esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging
### Virtual Cell Merging with ZF and MVC-MMSE Transmission
#### MVC-MMSE Transmission
Let:  
\[\mathcal{L}(\mathbf{G},\lambda)=\mathbf{J}(\mathbf{G})+\lambda(\text{tr} \bigg{\{}\mathbf{G}^{H}\mathbf{G}\bigg{\}}-1) \tag{12}\]  
\[\mathbf{R}=\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{m}}^{H}\mathbf{H}_{ \mathcal{K}_{m},\mathcal{B}_{m}}+\sum_{l=1,\underline{J}\
eq m}^{M}\mathbf{H}_ {\mathcal{K}_{l},\mathcal{B}_{m}}^{H}\mathbf{H}_{\mathcal{K}_{l},\mathcal{B}_{ m}}+\lambda\mathbf{I}_{\left|\mathcal{K}_{m}\right|} \tag{13}\]  
Then, define matrix \(\mathbf{U}\) and \(\mathbf{V}\) based on \(\mathbf{R}\) as:  
\[\left\{ \begin{array}{l}\mathbf{U}=\mathbf{R}^{\frac{1}{2}}\mathbf{G}\\ \mathbf{V}=\mathbf{R}^{-\frac{1}{2}}\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{ m}}^{H}\end{array} \right. \tag{14}\]rewrite (12) as the following quadratic form:  
\[\mathcal{L}(\mathbf{G},\lambda)=\|\mathbf{U}-\mathbf{V}\|^{2}-\text{tr}\left\{ \mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{m}}\mathbf{R}^{-1}\mathbf{H}_{\mathcal{ K}_{m},\mathcal{B}_{m}}^{H}\right\}+\sigma^{2}\left|\mathcal{K}_{m}\right|-\lambda \tag{15}\]  
From (15), we know that Equation (12) can be minimized only if \(\mathbf{U}=\mathbf{V}\). Therefore, we can get that the optimal multi-cell precoding vector is:  
\[ \begin{split}\mathbf{G}^{\text{opt}}&=\mathbf{R}^{ -1}\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{m}}^{H}\\ &=\left(\sum_{l=1}^{M}\mathbf{H}_{\mathcal{K}_{l},\mathcal{B}_{m} }^{H}\mathbf{H}_{\mathcal{K}_{l},\mathcal{B}_{m}}+\lambda\mathbf{I}_{\left| \mathcal{K}_{l}\right|}\right)^{-1}\mathbf{H}_{\mathcal{K}_{m},\mathcal{B}_{ m}}^{H}\end{split}  \tag{16}\]  
We can get the boundary condition \(\lambda=\sigma^{2}\) by letting \(\frac{\partial\mathcal{L}(\mathbf{G},\lambda)}{\partial\lambda}=0\) in (12). Therefore, (16) can be transformed into (10). Normalizing the columns of \(\mathbf{M}_{\mathcal{K}_{m},\mathcal{B}_{m}}\), we can get the precoding vector for each user \(k\) in \(\mathcal{K}_{m}\) under the MVC-MMSE transmission scheme:  
\[\mathbf{w}_{k,\mathcal{B}_{m}}^{\text{MVC-MMSE}}=\frac{\mathbf{m}_{k,\mathcal{ B}_{m}}}{\|\mathbf{m}_{k,\mathcal{B}_{m}}\|} \tag{17}\]  
where \(\mathbf{m}_{k,\mathcal{B}_{m}}\) is the column vector of \(\mathbf{M}_{\mathcal{K}_{m},\mathcal{B}_{m}}\) corresponding to user \(k\) in \(\mathcal{K}_{m}\).
# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
###### Abstract  
Carrier-based Unmanned Aerial Vehicle (CUAV) landing is an extremely critical link in the overall chain of CUAV operations on ships. Vision-based landing location methods have advantages such as low cost and high accuracy. However, when an aircraft carrier is at sea, it may encounter complex weather conditions such as haze, which could lead to vision-based landing failures. This paper proposes a runway line recognition and localization method based on haze removal enhancement to solve this problem. Firstly, a haze removal algorithm using a multi-mechanism, multi-architecture network model is introduced. Compared with traditional algorithms, the proposed model not only consumes less GPU memory but also achieves superior image restoration results. Based on this, We employed the random sample consensus method to reduce the error in runway line localization. Additionally, extensive experiments conducted in the Airsim simulation environment have shown that our pipeline effectively addresses the issue of decreased detection accuracy of runway line detection algorithms in haze maritime conditions, improving the runway line localization accuracy by approximately 85%.  
carrier-based unmanned aerial vehicles; deep learning; runway detection Article  
An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
# Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise
## 5 Simulation and Numerical Results
Figure 7 shows the secrecy rate against the SNR with varying channel estimation error, \(\rho^{2}\), where \(N_{a}\) and \(N_{t}\) were set to six and four, respectively. As we can easily deduce, the secrecy rate decreased as \(\rho^{2}\) increased. Finally, Figure 8 compares the bit error rate (BER) performances of the proposed scheme. We employed the BPSK modulation scheme and various numbers of \(N_{a}\) and \(N_{t}\) at Alice. It was shown that the BER performance at Bob decreased rapidly with SNR for varying estimation error. However, BER at Eve was nearly 0.5 for all SNRs, which indicated that there was no information leaked to Eve. Figure 6: Comparison of \(\bar{R}_{s}\) for the proposed scheme according to \(N_{a}\) and \(N_{t}\). Figure 7: Comparison of \(\bar{R}_{s}\) for the proposed scheme with varying \(\rho^{2}\).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.2 QoS Requirements  
Another major research issue is the provision of QoS in transmitting telemedicine traffic. This is mainly due to the sensitive nature of medical data with respect to patients' health. Therefore, provision of QoS is a key factor when transmitting data for telemedicine applications. Providing QoS in telemedicine networks is a major challenge, particularly in critical services such as live video streaming of surgeries and remote diagnosis tasks [37].
## 7 Bioremediation: Microbial Pathway  
As mentioned, microbes are involved in most bioremediation techniques as they are easy to maintain with feasible application. The adoption of cold-adapted microbes in degrading diesel has shown great efficacy in various research works [54; 55]. _Rhodococcus_, _Pseudomonas_ and _Sphingomonas_ are among the common bacterial species dominating the oil-polluted Antarctic environment and contributing to the mineralisation of hydrocarbon [62; 63; 64]. [PERSON] et al. [55] reported on microbial application in bioremediation and the factors limiting the rate of degradation including temperature and availability of nutrients, oxygen and contaminants. The ability of bacteria to degrade hydrocarbon, even the most persistent PAHs, is proposed primarily through the aerobic metabolic pathway (Figure 3).  
The importance of oxygen as a key electron acceptor in the microbial system has been postulated; hence, the system was later developed to employ microalgae to complement the oxygen demand via the photosynthetic cycle. Algae-bacteria consortia establish a symbiotic relationship described by [PERSON] and [PERSON] [61] as an ideal self-sustaining system. Effective synergism and improved hydrocarbon degradation have been reported with varying degrees of success using different consortia. A co-culture of _B. cepacia_ and freshwater algae performed 99% total petroleum hydrocarbon removal and exhibited higher tolerance upon phenol loading [66]. The robustness of microbial bioremediation co-stimulated with algal culture has sparked the study of axenic algae in phytoremediation, one of the enhanced in situ bioremediation techniques.
