esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### In Vitro Ion Release Test  
Figure 5 illustrates the relationship between pH and immersion time for the resin disks containing 0, 1, 3, and 5 wt% GaMBN: GaMBN0, GaMBN1, GaMBN3, and GaMBN5, respectively. The pH decreased with increasing GaMBN content, and generally decreased with immersion time for each disk.  
Figure 6 shows the Ca, P, and Ga release of each GaMBN resin. In terms of Ca ion release, more ions were released as the GaMBN concentration of the resin increased. For GaMBN0, the amount of Ca ions released after 1 day (2.4 \(\pm\) 0.1 ppm), 7 days (2.0 \(\pm\) 0.1 ppm), and 14 days (1.8 \(\pm\) 0.0 ppm) were similar. The Ca released by the GaMBN1 resin increased over the first 7 days and remained constant over the second 7-day period: 1 day (3.8 \(\pm\) 0.1 ppm), 7 days (7.2 \(\pm\) 0.2 ppm), and 14 days (7.1 \(\pm\) 0.1 ppm). The GaMBN3 resin exhibited a similar trend to GaMBN1 with a higher magnitude of Ca release: 1 day (6.4 \(\pm\) 0.1 ppm), 7 days (17.8 \(\pm\) 0.8 ppm), and 14 days (16.6 \(\pm\) 0.1 ppm). Unlike the other resins, the Ca released by the GaMBN5 resin increased with time: 1 day (6.3 \(\pm\) 0.2 ppm), 7 days (19.5 \(\pm\)0.3 ppm), and 14 days (27.2 \(\pm\) 0.8 ppm).  
Figure 5: Relationship between pH and immersion time for the resin disks containing 0, 1, 3, and 5 wt% GaMBN: GaMBN0, GaMBN1, GaMBN3, and GaMBN5, respectively.  
Figure 6: \(Cont.\)The release of P ions also increased with the GaMBN concentration of the resin. No P ions were released by GaMBN0 and the amount of P released by GaMBN1 was relatively consistent: 1 day (0.4 \(\pm\) 0.0 ppm), 7 days (1.2 \(\pm\) 0.0 ppm), and 14 days (0.8 \(\pm\) 0.0 ppm). For GaMBN3, the amount of P released by the resin increased over the first 7 days and remained constant over the second 7-day period: 1 day (1.0 \(\pm\) 0.0 ppm), 7 days (3.3 \(\pm\) 0.1 ppm), and 14 days (3.4 \(\pm\) 0.1 ppm). The P release of GaMBN5 followed the same trend as Ca release and increased with time: 1 day (1.3 \(\pm\) 0.1 ppm), 7 days (4.5 \(\pm\) 0.1 pp), and 14 days (6.4 \(\pm\) 0.1 ppm).  
The release of Ga ions also increased with the GaMBN concentration of the resin, and GaMBN0 did not release any Ga ions. For GaMBN1, Ga release increased with time: 1 day (0.2 \(\pm\) 0.0 ppm), 7 days (1.5 \(\pm\) 0.0 ppm), and 14 days (2.1 \(\pm\) 0.1 ppm). For GaMBN3, Ga release began to plateau after the first 7 days: 1 day (0.6 \(\pm\) 0.0 ppm), 7 days (4.8 \(\pm\) 0.3 ppm), and 14 days (5.7 \(\pm\) 0.5 ppm). The plateau in Ga release was more pronounced for GaMBN5: 1 day (0.5 \(\pm\) 0.0 ppm), 7 days (6.7 \(\pm\) 0.3 ppm), and 14 days (6.7 \(\pm\) 0.1 ppm).
## 3 The Proposed ReSiNet for Low-Memory Devices
### Construction of ReSTiNet
#### 3.2.5 Loss Function of ReSTiNet  
The custom loss function is utilized in this study, unlike Tiny-YOLO, which consists of three parts: error in prediction coordinate, error in IOU, and classification error.  
The error in coordinate prediction is described as follows:  
\[Error_{coord}= \tag{9}\] \[\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}\mathbf{ L}_{ij}^{obj}\big{[}(x_{i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y}_{i})^{2}\big{]}\] \[+\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B} \mathbf{L}_{ij}^{obj}\big{[}(\sqrt{w_{i}}-\sqrt{w_{i}})^{2}+\left(\sqrt{h_{i} }-\sqrt{\hat{h}_{i}}\right)^{2}\big{]}\]  
where \(s^{2}\) denotes the grid cell number of all scale. \(B\) represents the bounding-box number for every grid. \(L_{ij}^{obj}\) defines target of the \(i\)-th grid cell, which falls in the \(j\)-th bounding box. \((\hat{x}_{i},\hat{y}_{i},\hat{w}_{i},\hat{h}_{i})\) and \((x_{i},y_{i},w_{i},h_{i})\) represent the center coordinate, height, and width of the predicted box and the ground truth, respectively.  
