esa-sceva/satcom-chunk-collection huggingface dataset:
# Erasure Codes for Cold Data in Distributed Storage Systems
## 3 NewLib Code  
### Encoding  
#### 3.1.1 Encoding in the Liberation Code Pattern  
This paper focuses on the design of big data coding schemes in cold data storage applications. Cold data refers to the data that users seldom access, which means that they always have a low utilization rate. If they exist in the form of replication, there is a waste of storage space. Moreover, the requirements are relatively low for the availability. In order  
Figure 2: The encoding process is illustrated by the bit matrix–vector dot product.  
to minimize the use of the system resources, the speed of writing generally needs to be relatively high. Therefore, the encoding speed is the key consideration in the selection of coding schemes.  
Suppose that there are data chunks (defined as \(k\)) and parity chunks (defined as \(m\)) in the coding system. We call this EC policy \(k:\ m\). Each of the chunks has bits word length (defined as \(w\)). The erasure matrix can be described as \(w\times(k+m)\times wk\) in the finite field \(GF(2^{w})\). Liberation, EVENODD, RDP, and Cauchy Reed-Solomon were selected to compare the performance of the erasure matrix. We selected the one with the best performance to use in our system.  
Figure 3 shows the encoded parity matrix in Liberation code when k is 7 and \(w\) is 7. The black box represents one, while the white box stands for zero. It has been proved that the Liberation code is one of the MDS codes when \(k<w\). The coding matrices for EVENODD, RDP, and Cauchy Reed-Solomon are shown in Figure 8a-c in the [PERSON] article [25].  
For any given \(w\) and \(k\), the number of ones in the parity matrix of the Liberation code is \(kw+k-1\). Since there are \(kw\) ones in the unit matrix of the BDM matrix, the total number of ones is \(2 kw+k-1\) in the erasure matrix. The XOR operation number is determined by the number of ones. If the number of ones is \(x\) in an erasure matrix, the XOR operation number will be \(x-1\) in the encoding process. The number of XOR operations required in the Liberation code is shown as Equation (1):  
\[\frac{2 kw+k-1-2w}{2w}=k-1+\frac{k-1}{2w} \tag{1}\]  
The optimal value of Equation (1) is \(k-1\).  
We can obtain the number of XOR operations in the EVENODD code by the same method. This is shown as Equation (2):  
\[\frac{kw+(w-1)(k-1)+kw-2w}{2w}=\frac{3}{2}(k-1)+\frac{k-1}{2w} \tag{2}\]  
As we can see, the number of XOR operations in the EVENODD code is almost one and a half times that of the Liberation code. In addition, we can use the ratio of ones in the parity matrix to compare the various codes. The proportion of ones of the Liberation code is 16%, as shown in Figure 3, while that of the RDP code is 28%, and that of the CRS code is 21%.  
From the above comparison, we can see that the number of ones in the erasure matrix of the Liberation code is the lowest, which means that we can achieve the purpose of coding through fewer XOR operations in the actual coding process. Therefore, the coding scheme used in this paper is based on the Liberation code.
## 3 Capacity Optimization Model
### Evaluation Mechanism
#### 3.2.3 Environmental Evaluation  
The pollutants emitted from the CCHP system are mixed, mainly including \(CO_{2}\), \(CO\), \(NO_{X}\) and \(PM\), among which \(CO_{2}\) accounts for more than 99.5%. Therefore, carbon dioxide emission (\(CDE\)) is adopted as the environmental evaluation index [27].  
\[CDE=E_{grid}\mu_{e}+\big{(}F_{mt}+F_{gb}\big{)}\mu_{f} \tag{14}\]  
where \(CDE\) is the emission of \(CO_{2}\) (kg); \(\mu_{e}\) (g/kWh) is the \(CO_{2}\) emission coefficient corresponding to the purchased electricity; \(\mu_{f}\) (g/kWh) is the \(CO_{2}\) emission coefficient corresponding to the combustion of natural gas.
## 4 Results
### Mesh and Model Selection  
#### 4.2.1 Mesh  
The results from the twoPhaseEulerFoam solvers for the D24 and D36 meshes were nearly identical, requiring a choice between them for subsequent simulations. Figure 5a presents the computation time per simulated second, T (in h/s), to evaluate the computational costs. It is evident that the D36 mesh requires approximately eight times longer to simulate one second than the D24 mesh. Consequently, the D24 mesh was selected for this study as it offered a more efficient balance between computational cost and accuracy.  
The preliminary simulations revealed notable differences between the two models concerning mesh independence, requiring a decision on which solver to employ in subsequent simulations.  
Figure 6: Dimensionless velocity in the shear stress region.  
Figure 7: Air concentration as a function of the number of cells in the jet diameter: (**a**) r = 0.05 m, z = 0.20 m; (**b**) r = 0.10 m, z = 0.20 m.
