esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 The Roles of Antioxidants and Strength Training in Muscular Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Polyphenols’ Source**} & \multicolumn{1}{c}{**Sports Activity/Number of Subjects**} & \multicolumn{1}{c}{**Treatments**} & \multicolumn{1}{c}{**Effects of Polyphenol Supplementation**} & \multicolumn{1}{c}{**References**} \\ \hline \hline \multirow{8}{*}{900 mg cocoa flavanols} & & 1.5 h (time trial 1) and 3 h (time trial 2) pre-exercise & \(\bullet\) & Increased total antioxidant capacity & \\  & & 1.5 h (time trial 1) and 3 h (time trial 2) pre-exercise & \(\bullet\) & Increased serum & \\  & & (low- polyphenol) & & epicatechin concentrations & \\  & & diet consumed & \(\bullet\) & No change in plasma marker of & \\  & & 24 h pre-exercise) & \(\bullet\) & oxidative stress (malondialdehydedehyde) & \\  & & & \(\bullet\) & No change in plasma inflammatory & \\  & & & & markers (ILI, IL6 and TNF-a) & \\ \hline \multirow{8}{*}{40 g dark chocolate (54 mg catechins and 44 mg flavanols)} & & \multirow{8}{*}{20 active male cyclists} & \multirow{8}{*}{Twice per day for 2 weeks (last dose 2 h} & \multirow{8}{*}{*} & \multirow{8}{*}{No change in time to exhaustion} \\  & & & & & Increased plasma free fatty & \\ \cline{1-1} \cline{5-5}  & & & 2 weeks (last dose 2 h & & \\ \cline{1-1} \cline{5-5}  & & & & Decreased plasma markers of & \\ \cline{1-1} \cline{5-5}  & & & & oxidative stress (P2-isoprostanes, & \\ \cline{1-1} \cline{5-5}  & & & 48 h pre-exercise) & \(\bullet\) & oxidized low-density lipoproteins & \\ \cline{1-1} \cline{5-5}  & & & & No change in plasma markers & \\ \cline{1-1} \cline{5-5}  & & & & of inflammation & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._lower muscle synthesis following a training stimulus. This theory is confirmed by the findings of a placebo-controlled study on 42 female athletes, with statistically significant lower peak torque and total work in the control group supplemented with vitamin C and E (201). There is no doubt about the positive antioxidant effect on rapid recovery, limiting the damage generated by intense physical activity. This effect is of high utility in combat sports, where recovery times are limited. The results of a double-blind randomized study on 18 elite taekwondo athletes revealed a significantly better biological profile in the control group after supplementation with vitamins C and E, with lower values of muscle damage markers such as creatine kinase, myoglobin and hemolysis (129). Multiple studies support the positive vitamin E effect at high altitudes, limiting the deformation of erythrocytes (202). Consequently, although they may have detrimental effects on muscle hypertrophy, short-term and targeted supplementation of antioxidants are important aids for endurance and fast recovery, which is an important aspect for combat players between weigh-in and competition.
# Characteristics of Collimators Based on the Large-Mode-Area CMCF for Coupling Laser Beam
## 2 Theoretical Analysis of the Coupling Loss Mechanism
### Off-Axis Deviation  
Figure 3 shows the collimator coupling only with off-axis deviation. Usually, the guided mode in the collimator is a Gaussian beam, whose two-dimensional model is shown in Equation (3) [26; 27].  
\[E(x,y)=\frac{1}{\omega}\sqrt{\frac{2}{\pi}}\exp\biggl{[}-\frac{x^{2}+y^{2}}{ \omega^{2}}\biggr{]} \tag{3}\]  
where \(E\) is the electric field distribution of the beam, and \(\omega\) is the spot size of the Gaussian mode field at the collimator endface. At the operating wavelength \(\lambda\), \(\omega\) could be obtained from the mode field radius \(\omega_{0}\) of the tail fiber by the following equation [25; 28]  
\[\omega=\frac{\lambda}{\pi n_{0}\sqrt{A}\omega_{0}} \tag{4}\]  
where \(\omega_{0}\) is the mode field diameter of the tail fiber in both collimators.  
