esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Role of Pre-Runaway Chemical Heat Release in Radiation-Induced Thermal Runaway  
To isolate the effect of chemical heat release before thermal runaway, Figure 10 compares the maximum temperature evolution of Cell 2 with and without the chemical heat source term included in the calculations. Results without chemical heat release represent contributions solely from radiation, while the difference between the cases with and without chemistry effectively represents the contribution from chemical heat release. We can select a temperature as the onset temperature of thermal runaway during the temperature rise--for example, 450 K or 500 K--and evaluate the temperature differences at these selected reference points. Figure 10a plots the temperature evolution with and without chemistry in high triggering temperature case T\({}_{1}\) = 900 K. At the reference onset temperature T\({}_{\text{ref}}\) = 450 K, the temperature difference \(\Delta\)T between the cases with and without chemistry is around 2.2 K, indicating the role of pre-runaway chemistry. Considering the total temperature rise at the reference T\({}_{\text{ref}}\) - T\({}_{\text{ini}}\) = 450 \(-\) 293 = 157 K includes the combined effects of radiation and pre-runaway chemistry, the contribution of chemistry can therefore be reasonably isolated. Similarly, in the lower triggering temperature case shown in Figure 10b, the contribution of  
Figure 9: (**a**) Time-integrated net radiation heat flux at different triggering temperature; color indicates the time with zero net radiation flux. (**b**) Time-integrated net radiation heat flux versus integration time; color indicates thermal runaway triggering temperature.  
pre-runaway chemistry increases the battery temperature by 13.6 K, which is much higher compared to the higher triggering temperature case in Figure 10a.  
The ratio of temperature rise from pre-runaway chemistry to the total temperature rise is evaluated at different triggering temperatures. It is shown in Figure 11 that at high triggering temperatures, the contribution of pre-runaway chemistry is very small: below 1% for T\({}_{\text{ref}}\) = 450 K and below 5% for T\({}_{\text{ref}}\) = 500 K. This indicates the sole dominant role of radiation for high temperature radiation-induced thermal runaway, while for the low triggering temperature conditions, the contribution of pre-runaway chemistry increases to around 10% for reference temperature 450 K and nearly 30% for onset reference temperature 500 K. The impact of pre-runaway chemistry becomes comparable to radiation and, hence, cannot be neglected.  
Figure 11: The contribution of chemical heat release during radiation-induced thermal runaway with different T\({}_{1}\).  
Figure 10: Temperature evolution with and without chemistry (**a**) in high triggering temperature T\({}_{1}\) = 900 K and (**b**) in low triggering temperature T\({}_{1}\) = 602 K, showing the role of heat release from pre-runaway chemistry.
## 2 Configuration and Kinematics Modeling
### Forward and Inverse Kinematics Based on Geometrical Relationships  
According to the structure of CHCM, the 3-DOF position of end-effector is determined by the deflection of every 2-DOF joint module, which can be described by two parameters: rotation about \(X\)-axis by angle \(\varphi\) and rotation about \(Z\)-axis by angle \(\theta\). The world coordinate system \(\{W_{o}\}\) is defined, with its origin at the center of the base plate and \(y\) axis perpendicular to the base plate and pointing to the end-effector, as shown in Figure 3.  
Figure 1: The general structure of CHCM, consisting of the driving subsystem and the motion subsystem.  
Figure 3: The world coordinate system and joint angles definition.  
The purpose of forward kinematics analysis is to determine every joint angle and then compute the end-effector position when the lengths of three driving cables are given. Correspondingly, the inverse kinematics model is supposed to calculate the expected joint angles and cable lengths with given end-effector positions.
## 4 Measurement Formation Process
### MLMS Tree Construction and Maintenance  
In order to avoid a large number of control packets caused by constructing MLMS trees, after calculating the approximate minimum cost MLMS path \(T\), we let the sink node pack the information of \(T\) with the global seed into several packets. Through the nodes' relay of these packets the MLMS tree is constructed orderly (as shown in Figure 7), but for a large-scale network (meaning there are many interested nodes in each measurement and the path from the sink node to the interested nodes may be extremely deep), the complete path information will take up a lot of space in the packet head. Due to the length limitation of packets transmitted in the WSN, the sink node needs to compress and encode the information of \(T\).  
