esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 2 Related Work
[22] implemented modified ResU-Net to segment livers and their tumors. The modified system obtained 96.35% DSC and 89.28% accuracy 99.71% and 99.72% for liver and tumour segmentation. [PERSON] et al. [23] generated synthetic images utilizing a generative adversarial network (GAN). YOLOv3 detector and Resnet block localize the liver in the synthesized images. Moreover, DeeplabV3+ having Inceptionresnetv2 model as a base is implemented for better liver segmentation. [PERSON] et al. [24] implemented a novel network called residual attention-aware U-Net (RA-U-Net) to segment 3D images of liver 3 DIRCADb dataset. Moreover, [PERSON] et al. [25] implemented U-Net to segment liver and liver tumor and obtained a DSC value of 0.96 and 0.74. [PERSON] et al. [26] applied a semantic pixel-wise classification network called SegNet for tumor classification of the liver. The implemented network obtained an accuracy of 99.9% for liver tumor. The liver is an essential organ in the abdominal area, and overlapping the tumor region on the liver may cause trouble automatically segmenting the liver. Some surrounding tissues may also cause boundary problems for the automated liver segmentation task. The residual block in the segmentation model would provide efficient backpropagation gradient capability to train the deep learning model well and produce efficient segmentation results. However, due to the heterogeneity and variation in shapes of the liver and its potential tumors, segmenting CT images remains challenging, and many efforts have been made to address it.
## 2 Materials and Methods
### Calculation of POD
The parameters \(f_{\rm phen}\),\(f_{\rm light}\),\(f_{\rm min}\),\(f_{\rm temp}\),\(f_{\rm VPD}\),\(f_{\rm SWC}\) and \(f_{\rm O3 conc}\) were all expressed in relative terms (i.e., values between 0 and 1) as a proportion of \(g_{\rm max}\), where \(f_{\rm phen}\) represents the modification of \(g_{\rm max}\) owing to leaf phenological changes;\(f_{\rm light}\) represents the modification of \(g_{\rm max}\) by PPFD (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(f_{\rm min}\) represents the relative value of species-specific minimum \(g_{\rm sw}\) with respect to \(g_{\rm max}\);\(f_{\rm temp}\) represents the modification of \(g_{\rm max}\) by air temperature (T, \({}^{\circ}\)C);\(f_{\rm VPD}\) represents the modification of \(g_{\rm max}\) by VPD (kPa);\(f_{\rm SWC}\) represents the modification of \(g_{\rm max}\) owing to SWC (%); and \(f_{\rm O3 conc}\) represents the reduction in \(g_{\rm max}\) resulting from the acute effect of O\({}_{3}\) (ppb). We used the boundary line analysis to define the parameters of each/\(f\) in the relationships between the ratio of \(g_{\rm sw}\) to \(g_{\rm max}\) and environmental factors ([PERSON] et al., 2015). For details of each \(f\) and how to analyze the relationships using the boundary line analysis, please refer to [PERSON] et al. (2014)([PERSON] et al., 2015). Results of the parameterization of stomatal conductance models for the leaves of _F. crenata_, _Q. serrata_, _Q. mongolica_ var. _crispula_ and _B. platuphylla_ var. _japonica_ seedlingsare shown in Table S3. The POD\({}_{0}\) in the leaves of the tree species during both growing seasons are presented in Tables S2 and S4.
## 3 Result and Discussion
### Effects of pH and MWCNTs Concentration on NP Removal  
Figure 4a clearly shows the influence of pH on the NP removal. At alkaline pHs, NP adsorption decreased. The obtained results are in line with previously obtained ones that deprotonation of organic molecules (phenolic) took place and promoted diffusions [49]. Considering pH\({}_{\text{zpc}}\) of the modified MWCNTs (i.e., 2.3) and pK\({}_{\text{a}}\) of NP (i.e., 10.7; [39]), at neutral pHs (e.g., 4-10), the adsorption of NP onto the MWCNTs is favored due to the weak electrostatic interaction and H-bond formation between the negatively charged MWCNTs and NP. Figure 4b demonstrates the effect of the modified MWCNTs dosage (0.1 to 10 mg/L) on NP adsorption at pH 4. As the MWCNTs concentration increased, the NP adsorption increased to some extent possibly due to increased free binding sites. As shown in Figure 4b, the highest adsorption capacity of the modified MWCNTs for NP was estimated at 1040 mg/g at pH 4 and 25 \({}^{\circ}\)C. In any case, the adsorbent mass higher than 0.5 mg/L did not further improve the adsorption. Therefore, the modified MWCNTs of 0.5 mg/L was used in the following batch experiments.  
