esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Loss Functions  
#### 3.3.1 PatchNCE Loss  
As with the setting of CUT, we enable the network to maximize the mutual information between the input and output corresponding patches by using a noise contrastive estimation framework [34]. The core idea of contrast learning is to relate the two signals of a \"query\" and its \"positive\" example, not associated with other examples in the dataset (called \"negatives\"). Firstly, the probability of choosing \"positive\" instead of other \"negatives\" is represented by calculating the cross-entropy loss [35] as follows:  
\[\ell\big{(}v,v^{+},v^{-}\big{)}=-\log\left[\frac{\exp(v\cdot v^{+}/\tau)}{\exp (v\cdot v^{+}/\tau)+\sum\limits_{n=1}^{N}\exp(v\cdot v_{n}^{-}/\tau)}\right] \tag{1}\]  
where \(v\), \(v^{+}\in R^{K}\), and \(v^{-}\in R^{N\times K}\) indicate mapping query, positive, and \(N\) negatives to K-dimensional vectors, respectively. \(v_{n}^{-}\in R^{K}\) denotes the \(n\)-th negative. \(\tau\) is the parameter used to scale the distance between the query and the other examples, with the default value set to 0.07 in our experiments.  
The \(\mathrm{G}_{\mathrm{enc}}\) and \(\mathrm{H}_{l}\) are used to share weights to extract features from domain X and domain Y. The L layers are selected first and pass the feature mappings through \(\mathrm{H}_{l}\), as used in SimCLR [12]. A feature stack \(\left\{Z_{l}\right\}_{L}=\left\{H_{l}\big{(}G_{\mathrm{enc}}^{l}(x)\big{)} \right\}_{L}\) is then generated, where the output of the \(l\)-th selection layer is represented by \(G_{\mathrm{enc}}^{l}\). Similarly, we encode the output image G(x) belonging to domain Y into \(\left\{Z_{l}\right\}_{L}=\left\{H_{l}\big{(}G_{\mathrm{enc}}^{l}(G(x))\big{)} \right\}_{L}\). Our goal is to match the input and output patches corresponding to a specific position. The PatchNCE Loss [13] for the one-way mapping in this article is represented as follows:  
\[L_{\mathrm{PatchNCE}}(G,H,X)=\mathrm{E}_{x\sim X}\sum\limits_{l=1}^{L}\sum \limits_{s=1}^{S_{l}}\ell\Big{(}\hat{Z}_{l}^{S},Z_{l}^{S},Z_{l}^{S\setminus s }\Big{)} \tag{2}\]  
where the number of spatial positions in each layer is represented by \(S_{l}\). \(Z_{l}^{S}\in R^{C_{l}}\) represents the corresponding feature (\"positive\"), \(Z_{l}^{S\setminus s}\in R^{(S_{l}-1)\times C_{l}}\) represents other features (\"negatives\"), where the number of channels in each layer is represented by \(C_{l}\).
