esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### TR Propagation Barrier with Only Insulation Layer Added
The maximum TR temperature measured at each temperature measuring point during the TR process is shown in Figure 3f. The maximum temperature characteristics of the thermal insulation layer experiment using different materials show that the temperature of the front surface is always higher than that of the back surface; this is because the front surface is affected by self-generated heat and lateral heating, while the back surface is only affected by self-generated heat. The addition of an insulation layer can reduce the maximum temperature in the TR process to some extent. When the composite phase change material is used as the insulation layer, the maximum temperature of the TR of the battery can be significantly reduced because the phase change material plays a role in heat absorption. The physical images of the different thermal insulation materials are shown in Figure 4a, and the SEM images of the three materials are shown in Figure 4b. It can be seen from the SEM images that the diameter and thickness of the nano-ceramic fibers are different and that they have a crisscross distribution in space. The glass aerogels have dense fiber filaments, and they also fill a small amount of the aerogel particles between the fibers, which enhances their heat insulation ability. The composite phase change material has a large amount of liquid phase change and flame-retardant materials arranged between the nanofibers, which proves that it has a certain cooling and flame-retardant function when the TR occurs. The temperature-time relationship of the thermal diffusion of the different thermal insulation materials is shown in Figure 4c. The transient plane heat source method is used to measure thermal conductivity, and the thermal property parameters can be obtained using a mathematical model. The thermal conductivity in the thickness direction and the thermal diffusivity in the format direction of the composite phase change materials are superior to those of the other two materials, and it can be seen that the composite photo-materials have lower thermal conductivity in the vertical direction. The thermal diffusivity in the direction of the format is higher, which indicates its barrier performance for thermal diffusion between cells under TR conditions and its good heat dissipation performance under normal conditions.
## 3 Higher-Layer Protocols: MAC & Routing
### Fbr  
The Focus Beam Routing (FBR) [21] protocol is based on node location capabilities, and is able to find the path between two nodes in a random deployed network if each node knows its position and the position of the destination (gateway) node. Assuming a communication between nodes A and B, node A will send an RTS multicast packet to all the reachable neighbors. This packet will include the information of the source (node A) and final destination (node B). This request is a short control packet that contains the location of both the source node (A) and the final destination (B) node. Packet collisions can happen but always will involve short packets as the link is safe for data packets which have no risk of collisions. Although the chances of collision are small, if the source node detects a collision, it will receive signal but it will not be able to decode the information of the incoming data frame, it will resend the RTS once again.
# Evaluation of Green and Grey Flood Mitigation Measures in Rural Watersheds
## 2 Materials and Methods
### Valuation of the Flood Mitigation Scenarios  
The effectiveness of a particular flood mitigation scenario \(S\) can be measured by the reduction of flood risk in comparison to the baseline scenario. In other words, the benefits gained by implementing specific set of flood mitigation measures are quantified in terms of avoided damages, i.e., as the difference between flood damage \(D_{0}\) estimated without these measures (baseline scenario) and the damage \(D_{S}\) with these measures (flood mitigation scenario \(S\)).  
The expected annual damage EAD is the common indicator of the flood risk and can be obtained from the probability distribution of the damages [37]. In practice, it is calculated as the sum-product of the damages and their annual probabilities of exceedance:  
\[\text{EAD}=\sum_{i=1}^{M}D_{i}p_{i} \tag{1}\]  
where \(M\) is the number of probabilities of flood hazard \(p_{i}\) for which the damages \(D_{i}\) are evaluated. The expected annual benefit from the mitigation scenario \(S\) can then be computed based on all \(M\) considered return periods \(T_{i}=1/p_{i}\), and taking into account \(L\) loss categories:  
\[\text{EAB}_{S}=\sum_{i=1}^{M}\sum_{j=1}^{L}\bigl{(}D_{0,ij}-D_{S,ij}\bigr{)}p _{i} \tag{2}\]  
where \(\text{EAB}_{S}\) denotes expected annual benefit for flood mitigation scenario \(S\), \(D_{0,ij}\) are damages estimated for the baseline scenario (existing flood protection level in the watershed), and \(D_{S,ij}\) are damages under flood mitigation scenario \(S\). Subscript \(j\) indicates loss category, whereas subscript \(i\) is related to flood return period. Four different loss categories are considered in this study (\(L=4\)).  
For the long-term monetary analysis, it is useful to compute the total benefit as the net present value \(\text{NPV}_{S}\) from implementing specific flood mitigation scenario \(S\) by taking into account discount rate \(d\) and planning horizon \(N\), as follows:  
\[\text{NPV}_{S}=\sum_{t=1}^{N}\frac{\text{EAB}_{S}}{\left(1+d\right)^{t}} \tag{3}\]  
The long-term benefits are calculated for two planning horizons, namely 30 and 50 years, assuming the discount rate of 7%, as in the UNDP study [21].