Figure 4: \(Cont.\)  
Figure 3: Zeta potential of modified MWCNTs for different pHs.
## 3 Results and Discussion
### Assessment of Circular Economy Strategies
#### 3.1.3 Design for Standardization  
Design for Standardization received a high-medium importance score (RII = 0.76) and ranked 10 th in feasibility with a score of RII = 0.54 which places it in the medium level of feasibility. Respondents recognize that standardization plays a critical role in simplifying construction processes, enabling easier recycling, and facilitating the reuse of building materials. A1 elaborated on the significance of standardization, emphasizing its ability to streamline design processes, enhance recycling efficiency, and drive economic profitability. The participant commented _\"Standardizing materials provides conditions for more efficient recycling, particularly regarding narrowing the loop. By eliminating toxic materials and selecting mono-materials or materials with clear compositions, we create appropriate conditions for recycling. At the component level, standardization simplifies the design process, while standardizing connections makes overall design management more efficient without requiring standardization of individual elements. I think standardization would also be moderately feasible because it can create financial profit. It can be viewed this way: it is profitable for some companies, not necessarily with the intention of helping the environment, but with the intention of being economically profitable. Therefore, it can be followed more easily than design for disassembly\"_. Additionally, C1 acknowledged the advantages of standardization, highlighting how compatibility simplifies the construction process and facilitates easier maintenance and upgrades throughout the building's lifecycle. They also pointed out that _\"While the current building code does not mandate standardisation, it promotes an environment that encourages the adoption of standardised components and construction methods\"_. CN2 believed that resistance from building materials manufacturers hinder the adoption of standardized systems. C1 added that _\"They're often hesitant to change their organisational structures or business routines because it requires significant investment and can disrupt their established processes. This hesitation slows the progress toward broader adoption of standardisation\"_.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 3 The Proposed Robust Attack Path Tracing Algorithm
### RAPT Algorithm Design
#### 3.3.2 At the Core Router \(R_{j}\)  
In this subsection, a discussion is conducted on how core routers execute the RAPT algorithm related to them. The following elaborates on the steps in Algorithm 2:  
```
1 foreach\(Received\ packet\ (\{pkt\}\)do
2 if\((pkt.src=R_{i})\wedge(pkt.dst\
eq rtr.adr)\)then
3 /* i.e., pkt is a TracePack */
4  Send ACK(TracePack) \(\Longrightarrow\)\(R_{i}\)
5  TracePack.src \(\gets\)\(R_{j}\).IP
6  Append IP(\(R_{j}\)) to payload
7  Receive(RandPack) \(\Longleftarrow\)\(R_{i}\)
8  Send ACK(RandPack) \(\Longleftarrow\)\(R_{i}\)
9  Send TracePack \(\Longleftarrow\)\(R_{k}\)
10  Send RandPack \(\Longleftarrow\)\(R_{k}\)
11  Receive ACK(RandPack) \(\Longleftarrow\)\(R_{k}\)
```  
**Algorithm 1**Pseudo code for the RAPT algorithm at the edge router.  
When the core router \(R_{j}\) receives a packet that requires an ACK, this indicates that the received packet could be a TracePack packet. Recall that lines 19 and 21 of Algorithm 1 demand a hop-by-hop TCP connections. To verify this, \(R_{j}\) would lookup its Routing Information Base (RIB) for TracePack's source IP address. If a match is found and it is one hop away, in addition, TracePack's destination IP address is not any router's IP address (i.e., the destination IP for TracePack is a host machine IP address). Given that these two conditions are satisfied, \(R_{j}\) would infer that the received packet is a tracing packet:  
\(R_{j}\) replaces TracePack's source IP address with its own IP address;3. \(R_{j}\) appends its IP address to the payload of TracePack;
4. \(R_{j}\) sends an ACK to \(R_{i}\) to acknowledge receiving TracePack;