# A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
## 1 Introduction  
Brain tumors are new organisms, which grow in the cranial cavity and can be divided into primary and secondary tumors. According to cancer statistics in recent years, the incidence of brain tumors accounts for about 1.5% of whole-body tumors, but the death rate is as high as 3% [1]. Glioma is one of the most common malignant tumors in the brain--a tumor, which originates from glial cells. There are two types of gliomas: high-grade gliomas (HGG) and low-grade gliomas (LGG) [2; 3]. Magnetic resonance imaging (MRI) is one of the most commonly used tools in clinical medicine to examine the brain due to its non-invasive nature, which protects the body from ionizing radiation, and its superior soft tissue contrast [4]. The MRI scanner was adjusted with different parameters to obtain four modalities--T1, T2, T1 ce, and Flair--as shown in Figure 1, which are multimodal brain MRI images of the same patient with the same acquisition device using different parameters. Due to the widespread use of MRI equipment in brain examinations, a large amount of brain MRI image data are generated in the clinic, and it is impossible for physicians to manually annotate and segment all images promptly. The tissue structure in brain tumor images varies from individual to individual, from stage to stage, and from device to device, and manual segmentation of brain tumor tissue relies on the physician's personal experience. Therefore, the research focuses on how to segment brain tumors efficiently, precisely, and fully automatically [5].  
Due to the outstanding effect of methods such as machine learning in image classification and segmentation, intelligent analysis of medical images based on artificial intelligence has been vigorously developed, and some effective semantic segmentation networks have been proposed, one after another, such as SegNet [6], DeepLab [7], U-Net [8], and so on. In particular, the 2D U-Net structure is now widely used in medical image segmentation. [PERSON] et al. [9] proposed an improved U-Net with novel feature reorganization and recalibration modules and segmented the tumor substructures in a cascade fashion.  
[PERSON] et al. [10] proposed a two-stage cascaded U-Net model, where the first stage uses a variant U-Net to obtain a rough segmentation result, and the second stage fuses the input with the first stage rough segmentation result and uses two decoders to improve the performance of the algorithm. [PERSON] et al. [11] used two CNNs to process brain images for extracting global information and classifying voxels, respectively, after which the trained models were used for brain segmentation. [PERSON] et al. [12] proposed a multiple U-Net integrated network for brain tumor segmentation by extracting data in each of the three axes through three U-Nets, thus allowing multiple models to work in concert to achieve accurate segmentation of brain tumors. U-Net networks have been widely used in the field of brain tumor segmentation due to their powerful feature learning ability.
## 5 Conclusions  
This paper presents an ESN-MPC control strategy to enhance parameter robustness and control performance in PMSM drives. Initially, a conventional MPC-based PMSM control model is established to generate representative training data. Subsequently, the ESN is trained offline using extensive data samples and replaces the predictive model of the MPC by incorporating adaptive online learning capabilities. The main advantage of the proposed ESN-MPC method is that it can compensate for various system parameter mismatches while preserving excellent transient and steady-state control performance. Simulation studies indicate that the proposed ESN-MPC achieves performance that closely matches that of an FCS-MPC with accurate model parameters. However, the concept of integrating neural networks into the MPC method also has certain limitations. Although the proposed strategy can maintain acceptable resource utilization, the online learning process still introduces a considerable computation burden, particularly when a large reservoir size is required. This issue can possibly be mitigated by updating only a separated part of the matrix at each time step, thereby reducing the computational burden per control cycle.  
Figure 8: Test results under flux parameter mismatch with three control strategies.  
Figure 9: Test results under resistance parameter mismatch with three control strategies.  
Future research directions can include developing efficient ESN simplification methods to further reduce the computational load, as well as conducting experimental verification under extreme working conditions of actual PMSM drive systems.  
Conceptualization, X.Z., J.L. and P.Y.; Methodology, X.Z. and H.C.; Software, X.Z.; Validation, X.Z.; Formal analysis, X.Z.; Investigation, X.Z., J.L., P.Y., H.C. and D.L.; Resources, J.L.; Data curation, J.L. and Y.Z.; Writing--original draft, H.C. and D.L.; Visualization, P.Y.; Project administration, D.L.; Funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Smart Grid-National Science and Technology Major Project under Grant 2024 ZD0800300 and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQN25E070005.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company State Grid Zhejiang Power Co., Ltd., Wenzhou Power Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 State of the Art Energy Efficiency and IoT in SBE
### IoT Applications in SBE  
IoT technologies have redefined building management systems by integrating sensors, actuators, and analytics platforms. These tools enable granular monitoring of environmental and operational parameters, leading to enhanced resource efficiency and user-centric management. IoT-enabled systems allow continuous tracking of temperature, humidity, occupancy, and energy use. For example, IoT-driven HVAC systems dynamically adjust their operation based on real-time conditions, achieving energy savings of up to 30% [21]. Similarly, smart lighting systems optimise energy use by adapting to occupancy and daylight availability [27]. In addition, IoT-driven predictive maintenance implies sensor data to detect anomalies and anticipate equipment failures. Recent studies have shown that integrating machine learning algorithms with IoT platforms can significantly reduce energy waste by ensuring timely interventions [22].  