## 5 Conclusions  
To sum up, these studies showed that compounds synthesized from propenylbenzene derivatives, namely, hydroxy ketones and diols, showed high binding affinity to MAO-A in the active sites, as shown by ITC and molecular docking methods. Hydroxy ketones and diols binding to MAO-A may increase and regulate the level of serotonin in the body, which may result in better well-being, reduced appetite, and improved cognitive functions. The most effective MAO-A inhibitor was 1-(4-hydroxy-3-methoxyphenyl) propane-1,2-diol, which, both in the ITC study and in molecular modeling, showed high affinity for the enzyme, forming a stable bond in the region of the active site through the TYR amino acid residue. The newly synthesized compounds can be functional food ingredients or used in pharmacy. The results of this study will be used in future research on the beneficial effect of the health-promoting properties of propenylbenzene derivatives, evaluated in complex in vitro and in vivo models.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142310856/s1](https://www.mdpi.com/article/10.3390/app142310856/s1)]([https://www.mdpi.com/article/10.3390/app142310856/s1](https://www.mdpi.com/article/10.3390/app142310856/s1)), Figure S1: ITC raw data of interactions of MAO-A with (A) 1-(1,3-benzodioxol-5-yl)propane-1,2-diol, (C) 1-phenylpropane-1,2-diol, (E) 2-hydroxy-1-phenylpropan-1-one, (G) 2-hydroxy-1-(4-methoxyphenyl)propan-1-one, (G) 2-hydroxy-1-(4-methoxyphenyl)propane-1,2-diol, (K)1-(3,4-dimethoxyphenyl)propane-1,2-diol, (M) 1-(4-hydroxy-3-methoxyphenyl)propane-1,2-diol, Oxidation of 5-HT catalyzed by MAO-A at a presence of: (B) 1-(1,3-benzodioxol-5-yl)propane-1,2-diol, (D) 1-phenylpropane-1,2-diol, (F) 2-hydroxy-1-phenylpropan-1-one, (H) 2-hydroxy-1-(4-methoxyphenyl) propan-1-one, (J) 1-(4-methoxyphenyl)propane-1,2-diol, (L) 1-(3,4-dimethoxyphenyl)propane-1,2-diol, (N) 1-(4-hydroxy-3-methoxyphenyl)propane-1,2-diol. Curves in bottom figures show the fitting of data to a one-set model by the nonlinear Levenberg-Marquardt fitting algorithm supplied by the instrument.  
Conceptualization, J.G. and F.B.; methodology, J.G., H.P.-S., G.B., E.B. and F.B.; formal analysis, J.G., M.C.-B., I.G.-C. and D.H.; investigation, J.G., M.C.-B., I.G.-C. and D.H.; writing--original draft preparation, J.G.; writing--review and editing, H.P.-S., G.B., E.B. and F.B.; resources, D.H.; supervision, F.B.; funding acquisition, F.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Impact of Music Selection on Motivation and Performance during Cardiopulmonary Exercise Testing
## 1 Introduction
Participants performing a submaximal test on a cycle ergometer were able to maintain the prescribed intensity (60% of maximal heart rate) while consuming 7.4% less oxygen when listening to selected synchronous music during the test, compared to the same task while listening to asynchronous music [28], justifying its use for motivational purposes. The greatest effect is certainly exerted by melodies that meet the exercisers' needs in terms of internal and external characteristics of music. Music should be selected according to specific criteria if we want it to provide a source of motivation during physical activity. Motivational music has a fast tempo (>120 beats/min) and a strong rhythm to generate energy and cause bodily action [27; 31]. In order to select exercise music with satisfactory motivational qualities, it was necessary to construct an instrument for selecting musical tracks. For this purpose, the Brunel Music Rating Inventory--BMRI for music exercise experts [27] and the Brunel Music Rating Inventory-2 (BMRI-2) adapted for all levels of trainers and exercisers [24] were constructed, and the validity and reliability of the instrument were tested and confirmed. This questionnaire, as an instrument, can provide more precise information about the motivational qualities of music applied as asynchronous or synchronous music during exercise of all intensities. Its application can be in two directions--before training so that the exerciser can choose the musical tracks that motivate him or after exercise in order to evaluate the motivational qualities of the music chosen by the coach. Some results suggest that females may respond more positively than males to exercise-induced fatigue while listening to self-selected music during repeated bouts of high-intensity exercise [32]. The subject of this study was to examine the effect of applying synchronized music and appropriate music selection on motivation for exercise and achieving better results in females. The main aim of this study was to examine the response of cardiorespiratory parameters as well as RPE during CPET in 20 female participants, in two different testing conditions: without music and with the use of selected synchronized music. An additional purpose was to assess the significance of differences in responses between two groups of participants with slightly different levels of aerobic fitness and age. We hypothesized that: H1: the BMRI-2 scale is reliable; H2: music will affect the values of cardiorespiratory parameters during the load test; H3: greater effect of music on cardiorespiratory parameters during the CPET will be shown in recreational athletes compared to students; H4: RPE will show a lower value in the music testing condition than in the non-music condition.
