esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Computational Complexity  
The computational complexity (CC) was evaluated by exploring various parameters for the training and testing of the algorithms. This measure is related to the complexity of the DL algorithm, with the costs during training and testing are used to compute the measures of complexity for the DL models.  
The CC of the presented algorithms was investigated according to their training and testing times and was compared with those of six (6) pre-trained algorithms (AlexNet, AlexNet + DA, InceptionV3, InceptionV3 + DA, MobileNet, and MobileNet + DA) and two Light ConvNets (LAECNet, and LAECNet + DA). Here, we compare the CC scores of the DL algorithms. As depicted in Table 7, the InceptionV3 model exhibited the most lengthy testing and training time among the four models without DA. MobileNet + DA had the shortest training time and considerable testing time compared to other networks because MobileNet + DA adopted fewer parameters than the AlexNet, InceptionV3, and LAECNet network architectures. From the perspective of classification accuracy (Table 4), and reducing computing resources, the MobileNet model (with DA) achieved remarkable performance with less computational complexity, which also demonstrates the feasibility and superiority of this approach for automotive engine component crack classification.
## 4 Mathematical Model
### Thermal Balance on the Inner Surface of the Glazing (Non-Ventilated Air Gap)  
In the non-ventilated air layer between the inner surface of the glazing (\(x=e_{g}\)) and the outer storage wall surface (\(x=e_{g}+\delta_{1}\)), the average temperature of fluid \(T_{f1}\) is set equal to the average glazing and storage wall surface temperatures: \(T_{f1}=(T_{g}+T_{w})/2\). The thermal balance of the two walls (glazing and storage wall) is calculated by the following equations:  
\[\phi_{g}|_{x=e_{g}}=h_{r1}\left(T_{w,ext}-T_{g,int}\right)+h_{c1}\left(T_{f1}- T_{g,int}\right)+\xi_{g}Q_{glob,vert} \tag{9}\]  
\[\phi_{w}|_{x=e_{g}+\delta_{1}}=h_{r1}\left(T_{g,int}-T_{w,ext}\right)+h_{c1} \left(T_{f1}-T_{w,ext}\right)+\xi_{w}Q_{glob,vert} \tag{10}\]  
\(h_{r1}\) is the radiative heat transfer coefficient between the two surfaces (glazing and storage wall):  
\[h_{r1}=\frac{\sigma\left(T_{w,ext}^{2}+T_{g,int}^{2}\right)\left(T_{w,ext}+T_{ g,int}\right)}{1/e_{g,int}+1/\varepsilon_{w,ext}-1} \tag{11}\]  
The convective heat transfer coefficient \(h_{c1}\) in the non-ventilated air gap between the two walls is now calculated from the Hallands and Buchberg correlations [61; 62]:  
\[Nu=\frac{h_{c1}H}{\lambda_{f1}}=max\left\{1,0.288\left(\frac{Ra.W}{H}\right)^{ 1/4},0.039\left(\frac{Ra.W}{H}\right)^{1/3}\right\} \tag{12}\]  
\[Ra=Gr_{f1}.Pr_{f1}=\left(\frac{\beta_{f1}g\rho_{f1}^{2}\delta_{1}^{3}\left|T_{ g}-T_{w}\right|}{\mu_{f1}^{2}}\right)\left(\frac{\mu_{f1}c_{f1}}{\lambda_{f1}}\right) \tag{13}\]  
To increase the effectiveness of the wall, several parameters characteristic of the exterior cover become important, namely: the global transmission coefficient of double glazing \(\tau\) and the emissivity factor \(\varepsilon\) significantly influence both the solar radiation absorption by the opaque wall and the limitation of thermal heat loss. Due to multi-reflection between the two walls of the non-ventilated air layer, \(\xi_{g}\) and \(\xi_{w}\) indicate the global coefficients of total fluxes of incident shortwave solar radiation absorbed by glazing and wall:  
\[\xi_{g}=\frac{\tau_{g1}\alpha_{g2}}{1-\rho_{g2}\rho_{g1}}+\frac{\tau_{g1}\tau_ {g2}\rho_{w}\alpha_{g2}}{1-\rho_{w}\rho_{g2}}\left(1+\frac{\rho_{g2}\rho_{g1}} {1-\rho_{g2}\rho_{g1}}\right) \tag{14}\]  
\[\xi_{w}=\frac{\tau_{g1}\tau_{g2}\alpha_{w}}{1-\rho_{w}\rho_{g2}}+\left(\tau_{g 1}\tau_{g2}\alpha_{w}+\frac{\tau_{g1}\tau_{g2}^{3}\rho_{w}\rho_{g1}\alpha_{w}} {1-\tau_{g2}^{3}\rho_{w}\rho_{g1}}\right) \tag{15}\]