Noticing that Algorithm 2 divides the neighboring interested nodes into a group, we can use the effect of wireless communication to locally broadcast such packets at each branch node and thus all its neighbors can overhear them. Thus, in the branches of the MLMS path, we only need to encode the branch node information and add the local broadcast hops to the packet header, rather than the information of all interested nodes. In this way, we can compress the path information to reduce the number of control packets.  
As shown in Figure 7, all interested nodes and branch nodes are in four groups a, b, c and d, and we can guarantee that all interested nodes can receive the corresponding global seed simply by coding the information of branch nodes into the packet header, and the MLMS path can be constructed in disseminating such packets. Obviously, this mechanism is affected by the density of the interested nodes (_i.e._, the proportion of the number of interested nodes to the total number of network nodes), and we will evaluate it in Section 6.
## 4 Solution Methodology
### Grid Independence Analysis  
The simulations were conducted using the time-averaged technique with the SST turbulence model to analyze the flow field. Five grid configurations were tested to evaluate the impact of grid size on the simulation results, as illustrated in Figure 9. The grid independence test was conducted using averages of three key parameters (velocity, temperature, and humidity) to ensure that the simulation results were not dependent on grid resolution. As shown in Figure 5, the center, left, and right lines were divided into 200 points to accurately measure the flow characteristics. A systematic grid independence study was performed to minimize errors caused by grid variation. Based on this analysis, Grid Case 4 was selected for all subsequent simulations, as it had less than 5% variation compared to finer grids. This selection guaranteed a balance between computational efficiency and solution accuracy, ensuring reliable final results.  
Table 2 and Figure 9 present the use of PolyHex core grids for flow analysis, as recommended by ANSYS Inc. [26]. The preliminary results revealed that there were minimal fluctuations in temperature and humidity throughout the grid independence analysis, as shown in Figure 9. However, there were notable variations in velocity in the initial grid size, which progressively diminished as the grid resolution improved. The solution fluctuations stabilized, ultimately achieving less than a 5% variation in results compared to finer grids, ensuring accuracy and reliability for the selected grid size. The final grid size and grid distribution in the specific section plane are shown in Figure 10.  
Figure 9: Grid independence study comparing different grid resolutions.
# Digital Signal Processing of the Inharmonic Complex Tone
## 4 Experimental Results
### Simple Manipulation of the Recorded Signal  
As an example of basic signal manipulation, the exclusion of the first several partials is chosen. The modified signals can be used as test signals for pitch perception listening tests. The signal without content of the first channel (corresponding to the fundamental partial) is saved as an audio file Piano_A3_sum_min_01.wav. The signal without content of the first and second channel (corresponding to the fundamental partial and second partial) is saved as an audio file Piano_A3_sum_min_01_02.wav. Finally, the signal with only odd partials is saved as an audio file Piano_A3_sum_odd.wav. The files Piano_A3_sum_min_01.wav and Piano_A3_sum_min_01_02.wav are imported into MATLAB, and the spectra of the two modified signals are plotted together with the spectrum of the original signal. In Figure 14, the results for the signals with missing first and first two partials are given. It can be seen that the content is the same, except for the deliberately removed partials.
# Returning to Nature for the Design of Sportive Phases in Solid-Phase Microextraction
## 2 Cork
Studies comparing the extraction efficiency of cork-based microextraction techniques with commercial sorbents such as C18, nylon, Oasis(r) HLB (Hydrophilic-Lipophilic Balance), and DVB have been proposed in some applications. In a recent study, the recovery results showed that the cork reaches higher sorption efficiency than C18, DVB, and nylon, and achieves very similar recoveries to those of Oasis(r) HLB in which recoveries were between 67% and 98% for Oasis(r) HLB, while the cork reached values between 63% and 89% [37]. In another study, on applying the optimized conditions for all sorbents, positive results were obtained for the cork-based methodology where using the same amount of sample and sorbent, the renewable sorbent provided better results when compared with the commercial C18 [34]. Therefore, cork proved to be an excellent alternative to the synthetic phases, presenting the advantage of achieving similar efficiency with a cost-effective benefit. Moreover, cork-based devices have been demonstrating an incredible performance regarding reproducibility and reusability. On Dispersive Pipette Extraction (DPX) applications, studies were conducted to evaluate the cleaning step of the pipette tip and the cork stability under several extraction cycles. The results showed that a single DPX cork-based tip maintained around 80% of the extraction efficiency with good precision (relative standard deviation lower than 20%) for all analytes with up to 15 extraction/desorption cycles [32]. In addition, an efficient cleaning of the DPX device was achieved after four cycles with the desorption solvent, enabling the reutilization of the device without carryover [36]. The advances in cork-based techniques have led to remarkable improvements with regard to environmentally friendly aspects, in particular because cork harnessed as biosorbent is originated from cork stoppers that would be discarded and are reused for the development of several analytical methodologies. Moreover, the strategy of combining this cheap and natural biosorbent with high-throughput strategies [33; 38] that are able to perform fast and efficient extractions are promising trends that should be further investigated.
