esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
## 2 State of the Art and Background Information
### Programmable Networks and Network Virtualization
Furthermore, a flexible and programmable platform that separated the control and data planes at the RAN-level, aided by virtualized control functions and control delegation features, was proposed in [18], to support real-time RAN control applications. Nevertheless, the implementation of programmable networks and network virtualization, results in a number of challenges, such as the increased processing and signaling overheads [8; 9]. Furthermore, these emerging technologies seek a shift from device-driven management models to context-aware and QoS-aware management models, presenting important challenges associated with networkorchestration demands [19]. Note also that current architectural approaches are mainly centralized, posing scalability limitations, as a single entity is responsible for the collection and analysis of data associated with a large number of heterogeneous sources across multiple administrative domains [19]. Existing approaches in SDN-based D2D communications, include [20; 21]. In [20], a conceptual framework was introduced for wireless cognitive networks, following the cross-layer design approach and the component-based methodology, while accommodating network programmability at various capacities, decoupling control and data planes, and allowing for efficient and adaptable network management. The framework focused on peer relations between neighboring SUs with no centralized control and thus no provision for global optimization tasks. In [21], the authors proposed an SDN-based spectrum sharing technique for D2D communications, named as the harmonized SDN-enabled approach (HSA). The approach relied on distributed input reporting on spectrum availabilities in 5G heterogeneous networks, integrating the distributed sensing devices, cellular base stations, and SDN controller into an intrinsically amalgamated network. Database-assisted spectrum management had been assumed, implemented by the Base Stations (BSs) at the edge of the access network, but without specifying a channel assignment algorithm. Nevertheless, the aforementioned approaches have not been tested in a real-world testbed in order to confirm the feasibility and applicability of the proposed algorithms.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
###### Abstract  
Car rollovers are a class of serious traffic accidents that can easily cause heavy casualties and property damage, particularly for special operation vehicles. To enhance the driving stability of vehicles on forest roads, we developed a control strategy for wire-controlled auxiliary braking based on body-attitude detection and the overall design of the system. Moreover, the control system was further investigated and developed. A three-degrees-of-freedom (3-DOF) vehicle dynamics model with longitudinal, lateral, and lateral tilt was developed based on actual-vehicle test data. The lateral load-transfer rate (LTR) of the vehicle was selected as the early warning algorithm for vehicle rollover; the differential braking of the vehicle was realized by adjusting the pressure of the wheel cylinders; and automatic speed reduction was achieved according to the rollover attitude of the vehicle by combining the fuzzy-PID control algorithm. Finally, a vehicle dynamics model was developed, and the results verified the effectiveness of the anti-rollover control strategy under extreme operating conditions.  
vehicle; line-controlled auxiliary braking; LTR; fuzzy-PID control; co-simulation Article
## 2 Related Work
### Feature Extraction Based on Attention Mechanisms
#### 2.3.1 Channel Attention-Based Feature Extraction  
The channel attention mechanism identifies remote location dependencies of input data along two dimensions directions, and the channel attention structure for MARN is shown in Figure 3. The input matrices data are average pooled horizontally and vertically to compress the channel dimensions, followed by a two-layer \(1\times 1\) convolutional dimensionality reduction with simultaneous nonlinear activation; then, splitting along the spatial dimension is performed to obtain the direction-aware feature matrix and the location-sensitive feature matrix. Finally, the two matrices are weighted to obtain the channel attention feature matrix. This process can be represented as:  
\[H_{c}(M)=\sigma\Big{(}f_{x,y}^{1\times 1}\Big{(}S\Big{(}\sigma\Big{(}f^{1 \times 1}\big{(}\big{[}Argpool(M_{x}),Argpool(M_{y})\big{]}\big{)}\big{)}\Big{)} \Big{)}\Big{)}\Big{)}\Big{)} \tag{4}\]  
where \(M\) represents the input matrix; \(\sigma\) represents activation function; \(f^{1\times 1}\) represents convolution algorithm with 1 convolutional kernel; \(S\) represents the split operation; and _Argpool_ represents average pooling.  