The electric field distribution of the transmitted beam is substituted into Equation (2) when there is only off-axis deviation \(X\). The coupling loss caused by the off-axis deviation is calculated as shown in Equation (5). As \(\lambda\) = 1550 nm, \(\sqrt{A}\) = 0.326, and \(n_{0}\) = 1.5946, the relationship between the off-axis deviation and coupling loss for different \(\omega_{0}\) is shown in Figure 4. The coupling loss \(\eta_{X}\) increases as the off-axis deviation enlarges; meanwhile, the larger the mode field diameter of the tail fiber is, the higher the coupling loss under the same off-axis deviation is.  
\[\eta_{X}=4.343\biggl{(}\frac{\pi n_{0}\sqrt{A}\omega_{0}}{\lambda}\biggr{)}^{ 2}X^{2} \tag{5}\]
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.3 BEC  
BEC (business email compromise) is a form of phishing attack (a sub-type of spear phishing) that focuses purely on government, non-profit, and commercial organizations to inflict a negative effect (normally financial) on that organization. As the name implies, the aim is to compromise the corporate emails of the employees of the company and use the victim's access to inflict damage; this typically takes the form of data mining and invoice scams. This scheme has a knock-on effect whereby compromising one account can lead to the compromise or manipulation of another, also known as a launchpad attack [41]. It is common for phishers to spend weeks or months inside a company's networks to identify the perfect exploit. This can be done by analyzing, for example, the organization's billing system, its vendors, or a specific employee (preferably a high-ranking executive) [7]. Then, an email is sent requesting the transfer of funds according to the attacker's wishes. The advantage of this method of attack is that the phisher does not steal money directly but engineers the theft using another party.  
This methodology was first reported as an emerging threat in 2013 and since then has been closely monitored [42]. In 2018, the FBI was notified about 20,373 counts of BEC, which led to the loss of around USD 1.2 billion [11], representing the greatest loss recorded for any form of cybercrime.  
The Anti-Phishing Working Group (APWG) found that in BEC attacks the phishers use domain names they register (i.e., as close to the address they are impersonating as possible) to increase their chances of tricking targets. In recent years, this type of scam has seen an increasing use of gift cards as a method of \"cashing out\". In the third quarter of 2019, APWG found that gift cards were used in 56% of cases, with payroll diversion as the second most common method (25%), and the remaining 19% involving the use of direct transfer [7].  
The level of sophistication in these attacks can vary greatly; some are relatively simple and comprise only a single email from a disposable email account. In contrast to this, there are incredibly detailed and well-executed schemes. In these instances, immense research is conducted on the person the attacker wishes to impersonate, in addition to the target. Effort is made to make it appear as if phishing emails come from a genuine source, by either buying domains or stealing the login credentials of the individual the phisher wants to impersonate. The most commonly targeted position in this type of attack is the CEO, which make up 41% of targets. The CEO is also the position the attackers are most likely to attempt to impersonate, being used in 31% of attempts [43].  
This particular method of phishing is becoming increasingly hard to detect using automated tools. Thus, at the present time, the only real defense against this attack is user education [44].
# Research on Fault Diagnosis of Marine Diesel Engines Based on CNN-TCN-ATTENTION
## 3 Experiment
### Noise Experiment  
The sensor data of marine diesel engines are often influenced by various factors, such as environmental conditions and the operating status of the equipment, and are accompanied by various types of noise. To make the research method more representative of the actual working conditions of the diesel engine, noise can be introduced into the fault data to simulate the real data collected by sensors.  
To evaluate the performance of the CNN-TCN-ATTENTION diagnostic model under noisy conditions, random noise with an intensity of 10%, Gaussian noise with a signal-to-noise ratio (SNR) of 15, and salt-and-pepper noise with a noise density of 0.05 are added to the marine diesel engine dataset. The diagnostic accuracy of each model under these three noise conditions is calculated. The experimental environment and parameter settings remain the same as those introduced at the beginning of this section. The diagnostic accuracy in the test set under these conditions is shown in Table 9, and the results are illustrated in Figure 15.  