5. The last four lines of Algorithm 2 are similar to the last four lines of Algorithm 1 that were already discussed.  
3.3.3. At the Victim V  
When TracePack reaches the victim, which is potentially under or after a DDoS attack, it can extract the attack path from the payload of TracePack, which contains the source IP address of the assumed attacker/daemon. It should be noted that the IP address might be spoofed and not necessarily leading to the daemon host itself; however, this helps the victim to decide on dropping connection requests from that IP address. Figure 3 depicts the structure of the payload in the TracePack packet. The extraction of the payload is performed as follows:  
1. 32 bits for the attacker's source IP address;
2. 16 bits for the identification field of RandPack; and
3. The rest of the payload represents the IP addresses of routers in the path, each of which is 32 bits.  
The victim can take an action of dropping SYN packets coming from this source or multiple sources in the case of the DDoS attack.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 1 Introduction
First, the DDL module is integrated into the network's backbone to extract directional features of targets from initial feature maps and convert them into angles. Then, the rotating convolutional kernels adjust their rotation angles based on the output of this module, enabling them to accurately capture the target's directional information. Following this, BC-ROPN uses the boundary center features of the oriented bounding rectangle, along with the rotation angles and angle weights obtained from the DDL module, to generate boundary center offsets. These offsets direct the regression of the rotating anchor boxes within the confines of the oriented bounding rectangle. This approach results in a smoother convergence of the loss function and yields more nuanced and precise outcomes, effectively circumventing the issue of abrupt fluctuations in the loss function values. The novel contributions we present are outlined below. 1. We carried out a systematic analysis of the issue that the feature-level and sample-level alignment in existing RS target detection networks makes it difficult to match the spatial orientation of targets. 2. We introduced a rotating feature extractor that uses the early extraction of target directional features to pre-set the rotation direction of dynamic directional convolution kernels. By assigning an angle weight to each pre-set angle based on these features, we enhance the effectiveness of the convolution kernels during rotation. This method reduces redundant extraction of directional features compared to fixed pre-set rotation methods, conserves resources, and improves the efficacy of extracting directional features. 3. We proposed an anchor box convergence approach that utilizes the early extracted directional feature information to generate boundary center offsets for oriented bounding rectangle anchor boxes. This offset regression method mitigates the drastic changes in the loss function caused by angle regression and ensures that anchor boxes converge more tightly and accurately.
## 4 Experiment  
To verify the feasibility and the advantages of the new method, especially in terms of overcoming the data processing error and preventing high-order harmonic component distortion under the high lateral resolution conditions, an experimental device was constructed. As shown in Figure 7, the measuring device, which was constructed according to Figure 1, consisted of four pneumatic displacement sensors (Solartron DP/10/P) with the measurement range of \(10\text{ mm}\pm 0.05\text{ }\upmu\text{m}\) and was driven by a motorized linear stage. The spacing between \(P_{1}\) and \(P_{2}\) was \(16\pm 0.015\text{ mm}\) and \(14\pm 0.015\text{ mm}\) for \(P_{3}\) and \(P_{4}\). The measured objects were a pair of grinding marble rulers. Two feeler gauges, with thicknesses of \(0.2\pm 0.005\text{ mm}\) and \(0.4\pm 0.008\text{ mm}\), were stuck to marble rulers to simulate stepwise variations. The step distance of scanning was \(2\text{ mm}\pm 6\text{ }\upmu\text{m}\), and, when the measuring device stepped 39 times, probes collected measurement data for 40 times and the reconstruction length was 64 mm.  
The measured profiles were reconstructed using the measurement data obtained by the same scanning and different data processing methods. As the main purpose of the experiment was to  
Figure 6: Reconstruction result of the new method: (**a**) reconstruction profiles; (**b**) reconstruction errors; and (**c**) reconstruction profiles after depriving linear trends and translations.  