The IOU error is described as follows:  
\[Error_{IOU}=\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B} \mathbf{L}_{ij}^{obj}\big{(}C_{i}-\hat{C}_{i}\big{)}^{2} \tag{10}\] \[+\lambda_{noobj}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B} \mathbf{L}_{ij}^{noobj}\big{(}C_{i}-\hat{C}_{i}\big{)}^{2}\]  
where \(\hat{C}_{i}\) and \(C_{i}\) define the predicted and true confidence, correspondingly.  
The classification error is defined as follows:  
\[Error_{cls}=\sum\limits_{i=0}^{s^{2}}\mathbf{L}_{i}^{obj}\sum\limits_{c\in classes}(p_{i}(c)-\hat{p}(c))^{2}. \tag{11}\]  
where \(\hat{p}_{i}(c)\) denotes the predicted value, while \(p_{i}(c)\) denotes the target's true probability.  
From the above, the final loss function is shown in Equation (12).  
\[Loss=Error_{coord}+Error_{IOU}+Error_{cls} \tag{12}\] \[=\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B} \mathbf{L}_{ij}^{obj}\big{[}(x_{i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y}_{i})^{2} \big{]}\] \[+\lambda_{coord}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B} \mathbf{L}_{ij}^{obj}\big{[}(\sqrt{w_{i}}-\sqrt{w_{i}})^{2}+\left(\sqrt{h_{i} }-\sqrt{h_{i}}\right)^{2}\big{]}\] \[+\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B}\mathbf{L}_{ij}^{ obj}\big{(}C_{i}-\hat{C}_{i}\big{)}^{2}\] \[+\lambda_{noobj}\sum\limits_{i=0}^{s^{2}}\sum\limits_{j=0}^{B} \mathbf{L}_{ij}^{noobj}\big{(}C_{i}-\hat{C}_{i}\big{)}^{2}\] \[+\sum\limits_{i=0}^{s^{2}}\mathbf{L}_{i}^{obj}\sum\limits_{c\in classes }(p_{i}(c)-\hat{p}(c))^{2}.\]
## 5 Perceptual Audio Features Extraction Techniques
### Frequency Domain Perceptual Features  
Frequency-based features can also be defined on the perceptual frequency domain. This type of features are based in some signal properties measured taking into account the human auditory perception. The main perceptual properties represented by this type of features include:  
* Modulation-based
* Brightness-related
* Tonality-related
* Loudness-related
* Roughness-related  
The following paragraphs describe these subcategories of frequency-based perceptual features.
# Impact of Fiber Type on Chloride Ingers in Concrete: A MacroXRF Imaging Analysis
## 2 Materials and Methods  
To investigate the durability performance of concrete incorporating glass and polypropylene fibers against chloride ion ingress, circular test specimens with dimensions of 10 \(\times\) 20 cm were produced and subjected to analysis.  
Following the mix design method of the American Concrete Institute (ACI) and the Portland Cement Institute (PCI) and considering the adaptation to Brazilian conditions by the Brazilian Association of Portland Cement (ABCP), conventional concrete was mixed.  
This study analyzed a reference concrete (without the use of any type of fibers), concrete reinforced with glass fibers--added in proportions of 0.25% and 0.50% of the total concrete volume--and concrete reinforced with polypropylene fibers--added in the same proportions mentioned previously.  
The concrete and fiber content were dosed by volume to ensure compatibility between the volumes occupied by the different materials in the mixes. Glass and polypropylene fibers, having a much lower specific mass than components like aggregates and cement, were dosed in volumetric fractions. ACI 544.1R-96 [25] mentions typical volume fractions of fibers incorporated into concrete, relating them to changes in concrete properties. [PERSON] [26] explains that when the fiber content exceeds 0.5% by volume, difficulties may arise in the densification process, potentially incorporating air in an undesirable manner. Therefore, based on the mix ratio, adjustments were made for fiber-reinforced concrete by adding fibers in volumetric fractions of 0.25% and 0.50% relative to the total concrete volume. These percentages are consistent with several authors who have used fibers of the same composition, with contents ranging from 0.25% to 2.00% [4; 27; 28; 29].
