[\text{a}] }+n_{i}^{[\text{c}]}\\ &=&\frac{\sqrt{(1-\rho)P_{\text{r}}P_{\text{i}}}}{\|\widetilde{\mathbf{y}}_{i} \|}\mathbf{h}_{1}^{\text{T}}\mathbf{h}_{1}x_{1}+\frac{\sqrt{(1-\rho)P_{\text{r }}P_{\text{2}}}}{\|\widetilde{\mathbf{y}}_{\text{r}}\|}\mathbf{h}_{1}^{\text{ T}}\mathbf{h}_{2}x_{2}+\frac{\sqrt{(1-\rho)P_{\text{r}}}}{\|\widetilde{\mathbf{y}}_{ \text{r}}\|}\mathbf{h}_{1}^{\text{T}}\mathbf{n}_{\text{r}}^{[\text{a}]}\\ &+\frac{\sqrt{P_{\text{r}}}}{\|\widetilde{\mathbf{y}}_{\text{r}}\|}\mathbf{h}_{ 1}^{\text{T}}\mathbf{n}_{\text{r}}^{[\text{c}]}+n_{i}^{[\text{a}]}+n_{i}^{[ \text{c}]}\end{array} \tag{26}\]  
where \(n_{i}^{[\text{a}]}\) and \(n_{i}^{[\text{c}]}\) are defined below Equation (11). Assuming that \(\rho\), \(P_{\text{r}}\), \(\{h_{1,m}\}_{m=1}^{M}\), \(\{h_{2,m}\}_{m=1}^{M}\), and \(\|\widetilde{\mathbf{y}}_{\text{r}}\|\) are known at T\({}_{i}\), an estimate of the intended signal \(x_{j}\)\((j=1,2;j\
eq i)\) can be obtained as:  
\[\hat{x}_{j}=\frac{y_{i}-\sqrt{(1-\rho)P_{\text{r}}P_{\text{i}}}\mathbf{h}_{i}^ {\text{T}}\mathbf{h}_{i}x_{i}/\|\widetilde{\mathbf{y}}_{\text{r}}\|}{\sqrt{(1- \rho)P_{\text{r}}\mathbf{h}_{i}^{\text{T}}\mathbf{h}_{j}}/\|\widetilde{ \mathbf{y}}_{\text{r}}\|}. \tag{27}\]  
Let \(\gamma_{ji}\) denote the instantaneous SNR of \(\hat{x}_{j}\) at T\({}_{i}\). It is straightforward to show that:  
\[\gamma_{ji}=\frac{(1-\rho)P_{\text{r}}P_{\text{r}}\big{|}\sum\limits_{|m=1}^{M }h_{i,m}h_{j,m}\big{|}^{2}}{\big{(}(1-\rho)\sigma_{2}^{2}+\sigma_{\text{c}}^{2 }\big{)}R\sum\limits_{m=1}^{M}\big{|}h_{i,m}\big{|}^{2}+\big{(}\sigma_{2}^{2} +\sigma_{\text{c}}^{2}\big{)}\Big{[}(1-\rho)\Big{(}P_{1}\sum\limits_{m=1}^{M} \big{|}h_{1,m}\big{|}^{2}+\mathbb{P}_{2}\sum\limits_{m=1}^{M}\big{|}h_{2,m} \big{|}^{2}+M\sigma_{\text{c}}^{2}\Big{)}+M\sigma_{\text{c}}^{2}\Big{]}} \tag{28}\]  
and the corresponding end-to-end BER, \(\overline{P}_{\text{e}}\), is the average BER associated with \(\gamma_{ji}\). Unfortunately, it is difficult, if not impossible, to find the PDF of \(\gamma_{ji}\). Therefore, the end-to-end BER has no closed-form expression and is obtained by means of simulations.
## 5 Conclusions  
The experimental results show that both the developed IMU glove and a smartphone device could be viable tools for wrist ROM measurement compared to a goniometer in terms of inter-observer reliability in all directions of wrist motion. The advantage of a smartphone device is its availability, but it could be troublesome and inaccurate because it tends to slip off when joint motion occurs. The developed IMU glove requires additional hardware, but it is easier to use and more suitable for measuring and monitoring dynamic motion than a smartphone device or a goniometer. However, patients with a wound on their hand might require customization or have difficulty wearing the glove.  
