esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sea Surface Sun Glint Suppression and Tiny Human Object Detection Based on Visual Saliency  
### Characteristic Analysis of Maritime UAV Images  
In SAR tasks, compared with field scenarios, in maritime scenarios, the background is relatively flat and generally includes the elements of sea surface, glint, and sky. The visual characteristics of the background and objects in maritime UAV images are as follows:For the areas of sea surface and sky, there is almost no large-area shading caused by occlusion. The color distribution of the whole image is uniform, with few color mutations.
2. Sun glint on the sea surface, which is due to the variable trajectory and view of UAVs, appears white in RGB images. The visual saliency of tiny objects concealed by sun glint is decreased significantly. However, the color and brightness of maritime objects are different from those of sun glint and background.
3. Because of the large field of view of UAVs, maritime objects generally present a clustered spatial distribution. The characteristics at the pixel level reflecting the above visual features are as follows:
1. The pixel intensity values of the sea surface and sky change continuously and evenly on the grayscale, and those of objects such as human targets and boats differ from those of the background, which can form pixel peaks or valleys.
2. The pixel intensity values of sun glint on the grayscale and in different RGB channels are close to 255. Objects also have high pixel intensity values on the grayscale, and when they are concealed by sun glint, it is difficult to separate them from glint noise on the grayscale. However, tiny objects have different pixel distributions in the RGB channels compared with sun glint.  
As shown in Figure 2, the high pixel intensity values of sun glint and sky on the grayscale significantly interfere with object detection based on pixel saliency. Against backgrounds that appear as pixel surfaces, objects have much higher pixel intensity values and are well distinguishable, but when they are concealed by sun glint, their visual saliency decreases significantly. Therefore, the difficult task of separating the salient points of objects and glint to achieve tiny object detection based on visual saliency needs to be addressed.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 3 System Model and Problem Formulation
### Energy Consumption Model
The constraints related to Equation (9) are as follows:  
\[\sum_{c\in C}x_{sc}=1,\forall s\in S \tag{10}\]  
\[x_{sc}\leq z_{c},\forall s\in S,\forall c\in C \tag{11}\]  
\[P_{in}^{j}=f(R_{in}^{j})P_{in}^{\max} \tag{4}\]0  
\[P_{in}^{j}=f(R_{in}^{j})P_{in}^{\max} \tag{4}\]1  
Constraint (10) ensures that each satellite \(s\) is controlled by exactly one controller \(c\). Constraint (11) guarantees that the switch is connected to a node where a controller resides; \(z_{c}\in\left\{0,1\right\}\) equals \(1\) if the satellite node \(c\) is selected as the controller and equals \(0\) otherwise. Constraint (12) means that the control paths need to meet the latency constraint \(L_{b}\); \(d_{sc}\) represents the propagation latency of the path from satellite \(s\) to controller \(c\). Constraint (13) ensures that controller \(c\) controls at most \(N_{c}\) number of switches; \(N_{c}=\lceil\frac{|S|-|\mathbb{C}|}{|\mathbb{C}|}\rceil\); \(|S|\) is the number of satellites; and \(|C|\) is the number of controllers.
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 3 Applications of Nuclear Sensors
### Environmental Monitoring  
The detection and monitoring of toxic compounds in the air, water, and soil proves to be an ongoing challenge for environmental scientists. Not only can nuclear sensors help prevent radiation exposure of humans and wildlife from reaching a harmful level but they show great promise in detecting the toxic compounds in the air, water, and soil [8]. The advancement of nuclear sensor technology improves the detection of unwanted compounds in the air, water, and soil, thereby improving the safety and health of humans and wildlife.
## 4 Results and Discussion
### PU and PELI in CMP Experiments  
Figure 9 shows the change of PELI (pad effective lifetime index) and PU (pad non-uniformity) value in CMP experiments. The PELI decreases to 30.5% after 50 rounds of CMP experiments and PU increases to 61.9% in the same time. Figure 10 shows the re-mapped pad surface profile from the measuring data. After 50 rounds of CMP process with conditioning between each polishing, a dish-type pf pad profile is measured. The dish-type shape is formed because of the difference of relative speed with pad radius. The pad cutting rate (PCR) is higher at inner area due to the higher relative speed. The scanning electron microscope(SEM) photos of cross-section of pad after 50 rounds of tests are shown as Figure 11 to verify the change of the groove depth during CMP process. Figure 11a shows the locations to take the SEM pictures. The pad's area is separated into 6 ring sections from outer ring to inner ring. Figure 11b is the cross-section on groove of new IC1000 pad. Figure 11c-h shows the wear of pad's groove from outer area to pad center.  
