esa-sceva/satcom-chunk-collection huggingface dataset:
# Rate of Penetration Estimation with Parameter Correction
## 2 Well Depth Correction
### Downhole Forces
* If \(\Delta F_{h}<0\), meaning the calculated hook load is lower than the measured value, WOB is decreased to bring the computed values closer to the measured ones. A correction step is applied iteratively, updating WOB as follows: \[WOB_{n+1}=WOB_{n}-\alpha\Delta F_{h},\] (9) where \(\alpha\) is a tuning parameter that controls the step size of the correction. This process is repeated until \(|\Delta F_{h}|\) is within a predefined threshold, ensuring that the calculated hook load sufficiently matches the measured value. Each update to WOB propagates through the force balance equations, leading to updated values of axial tension force \(F_{t}\) and hydraulic force \(F_{\text{hydraulic}}\). The iteration continues until the following convergence condition is met: \[|\Delta F_{h}|\leq\epsilon,\] (10) where \(\epsilon\) is a predefined threshold value ensuring numerical stability. Once the algorithm converges, the final corrected WOB is obtained, along with the updated values for \(F_{t}\) and \(F_{\text{hydraulic}}\). This iterative approach improves the accuracy of WOB estimation by systematically refining the value until the calculated hook load aligns with the measured hook load. Figure 1 presents the flow process used for the calculation of the WOB for given data points. The inputs besides the aforementioned are the friction factor, well trajectory, BHA configuration, and fluid density. By incorporating these corrections, the measured WOB provides a more accurate estimate of the actual force applied by the bit to the formation.
## 2 Non-Orthogonally Multiplexed Cognitive Radio (NOMCR) System  
### Conventional OFDM-LDM  
Cloud transmission network (Cloud-Txn) with layer division multiplexing (LDM) is a non-orthogonal multiplexing transmission technique which provides a very high data rate [2; 3; 4]. Conventional multicarrier LDM adopts a technique similar to OFDM based power domain NOMA. In such a scheme, waveforms corresponding to multiple services such as digital broadcasting or multicasting are transmitted in different signal layers within a single radio frequency (RF) channel. Contrary to traditional time division multiplexing (TDM) or frequency division multiplexing (FDM) systems, each transmission layer in LDM occupies the full frequency spectrum and full-time duration. As evident, the LDM scheme provides full frequency diversity and time diversity to the waveforms in all the layers of the system. For a 2-layer LDM, the upper layer (UL) is responsible for delivering service to mobile users under low SNR and harsh multipath conditions. Hence it is designed to possess high transmission power and use very strong coding and digital modulation schemes such as \(\frac{1}{4}\)-low density parity check code (LDPC) with QPSK. On the other hand, the lower layer (LL) is responsible for providing high data rate service to fixed users which may use large directional antennas to provide high antenna gain. Hence the LL is designed to use lower transmission power, weaker channel coding and larger signal constellation sizes, for e.g., 64-QAM, 256-QAM etc. Details of the OFDM based LDM transceiver can be obtained from Reference [4]. The traditional LDM concept can be extended to the cognitive radio (CR) scenario. In a CR system based on interference temperature (IT) constraint [11], the secondary user (SU) can allocate its data on those portions of the frequency bands where the tolerable IT is not exceeded. So, in those sub-bands, the SU can superimpose its modulated data streams on the primary user (PU) data. In essence, this is a non-orthogonal multiplexed cognitive radio (NOMCR) transmission scheme where the SU transmission is opportunistically superposed on the PU data in a non-orthogonal fashion. This kind of non-orthogonal cognitive multicarrier system can lead to higher spectral efficiency. However, due to the use of rectangular pulse shaping filters, the out-of-band (OOB) radiation in conventional OFDM based systems is high leading to harmful adjacent channel interference (ACI) to the PU transmissions. Hence there is need to use alternative multicarrier processing schemes for the NOMCR transmissions.
