esa-sceva/satcom-chunk-collection huggingface dataset:
# Inorganic Waste Generated in Kraft Pulp Mills:
## 3 Main Properties of the Inorganic Wastes
### Chemical composition  
Table 2 shows the composition expressed in oxides, determined by XRF, and also the loss on ignition (LOI).  
These data demonstrate that GLD, SG, and LM are particularly rich in Ca, while BFA contains less of this element. Indeed, BFA is rich in Si with noteworthy content in Al and Fe. In addition to Ca, GLD also contains noticeable Na and Mg content. The concentration of K and P in BFA can be interesting for the production of fertilizers. The high LOI associated with these wastes is normally due to the decarbonation of calcite instead of organic matter, which generally is low [30].  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Wastes** & **Industrial Information (a)** & **[3]** & **[20]** & **[21]** & **[22]** & **[23]** \\ \hline GLD & 12 & 10–20 (b) & 12 & 15 & 4–20 & 12.8 \\ SG & 10 & & 7 & 16 & & & \\ LM & 25 & 10–20 & 15 & 13 & & & \\ BFA & 30 & 9 (c) & 20 & 5 & & & \\ \hline \hline \end{tabular} (a) Data provided by the Portuguese Industry (The Navigator Company); (b) includes green liquor dregs (GLD) and slaker grits (SG); (c) may be higher if biomass from external sources is also used; AD – air dried pulp.  
\end{table}
Table 1: Specific waste generation in kraft pulp mills (kg/t AD).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & **GLD** & \multicolumn{4}{c}{**SG**} & \multicolumn{4}{c}{**LM**} & \multicolumn{4}{c}{**BFA**} \\ \hline  & [4] & [24] & [25] & [5] & [24] & [26] & [27] & [28] & [5] & [23] & [26] & [29] \\ CaO & 34.3 & 33.0 & 34.9 & 49.45 & 55.8 & 44.4–52.0 & 57.12 & 54.1 & 16.7 & 34.9 & 0.8–10.4 & 16.5 \\ MgO & 10.9 & 4.65 & 5.94 & 0.45 & 0.47 & 0.6–3.4 & 0.91 & 0.86 & 3.44 & 4.4 & 0.7–1.9 & 3.07 \\ SiO\({}_{2}\) & 0.23 & 2.35 & ni & 0.47 & 1.31 & 3.4–11.0 & 3.58 & 0.34 & 38.5 & 11.6 & 33.9–59.7 & 34.0 \\ Al\({}_{2}\)O\({}_{3}\) & 1.94 & 0.69 & 0.47 & 0.29 & 0.42 & 0.5–1.4 & 0.07 & 0.07 & 14.8 & 4.4 & 16.5–35.4 & 13.5 \\ Fe\({}_{2}\)O\({}_{3}\) & 0.61 & 0.65 & 0.59 & 0.05 & \(<\)0.1 & 0.2–1.2 & 0.20 & 0.15 & 5.94 & 2.6 & 1.5–19.7 & 4.95 \\ Na\({}_{2}\)O & 2.10 & 11.7 & 9.49 & 4.52 & 0.60 & ni & 2.32 & 0.91 & 1.53 & 1.4 & ni & 1.52 \\ K\({}_{2}\)O & -0.1 & 1.03 & 0.37 & 0.27 & \(<\)0.1 & ni & 0.26 & 0.06 & 5.97 & 6.5 & ni & 5.49 \\ P\({}_{2}\)O\({}_{5}\) & ni & 0.33 & 0.37 & 0.38 & 0.65 & ni & 0.03 & 0.96 & 1.12 & 1.6 & ni & 1.11 \\ TiO\({}_{2}\) & ni & \textless{}0.1 & ni & ni & \textless{}0.1 & ni & ni & 0.76 & 0.25 & ni & 0.65 \\ MnO & 4.21 & 0.37 & 0.06 & ni & \textless{}0.1 & ni & ni & 0.09 & 0.50 & 1.4 & ni & 0.45 \\ SO\({}_{3}\) & 3.6 & 2.82 & ni & 1.86 & 0.11 & ni & 0.4 & ni & 2.66 & 11.4 & ni & 2.77 \\ LOI & ni & 42.10 * & ni & 41.1 & 40.10 & 31.5–43.5 & ni & ni & 6.38 & 15.8 & 1.2–33.6 & 14.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of green liquor dregs (GLD), slaker grits (SG), lime mud (LM), and boiler fly ash (BFA) determined by XRF (wt %).