Further research could also be done with a larger group of participants including the patients to validate the results, especially a patient with a wound that requires customization of the IMU glove. More experiments with novice operators, including caretakers or patients, could also highlight the difference in the ICC between different levels of operators for the three devices. Nevertheless, these patient-friendly approaches could support and provide remote quantitative monitoring for wrist rehabilitation.  
[PERSON] developed the prototype and was involved in all facets of the study and manuscript preparation. [PERSON] and [PERSON] performed the experiments and data analysis. P.S.-i. and [PERSON] were involved in manuscript preparation. [PERSON] and C.T. were involved in the prototype development process. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Science and Technology Development Agency (NSTDA), Thailand.  
The experiment was conducted according to the principles expressed in the Declaration of Helsinki, the Belmont Report, and the International Conference on Harmonization Good Clinical Practice (ICH GCP). It was reviewed and approved by the human research ethics committee of the Royal Thai Army medical department. The research study number and the study document number are R1111/61 and IRBRTA 1754/2561, respectively. The clinical trial was registered according to the WHO International Clinical Trials Registry Platform (WHO-ICTRP) at Thai Clinical Trials Registry (TCTR). The registry id is TCTR2020110006.  
All of the participants provided their written informed consent to participate in this study.  
The datasets generated for this study are available on request to the corresponding author.  
We would like to thank the orthopedic division of Phramongkutkloa hospital and the Maha Vajiralongkorn foundation for their kindness during the research. We would also like to thank [PERSON]; [PERSON]; [PERSON]; and [PERSON]. This research would not be completed without support from them.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.
# Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems
## 5 Experimental Results
### Analysis of the 4 SICS 2015 Dataset  
For this dataset, we performed data splitting as follows: 70% for training data and 30% for testing data. The results obtained are presented in the following sections.  
The RF model demonstrated superior performance in detecting cyberattacks in the 4 SICS dataset, achieving an accuracy of 93%, as shown in the classification report in Figure 18, and an AUC-ROC of 0.930, as presented in Figure 19, both on the training and testing data. The model successfully classified most of the data, maintaining a balance between precision and recall. The precision for the attack class is 77%, indicating a fairly accurate classification of malicious events, while the recall of 93% shows that the model effectively detects attacks, minimizing the number of false negatives. Additionally, the F1-score of 0.84 confirms that the RF model is well-calibrated for this task. The confusion matrices, presented in Tables 9 and 10, highlight a low false alarm rate, which is crucial for a robust intrusion detection system.  
Thus, Table 13 presents a summary of the findings discussed above for the 4 SICS dataset. Based on this, we can draw the following conclusions: RF outperforms SVM across all key metrics, proving to be more reliable for attack detection, at least within this dataset. SVM has a good recall, indicating its ability to detect malicious events, but its lower precision makes it more prone to false alarms. The AUC-ROC score further confirms that RF is superior in correctly classifying the data.
## 4 Linear Regression Algorithm against Device Diversity Problem  
In the existing experimental systems, the same device is used to collect the RSS values in both the offline phase and the online phase. However, when the crowdsourcing method is widely applied to the indoor localization systems, a large number of different mobile devices have been used in the establishment of the radio map. In the online phase, a variety of mobile devices are also used by the users which are different from the device used to build the radio map. In this section, the linear regression (LR) algorithm is proposed to solve the device diversity problem in RSS-based crowdsourcing localization system.  
Figure 3: The system view of the proposed G-SSL based Localization.  
We define \(\mathcal{X}\) and \(\mathcal{Y}\) are the signal space of different devices. Assume that the fingerprint \(r_{\mathcal{X}}\) belongs to \(\mathcal{X}\) is the nearest neighbor to the online point \(r_{\mathcal{Y}}\) belongs to \(\mathcal{Y}\). As described above, although they were collected at close physical locations, the RSS values have obvious difference. In order to solve the device diversity problem, the relationship between different devices has to be studied. Therefore, these RSS values collected by different devices could be processed to make the \(r_{\mathcal{Y}}\) in closer to \(r_{\mathcal{X}}\). Mathematically  
\[\mathcal{X}\approx f(\mathcal{Y}), \tag{5}\]  
By learning \(f\), the radio map build by the training device could be used to localize any other devices.  