# Enhancement of the Neuroprotective Effect of
## Materials and Methods  
### Preparation of Fermented S. maxima _Fermentation_  
The marine alga _Spirulina maxima_ used in this study was supplied by the Korea Institute of Ocean Science & Technology (KIOST, Jeju, Korea). To prepare the _S. maxima_ fermentation extract, 100 g of _S. maxima_ powder was mixed with 1 L of distilled water containing 0.5% (w/v) yeast extract, 1% peptone, 2% glucose, 0.01% magnesium sulfate, 0.005% manganese sulfate, 0.2% potassium phosphate and 0.1% (v/v) polysorbate 80 (All chemicals were purchased from Sigma, St. Louis, MO, USA). The culture medium was inoculated with 1 \(\times\) 10\({}^{5}\) CFU of _Lactobacillus plantarum_ HY-08 (Accession number: MG547899) as an initial seed volume, which was incubated at 37 \({}^{\circ}\)C for four days under anaerobic conditions. The whole culture broth was then treated by an ultrasonic extractor (AUG-R3-900, ASIA ULTRASONIC, Gyeonggi-do, Korea). Based on the previous research, we set the ultrasound conditions as follows: 40 kHz for 6 hr at room temperature [22]. To prepare two different control samples, 100 g of _S. maxima_ powder was first added to 1 L of distilled water and extracted at 100 \({}^{\circ}\)C for 12 h, and the ultrasonic extract was prepared only by ultrasonic extractor treatment (AUG-R3-900, ASIA ULTRASONIC, Gyeonggi-do, Korea) at 40 kHz for 12 hr at room temperature. Both of the obtained extracts were filtered using a vacuum filtration pump (KNF Laboport Pressure Pump, Cole-Parmer, Vernon Hills, IL, USA) and 20-25 \(\upmu\)m filter paper from Whatman Co. (Piscataway, NJ, USA). The filtered extracts were concentrated using a rotary vacuum evaporator (Rotary Vacuum Evaporator N-N series, EYELA, Rikakikai Co., Tokyo, Japan), and powdered using a freeze dryer (PVTFA 10 AT, ILSIN, Suwon, Korea).  
Determination of \(\beta\)-Carotene Content in the _S. maxima_ extracts from the three different processes was analyzed using high-performance liquid chromatography (HPLC, Dionex 3000, Dionex Co., Sunnyvale, CA, USA) with a C\({}_{18}\) column (Jupiter C\({}_{18}\) column, 4.6 \(\times\) 250 mm, 5 \(\upmu\)m, 300 A, 00G-4053-E0, Phenomenex, CA, USA). In the analysis, the mobile phases of solvent A (60% methanol, 10% butanol, 30% water) and solvent B (89.5% methanol, 10% butanol, 0.5% water) were flowed under gradient conditions of 0-8 min (75:25), 8-50 min (90:10), 50-55 min (90:10), 55-57 min (75:25), and 57-65 min (75:25) at a flow velocity of 1.0 mL/min, along with a standard \(\beta\)-carotene (C4582, Sigma-Aldrich, St. Louis, MO, USA). The analysis was conducted by simultaneously evaluating the wavelengths 452 nm and 325 nm [17].
# Operational Temperature Effect on Positioning Accuracy of a Single-Axial Moving Carrier
###### Abstract  
This study investigated the ambient environmental temperature effect on the positioning accuracy of a periodically-moving carrier. The moving carrier was operated in an environmental chamber in which the operational temperature could be controlled by an air conditioning system. Different operational temperature modes, including a stable environment, a rise in temperature, a decline in temperature, summer daytime hours, and winter nighttime hours in terms of seasonal climate change in Taiwan, were generated within the environmental chamber by an air conditioning system to investigate the operational temperature's effect on positioning accuracy. From the experimental measurements of a periodically-moving carrier, it is found that the operational temperature conditions can significantly affect the positioning accuracy of the moving carrier, especially in the case of an operational temperature decline. Under stable operational conditions, the positioning accuracy of the moving carrier can be considerably improved. In comparison to the case of an operational temperature decline, the positioning accuracy improvement can reach 29.6%. Moreover, the effect of the temperature distributions within the chamber on the positioning accuracy was further investigated. It was found that, with a parallel flow pattern in the chamber, the positioning accuracy can be further enhanced.  
Keywords:positioning accuracy; environmental temperature; machine tool; environmental chamber +
Footnote †: journal: Applied Physics Letters
## Materials and Methods
### Time-Series Smoothing Methods
#### 2.2.1 Polynomial Local Filtering (SG and LO)  
The principles of SG and LO are similar in that both methods replace each data value by a combination of adjacent values in a window using a least-squares second-order polynomial fit (Figures 3 and 4). The size of the filtering window determines the degree of smoothing. If rapid variations are encountered in the window, both methods are in TIMESAT modified to automatically decrease the window size. This enables quite sudden variations in the NDVI trajectory to be modelled. In this study, the definition of the filtering window size \(r\) in both smoothing methods is the same as in TIMESAT, i.e., \(r=2q+1\), where \(q\) is the number of time steps from right/left to the middle point \(m\).  