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 3 Fundamentals of Different LAS Measurement Schemes  
Absorption is based on signal difference between the light intensities before and after propagation through a medium. When the incident light source has a spectral linewidth much narrower than the absorption transition feature, the so-gained spectral information can be regarded as single-wavelength without the need for light dispersion elements which can potentially introduce a reduction in spectral resolution. The use of semiconductor lasers which can be easily and fast tuned with injection current and temperature also allows the access to a range of wavelengths and absorption feature profiles with a single laser device. A detailed description of typical laser sources employed in LAS including distributed feedback lasers (DFBs), vertical-cavity surface-emitting lasers (VCSELs), Fourier-domain mode-locked lasers (FDML), quantum cascade lasers (QCLs), interband cascade lasers (ICLs), and their corresponding features, such as linewidth, power, and tunability has been presented by [PERSON] et al. (2012) and [PERSON] et al. (2013).  
The two most commonly employed schemes in realizing a LAS sensor with wavelength-tunable, narrow-linewidth lasers include direct absorption spectroscopy (DAS) and wavelength modulation spectroscopy (WMS), and each can be further associated with fixed- or scanned-wavelength schemes.  
Figure 3: Absorption linestrengths for O\({}_{2}\), CO, CO\({}_{2}\), CH\({}_{4}\), NO, NO\({}_{2}\), NH\({}_{3}\), and H\({}_{2}\)O at (**a**) 650 K, (**b**) 1800 K. Transitions with line strengths less than \(1\times 10^{-23}\) cm/molecule are not shown. All data taken from HITRAN2016 [47].  
For fixed-wavelength DAS, as the laser wavelength is not tuned or modulated, the detection bandwidth is solely limited by the detector bandwidth and the sampling rate of the data acquisition system. Therefore, this scheme is often used in applications where an extremely high temporal resolution at levels of 1 MHz or above is needed. However, it requires the absolute laser wavelength to be accurately known and precisely controlled. In contrast, the scanned-wavelength scheme is widely used for its higher robustness and better immunity against fluctuations in practical combustion system monitoring.  
The basic principles of DAS and WMS have been detailed elsewhere [48]. Here, from a different perspective, we will give a brief summary of each technique and elucidate the fitting algorithms used to infer gas conditions.
## 3 Motivation  
ECC is widely used to reduce the bit error rate of NAND flash memory devices. Combining ECC with the different location of the data, data of the MSB pages has much lower error rates. We tested the raw bit error and the effect of ECC using Micron's MLC flash package 29F64 GCBAAA with capacity of 64 Gb and a maximum of 3000 P/E cycles. The results are shown in Figure 3. The raw bit error rate for two different locations of the data is compared in Figure 3a. In this experiment, we program both of LSB pages and MSB pages in ten blocks and analyze the raw bit error rate of the LSB page and the MSB page, respectively. It can be observed that the data allocation has great impact on the raw bit error rate, and the data of MSB pages can achieve a much lower level raw bit error rate than the data of LSB pages. By combining this with ECC, the uncorrectable page error rate can be further reduced (by up to 98.10% of page errors) as shown in Figure 3b. At the time of the maximum 3000 P/E cycles of the lifetime of the tested MLC NAND flash and using 3-bit error correctable BCH code, page errors of over 50% were incurred for the LSB pages, while a reasonably low error rate was maintained by allocating data into MSB pages even after the lifetime of the MLC NAND flash.  