## 2 The Proposed Method
### Improved Particle Swarm Optimization Algorithm
#### 2.1.1 Fitness Evaluation Function  
The value of the evaluation function corresponding to each particle determines the next update direction of the particle, which is related to whether a better image enhancement effect can be achieved. The coal mine image has the characteristics of low contrast and blurred details, while image enhancement requires information enhancement, and the effect of image enhancement after particle iteration should meet some evaluation indexes. In this paper, information entropy, gradient and variance are selected to form the suitability evaluation function\(f_{fitness}\), and\(f_{fitness}\) is defined as:  
\[f_{fitness}=\lambda_{1}S+\lambda_{2}H+\lambda_{3}D \tag{3}\]  
where \(S\) is the information entropy, \(H\) is the average gradient, \(D\) is the variance, \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) are the weighting coefficient, and the value of \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) are 0.3, 0.5 and 0.2, respectively.  
Information entropy is a common index for image quality evaluation, which reflects the richness of image information. Generally speaking, images with high information entropy are more colorful and of better quality. The definition of information entropy is as follows:  
\[S=-\sum_{i=0}^{255}p_{i}\log p_{i} \tag{4}\]  
where \(p_{i}\) is the proportion of pixels with grayscale values in the image.  
The gradient is the index that can reflect the difference between pixels. When the gradient is large, there is a significant difference in grayscale at the edge of the image, and the image is sharper. For an image \(f(i,j)\), the gradient is defined as:  
\[\
abla f(i,j)=\sqrt{\left(\frac{\partial f}{\partial i}\right)^{2}+\left(\frac{ \partial f}{\partial j}\right)^{2}} \tag{5}\]  
For an image of size \(L=m\times n\), the average gradient is:  
\[H=\frac{\sum_{i,j\in L}\
abla f(i,j)}{m\times n} \tag{6}\]  
Variance \(D\) reflects the degree of dispersion of the image pixel value from the mean value, and a large standard deviation represents a clear image edge. For an image \(f(i,j)\), the mean and variance are recorded as \(\mu\) and \(\sigma^{2}\), respectively:  
\[\mu=\frac{\sum_{i,j\in L}f(i,j)}{m\times n} \tag{7}\]  
\[D=\sigma^{2}=\frac{\sum_{i,j\in L}\left(f(i,j)-\mu\right)^{2}}{m\times n} \tag{8}\]  
The calculation flowchart of the fitness evaluation function proposed in this paper is shown in Figure 1.
# Privacy Goals for the Data Lifecycle
## 6 Step 3-Aligning the Privacy Goals to GDPR and the Data Lifecycle
### GDPR Principle 1: Lawfulness, Fairness and Transparency  
The definition of lawfulness, fairness and transparency under GDPR, is that business must ensure that any data processed has a valid legal basis for being processed (Article 5(1)(a)). GDPR then goes on to further outline the permitted legal basis (or conditions) for lawfulness in Articles 6 and 9. Further, lawfulness and fairness are discussed in Recitals, 45 (lawfulness of processing), 50 (lawfulness of processing beyond original purpose), 63 (data subject right to of access) and 138. Further, GDPR includes transparency as part of principle 1 (Article 5(1)(a)) and further considers this principle in Article 88(2) and Recitals 13, 39, 58, 60, 71, and 78 (keeping data processing activities transparent). Article 12(1) and 13(2), 14(2) and Recitals 39, 58, 60 (keeping data subjects informed). Article 43(3) and Recital 100 (establishing privacy certification mechanisms), Article 53(1) and Recital 121 (transparency of decisions made by governing authorities).  
Comparing this to how the other frameworks have treated these concepts, ENISA and Schleswig-Holstein DPA have separated out transparency as a separate privacy goal, while LINDDUN and PRiS do not consider either of these concepts as privacy goals at all. The other two aspects, (lawfulness and fairness) are mentioned by ENISA and Schleswig-Holstein DPA, but not as a goal, rather, they contend these should be overriding considerations that support the protection goals [50; 51]. Turning to ISO29100, 'purpose legitimacy and specification' are considered as part of privacy principles 2 and transparency as part of principle 7: 'openness, transparency and notice'. Thus, in 7-DL this means that decisions around lawfulness and fairness should be considered as an overarching consideration that can influence the whole of the data lifecycle. Therefore, in 7-DL this will be incorporated into the governance stage as decisions will need to be made before data collection and processing commences and appropriate policies and procedures put in place. It will also figure as part of the Collection (7-DL:2), Transformation (7-DL:3), Access/Release (7-DL:5), and Post-Access (7-DL:6) stages to ensure data is processed in accordance with this principle.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Evaluations
#### 4.6.3 Discussions  
Since the measured _LQI_ is frequently fluctuating, the moving average should be used instead of the instantaneous value to reduce misdetections. Then, the time period of the average, or the detection interval should be optimized to maximize the detection accuracy.  
