esa-sceva/satcom-chunk-collection huggingface dataset:
# Working State of ECC Link Slabs Used in Continuous Bridge Decks
## 3 Experimental Program
### Loading Procedure and Measuring Scheme  
A monotonic cyclic loading procedure simulating the traffic loads was performed up to the ultimate failure or the rotation angle of 0.04 rad [26]. This test system was operated conforming to the deflection-control method. The deflection increased from zero to the peak value (\(d_{\text{peak}}\)) and then decreased to zero within one loading cycle. The growth rate of \(d_{\text{peak}}\) was 0.5125 mm per cycle (i.e., 0.0005 rad/cycle). As shown in Figure 1, the steel girder ends of the inverted model were subjected  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cement**} & \multicolumn{3}{c}{**Binder (wt. \%)**} & \multirow{2}{*}{**Micro Sillica**} & \multirow{2}{*}{**W/B**} & \multirow{2}{*}{**S/B**} & \multirow{2}{*}{**V\({}_{\text{PFA}}\)**} & \multirow{2}{*}{**Superplasia-Cizer (wt. \%)**} \\  & & & & & & & \\ \hline
40 & 50 & 4 & 4 & 2 & 0.268 & 0.364 & 2.0 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mix formulations for ECC.  
Figure 1: Experimental continuous bridge deck model.  
to the vertical monotonic cyclic loads. The relationship between applied load (\(F\)) and \(d_{\text{peak}}\) in each loading cycle was recorded.  
As shown in Figure 2, a linear variable differential transformer (LVDT) was installed at the mid location of each ECC link slab (on the top surface) to monitor the vertical deflection. The strain gauges numbered S1, S2, S3, S4, and S5 were placed at equal intervals along the side midline of each ECC link slab. Meanwhile, the strain gauges numbered T1, T2, T3, and T4 were placed along the top midline of each ECC link slab. Additionally, the strains of CFT grids (G1, G2, G3, etc.), CFRP bars (B1, B2, B3, etc.), and reinforcing steels (R1, R2, R3, etc.) embedded in the link slabs were monitored and those measuring points are exhibited in Figures 3 and 4. It should be noted that the healthy measuring points are continuously numbered herein, excluding the broken ones. The relationship between measured strains (\(\varepsilon\)) and \(d_{\text{peak}}\) within each loading cycle was recorded as well. Through the analyses of \(F\)-\(d_{\text{peak}}\) and \(\varepsilon\)-\(d_{\text{peak}}\) curves, the dependence of strains on each loading step (every 1 kN) can be determined to further study the structural performance.  
Figure 4: Strain measuring points for the reinforcing steels embedded in different types of ECC link slabs: (**a**) Type I ECC link slab; (**b**) Type II ECC link slab; and (**c**) Type III ECC link slab.  
Figure 3: Strain measuring points for the fiber reinforced polymer (FRP) composites: (**a**) carbon fiber textile (CFT) grids and (**b**) carbon fiber reinforced polymer (CFRP) bars.  
Figure 2: Strain and displacement measuring points for the inverted engineered cementitious composites (ECC) link slab.
# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 4 Discussion  
Currently, X-ray is the basic routine imaging modality for examining a patient with OA potentials clinically. While X-ray is more cost-efficient than MRI, it is not as sensitive as MRI, which can show much more structure and tissue details. Therefore, MRI is considered as an alternative imaging tool, especially for detecting early osteoarthritis with slight structure change.  
In the 5-category results of this study we found that MRI had higher accuracy in classifying KL = 0 and KL = 1 (Table 6). This aligns with the previous studies that found MRI to be better at capturing detailed and small structure change and therefore more sensitive to early signs of OA development. When classifying the category KL = 4, X-rays have higher accuracy than MRI, indicating that X-rays are better at detecting OA in a more severe situation. The 2-category results in this study were consistent with those in the 5-category, in that X-ray has higher accuracy for detecting severe OA cases while MRI is more sensitive to small structure changes and early indicators of OA (Table 7). The complementary performance of the two imaging modalities is interesting and indicates the possibility that they could be combined to develop a comprehensive and more accurate diagnosis system than using each individual imaging modality alone.  
A limitation of this study is that we have a limited number of samples. This is because we have to include patients with both MRI and X-ray scanned on the same knee. Another limitation is that MRI is not widely used in clinical diagnosis due to the cost. However, MRI is a new trend of imaging to study the pathology of knee OA in many clinical trials since MRI can offer a better view of soft tissues such as cartilage, bone marrow lesions, and effusions.  
