esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Roles of Creatine, Caffeine, Phosphate, Beta-Alanine and Carnitine
### Beta-Alanine  
Beta-alanine is a rate-limiting precursor to carnosine, an endogenous intracellular (muscle) buffer, and one of the first lines of defense against protons building up in the expanding muscles during exercise [164]. For a minimum of 2-4 weeks, routine supplementation with 3.2-6.4 g (\(\sim\)65 mg/kg BM) of beta-alanine might raise muscle tissue carnosine content (65% over resting levels), enhancing tolerance for maximal activity sessions lasting 30 s to 10 min [165]. Beta-alanine supplementation has been demonstrated to provide small but noticeable advantages (\(\sim\)2-3%) in both continual and sporadic exercise testing; however, sport-specific studies that emphasize the practical consequences for sporadic sports are notably absent [165]. When supplementation regimens are prolonged to 10-12 weeks (80% above resting levels), muscle carnosine concentration could be further increased; however, the relationship between muscle alterations and the amount of predicted performance remains unknown [165]. Supplementing with beta-alanine may not be as helpful in well-trained athletes as it is in less-trained athletes, possibly due to carnosine's reduced function in intramuscular pH control in people who already have a high retention time [166]. Nevertheless, in the context of an actual competition situation, the slight performance improvements reported in well-trained athletes to date (\(\sim\)0.2-1.3%) may still be significant.  
To decrease the probability of health consequences, beta-alanine dosing approaches usually involve split doses absorbed throughout the day (i.e., 0.8-1.6 g every 3-4 h) and/or controlled-release preparations [165]. However, following beta-alanine supplementation, large interindividual variability in muscle carnosine synthesis has been revealed, which is thought to have an inverse relationship with (a) the individual's preingestion carno-sine levels, (b) the person's training status and (c) the proportion of fast-twitch muscle fibers [167]. Nevertheless, an in-depth study and assessment of the existing research by [PERSON] et al. [168] suggest that a total of 230 g of beta-alanine should be ingested, including a current consumption range of 1.6-6.4 g/day, in order to obtain around a 50% rise in muscle carnosine. In any case, where practicable, a tailored strategy for beta-alanine supplementation should be investigated [168].  
A study on 23 highly trained judokas identified improvements in judo-related performance after 4-week supplementation with 6.4 g/day of beta-alanine [169]. Ten weeks of beta-alanine supplementation resulted in significant performance improvement with no significant side effects in nine male amateur boxing athletes [170]. Bicarbonate may have a synergic additive effect when used in combination with beta-alanine, as demonstrated by a study performed on 37 judo and juu-jitsu players [171]. We can conclude that beta-alanine has a place in most combat athletes' nutrition with ergogenic potential when used alone or in combination with other products.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI in Neuro-Ophthalmology
#### 4.10.2 AI in Optic Nerve Head (Optic Disc) Anomalies  
The optic nerve head is the proximal termination of the optic nerve. Clinically, the integrity of the optic nerve head (optic disc) can be assessed by means of direct ophthalmoscopy or fundus photography. Optic nerve injuries lead to visible disc modifications, like swelling, pallor, \"cupping\" (e.g., in glaucoma), or infiltration.  
A large reserve of optic disc images in glaucomatous eyes allows DL to automatically detect glaucoma on digital images of the fundus [74], based only on optic disc appearance or in combination with imaging (OCT) [101].  
Several studies have automatically determined optic disc laterality using DL and transfer learning [104], as well as detecting abnormalities of the neuro-ophthalmic optic nerve head [105].  
Research by means of fundus images have shown that feature selection and extraction using ML (e.g., SVM) and powerful statistical methods (e.g., with gray-level co-occurrence matrix) can detect optic discs with papillary edema with high precision (93%) [106]. They can also classify the severity of the disease in high agreement with a neuro-ophthalmologist (Kappa score = 0.71) (see Appendix B).  
