esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 3 Results and Discussion
### Effects of the Air Vent Location and Number on the Air Demand  
As the distance (\(X_{2}\)) from air vent #2 increases, as shown in Figure 7, the total air discharged through the air vent decreases slightly. The air discharge (\(\Omega_{a,1}\)) and the percentage of air discharged from air vent #1 increase, which could be explained by the pressure in air vent #1 decreasing when air vent #2 moves downstream, as shown in Figure 8. The air discharged through air vent #3 changes only slightly because of its greater distance from the gate. Consequently, the optimal placement of air vents can improve the negative pressure distribution in the tunnel and increase the total air discharge. Considering topography, technical, and economic factors, air vents should be positioned as close to the gate as possible.  
Figure 5: Relative air pressure distribution (\(p\)) along the tunnel for different air vent areas.  
Figure 6: Percentage of the total air discharge along the tunnel as a function of air vent area (\(a\)).  
Figure 7: Percentage of the total air discharge as a function of the location (\(X_{2}\)) of air vent #2.  
As shown in Figure 9, reducing the number of air vents decreases the total air discharge (\(Q_{a}\)) through air vents and increases the air velocity (\(U_{a,1}\)) through air vent #1. The air discharge (\(Q_{a,1}\)) through air vent #1 in the model with air vents #1 and #3 is greater than that in the model with air vents #1 and #2, which is attributed to the greater distance of air vent #3 from the gate than that of air vent #2, resulting in a lower pressure at air vent #1 in Figure 10. The spillway tunnels with air vents #1, #2, and #3 present the lowest pressure difference and the most uniform pressure distributions along the tunnel. Therefore, the number of air vents significantly influences the air discharge of each vent. Rationally designing multiple vents for large structures with high heads can effectively increase the air discharge, reduce the air velocity, and considerably decrease the negative pressure along the spillway tunnel.  
Figure 8: Relative air pressure distribution along the tunnel as a function of the location (\(X_{2}\)) of air vent #2.  
Figure 10: Relative air pressure distribution along the tunnel for different number of air vents.  
Figure 9: Percentage of the total air discharge with different number of air vents.
## 1 Introduction  
In recent years, shape memory polymers (SMPs) have attracted much attention owing to their excellent properties and potential applications such as biomedical devices, actuators, textiles, and packaging materials [1; 2; 3]. SMPs have an ability to change their shapes from temporary to original upon external stimuli, such as heat, light, and pH [4; 5; 6; 7]. Among them, thermo-triggered SMPs are the most widely studied, and a number of thermally-actuated SMPs have been reported, such as polyurethane, polyesters, polyacrylates, polynorhene, and polyimide [8; 9; 10; 11; 12; 13; 14]. Typical thermo-responsive SMPs could be described as a netpoint-switch model as reported in our previous work [15], where the netpoints could be of physically or chemically cross-linking, determining permanent shapes, and the switches refer to amorphous or crystallization soft phase responsible for temporary shapes. Inspired by spider silks, we recently reported shape memory biopolymers based on \(\beta\)-sheet structures of polyalanine segments [16]. The \(\beta\)-sheet structures act as netpoints responsible for the achievement of excellent shape recovery ability and high shape fixity.  
