esa-sceva/satcom-chunk-collection huggingface dataset:
# Green IoT: A Review and Future Research Directions
## 4 Energy-Efficient M2M Communications
### Energy-Efficient Data Transmission  
The approaches to improving the energy efficiency of M2M data transmission that are listed in the literature can be summarized as follows:  
1. _Data compression_: Compressing data before transmission can reduce the amount of energy used to transmit it. Data compression techniques that are straightforward to use and have low computational requirements often use less energy [20]. However, the balance between the amount of data that is compressed and the energy needed can be complex, as techniques that compress data more may require more energy [21]. Table 1 provides a summary of data compression techniques, highlighting their respective advantages and disadvantages. Based on information from existing reviews or research in the field [22; 23; 24], these factors were determined by the authors of this article.
2. _Cooperation between devices_: M2M devices can collaborate in the transmission of data and share resources such as power, bandwidth, and processing capabilities to lower energy consumption and enhance system performance [25; 26; 27; 28; 29; 30].
3. _ML algorithms_: ML algorithms can be used to minimize data redundancy and optimize the rate of data transmission in multicell networks. The type of algorithm depends on the specific needs of the M2M application, including the amount and complexity of available data and the required accuracy. Testing different algorithms and evaluating their performance can help determine the most suitable one for a specific M2M application [31; 32; 33; 34].
4. _Power control_: By carefully adjusting the transmission power of M2M devices, it is possible to reduce the energy required for data transmission. There are several techniques that can be implemented to control power and improve energy efficiency performance in M2M data transmission, such as power-aware routing, adaptive modulation and coding, and duty cycling [35].
5. _Energy-efficient protocols_: There are numerous communication protocols that have been designed specifically to improve the EE of data transmission in a green M2M network. However, each of these protocols has its own unique set of advantages and disadvantages, and the most suitable protocol can be chosen based on the specific  
Figure 4: Classification of energy-efficient techniques for M2M communications.  
requirements of the M2M application. Details of the transmission protocols and technologies are discussed in the following sections.
## Low-Temperature Plasma Nitriding of Mini-/Micro-Tools and Parts by Table-Top System
###### Abstract  
Miniature products and components must be surface treated to improve their wear resistance and corrosion toughness. Among various processes, low-temperature plasma nitriding was employed to harden the outer and inner surfaces of micro-nozzles and to strengthen the micro-springs. A table-top nitriding system was developed even for simultaneous treatment of nozzles and springs. A single AISI316 micro-nozzle was nitrided at 673 K for 7.2 ks to have a surface hardness of 2000 HV0.02 and nitrogen solute content up to 10 mass%. In particular, the inner and outer surfaces of a micro-nozzle outlet were uniformly nitrided. In addition, the surface contact angle increased from 40\({}^{\circ}\) for bare stainless steels to 104\({}^{\circ}\) only by low-temperature plasma nitriding. A stack of micro-nozzles was simultaneously nitrided for mass production. Micro-springs were also nitrided to improve their stiffness for medical application.  
plasma nitriding; micro-nozzle; micro-spring; nitrogen supersaturation; hardening; hydrophobicity; stiffness control
## 2 Related Research
### Method Using Statistical Models  
#### 2.2.1 State-Space Model  
By building a state-space model like Figure 3, indoor position estimation can be considered as a state estimation problem. The current position of the measurement point at time \(t\) is considered to be related to the position and state at time \(t-1\). The discrete-time state-space model [26] can be used to implement the position estimation problem.  
\[x_{t} =F(x_{t-1})+\omega_{t} \tag{1}\] \[z_{t} =H(x_{t-1})+v_{t} \tag{2}\]  
The state Equation (Equation (1)) represents the relationship between the states of the target at different moments. The observation Equation (Equation (2)) is used to represent the relationship between the observed values and the state of the target.  
\(\omega_{t}\) is the process noise, representing the uncertainty of the movement. \(v_{t}\) is the observation noise, representing the error of the observation. \(\omega_{t}\) and \(v_{t}\) are independent of each other.  
Figure 3: State-space model.  
Figure 2: k-NN-based fingerprinting method.