## 2 Related Works
Considering the energy efficiency problem, the purpose of the original four-dimensional matching among D2D pairs, relay selections and resource blocks (RBs) reused in the first and second hop of two-hop D2D links is to maximize the EE of D2D links. In [11], the authors formulated the joint resource allocation problem as a one-to-one matching problem under two-sided preferences and employed the Gale-Shapley (GS) algorithm to match D2D pairs with cellular UEs. In [12], a three-dimensional iterative matching algorithm was proposed to maximize the sum rate of D2D pairs weighted by the intensity of social relationships. The work in [26] focused on the energy-efficient context-aware resource allocation problem. The authors transformed the NP-hard optimization problem into a one-to-one matching problem under two-sided preferences and proposed an energy-efficient matching algorithm. In [27], a two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage and a context-aware D2D peer selection problem in the second stage, was investigated. The authors proposed an iterative power allocation algorithm to optimize EE under a specific match and proposed an iterative matching algorithm to solve the combinatorial problem. The difference between previous works and this work is summarized as follows. First of all, we focused on the multi-hop D2D scenario, which was not investigated by [11; 12; 26; 27]. Second, the optimization problem involves a four-dimensional matching among D2D pairs, relay selections and resource blocks (RBs) reused in the first and second hop of two-hop D2D links, which is much more complicated than the problems studied in [11; 12; 26; 27]. Last, but not least, the solutions proposed in previous works cannot be directly applied to this work. To provide a tractable solution, we firstly decompose the origin problem into a two-dimensional matching among D2D TRs and RBs for the second hop and a three-dimensional matching problems among D2D TRs, RSs and RBs for the first hop. Then, we employ the proposed pricing-based iterative two-dimensional matching and pricing-based iterative three-dimensional matching algorithm to provide a tractable solution. In summary, the system model, problem formulation and proposed solution of this work are completely different from our previous works in [11; 12; 26; 27].
## 4 Background
### Low-Rate Distributed Denial of Service (LDDoS) Attacks  
An LDDoS attack is a variant of a DDoS attack. However, the attack traffic launched by LDDoS is always lower compared to DDoS attack where target network's links and nodes are overwhelmed with high traffic. In an LDDoS attack, only 10 to 20 percent of the traffic toward the target is generated, which is hidden in the normal data flow causing no obvious anomaly for network monitors [73]. Although the nature of the attack is small, it will lead to massive destruction at the level of the target network or system. Multiple individual LDDoS attacks producing bursts of pulses, each consisting of low throughput rate with a specific duration, are injected into the network. These bursts can overload the target network nodes or links causing high packet drop, network performance degradation, or even unavailability of service. In short, although the average throughput of an LDDoS attack is low, its impact is comparable to that of a DDoS attack [74]. In the following points, the main characteristics of LDDoS attacks are listed:  
* LDDoS is launched to specific target victims. Before attacks are launched, the attacker 1 obtains the application service information of the victim or the vulnerability existing in network protocols.
* An attacker 1 transmits attack data packets in a low-density and periodic mode so that the network resources of the attacker 1 are exhausted, and the network and service performance is reduced.
* LDDoS attack behavior is extremely covert.  
Figure 4 illustrates the concept of LDDoS attack targeting two network components: a network link (Target 1) and a node (Target 2). Target 1 is the link between router 4 and router 6, as well as between router 6 and the server. Target 2 is the server itself. Servers are considered the most frequently attacked devices, as they are considered the data repository needed by the users. As shown in the figure, two attackers (Attacker 11 and Attacker 2) launch LDDoS attacks into the network [74]. The traffic of LDDoS attack generated is indicated by the burst of rectangular pulses. The pulses describe a period of packet transmission in short duration and are repeated at a given frequency. The square  
pulse amplitude and length define the attack's energy. In other words, the LDDoS attack traffic is averaged across time in the square pulse [75].