## 2 Theory of the SPH Wind and Sand Flow Modeling Approach
### Construction of SPH Control Equations for Wind-Sand Flow  
Wind-sand flow is a complex, nonlinear, self-organizing two-phase flow [14]. In the SPH approach, we simplify the wind-sand flow into the following features:  
1. In terms of fluid viscosity, the wind-sand flow is considered a Newtonian fluid.
2. In terms of flow characteristics, the wind-sand flow is regarded as incompressible flow.  
(3) In the near-surface layer flow, the gas flow that can drive the sand particles to jump and move is basically in a turbulent state [15].  
Wind-sand flow is the constant movement of sand particles entrained by air currents. Since the velocity variation of the wind field in the longitudinal direction in the near-surface plane is very small, the near-surface layer is simplified to a two-dimensional space in this study [2]. The equations governing the flow in the near-surface layer are mainly described as conservation of mass and momentum, to which the SPH particle approximation is applied, and the discretization yields [16]:  
Continuous density equation:  
\[\frac{\partial\rho_{i}}{\partial t}=\rho_{i}\sum\limits_{j=1}^{N}\frac{m_{j}} {\rho_{j}}u_{ij}^{\beta}\cdot\frac{\partial w_{ij}}{\partial x_{i}^{\beta}} \tag{8}\]  
where \(m_{j}\) is the mass of particle \(j\), \(\rho_{i}\) is the density of particle \(j\), \(\beta\) denotes the direction of the coordinate axis.  
Momentum equation:  
\[\frac{d\overrightarrow{v}_{i}^{\alpha}}{dt}=\frac{1}{\rho_{i}}\sum\limits_{j =1}^{N}\frac{m_{i}}{p_{j}}\sigma^{\alpha\beta}\frac{\partial W_{ij}}{ \partial\overrightarrow{x}_{i}^{\beta}} \tag{9}\]  
where \(\alpha\) denotes the direction of the coordinate axis.
## 2 Related Work
### Applications of Semantic Segmentation for Agriculture  
Overall, several studies have successfully applied semantic segmentation models to the agricultural data domain. One study applied a feature pyramid network to classify seven land types such as urban land, agricultural land, and water [29]. Other works include but are not limited to applying semantic segmentation models to detect weed plants or identifying cranberry fields [30; 31]. In our study, we propose a framework based on state-of-the-art semantic segmentation models including U-Net [19], SegNet [20], and DeepLab V3+ [32], to detect the cabbage fields at a pixel-level in the South Korea highlands. We conducted various experiments to compare model performances for identifying cabbage pixels under different spatiotemporal conditions unseen from the training dataset.
## 4 Performance Evaluation
### Ablation Study  
These experiments demonstrated the effectiveness of the sub-models and information fusion because our model defeated the sub-models in all evaluation metrics. Table 1 showsthe results of the ablation study, sub-models, and our model on varying evaluation metrics. Our model achieves an accuracy of 93.67%, precision of 93.71%, recall of 93.67%, \(F_{1}\)-score of 0.9366, and AUC of 0.994. Followed by the MobileViT-Plus, it also achieves decent performance, with an accuracy of 88.00%, a precision of 87.97%, a recall of 88.00%, an \(F_{1}\)-score of 0.8797, and an AUC of 0.963. The performance of ResNet101 in terms of its accuracy, precision, recall, and \(F_{1}\)-score is slightly above 0.81, and the AUC is 0.943. The results indicate that our hybrid framework combines the strengths of both sub-models and effectively improves the network's generalization performance. Figure 5a-c, respectively, show the training loss and test loss of each epoch and the ROC curves of the models. The curve of our model is closer to the ideal state of (0, 1), and its AUC is also the highest. MobileViT-Plus performs the second best. The test performance of ResNet101 is better than its training performance because dropout was used during training to prevent overfitting, whereas all neurons participated in the classification task during testing.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 3 Preliminaries
### Geometrical Invariances of MFrLFMs
#### 3.3.1 Rotational Invariance  
Suppose that \(f_{\mathbb{C}}(r,\theta)\) denotes the original image and \(f_{\mathbb{C}}^{\beta}(r,\ \theta)\) denotes the rotated image with a rotational angle \(\beta\) where  