## 2 Materials and Methods
### Development of the DL Model and Performance Analysis  
We developed a vision transformer (ViT) model to automatically identify and categorize two types of frames in the esophagus: normal mucosa and pleomorphic lesions. The complete dataset was divided into two main groups for our study: one for training and validation, and the other for testing. To ensure consistency, frames from the same patient were grouped together during this division, following a patient-split design. The training set, comprising 70% of the data, was employed to train the model, while the validation set (20%) helped fine-tune its parameters. The remaining 10% of the data constituted the testing set, used to independently evaluate the diagnostic performance of our ViT model. The graphical representation of our study design can be found in Figure 1.  
Our ViT model was initialized using pre-trained weights from ImageNet, a comprehensive image dataset specializing in object recognition [16]. We retained the feature extractor weights to leverage knowledge from ImageNet and defined our own fully connected layers to adapt the pre-trained model to our specific task. To prevent overfitting, we have included between these fully connected layers dropout layers with a dropout rate of 0.2. Subsequently, a dense layer was added to determine the binary classification result (normal or pleomorphic esophageal lesions). The hyperparameters, including the initial learning rate, batch size (32), and the number of epochs, were determined through trial and error. Common data augmentation techniques, such as image rotation and mirroring, were applied during the training stage. Our computational setup consisted of a NVIDIA RTX A6000 graphic processing unit (NVIDIA Corp, Santa Clara, CA, USA) and a dual AMD EPYC 7282 16-Core processor (AMD, Santa Clara, CA, USA).  
The model outputs the probability of each frame being labeled as normal or having a pleomorphic esophageal lesion. Based on the highest calculated probability, each frame was assigned one of these labels (Figure 2). We also generated heatmaps to visualize the frame features that contributed most to the model predictions (Figure 3). To establish a reference point, we compared the final classification made by our algorithm with expert assessments provided by three gastroenterologists, recognized as the gold standard.  
Figure 1: Diagram that shows how the study was designed. AUC-PR: area under the precision–recall curve; AUC-ROC: area under the conventional receiver operating characteristic curve; CE: capsule endoscopy; CCE: colon capsule endoscopy; NPV: negative predictive value; and PPV: positive predictive value.
# More Time-Space Tradeoffs for Finding a Shortest Unique Substring
## 3 Tradeoffs with Sketching  
For Karp-Rabin pattern matching, we must keep track of characters leaving a sliding window, for which we need either enough memory to store the contents of the sliding window--which, by assumption, we do not have--or random access to \(T\). However, [PERSON] and [PERSON] [14] gave a Monte-Carlo randomized sketching algorithm that takes \(d\) patterns of maximum length \(\ell\), scans \(T\) one character at a time, and, for each position, reports the longest pattern with an occurrence ending at that position with probability of failure inversely proportional to any fixed polynomial in \(n\). Their algorithm uses \(O(\log\log\sigma)\) time per character of \(T\) and \(O(d\log\ell)\) space and does not use a sliding window, so it needs only sequential access to \(T\). Replacing [PERSON] pattern matching with [PERSON] and [PERSON] and searching for substrings of length \(\ell\) in batches of size \(\Theta(m/\log\ell)\), so that we stay within our workspace bound \(m\), we obtain the following result:  
**Theorem 2**.: _With \(m=\omega(\log L)\) words of workspace, with high probability we can find an SUS for \(T[q]\) in \(O(n(L/m)\log^{2}(L)\log\log\sigma)\) time using \(O((L/m)\log^{2}L)\) sequential passes over \(T\)._  
Because [PERSON] and [PERSON]'s algorithm is Monte Carlo, so is our result; unlike Theorem 1, we cannot easily make it Las Vegas, since verifying matches requires random access. Again, our batch size depends on the ratio of the word-size to \(\log_{2}n\) and the power of \(n\) we want in the denominator in the probability of failure. The requirement that \(m=\omega(\log L)\) means the probability of failure can still be made inversely proportional to any fixed polynomial of \(n\).  