Polypeptide is one of the most important synthetic protein-mimicking materials that consist of a specific amino acid sequence linked by peptide bonds, which has been widely used in various applications such as gene delivery, drug delivery, and tissue engineering [17; 18]. The sequences could adopt secondary structures of \(\alpha\)-helices, \(\beta\)-sheets, etc., through hydrogen bonds within peptide backbones, enabling interesting self-assembly behaviors and bioactivity of polypeptides [19; 20]. Currently, ring-opening polymerization (ROP) of \(N\)-carboxyanhydrides (NCAs) has been the most common method used for large scale preparation of high molecular weight polypeptides [18]. Many stimuli-responsive polypeptides have been prepared by the ROP of \(\alpha\)-amino acid NCAs based on the conformational transitions between secondary structures [21; 22; 23; 24]. However, there are few reports on shape memory polypeptide. For SMPs-based biomaterials, in addition to suitable shape recovery performances, their mechanical properties, biodegradability, biocompatibility, and sterilizability need to be considered [25]. Polypeptides can satisfy the outlined above requirements. In this work, shape memory poly(\(\gamma\)-benzyl-L-glutamate) (PBLG-PPG-PBLG) was synthesized, and its second structure and properties were studied. This work will provide new insight for designing shape memory biopolymers, and promote the development of interdisciplinary subjects of bionics and intelligent materials.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Ground-Based Platforms
#### 3.1.2 Unmanned Ground Vehicles  
The use of UGVs in the dismantling of nuclear reactors, and in the aftermath of the accidents that occurred in the nuclear reactors of Chernobyl (1986) and Fukushima (2011), allowed them to operate in places with high levels of radiation, transport various sensors, and to perform measurements in real time [10]. However, limitations to the use of these vehicles, include inter alia, problems in terms of the sensitivity and performance of electronics in high radiation fields, difficulty in establishing communications, and reduced mobility of these vehicles/robots (e.g., descending and climbing stairs and overcoming some obstacles). Considering the tasks required to be performed in extreme scenarios, the use of UGVs will be limited, with human intervention (first responders and radiation task forces) being ultimately required.
## 5 Discussion  
To increase the capacity of managing drought and reducing the adverse effects, timely and effective drought monitoring is indispensable in agricultural management. This study improved the spatial resolution of SIF to 1 km and explored the potential of downscaled SIF in drought detection. Some important and interesting discoveries are discussed in this section.
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 4 Methodology
### Semisupervised Alternative  
In the semisupervised alternative, we consider an scenario where rather than blindly dealing with any domain, we can previously obtain a small portion of data with manual annotation. Thanks to these data, we optimize the system to the domain under analysis. The main drawback for this option is the cost of data gathering and annotation, creating a trade-off between potential performance benefits and cost. In this line of research, we propose two different semisupervised options developed around previously described systems from Sections 4.5 and 4.6. These systems take into account three types of input embeddings: \(\mathbf{\Phi}\),\(\mathbf{\Phi}_{\text{enroll}}\) and \(\mathbf{\Phi}_{\text{in}}\), respectively, obtained from the audio under analysis, the enrollment audios and the new labeled in-domain data.  
The first semisupervised option is a modification of the system described in Section 4.5 and illustrated in Figure 9. In this system, we modify the subset of embeddings playing the role of \(\mathbf{\Phi}_{\text{calib}}\) for the threshold adjustment. While in the unsupervised system, this role is played by the whole development subset with potentially out-of-domain data, we now make use of the annotated in-domain data \(\mathbf{\Phi}_{\text{in}}\).  
Figure 8: Flowchart for the hybrid approach. Red and yellow boxes, respectively, stand for the embedding extraction pipelines for the evaluation audio \(\Omega\) (online) and the enrollment audios \(\Omega_{\text{enroll}}\) (offline). Both sets of embeddings are used in the new hybrid clustering and identity assignment block.  
Figure 7: Diagram of the hybrid approach. Embeddings (\(\mathbf{\phi}_{1}\),...,\(\mathbf{\phi}_{4}\)) are sequentially assigned to the available clusters at each time \(t\). Initially, the available clusters are only those for the enrolled speakers. When the embedding is not assigned to an existing cluster (t = 3), it is responsible for an extra cluster for an unknown speaker (red arrow). This new cluster is then available along the posterior assignments.  
The second semisupervised alternative works with the system from Section 4.6. This alternative modifies the definition of anchor embeddings. While the unsupervised hybrid system builds the anchor clusters exclusively based on the enrollment embeddings \(\mathbf{\Phi}_{\text{enroll}}\), the semisupervised version now considers the joint work of enrollment embeddings as well as those from the new labeled in-domain data \(\mathbf{\Phi}_{enroll}\cup\mathbf{\Phi}_{in}\). By doing so, anchor clusters are reassured to contain traces of in-domain audio regardless of the enrollment audio nature.  
In both presented systems, the influence of the new in-domain data is restricted to the assignment algorithms, keeping the backend models unmodified. This choice is made despite the potential benefits of model adaptation to make a fair comparison between unsupervised and semisupervised styles.
