esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 4 Experiments and Results
### Comparisons on Multi-Organ Segmentation Dataset  
We demonstrate the experiments on an abdomen multi-organ MRI-T2 SPIR dataset, and the corresponding organs include the liver, right kidney, left kidney, and spleen. Table 2 depicts the comparative performance of multiple methods with noise ratios of 25%, 50%, and 75% and the noise level of 1-5 pixels. The noisy labels are generated in the same way as the liver segmentation. We report the average dice value (%) of these four organs. The upper bound with the average dice value of 74.48 is acquired by training the 3D segmentation model on a clean-labeled dataset. Other results are obtained by training the model on a dataset with corrupted labels. As illustrated, our proposed hybrid architecture outperforms other methods, which demonstrates the effectiveness of our hybrid noise-robust architecture.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Noise Level 1**} & \multicolumn{3}{c}{**Noise Level 2**} \\ \cline{2-7}  & **25\%** & **50\%** & **75\%** & **25\%** & **50\%** & **75\%** \\ \hline Plain [35] & 70.94 \(\pm\) 0.96 & 68.97 \(\pm\) 0.43 & 65.24 \(\pm\) 1.32 & 59.61 \(\pm\) 0.41 & 56.82 \(\pm\) 0.19 & 48.07 \(\pm\) 2.38 \\ Pick-and-learn [14] & 65.67 \(\pm\) 0.44 & 59.36 \(\pm\) 0.73 & 54.10 \(\pm\) 0.86 & 50.91 \(\pm\) 0.59 & 47.31 \(\pm\) 0.26 & 40.32 \(\pm\) 0.45 \\ Disagreement [33] & 71.43 \(\pm\) 1.10 & 69.88 \(\pm\) 0.24 & 67.58 \(\pm\) 0.28 & 66.64 \(\pm\) 0.07 & 55.72 \(\pm\) 0.19 & 47.23 \(\pm\) 0.25 \\ INT [45] & 77.34 \(\pm\) 0.14 & 75.33 \(\pm\) 0.08 & 71.67 \(\pm\) 0.06 & 70.38 \(\pm\) 0.81 & 60.80 \(\pm\) 0.64 & 53.56 \(\pm\) 0.67 \\ Area-aware [44] & 76.62 \(\pm\) 1.75 & 74.92 \(\pm\) 1.27 & 69.45 \(\pm\) 1.96 & 70.63 \(\pm\) 1.32 & 60.44 \(\pm\) 1.98 & 54.82 \(\pm\) 1.56 \\ Ours & 78.31 \(\pm\) 0.46 & 76.29 \(\pm\) 0.63 & 72.78 \(\pm\) 0.60 & 71.72 \(\pm\) 0.18 & 64.05 \(\pm\) 0.30 & 56.99 \(\pm\) 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results on the CT dataset for liver segmentation under different noise settings. We report the mean value (\(\pm\)std) of the dice score (%) with 3 runs.
# Improving User Experience with Recommender Systems by
## 4 Analysis and Results
What Is the Effect of Message Design (Characteristics) on a User's Attitudes and Behavioral Intentions toward the RS and Its Recommendations? Information transparency positively impacted recommendation confidence (H15: b = 1.0743, \(p\) < 0.0001), which was also affected by RS usefulness (H20: b = 0.9111, \(p\) < 0.0001) and in turn positively influenced intention to accept the recommendation (H21: b = 1.715, \(p\) < 0.0001); in addition, this intention was also affected by RS confidence (H24: b = 1.5625, \(p\) < 0.0001). The ease of use of the system positively impacted both system usefulness (H16: b = 0.993, \(p\) < 0.0001) and system satisfaction (H17: b = 1.2254, \(p\) < 0.0001). Usefulness positively impacted system satisfaction (H18: b = 1.7252, \(p\) < 0.0001) and system confidence (H19: b = 1.5487, \(p\) < 0.0001). Recommendation confidence had a positive effect on system confidence (H22: b = 1.8567, \(p\) < 0.0001), which positively impacted system trust (H23: b = 1.402, \(p\) < 0.0001) and finally system satisfaction (H25: b = 1.7122, \(p\) < 0.0001). System satisfaction positively impacted the intention to use the recommender system (H26: b = 1.5809, \(p\) < 0.0001). Lastly, all system-level mediating constructs demonstrated a good explanation of the variance in their respective DVs, including 45.2% for recommendation acceptance intention and 49.2% for system use intention, as shown in the validated model (see Figure 5a and Table 3). Figure 5: (**a**) Validated research model (self-reported data); (**b**) Validated research model (behavioral data).