[PERSON] et al. used ML to classify normal discs, bulging discs due to certain optic neuropathies, and pseudopapilledema. By means of data augmentation to prevent overfitting and a traditional CNN with TensorFlow and transfer learning, the researchers differentiated true optic disc swelling from pseudo-swelling with \(\sim\)95% precision.  
[PERSON] and his team were able to train a DL system with 14,341 images acquired from 6779 patients from 19 neuro-ophthalmology world stations (BONSAI syndicate), along with 9156 normal optic disc images, 2148 discs with validated papilledema, and 3037 discs with other anomalies [105]. Assessed using a different set of 1505 fundus photographs acquired retroactively from five other medical stations, integrating different ethnic populations, BONSAI-DLS had high accuracy for classifying healthy discs, discs with papilledema,and discs with different anomalies (e.g., non-arteritic ischemic optic neuropathy, disc degeneration, optic disc drusen) with AUCs of 0.98, 0.96, and 0.96, respectively [105].  
DL provides a real opportunity for healthcare staff who are not experienced in performing ophthalmoscopy to automatically, and with good results, analyze vision and detect dangerous situations on fundus images. Physicians and neurologists in intensive care units may also benefit from automated optic disc analysis in their regular assessments of patients, particularly those with limited access to neuro-ophthalmologists.
## 3 Results and Discussion
### Gas Recovery Efficiency and Interlayer Compatibility  
In this work, the gas production coefficient together with its interference index is introduced to quantitatively assess the impact of commingled multilayer production on the gas production and recovery. The gas production coefficient \(W\) represents the incremental in gas recovery per unit pressure drop, while the interference index \(F\) measures the reduction ratio in \(W\) during commingled multilayer production compared to that of single-layer production. Their expressions are given as follows:  
\[W=\Delta E_{R}/\Delta p_{w} \tag{1}\]  
\[F=\left(W_{1}-W_{n}\right)/W_{1}\times 100\% \tag{2}\]  
where \(\Delta E_{R}\) is the incremental in gas recovery, %; \(\Delta p_{w}\) is the change in outlet pressure, MPa; and \(W_{1}\) and \(W_{n}\) are the gas production coefficients for single-layer and multilayer extraction, respectively, MPa\({}^{-1}\).
# 10 Gb/s Bidirectional Transmission with an Optimized SOA and a SOA-EAM Based ONU
## 3 10 Gb/s Bidirectional Transmission for a DFB-EAM-SOA-Based ONU
### Results and Discussion
These are 0.3 dB and 0.6 dB larger than the unidirectional US transmission for BtB and 20 km fibers, respectively, and the penalty between BtB and 20 km fibers in bidirectional US transmission is 0.6 dB (0.3 larger than the unidirectional US transmission) due to backscattering and reflection effects in the bidirectional system [25]. After accomplishing the bidirectional transmission of the single ONU, a neighboring ONU2 was added with 50 GHz spacing. The added ONU2 and transceiver2 used amplitude modulators, as shown in Figure 2. Compared with the single-ONU transmission, the multi-ONU transmission needed to consider the negative effect of crosstalk and cross-phase modulation between signals [26]. As Figure 4b shows, at BER = 2.4 \(\times\) 10\({}^{-4}\) for 10 Gb/s, for US, the sensitivities reach \(-\)28.8 dBm and \(-\)28.2 dBm for BtB and 20 km fibers, respectively; for DS, the received sensitivities are \(-\)33 dBm and \(-\)32.6 dBm for BtB and 20 km fibers, respectively. Compared with the bidirectional transmission in Figure 4a, there is a 0.7 dB  
Figure 3: The BER against the received power in a 10 Gb/s unidirectional transmission: (**a**) the upstream (US) BER curves for input power (Pi) values; (**b**) the US BER curves for the peak–to–peak RF voltage (V\({}_{\rm PP}\)) values; (**c**) the US BER curves for the bias current (I\({}_{\rm bias}\)) values; (**d**) the US and downstream (DS) BER curves for back–to–back (BtB) and 20 km fibers. and a 0.5 dB power penalty between the single-ONU and the multi-ONU in US and DS transmissions, respectively. It can be seen that the sensitivity penalty caused by the interference between multiple channels was less than 1 dB, so the influence of the neighboring ONU2 on ONU1 was not serious.