[PERSON] and [PERSON] (2015) considered a harder version of the problem [PERSON] and [PERSON] studied, in which we are given a distance \(k\) and, for each position in \(T\), we should report all of the patterns within Hamming distance \(k\) of substrings of \(T\) ending at that position. They gave a Monte-Carlo randomized sketching algorithm that searches for \(d\) patterns of length at most \(\ell\) using \(O(klog^{k}d\operatorname{polylog}{(\ell)}+\operatorname{occ})\) time per character of \(T\), where \(\operatorname{occ}\) is the number of matches reported ending at that character, and \(O(kd\log^{k}d\operatorname{polylog}{(\ell)})\) space. Replacing [PERSON] and [PERSON]'s algorithm with [PERSON] and [PERSON]'s and searching for substrings of length \(\ell\) in batches of size \(\Theta(m/n^{\epsilon})\) for some positive constant \(\epsilon\), so that we stay within our workspace bound \(m\) for constant \(k\), we obtain the following result:  
**Theorem 3**.: _For constant \(k\) and with \(m=\omega(n^{\epsilon})\) words of workspace, with high probability we can find a \(k\)-mismatch SUS for \(T[q]\) in \(O(n^{1+\epsilon}L/m)\) time using \(O(n^{\epsilon}L/m)\) sequential passes over \(T\)._  
Like Theorem 2, this result is Monte Carlo and the probability of failure is inversely proportional to any fixed polynomial in \(n\).
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 5 Application Layer
### Chain Construction Protocol  
In Chain-based protocol, the technique consists of constructing a transmission chain that connects all nodes to optimize energy consumption in each round of data collection [47]. A linear chain connects all the sensor nodes within the network. In this technique, data are transmitted from one end of the chain to the other end, where each node appends data to the received message in order to construct a larger packet, then forwards it to the next node. To optimize energy consumption, sensor nodes of the network keep the samesize of the packet header. Hence, WSN nodes transmit the same number of bits except for the end nodes, as they only transmit their data. Using this technique of distributing the energy usage among nodes, the WSN prevents nodes from being overused. When a WSN node dies, the transmitter node skips the dead node and transmits data to the next living neighbor in the chain.
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 4 Skin and Diffusion through the Skin
### Skin Structure  
The skin acts as the largest interface connecting the body to the external environment. Its primary function is to offer protection against various external hazards such as chemicals, heat, and physical injuries. Consisting of several layers, the skin is composed of the epidermis, dermis, and hypodermis (Figure 3). Although the dermis and hypodermis have minimal participation in drug delivery, the outermost layer, the epidermis, is essential for providing protection. The epidermis comprises two main layers: the viable epidermis and the nonviable epidermis, which includes the stratum corneum [56]. The stratum corneum, the thin outer layer of the epidermis, is accountable for the skin's exceptional barrier properties [57]. Unlike other body tissues, the stratum corneum consists of corneocytes surrounded by an extracellular environment of lipids organized in lamellar bilayers. These lipids act as a barrier. However, they permit the passage of lipid-soluble and low molecular-weight drugs. The existence of lipids presents a notable challenge in drug administration through the skin [58]. The lipid-rich extracellular matrix of the stratum corneum has several properties that contribute to its exceptionally low permeability to water-soluble drugs. One of the exceptional features is that of the organization of the lipid-rich extracellular matrix and its organization into a highly tortuous and convoluted extracellular pathway [58].  
Ref. [56] The characteristics of the lipid phase in the stratum corneum are distinguished by its lipid content, which differs from that of other tissues' membranes. This characteristic of the epidermis impedes drug delivery through the skin [56]. The unique structural characteristic of skin makes it a strong barrier against TDDSs. Therefore, effective transdermal drug delivery requires novel designs and approaches.  
Figure 3: Layers of the skin: understanding skin structure.
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 5 Simulation Results and Discussions
### Disturbance Estimation for Single Area 2 Using the Disturbance Observer  
Three disturbance scenarios are considered as follows:  
* Disturbance 1: a small disturbance (0.01 p.u),
* Disturbance 2: a medium disturbance (0.05 p.u),
* Disturbance 3: a large disturbance (0.1 p.u).  