Aiming to explore the mapping function between RSS values collected by distinct devices, the comparison results of RSS values across different training/tracking devices are plotted in Figure 4. Every point on the figure represents RSS values from two different devices measured at the same location from the same AP at the same location. For example, the top right subplot in Figure 4 represents the RSS values measured by Lenovo laptop and Huawei mobile device. From Figure 4, we can get a linear correlation between the RSS values measured by different devices. Hence, the following linear regression method can be employed as the mapping function.  
\[r_{\mathcal{Y}}=ar_{\mathcal{X}}+b \tag{6}\]  
where \((a,b)\) are the coefficients in the mapping function.
## 5 Proposed Multi-EC Enabled Air-Ground VN Scenarios
### ML for Computation Offloading in Multi-Service Multi-EC Vehicular Environments
#### 5.3.3 Proposed Solution  
Preference Weighted Network Function Placement for a Heterogenous Vehicular Service Slices  
Each VUs service request comes with a specific demand. Required service latency and the data rate are two main characteristics of demanded services. The VN services can be differentiated into two main categories. A set of slices with high data rate requirements can be grouped as eMBB Service slices. Services with critical latency requirements are grouped into URLLC classes. Each of these services can be deployed as a chain of VNFs.  
For avoiding the excess transmission delay between VNFs, to satisfy the critical latency requirement of service, it is preferred to place VNFs associated with URLLC services in proximity. On the other hand, for satisfying the user data rate requirements of eMBB, several VNFs of eMBB slices can be placed on platforms with large computation capabilities. Such an approach can have many benefits for serving many users with efficient utilization of EC resources. However, such placement can not be done with a random guess, and a proper strategy is required. In one of our past works, we have considered a network operator-biased approach for multi-service network function placement in a 5G network slicing architecture [59]. In this approach, we have defined a preference function that assigns a proper weigh value for each slice's functions based upon the slice category and the function types. A similar strategy can be adapted for VUs slices over a Multi-EC enabled VN. An ML-based intelligent strategy can be applied for designing a preference function for associating a proper weight for each VNFs placement over different platform layers.  
Thus, by analyzing the individual VUs service request data and the available resources of EC platforms, an intelligent approach can be defined, for solving both VU slice function placement and resource allocation problem over a Multi-EC enabled VN. Table 6 presents the important features of a proposed solution for the NFP problem for vehicular services over multi-service Multi-EC vehicular environments.
## 2 Materials and Methods  
The developed LMD-w-system consists of a laser process head, a coaxial wire-feeding system, the integrated OCT measuring system, and a control unit that controls all subsystems. Figure 2 shows the set-up, whereby it should be mentioned that the OCT components of the SLD (super luminescent diode), the spectrometer, and the reference arm are coupled to the process head by a single mode optical fiber. The length of the fiber was chosen so that the optical and electronical components were protected and placed in a distance from the welding process. The coaxial wire guidance of the system offers the advantage of multidirectional weld paths which can be realized, in contrast to lateral wire guidance, which adds flexibility to the process but creates some optical design challenges that were addressed in this work. To avoid diffraction effects and shielding by the wire, a beam shaping design using an axicon and optical prisms was chosen. Custom-built optics, such as the focusing lens, differ from ordinary optics in that their geometry allows for central wire guidance. To validate the optical setup, optical simulations were carried out, which verified a ring-shaped scan image on the sample. With the help of the simulations, the shape and arrangement of the optics could be determined. The shape of the conic optics significantly influences the shape of the laser beam focusing on the substrate in the processing area.
## 2 Materials and Methods
### Dispersion of SWCNTs in NR  
To improve the dispersion of SWCNTs in NR, a masterbatch of SWCNTs was prepared. Five grams of NR were dissolved in 40 mL of xylene; separately, 0.7 g of SWCNTs were dispersed by ultrasonication in 50 mL of xylene using an ultrasonic bath, Model 3510 (Branson Ultrasonic Corporation, Brookfield, CT, USA) for 20 min at 6 W. Both dispersions were mixed and sonicated for an additional 30 min. Then, the solvent was removed at \(110\,^{\circ}\)C with continuous agitation. The SWCNTs masterbatch was used as a source of SWCNTs for the nanocomposites prepared in this work.