## Materials and Methods
### Chemical Characterization
#### 2.3.3 HPLC-DAD/FLD Profile  
The phenolic profile of all the samples was characterized by HPLC analysis. The separation was carried out according to the procedure described by [PERSON] et al. [16], on an ODS column (Eurosphere II, C\({}_{18}\) stationary phase, 250 \(\times\) 4.6 mm \(\times\) 5 \(\upmu\)m, Knauer, LabService Analytica, Anzola dell'Emilia, Bologna, Italy) installed on a Nexera X2 UHPLC system (Shimadzu, Milano, Italy) equipped with a UV-Vis diode array detector (DAD, sampling rate 12.5 Hz, time constant = 0.320 s, scan range = 200-800 nm, 1.2 nm slit width) and fluorescence detector (FLD, 10 Hz sampling rate, \(\lambda_{\text{exc}}\) = 276 nm, \(\lambda_{\text{em}}\) = 316 nm, with 1\(\times\) gain) in series. The HPLC mobile phase was formed by solvent A (0.1% formic acid in degassed millIQ water) and solvent B (0.1% formic acid in acetonitrile). The gradient method was: 0-2.5 min 1% B, 2.5-50 min 1-25% B, 50-51 min 25-99% B, 51-55 min 99% B, 55-56 min 99-1% B, 56-60 min 1% B. The HPLC flow rate was 0.7 mL-min\({}^{-1}\). The HPLC peaks were reported as integrated areas vs. retention times using the automatic integration provided by the software (LabSolutions, Shimadzu). The peaks alignment was performed manually. A series of standard compounds were also injected to obtain reference retention times and external calibrations with DAD and FLD (see Table S1 in Supplementary Materials). Peaks of (+)-catechin and (-)-epicatechin were used to correct the 0.3 min retention time shift between DAD and FLD.
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 3 Methodology
### Models  
**Support Vector Machine (SVM)**  
_Parameters_: The model was trained with class_weight = 'balanced' to address class imbalance and probability=True to enable probability-based predictions. Linear kernel was utilized.  
_Model Training_: An SVM classifier was trained using the linear kernel to optimize the decision boundary for spam vs. ham classification.  
**Logistic Regression**  
_Parameters_: Logistic Regression was trained with max_iter = 1000 to ensure convergence, particularly due to the complexity of the dataset.  
_Model Training_: The model was trained to classify spam and ham images, applying a linear decision boundary.  
**XGBoost**  
_Parameters_: XGBoost was trained with max_depth = 6 to balance training time and model complexity.  
_Model Training_: XGBoost, a gradient-boosting model, was trained to classify spam and ham images using ensemble learning to improve accuracy.  
Figure 6: Grayscale conversion of spam images.  
**LightGBM**  
_Parameters_: LightGBM was trained with boosting_type = 'gbdt', learning_rate = 0.05, and max_depth = 7. The model utilized 100 estimators and was trained over 20 epochs with a batch size of 32 to balance training efficiency and model accuracy.  
_Model Training_: LightGBM, a gradient boosting model, was trained to classify spam and ham images. It used an ensemble of decision trees to improve classification accuracy through boosting.  
**VGG16**  
_Parameters_: VGG16 was fine-tuned with a pre-trained ImageNet base. The convolutional layers were frozen, and more fully connected layers were added to classify spam and ham images. Training was carried out with a batch size of 32 and 10 epochs.  
_Model Training_: VGG16, a CNN, was used for classifying spam and ham images. The pre-trained weights from ImageNet allowed for effective extraction of features, and the model was fine-tuned with additional dense layers for classification. As VGG16 is optimized for RGB images of size 224 \(\times\) 224, this was utilized for training the model.  
**ResNet50**  
_Parameters_: ResNet50 was initialized with pre-trained ImageNet weights, the base model layers were frozen to retain learned features, and a custom head was added, which consisted of a GlobalAveragePooling2D layer, followed by a dense layer with 128 ReLU units.  
_Model Training_: ResNet50, a deep convolutional neural network, is highly utilized for transfer learning and classification tasks. It can extract high-level image features and is trained for the binary classification task of distinguishing between spam and ham images. ResNet50 is also optimized for 224 \(\times\) 224 RGB images and these images were used for training the model.