## 4 Discussion
AST and ALT are important transaminases that act as crucial links between protein and carbohydrate metabolism and the mobilization of amino acids for gluconeogenesis [9]. Increased activity of AST and ALT after exposure and recovery period suggests enhanced transamination for the channeling of amino acids in the citric acid cycle [55]. The liver is the main site for detoxification and removal of xenobiotics, so a high concentration of transaminases is found in hepatocytes. Hepatic injuries and disruption of the cell membrane by xenobiotics elevate the activity of AST and ALT [55; 66; 67]. Enhanced activity of transaminases after TCS exposure has also been reported in _L. rohita_ and _Catla calta_[9; 28] and in embryos of _L. rohita_, _Cyprinus carpio_, _Ctenopharyngolodon idella_ and _Cirrhinus mrigala_[11; 68]. ALP is an important enzyme present in the liver and cellular membranes and plays a key role in detoxification and membrane transport. The cells that line the bile duct of liver produce this enzyme [69]. The declined activity of ALP after exposure could be due to disruption of the plasma membrane, the inability of ion exchange channels to function and liver necrosis [55]. Exposure to TCS for 14 days and chlorpyrrifos for 21 days was observed to significantly decrease the activity of ALP in catfish _Pseudoplatystoma magdaleniatum_[70] and _C. carpio_[55], respectively. However, increased ALP activity after recovery indicates damage to bile ducts [71]. During oxidative stress, rapid mobilization of metabolites and necrosis of cells can be linked with enhanced ALP activity [72], which directly supports our observations of a consecutive concentration-dependent increase in ALP activity and necrotic cells during exposure and recovery period. U and UA are the final catabolic products of protein and purines, respectively, and their contents indicate the renal functioning of the organism [73]. Pesticidal exposure is suggested to cause various histopathological changes in the kidneys of fish (e.g., glomerular lesions, reduced nephron numbers, necrosis of hematopoietic tissue and degeneration of renal tubules) [74]. These could be the reasons for the declined contents of U and UA in the hatchlings in the present study. Hemalatha et al. [9] reported that TCS induced shrinkage of glomeruli and necrosis of renal tubules in _L. rohita_. The decline in U and UA in the present study could also be attributed to the inability of the liver to metabolize proteins and synthesize purines, respectively, as suggested by Adamu and Kori-Siakpere [75]. It seems that defects in renal reabsorption could have caused an increase in U at the highest concentration of TCS. The depletion of Tbili after exposure also indicates hepatic dysfunction, because bilirubin is a predominant hepatic pigment formed from the breakdown of heme and porphyrin rings [76]. A decline in Tbili content has also been reported in fresh water fish _C. punctatus_ after exposure to malathion [53]. The significant elevation in Tbili after recovery could be due to obstructions in bile ducts and liver damage, as suggested by Jyoti and Narayan [77].
# An Experimental Study on the Migration of Pb in the Groundwater Table Fluctuation Zone
###### Abstract  
As a result of fluctuations in the shallow groundwater table, hydrodynamic conditions change alongside environmental conditions and hydrogeochemical processes to affect pollutant migration. The study aimed to investigate the migration, adsorption, and desorption characteristics of Pb on fine, medium, and coarse sand in the water table fluctuation zone by using several laboratory methods, including the kinetic aspects of Pb\({}^{2+}\) adsorption/desorption and water table fluctuation experiments. The results showed that the adsorption and desorption curves fit the Elovich equation well at a correlation coefficient above 0.9. In the adsorption and desorption kinetic experiments for fine, medium, and coarse sand collected and from the floodplain, the maximum adsorption capacity of Pb\({}^{2+}\) was 2367 mg\(\cdot\)kg\({}^{-1}\), 1848 mg\(\cdot\)kg\({}^{-1}\), and 1544 mg\(\cdot\)kg\({}^{-1}\), respectively. The maximum desorption capacity of Pb\({}^{2+}\) was 29.18 mg\(\cdot\)kg\({}^{-1}\), 62.38 mg\(\cdot\)kg\({}^{-1}\), and 81.60 mg\(\cdot\)kg\({}^{-1}\), respectively. In environments with pH greater than 4, the adsorption capacity was proportional to the pH, but the desorption capacity decreased as the pH increased in water. As the water table varied, the lowest pH occurred in the polluted medium we set initially. When the distance between the pollutants and sample solution grew further, pH increased, and the Pb\({}^{2+}\) concentration decreased in the sample solution. In the column experiment of water table fluctuations on coarse sand, Pb\({}^{2+}\) migrated nearly 5 cm upward from the original pollutant and migrated less than 10 cm downward from that. In our experiments on medium and fine sand, the upward and downward migration distances were <5 cm. The groundwater table fluctuations, pH variation, and Pb concentration currently influence the migration of Pb.  