Then, the results in Figures 4 and 5 indicate that the detection accuracy is improved until the interval becomes 40 s. After that, the accuracy is saturated, where the number of fingerprints is also saturated. Thus, 40 s is selected as the best detection interval.  
It is noted that the results were obtained when all the transmitters were stationary or not moving. The detection interval should be optimized when transmitters are sometimes moving in the field. However, variations of transmitter/user movements are much more diverse, including source/destination locations, moving speeds, and paces. A lot of experiments will be necessary to find the optimal interval. Thus, it will be in future works.  
With the fixed detection interval of 40 s, Figure 6 shows a sufficiently high detection accuracy of higher than 98% for any room. Figure 7 shows the number of fingerprints generated by the proposal. For _D208_ and _D308_, only four fingerprints are necessary and are smaller than the other rooms. The reason is that both rooms are located at the end of each floor in the two-floor field and are isolated from the other rooms. It will cause less confusion with other rooms. On the other hand, the other rooms are surrounded by several rooms and need many fingerprints to reduce confusion among them.
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.4 Blended Solid Polymer Electrolytes
Another unrelated work has compared the usefulness of the blending approach with grafting technique on LiClO\({}_{4}\)-containing SPE system with sulfonated polyether ether ketone (SPEEK) and polyethylene glycol (PEG) as polymer hosts [101]. The SPEEK grafted with PEG electrolyte gave higher conductivity (around 10\({}^{-5}\) S cm\({}^{-1}\)) when compared to SPEEK-PEG blended electrolyte (10\({}^{-6}\) S cm\({}^{-1}\)) at 30 \({}^{\circ}\)C, thus implying the grafting method can suppress PEG crystallinity more than the blending technique [101]. [PERSON] and co-workers [102] have combined both chemical (crosslinking) and physical (blending) routes together, i.e., crosslinked (3-glycidyloxypropyl)trimethoxysilane (GLYMO) with polyetheramine and also crosslinked poly(ethylene glycol) diglycidyl ether (PEGDGE) with polyetherdiamine before blending their products at 70:30 ratio in THF solvent and added with LiClO\({}_{4}\) salt. The [EO]/[Li] ratio was optimized at 16:1. Such a unique SPE exhibited the conductivities of 1.20 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\) and 8.30 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\) at 30 and 80 \({}^{\circ}\)C, respectively [102]. The \(T_{g}\) value was \(-43.6\)\({}^{\circ}\)C, and the transference number of Li\({}^{+}\) ions was 0.28 at 70 \({}^{\circ}\)C. This electrolyte was thermally stable up to 260 \({}^{\circ}\)C and gave the capacities of 110 mAh g\({}^{-1}\) (The first cycle) and 61 mAh g\({}^{-1}\) (100 th cycle) when applied in LiFePO\({}_{4}\)/Li battery at 0.2 C rate [102]. As a summary, blended SPEs show superior properties than SPEs containing single polymer matrix.
# An Enhanced Photonic Quantum Finite Automaton
## 4 Photonic Implementation of the 1 qfa  
A photonic implementation of the 1 qfa described in the previous section was proposed and demonstrated in [6]. Figure 1 depicts the main elements of the enhanced version of the automata we will describe in the following.  
The state of the automaton is encoded in the polarization of single photons, and the Hilbert space is \(\mathcal{H}=\text{span}\{|H\rangle,|V\rangle\}\). A single photon source generates a horizontal-polarized state, \(|H\rangle\), which is sent to \(k\) rotators of polarization, \(a^{k}\) being the input word to be processed. Each rotator corresponds to a unitary rotation of an amount \(\theta_{m}\), which is thus language-dependent. After the rotators, the single photon state reads  
\[|k\theta_{m}\rangle=\cos(k\theta_{m})|H\rangle+\sin(k\theta_{m})|V\rangle \tag{10}\]  
and it is sent to a polarizing beam splitter (PBS; see Figure 1) that reflects the vertical polarization component and transmits the horizontal one. Finally, two photodetectors placed after the PBS realize the projective measure of \(P^{H}\) and \(P^{V}\). As the reader can see, the scheme is almost the same of that proposed in [6], but here we will implement a new inference strategy exploiting the outcomes from both the detectors.  