Future work includes further examination of the KL categories with lower accuracy, e.g., the KL = 2 category, which was often misclassified into KL = 1 (non-OA) by the model. This may be solved with a weighting system during training. Additionally, our current preprocessing uses a fixed offset for cropping as well as a fixed range for slice removing. This could be updated as a dynamic setting for each sequence, which may retrieve more accurate information and therefore generate better classification performance. Combining the two imaging modalities for a comprehensive and more accurate model is also a promising direction.
# Progress of Capillary Flow-Related Hydrovoltaic Technology: Mechanisms and Device Applications
## 2 Current Understandings of Capillary Flow-Related Electricity Generation Mechanisms
### Ion Concentration Gradient
[14] with permission from John Wiley and Sons, copyright 2015.  
process could reach up to 62%, significantly surpassing other forms of power generation, such as piezoelectric and fluidic-electric systems [14]. This efficiency underscores the potential of leveraging thermodynamic insights to optimize hydrovoltaic devices. Due to the resemblance between the ion concentration gradient mechanism and the working principle of a diode, many studies have utilized the diode mechanism to design and explain moisture-induced electricity devices, achieving notable success [41; 42; 43]. Specifically, [PERSON] et al. recently demonstrated the effectiveness of using an ion-diode-like heterojunction for improving electricity generation from water droplets through capillary infiltration. This structure, consisting of materials with opposing charges, significantly enhances charge accumulation by suppressing undesirable reverse diffusion currents. The incorporation of this heterojunction provides a new approach to increasing the efficiency of the ion concentration gradient mechanism in hydrovoltaic devices [42]. To further address the challenge of achieving continuous and long-term electricity generation, [PERSON] et al. developed a nanofluidic diode-type hybrid membrane capable of generating power from ambient humidity for at least one month. This work leverages a built-in electric field formed in a PN junction to achieve steady-state, one-way ion transport, which is crucial for maintaining continuous power output. This advancement highlights a promising path for overcoming the limitations of transient pulse signals in moisture-enabled electricity generation [43]. We consider the diode theory to be fundamentally the same as the ion concentration gradient mechanism. This mechanism has been highly effective in explaining current formation and predicting device performance. Following the initial pioneering work, subsequent studies have significantly expanded the range of materials by incorporating those with various functional groups, such as polymeric gels [44; 45], porous carbon [46], and MoS\({}_{2}\)[47]. The open-circuit voltage has been impressively enhanced to 1000 V when scaled up [48], and the electricity output has improved from transient pulse to nearly continuous 24-h generation [28]. While the ion concentration gradient mechanism has achieved great success, challenges remain. One disadvantage is the lack of accurate mathematical models to describe the whole process in a quantitative way. Current explanations, though reasonable, rely only on phenomenological observations and qualitative deductions. Another difficulty of this mechanism is achieving both steady high current and continuous output. To maintain a concentration gradient for continuous electric current requires slow ion movement, which contradicts the need for a strong electric current. This dilemma calls for further study.
## 1 Introduction  
Cellular networks' future is in heterogeneous networks. These networks consist of different radio and architecture technologies. On the other hand, the growing services and technologies increase the complexity of these networks and the internet of things (IoT) daily. For operators' survival in today's competitive world, it is necessary to use a new strategy to manage these networks. Using self-organizing networks (SONs) is the most common solution to achieve this goal. The architecture of SONs consists of three general parts: self-configuration, self-healing, and self-optimization [1, 2].  
Self-regulating networks include different phases, including designing, updating, or developing the network. Transmitter stations, base receivers, nodes, and other network terminals need to set related parameters, especially with the emergence of LTE networks based on IP and with flat architecture [3, 4].  
With the increase in the complexity of cellular networks, self-healing networks have become more popular. However, the rate of published studies and articles in this field is much more limited than in other areas. These networks can manage faults and system failures by automated detection and identify their causes. Finally, the self-optimizing network calculates and values the network parameters that need to be reset, according to the network status, traffic, and services used by the users [5].  
Therefore, actions related to self-regulating networks are in the development phase, or any activities related to resetting the network are in the operational stage. The self-optimization starts working in cases where there is no apparent problem in the network, but the cell performance is average. Finally, the measures related to the network's self-healing capability are used when the quality and efficiency of the network severely decline. Our focus in this paper is on self-healing networks. This research seeks to find some solutions to improve the process that is currently used in the industry to detect faults and identify the cause of faults. Therefore, all assumptions and modeling have been made according to the needs of the sector and consultation with experts in this field. In addition, according to experts in this field, it improved the quality of operational work.