kinetic adsorption and desorption; groundwater table fluctuations; Pb; migration; experimental study +
Footnote †: journal: Applied Sciences
## 2 System Model  
We consider an EH relay network with one S, multiple Ds, and one R as shown in Figure 1[13; 14]. In this system model, S, R, and D are working in a half-duplex (HD) mode with one antenna. The channel gain between node S and the relay R (\(l_{RSD}\)) is a Nakagami-m fading channel, so between the relay R and the destinations, D\({}_{i}\) as \(h_{RDI}\) is represented by Rayleigh fading channels. In this model, the direct link between S and D nodes is too weak without the help of a relay. The EH and information transmission (IT) for this proposed model system is presented in Figure 2. In this model, the transmission length time \(T\) is divided into two slots. In the time slot T/2, R harvests energy in \(\rho P_{s}\) and receives information in (1\(-\rho\))\(P_{s}\) from S. The remaining half-time slot T/2 is used for information transferring from R to D as in [15; 16]. In the first transmission phase, the received signal at R in period \(T\)/2 time can be formulated by the following:  
\[y_{r}=\frac{1}{\sqrt{d_{sr}^{m}}}\sqrt{(1-\rho)}h_{sr}x_{s}+n_{r}. \tag{1}\]  
In this equation, \(h_{sr}\) is S to R channel gain, \(d_{sr}\) is the distance between S and R, and \(m\) denotes the path loss exponent. Here, \(x_{s}\) is the transmitted signal at S, \(n_{r}\) is the additive white Gaussian noise (AWGN) with variance N\({}_{0}\), and \(0<\rho<1\) is the PS ratio at the relay R. Moreover, \(\mathrm{E}\big{|}\{x_{s}\}^{2}\big{|}=P_{s}\), \(\mathrm{E}\{\bullet\}\) is the expectation operator, and \(P_{s}\) is the average transmit power at S.  
After the first-time slot \(T\)/2, the harvested power at R could be obtained by  
\[P_{r}=\frac{E_{h}}{d_{sr}^{m}(T/2)}=\frac{\eta\rho P_{s}|h_{sr}|^{2}(T/2)}{d_{ sr}^{m}(T/2)}=\frac{\eta\rho P_{s}|h_{sr}|^{2}}{d_{sr}^{m}} \tag{2}\]  
where \(0<\eta\leq 1\) is the energy conversion efficiency. In the second time slot \(T\)/2, R retransmits the information from S to D\({}_{\mathrm{i}}\). The received signal at the \(n^{th}\) destination at the second slot time can be expressed as  
\[y_{d_{i}}=\frac{1}{\sqrt{d_{i}^{m}}}h_{rd_{i}}x_{r}+n_{d_{i}} \tag{3}\]  
where \(i\in(1,2,\ldots,K)\), \(h_{nli}\) is the R to the \(i^{th}\) D channel gain, \(d_{i}\) is the R to the D distance, \(n_{ali}\) is the additive white Gaussian noise (AWGN) with variance N\({}_{0}\), and \(\mathrm{E}\big{|}\{x_{r}\}^{2}\big{|}=P_{r}\). Here, the AF protocol is considered, and the amplifying factor can be given by  
\[\beta=\frac{x_{r}}{y_{r}}=\sqrt{\frac{P_{r}}{\frac{(1-\rho)P_{s}|h_{sr}|^{2}} {d_{sr}^{m}}+N_{0}}}. \tag{4}\]  
Figure 1: System model. Figure 2: EH and IT of the proposed system. From Equations (1), (3) and (4), the received signal can be rewritten as  
\[ \begin{array}{ll}y_{d_{i}}&=\frac{1}{\sqrt{d_{n_{d_{i}}}^{m}}}h_{rd_{i}}\beta y _{r}+n_{d_{i}}=\frac{1}{\sqrt{d_{n_{d_{i}}}^{m}}}h_{rd_{i}}\beta\Big{[}\frac{1}{ \sqrt{d_{g}^{m}}}\sqrt{(1-\rho)}h_{sr}x_{s}+n_{r}\Big{]}+n_{d_{i}}\\ &=\underbrace{\frac{\sqrt{(1-\rho)}h_{sr}x_{s}h_{rd_{i}}\beta}{\sqrt{d_{i}^{m} }\sqrt{d_{sr}^{m}}}}_{signal}+\underbrace{\frac{1}{\sqrt{d_{i}^{m}}}h_{rd_{i} }\beta n_{r}+n_{d_{i}}}_{noise}\end{array}
# Effect of Dune Sand on Drying Shrinkage Cracking of Fly Ash Concrete
###### Abstract  
In this study, the drying shrinkage cracking characteristics of concrete containing 30% fly ash were investigated for various dune sand (DS) replacements and unit water contents. In the results of compressive strength, the mixture with a DS-to-fine aggregate (DS/FA) ratio of 10% showed the highest value, which was 17.6% higher than the lowest value. However, in the results of restrained drying shrinkage cracking, the mixtures with a DS/FA ratio of 20% showed the highest crack resistance, which was 24% higher than the lowest crack resistance. Therefore, it is necessary to consider DS replacement according to the required performance of concrete. The restraint effect factors of the aggregates were analyzed based on the relationship between the volume change of each aggregate and the change in the net time to cracking. The restraint effect factor of crushed sand was 0.71-0.98 and that of DS was 0.56-0.90 when that of the coarse aggregate (CA) was 1.  