Based on WAMS, decentralized monitoring of the power system can also be done. Here we assume that each of the four areas is under the monitoring of an operator specifically responsible for that area. We only considered the results for area 2 because we assumed that in the other three areas no disturbance occurred. In all the simulated scenarios, a disturbance occurred at \(t=1\), as shown in Figures 5-7. In all cases it took the disturbance observer only 0.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 3 Materials and Methods
### Motor and Rudder Servo Sizing
Reynolds number characterizes the type of flow (laminar or turbulent) of the fluid, which affects the calculation of the friction coefficient \(C_{f}\left[+\right]\):  
\[\text{Laminar flow}\quad\text{ If }Re<5\times 10^{5}\longrightarrow C_{f}=1.33 \cdot Re^{\frac{-1}{2}} \tag{4}\]  
\[\text{Turbulent flow}\quad\text{ If }5\times 10^{5}<Re<10^{7}\longrightarrow C _{f}=0.074\cdot Re^{\frac{-1}{5}} \tag{5}\]  
With this in mind, we can calculate the drag forces \(F_{Drag}\left[N\right]\) opposing surge motion by identifying all the contact surfaces that contribute to the drag, both in water and air. Figure 5 shows all the surfaces contributing to the drag force and their fluid of origin, which lets us, in conjunction with Equation (6) calculate all the surge drag forces at play. \[F_{Drag}=\frac{1}{2}\cdot\rho\cdot u^{2}\cdot C_{f}\cdot A \tag{6}\]  
where \(\rho\) is the density of the fluid which drag force is being calculated, \(u\) is the surge speed, \(C_{f}\) is the friction coefficient and \(A\left[m^{2}\right]\) is the front surface area of the marked elements in Figure 5. A value of \(5\;m\;s^{-1}\) surge speed was selected to ensure compliance with the requirements established by Table 1. The high value (regarding the requirement) of this design parameter selection is due to the necessity of a safety margin due to the possible inaccuracies of the model and assumptions made. This also ensures the inclusion of top speed headroom above the required cruise speed. We can calculate then the necessary power \(P\left[W\right]\) to overcome the surge drag forces as:  
\[P=\sum F_{Drag}\cdot u \tag{7}\]where \(\sum F_{Drag}\) is the sum of all surge drag forces and \(u\) is the surge speed.
# A Review of Sound Field Control
## 2 Sound Field Reproduction
### Sound Field Recording  
Sound field recording is the foundation for reproducing the desired sound field precisely. This technology aims to accurately estimate the distribution of the original sound field in an entire target area through limited acoustical data measured by microphone arrays.  
Expressing a 3D sound field into spherical harmonics reveals the underlying characteristics of the sound field, and thus enables a high accuracy recording of the sound field [16; 17; 18; 19]. The spherical harmonics expansion for sound pressure at any point in a source-free region is of the form:  
\[p(r,\vartheta,\varphi,k)=\sum_{n=0}^{\infty}\sum_{m=-n}^{n}\alpha_{n}^{m}(k)j_ {n}(kr)Y_{n}^{m}(\vartheta,\varphi) \tag{1}\]  
where \(r\), \(\vartheta\), and \(\varphi\) are radius, elevation, and azimuth angles, respectively; \(k=2\pi\vartheta/c\) is the wave number; \(f\) is the frequency; \(c\) is the speed of the sound propagation; \(j_{n}(kr)\) is the \(n\)th order spherical Bessel function; \(Y_{n}^{m}(\vartheta,\varphi)\) is the spherical harmonic of order \(n\) and degree \(m\); and \(\alpha_{n}^{m}(k)\) is the spherical harmonic coefficients. The sound field in the target area can be represented by these spherical harmonic coefficients.  
Because the sound pressure received by a spherical array is suitable for the spherical harmonic transform [18], the spherical microphone array is considered to calculate the above-mentioned coefficients. However, the placement of the microphones in a spherical array must follow a strict rule. A great number of microphones are required especially when the target area is large and the frequency is high. Therefore, the practical use of spherical arrays is limited. An array of higher order microphones (HOMs) has been proposed to decrease the number of microphones [20; 21]. The array of HOMs is distributed enclosing the large area of interest. The local spatial recordings by the HOMs are translated to the global sound field coefficients of interest based on the Graf's addition theorem. This method replaces the omnidirectional microphone with the HOM, which reduces the microphone units of an array at the expense of the additional complexity of each unit. Microphone arrays with 2D geometry are easier to implement than a spherical array and were therefore designed to record 3D sound fields [22]. The planar array utilizes both omnidirectional and first-order microphones to calculate the even and odd coefficients, respectively. The proposed 2D microphone array in [22] has good accuracy of sound field recording, and its geometry is more feasible than traditional spherical microphone arrays in real-world applications.