Figure 7: Experimental device.  
compare the ability to overcome the data processing error of the new method and the existing method, the comparison of the reconstruction results on stepwise variation parts can reflect the advantage of the new method in this respect. Using the measurement data of \(P_{1}\), \(P_{2}\), and \(P_{3}\), the reconstruction profiles calculated from Equations (12)-(14) are shown as \(f_{1}\) and \(g_{1}\) in Figure 8, while \(f_{2}\) and \(g_{2}\) in Figure 9 are reconstruction profiles obtained from the new method using measurement data of \(P_{1}\)-\(P_{4}\). It is obvious that the stepwise variations are smoothed in \(f_{1}\) and \(g_{1}\) because of the data processing error and the new method can reflect the characteristics of measured profiles better. That is to say, the new method has a better performance when the exact reconstruction is required, especially when there are stepwise variations in measured profiles.
## 2 Related Work
### Machine Learning and Anomaly Detection  
The integration of machine learning techniques has addressed several of the limitations of traditional approaches, particularly in anomaly detection. Supervised learning methods, including Support Vector Machines (SVM) and Random Forests, have shown promise in detecting network intrusions [22]. Notable work by [23] introduced an adaptive one-class SVM framework for industrial control systems, combining anomaly- and signature-based detection for comprehensive threat coverage. However, these approaches often face challenges with high false-positive rates and struggle with scalability issues in operational environments [24]. The need for extensive feature engineering and difficulty in handling complex, multi-stage attacks further limits their effectiveness.
## 6 Analysis of Results
### Problem 1  
The minimum cost SNDP that satisfies precision and estimability constraints for a set of key variable estimates is formulated as follows  
\[\begin{array}{ll}Min&\mathbf{c^{T}q}\\ s.t.&\\ &\hat{\sigma}_{j}(\mathbf{q})\leq\sigma_{j}^{*}&\forall\ j\in S_{\sigma}\\ &E_{k}(\mathbf{q})\geq 1&\forall\ k\in S_{E}\\ &\mathbf{q}\in\{0,1\}_{\sigma}^{n}\end{array} \tag{12}\]  
where \(\mathbf{c^{T}}\) is the acquisition cost vector; \(\hat{\sigma}_{j}\) is the standard deviation of the _j_-th variable estimate after a data reconciliation procedure is applied and \(E_{k}\) stands for the degree of estimability of variable \(k\). For this formulation \(E_{k}\) is set equal to one, consequently only a variable classification procedure run is needed to check its feasibility. Furthermore \(S_{\sigma}\) and \(S_{E}\) are the set of key process variables with requirements in precision and estimability respectively.  
For Problem 1 five case studies are solved that are presented in order of increasing complexity.  
a) Cases 1 to 4  
The selection of flowmeters for an industrial steam metering network is performed. The process consists of 11 units interconnected by 28 streams. It is assumed that there is no restriction for the location of sensors on any stream, therefore the search space is made up of 2\({}^{28}\) solutions. Data of cost and standard deviation for measurement errors are obtained elsewhere [1]. This flowsheet is frequently used to test new methodologies in the area of process monitoring.  
Different case studies are run increasing the number of streams subject to estimability and precision constraints. In this sense, the set of key variables for Cases 1 and 2 is constituted by only three flowrates, but they are associated with distant streams for the second case. The number of key variables is increased to 6 and 10 for Cases 3 and 4 respectively. In Table 1 this information is summarized.  
All cases were previously addressed using the HGA procedure. Furthermore Cases 1, 3 and 4 were presented in [PERSON] et al. [15-16] where it was reported that the technique SO-TS outperforms both HGA and C-TS.  
b) Case 5  
The flowmeter network design for a simplified ethylene plant is conducted. The process involves 82 streams and 47 units. It is assumed that the standard deviation of flowmeters is 2.5% of the corresponding true flowrates [15]. The search space of this problem is made up of 282 solutions, therefore it is large in comparison with other cases presented in the literature. In Table 1 the constraints of the optimization problem are included.  
Case 5 was previously solved using the method SO-TS, which also showed better performance than C-TS and HGA to address this design problem.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 7 Tradeoff Between Privacy and Utility
### Experimental Setup  
To evaluate the effect of interference defence on Wi-Fi bandwidth, we introduce a server-client pair to the experimental environment. Utilising the same setup illustrated in Figure 12, we position a Transmitter (Tx) in the channel, which propagates ping packets at a rate of 1000 Hz to a CSI measurement device. Meanwhile, an interference device is positioned adjacent to Tx. The independent variable is the rate of interference packet propagation, which we vary between 0 Hz and 1000 Hz. Concurrently, the server-client pair on the same Wi-Fi channel (2.4 GHz, Channel 2) transfers a 200 MB file over the wireless link. The dependent variable is the time taken for this file to transfer from the client to the server, which we subsequently convert to a bandwidth in Mbps.