As we observed in the previous section, the automata \(\mathcal{A}_{1}\) and \(\mathcal{A}_{2}\) accept with certainty a word \(a^{k}\) that belongs to \(L_{m}\). However, there is a high probability that an incorrect word, such as \(a^{k_{1}}\) with \(k_{1}\mod m\
eq 0\) can be accepted, as we can see from Equations (6) and (9). Therefore, strategies based on a single-photon shot may not be the optimal way to recognize an arbitrary word \(a^{k}\).
## 4 Conclusions
### Possible Applications of the Proposed Methodology  
Accordingly, this approach is particularly promising in centralised installations or district heating systems, which interconnect a plurality of generation and distribution equipment. These scenarios are increasingly becoming common, driven by the incorporation of renewable energy sources and the hybridisation of thermal systems. Such systems are aligned with the objectives of ZEB, offering a comprehensive solution to improve the performance, sustainability, and operational efficiency of thermal installations in buildings.  
In summary, this methodology is a fundamental element for improving the energy efficiency of thermal systems in buildings, contributing to reducing consumption throughout their useful life and guaranteeing the comfort of users, with the aim of decreasing the environmental impact and improving the quality of life for future ZEBs.
## 5 Experiment
### Training Efficiency Analysis  
In Figure 3 (left), it is evident that DeepChaos+ (medium size) achieves fast convergence to a BER of approximately \(2\times 10^{-5}\) in \(\mathcal{X}^{test}\). However, the other learning models, such as GRU-D and BiLSTM, also exhibit fast convergence, but the only reach a BER of around \(2\times 10^{-3}\). Despite utilizing 60% of the original signal set \(\mathcal{X}\) and having larger model sizes compared to DeepChaos+, they do not attain the same level of accuracy. This highlights the superiority of DeepChaos+ in effectively capturing the underlying patterns and optimizing the BER, even with a smaller proportion of training data. The key factor behind this phenomenon is the ability of DeepChaos+ to generate additional data variants that effectively represent the original data using only 60% of the original data. It selectively learns from the best-performing generated data, resulting in the lowest BER for the \(\mathcal{X}^{test}\) set. Over time, DeepChaos+ autonomously generates data that accurately capture theunderlying features of the remaining 40% of the data, further enhancing its performance and ultimately achieving the lowest BER among the three methods.  
On the other hand, in Figure 3 (right), an interesting observation is made when experimenting with different sizes of the student model for the remaining 40% subset. It is noted that DeepChaos+ did not converge to 100% accuracy on the test set with the tiny and small sizes. This suggests that these smaller-sized models would have a higher bit error rate (BER). However, starting from the medium-sized model and above, DeepChaos+ achieved nearly 100% fit with a recorded BER of \(2.3\times 10^{-4}\). This observation emphasizes the significance of model size in attaining higher accuracy. Larger-sized models, such as medium and above, possess the ability to effectively capture and represent the underlying dynamics of the data, leading to better convergence and lower BER. Additionally, it is worth noting the training time (red line in the figure), which significantly increases with each model size. In our experiments, the medium-sized student model proved to be the most suitable choice, as it offered a balance between fast training time and achieving accuracy comparable to the large-sized model.
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
## 4 Simulation Results
Compared with MTE and PC-3, TSCR-2 has higher end-to-end transmission delay and lower energy efficiency, while the network lifetime of TSCR-0 is lower than the FA and FACR. These prove the trade-off between end-to-end energy cost and network balance;
3. Since TSCR-1 has better energy efficiency and longer network lifetime than all the existing schemes, it can be regarded as a good trade-off between end-to-end performance and network energy balance. 4. With different setting of \(x\), the proposed TSCR scheme can meet different performance requirements.