## 1 Introduction  
In basketball, the lower limbs of athletes are subjected to large impact forces during each landing [1]. Players complete 70 jumps/landings in a single game and attenuate impacts of up to nine times their body weight every time [2], which increases the risk of knee and ankle injuries [3]. Reducing impact forces (which includes both magnitude and loading rate characteristics) may help prevent foot injuries [4]. The shock absorption characteristics and comfort of basketball shoes, as core equipment of the sport, have important influences not only on the performance of the players but also on the prevention of lower extremity injuries [5]. The impact force and pressure distribution characteristics between the feet and the shoes must be understood to effectively optimize the technical movements, reduce foot injuries, and improve the design of specialized shoes [6]. However, the existing studies on foot loading have mainly focused on common gait characteristics, such as walking and running [7, 8], and studies on specific sports maneuvers, such as cross-over running and lay-up jumping [9, 10, 11], are limited.  
Meanwhile, comfort and stability have been identified as the principal factors of specialized sports shoes [12], in addition to meeting the functional requirements of specific sports and strengthening the foot protection function [13]. However, these factors are easily neglected. The comfort of basketball shoes is closely related to the performance of basketball athletes, as well as to ankle injury and its prevention [2]. The subjective perception scale has been proven to be an effective and credible method for assessing the aforementioned variables [14; 15]. [PERSON] et al. [16] found that a close relationship exists between runners' subjective perception of shoes' cushioning performance and their impacts on plantar pressure during running. Meanwhile, foot comfort is also closely related to the impact load on the lower limbs at initial contact [17]. Thus, the functionality and comfort of basketball shoes for typical maneuvers in basketball must be explored through both the subjective perception and foot biomechanical tests [18].  
Therefore, the current study aims to determine the effect of different basketball shoes on the ground reaction force (GRF) and plantar pressure characteristics in three typical basketball maneuvers and to further understand foot loading characteristics in basketball and their relationship with comfort through the subjective perception scale evaluation of basketball shoes. It was hypothesized that wearing different shoes would affect comfort perception, and correspondingly change GRF and plantar pressure characteristics during basketball maneuvers.
## 2 Materials and Methods
### Response Surface Methodology Based Experimental Design  
Response surface methodology (RSM) via Central Composite Design (CCD) in the Design Expert software was used to optimize the coagulant dosage, pH of the medium, and initial turbidity (independent variables) for removal of turbidity and coagulation activity (dependent variables) by means of coagulation-flocculation using the bio-coagulant deshelled _Carica papaya_ seeds. The dependent variables were predicted based on the Equation (2).  
The CCD consisted of a duplication of \(2^{3}\) factorial design, complemented by six axial points and five central points; a total of 33 tests were performed randomly. After reviewing the literature and conducting the preliminary trials, the high and low levels were then selected. The coagulant dosage levels, pH, and initial turbidity were studied (Table 2). For pH, initially, the factorial design was run to obtain the Pareto effect. In this run, pH 3.0 and 7.0 were used. For CCD analysis, minor revision was done on pH level. The pH was slightly changed to pH 4.0 and 6.5, taking into consideration for more feasible axial point (\(-\alpha\)). If the low range of pH 3.0 was continuously used in the subsequent CCD analysis, the \(-\alpha\) can be about pH 2.0, which then can potentially deteriorate the bio-coagulant.  
\[\text{Y}=\beta_{\text{o}}+\ \sum_{i=1}^{n}\beta_{i}X_{i}+\sum_{i=1}^{n}\beta_{ ii}x^{2}_{i}+\sum_{i=1}^{n}\sum_{j=i+1}^{n}\beta_{ij}x_{i}x_{j}+\varepsilon \tag{2}\]  
where,  
Y = Predicted responses (turbidity removal and coagulation activity)  
\(\beta_{\text{o}}\) = Intercept coefficient;  
\(\beta_{i}\) = Linear coefficient;  
\(\beta_{ii}\) = Quadratic coefficient;  
\(\beta_{ij}\) = Interactive coefficient; \(x_{i}x_{j}\) = Influenced factors;  
\(\varepsilon\) = Random error.  