The most recent significant activity in the WGoC was the 2020-2021 seismic sequence [7; 45; 46], which started on 23 December 2020 with a moderate event near Marathias and evolved in three stages, first triggering earthquake clusters towards Trizonia Island involving some strike-slip events and later producing an M\({}_{\rm w}\) = 5.3 earthquake offshore of Psa thopyrgos that exhibited peculiarities in terms of its complicated rupture characteristics [47]. In this study, we reassess the seismic hazard in the WGoC utilizing a well-established methodology and incorporating recent data. The term \"seismic hazard\" describes the expected ground motion level for potential earthquake occurrences within a study area. The methodologies used to evaluate seismic hazard are grouped into two major categories: probabilistic and scenario-based. The first exploits data from historical and instrumental earthquake catalogues and integrates a seismotectonic model for its application. Subsequently, given an earthquake occurrence model, results for the maximum expected ground motion for selected return periods are generated. The second methodology is based on individual earthquake scenarios, with which synthetic waveforms are produced and analyzed. Strong ground motion may be a catalyst for other secondary hazards, the most important of which are rock falls, landslides, and liquefaction phenomena. Therefore, the visualization of the spatial distribution of peak ground motion parameters can be of great importance for scientists and decision makers. The advantage that the study area offers is the wealth of earthquake data. The latter is critical because seismic hazard can be assessed without adopting algorithms that cope with this difficulty, such as the one proposed by [PERSON] et al. [48]. A plethora of seismic hazard studies have been conducted for the Greek territory, either as a whole or for specific areas, such as [49; 50; 51; 52; 53; 54; 55; 56; 57; 58]. The applied methods for the assessment of seismic hazard are described in detail in Section 2. Patras, the third largest city in Greece and one of its largest harbors, is situated at the western edge of the study area, on the southeastern shores of the Patras Gulf. The nearby town of Rion hosts important infrastructure, such as the University of Patras and the University General Hospital of Patras, while the Rion-Antirrion bridge connects Peloponnese with Central Greece. Other important towns on the shores of the WGoC include Aigion and Nafpaktos. As the seismic risk strongly depends on the exposure to seismically prone areas, the aforementioned city, towns, and infrastructure may suffer increased economic losses from potentially destructive earthquakes, both in terms of human lives and structural damage. As a consequence, the proper assessment of seismic hazard in the WGoC area, both probabilistic and with specific earthquake scenarios, is required for the subsequent estimation of seismic risk and consideration of means for its mitigation.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture
### Edge Deployment Considerations  
Several model compression techniques were implemented to optimize performance on edge devices. Quantization improved inference speeds by up to 4.8 times, reducing processing time from several seconds to under 1 s on a Raspberry Pi 4, depending on the model's complexity and quantization techniques applied. Previous studies have demonstrated that full-integer quantization can achieve latencies as low as 2.19 s for deep learning models, while optimized inference frameworks can lead to 79% energy savings and significant execution time reductions [23; 24]. These findings reinforce the importance of quantization in enhancing edge AI efficiency for on-device anomaly monitoring.  
By reducing the model's complexity without significant loss of accuracy, real-time anomaly detection remains feasible on low-power devices.  
Beyond computational efficiency, energy consumption is a critical factor in IoT-based systems. Since many edge devices are battery-powered, our system incorporates energy-efficient execution strategies to minimize power usage. By leveraging event-triggered processing, the model remains idle until an anomaly is suspected, reducing unnecessary energy expenditure. Furthermore, dynamic power management techniques allow the edge device to adjust its processing power based on workload demands.  