Figure 2: Residual structure.
## 1 Introduction
In both cases, direct pattern projection and LED arrays, the projected pattern is generated at a certain layer within the depth of focus only, restricting it to two-dimensional applications mainly. Also, due to the incoherent emission of the single emitters, LED arrays cannot be used for beam shaping purposes to achieve a focal point in different depths. Another problem that arises in many optical systems is that of system- and sample-induced aberrations like chromatic [30; 31; 32] or geometric [33; 34] aberrations. Often, these are corrected for in silico or using dedicated devices [35]. The requirements can be fulfilled entirely by holographic light projection with spatial light modulators (SLM) as the key components, which allow for direct wavefront shaping by means of computer generated holograms (CGH) [16; 36; 37; 38], described as digitally programmed light. These devices have already proven beneficial for light shaping in a lot of complex environments, e.g., structured light transmission through multimode fibers [39; 40; 41; 42; 43]. The scientific question is whether these methods can be employed to realize an optogenetic stimulation platform that can entirely cope with the aforementioned requirements in order to advance optogenetics studies of stem-cell-derived cell cultures. Especially, we investigated the correction of system-inherent optical aberrations with the same light-field shaping device. With the availability of such a stimulation system, we are heading towards in vitro arrhythmia investigations using cardiomyocytes (heart muscle cells) and connectivity analysis in developing neural networks.
## 2 Islanding Detection Methods
### Remote Methods
#### 2.4.1 Power Line Carrier Communication Method (PLCC)  
This method uses a transmitter that is installed on the grid side to emit a communication signal along with the power line. Additionally, the receiver is set up on the DG side. A low-energetic signal is sent to the receiver once the main grid is connected. The PLCC signal will be interrupted during the islanding state, indicating that the microgrid is in islanding mode [23].  
In this method, there is no NDZ, the power quality is not degraded, and there is no effect on the grid transient response. Moreover, the effectiveness of this method is high when it is used in multiple inverter systems. However, economically this method is expensive, especially in low-density DG systems. Therefore, it is used in the microgrid with high-density DG systems.
# Fast and Effective Tag Searching for Multi-Group RFID Systems
## 2 Related Work
### Missing Tag Detection Protocols in Multi-Group RFID System  
In the scenario of multi-group RFID systems, the most popular problem investigated by researchers is the missing tag detection. The objective of missing tag detection is to determine if a missing tag event has occurred in the system. When the number of missing tags in the system is larger than a certain threshold, a missing tag event is considered to have occurred. Missing tag detection is flexible and useful in some practical scenarios.  
For multi-group RFID systems, the reader needs to recognize whether a missing tag event occurs within a group. To solve this problem, researchers have proposed many multi-group missing tag detection protocols. [PERSON] et al. [18] developed a series of missing tag detection protocols by incorporating an improved Bloom filter design and parameter tuning in multi-group and multi-region RFID systems. Shortly after, a Simultaneous Missing Tag Detection protocol (SMTD) was proposed by [PERSON] et al. [19]. It can decode out multiple frame occupation vectors from one actual time frame, and each frame occupation vector can complete the missing tags detection within a group. Combined with the Category Clustering protocol (CC) which can cluster the tag categories into one batch, SMTD can significantly reduce the time for multi-group missing tag detection. [PERSON] et al. [20] developed an Accurate and Expeditious Multi-group Missing Tag Detection protocol (AEMD), which implants a built-in vector to tags in the offline phase, and the reader implements multi-group missing tag detection by collecting the built-in vectors of tags.