The effect of the variables coagulant dosage, pH and initial turbidity were evaluated by analysis of variance (ANOVA). The order of statistical significance along with the model's hierarchy criterion were used as a basis to determine either the terms would or would not remain in the prediction models. After attaining the prediction models, three-dimensional (3D) response surface plots were generated to visualize and study the effects of the variables and the interactions.
###### Abstract  
Thermal and non-thermal technologies used in food processing should be not only effective in terms of decontamination and preservation but also minimize undesirable losses of natural bioactive compounds. Arabica (_Coffea anabica_) is the most cultivated variety of coffee, making it a valuable source of phytontrients, including antioxidants. In the present study, green and roasted Arabica coffee beans were treated with slow freezing (SF), fast freezing (FF), microwave radiation (MWR) and cold atmospheric plasma (CAP). Moisture content (MC) of coffee beans and antioxidant activity (AOA) of aqueous extracts were measured. Green coffee showed a decrease in MC after MWR treatment, and roasted coffee showed an increase in MC after freezing. After SF and FF at \(-19\)\({}^{\circ}\)C for 24 h, all extract samples showed an increase in AOA by 4.1-17.2%. MWR treatment at 800 W for 60 s was accompanied by an increase in the AOA of green coffee extracts by 5.7%, while the changes in the AOA of roasted coffee extracts were insignificant. Sequential combined treatments of SF + MWR and FF + MWR resulted in an additive/synergistic increase in the AOA of green/roasted coffee extracts, up to \(+23\)%. After CAP treatment with dielectric barrier discharge (DBD) parameters of 1 \(\mu\)s, 15 kV and 200 Hz for 5 and 15 min, green coffee showed a decrease in the extract AOA by 3.8% and 9.7%, respectively, while the changes in the AOA of roasted coffee extracts were insignificant. A high positive correlation (r = 0.89, \(p\) < 0.001) between AOA and MC was revealed. The results obtained indicate that SF, FF, MWR and combined treatments may be applied at the pre-extraction stage of coffee bean preparation in order to increase the yield of antioxidant extractives.  
coffee; Arabica; freezing; microwave; cold atmospheric plasma; antioxidant activity; moisture content; pre-extraction preparation; extraction yield +
Footnote †: journal:
## 4 Discussion
### RQ2: Communicative Competence  
The analysis of students' logs has further shown that students' communication was more intense and fluid when they used the digital version to accomplish the given learning task. The number of turns and messages exchanged between students from the ExpGr was considerably higher than that from the ContrGr. Consequently, students from the ExpGr were exposed to more significant interaction with their peers and thus obtained more opportunities to negotiate and exchange information in the target language than those from the ContrGr. Therefore, the data suggest that the digital version provides more suitable conditions for developing students' communicative competence.  
Nonetheless, despite being exposed to more significant interaction with their peers, students from the ExpGr were not more successful in arranging their appointments than those from the ContrGr. Bearing in mind that communicative competence refers to the ability of a speaker to communicate effectively with other native or nonnative speakers [56], the results suggest that the digital version is not more effective than the paper version for solving the given learning task. This implies that the digital version of _Terminkalender_--against the authors' initial assumption--does not enhance students' communicative competence more than the paper version does.
# Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis
## 4 The GRALG Classification System
### Granulator and Alphabet Synthesis  
The set of candidate information granules \(\mathcal{B}\) is forwarded to the Granulator block, which is in charge of extracting a suitable subset \(\mathcal{A}\subset\mathcal{B}\) of meaningful granules of information to be collected in an alphabet of symbols \(\mathcal{A}\). In order to accomplish this task, the Granulator runs a clustering procedure [16] and the representative elements of well-formed clusters are considered as meaningful granules of information.  
Specifically, the Granulator receives \(\mathcal{B}\) from the Extractor block and runs a Basic Sequential Algorithmic Scheme (BSAS) free-clustering procedure on it. BSAS is driven by the following inputs:  
* a dissimilarity measure between graphs;
* a value \(\theta\) which determines the maximum radius of the resulting cluster;
* a value \(Q\) which determines the maximum number of clusters that can be spawned;
* a suitable way to determine the representative element of a cluster: since BSAS will work in the graph domain, the choice fell on using the medoid of each cluster as its representative element [39].  