By integrating these optimization strategies, the proposed system ensures that on-device anomaly monitoring is accurate, scalable, energy-efficient, and deployable on diverse edge hardware platforms.  
Latency tests showed that the quantized LSTM Autoencoder model achieved inference speeds under 32.1 ms on an NVIDIA Jetson Nano, making it suitable for real-time applications.
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Problem Formulation  
Given a set of cluster-heads \(\mathbf{CHs}[CH_{i},i=1,\ldots,n]\) and a set of checkpoints \(\mathbf{CPs}[CP_{j},j=1,\ldots,m]\) in a multi hopping WSN, the dispatched ferry need to move along a path to collect data from associated nodes when stopping at the checkpoints return back to the base station while satisfying the following two main goals:  
_Goal one_: Minimizing the overall tour time of the ferry (delay):  
\[\text{Tour\_Time}(s)=\text{Travel\_Time}\ (s)+\sum_{j=1}^{m} \text{ST}_{s}(j) \tag{6a}\] \[Tmax=\text{Max}(\text{Travel\_Time}\ (s)+\sum_{j=1}^{m} \text{ST}_{s}(j)\ ) \tag{6b}\]  
such that:  
\[\text{Tour\_Time}(s)\ <\text{Tmax} \tag{7}\]  
where s = 1,2,3... corresponds to the round of data collection, Tour_Time is the total round trip travelling time of the ferry from the base station to each checkpoint j plus the stopping time at each check point of rounds:  
\[\text{Travel\_Time}\ (s)=\frac{D_{s}(BS,CP1)+\sum_{j=1}^{m-1}D_{s}(CP(j),CP(j+1))+D_{s}( CP(m),BS)}{\text{Ferry\_Speed}} \tag{8}\]  
where m is the total number of checkpoints, n is the number of cluster heads, Travel_Time is the round trip travelling time of the ferry from the base station to each checkpoint j and returning back to the base station. \(D_{s}(BS,CP1)\) is the distance from the base station to the first checkpoint and \(D_{s}(CP_{m},BS)\) is the distance from the last checkpoint visited by the ferry to the base station. \(Ferry\_Speed\) is the assumed fixed speed of the ferry.  
_Goal two_: Minimize the overall energy consumption of the network by applying our FNRCA in each virtual grid _i.e._, finding the:  
\[Min\sum_{i=1}^{n}E(i) \tag{9}\]  
and by minimizing the sum distance from the checkpoints and their associated cluster heads as:\[Min\sum_{cp=1}^{m}\sum_{ch=1}^{n}D(ch,cp) \tag{10}\]  
such that:  
\[\forall\;ch\in CHS,\exists\;cp\in CPS:D(ch,cp)\leq r \tag{11}\]  
where \(n\) is total number of cluster heads, \(ch\) is a cluster head, \(CHS\) is the cluster heads list, \(cp\) is a checkpoint and \(CPS\) is the list of checkpoints.  
In order to choose the optimum path of the ferry and achieve the above goal and constraints a weighting scheme is used to order the checkpoints into the sequence to be visited by the ferry.
# Optimizing Foreign Language Learning in Virtual Reality:
## 4 VR-CCL Framework for Optimized Language Learning
### Interactive Engagement (Based on Constructivist Learning Theory)
#### 4.1.3. Task and Goal-Oriented Interaction in VR  
Virtual reality (VR) furnishes a singular conduit for mission-centric and objective-driven engagements, especially within linguistic acquisition. Contrasting conventional techniques, wherein assignments might be circumscribed to textual endeavors or rudimentary exchanges, VR proffers an engulfing milieu wherein scholars can plunge into verisimilar tableaux, necessitating lingual dexterity to fulfill determinate ends.  
Visualize, for a moment, an undertaking within VR that mandates steering through a non-native metropolis solely employing the focal dialect. Disciples can pinpoint a dis-tinct locale, requisitioning sustenance or deciphering a conundrum, all while articulating solely in the vernacular under scrutiny. These tableaux not merely assay their lingual faculties but also their prowess in harnessing them in tangible, quotitien predicaments.  
The engulfing essence of VR ascertains that those disciples remain wholly enmeshed in the designated assignment. External diversions wane, and the palpable immersion within the virtual realm augments the gravitas of the mission, rendering efficacious discourse paramount. The intensified involvement and authenticity render VR endeavors more resonant and indelible, culminating in the superior consolidation and employment of linguistic paradigms.  