## 1 Introduction  
With the rapid development of modern communication technology, there has been an expanding trend in the types and complexity of modulation methods used in wireless communication [1]. Furthermore, the exponential growth in user demands and data volume continues to drive the increasing complexity of communication environments [2]. Consequently, the rapid and automated identification and classification of communication signals have become particularly crucial. AMC plays a key role in numerous civilian and military applications of wireless communication, including cognitive radio, electronic warfare, spectrum monitoring, surveillance analysis, and radio fault detection [3; 4; 5; 6]. Serving as an intermediary between signal detection and demodulation, AMC greatly facilitates the effective classification of the modulation types. Thus, having a reliable and efficient AMC model is of paramount importance.  
Traditional AMC methods can be categorized as decision theory-based and feature-based [7]. The former constructs a maximum likelihood function using statistical characteristics to determine modulation type through optimal decision thresholds. Methods like ALRT [8], GLRT [9], and HLRT [10] fall into this category. However, these methods require substantial prior knowledge and complex computations, posing challenges in non-cooperative communications. The latter identifies modulation type by comparing artificial features with theoretical values, requiring effective feature extraction techniques like constellation diagrams [11], fourth-order spectra [12], and cyclo-spectrograms [13]. While feature-based methods are more computationally efficient and require less prior knowledge, they have lower recognition performance in low SNR scenarios. Thus, extracting effective features remains crucial.
# Semantic Image Segmentation Using Scant Pixel Annotations
## 2 Related Work
### Proposed SSPA Approach
The authors stated that only 30% of training data surpassed the baseline trained with complete ground-truth annotations. Even though this approach allows recovering the scores obtained without incorrect/incomplete labels, it relies on the use of a perfectly labeled sub-dataset (100% clean labels). This approach was further extended to an approach called INERRANT [41]to achieve better confidence estimation for the initial pseudo-label generation, by assigning a dedicated confidence network to maximize the number of correct labels collected during the pseudo-labeling stage. [PERSON] et al. [42] proposed a label-efficient hybrid supervised framework for medical image segmentation, where the annotation effort is reduced by mixing a large quantity of weakly annotated labels with a handful of strongly annotated data. Mainly two techniques, namely dynamic instance indicator (DII) and dynamic co-regularization (DCR), are used to extract the semantic clues while reducing the error propagation due to strongly annotated labels. Specifically, DII adjusts the weights for weakly annotated instances based on the gradient directions available in strongly annotated instances, and DCR handles the collaborative training and consistency regularization. The authors stated that the proposed framework shows competitive performance only with 10% of strongly annotated labels, compared to the 100% strongly supervised baseline model. Unlike SSPA, their approach assumes the existence of strongly annotated data to begin with. Without using directed expert annotation as done in SSPA, it is highly unlikely that a handful of strongly annotated samples chosen initially will cover all the variations of the data, and hence we argue that the involvement of experts in a directed manner guided by model predictions is important, especially in sensitive segmentation application domains, such as medical and material science. [PERSON] et al. [43] recently proposed a watershed transform-based iterative weakly supervised approach for segmentation. This approach first generates weak segmentation annotations through image-level class activation maps, which are then refined by watershed segmentation. Using these weak annotations, a fully supervised model is trained iteratively. However, this approach carries many downsides, such as no control over initial segmentation error propagation in the iterative training, requires many manual parameterization during weak annotation generation, and lack of grasping fuzzy, low-contrast and complex boundaries of the objects [44, 45]. Segmentation error propagation through iterations can adversely impact model performance, especially in areas requiring sophisticated domain expertise. In such cases, it may be best to seek expert help in generating segmentation ground truth to manage boundary complexities of the objects and mitigate the error propagation of weakly supervision. Our experiments show that the SSPA models outperform the watershed-based iterative weakly supervised approach.