Figure 8: Results of metrological experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Platen Rotation Speed** & **1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 rpm** \\ \hline Swing Arm Rotation Speed & 1\({}^{\circ}\)/s \\ Sampling Rate & 1000 Hz \\ Measurement times & 50 times \\ Scan time & 50 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of metrological experiments.  
Figure 9: Results of PELI and PU for 50 rounds of CMP experiments.  
Figure 10: Re–mapped pad surface profile from round 1 to round 50.  
Figure 11: Cont.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 2 Literature Review of PLS Techniques for IoT
### Information-Theoretical Fundamentals of Physical Layer Security
[15] established the secrecy capacity theory for Gaussian broadcast channels; Refs. [16; 17] analyzed the secrecy capacity for multi-antenna channels; Ref. [18] discussed the secrecy capacity over quasi-static fading channels; Ref. [19] investigated the secrecy capacity over block fading channels without the knowledge ofthe eavesdropper channel; Ref. [20] derived the secrecy capacity over frequency selective channels; Ref. [21] analyzed the impact of noisy channel feedback on the eavesdropper; Ref. [22] discussed the secure transmission problems with limited feedback. With the development of multi-user information theory, the focus of information-theoretic security research is also shifted from the point-to-point systems to multi-user systems. In multi-user systems, the inter-user cooperation and interference exist among different users, which brings in great challenges to the analysis of the system secrecy capacity and the design of PLS strategies, and provides important opportunities as well. Ref. [23] proposed the notion of cooperative jamming, and analyzed the achievable secrecy performance for two-way Gaussian wiretap channel and Gaussian multiple-access (MAC) channel with multiple eavesdroppers. Ref. [24] studied the trade-off between cooperation and secrecy in relay channels with secret messages. Ref. [25] analyzed the secrecy rate of cognitive channels with security constraints, where the capacity-equivocation regions were obtained for the discrete memoryless cognitive interference channel and the Gaussian cognitive interference channel. The aforementioned research outcomes in information-theoretic studies lay the foundation for the design of practical wiretap coding strategies and anti-eavesdropping transmission schemes, and provide the analytical tools for the performance evaluation of the physical layer security techniques.
## 3 Results and Discussion
### Factors Influencing the Quality of Classification Results  
#### 3.1.1 The Presence and Influence of Shadows  
Table 2 indicates that shadows represent in average 13% of the whole area, increasing up to 23% in one case (\"Transitional,' 2015). The reclassification of this feature depended on the urban configuration at each site. Shadows were generally reclassified as 'Buildings' and 'HighVeg' in the 'Urban' area. In the other two areas, shadows were mostly changed to 'HighVeg,' 'LowVeg,' and 'Bare land' due to the low building density. Although shadows from clouds were observed in two images, the classification algorithm did not recognize them, but classified the pixels as other LULC classes found on the surface. Shadows lying on streets were not reassigned to the respective layers, since they were subsequently superimposed. That reduced the effort of manual editing. The percentage transformed to 'Water' was near zero, with a maximum value of 0.7% from only one case. Nevertheless, that transformation allowed us to define the location of the river in the raster. In summary, the percentage of shadows went down to zero in all cases, improving, theoretically, the results.
# Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents
## 2 Experimental Setup  
### Model and Instrumentation  
A physical model of a typical spillway tunnel was built at the State Key Laboratory of Hydraulics and Mountain River Engineering (SKLH). The model comprises a pressurized section, a gate chamber, an open-channel section, and a tailwater channel section (Figure 1). The pressurized section had a circular cross-section with a diameter of 35 cm, tapering into a square cross-section of 0.35 m \(\times\) 0.15 m. A plain gate without gate slots was installed to regulate the flow depth (\(h_{\omega}\)), ranging from 0.04 m to 0.10 m. The open flow section of the spillway tunnel model had a length (\(L\)) of 8 m, a width \(B\) of 0.35 m, and a height (\(h\)) of 0.16 m for the downstream tunnel section, with a slope (\(S\)) of 0.03. All the channel walls were glass with a hydraulic roughness of 12 \(\upmu\)m, and the hydraulic roughness (\(\Delta\)) of the invert was adjusted by placing different grades of sandpaper ranging from 12 \(\upmu\)m to 240 \(\upmu\)m. The three air vents (#1, #2, #3) in the open-flow section had a height (\(h_{a}\)) of 1.47 m, width (\(B_{a}\)) of 0.07 m, and length (\(L_{a}\)) ranging from 0.04 m to 0.20 m and were installed at different positions (\(X_{2}\) and \(X_{3}\)) of the open-flow section. Prior to the experiments, all sensorsunderwent three-stage calibration: factory calibration with NIST-traceable standards; pre-experiment verification using reference instruments; and post-experiment drift checks, showing <0.5% deviation. All measurements were conducted under identical conditions, with each parameter recorded 20 times and averaged to ensure reliability. Air velocity (\(U_{a,i}\)) and air discharge (\(Q_{a,i}\)) were measured using a GM8903 thermal anemometer (range: 0.1-30 m/s; accuracy: \(\pm\)0.001 m/s \(\pm\)1.5% of reading), yielding a mean absolute percentage error (MAPE) of 4.95% during model testing. Pressure measurements were obtained via a Fluke 922 barometer (\(\pm\)4000 Pa range, 1 Pa resolution), achieving an MAPE of 7.68%. Water discharge (\(Q_{w}\)) in the tailwater channel section was measured using a sharp-crested weir, with a measurement accuracy of \(\pm\)0.1%.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 3 Proposed Method
### Deep Semantic Fusion Module
Subsequently, the pooled features \(Y_{5}\) and \(Y_{6}\) are combined along the channel axis, resulting in a fused representation \(Y_{7}\) that incorporates a variety of statistical information. Next, a convolution layer is applied to further compress the channel information, strengthening spatial correlations. Once the features are compressed, a Sigmoid activation function generates the spatial attention weight \(Y_{8}\), which is used to adjust the spatial positions within the input feature map. This attention weight performs pixel-wise scaling, allowing the model to dynamically emphasize high-response regions and suppress background noise or irrelevant areas. This optimization improves the model's spatial awareness of targets. This process can be as follows:  
\[Y_{5} =\text{Mean}(Y) \tag{9}\] \[Y_{6} =\text{Max}(Y)\] (10) \[Y_{7} =\text{Concat}(Y_{5},Y_{6})\] (11) \[Y_{8} =\text{Sigmoid}(\text{Conv}(Y_{7}))\] (12) \[Y_{out} =Y_{8}\otimes Y\oplus Y \tag{13}\]  
where Mean and Max refer to average pooling and max pooling performed along the channel dimension, respectively, Conv represents a 1 \(\times\) 1 convolution, and \(Y_{out}\) refers to the final output of the DSFM module. DSFM effectively captures deep semantic information and adaptively adjusts feature representations across different scales and contextual environments, ensuring that target features maintain strong discriminative capabilities even in complex backgrounds. Moreover, by employing a progressive fusion strategy, DSFM refines semantic features across various detection stages, boosting the model's overall perception, and ultimately enhancing the precision and reliability of object detection. The choice of the squeeze-and  
Figure 4: Structure of deep semantic fusion module. excitation (SE) attention mechanism is motivated by its effectiveness in enhancing channel interdependencies with minimal computational overhead. In remote sensing scenarios, where targets are often small and embedded in cluttered backgrounds, SE helps emphasize category-relevant feature channels by adaptively recalibrating channel-wise responses. Unlike more complex attention mechanisms (e.g., non-local or transformer-based attention), SE is lightweight, easy to integrate into existing CNN backbones, and has been widely validated to improve performance without significantly increasing model size. Therefore, SE offers an ideal balance between effectiveness and efficiency for our deep semantic fusion design.