In order to analyze the data at different levels of granularity (i.e., resolution), BSAS runs several times for different values of \(\theta\). Without loss of generality, the considered values of \(\theta\) are collected in a vector \(\mathbf{\theta}\). Regardless of the value of \(\theta\), each cluster in each partition is then evaluated by means of a cluster quality index that jointly takes into account its cardinality (i.e., number of patterns) and compactness (i.e., average pattern-to-center distance) and only the centers of well-formed (i.e., compact and populated) clusters are admitted to be part of the alphabet. Mathematically speaking, the cluster quality index is defined as:  
\[F(\mathcal{C})=\eta\cdot\Phi(\mathcal{C})+(1-\eta)\cdot\Psi(\mathcal{C}) \tag{7}\]  
namely, as a linear convex combination between the compactness of the cluster \(\Phi(\mathcal{C})\) and its cardinality \(\Psi(\mathcal{C})\) weighted by a trade-off parameter \(\eta\in[0,1]\). The compactness of the cluster is defined as the average pattern-to-center distance:  
\[\Phi(\mathcal{C})=\frac{1}{|\mathcal{C}|-1}\sum_{i}d(g^{*},g_{i}) \tag{8}\]  
where \(g_{i}\in\mathcal{C}\) is the \(i^{\text{th}}\) pattern of cluster \(\mathcal{C}\), and \(g^{*}\) is the representative element of the cluster. Instead, the cardinality of the cluster is defined as the relative size of the cluster \(\mathcal{C}\) with respect to the overall number of candidate information granules:  
\[\Psi(\mathcal{C})=1-\frac{|\mathcal{C}|}{|\mathcal{B}|} \tag{9}\]  
Since both \(\Phi(\mathcal{C})\) and \(\Psi(\mathcal{C})\) are negative-oriented, a cluster is considered for being part of the alphabet \(\mathcal{A}\) if and only if its quality index \(F(\mathcal{C})\) is below a threshold \(\tau_{F}\in[0,1]\). The medoids whose clusters satisfy such condition compose the alphabet of symbols \(\mathcal{A}\).
## 6 Discussion
#### Threats to Validity and Mitigations  
While this study provides valuable insights into the integration of Scrum and DevOps for VSEs in South America, several limitations and potential threats to validity should be acknowledged.  
1. The evaluation of the implementation guide relied primarily on expert reviews, which may not fully reflect its real-world effectiveness in diverse VSEs settings. Although the experts provided constructive feedback and identified areas for improvement, their assessments may be subject to subjectivity bias, as personal preferences and preconceived notions about Scrum and DevOps could influence their evaluations. To mitigate this risk, we employed structured evaluation forms and clear guidelines, but the potential for bias cannot be eliminated. Furthermore, we must also acknowledge the potential for social desirability bias where experts may be inclined to offer overly positive evaluations. To address this, experts were guaranteed anonymity and confidentiality to encourage honest feedback.
2. The study's focus on VSEs in South America limits the generalizability of the findings to other regions with different cultural, economic, and technological contexts. The guide's effectiveness may vary depending on factors such as organizational culture, team size, project complexity, and available resources. Therefore, caution should be exercised when applying the guide in settings outside of the South American context. This poses a threat to external validity.
3. The reliance on expert opinion, while valuable, represents a threat to internal validity. Improvements noted may not be causally linked to the guide and may be influenced by extraneous variables. Future studies could incorporate control groups or longitudinal designs to strengthen causal inferences.
4. The validity of the characterization instrument is crucial for accurately assessing the maturity of Scrum and DevOps adoption in VSEs (construct validity). While the instrument was developed based on established frameworks and expert knowledge, further validation is needed to ensure that it accurately captures the intended constructs.
5. The study did not assess the long-term impact of the guide on VSE performance. Future research should investigate the sustained effects of the guide on key metrics such as software quality, time to market, and customer satisfaction. Addressing these limitations and threats to validity in future research will further strengthen the evidence base for the effectiveness of the proposed implementation guide and enhance its practical applicability for VSEs worldwide.