\[f_{\mathbb{C}}^{\beta}(r,\ \theta)=f_{\mathbb{C}}(r,\theta-\beta) \tag{7}\]  
The MFrLFMs of \(f_{\mathbb{C}}^{\beta}(r,\ \theta)\) is computed as follows:  
\[\begin{split} MFrM\ _{pq}^{R}\left(f_{\mathbb{C}}^{\beta}\right)=\frac{2 p+1}{2\pi}\ \int_{0}^{2\pi}\int_{0}^{1}f_{c}(r,\theta)L_{p}(\alpha,r)e^{-iq\theta}\ \ e^{-iq\beta}\ \ rdrd\theta\\ =MFrM\ _{pq}(f_{c})e^{-iq\beta}\end{split} \tag{8}\]  
Sparingly, it can be written as  
\[MFrM\ _{pq}^{R}\left(f_{\mathbb{C}}^{\beta}\right)=e^{-iq\beta}MFrM\ _{pq}(f_{c}),\ C\ \in\{R,G,B\} \tag{9}\]  
Since, \(\left|e^{-iq\beta}\right|=1\); then  
\[\left|MFrM\ _{pq}^{R}\left(f_{c}^{\beta}\right)\right|=\left|\ MFrM\ _{pq}(f_{c})\right| \tag{10}\]  
Equation (10) proves the rotation invariance of MFrLFMs.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 6 Learning Analysis
### Binary Classification  
For the binary classification we compare the performance of _k-means_ and SVC, based on the following parameters:  
* Precision: it is the ratio of correctly predicted positive observations to the overall positive observations. It shows the probability that a predicted congestion event corresponds to an actual one, so it yields how trustworthy the positive prediction is.
* Recall (sensitivity): it is defined as the ratio of correctly predicted positive observations to all actual positive observations. This metric tells us the probability of detection of a congestion event.
* F1-score: it combines the two metrics above. It is typically used when both metrics, precision and recall have comparable weights, and it is defined as \(F_{1}=2\cdot\frac{\text{Prec}\times\text{Recall}}{\text{Prec}+\text{Recall}}\)  
In Figure 9 we represent the performance of both _k-means_ and SVC for a distance of 40 m between user and base station, and for different data rates. The _k-means_ performance in Figure9(a) yields interesting results. First, for low data rates the unsupervised algorithm provides a reasonably good performance. The precision is high, so that the probability of having false positives is rather low. In addition, we can observe that such precision decreases as we increase the data rate, which matches the conclusions of the correlation analysis discussed before. In addition, the results indicate that most of the congestion events are successfully detected, as can be inferred from the recall values. As can be seen, the recall metric shows a similar trend as that observed for the precision, so its value decreases for higher values of the application data rate.  
Figure 9(b) compares _k-means_ with the supervised learning approach, SVC. We can observe that both solutions yield similar performance for low rates. However, when the traffic rate is high, SVC clearly outperforms the unsupervised learning, as could have been expected. Although in some cases, we could have inferred that _k-means_ performs better than SVC under some circumstances, the _F1-score_ of the latter is always higher.  
Figure 9: Performance of binary classification for a distance of 40 m and the different data rates.
## 2 Local Path Generation Based on Spatial Perception  
### Dynamic Control Point Determination  
Local obstacle avoidance path planning for intelligently driving sightseeing vehicles presents a nuanced challenge. Encountering obstacles necessitates the vehicle integrating global path data with real-time environmental inputs [29]. Informed by these combined insights, the vehicle then ascertains control points for immediate obstacle circumvention, facilitating the refinement and calibration of the local trajectory. This systematic approach ensures stable and safe navigation around impediments [30].
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 2 Data and Methods
### Image Nonuniformity and Correction  
Due to the influence of some imaging conditions, such as illumination conditions, sensor performance, clouds, and other factors, some HY-1C/D satellite images in the Antarctic region exhibit nonuniformity. Figure 1 shows an example of the nonuniform luminance phenomenon on Antarctica, which causes great difficulties for the subsequent image mosaic and color consistency process. Therefore, it is necessary to correct the nonuniformity luminance of the single-field image first.  