Figure 4: Examples of LOESS (LO) smoothing with different window sizes, \(r=2q+1\).  
Figure 3: Examples of Savitzky-Golay (SG) smoothing with different window sizes, \(r=2q+1\).  
Figure 2: Examples of the effects of modifying parameters for upper-envelope adaptation in TIMESAT using double logistic (DL) fits to data. \(u\): the number of iterations, and \(a\): is the adaptation strength.  
The difference between SG and LO is in the weights of each data point in the filtering window. In SG, all points of the fitting window have weight 1.0, whereas in LO weight \(w_{i}\) of point \(i\) decreases outwards from the middle point \(m\) of the filtering window  
\[w_{i}=\left(1-\left(\frac{|m-i|}{r}\right)^{3}\right)^{3} \tag{1}\]  
If the same window size is applied to both methods, the different weighting systems cause LO to have lower total data weight than SG. In other words, LO has to have a larger window size than SG to result in the same data smoothing effect as SG. A benefit of LO is that it prevents some of the sharp peaks often encountered in SG filtering.  
In this study, we tested varying \(q\) in SG from 2 to 7, and equivalently varying \(q\) in LO from 4 to 12 (Table 2).
## 3 Results and Discussion
### Spatial Differences of VOCs  
The total VOCs concentrations (the sum of the mean concentrations of individual VOCs during the sampling period) varied across sampling sites during all seasons in 2020. The total VOCs variations were from 233 to 420, from 231 to 437, from 48 to 151, from 46 to 133, and from 72 to 393 \(\upmu\)g m\({}^{-3}\) in sampling days in January, April, April-May, July, and October, respectively. TVOCs concentrations decreased on 28, 30 April, and 3 May by an average of 74 and 67% compared to 15, 17, 19 January and 3, 5, 7 April, respectively.  
The spatial distribution of TVOCs (Figure 4) varied from the south to the north. All sampling seasons had a similar spatial profile, with lower TVOCs concentrations in the south and higher in the north of Almaty, where CHPs are located. 15, 17, 19 January and 21, 23, 25 October were characterized by a strong negative correlation between TVOCs level and elevation of sampling sites with r = \(-\)1.0 and r = \(-\)0.89, while a moderate negative correlation was observed in sampling days in April, April-May, and July (Figure S2 in Supplementary Materials). These correlations indicate the possible effect of heating season on TVOCs spatial distribution due to increased coal consumption [4] at low ambient temperature.  
For the studied periods, the most polluted sampling site in Almaty was site S4 (Pa-panin str.-Suyunbay ave.). The sampling site S4 is at 12 km from CHP-3 and 14 km from CHP-2 and the lowest elevation above the sea level (700 m) (Table 1). There are a number of private houses close to S4 sampling sites that use solid fuel for heating (coal) and avenues with heavy traffic, which could contribute to air pollution around the S4 sampling site. The lower TVOCs concentrations were obtained in sampling sites S1 and S2 during all sampling periods. These sampling sites are located at a higher level (978 and 944 m) close to mountains and far from CHP-3 (22-26 km) and CHP-2 (13-15 km).
# Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems
## 2 Background
### System Model  
This paper considers a MIMO system with \(N_{t}\) transmit and \(N_{r}\) receive antennas. Let \(\mathbf{\hat{H}}\in\mathbb{C}^{N_{r}\times N_{t}}\) denote the channel matrix, where \(\tilde{h}_{i,j}\) is the complex channel gain between the \(j\)th transmit antenna and the \(i\)th receive antenna. Let \(\mathbf{\tilde{s}}\in\mathbb{C}^{N_{t}\times 1}\) be a transmit signal vector whose elements take values from a complex constellation \(\mathbb{\hat{A}}\) of QAM modulation. An \(N_{r}\times 1\) received signal vector \(\mathbf{\hat{y}}\) is given by the complex signal model  
\[\mathbf{\hat{y}}=\mathbf{\hat{H}}\mathbf{\hat{s}}+\mathbf{\hat{n}}, \tag{4}\]  
where \(\mathbf{\hat{n}}\in\mathbb{C}^{N_{r}\times 1}\) is a noise vector consisting of independent and identically distributed (i.i.d.) complex additive white Gaussian noise (AWGN) samples with zero mean and unit variance \(\sigma_{n}^{2}\).  