dune sand; fly ash; drying shrinkage cracking; net time to cracking; restraint effect factor
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 1 Introduction
The remainder of the paper is organized as follows: A description of the QSM general system model with a brief explanation of the QSM implementation, the channel effects at the QSM receiver, and the considerations for an ML detection are presented in Section 2; Section 3 describes the low complexity detection algorithm implemented at the MIMO-QSM receiver; Section 4 show a full description of the proposed architecture for the MIMO-QSM receiver; Section 5 shows the results of BER performance and the proposed architecture to implement the receiver for the MIMO-QSM system. Finally, conclusions are summarized in Section 6. _Notation_: Uppercase boldface letters denote matrices, whereas lowercase boldface letters denote vectors. The transpose, Hermitian transpose, and Frobenius norm of \(\mathbf{A}\) are denoted by \(\mathbf{A}^{T}\), \(\mathbf{A}^{H}\), and \(\|\mathbf{A}\|_{F}^{2}\), respectively. Finally, \(\mathcal{CN}(\mu,\sigma^{2})\) is used to represent the circularly symmetric complex Gaussian distribution with mean \(\mu\) and variance \(\sigma^{2}\).
## 3 Post-Processing Methodologies
### Frequency-Domain Analysis
#### 3.2.1 Single-Bin Evaluation: Frequency-Domain Tomography (FDT)  
In Ref. [21], defect detection is performed by evaluating the phase at arbitrary frequencies. In contrast, the present approach focuses on a systematic search of the maximum phase contrast and its corresponding frequency, analogous to DTT. The phase contrast was calculated by either subtracting the phase profile of a single known sound pixel, the mean phase value of a known sound area, or the mean phase value of the entire inspected sample, from the phase profile of every pixel [36]. In this study, the phase contrast was calculated by the latter approach. Similar to the temperature contrast curve, the phase contrast curve of a defected pixel exhibits a maximum (negative) phase contrast \(\Delta\varphi_{max}\) at a corresponding frequency of maximum phase contrast \(f_{\Delta\varphi,max}\); their surface maps are termed '_maxigram_', and '_frequencygram_', respectively. This is schematically illustrated in Figure 4c. Evaluation of these two quantities, i.e., \(\Delta\varphi_{max}\) and \(f_{\Delta\varphi,max}\), can be classified as a single-bin evaluation technique in the frequency domain. In analogy with DTT, this procedure is termed frequency-domain tomography (FDT). The deeper a defect, the lower the frequency of maximum phase contrast \(f_{\Delta\varphi,max}\) (see Equation (3)). Since the frequency of maximum phase contrast is typically found at very low frequencies, deep defects might be attributed the same \(f_{\Delta\varphi,max}\) due to a limited frequency resolution in the experimental data [19]. Recording for a longer duration would increase the frequency resolution (defined by their inverse relationship, \(\Delta f=1/t_{end}\)), but the defect detectability might become worse since the additionally evaluated part is noisy and strongly affected by lateral heat diffusion effects. Due to this reason, defect depth inversion through FDT becomes challenging for deeper defects.
## Appendix A
#### a.1.2 Atat  
Since the number of tasks has been equated to the number of packets, the energy consumption outcome for tasks in ATAT [15] will equivalently apply to the corresponding number of packets. Thus, for 500,000 packets, the energy consumption corresponds to 15 KWH. For instance, for a mere 20 packets, the energy consumption is calculated as (15 \(\times\) 20)/500,000 KWH, which converts to (15 \(\times\) 3600 \(\times\) 20)/500,000 KJ, resulting in 2.16 KJ.  
Similarly, with 1,000,000 packets, the energy consumption aligns with 23 KWH. For 40 packets, the energy consumption computes as (23 \(\times\) 40)/ 1,000,000 KWH or (23 \(\times\) 40 \(\times\) 3600)/1,000,000 KJ, amounting to 3.3 KJ.  
Furthermore, for 1,500,000 packets, the energy consumption value corresponds to 28 KWH. Consequently, for 60 packets, the energy consumption is determined as (28 \(\times\) 60)/1,500,000 KWH or (28 \(\times\) 60 \(\times\) 3600)/1,500,000 KJ, resulting in 4.03 KJ.  