Also, IoT facilitates the integration of renewable energy systems into building management. By dynamically balancing supply and demand, IoT platforms optimise the use of on-site energy generation, such as solar photovoltaics, and reduce reliance on grid electricity [19]. Lastly, occupant behaviour plays a crucial role in energy efficiency. IoT technologies, within machine learning, enable personalised SBE by analysing user preferences and occupancy patterns. Such approaches not only enhance comfort but also optimise energy performance [27].
## 3 Overview of Layered Backhaul (LayBack) Network Architecture
### Management in LayBack  
The unifying SDN orchestrator in LayBack has three main tasks: (1) it creates a common platform for coordinating among all the wireless service operators and heterogeneous network technologies across its layers; (2) it maintains the current topology information of the entire network and tracks the network capabilities; (3) it enables each of the layers to flexibly reconfigure the network by allocating resources in response to their time-varying needs, while maintaining long-term performance requirements that define the service guarantees. The networks maintained by different operators periodically communicate their requirements and reconfiguration capabilities to the SDN orchestrator to fulfil its tasks. Next, we show how these tasks can be  
Figure 1: Illustration of LayBack architecture and multi-timescale optimization decomposition in context of cellular networks: LayBack partitions the wireless backhaul infrastructure into radio node layer, gateway layer, SDN switching layer, and core network layer. The entire network is controlled by the central unifying SDN orchestrator. This case study decomposes the optimization of the sharing of the backhaul bitrate of multiple operator core networks into fast-timescale sub-problems at the radio nodes and progressively slower timescale sub-problems at the gateways and operator core networks; whereby all sub-problems are coordinated through a root problem at the SDN orchestrator.  
combined with an online optimal resource sharing task that leverages our multi-layer multi-timescale NUM framework.
## 6 Conclusions  
In this paper, we first analyzed the influence of channel structure, coolant flow rate, and hydraulic diameter of the channel on the heat dissipation capacity of microchannel heat sink by means of finite element analysis, and then optimized the design of heat sink structure, and studied the influence of surface roughness, dimensional accuracy and thermal conductivity of copper parts prepared by laser 3D printing on heat dissipation capacity of heat sink, and the following conclusions can be obtained.  
1. Among the three structures of microchannel heat sinks of pin-fin, honeycomb, and double-layer reflow type, the double-layer reflow type has the lowest heat source surface temperature, the smallest chip thermal resistance, and the best heat dissipation effect. The optimal solution for the structural design of the double-layer reflow microchannel heat sink is obtained by comprehensively weighing the chip thermal resistance and the pipe pressure that the micro-channel can withstand: The hydraulic diameter is 0.5 mm, the coolant flow rate is 24 L/h, and the chip thermal resistance is 0.258 \({}^{\circ}\)C/W.
2. The inner wall roughness of pure copper microchannel prepared by 3D printing technology is 7.08 \(\upmu\)m, and the heat sink thermal resistance is reduced by 0.7% compared to that when the smooth channel wall is used. The deviation of microchannel diameter from the design size (500 \(\upmu\)m) is \(-\)10 \(\upmu\)m, and the heat sink thermal resistance is reduced by 0.8% compared to the theoretical value, which shows that the surface roughness and size deviation of 3D printed microchannel can be regarded as a beneficial factor to enhance heat dissipation. At present, the highest thermal conductivity achievable for laser 3D printed formed copper parts is 310 W/m-K, at  
Figure 17: Thermal resistance varies with thermal conductivity of microchannel materials.  
which the maximum chip surface temperature is 35.3 \({}^{\circ}\)C and the heat sink thermal resistance is 0.306 \({}^{\circ}\)C/W, which can fully meet the chip (\(-\)45 \({}^{\circ}\)C\(\sim\)80 \({}^{\circ}\)C) operating temperature range.  
Conceptualization, A.Y. and X.L.; methodology, A.Y.; software, X.L.; validation, A.Y., X.L.; formal analysis, A.Y.; investigation, X.W.; resources, Z.W.; data curation, A.Y.; writing--original draft preparation, A.Y.; writing--review and editing, X.W.; visualization, A.Y.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 4 Mechanism of Microbial Decontamination  
PL leads to the inactivation of microbes based on various extensive physicochemical processes. The microbial destruction is generally attributed to mechanisms like photo-chemical, photo-thermal, and photo-physical effects. These mechanisms are responsible for the inactivation of microorganisms independently or in combination with each other, depending on treatment conditions and target organisms [41]. Although photo-thermal and photo-physical effects might have an influence on inactivation, most works indicate the photo-chemical effect of light to be the main factor that drives the destruction of microorganisms.  