Given an N-bit physical page, let the raw bit error rate be R, and the ECC scheme can correct E bits of errors. Then, the probability that there will be more than E faulty bits on a page is [37]:  
\[1-\sum_{i=0}^{E}\binom{N}{i}\cdot(1-R)^{i}\cdot R^{N-i} \tag{3}\]  
The bit error rate of one MSB pages is \(\sigma*R\), where \(\sigma\) denotes the ratio of reducing errors of the date in MSB pages than that of LSB pages. For example, for the case in Figure 3, \(\sigma\) is 0.5. Then, the probability that there will be more than E faulty bits on a page, or the uncorrectable page error, is:  
\[1-\sum_{i=0}^{E}\binom{N}{i}\cdot(1-\sigma*R)^{i}\cdot(\sigma*R)^{N-i} \tag{4}\]  
Figure 2: SLC and MLC NAND flash coding and the corresponding reference voltage.  
Based on the above equations, we can calculate the uncorrectable page error rate for LSB pages and MSB pages. The results are presented in Table 1. Compared to date of the LSB pages, allocating data into MSB pages can significantly reduce the uncorrectable page error rate. If we allocate all of the metadata into MSB pages, instead of allocating it into both LSB pages and MSB pages randomly and symmetrically, the metadata will achieve a much lower uncorrectable page error rate following this asymmetric programming manner. Then the reliability of the whole system will be improved. Also, different from redundancy methods which will induce extra space overhead and eventually harm both the I/O performance and endurance of NAN flash memory, LSB pages is still available when allocating the metadata into MSB pages, which means that there will be no space overhead. This observation motives us to present a fault-tolerant scheme to provide a more reliable MLC NAND flash memory storage system.
## 3 Proposed Scheme
### Data Extraction and Recovering Procedure  
In this section, we introduce the process of data extraction and image restoration from the proposed model. If the receiving side has only the encryption key EK or data secret key DK, image restoration or extraction is possible. If both keys are present, image restoration and data extraction are possible. If we do not have the encryption key and data secret key, image restoration and data extraction cannot be performed. These features can not only protect images from attackers, but also hide data. We describe the process of extracting hidden information from the marked images \(I^{\prime\prime}\) created in Section 3.2 and restoring the original image. The detailed process is described in stages.  
**Input**: An encrypted AMBTC cover image \(I^{\prime\prime}\) sized \(N\times N\), and both keys EK and DK.  
**Output**: A secret message and the cover image \(I\) sized \(N\times N\) and \(S^{\prime}=(s_{1},s_{2},\ldots,s_{n})\).  
**Step 1:**: Read the \(i\)-th \(\mathit{trio}(Q_{1},Q_{2},M^{\prime})\) from the encrypted cover image \(I^{\prime\prime}\), where \(Q_{1}\) and \(Q_{2}\) are the quantization levels and \(M^{\prime}\) is a bitmap \(M^{\prime}=(m^{\prime}_{1},m^{\prime}_{2},\ldots,m^{\prime}_{15},m^{\prime}_{16})\).
**Step 2:**: Assign the bitmap \(M^{\prime 15}_{1}\) to codeword \(c\), i.e., \(c=M^{\prime 15}_{1}\).
**Step 3:**: Calculate the syndrome \(\hat{\eta}=H\cdot c^{T}\) for codeword \(c\), and then assign the syndrome value \(\hat{\eta}^{T}\) to the secret bits \(s^{\prime}_{i+4}\), i.e., \(s^{\prime}_{i+4}=\hat{\eta}^{T}\), \(i=i+4\). For the extracted 4-bit data, decoding is performed using \(r\) created by DK using a standard stream cipher. That is, \(s_{i+4}=s^{\prime}_{i+4}\oplus r_{i}\).
**Step 4:**: Repeat _Steps 1_ and 3 for all blocks. Then, the secret data are recovered completely.
**Step 5:**: The original bitmap containing hidden bits can be recovered from the encrypted image by the EK. For decryption, the exclusive-or results of the encrypted bits and pseudo-random bits are calculated via Equation (16). This restores the bitmap \(M=(m_{1},m_{2},\ldots,m_{16})\).  
\[m_{j}=\sum_{j=1}^{16}m^{\prime}_{j}\oplus r_{j} \tag{16}\]
**Step 6:**: In the data hiding process, the location \(\hat{\eta}\) of the codeword was changed, and to restore it during the image restoration process, the payload was attached to the back of \(M^{\prime}\) and recorded. In this was, the bitmap \(M\) is restored. That is, after obtaining the changed matrix position(row, column) from the bitmap, the restored bitmap \(M\) is obtained using Equation (17), where \(rc(1)\) is the row and \(rc(2)\) is the column position of the location \(\hat{\eta}\).  