The complex signal model (4) can be converted to its equivalent real signal model \(\mathbf{y}=\mathbf{H}\mathbf{s}+\mathbf{n}\), i.e.,  
\[\left[\begin{array}{c}\Re(\mathbf{\hat{y}})\\ \Im(\mathbf{\hat{y}})\end{array}\right]=\left[\begin{array}{cc}\Re(\mathbf{ \hat{H}})&-\Im(\mathbf{\hat{H}})\\ \Im(\mathbf{\hat{H}})&\Re(\mathbf{\hat{H}})\end{array}\right]\left[\begin{array} []{c}\Re(\mathbf{\hat{s}})\\ \Im(\mathbf{\hat{s}})\end{array}\right]+\left[\begin{array}{c}\Re(\mathbf{ \hat{n}})\\ \Im(\mathbf{\hat{n}})\end{array}\right], \tag{5}\]  
where there are \(N=2N_{t}=2N_{r}\) layers, \(\mathbf{y}\), \(\mathbf{x}\), and \(\mathbf{n}\) are the \((N\times 1)\)-equivalent real received, transmitted, and AWGN noise signal vectors, respectively, and \(\mathbf{H}\) represents the \((N\times N)\)-equivalent real channel matrix. Finally, \(\mathbb{A}=\{\pm 1,\pm 3\ldots,\sqrt{\Omega}-1\}\) is the equivalent real-valued constellation set of \(\mathbb{\hat{A}}\), where \(\Omega\) indicates the modulation order of \(\Omega\)-QAM signaling.  
Based on the signal model in (5), the maximum likelihood (ML) solution can be expressed as  
\[\mathbf{s}_{\text{ML}}=\arg\min_{\mathbf{s}\in\mathbb{A}^{N}}\phi(\mathbf{s}), \tag{6}\]  
where \(\mathbb{A}^{N}\) is the set of all possible transmitted signal vectors and \(\phi(\mathbf{s})=\|\mathbf{y}-\mathbf{H}\mathbf{s}\|^{2}\) is the ML metric of \(\mathbf{s}\). The computational complexity of the ML detection in (6) grows exponentially with \(N\), which makes it computationally prohibitive for large MIMO systems in which \(N\) is large.
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Semisupervised Discriminant Analysis
#### 3.2.2 PCA  
Let \(\{y\}_{i=1}^{u}\) be a set of \(u\) unlabeled RSSI observations and \(S^{p}\) be the total scatter matrix, given by  
\[S^{p}=\sum_{i=1}^{u}(y_{i}-\mu)(y_{i}-\mu)^{\mathrm{T}}, \tag{20}\]  
where \(\mu\) is the mean of all the observations:  
\[\mu=\frac{1}{u}\sum_{i=1}^{u}y_{i}. \tag{21}\]Equation (20) can be also expressed in a pairwise form as  
\[S^{p} = \sum_{i=1}^{u}y_{i}y_{i}^{\mathrm{T}}-n\mu\mu^{\mathrm{T}} \tag{22}\] \[= \frac{1}{2}\sum_{i,j=1}^{u}y_{i}y_{i}^{\mathrm{T}}-\frac{1}{2}\sum _{i,j=1}^{u}y_{i}y_{j}^{\mathrm{T}}\] \[= \frac{1}{2}\sum_{i,j=1}^{u}W_{ij}^{p}(y_{i}-y_{j})(y_{i}-y_{j})^{ \mathrm{T}},\]  
with \(W_{ij}^{p}=1/u\). PCA finds the transformation matrix \(T^{pca}\) where  
\[T^{\mathrm{PCA}}=\mathrm{argmax}_{T}\left[\mathrm{tr}\left(T^{ \mathrm{T}}S^{p}T(T^{\mathrm{T}}T)^{-1}\right)\right]. \tag{23}\]  
Note that PCA is an unsupervised method that does not require label information such as floor level. Thus, PCA alone cannot be used for a supervised problem such as floor classification and landmark detection in this paper.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### Positioning Domains  
In terms of measurements, there are multiple domains from which they can be extracted from, as long as there are means to do so in the devices. For that reason, we briefly present three of the main domains we consider of interest for an IoT positioning system:  
* Power or signal strength-based;
* Time-based;
* Space-based.  