Likewise, with 2,000,000 packets, the energy consumption value aligns with 35 KWH. For 80 packets, the energy consumption computation stands at (35 \(\times\) 80)/2,000,000 KWH or (35 \(\times\) 80 \(\times\) 3600)/2,000,000 KJ, leading to 5.04 KJ.  
Similarly, for 2,500,000 packets, the energy consumption corresponds to 42 KWH. Correspondingly, for 100 packets, the energy consumption calculates as (42 \(\times\) 100)/2,500,000 KWH or (42 \(\times\) 100 \(\times\) 3600)/2,500,000 KJ, resulting in 6.04 KJ.  
This pattern continues, extending the computation for larger numbers of packets, which demonstrates a consistent relationship between energy consumption and the number of tasks, underscoring the scalability of the presented results.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### Model Classification Performance
This result is evident given that the alpha band uses 16 samples per second and two wavelet coefficients, while the beta band uses 32 samples per second and five wavelet coefficients. This demonstrates that in this context, alpha band analysis is significantly more computationally efficient than the beta band. Furthermore, given the algorithm's high processing speed in the alpha band and efforts to minimize computations, there is clear potential for deploying this model in low-cost device environments or simple applications. The obtained results represent the culmination of multiple experiments. The proper selection of training data, their quality, and quantity leads to superior models. These results provide some key insights into their performance in classifying EEG signals: the metrics of sensitivity, specificity, and precision demonstrate the model's ability to accurately detect seizures and categorize them. Models constructed with prefiltered data consistently demonstrates superior performance compared to those built with unfiltered data, as evidenced by higher average accuracy for both the alpha and beta bands. This highlights the importance of prefiltering signals beforehand to enhance the quality of the analyses. Figure 8: EEG classification accuracy results for Helsinki filtered and unfiltered signals. Figure 7: EEG classification accuracy results for CHB-MIT model.
## 3 Results and Analysis  
### Tool Overlapping Results  
Six pockets have been machined on alumina (Al2O3) material under various tool overlapping percentages (0%, 5%, 10%, 15%, 20% and 25%). Figures 7 and 8 represent the results of roughness (Ra and Rt) and the morphology of the fabricated pockets surfaces respectively. As mentioned in the previous section, Ra and Rt were measured at six random locations across the feed direction. The results of the Ra and Rt revealed in Figure 7 are the average of these six readings. As shown in Figure 7, the values of Ra and Rt decreased with the increasing of the tool overlaps. The standard deviations among the values of Ra and Rt were 0.1718 \(\upmu\)m and 5.2707 \(\upmu\)m respectively. The values of Ra and Rt decreased by 70% (from 0.723 to 0.21 \(\upmu\)m as shown in Figure 7a) and 0.87% (from 13.1 to 1.73 \(\upmu\)m as shown in Figure 7b) while increasing the tool overlaps from 0% to 20%. This is due to the reason that the number of passes in the same surface increased at a higher value of overlapping (20-25%). This results in a smooth surface of the machined pockets. In contrast, at a low value of overlapping (0-10%), a single-pass took place at the same surface with the un-machined line between the two passes, which make the surface of the pocket rougher. Figure 8 shows the typical examples of the surface roughness profiles of the pockets machined at 0% and 20% of tool overlaps. Duo to un-machined regions produced as a result of the machining under low level of tool overlapping (0%), the rough surface with peaks can be clearly observed at the pocket's bed (see Figure 8a).  
Figure 7: Effects of tool overlapping on (**a**) Ra and (**b**) Rt.  
The SEM images shown in Figure 9 present the surface morphology of the pockets machined on the alumina bio-ceramic material at a different percentage of the tool overlapping. It can be observed from Figure 9 that the surface morphology of the machined pockets changes with varying the percentage of the tool overlapping. The SEM images indicate that, in agreement with observations documented in [1,17] whereas in RUM of ceramics, two material removal modes exist: brittle fracture and ductile fracture. Brittle fracture is observed as tear-outs forming on the pockets surfaces machined at a low percentage of overlapping (0-10%) as shown in Figure 9a,b. The presence of smooth areas where the ductile fracture mode seems to be dominant resulting in a smooth surface of the machined pockets at a higher percentage (20%) of the tool overlapping (see Figure 9c). It should be noted that, since there is no difference between the values of Ra and Rt obtained from the overlapping of 20% and 25%, and in order to reduce the machining time, the overlapping of 20% was selected and kept constant in the tool path strategy test.  
Figure 8: Typical example of the pocket’s profiles machined at (**a**) 0% tool overlapping and (**b**) 20% of the tool overlapping.