\[M=\sum_{u=1}^{4}\sum_{v=1}^{4}\left\{\begin{matrix}\text{flip}(M^{\prime}_{u,v }),\;if(u=rc(1)\text{ and }v=rc(2)),\\ M^{\prime}_{u,v},\;\text{ otherwise.}\end{matrix}\right. \tag{17}\]
**Step 7:**: Repeat _Steps 1_ and 6 for all blocks. Then, all bitmaps are recovered completely.  
The image \(I\) restored by this process is the cover image and becomes the original AMBTC image.
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells
### Materials for Organic Solar Cells
Different metals can have peculiar and dissimilar effects on the optoelectronic properties of the nanocomposites and their performance in the context of solar cells [76; 77; 78; 79; 80]. The incorporation of silver (Ag) and gold (Au) nano particles in polymer bulk heterojunction solar cells enhances their performance owing to the improved absorption of sunlight. In the context of OSC, conjugated polymer nanowires are mostly used due to their ability to create percolation pathways for both electrons and holes, leading to higher device efficiency [81; 82; 83; 84]. The applications of polymer nanowires have several advantages such as (a) better control over morphology, (b) appropriate matching of the exciton diffusion lengths to the widths and lengths of polymer nanowires, (c) large interfacial area between donor and acceptor, (d) existence of electrically bi-continuous morphology, (e) achievement of high absorption coefficient and high carrier mobilities, (f) possibility of manufacturing devices with high surface areas, and (g) the avoidance of blend phase-separation difficulties [85; 86]. Thermal annealing of the P3 HT/PCBM mixture at 120 \({}^{\circ}\)C for 60 min renders P3 HT nanowires with improved crystallinity of P3 HT and enhances the demixing between P3 HT and PCBM [87].
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Effects of Agro-Wastes on RC Elements
#### 4.3.2 Density and Insulation Properties of Concrete  
Studies have shown that lightweight concrete produced with palm oil shells has a lower density and better insulation properties compared to conventional lightweight concrete [74]. Furthermore, the results of shear strength tests on foamed concrete reveal that lower-density concretes tend to exhibit lower shear strength. This observation suggests that while lightweight aggregates can optimize the density properties of concrete, they may also introduce certain mechanical limitations [69].  
The use of PO shells as lightweight aggregates has been highlighted for its positive impact not only on density but also on thermal insulation performance. The incorporation of such AWs in RC structures presents significant potential for developing sustainable construction materials and enhancing energy efficiency in buildings.
## 3 Calorimetric Measurements
### Discussion  
The thermal power delivered by plasma has been determined, in several conditions of both power supply and distance, in different metallic foil samples. The performed analyses allowed to identify resistive coupling as the dominant mechanism for power deposition on the substrate. As a consequence, in order to precisely determine the heat load coupled upon a plasma treatment, it is necessary to infer the target resistivity. For this reason, extrapolating the load to biological samples is not trivial, since the substrate resistivity is not known and depends on the specific nature of the substrate. It could be useful, in this context, to use alternative materials, such as the soft gels described in [PERSON] et al. (2014) [35], used as a model to simulate the transport of reactive species from plasma to biological tissues. However, even in that case, the thermal and electrical properties of such materials are almost unknown. So we preferred estimating the power carried by the plasma through the use of materials, of which we can precisely infer the chemical-physical properties.  
Figure 5: Calorimetric estimates of power at different frequency. (**a**) Comparison between grounded and floating samples. (**b**) Comparison between grounded samples of different thickness.  
Figure 6: Calorimetric estimates of power at different distances from the PCC.  