## 5 Data Processing and Definition of the EAR Index
### Definition of the Performance Coefficients
#### 5.2.3 Noise Coefficient  
The last parameter related to the characteristics of the mixtures that have been considered is related to the acoustic emissions. It is known from the literature that acoustic performances are strictly related to surface texture, temperature, size, and shape of the aggregates. Moreover, noise represents an environmental parameter that, in the opinion of the authors, is worth taking into account as it has a relevant impact on people's health and quality of life. In order to evaluate the performance of surface mixtures from this point of view as well, it has been decided to adopt the model proposed by [PERSON] et al. in 2013 [36] that links acoustic emissions, represented by the close proximity rolling noise index \(CP\)\(\underline{\text{L}}\)X to the sieve diameter associated with the 95 th percentile of the passerby \(D_{95}\), the fractal dimension \(D_{\text{f}}\), and characteristics in terms of percentage of voids in the asphalt mixture \(V_{\text{A}}\) and percentage of voids in the mineral aggregates _VMA_:  
\[CP\underline{\text{L}}=a_{1}+a_{2}\log\frac{S}{S_{0}}+\bigg{[}a_{3}+a_{4}\log \frac{S}{S_{0}}\bigg{]}\frac{D_{95}}{D_{\text{f}}}+\bigg{[}a_{5}+a_{6}\log \frac{S}{S_{0}}\bigg{]}\frac{V_{\text{A}}}{V\underline{\text{M}}A} \tag{17}\]  
\(S\) is the reference speed assumed equal to 130 km/h and \(S_{0}\) is the reference speed equal to 40 km/h. More specifically, the term associated with the ratio \(\
icefrac{{D_{95}}}{{D_{\text{f}}}}\) represents the granulometric properties of the mixture while the one associated with the ratio \(\
icefrac{{V_{\text{A}}}}{{V\underline{\text{M}}A}}\) represents the properties of the compacted asphalt mixtures and in particular, the contribution of the voids to the reduction of noise.  
Finally, the index related to noise emissions associated with the EAR index is evaluated as:  
\[I_{3}=\frac{CP\underline{\text{L}}}{CP\underline{\text{X}}L_{\text{ref}}} \tag{18}\]  
where \(CP\underline{\text{X}}L_{\text{ref}}\) is the reference limit value assumed equal to 103.5 dBA. The \(I_{3}\) index is applied to all surface mixtures while it is set equal to 1 for all base, binder, and base-binder mixtures.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 4 Simulation Results and Analysis  
### Experiment Environment and Parameters  
The simulation was conducted using PyTorch as the backend on a GTX 1080 Ti platform. To assess the classification performance and model complexity of the lightweight CVResNet, we used the open-source dataset RadioML 2016.10A [58]. Five modulated scheme data were selected from this dataset, with a modulation scheme model pool of \(\mathbb{P}=BPSK,QPSK,8 PSK,QAM16,QAM64\) and an SNR of 2 dB. The dataset contained 1000 samples for each modulated type per SNR. For training, validating, and testing, we used 420, 180, and 400 samples, respectively. During the neural network training process, the best model on the validation set was saved as the final classification model. The simulation parameters are shown in detail in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Parameters** & **Values** \\ \hline Data dimension & \(2\times 128\) \\ Number of \(D\) & 3000 \\ Number of \(D_{val}\) & 2000 \\ Optimizer & Adam \\ Loss & Cross Entropy Loss \\ Epoch & 100 \\ Batch-size & 512 \\ Learning rate & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## The Proposed Protocol
### SRPL-RP Implementation  
Based on the creation of the rank attack and version number attack, a timestamp is attached to the DIO control messages. The timestamp is used to monitor and track the time of exchange of the DIO control messages. The time difference between DIO messages should be within a threshold value that is registered as a timestamp and transmitted with the DIO message. If the time of the DIO message is above the threshold value, the DIO message will be discarded because it is indicated as a malicious activity. In addition, if the time of the DIO message is less than the threshold value, the legitimacy of the sender node will be verified by the receiver node for more security by checking the ID of the sender nodes against the values in the monitoring table that was created during the establishment of the RPL DODAG by the root node. If it is valid, the node will be added to the monitoring table. After that, if the node version number in the DIO message is greater than the default version number in the root node, the rank attack will be checked. The rank value of the rank must be checked according to the comparison strategy.