# Anomaly Detection in Blockchain: A Systematic Review of Trends, Challenges, and Future Directions
## 4 Discussion
### Open Challenges and Future Directions
Cluster #6 publications [58; 113; 116; 117] focus on the implementation of anomaly detection in operational technology and industrial control system environments. These settings, characteristic of Industry 4.0, require context-specific models that support real-time monitoring and low-latency decision-making. Cluster #10 publications [98; 133; 134] address the distinct challenges of securing resource-constrained IoT devices that operate within or alongside blockchain frameworks. Given the heterogeneous nature of IoT ecosystems and their vulnerability to both device-level and network-level threats, this area underscores the need for lightweight, efficient, and adaptive anomaly detection methods. These thematic concentrations point toward several key directions for future research:
* developing specialized anomaly detection models for industrial blockchain systems in the context of Industry 4.0;
* creating lightweight algorithms for resource-constrained IoT devices in blockchain networks;
* integrating blockchain security with traditional critical infrastructure security systems. * _Standardization and regulatory frameworks._ Numerous review studies [31; 36; 38; 105; 133] consistently highlight persistent challenges, open questions, and the lack of unified methodologies, all of which hinder the large-scale adoption and interoperability of anomaly detection systems across different blockchain platforms. This fragmentation not only complicates technical integration but also impairs collective efforts to combat cyber threats in blockchain ecosystems. Moreover, research focusing on anomaly detection within highly regulated or financially sensitive domains--such as finance, emergency management, healthcare, cybersecurity, and critical energy infrastructure--emphasizes the urgent need for international cooperation [19; 69; 96; 103; 125; 147; 148]. In these sectors, combating cybercrime, including fraud and anomalous behavior, is a critical priority. Addressing this challenge requires the establishment of harmonized regulatory frameworks and international standards that can ensure the effectiveness and interoperability of anomaly detection systems, regardless of their underlying architecture or blockchain platform. Accordingly, future research and policy development should prioritize the following:
* developing harmonized methodologies for evaluating the effectiveness of anomaly detection systems in blockchain;
* forming international standards for the interoperability of anomaly detection systems across different blockchain platforms.
## 4 Energy Management Strategies
### Energy Consumption Optimization  
Optimizing energy consumption is critical for extending the operational life of sensor nodes [44; 74]. Various strategies and techniques are employed to minimize energy usage during different operational phases.  
* Adaptive Sampling: Adjusts the frequency of data collection based on environmental conditions or application needs.
* Compression Techniques: Reduces the amount of data transmitted by compressing sensor data before transmission.
* Duty Cycling: Alternates between active and sleep modes to reduce energy consumption during idle periods.
* Low-Power Communication Protocols: Utilizes energy-efficient communication protocols that minimize power usage during data transmission.
* Efficient Algorithms: Implements algorithms that minimize computational complexity and energy consumption.
* Edge Computing: Processes data locally on the sensor node to reduce the amount of data transmitted and save energy.  
Figure 6 illustrates various energy consumption optimization techniques, including adaptive sampling, compression, duty cycling, and low-power communication protocols.