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 4 Performance Metrics for the SCAR-Phase of Spectrum Sensing  
### Detection Performance  
The detection performance of the SCAR-Phase of Spectrum Sensing is characterized in terms of the overall probability of false alarm and the overall probability of detection. Since this approach combines two detectors as in [26], a false alarm occurs in the following two cases:  
(i) \(\Phi_{\rm{\it cal}}(\mathbf{x})>\lambda_{\rm{\it ed}}\) when \(\mathcal{H}_{0}\) is true.  
(ii) \(\Phi_{\rm{\it ST}}(\mathbf{x})>\lambda_{\rm{\it st}}^{\prime}\) given that \(\Phi_{\rm{\it ed}}(\mathbf{x})\leq\lambda_{\rm{\it ed}}\) when \(\mathcal{H}_{0}\) is true.  
Therefore, the overall probability of false alarm, denoted by \(P_{\rm{\it FA}}\), of the SCAR-Phase of spectrum sensing is:  
\[P_{\rm{\it FA}}=P_{\rm{\it fa}}^{\rm{\it ed}}+\left(1-P_{\rm{\it fa}}^{\rm{\it ed }}\right)P_{\rm{\it fa}}^{\rm{\it st}}. \tag{38}\]  
Whereas a correct detection occurs in the following two cases:  
(i) \(\Phi_{\rm{\it ed}}(\mathbf{x})>\lambda_{\rm{\it ed}}\) when \(\mathcal{H}_{1}\) is true.  
(ii) \(\Phi_{\rm ST}(\mathbf{x})>\lambda_{st}^{\prime}\) given that \(\Phi_{\rm ed}(\mathbf{x})\leq\lambda_{ed}\) when \(\mathcal{H}_{1}\) is true.  
Hence, the overall probability of detection of the SCAR-Phase of spectrum sensing, denoted by \(P_{\rm D}\), is:  
\[P_{\rm D}=P_{\rm d}^{ed}+\left(1-P_{\rm d}^{ed}\right)P_{\rm d}^{st}. \tag{39}\]  
Note that both \(P_{\rm FA}\) and \(P_{\rm D}\) depend on the values \(\lambda_{ed}\) and \(\lambda_{st}\). Consequently, the values of both thresholds that maximizes \(P_{\rm D}\) subject to a false alarm rate constraint \(\beta\) must be determined. Since the optimal \(P_{\rm D}\) is attained by \(P_{\rm FA}=\beta\), as demonstrated in [26], this problem can be written as:  
\[\max_{(\lambda_{ed},\lambda_{st}^{\prime})}P_{\rm D}\left(\lambda _{ed},\lambda_{st}^{\prime}\right)\] \[\text{s.t.}\;P_{\rm FA}=\beta. \tag{40}\]  
From (38) and a given false alarm rate constraint \(\beta\), the probability of false alarm for the energy detector is \(P_{\rm fa}^{ed}=(\beta-P_{\rm fa}^{st})/(1-P_{\rm fa}^{st})\). Hence, using (10), \(\lambda_{ed}\) can be written as a function \(g(\lambda_{st}^{\prime})\), i.e.,  
\[\lambda_{ed}=g(\lambda_{st}^{\prime})=\sqrt{N\sigma^{4}}\mathcal{Q}^{-1}\left( \frac{\beta-P_{\rm fa}^{st}}{1-P_{\rm fa}^{st}}\right)+N\sigma^{2}. \tag{41}\]  
Therefore, as shown in [26], the optimization problem in (40) can be simplified as follows:  
\[\max_{\lambda_{st}^{\prime}}P_{\rm D}\left(g(\lambda_{st}^{\prime}),\lambda_{ st}^{\prime}\right). \tag{42}\]  
Thus, from (42) and (41) the optimal \(\lambda_{st}^{\prime}\) and \(\lambda_{ed}=g(\lambda_{st}^{\prime})\) can be found.