## 3 Case Study Analysis
### Case Study 2: Pumping System  
This case study is related to an offshore centrifugal pumping system that has redundant equipment (2 out of 3) and is monitored. The technical condition is monitored and transferred using IoT to the cloud. Some of Industry 4.0 technologies and principles have been used to keep the system availability as high as possible and cut down maintenance costs as much as possible. Therefore, two actions were taken, as follows: (1) to improve the condition monitoring system (CMS) and (2) to use a reallocation policy and prescribe maintenance needs. The pumping system came with a built-in CMS; however, the maintenance department found that there are some critical failure modes that are not covered. The maintenance department decided to modify the system and upgrade its monitoring system in order to reduce corrective maintenance work, offer early preventive maintenance when the failure consequence is low (before the level of damage increases), and reduce the planned maintenance workload (the estimated maintenance manning). The maintenance department also plans to change the operating policy from conditional standby (the standby equipment works if one of the two active equipment fails) to a planned standby policy (reallocation is performed based on the schedule or technical condition). The aim is to better utilize the equipment (especially the standby one), enabling better maintenance planning and offering planned opportunistic maintenance intervals. Although reallocation is conducted in a manual manner, it is a good example of the potential use of prescriptive maintenance and cyber-physical systems to offer automatic reallocation based on either technical conditions or predefined schedules.  
These two modifications (upgrade of the monitoring system and planned standby policy) require a well-defined work process to engineer and determine the technical specifications and integrate them into the overall maintenance program. Thus, several emergingpractices have been observed in this case, summarized in Table 4. The first emerging practice is related to a new guideline to incorporate prescriptive maintenance (PsM) into the risk-based maintenance program. This guideline has impacted mainly the maintenance and testing concept, and inserted \"Prescriptive Maintenance\" into the \"Maintenance Program\" function as a new maintenance concept that should be evaluated while selecting the maintenance concept. Consequently, the second emerging practice is a new model to estimate the remaining useful time under prescribed actions. The third emerging practice is a new data flow, covering the predicted remaining useful time (without prescription) and the predicted remaining useful time with prescribed actions. The fourth emerging practice is the new guideline for managing maintenance notifications (generated by the prescriptive model) and reinforce the prescriptive model.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 3 Subjective Listening Tests Using the MUSHRA Methodology
### Experiment 2-Multiple Point Audio Sources
#### 3.5.4 Results  
Aggregated MUSHRA scores by encoding scheme (with mean values and 95% confidence intervals) are shown in Section 4.6.  
Multiple point audio sources are denoted in this paper as concatenations of fixed and dynamic audio source labels. Audio sources with fixed localizations are denoted with suffix \"F\". Sources moving vertically (i.e., dynamic elevation) are denoted with suffix \"El\" and moving horizontally (i.e., dynamic azimuth) are denoted with suffix \"Az\". For example, an audio source denoted as \"pianoFxyEl\" represents multiple point audio source with one piano sound source at fixed location and one xylophone sound source moving vertically (i.e., with dynamic elevation).  
Experiment 2 confirmed that both Ambisonics order and audio channel compression affects localization accuracy of multiple point audio sources. All third-order Ambisonics above 16 kbps per channel (i.e., 3 OA512, 30A384, 3 OA256) and one first-order Ambisonics with 32 kbps bit rate per channel (i.e., FOA128) were rated \"excellent\" or \"good\" in regards to localization accuracy.  
The results of these experiments have been used to optimize (experiment 1) and validate (experiment 2) a new objective quality model for spatial audio described in next sections of this paper.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation  
Our evaluation process prioritizes real-world application over theoretical optimality. All experiments were carried out in a standardized Google Colab environment, utilizing production-grade cryptographic libraries: PyQC for post-quantum primitives and TenSEAL for homomorphic operations. To ensure the stability of our findings, each FHE performance metric was measured and averaged over several runs.
## 4 Current Status and Identified Needs
### Level 1 TOA Radiometry and Geometry
#### 4.1.3 Vicarious Calibration with Desert Targets  
The most widely used vicarious targets are the Pseudo-Invariant Calibration Sites (PICS); these are desert sites, which were carefully selected based on their low cloud coverage, the high reflectance, and the good stability and uniformity in terms of temporal, spatial and spectral characteristics [75]. The six CEOS-endorsed reference PICS sites are Libya-4, Mauritania-1, Mauritania-2, Algeria-3, Libya-1 and Algeria-5 [76], which were chosen based on the large number of data sets from multiple sensors that already exist in the archives and the long history of characterisation performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. Consequently, these PICS are highly suitable to evaluate the long-term stability of an optical sensor in the visible and short-wave infrared spectral regions and to facilitate inter-comparison with other sensors [73].  