Other domains, such as natural or artificial fields, e.g., geo-magnetic field, light, sounds, or smell are out of the scope of our study, but could also serve as relevant sources of information for future IoT positioning systems.  
The following subsections provide a short summary of main challenges in each of these three positioning domains and their system-wide impacts.
###### Abstract  
Device-to-device (D2D) communication is proposed as a promising technique of future cellular networks which fulfills its potential in terms of high resource utilization. In this paper, in order to improve the achievable rate of D2D communication and the spectrum utilization, we consider the scenario that multiple D2D pairs can share uplink spectrum resources with multiple cellular users (CUs). We aim to maximize the overall system spectrum efficiency while satisfying the rate requirements of all CUs and guaranteeing that the system gain is positive. We formulate the joint optimization problem of subcarrier assignment and power allocation which falls naturally into a mixed integer non-linear programming form that is a difficult problem to solve. Hence, we propose a two-stage resource allocation scheme which comprises a subcarrier assignment by employing a heuristic greedy strategy, as well as a power allocation algorithm based on the Lagrangian dual method. Numerical results demonstrate the advantageous performance of our scheme in greatly increasing the system sum spectrum efficiency.  
cellular network; multiple device-to-device (D2D) communication; mixed integer non-linear programming; multi-subcarrier reusing; power allocation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Field Experiment and Sample Collection
#### 2.1.1 Design and Location  
Field experiments were conducted from 2021 to 2024 in Brodnica County, Kuyavian-Pomeranian Voivodeship, Poland (GPS: 53.1912 N, 19.4337 E). The soil was classified as quality class IV, with a granulometric composition of >1.0 mm: 5%, 1.0-0.1 mm: 52%, 0.1-0.02 mm: 22%, and <0.02 mm: 21%. Soil pH (1 M KCl) ranged from 6.4 to 6.6. Available nutrients included P\({}_{2}\)O\({}_{5}\): 27.3-42.0 mg/100 g, K\({}_{2}\)O: 13.6-20.2 mg/100 g, and Mg: 4.1-6.2 mg/100 g. Organic matter content remained between 1.42% and 1.51%.  
Five randomized blocks (each 0.172 ha) were established, with 2 \(\times\) 2 m microplots replicated five times per treatment. The total cultivated area was 4 ha.
# Viability of ABO Blood Typing with ATR-FTIR Spectroscopy
## 3 Results and Discussion  
The actual blood distribution of the Spanish population, according to Red Cross data [15], shows a very low presence of certain types (Table 1). Thus, keeping this proportion in the training and test datasets could lead to poor results because of the low amounts. Therefore, blood samples were selected in order to have a proper amount of each type, regardless of a possible overrepresentation compared to the natural distribution (Table 1).  
The spectroscopic study was carried out using two different approaches, a first considering both ABO blood type and Rh factor and a second taking into account only ABO blood type. The average spectra for every class are depicted in Figure 1, showing no evident differences with the ABO type or the Rh factor. Thus, the ABO typing was evaluated from a mathematical approach using IA tools. For this, the area under the most relevant peaks, a total of 18, were selected as input variables in the different algorithms (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **0+** & **A+** & **B+** & **AB+** & **0-** & **A-** & **B-** & **AB-** \\ \hline Spanish & 35\% & 36\% & 8\% & 2.5\% & 9\% & 7\% & 2\% & 0.5\% \\ \hline Number of samples & 24 (24\%) & 21 (21\%) & 36 (36\%) & 5 (5\%) & 5 (5\%) & 2 (2\%) & 3 (3\%) & 4 (4\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ABO Blood-type distribution and number of samples.  
These peaks are mainly related to the amide I (1639 cm\({}^{-1}\)), amide II (1537 cm\({}^{-1}\)), and amide III (1300 cm\({}^{-1}\)) bands characteristic of proteins and also of carbohydrates, with strong very wide bands between 3520 cm\({}^{-1}\) and 3100 cm\({}^{-1}\)[16].