# Interactions between Phase-Separated Liquids and Membrane Surfaces
## 5 Biomolecular Condensates Promoting Membrane Protein Clustering Influence Protein Function
### Phase Separation in LAT Clusters Has a Functional Contribution to T Cell Signaling  
LAT is a transmembrane protein that is phosphorylated upon activation of T cell receptors (TCRs) and binds several downstream signaling elements when phosphorylated; downstream signaling does not occur in its absence [63]. LAT and some binding partners formed clusters upon TCR activation that were mediated by protein-protein interactions rather than cytoskeletal or membrane structures [64]. These clusters were phase-separated domains requiring multivalent binding partners to link LAT together. When reconstituted on a lipid bilayer, these liquid-like biomolecular condensates enriched a LAT kinase and actin polymerases and excluded a LAT phosphatase, strengthening signaling and promoting localized actin polymerization [3]. The proteins Nck and N-WASP can latch LAT domains to actin and lead to biomolecular condensate movement by different actin networks depending on the amount of Nck present, representing a model of controlling the location of biomolecular condensates and all of their components simultaneously [65].  
Son of Sevenless (SOS) is one LAT binding partner necessary for phase separation whose signaling activity is activated by prolonged presence at the membrane [66]. The long average dwell time for which SOS must occupy the membrane before activation leads to a situation in which SOS is likely to be activated when part of a long-lived biomolecular condensate but unlikely to be activated when recruited to the membrane by one-off, transient interactions. This phenomenon is referred to as kinetic proofreading of LAT signaling [67]. [PERSON] and [PERSON] have recently speculated that prolonged dwell times as a consequence of phase-separated signaling domains could represent a broadly applicable phenomenon, as a similar process occurs in nephrin signaling [68].
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 7 Challenges and Future Research Trends
### Sensor Communication-Related Challenges  
The IoD sensor's communication protocols are designed with lightweight and highly sensible objects which have a substantial chance for data loss or receive the wrong data from other nodes. Furthermore, sensors face routing issues when communicating with multiple drones with a network center. Some of the drone manufacturers use cheap hardware components which increase the chance of other network communication issues namely, high throughput, latency, and delay between the device and center [82]. To address these issues, next-generation network technologies such as 5G, intelligent routing, narrowband-Internet of things, LoRa-based IoT systems, Sigfox, NB-IoT, and LTE-M need to support connection choices in the future. Lastly, the standardized policy should be developed to use the authorized component for drones' communication protocol.
# Mass Processing of Sentinel-1 Images for
## 1 Introduction
Following the Copernicus's \"free, full and open\" data access policy, all the images (\"full\") are made available to all users (\"open\") at no cost (\"free\") through the Copernicus Open Access Hub (formerly known as Sentinels Scientific Data Hub), while dedicated data access is provided to international partners (International Hub), collaborative ground segments (Collaborative Hub) and Copernicus Services (Copernicus Services Hub) [18]. Sentinel-1 operates in a predefined observation plan, which dictates the areas that will be monitored and the modes and polarisations that will be used [19]. This plan responds to a number of user requirements and technical constraints [20]. According to the current operation, Sentinel-1 monitors the entire Mediterranean (\"Med\") Sea in IW mode and VV + VH polarisation. Each satellite has an orbit cycle of 12 days; this means that the satellite can monitor the same area on the ground from the same orbital position every 12 days. Due to overlaps between image swaths in ascending and descending orbits and between swaths in adjacent orbits, the revisit time of a point on the ground can be much shorter than 12 days: at the Med latitude, the revisit time can be as short as three days using the acquisitions of one of the satellites only, and 1.5 days using the acquisitions of both satellites. The Sentinel-1 satellites fly in a sun-synchronous near-polar orbit. At low and medium latitudes (which includes the Med Sea) the overhead passes are confined to narrow time windows around 6:00 a.m. and 6:00 p.m. local time. The objective of the study was to test automatic ship detection processing capabilities in large volume maritime monitoring campaigns that leave no room for manual verification of the detection results to assess Sentinel-1's contribution in a multi-source AIS + SAR monitoring campaign (e.g., number of non-reporting ships detected in the Sentinel-1 images), and to quantify how the use of repeat acquisitions can improve the monitoring results (by analysing the recurrence of targets). It builds on the experience gained in earlier analyses of Sentinel-1 images [21; 22].