At present, a commonly used algorithm to correct nonuniformity images is based on the MASK principle. The image luminance distribution can be better simulated using this algorithm. The luminance distribution is used as the background image, which is subtracted from the original image. Then, an artificial light source is added, and a relatively ideal uniform light result can be obtained [41]. However, when using this method, for areas with great contrast in the original image (such as the coastal zone), different background simulation filters or different stretching modes may cause inconsistent stretching effects between the darker (seawater) area and the brighter (ice sheet) area, resulting in obvious luminance differences between the coastline land area and the inland area. To eliminate this effect, the nonuniformity luminance correction method proposed by [PERSON] et al. [21] is adopted in this study. In this method, a Gaussian filter with an auxiliary mask is introduced to separate the nonuniform illumination information (feature map) from the image to effectively eliminate this effect. The auxiliary mask can be obtained by a simple threshold  
Figure 1: HY-1/CZI example image of Antarctica (nonuniformity luminance phenomenon).  
method, which mainly identifies the image as a light and dark target (e.g., glacier and seawater). Based on MASK theory, image nonuniformity luminance correction includes the following four steps: (1) luminance feature image extraction, (2) uneven luminance removal, (3) calculation of artificial light sources, and (4) information integration to generate a uniform light image.
## 4 Results and Analysis
### PSF Used for the Results  
Since the L-R iterative algorithm is a non-blind deconvolution method, the selection of the PSF is important. In our algorithm, the PSF is acquired by the simulation result in FEKO with each frequency being the same as the experiment and the size of the PSF and image are both 51 \(\times\) 101. In Figure 6, the PSF values of different methods at 3 GHz are shown: a is the PSF of the direct deconvolution algorithm; b is the polar transformation's PSF; c is the PSF of our method, whose axes are set with the same range as the acquisition image; d is the 9 sub-PSFs of the PSF set in the segmentation method which includes the sub-PSFs acquired at different locations. The axis represents the pixel number of each sub-block. which is usually 17 \(\times\) 33 or 17 \(\times\) 35.  
Figure 5: Flow chart of log-polar transformation and L-R algorithm super resolution.  
Figure 6: The 3 GHz PSFs of different methods: (**a**) the PSF of direct deconvolution algorithm; (**b**) the polar transformation’s PSF; (**c**) the PSF of our method; and (**d**) the PSF of the segmentation method.
## 3 System Architecture and Assumptions
### Network Model  
In this paper, a spatial model of a 1000 m \(\times\) 1000 m \(\times\) 1000 m underwater acoustic network is constructed based on the classical panda hunter model [26], as illustrated in Figure 1. Sensor nodes are randomly placed in the underwater environment for sensing and transmitting data packets. Each sensor node is equipped with a depth sensor. Assuming that the network time is synchronized, all nodes work in half-duplex mode, transmitting and receiving packets at different time periods [27]. In addition, each node possesses a unique ID number and the same transmission capacity, buffer space, and computational power. The sink node is a strong and stable node deployed in the middle of the water, which is the ultimate destination of all source packets. An attacker lurks near the sink node but is unable to attack objects within a one-hop range from the sink node due to the region's robust surveillance capability [28]. Once the target object is detected, the sensor node closest to the object is promptly activated to serve as the source node and reports the information about the monitored object to the sink node. It is assumed that the monitored objects are randomly displayed within the network and only one node acts as the source node at any given time. Any two sensor nodes in the network communicate in either one-hop or multi-hop mode. It is assumed that each node has N transmission frequencies, and each node can switch its frequency to send or receive packets.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
###### Abstract  
In wireless sensor networks (WSNs), the accuracy of location information is vital to support many interesting applications. Unfortunately, sensors have difficulty in estimating their location when malicious sensors attack the location estimation process. Even though secure localization schemes have been proposed to protect location estimation process from attacks, they are not enough to eliminate the wrong location estimations in some situations. The location verification can be the solution to the situations or be the second-line defense. The problem of most of the location verifications is the explicit involvement of many sensors in the verification process and requirements, such as special hardware, a dedicated verifier and the trusted third party, which causes more communication and computation overhead. In this paper, we propose an efficient location verification scheme for static WSN called mutually-shared region-based location verification (MSRLV), which reduces those overheads by utilizing the implicit involvement of sensors and eliminating several requirements. In order to achieve this, we use the mutually-shared region between location claimant and verifier for the location verification. The analysis shows that MSRLV reduces communication overhead by 77% and computation overhead by 92% on average, when compared with the other location verification schemes, in a single sensor verification. In addition, simulation results for the verification of the whole network show that MSRLV can detect the malicious sensors by over 90% when sensors in the network have five or more neighbors.  
sensor; location verification; static wireless sensor networks (WSNs) Article