Other than the physicochemical mechanisms, the spectral range and microbial morphology also govern the microbial destruction by PL to an extent. The effect of spectral range of light pulses has also been extensively studied. The spectral range found to be most efficient in microbial destruction was UV range [23], with UV-C component (200-280 nm) having the major role and a theoretical inactivation peak at 253.7 nm [41]. [PERSON] et al. [42], however, reported that maximum reduction of _E. coli_ took place at 270 nm, in the UV-C range. [PERSON] et al. [43] also obtained greater inactivation in fresh-cut avocadoes by using the UV-C component rather than Vis-NIR spectra PL. However, the IR and visible regions of light radiation were not effective in PL-based inactivation process [23]. The IR and visible regions only had the antibacterial effect at higher powers [13].  
Microbial morphology does not show consistency with respect to PL treatment. [PERSON] et al. [44] concluded the susceptibility of microorganisms to PL in the manner: Gram-negative bacteria > Gram-positive bacteria > fungal spores. In another study, [PERSON] et al. [45] observed similar trend in microbial sensitivity. Contrarily, [PERSON] et al. [46], however, did not report similar pattern of susceptibility in their studies. Spores have lower susceptibility than bacteria vegetative cells which was stated to be due to inherent DNA repair mechanisms of cell [45]. However, this tendency of repairing of DNA is highly affected by PL application as compared to continuous UV application, which is why PL is more effective [47].  
Pigments in microorganisms can also be a determinant factor for PL efficacy. [PERSON] et al. [45] inferred that spore colors may have an effect on the action of PL. They showed that dark colored spores of the mold _Aspergillus niger_ were resistant to PL rather than pink colored _Fusrium culmorum_, due to a greater absorption of UV-C light.
## 5 Challenges and Future Directions
### The Future of UAVs  
While AI approaches, such as machine learning and neural networks, have been implemented in UAVs, deep learning and reinforcement learning solutions have yet to be fully adopted [223; 224]. This is mainly due to power and processing limitations on drones. Researchers should develop novel deep learning strategies tailored to UAVs, especially for search-and-rescue (SAR) missions. Such schemes can provide context-aware decisions and trajectory-based learning. The compact size of UAV platforms restricts the use of more advanced AI, such as deep learning and reinforcement learning solutions. Developing deep learning solutions that work within the power and processing constraints of drones remains an open challenge. This represents an opportunity for AI research to unlock autonomous decision-making and learning capabilities for UAVs used in search-and-rescue and other applications.  
Power Supply and Flight Duration  
To enable extended UAV missions, researchers should investigate novel strategies for energy harvesting and advanced battery materials. Developing new lightweight, high-efficiency batteries can support increased flight times. Further studies need to address efficient power control and energy consumption mechanisms for drones. There is a need to explore new ways of harvesting energy and improving battery technology to overcome the limitations of UAV flight endurance. Lightweight, high-density battery chemistries and intelligent power management can help optimize energy usage during flights. Advancing UAV capabilities require prioritizing the research into onboard power systems and energy consumption. Moreover, further research into optimizing aerodynamic designs, lightweight structures, and energy-efficient components can improve the power consumption and range in UAVs. Investigating the mechanisms for in-flight charging or swappable power packs also offers flexibility to extend UAVs' flight duration. As researchers make progress in harvesting energy, storing power, and managing consumption efficiently, UAVs will be able to take on more ambitious applications in science, commerce, and remote sensing.
# Approaching Electroencephalographic Pathological Spikes in Terms of Solitons
## 6 Solitons' Generation
### Generation of Neuronal Solitons  
We stated that surface solitons might emerge from (and may interact with) electromagnetic fields like the ones detected on the scalp, a question arises: how could solitary waves be physically produced in the nervous tissue? Different theoretical possibilities might be considered:  
1. The nervous solitons detectable on the scalp are electric waves produced by the underlying neuronal tissue. In this case, solitons are produced INSIDE the electric fields.
2. The nervous solitons detectable on the scalp are pressure waves that only later become electric currents. In this case, solitons are produced OUTSIDE the electric fields. This can be accomplished in several ways, including the piezoelectric effect, e.g., a simple electromechanical interaction between the mechanical stress and the electrical fields. Another distinction could be made concerning the anatomical source of these theoretical nervous solitons:
3. The nervous solitons can be naturally produced in the healthy brain.
4. The nervous solitons can be produced in the pathological brain, e.g., by the spared tissue adjacent to injured areas.  
In summation, further studies are needed to elucidate the possible mechanisms underlying solitons generation in the central nervous system.