VR's inherent malleability sanctions assignments to be modulated in alignment with individual adeptness tiers. Neophytes might be embroiled in more elementary liaisons such as procuring commodities from a mercantile, while seasoned learners could broker commercial pacts or orate discourses.  
In its quintessence, mission-centric and objective-driven liaisons within VR proffer a vivacious, captivating, and adaptable modality for linguistic acquisition, melding theoretical cognizance with pragmatic deployment.
## 2 Modeling of the Taper-Leaf and the Multi-Leaf Spring
### The Mechanical Model of the Single Taper-Leaf Spring  
The half of the single taper-leaf spring is taken as the research object. Its mechanical model is shown in Figure 2. Similarly, the half of the single taper-leaf spring also can be regarded as an  
Figure 1: The mechanical model of the multi-leaf spring.  
elastic beam. It is fixed at one end. A concentrated load \(F\) is applied at the other end. The geometric parameters and the coordinate system are marked in Figure 2. \(L\) is the half length of the single taper-leaf spring; \(h_{2}\) is the thickness for \(x\)(\([l_{2},L]\); \(h_{1}\) is the thickness for \(x\)(\([0,l_{1}]\). \(h(x)\) is the thickness for \(x\)(\([l_{1},l_{2}]\). \(\gamma\) is defined as the thickness ratio and \(\gamma=h_{1}\)/\(h_{2}\).  
Based on the model in Figure 2, when the load \(F\) is applied at the free end, the same normal stress at any position for \(x\)(\([l_{1},l_{2}]\) is [9]:  
\[\sigma_{x}=\frac{6 Fx}{bh^{2}(x)} \tag{2}\]  
From Equation (2), the normal stress \(\sigma_{B}\) of Section B for \(x=l_{2}\) can be expressed as:  
\[\sigma_{B}=\frac{6 Fl_{2}}{bh_{2}^{2}} \tag{3}\]  
In order to meet the requirements of the equal stress at Section B for \(x=l_{2}\), \(\sigma_{x}=\sigma_{B}\) must be satisfied. According to Equations (2) and (3), \(h(x)\) can be expressed as:  
\[h(x)=h_{2}\sqrt{\frac{x}{l_{2}}} \tag{4}\]  
According to Equation (4), \(h_{1}\) can be expressed as:  
\[h_{1}=h_{2}\sqrt{\frac{l_{1}}{l_{2}}} \tag{5}\]  
When the load \(F\) is applied at the free end, the deformation energy \(U\) can be expressed as:  
\[U=\int_{L}\frac{(Fx)^{2}}{2 EI}\mathrm{d}x=\int_{0}^{l_{1}}\frac{(Fx)^{2}}{2 EI_ {1}}\mathrm{d}x+\int_{l_{1}}^{l_{2}}\frac{(Fx)^{2}}{2 EI_{2}}\mathrm{d}x+\int_{ l_{2}}^{L}\frac{(Fx)^{2}}{2 EI_{3}}\mathrm{d}x \tag{6}\]  
where \(I_{1}\), \(I_{2}\), \(I_{3}\) are the inertia moments at different thicknesses and \(I_{1}=\frac{bh_{1}3}{12}\), \(I_{2}=\frac{bh^{3}(x)}{12}\), \(I_{3}=\frac{bh_{2}3}{12}\), respectively.  
According to the Castigliano Second Theorem [37], the end deformation of the spring can be expressed as:  
\[y=\frac{\partial U}{\partial F}=\frac{4F\big{[}L^{3}+l_{2}^{3}\big{(}1-\gamma ^{3}\big{)}\big{]}}{Ebh_{2}^{3}} \tag{7}\]  
Based on Equation (7), the stiffness of the half of the single taper-leaf spring can be expressed as:  
\[K_{s}=\frac{Ebh_{2}^{3}}{4\big{[}L^{3}+l_{2}^{3}(1-\gamma^{3})\big{]}} \tag{8}\]  
Figure 2: The mechanical model of the single taper-leaf spring.  
According to Equation (8), the design formula of the root thickness can be expressed as:  
\[h_{2}=\sqrt[3]{\frac{4K_{s}\left[L^{3}+l_{2}^{3}(1-\gamma^{3})\right]}{Eb}} \tag{9}\]