## Abstract  
Inappropriate soil management practices and specific climatic conditions in semi-arid region cause loss of soil organic matter (SOM), decline soil fertility, and trigger soil erosion processes and desertification. A two-year field study was carried out to investigate the effects of tillage intensity and fertilizer regime treatments on the productivity of sunflower (_Helianthus annuus_ L.) and soil physicochemical properties in the semi-arid highland region in northwest Iran (37\({}^{\circ}\)31\({}^{\prime}\) north (N), 46\({}^{\circ}\)53\({}^{\prime}\) east (E)). Five fertilizer treatments were included under conventional (CT) or reduced tillage systems (RT): F1, no fertilizer application; F2, 20 t-ha\({}^{-1}\) farmyard manure (FYM); F3, 40 t-ha\({}^{-1}\) FYM; F4: 20 t-ha\({}^{-1}\) FYM + 50% of the recommended dose of nitrogen-phosphorus-potassium (NPK) chemical fertilizer; F5: full dose of the recommended chemical NPK fertilizer. Results showed that utilization of FYM decreased bulk density (BD); its effects were more evident under the highest SOM content for the F2 and F3 treatments, whereas application of mineral fertilizer had no significant effect upon SOM content, and elevated levels of FYM preserved higher organic carbon contents in topsoil. The highest N, P, and K contents of the soil were obtained with FYM plus inorganic fertilizer application in the RT system. Hydraulic conductivity and soil moisture content were significantly improved by RT and FYM application. The highest root growth was observed for F3 and F4 under the RT system. The effect of fertilizer and tillage treatments was more pronounced during the second year. Traits related to growth and seed quality such as achene oil content, leaf area, and harvest index were enhanced by chemical fertilization in the CT system. The highest achene yield and oil percentage were recorded for plants grown with F3 and F4. The best option for enhancing sunflower productivity and quality in semi-arid, high-altitude environments is the application of organic fertilizers amended with reduced amounts of chemical fertilizers.  
conservation tillage; organic-inorganic fertilizers; reduced tillage; semi-arid environment; soil physicochemical properties; sunflower
# Evaluation of Rice Straw Ash as a Pozzolanic Addition in Cementitious Mixtures
## 3 Results
### Analysis of RSA Reactivity
#### 3.2.2 Frattini Test  
Table 3 shows the data obtained by the Frattini test of the studied PC/RSA and PC/RSAW mixtures (replacements of 10%, 20% and 25%, at 8 and 15 days.  
The data confirmed that RSA presented pozzolanic activity and complies with the Frattini test for all the studied percentages, except for 10% RSA at both 8 and 15 days.  
After the washing treatment, (PC/RSAW) the results were similar. The sample with 10% washed ash still brought about a negative Frattini test as, apparently, there was no positive effect. However, this washing method has already been used to improve pozzolanic reactivity. [PERSON] et al. [31] followed this washing method with mineral wool manufacturing waste, cupola dust, and [PERSON] et al. [32] removed chlorides using this washing process applied with municipal solid waste incineration fly ash.  
Figure 8 offers these data at 8 days versus those obtained by other pozzolans: RHA and SF. As observed, these pozzolans presented positive tests for 10% which can be attributed to the higher amorphous silica content in ash vs. RSA [33; 34].  
[PERSON] et al. [18] ran a Frattini test with an RSA sample, and reported positive results for 8 days for those samples with 10% and 25% ash. This behavior was attributed to the method followed to prepare ash: in this case, controlled muffle burning at 650 \({}^{\circ}\)C of a small biomass sample was carried out, while ash was herein obtained from uncontrolled burning in large quantities. The high temperature reached (crystallizing a small part of silica) and the significant LOI percentage (14.6%) led to a moderate decrease in ash reactivity.
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
## 3 Anti-RAS-p21 Peptides from Expression Libraries  
As noted in the Introduction section, X-ray crystallographic structures of wild-type and oncogenic RAS-p21 bound to GDP and GTP suggested that there is an absence of surface-exposed clefts to which drug molecules can bind, suggesting that RAS-p21 may be \"undruggable [12; 13]\". This impression has proved false by a series of studies that have resulted in a number of cyclic peptides [29; 30] and small molecules that bind to mutant RAS-p21 proteins in their switch 2 domains in a binding \"pocket\" involving the switch 2 loop (residues 55-67) and the \(\alpha\)-2 helix (residues 67-73) [31]. This binding is very much dependent on the specific amino acid substitutions that occur. When these agents bind, they strongly inhibit either GTP-bound RAS-p21 or GDP-RAS-p21. In the former case, GTP-bound RAS-p21 cannot bind to the RBD of ras, and in the latter case, GDP-bound RAS-p21 is \"frozen\" in an inactivated state such that it cannot exchange GDP for GTP and, since it cannot bind to GTP, it cannot adopt an activated conformation in which it presumably interacts with RAF.