It must also be said that the estimated power values are comparable with those reported in [PERSON] et al. (2009) [36]. In that work, as in ours, it is shown how the power decreases with the distance from the source. In addition, the importance of controlling the plasma dose was emphasized to avoid deleterious effects on living tissues. To our knowledge, there are not many publications showing the thermal output of the plasma, like the one we reported. However, the importance of avoiding overheating of the biological substrate is widely recognized to avoid cellular damage [37; 38]. Among the latter, we can report the denaturation of proteins, evaporation of cellular liquid, structural alterations of the cell membrane, up to cell death [37; 39]. The heat dose control is therefore fundamental for the clinical applications of cold plasmas. This control can be done in multiple ways--by varying the power coupled to the plasma, controlling the distance and the treatment time. In this context, PCC is a useful source of plasma for biomedical applications, thanks to its flexibility. We have shown that it is indeed possible to vary the power coupled to the plasma in such a way as to induce coagulation but avoiding, at the same time, the appearance of deleterious effects on biological tissues. Our calorimetric analysis therefore serves as a preliminary activity for tuning such a plasma dose. It must be said however that a direct thermographic measurement on the biological sample could be helpful to avoid undesired thermal loads. Furthermore, it shall be noticed that hand-held use of plasma sources would hardly grant a mm precision in the distance source-substrate, while mechanical suspension has to be preferred where possible (e.g., in vitro).
# A Patch/Dipole Hybrid-Mode Antenna for Sub-6 GHz Communication
###### Abstract  
A low-profile antenna with a high gain and broad bandwidth is proposed for Sub-6 GHz communication in this paper. A narrow-band patch mode and a narrow-band dipole mode are shared in one radiator and simultaneously excited to broaden the bandwidth. A compact prototype with a projection size of 0.90 \(\lambda_{0}\)\(\times\) 0.78 \(\lambda_{0}\) and a profile of 0.13 \(\lambda_{0}\) (\(\lambda_{0}\) is the wavelength in the free space at the center of the operating frequency) is fabricated and measured. The measurement demonstrates an impedance bandwidth of 67.50%, covering the frequency range from 2.75 GHz to 5.45 GHz and an average gain of 8.4 dBi in the operating band of 3.0-5.0 GHz.  
hybrid mode; slotted patch; low profile; Sub-6 GHz communication +
Footnote †: journal: Sensors
# MRI Diffusion Connectomics-Based Characterization of Progression in Alzheimer's Disease
## 4 Discussion  
Alzheimer's disease is a complex and widespread [20] neurodegenerative disease that manifests itself in multiple ways [6; 70], including atrophy of the hippocampus, entorhinal region, and middle temporal regions, as well as accumulation of A\(\beta\) and Tau proteins. Tractography measurements derived from diffusion-weighted images appear to show limited potential as a single scan test capable of offering predictions for many of the traditional neuropsychological and neurobiological assessment metrics used today; however, they may still contribute as an important tool to a comprehensive approach to understanding the complexity of AD and characterization of disease staging for some of these clinical variables.  
Assessing and diagnosing Alzheimer's disease remain complex and challenging due to the lack of a complete model that can identify the disease in any stage. Currently, diagnosis often relies heavily on subjective assessments derived from neuropsychological and neurobiological tests carried out in primary care settings. These tests, while valuable, can be influenced by various factors, such as the examiner's expertise, the time of day when the test is administered, the testing environment, and the patient's physical and emotional state at the time of testing [71].  
The inherent variability and subjectivity in these evaluations can lead to inconsistent diagnoses, particularly in the early or preclinical stages of Alzheimer's disease, where symptoms may be subtle or overlap with other conditions, such as ageing. Clinic pathological studies have shown that the diagnostic sensitivity of clinicians is between 70.9% and 87.3% and the specificity is between 44.3% and 70.8% [69]. Additionally, traditional diagnostic methods may fail to capture the full spectrum of neuropathological changes associated with Alzheimer's, limiting their effectiveness in early detection and intervention. In light of this, the aim of our study was to elucidate the contribution that diffusion-weighted imaging can make to improving model development towards the early detection of Alzheimer's. Machine learning models hold significant promise for improving the evaluation of Alzheimer's disease by offering more objective, accurate, and scalable diagnostic tools. Machine learning can uncover subtle and complex relationships within the data that may not be apparent through traditional methods, potentially leading to earlier and more accurate diagnoses, a better monitoring of disease progression, and personalized treatment plans.  
Figure 6: Receiver operating characteristic plot and corresponding precision recall plot for eXtreme gradient boosting model predicting subjects who exhibit worsening disease stages within three years (Converters).