## 4 Use Case Example: Deriving Electromagnetic Requirements for An Elevator Application
### Deriving Electromagnetic Requirements  
This phase is reached when the aforementioned requirements are considered to be good enough by both parts to start the design phase. However, the designer/manufacturer must derive more into specific-domains in order to detail better his objectives with the design. Following the framework, these requirements must be derived from the technical requirements of the different disciplines, not directly from the integrator's requirement, if the need exists, first a copy should be made into a discipline before deriving into a domain-specific package. The same process backward if the creation of a new requirement needs to go to a higher level package.  
Figure 15 shows an example of electromagnetic requirements derivation. In this case, the decision to use an already available design by selecting the stamping die for the stator and rotor laminations is taken based on the motor frame dimensions, type of motor, and cross-domain objective of comfort. This contains three requirements specifying the laminations for the stator, rotor, and type of steel. The stator and rotor laminations instances, also shown with its attributes in the diagram, satisfy these requirements. As a consequence of this requirement, the air gap is fixed to 1 mm. In addition, this requirement affects other domains; therefore, a copy is made to the manufacturing package. The torque ripple shall be less than 2% of the rated torque, because comfort is one of the main objectives and the requirement of the noise level. Finally, mechanical and electrical requirements, such as rated voltage, speed, torque, and others are copied.
## 1 Introduction  
Cerium (IV) dioxide (ceria) is one of the most common forms of cerium and it has a wide range of uses in applications mostly for its physical and mechanical properties. It is, for example, used as a polishing agent, an additive for heat-resisting alloys and in ceramics [1, 2]. It is also added to thorium oxide gas lantern mantles. Without the presence of ceria, the flame would be practically colorless with minimal luminosity [3]. Another interesting application is its usage as a surrogate material for nuclear fuels [4].  
On the other side, ceria nanoparticles are well known for their catalytic properties, which are widely used as an electrolyte material [5], in solid oxide fuel cells [6], in solar cells [7], for fuel oxidation [8], as automotive three-way catalysts to oxidize exhaust gases [9] and as part of composite materials [10]. Recently, lots of studies have also reported seeing the potential for usage of ceria nanoparticles in biotechnology, environmental chemistry and medicine [11; 12].  
Such a wide field of applications goes hand in hand with ceria nanoparticles' exceptional redox properties, which are mainly granted by the very fast redox-cycling between (3+) and (4+) states of cerium [13]. This cycling is caused by many oxygen vacancies that occur exclusively on the surface of nanoparticles, and the occurrence of these vacancies is not random [14]. The most stable vacancies are located at the (111), (110) and (310) crystal planes, whereas vacancies occurring in the bulk form are unstable [15]. The more the nanoparticle's crystallite size decreases, the more oxygen vacancies occur [16]. This undoubtedly has an impact on the nanoparticles' properties, which are therefore mainly controlled by the crystallite size of the nanoparticles and by the oxidation state of the cerium [17]. Precise control over the crystallite size of the ceria nanoparticles led us to the synthesis of a new, highly specifically working catalyst with unique properties [18].  
Vast numbers of papers have been published referring to nanoparticle synthesis of ceria and other nanoparticles in general. The crystallite size of the nanoparticles can be modified both during and after synthesis. A dominant synthetic route in the case of the ceria nanoparticles is hydrothermal synthesis [19]. This method can be modified by e.g., use of different solvents [20] at different temperatures [21], by ultrasound [22], by use of various atmospheres [23] or by irradiation [24; 25]. Methods having the most significant impact on the crystallite size of already synthesized nanoparticles are, in general, based on milling [26; 27]. In this study, the modification of ceria nanoparticles' crystallite sizes was achieved using thermal treatment, and the as-prepared nanoparticles with different particle sizes were analyzed using a broad spectrum of analytic techniques. Because immobilization through thermal treatment of these materials is crucial for possible future applications, nanoparticles of various sizes were also examined for the catalytic decomposition of selected chlorinated phenols.
# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 1 Introduction
Due to the high complexity of human emotions, manually annotating emotional data is extremely costly, making it difficult to scale dataset size beyond 100 h. To address this challenge, adopting an automatic annotation pipeline for constructing an emotional speech dataset is essential. This approach not only removes the dependency on manual labeling, significantly reducing costs, but also contributes to expanding the dataset scale, ensuring a more extensive and scalable data foundation for emotional speech synthesis. In this paper, we propose Easy-Control Emotion TTS (ECE-TTS), a zero-shot emotion-controllable TTS system that simplifies emotion control while enhancing both the naturalness and expressiveness of synthesized speech. Built upon a flow-matching-based zero-shot TTS architecture, ECE-TTS simplifies model design by removing the need for phoneme alignment, duration predictors, and text encoders. By leveraging ConvNeXt V2 modules, it refines text representations and enhances text-speech alignment during in-context learning, leading to improved prosody and rhythm. To achieve fine-grained emotional style control, we employ the Emotion-Adaptive Coordinate Transformation, which converts VAD values into EASV representations. Compared to conventional approaches that rely on discrete emotion labels or reference-based style transfer, this method allows for more precise and adaptable emotional expression. For emotion intensity modulation, we adopt an emotion arithmetic vector method, leveraging weight-space interpolation techniques to adjust emotion intensity in a smooth and controllable manner. By applying a scaling factor \(\alpha\), this approach enables continuous emotion intensity transitions, overcoming the limitations of conventional ranking-based or embedding-space manipulation methods. To further strengthen the model's performance, we construct a 200 h high-quality emotional speech dataset through an automatic annotation pipeline, eliminating the need for manual labeling while scaling up the dataset. Extensive experiments demonstrate that ECE-TTS effectively generates highly natural and expressive speech while providing precise and flexible control over both emotional style and intensity, making it a practical and efficient solution for zero-shot emotional speech synthesis.
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 1 Introduction
### Relevance of the Subject Matter
In particular, the correct operation, active and reactive power consumption, and the temperatures obtained by crucial elements are important to the end user. These issues have a direct influence on the quality of operations and financial issues. Ref. [33] presents a 2.022 mW real-time intelligent management system. The system is powered by a 66 mW solar cell with additional supercapacitor energy bank, which uses LoRaWAN technology with real conditions ranging at the level of 761 m. The authors of the article performed evaluation of the performance of this communication system in real operational conditions. The article [34] presents a lead acid battery monitoring system that uses an embedded web server which sends information on the battery charge status over Ethernet. It was created for PV-powered lighting systems with a MPPT charge controller. The system allows for the fast interpretation of a system's technical faults by reporting critical operating conditions. An interesting implementation of neural networks to predict energy production and consumption is presented in [35]. The research carried out analyzes the cooperation of the PV farm with an electric load in terms of the timing of the on and off switching of the load in order to minimize the energy flow outside the local power system.
## 5 D-MSR Management Phases
### Coping with Dynamicity, Reservation Conflict and Interference in the Network (Phase-6)
#### 5.6.3 Coping with Internal Interference in the Network  
In a realistic setting, the interference range of a node may be much larger than its transmission range. Concurrent transmission in the same cell may cause interference even when the edges are two hops away from each other. Figure 17 illustrates how the communication on edge (C, D) that is outside of the two-hop neighborhood of edge (A, B) interferes with edge (A, B). Thanks to the scheduled communications concept, internal interference caused by communications outside of the two-hop neighborhood, happens in specific timeslot-channel cells that can be recognized by (1) observing the constant packet loss in those cells after reservation or (2) by performing CCA before reservation. By considering the _virtual_ links that represent the interfering links, adding these in the schedule-matrix and by subsequently avoiding to use those timeslot-channel cells, the internal interference can be solved in a distributed manner. In Section 6.5.1, we evaluate the effect of this scheme in improving the packet delivery ratio in case of internal interference in the network.
## 5 Conclusions  
This paper proposes a detection of hidden corrosion at the riveting site in a multilayer structure method using electromagnetic testing and machine learning. The electromagnetic testing system uses a Hall sensor array for measuring the distribution of the electromagnetic field of the rivet and hidden corrosion in a multilayer structure. Machine learning algorithms were used to enhance the detectability of the hidden corrosion. PCA was used to auto-extract features of electromagnetic signal; e.g., it significantly reduces the number of features from 4480 to 53 while keeping 99% of the signal information. Then, machine learning classifiers such as SVM, LG, RF, KNN, and Naive Bayes were used to detect both the presence of the hidden corrosion and its location around the rivet (forward, backward, left, and right position). Among the classifiers, SVM shows the best performance, which provides an accuracy of about 99% with 60-80% data for training.  