# Modeling the Directional Clumping Index of Crop and Forest
## 3 Results and Evaluation
### Assessing the Variation Magnitude of \(\Omega(\theta)\)  
Using the proposed models, we assessed the relative variation magnitude of \(\Omega(\theta)\) to indirectly reflect its potential influence on LAI/GPP estimation.  
We selected three scenarios, one representing a typical crop example, and the other two matching forest measurements. We first calculated the hemispherical average clumping index (\(\widetilde{\Omega}\)) for each group by angular integration, where \(\widetilde{\Omega}\) represents the commonly used single value of clumping index. The uncertainty due to using \(\widetilde{\Omega}\) rather than angle specific \(\Omega(\theta)\) can be assessed from the position of \(\widetilde{\Omega}\) in the range of \(\Omega(\theta)\) (Figure 10). For crop canopies, the discrepancy between \(\Omega(\theta)\) and \(\widetilde{\Omega}\) is more obvious before the zenith angle reaches the range angle. Since the range angle of \(\Omega(\theta)\)\(\sim\)\(\theta\) curve is smaller than 20\({}^{\circ}\), the marker of \(\widetilde{\Omega}\) is out of the box. For forest canopies, the angular variation of the black spruce forest is more intense than the larch forest, due to the different \(\Omega(\theta)\) variation range determined by their tree species and growth status. However, all of the variation amplitude relative to \(\widetilde{\Omega}\) are large and they should not be neglected.  
To further quantify the potential uncertainty caused by ignoring variation of \(\Omega(\theta)\) with zenith angle, we estimated the relative angular variation magnitude of \(\Omega(\theta)\) using the index  
\[A_{V}=\frac{\Omega_{max}-\Omega_{min}}{\widetilde{\Omega}} \tag{16}\]  
where \(A_{V}\) values for the example clumping index datasets are listed in Table 3. \(\Omega(\theta)\) variation amplitude for crops can reach 32.4% of its average value. Black spruce has tall and slender crowns, i.e., larger crown shape factor (Section 4.2.4), which leads to larger \(\Omega(\theta)\) variation range, with Av reaching 102.3%. Larch forest canopy \(A_{V}\) is somewhat lower, mainly due to its relatively smaller \(\Omega(\theta)\) variation range related to its crown shape factor. This demonstrates that potential uncertainty can arise by ignoring \(\Omega(\theta)\) variation in practice. Comparing the relative variation magnitude with the  
Figure 9: Simulated and measured gap fraction for Larch stands. Parameters are listed in Column 2 in Table 2.  
allowable error range will help to determine whether or not \(\Omega(\theta)\) should be treated as constant in practical situations.  
Another main application for \(\Omega(\theta)\) is to estimate the quantity of the terrestrial carbon sink and its dynamic variability. One representative parameter is GPP [33; 50]. The proposed method can be used in GPP estimation to explore whether it would improve the accuracy, which would be a fruitful future research direction.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 6 Open Challenges and Future Research Directions
### Resource Constraints of IoT Devices  
One primary challenge is the _resource constraints_ of IoT devices [54], which make it difficult for them to perform essential NDN functions such as maintaining FIB and PIT entries, especially in mobile scenarios where devices frequently change locations. The heterogeneity of IoT devices and their mobility complicate reliable data delivery, as broken reverse paths and limited processing capabilities hinder efficient communication. Several open challenges have been identified in the deployment of NDN in IoT environments [55]. The heterogeneity of IoT devices and their mobility complicate the delivery of reliable data, as broken reverse paths and limited processing capabilities hinder efficient communication [56; 57]. To address these issues, an edge-assisted NDN-based IoT framework (ENIPC) has been suggested that takes advantage of edge devices with abundant resources to handle routing, caching, and forwarding functions [54]. This hierarchical architecture enables IoT devices to offload resource-intensive tasks to edge nodes, supporting mobility and multiple data retrievals through dynamic name sets and edge-based FIB management [54].