As shown in Figure 15, different types of noise have a certain degree of impact on the accuracy of each network model in the test set. Specifically, the accuracy of all models exhibits a downward trend. However, the CNN-TCN-ATTENTION model experiences the least decrease, with its diagnostic accuracy remaining above 90%. This demonstrates the model's strong noise resistance while maintaining highly favorable precision. This  
Figure 15: Test accuracy under different noise conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Diagnostic Model** & **Noise Class** & **Test Accuracy** \\ \hline \multirow{4}{*}{CNN-TCN–ATTENTION} & Noiseless & 100\% \\  & Random noise & 98.75\% \\  & Gaussian noise & 97.5\% \\  & Impulse noise & 94.37\% \\  & Noiseless & 91.87\% \\  & Random noise & 82.5\% \\ CNN-TCN & Gaussian noise & 76.25\% \\  & Impulse noise & 78.12\% \\  & Noiseless & 89.38\% \\  & Random noise & 78.12\% \\ TCN & Gaussian noise & 77.5\% \\  & Impulse noise & 75.62\% \\  & Noiseless & 90.62\% \\  & Random noise & 72.5\% \\ CNN & Gaussian noise & 73.12\% \\  & Impulse noise & 70\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Diagnostic accuracy under different noise conditions.  
can be attributed to the ability of the CNN to extract local features, the ability of the TCN to process time-series data, and the focusing effect of the attention mechanism on key information when noise is present at various levels and in different features. These factors make the model resilient to noise, enabling it to filter out irrelevant information and enhance robustness. This confirms that the model possesses high robustness under various noise conditions, making it suitable for marine diesel engine fault diagnosis.
## 5 Results and Discussion
Some examples of those studies were presented in Section 2.3 and a comparison between the results presented in this study with the ones found in the literature can be made:* Similar to [36], it accomplished a reduction in walking distance and ergonomic improvements (through the 5S measures and work instructions proposed);
* Similar to [37], it accomplished a reduction in time spent on activities caused by the inefficient organization of the Gemba (through the Kanban poster proposed) and the elimination of factors causing defects (through the Poka-Yoke developed);
* Similar to [38], a decrease in NVA and a decrease in the process Lead Time was obtained (using 5S, Standardized Work, and Kaizen measures);
* Similar to [20], the walking times were reduced and there was an improvement in the time spent searching for materials (through the codification of all products developed as well as the code standard proposed), a reduction in operating time, and a reduction in waiting time (using 5S, Standardized Work, and Kaizen measures);
* Similar to [39], this study accomplished an improvement in work safety (through 5S measures and work instructions based on Standardized Work), reduction in stock levels (as it eliminated the waste of Overstocking and lack of stocks through the development of the DDS for stock control), and a reduction in the Lead Time, including the time looking for tools and elimination of unnecessary travel or movements. The DDS developed for stock control presents a methodology that can be adopted by companies of all industries and dimensions, as it allows for a simple solution to control quantities in stock at all times, only needing to adapt the information on the database. The Control Sheet, as well as the code standard and the simple label proposed, can also be used by any company. In the upholstered furniture industry, this DDS most likely does not need any adaptation; in other industries, the only change necessary would be the division of stocks into other categories according to the product used in all kinds of productive processes and activities. Regarding the ideas and solutions proposed using the Lean tools, all can also be adapted and used in different industries, namely, the way the tools are used to eliminate, for example, unnecessary movements and unnecessary waiting times, which happens in all processes; however, most importantly, they can provide a guide for SMEs of the upholstery industry on how to improve efficiency in their activity and how Lean tools can be implemented in their everyday life without much monetary investment. It is also very important to notice the contribution this paper gives to a simple Poka-Yoke for companies involved in vehicle seat upholstery production/maintenance, which can make a great difference in not only quality rates but also in competitiveness towards the market and demand satisfaction.
# Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience
## 3 Results of the 1 st Macro-Phase: The GLANCE Project  
As we already observed in the Introduction, our first objective was to overcome the limits that customary approaches have with respect to requirements of network management, in particular we wanted to abandon the perspective of streams of notifications displayed one after the other through badges with a unified format and inserted in a scrollable list based on timestamps, to achieve _a notifier acting as an ambient or an alerting display based on severity-based coding strategies, retaining the information expressiveness of the one-notification-at-the-time perspective, and enriching it with overview capabilities_. The objective is to guarantee (possibly subliminal) long-term local and global content _comprehension_ and prompt _reaction_ when the _interruption_ from the foreground task is dictated by the fault severity (the three parameters _Interruption (I)_, _Reaction (R)_, and _Comprehension (C)_ have been proposed by [36] as pillars of the so-called IRC framework). Context-of-use analysis, user observation, field studies, and brainstorming meetings with domain experts in the initial phases led to the founding idea of a system responsible for selecting a Level-Of-Detail (LOD) and dynamic stimuli consistent with the urgency degree of the notification, based on a design \"serving\" the IRC parameters by (1) a _visual coding technique_ (which is the leading aspect of the study) oriented to comprehension and reaction and (2) a _transition strategy_, oriented to interruptions and reaction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & **Main Objective** & **Evaluation** \\ \hline
1 st iteration & dealing with individual alarms & expert based \\
2 nd iteration & dealing with multiple alarms & expert based + user based + dissemination \\
3 rd iteration & generalization of results and definition of the GLANCE model & expert-based + user based \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall project plan.  
In this section we discuss the design of GLANCE, addressing separately the two cases of single alarm and multiple alarms, faced from both the research and problem-solving point-of-view in the first two iterations (in Sections 3.1 and 3.2, respectively), and then discussing the generalizability of results (in Section 3.3).
## 4 Discussion
### Technical Indicators and the Efficient Market Hypothesis  
The inclusion of technical indicators in RL agents provides mixed results, presenting a challenge to the EMH, which posits that all available information is reflected in price alone. While the EMH suggests that additional data such as technical indicators should offer no advantage, the empirical results demonstrate that, in some cases, these indicators can improve decision-making.  
For instance, in the case of the _DDPG-MLP_ agent, using technical indicators with a 28-period lookback and continuous rebalancing led to an annualized return of 19.2%, accompanied by a Sharpe ratio of 0.72 and a Sortino ratio of 0.99. In contrast, the same agent relying solely on OHLC data achieved a lower annualized return of 18.1%, with a Sharpe ratio of 0.66 and a Sortino ratio of 0.88. This suggests that the additional information from technical indicators enabled more efficient asset allocation. Similarly, the _DQN-CNN_ agent with technical indicators outperformed its OHLC-only counterpart, yielding an annualized return of 18.1%, with a Sharpe ratio of 0.88. This suggests that the additional information from technical indicators enabled more efficient asset allocation.
# Potential Applications of Yeast Biomass Derived from Small-Scale Breweries
## 7 Yeast Cell Walls and Their Derivates
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product** & **Application** & **Advantages** & **Reference** \\ \hline \multicolumn{4}{c}{**Food and fodder additives**} \\ \hline Whole yeast cell & Supplementation of broiler diet (1–5\%) & Enhancement of bone health & [52] \\ \hline Purified beta-glucan & Addition of beta-glucan to flour in bread production & Improved texture of bread & [60] \\ \hline Purified beta-glucan & Low-fat mayonnaise & Reduced fat content, with acceptable changes in taste & [61] \\ \hline Purified mammoptein & Emulsifier in mayonnaise & Replacement of xanthan gum without deterioration of stability and taste & [62] \\ \hline \multicolumn{4}{c}{**Medical and special applications**} \\ \hline Peptide derived from yeast, fraction below 3 kDa & Orally applied solution (rats) & Blood pressure reduction comparable with ecaptopril use & [63] \\ \hline Selenium-rich peptide derived from yeast, fraction below & Intragastric applied solution (mice) & Protection against UV-induced skin damage & [64] \\ \hline Yeast extract, yeast extract after additional enzymatic & & Protection against stomach ulceration caused by 99.9\% & [65] \\ hydrolysis fraction below 3 kDa & Oral administration of water solution (rats) & ethanol & [65] \\ \hline Yeast extract & Addition to medium used in in vitro culture of Chinese & Growth enhancement & [39] \\ \hline Yeast extract after additional enzymatic hydrolysis & Addition to serum-free medium used in in vitro culture & & \\  & of skeletal muscle cells & Restoration of cell culture growth & [66] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Examples of applications for products obtained from bewery spent yeast cell walls and extracts.
# A Study of OWA Operators Learned in Convolutional Neural Networks
## 4 Methods
### Feature Map Weighted Aggregation  
Once the \(C_{in}\) input channels have been sorted by one of the measures of Section 4.2, we can proceed to the second critical stage, their weighted aggregation. For this purpose, we define \(C_{f}\) independent OWA operators, each one represented by a weighting vector composed of \(C_{in}\) weights.  