However, the PICS are not fully invariant, since dunes topography or sand grain size and composition can induce residual directional and shadowing effects, with intra-annual variations related to the seasonal changes of illumination conditions and potential inter-annual changes caused by dune displacement [77]. It is thus generally recognised that the list of endorsed PICS sites should be critically reviewed in order to improve the accuracy of the derived radiometric assessment. This critical review was recently undertaken as part of an ESA-funded study [78] and it is also being pursued in the frame of a CEOS supported initiative [79].  
Another challenge in vicarious approaches using PICS resides in the inaccuracies of the RTMs used to simulate the at-sensor signal. These are induced by approximations in modelling the atmosphere-surface radiative interactions (e.g., plane parallel atmosphere) and the use of inaccurate ancillary data (e.g., atmospheric concentration profiles, molecular cross-sections, aerosol properties). As a matter of fact, the current discrepancies between commonly used RTMs for simulating the radiance over the Libya-4 desert site has been demonstrated to be in the order of 2 to 3% [80]. These challenges were the main motivations to start the development of a new open-source Monte-Carlo ray-tracing 3-D RTM, called Eradiate, which is currently being supported by ESA and the European Commission (EC) [81].
# Thermo-Optic Numerical Research on Segmented Circular LD Arrays Side-Pumping a Nd:YAG Laser Rod
## 4 Conclusions  
In summary, the thermo-optic numerical research on LD arrays side-pumping an Nd:YAG laser rod was demonstrated for three different side-pumping schemes and a numerical analysis method as an efficient analysis design tool is provided for compact and miniature side-pumping lasers. The temperature distributions and thermo-optic analyses of laser rods in the segmented circular LD array side-pumping configuration were examined in contrast with those in the annular liquid-cooling and compensated semicircular LD array side-pumping configurations The numerical results revealed that the segmented side-pumping scheme has a higher beam quality and provides an effective method to remove the complex liquid cooling system in medium power applications. The mobility and portability were enhanced due to the small volume of the laser system. We predict that this scheme has the potential to be used in compact and miniature laser systems.  
Conceptualization, W.W.; methodology, W.W. and Q.Z. ([PERSON]); formal analysis, Q.Z. ([PERSON]) and Z.H.; investigation, Q.Z. ([PERSON]), W.G. and S.Y.; writing--original draft preparation, W.W. and Q.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the open fund of Science and Technology on Electro-Optical Information Security Control Laboratory, grant number 61421070201. The authors declare no conflict of interest.
# Targeting Antigen-Presenting Cells in Multiple Sclerosis Treatment
###### Abstract  
Multiple sclerosis (MS) is common neurological disease of the central nervous system (CNS) affecting mostly young adults. Despite decades of studies, its etiology and pathogenesis are not fully unraveled and treatment is still insufficient. The vast majority of studies suggest that the immune system plays a major role in MS development. This is also supported by the effectiveness of currently available MS treatments that target immunocompetent cells. In this review, the role of antigen-presenting cells (APC) in MS development as well as the novel therapeutic options targeting those cells in MS are presented. It is known that in MS, peripheral self-antigen-specific immune cells are activated during antigen preparation process and they enter the CNS through the disrupted blood-brain barrier (BBB). Myelin-reactive CD4+ T-cells can be activated by dendritic cells, infiltrating macrophages, microglia cells, or B-cells, which all express MHC class II molecules. There are also suggestions that brain endothelial cells may act as non-professional APCs and present myelin-specific antigens with MHC class II. Similarly, astrocytes, the major glial cells in the CNS, were shown to act as non-professional APCs presenting myelin antigens to autoreactive T-cells. Several currently available MS drugs such as natalizumab, fingolimod, alemtuzumab, and ocelizumab may modulate antigen presentation in MS. Another way to use this mechanism in MS treatment may be the usage of specific tolerogenic dendritic cells or the induction of tolerance to myelin antigens by peptide vaccines.  