The SVM method was also evaluated with a smaller amount of data for training (e.g., only 20%) at small sizes of corrosion (e.g., 15.0 to 25.4 mm\({}^{3}\) volume). The SVM method provides an 89.48% average accuracy, which is over 26.47% compared to the previous SoA feature method. Among the different locations of corrosion, the right corrosion is the easiest to detect (91.67% of accuracy). In addition, the POD of the system was also improved using the SVM method. The system could detect backward, forward, left, and right corrosion with a POD of 90% and confidence of 95% (a\({}_{90/95}\)) for a volume of 48.08, 24.85, 41.53, 63.16 mm\({}^{3}\), respectively. The results also suggest that we should train the machine-learning model with small corrosoins rather than training with large corrosoins. Once the model is trained well with small corrosoins, it will be easy to detect large corrosion. However, it should not be so small that the model could be underfitting since it is hard to detect very small corrosion.  
Conceptualization, J.L.; Data curation, V.S.L. and D.-K.L.; Formal analysis, M.L.; Funding acquisition, J.L.; Methodology, J.L.; Project administration, J.L.; Resources, V.S.L., D.K.N., D.-K.L. and J.L.; Software, M.L. and D.K.N.; Supervision, M.L.; Validation, V.S.L., D.K.N. and D.-K.L.; Visualization, M.L., V.S.L. and D.K.N.; Writing--original draft, M.L.; Writing--review & editing, J.L. All authors have read and agreed to the published version of the manuscript. This study was supported by research fund from Chosun University, 2020. The authors declare no conflict of interest.
# STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
## 5 Experiments  
### Performance Evaluation Method  
This study used four metrics--Dice, IoU (Intersection over Union, Jaccard index), precision, and recall--to evaluate the model's performance. Dice is a metric used to measure the similarity between the predicted and actual values, and its value ranges from 0 to 1. The calculation equation is the same as that of F1-score, but there is a tendency to emphasize Dice more in the medical image segmentation field. Dice is also expressed as Dice coefficient, Dice similarity coefficient (DSC), and Dice score, depending on papers. IoU is a performance metric used in object detection and semantic segmentation studies and refers to the ratio of the intersection area to the union area for the predicted values and the actual values. IoU has the same meaning as Jaccard index. Precision refers to the percentage of the pixels predicted accurately in the prediction result of the model. Recall refers to how well the model detected the ground truths.  
Dice, IoU, precision, and recall can all be calculated using True Positive (\(TP\)), True Negative (\(TN\)), False Positive (\(FP\)), and False Negative (\(FN\)) of the classification confusion matrix. Equations (6)-(9) show the calculation methods of the performance metrics, respectively.  
\[Dice=\frac{2 TP}{2 TP+FP+FN} \tag{6}\]  
Figure 6: Architecture of the proposed STHarDNet model.  
\[IoU=\frac{TP}{TP+FP+FN} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
As explained in Section 3, the ATLAS dataset has 3D MRI scan images, and the model's performance was evaluated by measuring the performance at the patient level. In other words, instead of measuring the performance in the prediction result of one image, the disease of a patient was predicted using 189 MRI scan images captured for the patient as the input values of the model. In this study, the data of 52 patients were used as the validation data and obtained a total of 52 prediction results, which were then averaged
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Contactors  
Most battery packs have the requirement to be able to galvanically disconnect at least one of two battery poles. To fulfill this requirement, the battery pack needs Contactors that are large enough to carry the main current, as well as being able to cut off large DC currents in the event of a hazardous fault. Since DC--unlike Alternating Current (AC)--has no repetitive zero-current events, disconnecting and extinguishing the electric arc on the Contactors' blades needs special care in the case of DC. Therefore, Contactors have magnetic arc extinguishers [14], which push the arc away from the contact blades. This results in a preferred current flow direction when using these devices. This is important for the developer of a battery system to obey, otherwise the switch off performance can be reduced by a large amount [15]. Contact welding is very dangerous for a battery pack because then the pack cannot be disconnected entirely from the application. Contact welding happens when the blades are hot and are pushed against each other heavily--this scenario must be avoided at all times. When switching a contactor off under full loads, there will be an electric arc, but the contact blades will not touch in the heated and off position. Another, more hazardous scenario poses the switch-on action while there resides a potential across the not yet closed contact blades (resulting from not yet charged DC-link capacitors). If the blades are closed, they will bounce (like all mechanical switches do), and, on every bounce, an electric arc will be drawn, heating up the blades more and more. When the bouncing ends, the blades will finally close and the very hot blades are pushed towards each other, and they will weld eventually, especially at the very high current densities that occur while the contact blades are not yet fully in contact. To avoid this from happening, a special circuit has to ensure zero potential across the contacts: zero switching power has to be ensured during any switching action, and a pre-charge unit is obligatory. The pre-charge unit consists of another contactor in series with a resistor. This device is connected across the main contactor and will be activated first, to slowly increase the DC link voltage to a level equal to the pack voltage so that afterwards the main contactor can be switched on powerless [16].