# A Wideband Terahertz Transmissive Polarization Manipulator Based on Metasurfaces
## 3 Results and Discussion
### Comparison with Previous Works  
Table 1 compares the major characteristics of the proposed polarization converter with other state-of-the-art works. Compared with other linear to circular polarization converters [51; 52; 53; 54; 55; 56; 57; 58], the  
Figure 11: Transmission characteristics of the receive filter.  
Figure 12: Electric field distribution with incident slant (xy) polarization at 0.91 THz frequency.  
proposed converter works for 43.9% relative bandwidth. Compared with other multifunctional polarization manipulators [27; 69; 70; 71], the proposed design works over a wider frequency range.
# Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording
## 4 Simulation and Results
Next, we considered the BER performance of the proposed model in the BPMR channel with the TMR effect (\(\Delta_{off}\
eq 0\)). We assumed that our model suffers from a 10% TMR. We compared the proposed model with serial detection [24] and the model used in [18]. The results are shown in Figure 5. When the TMR effect occurs, the ITI is becomes asymmetric. However, our proposed model can estimate and compensate for the change in ITI and still achieve the best performance. To investigate resistance to the TMR effect, we changed the level of TMR from 10% to 30%, with an SNR of 15 dB to compare the BER performance. The results are presented in Figure 6.  
Based on the results in Figures 5 and 6, we can see that the proposed model can achieve the best performance when the level of the TMR effect is less than 20%. When the TMR effect is more than 20%, the performance of our proposed model is the same as that of serial soft output [24]. Therefore, the proposed model can resist a TMR effect of less than 20%. In the next experiment, we simulated our proposed model on the BPMR channel with 6% position fluctuation. Figure 7 shows that our proposed model improved the BER performance compared to the serial soft output in [24] and three-way GPR target with feedback in [18] with 6% position fluctuation. When position fluctuation occurs, both ITI and ISI change. However, in this case, the ITI is still larger than the ISI. Therefore, estimating and compensating for the change in ITI is a major factor for improving the BER performance. In addition, we simulated according to the degree of position fluctuation, and the results are shown in Figure 8. We can see that the proposed model can resist position fluctuation of less than 18%. When the position fluctuation is more than 18%, the performances of all models converge. Figure 5: BER performance of the proposed model with 10% TMR [18; 24]. Figure 6: BER performance of the proposed model according to TMR at SNR = 15 dB [18,24]. Figure 7: BER performance of the proposed model with 6% position fluctuation [18,24].
# Performance Analysis of Direct Torque Controllers in Five-Phase Electrical Drives
## 4 Results and Discussion
### Step-Speed and Reverse Speed Tests  
Then, experimental tests were conducted to evaluate the step-speed response of the system. Figure 12 reviews the results obtained. In the experiment, the machine is initially magnetized and the reference speed is changed from 0 to 500 rpm at t = 0.2 s. The performance of the mechanical speed (upper row, left plot), the electrical torque (upper row, right figure), stator phase currents (bottom row, left illustration), and the stator flux (bottom row, right drawing) is shown. Again, it can be observed that the DTC scheme provides accurate tracking of the reference speed and electrical torque, while the stator flux is maintained constant and equal to the reference value.  
Finally, a reverse speed test is generated, where a change in the reference speed from 500 to -500 rpm is forced at \(t\) = 0.2 s. The obtained results are shown in Figure 13. Appropriate tracking is observed in the speed and the electrical torque, as can be seen  
Figure 11: Experimental response of the controlled system in a load torque rejection test. The reference speed is 500 rpm and a load torque is applied at 0.5 s. The upper row shows the speed and torque responses. The lower row shows the stator current waveform and modulus of the stator flux during the test.  
Figure 12: Experimental response of the controlled system in a step speed test. The reference speed is changed from 0 to 500 rpm at 0.2 s. The upper row shows the speed and torque responses. The lower row shows the stator current waveform and modulus of the stator flux during the test.  
in the upper row plots, left and right ones, respectively, while the expected stator phase current performance is observed close to the zero-speed crossing operating point (see lower row, left plot). Note that a saturation value of the reference torque (T\({}_{\text{MAX}}\)) is set to 3.25 N\(\cdot\)m, being the stator flux constant and equal to the reference value.