multiple sclerosis; autoimmunity; antigen-presenting cells; dendritic cells; tolerance induction
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.2 Leach-Dcs  
The stochastic CH selection in LEACH is enhanced in [68], which modifies the CH selection threshold using a deterministic approach. Two modifications of \(T(n)\) have been considered to enhance the network lifetime. The first approach multiplies the remaining battery power level (\(E_{current}\)) of each node and is given as:  
\[T(n)=\frac{P}{1-P\Big{(}rmod\frac{1}{P}\Big{)}}\times\frac{E_{current}}{E_{ max}} \tag{3}\]  
where \(E_{max}\) is the initial node energy. However, after a few rounds, the network becomes stuck, with nodes having high energy levels alive for data transmission. Hence, the \(T(n)\) value is further expanded as:  
\[T(n)=\frac{P}{1-P\Big{(}rmod\frac{1}{P}\Big{)}}\times\Bigg{[}\frac{E_{current}}{ E_{max}\Big{(}r_{s}div\frac{1}{P}\Big{)}\Big{(}1-\frac{E_{current}}{E_{ max}}\Big{)}}\Bigg{]} \tag{4}\]  
where \(r_{s}\) represents the number of consecutive rounds in which a node has not acted as CH. The method proved to be energy efficient as nodes themselves decide to be the CH, as no global information needs to be stored in BS. With the modified values of \(T(n)\), the network lifetime was enhanced by 30%, but the frequent cluster formation process degraded the network performance and demanded recursive cluster maintenance.
# Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
## 4 Experiments and Results
### Implementation Details and Evaluation Metrics
#### 4.2.2 Evaluation Metrics  
We use overlap and surface distance measures to evaluate the segmentation, including Dice, Jaccard, the average surface distance (ASD), and the 95% Hausdorff Distance (95 HD).  
(1) Dice and Jaccard Coefficients : Given two binary segmentation masks, \(A\) and \(B\), the Dice \(D\) and Jaccard coefficient \(J\) are defined as:  
\[D=\frac{|A\cap B|}{|A|+|B|},J=\frac{|A\cap B|}{|A\cup B|} \tag{2}\]  
where \(|\cdot|\) gives the cardinality (i.e., the number of non-zero elements) of each set. Maximum and minimum values (1.0 and 0.0, repectively) for Dice and Jaccard coefficient occur when there is 100% and 0% overlap between the two binary segmentation masks, respectively.  
(2) Average Surface Distance and 95% Hausdorff Distance: Let, \(S_{A}\) and \(S_{B}\), be surfaces (with \(N_{A}\) and \(N_{B}\) points, respectively) corresponding to two binary segmentation masks, \(A\) and \(B\), respectively. The average surface distance (ASD) \(S\) is defined as:  
\[S=\frac{1}{2}\Big{(}\frac{1}{N_{A}}\sum_{p\in S_{A}}d(p,S_{B})+\frac{1}{N_{B}} \sum_{q\in S_{B}}d(q,S_{A})\Big{)} \tag{3}\]  
Similarly, Hausdorff Distance (HD) \(H\) is defined as:  
\[H=\max\biggl{(}\max_{p\in S_{A}}d(p,S_{B}),\max_{q\in S_{B}}d(q,S_{A})\biggr{)} \tag{4}\]  
where  
\[d(p,S)=\min_{q\in S}d(p,q) \tag{5}\]  
is the minimum Euclidean distance of point \(p\) from the points from the points \(q\in S\). Hence, MSD computes the mean distance between the two surfaces, whereas, HD computes the largest distance between the two surfaces, and is sensitive to outliers.  
Four complementary segmentation metrics are introduced to quantitatively evaluate the segmentation results. Dice and Jaccard, two region-based metrics, are used to measure the region mismatch. Average surface distance (ASD) and 95% Hausdorff Distance (95 HD), two boundary-based metrics, are used to evaluate the boundary errors between the segmentation results and the ground truth.