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 3 Proposed LBN-Aided Positioning Scheme
### LBN for RSSI-Based Indoor Positioning  
Building on existing normalization techniques, we propose a novel approach called LBN, specifically designed for RSSI signal-based indoor positioning. The LBN technique divides the input feature map into spatially localized patches, applying normalization within each patch. This localized strategy allows the model to better capture variations in RSSI signal distributions, thereby improving its performance in indoor localization tasks.  
As shown in the pseudocode in Algorithm 1, the method operates as follows:  
1. LBN divides the input tensor into non-overlapping spatial patches of size block_size \(\times\) block_size, performing localized normalization within each patch.
2. The technique involves initializing learnable scaling (\(\gamma\)) and shifting (\(\beta\)) parameters, set to 1 and 0, respectively. Additionally, momentum and epsilon (\(\epsilon\)) are introduced to control the moving averages and ensure numerical stability.  
During forward propagation, LBN extracts the input tensor dimensions, including batch size, height, width, and number of channels, and calculates the block dimensions (block_h and block_w) to divide the input into patches.
4. For each patch, localized mean and variance are computed to enable normalization specific to the spatial region.
5. In training mode, patches are normalized using the calculated statistics: \[normalized\_patch=\frac{(patch-mean)}{\sqrt{(variance+\epsilon)}}\] (10)
6. The patches are then reshaped to their original dimensions.
7. Scaling and shifting are applied: \[output=\gamma\cdot normalized\_patch+\beta\] (11)  
During inference, precomputed moving averages of the mean and variance are used to ensure stable predictions. By incorporating spatial locality into the normalization process, LBN enhances the CNN's ability to learn localized features, improving the model's robustness and positioning accuracy. LBN is applied within the CNN model, as shown in Figure 2.  
```
1. Input: Input tensor inputs, Block size block_size, Momentum momentum, Epsilon \(\epsilon\)
2. Initialize: Trainable weights \(\gamma\) and \(\beta\) for each channel
3. Extract input dimensions: batch_size, height, width, channels
4. Compute block dimensions: block_h = height // block_size, block_w = width // block_size
5. Divide input into block_size \(\times\) block_size patches
6. Compute mean and variance for each patch
7. if training mode then
8. Normalize patches: (patch - mean) /\(\sqrt{(variance+\epsilon)}\)
9. end if
10. Reshape patches to original dimensions
11. Apply weights: outputs = \(\gamma\) - patches + \(\beta\)
12. Return: Normalized output tensor outputs
```  
**Algorithm 1** Localized Batch Normalization  
Figure 2: Flow graph for position estimation with LBN integrated into CNN.
###### Abstract  
As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.  
h 2022122013 May 202213  
Under the influence of harsh industrial environments (such as noise, exhaust gas, dust, and high temperature), industrial wireless sensors pose severe challenges to the survival and stability of the network, which becomes more prone to cascading failures;
* High topology complexity means that the network node layout in the industrial site is more complex, and the coverage is more considerable. Using the simple networking topology (e.g., star topology) is not suitable;
* The real-time performance of data transmission, in some scenarios, requires data to be transmitted at a breakneck speed to reduce the risk of accidents.  
Due to the above differences, it is essential to explore the cascading failure phenomenon in the hierarchical topology of IWSNs. The robustness of the network should integrate the network-efficiency value and the node-survival number under cascading failures. This paper provides theoretical guidance for building industrial sensor networks, thereby reducing the probability of large-scale losses caused by cascading failures.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 4 FoMs Analysis  
In previous research, BPSK(1), BPSK(4), BPSK(8), \(\text{BOC}_{\text{s}}\)(1,1), \(\text{BOC}_{\text{c}}\)(4,4), \(\text{BOC}_{\text{s}}\)(4,4), and multiplexed BOC (MBOC) were studied as future S-band navigation signals [17; 22]. Meanwhile, one of the concerns regarding using the S-band is the OOBE interference due to the adjacent band systems [31; 32; 50; 51]. If the RNSS signal has a large amount of signal power near the edge of the S-band, more serious interference is expected. Therefore, we considered additional candidates that have less power at the edge of the frequency band.  
Furthermore, the cosine-phased BOC modulation with the same chip rate and subcarrier frequency of the sine-phased BOC scenarios were examined as candidates. BPSK(1) and \(\text{BOC}_{\text{s}}\)(5,2), which are adopted modulation schemes in the NavIC S-band signals, were also considered. Table 3 specifies the carrier frequency and modulation schemes of the candidate signals investigated in this study.