## 3 Approach for Reliable Data Collection in IoT-Based Smart Farming
### Motivation  
Let us consider a Smart Coffee Farm as a scenario involving various IoT devices, such as network-connected weather stations and wireless sensors. These IoT devices collect significant and heterogeneous data about the environment and the coffee crop. These data are the primary input for IoT-based applications like data analysis to predict coffee production, traceability to track the coffee source, alarm to inform about variables out of normal ranges, and irrigation systems [30; 31]. Since a coffee farmer makes decisions based on the information that applications provide, data supporting them must be reliable. For instance, if the weather variables' monitoring system fails due to information loss during the data collection, the coffee production estimation may be inaccurate. This problem may affect farmers' annual schedules, avoiding a suitable organization of resources, storage space, and recruitment. In addition, if a coffee quality variable gets outside the range of the standard parameters, such as pH levels at the fermentation phase, the coffee may not achieve exportation quality. This issue may cause the farmer to lose much money (approximately USD 1000 per hectare). Therefore, the data collection must be reliable, aiming to support the smart coffee farm proper operation.  
Considering the above scenario, we argue that the Things-Fog-Cloud continuum requires new ways to ensure reliable delivery of data retrieved by IoT-based devices for guaranteeing the proper operation of applications located at the Fog and Cloud tiers. In this sense, the approach proposed in this paper is more full-fledged than the ones presented in Section 2 because it includes a reliability-oriented and Fog-based architecture that incorporates mechanisms for detecting outliers and inferring data to recovering from them in the data collection process.
## 3 Methodology
### Optimization of Hyperparameters  
The choice of GNG-based hyperparameters is very important for its performance. In this paper, the Bayesian optimization method is adopted to select optimal hyperparameters for the proposed BOA-GNG model. Bayesian optimization, which mainly consists of the probabilistic surrogate model and acquisition function, is an efficient global optimization algorithm [28; 29]. Its objective function is designed as follows.  
\[f_{p}(x)=\frac{1}{N_{v}}\sum_{i=1}^{N_{v}}x\in p^{c}-\left\|x-\omega_{x}\right\| \tag{5}\]  
where \(p\) is the set of parameters to be optimized, \(v\) is the validation set, \(x\) is the instance from validation set, \(N_{v}\) is the total number of validation set, and \(\omega_{x}\) is the neuron nearest to \(x\). We hope to find the optimal parameter \(p\), which minimizes the average distance from \(x\) to the existing neurons and maximizes the value of the objective function at the same time. Taking the objective function as the optimization goal, the process of Bayesian optimization can be described below.  
1. Initialization: Initialize the search scope of parameter set \(p\). Start by randomly selecting a small set of initial sampling points from the search space. Evaluate the objective function to obtain initial observations.  
2. Build a Probabilistic Surrogate Model: Based on the initial observations, construct a probabilistic surrogate model (defined as a Gaussian process in this paper) that approximates the objective function. This model captures both the mean prediction and the uncertainty of the objective function.  
3. Optimize the Acquisition Function: Define an acquisition function (probability of improvement is selected as the acquisition function in this paper) that quantifies the utility of sampling at a given point based on the current surrogate model. Optimize this acquisition function to find the next point to sample.  
4. Sample the Next Point: Evaluate the objective function at the point identified by optimizing the acquisition function. This new observation is used to update the surrogate model.  
5. Iterate: Repeat steps 3 and 4, continuously updating the surrogate model and optimizing the acquisition function until a stopping criterion is met (e.g., a maximum number of iterations is reached or the objective function value improves below a certain threshold).  
6. Return the Best Solution: At the end of the optimization process, return the point in the search space that corresponds to the best objective function value observed.  
By balancing exploration and exploitation, it can efficiently navigate the search space to find high-quality solutions.