## 3 Experiments and Results
### Data Preprocessing  
For preprocessing the existing textual data, comment sentences are segmented into words and stop words are removed using the Harbin Institute of Technology stop word list. Based on the given corpus, Word2 Vec can represent a word as a vector. In this paper, the skip-gram model is used to establish the word embedding model. We suppose the dimension of the word vector (or characters vector) is \(k\). A sentence consists of \(n\) words (or characters). If the word vector (or characters vector) of the \(i\)th word is \(n\), the sentence can be described as \(X_{i}\) as shown in Equation (12):  
\[X_{1:n}=X_{1}\oplus X_{2}\oplus\cdots\oplus X_{n}, \tag{12}\]  
where \(\oplus\) represents the connection operator.  
At the same time, through the string slicing function, a word function for word segmentation is established, and then Word2 Vec is used to convert it to a character vector. Similar to the word vector, if the dimension of the characters vector is \(k\), a sentence consists of \(n\) characters, and the character vector of the first character is \(Y_{i}\); then, the sentence can be described as shown in Equation (13):  
\[Y_{1:n}=Y_{1}\oplus Y_{2}\oplus\cdots\oplus Y_{n}. \tag{13}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Real Class** & **Positive** & **Negative** \\ \hline Positive & TP (True Positive) & FN (False Negative) \\ Negative & FP (False Positive) & TN (True Negative) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification confusion matrix.
## 2 Materials and Methods
### Traditional Parameters for the Implementation of the Flight Project
It allows the stereoscopic overlap and the 3D representation. Typical forward overlap values range between the minimum that is applied in conventional photogrammetry (60%), and those applied to generate orthopotographs (80-90%). A small forward overlap implies little stereoscopic aliasing. On the other hand, a high value implies developing flights with many more photographs, which can imply a problem for the management of the data. However, a high value is usually adopted in order to guarantee good 3D models and orthopotographs. * Lateral overlap. It is defined as the overlap between two consecutive passes of a same photogrammetric flight. It is usually expressed as a percentage of the surface covered, and it permits the stereoscopic overlap and the 3D representation. Typical values of lateral overlap range between the minimum applied in conventional photogrammetry (20%) [12] and the one applied for orthophotographs (80%). Again, a small lateral overlap implies little stereoscopic overlapping, while a high value implies more. A high value implies flights with many more passes, which can be a problem for the processing of data. As it happens with the forward overlap, a high value is usually applied to ensure overlapping and, therefore, good models and orthomosaics. * Inclination angle of the sensor. It is defined as the angle that the optical axis of the camera forms with the vertical direction, measured from the nadir. Conventional photogrammetry is characterized by being of vertical axis. The gimbal suspension that is embodied in the sensor keeps it perpendicular to the focal plane and avoids possible deviations. Nevertheless, due to the existence of certain elements of structures, a change in the inclination angle can be interesting to obtain images with more altimetric information of the objects that are desired to model (buildings, etc.) [14]. Finding recommendations about this parameter is not easy, and it is undoubtedly one of the most important to avoid gaps in the geometric model, and to provide a high visual value. In this regard, and considering the relative position of the surface the element to be captured and the principal axis of the camera [15], examples of perpendicular [16], oblique, and a combination of both types of shots can be found in literature [17].
# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 2 Research Contents and Methods
### Image Preprocessing  
We collected images from the ADNI and PPMI database, and preprocessed the images to improve the recognition accuracy. The preprocessing process is divided into three parts, including gray level transformation, histogram equalization and wavelet soft-threshold denoising. The flow chart of the preprocessing is shown in Figure 6. In the following, we will discuss them separately.  
Gray level transformation maps image gray-scale from 0 to 255. We histogram equalize the image to adjust contrast after linear gray level transformation, which will increase  
Figure 4: Basic structure of convolution neural network.  
Figure 5: (a) AD patient’s PET image. (b) AD patient’s T2-MRI image.  