## 4 Experimental Analysis
### Additional Comparisons
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Genotype time**} & \multicolumn{4}{c}{**Phenotype time**} & \multicolumn{4}{c}{**Space**} \\ \cline{2-13}
**Scenario** & **1 k** & **10 k** & **100 k** & **1 M** & **1 k** & **10 k** & **100 k** & **1 M** & **1 k** & **10 k** & **100 k** & **1 M** \\ \hline Vectors 1 & 0.023 & 0.075 & 1.426 & - & 0.002 & 0.022 & 0.203 & - & 11.7 & 82.4 & 800.6 & - \\ Segments 1 & 0.032 & 0.039 & 0.019 & 0.019 & 0.027 & 0.027 & 0.014 & 0.019 & 4.5 & 5.3 & 13.2 & 90.7 \\ \hline Vectors 2 & 0.022 & 0.145 & 2.964 & - & 0.002 & 0.019 & 0.190 & - & 11.7 & 82.5 & 801.4 & - \\ Segments 2 & 0.065 & 0.072 & 0.069 & 0.069 & 0.027 & 0.020 & 0.023 & 0.022 & 4.6 & 6.2 & 21.9 & 177.0 \\ \hline Vectors 3 & 0.035 & 0.242 & 4.678 & - & 0.002 & 0.021 & 0.201 & - & 11.9 & 82.7 & 801.6 & - \\ Segments 3 & 0.152 & 0.159 & 0.143 & 0.148 & 0.022 & 0.028 & 0.030 & 0.035 & 4.9 & 5.7 & 13.6 & 91.1 \\ \hline Vectors 4 & 0.095 & 0.563 & 11.14 & - & 0.000 & 0.017 & 0.207 & - & 12.2 & 83.2 & 803.5 & - \\ Segments 4 & 0.436 & 0.429 & 0.432 & 0.435 & 0.037 & 0.042 & 0.045 & 0.055 & 5.7 & 8.3 & 34.8 & 295.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of time (in seconds) and space (in MB) required for genotype and phenotype computation in experimental scenarios 1–4 by vector and segment representation, for one thousand to one million markers. A greater than ten-fold decrease by segments is shown in blue, while green denotes entries where vector representation ran out of memory. In this case, the increased number of generations does not induce notable over-fragmentation in the segment representation, since the progeny generation time (and space, not shown) stays at a low level across generations. The total time for genotype and phenotype computation for the 11 generations is an order of magnitude faster with segments than with vectors (1.6 _vs._ 19 s).
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) Version 26.0 (IBM, Armonk, NY, USA) software. Correlation analysis was performed between the RCOF during peak GRF and initial foot contact and the resultant JRFs at the lower-extremity joints (i.e., hip, knee, and ankle). Correlation analysis was also performed between the PSI and JRFs at the lower-extremity joints in all directions (anterior-posterior, medial-lateral, and proximal-distal) and between the COP-COM distance and JRFs at all lower-extremity joints in both directions (anterior-posterior and medial-lateral). To evaluate the stability parameter response during slope walking in comparison to level walking, the non-parametric Kruskal-Wallis test was performed using the mean value after the Shapiro-Wilk normality test was completed. The Pearson correlation coefficient was calculated to quantify the relationship between stability parameters (the PSI, RCOF, and COP-COM distance) and the JRF during incline and decline walking. Statistical significance was set at \(p<0.05\). The correlation coefficient was expressed as \(r\) and determines the strength and direction of the relationship between the two variables investigated, which were scaled within ranges from \(-1\) to \(+1\). When r = 0, there is no linear relationship between the variables, and as the value of r increases and eventually becomes a straight line approaching \(-1\) or \(+1\), the relationship becomes stronger, either negatively or positively [16].
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 4 Design and Implementation
### Namespace  
To realize CoAP communication, the CoAP Handler utilizes the namespace depicted in Figure 3. A CoAP server is identified by a Host-URI and it may be part of multiple CoAP groups. The namespace within the ICN enabled operator network is grouped under a single scope (illustrated with Root). A CoAP request to a CoAP server is a publication to its fully qualified domain name (FQDN) represented by the Request Identifier, which is generated by hashing over Host-URI as shown in Figure 3. In the case of CoAP group communication, a request to a CoAP group is a publication to the hash of the Group-name. A response is represented by Response Identifier which is generated by hashing over URL as shown in Figure 3.