These weights constitute a raw unconstrained version of the OWA, and we transform them before applying the aggregation to ensure that they correspond to proper weighting vectors. The main reason to work with the weights in their unconstrained form is to easily integrate them into the main backpropagation loop, allowing them to be learned with the rest of the parameters in the network.  
To translate the weights into a valid OWA operator, as defined in Section 2.2, we need to ensure that \(w_{i}\in[0,1]\) for every \(i=1,\ldots,n\) and that \(\sum_{i=1}^{n}=1\). The easiest way to comply with these constraints is to first clip the negative weights to achieve \(w_{i}\in[0,\infty)\) for every \(i=1,\ldots,n\), and then normalize to guarantee \(\sum_{i=1}^{n}=1\), which implicitly ensures that \(w_{i}\in[0,1]\) for every \(i=1,\ldots,n\).  
To clip the negative weights, we employ the ReLU function. This is a well-known function that integrates well in the common gradient descent learning algorithm, with a very fast implementation. It is defined as  
\[ReLU(w_{i})=\text{max}(w_{i},0)\text{ for every }i=1,\ldots,C_{in}\,. \tag{20}\]Then, we normalize the weights by dividing each weight by the total sum of the weights, ensuring that the weighting vector adds up to one. The final weights are calculated as  
\[w_{i}^{\prime}=\frac{ReLU(w_{i})}{\sum_{j=1}^{C_{f}}ReLU(w_{j})}\text{ for every }i=1,\dots,C_{in}\,. \tag{21}\]  
The resulting weighting vector is a proper OWA weighting vector.  
With the final set of \(C_{f}\) weighting vectors calculated, which can be seen as weighting matrix of size \(C_{f}\times C_{in}\), the aggregation itself is straightforward. Each new channel \(Y_{k}\), \(k=1,\dots,C_{f}\), is given by  
\[Y_{k}(i,j)=\sum_{l=1}^{C_{in}}w_{l}^{\prime}\times X_{\sigma(l)}(i,j)\,, \tag{22}\]  
for every \(i=1,\dots,I\), \(j=1,\dots,J\). The input feature maps \(X_{\sigma(l)}\) are sorted according to one of the channel metrics presented in Section 4.2, defining a permutation \(\sigma(l)\) such that \(\text{Metric}(X_{\sigma(1)})\geq\dots\geq\text{Metric}(X_{\sigma(n)})\).
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 3 Results and Analysis
### Comparison of Different Backbones Based on YOLOv7  
In this experiment, to verify the feature extraction capabilities of different backbones, we select MobileNet-V2, MobileNet-V3, ShuffleNet-V2, and LW-YOLOv7 for comparison with the YOLOv7 algorithm. The comparison results are shown in Table 4. The precision of the YOLOv7+MobileNet-V2 algorithm decreased by 1.3%, the recall decreased by 1.8%, the mAP decreased by 1.8%, and the number of parameters decreased by 25.2 M. The precision of the YOLOv7+MobileNet-V3 algorithm decreased by 3.4%, the recall decreased by 0.4%, the mAP decreased by 2.9%, and the number of parameters decreased by 27.5 M. The precision of the YOLOv7+ShuffleNet-V2 algorithm decreased by 5.6%, the recall increased by 0.7%, the mAP decreased by 3.6%, and the number of parameters decreased by 28.7 M. The precision of the LW-YOLOv7 algorithm decreased by 1.6%, the recall increased by 3%, the mAP increased by 0.5%, and the number of parameters decreased by 15.2 M.  
Based on the comparison results, it can be observed that using lightweight models may result in a decline in precision. Among them, the YOLOv7+ShuffleNet-V2 algorithm experienced the most significant decrease in precision, while the LW-YOLOv7 algorithm exhibited a relatively minor drop compared to other models. This is because lightweight models often adopt simplified network structures, which may reduce their feature representation capability and subsequently impact the models' accurate object detection. However, lightweight models have advantages in reducing parameter quantity and computational resource requirements, making them suitable for resource-constrained situations. Additionally, we can introduce methods such as CBAM and BiFPN to improve the detection accuracy of the model. This is because they can enhance the representation and fusion capabilities of feature maps.