## 3 Cluster Formation
### Optimal Clustering  
In the LEACH algorithm, cluster formation is based on minimizing the energy expenditure of cluster members. Sensor nodes join the nearest CHs by calculation of their distance from CHs through the signal strength of the received advertisement messages. This method of clustering does not consider the size of the constructed clusters or even distribution of energy expenditure within the clusters. While most of CH selection schemes accept the cluster formation method proposed by LEACH and they address the  
Figure 7: The taxonomy of the cluster formation phase.  
clustering issues in CH selection phase, some researchers [13, 30, 79, 80, 81, 82, 83, 84] propose resource-aware algorithms for cluster formation stage separately.
# Monitoring Safron Crops with UAVs
###### Abstract  
The use of information technology in agriculture has brought significant benefits to producers, such as increased profits and better product quality. Modern technology applications in agriculture rely on the use of unmanned aerial vehicles (UAVs) and wireless ground sensors to provide real-time information about fields and crops. In Europe, these techniques, referred to as Smart Farming (SF), are still in their infancy despite the large agricultural production of a wide range of products. For this reason, in this study, we experimented with the technologies of SF in the cultivation of Greek saffron, a rare spice with many uses. For this reason, and also because its harvest is quite labor intensive, it is one of the most expensive spices. Since the field of SF is relatively new and has not yet been used for this particular crop and area, the equipment and methods of data processing were selected experimentally after a review of the literature. The aim of the study was to remotely acquire image data of the crops and train a machine learning model to detect important objects such as saffron flowers and weeds.  
Smart Farming; UAV; photogrammetry; machine learning +
Footnote †: journal:  
0000-0002-4010-56526
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.2 Influence of Adsorbent Dose  
The mass of an adsorbent shows its capacity for a given concentration of dye. The influence of the adsorbent dose was studied at 25 \({}^{\circ}\)C by varying the sorbent dose to 0.005, 0.01, 0.2, 0.04, and 0.08 g. For all of these runs, the concentration of the IV2R dye was fixed at 10 mg L\({}^{-1}\), and the results are illustrated in Figure 7. An increase in the percentage of removal was observed when the dose was increased from 0.005 to 0.08 g, and the maximum removal was obtained at the adsorbent dose of 0.08 g with a percentage of 98.32% for the IV2R dye in the MWCNTs. The obtained data show that the adsorption of dye increased gradually with an augmentation in the amount of MWCNTs due to the greater availability of dye-binding sites of the surface area at higher concentrations of the adsorbent [51]. [PERSON] et al. [17] studied the treatment of methyl orange dye by MWCNTs, and it was found that, as the mass of MWCNTs increases, the whole adsorptive capacity of MO increases because of the increase in the number of reaction sites available and surface area to MO.
# Grafting of (3-Chloropropyl)-Trimethoxy Silane on Halloysite Nanotubes Surface
## 3 Results and Discussion
### Effect of Solvent  
Choosing the appropriate solvent is of great importance for the silanization of halloysite nanotubes using (3-chloropropyl) trimethoxy silane due to the effect of solvent on both the rate and thermodynamic route of the chemical modification process. The solvent can act as a source of protons or act as a base. Moreover, the solvation of the reactant can be also considered. In the present work, many solvents have been tested for the silanization of halloysite nanotubes using (3-chloropropyl) trimethoxy silane, such as ethanol, toluene, tetrahydrofuran, 1,4-dioxane, and n-hexane. The nature of these solvents affects the grafting degree of CPTMS onto the HNT surface as mentioned in Table 1. The degree of grafting of CPTMS onto HNTs using ethanol, toluene, tetrahydrofuran, 1,4-dioxane, and n-hexane was 10.75, 24.29, 11, 14.3, and 15.32%, respectively. Relying on the data obtained from elemental analysis, it was found that toluene is the most effective solvent for the grafting of (3-chloropropyl) trimethoxy silane onto the HNT surface [18; 30].
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 4 PSIJ Sensitivity Study of Two-Stage Driver
### Two-Tone Jitter Superposition  
This investigation aims to validate the jitter superposition condition under small and large-signal distinct PSV variations for both driver's stages [18; 22; 23].