Figure 6: Flow diagram of preprocessing.  
the local contrast of the original image. Histogram equalization can be expressed by the following formula:  
\[\begin{split} s_{k}&=T(r_{k})=(L-1)\sum_{j=0}^{k}p_{r} \big{(}r_{j}\big{)}\\ &=\frac{(L-1)}{MN}\sum_{j=0}^{k}n_{j},k=0,1,2,\ldots,L-1\end{split} \tag{1}\]  
where \(L\) is the number of possible gray levels in the image, \(MN\) represents the image pixels number, and \(n_{k}\) represents pixels number in \(r_{k}\) gray scale. Figure 7 demonstrates the effect of histogram equalization.  
After the gray level transform is the denoising process. The denoising algorithm implemented in this paper is based on the wavelet soft-threshold algorithm [20], which can be expressed in the following piecewise function:  
\[f(x)=\left\{\begin{array}{cc}\text{sign}(x)\circ(|x|-\alpha),&|x|\geq\alpha \\ 0,&|x|\leq\alpha\end{array}\right. \tag{2}\]  
where \(x\) represents the pixel's gray value, \(\alpha\) stands for the threshold value of the wavelet soft-threshold algorithm, and this threshold value is defined by the following formula:  
\[\alpha=\sigma^{2}\sqrt{2\log(N^{2})} \tag{3}\]  
where \(N\) represents the signal dispersion number, and through the experiment we set \(\sigma=25\). Figure 8 shows the denoising effect with the algorithm.  
Figure 7: (**a**) Original image. (**b**) Histogram equalization image. (**c**) Original image histogram. (**d**) Histogram after equalization. In the histogram, the abscissa represents the gray scale and the ordinate represents the frequency.  
The next step is contrast stretching. We used three stage contrast stretching methods in this paper. The basic form of piecewise linear function is as follows.  
\[\text{f(x)}=\left\{\begin{array}{ccc}\frac{y_{1}}{x_{1}}x&x<x_{1}\\ \frac{y_{2}-y_{1}}{x_{2}-x_{1}}(x-x_{1})+y_{1}&x_{1}\leq x\leq x_{2}\\ \frac{250-y_{2}}{255-y_{1}}(x-x_{2})+y_{2}&x<x_{2}\end{array}\right. \tag{4}\]  
The specific gray-scale restricting rule is shown in Figure 9, and in Figure 10 we give the image before and after contrast stretching.  
Figure 8: (a) Image before denoising. (b) Image after denoising.  
Figure 10: (a) Previous image. (b) After contrast stretching.  
Figure 9: Gray-scale restricting rule.
# Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater--Making the Right Choice
## 3 Results and Discussion
### Central Supply Approach  
In order to compare costs between the centralised and local approaches, the scope of the centralised systems must be decided upon and the investment and operational costs estimated based on the assumption that under both approaches safe and good quality drinking water will be provided to all households in the municipalities investigated.  
To demonstrate the differences between the local supply and central supply approaches, it is necessary to re-evaluate which settlements are to be connected. Making rational decisions concerning the configuration of a centralised water supply system is considerably more complicated than the local supply approach. After identifying a nearby good quality water source with a sufficient quantity of water to supply an entire municipality, there are two steps that must be performed to complete the analysis.  
First, a preliminary analysis must decide which settlements will be included in the central system. Water transmission pipelines for long-distance transport are expensive to build and maintain. The preliminary distribution network analysis should be of sufficient in detail to include or eliminate settlements based on how economical they are to connect to the centralised water source. If the pipelines required to reach a settlement are considerably more expensive than a local WTP, then that settlement should be eliminated from the planned central supply system. The distance among settlements is of course the most significant factor, but the topography of the land and the presence of major rivers and other natural barriers should also be considered.  
The final step of the analysis considers each settlement connected to the central system, and examines in detail the drinking water treatment technology, transmission pipelines, pumping stations and pumping infrastructure, and the local distribution system required. It is important to note that some settlements in the two municipalities studied have, therefore, been excluded from the central supply systems modelled based on these connection criteria, and settlements from other municipalities have been added.