## 4 Three-Dimensional Numerical Simulation of Hydraulic Fracturing
### Simulation Results and Analysis  
The following conclusions were drawn from the simulation results:  
1. The cracking location of the hydraulic fracturing in the multi-layered coal seam was always in the soft coal layer. When the depth was 300 m, the initiation pressure of the multi-layered coal seam was 12.6 MPa. When the depth reached 800 m, the initiation pressure was 27.5 MPa, which marks an increase of 2.2 times. Figure 11 shows the variation of the initiation pressure of the multi-layered coal seam under different depths and geo-stress states. As can be seen, the initiation pressure of the multi-layered coal seam linearly increased with the increase of the coal seam depth. As shown in Figure 11, the theoretical calculation results are in good agreement with the numerical simulation results.
2. The hydraulic fracturing cracks occurred along the X direction (minimum principal stress direction) in the soft coal layer, and propagated along the Z direction (maximum principal stress direction), without penetrating the hard coal layer. The direction of hydraulic fracture propagation in multi-layer coal seams is consistent with that of single coal seam. The half width of the fracture was approximately 0.02 m at different coal seam depths, when the water injection time was 20 s. When the water injection time reached 300 s, the half width of the fracture in the multi-layered coal seam with a simulated depth of 300 m was 0.033 m, and the half width of the fracture in the multi-layered coal seam with a simulated depth of 800 m was 0.063 m (Figure 12). As the burial depth and three-dimensional geo-stress increased, the width of the hydraulic fractures in the soft layer of the multi-layered coal seam significantly increased. The research results are similar with the rules shown in Reference [35].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Unit** & **Hard Coal Layer** & **Soft Coal Layer** \\ \hline Elastic modulus & GPa & 2.2 & 0.4 \\ \hline Poisson’s ratio & & 0.39 & 0.15 \\ \hline Geo-stress \(\sigma\) x/\(\sigma\) y/\(\sigma\) z(300 m) & & 5/8/10 & 5/8/10 \\ Geo-stress \(\sigma\) x/\(\sigma\) y/\(\sigma\) z(400 m) & & 7/11/13 & 7/11/13 \\ Geo-stress \(\sigma\) x/\(\sigma\) y/\(\sigma\) z(500 m) & MPa & 8/13/15 & 8/13/15 \\ Geo-stress \(\sigma\) x/\(\sigma\) y/\(\sigma\) z(600 m) & & 10/16/18 & 10/16/18 \\ Geo-stress \(\sigma\) x/\(\sigma\) y/\(\sigma\) z(700 m) & & 13/19/21 & 13/19/21 \\ Geo-stress \(\sigma\) x/\(\sigma\) y/\(\sigma\) z(800 m) & & 15/21/23 & 15/21/23 \\ \hline Permeability coefficient & m/s & \(1\times 10^{-7}\) & \(1\times 10^{-7}\) \\ \hline Fluid leak off & & \(1\times 10^{-14}\) & \(1\times 10^{-14}\) \\ \hline Pore ratio & & 0.2 & 0.2 \\ \hline Fluid gravity & N/m\({}^{3}\) & 9800 & 9800 \\ \hline Saturation & & 1.0 & 1.0 \\ \hline
2D flow & m\({}^{2}\)/s & 0.01 & 0.01 \\ \hline Liquid viscosity & Pas & 0.001 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model parameters.
## 3 Results and Discussions
### Photovoltaic Studies  
The photovoltaic performance of the fabricated DSSC is shown in Figure 7. Photocurrent density of Ba/TiO\({}_{2}\) was higher than that of the TiO\({}_{2}\) photoanode. This increment was due to the increased photon absorption of dye molecule as compared to the TiO\({}_{2}\) counterpart. The Ba/TiO\({}_{2}\) photoanode had higher surface area than the TiO\({}_{2}\) photoanode, which led to enhanced dye-loading capacity. These results were also confirmed by the XRD and UV analysis. Another important factor was that the increment in photocurrent density was supported by the recombination rate or dark current reduction. The Ba/TiO\({}_{2}\) photoanode had the high recombination resistance compared to the TiO\({}_{2}\) photoanode, which reduce dark current production between the MOS and electrolyte.  
Figure 6: EIS analysis of synthesized (**a**) TiO\({}_{2}\), (**b**) Ba/TiO\({}_{2}\) photoanode and along with the equivalent circuit of corresponding EIS.  
Figure 7: Photovoltaic response of synthesized TiO\({}_{2}\) and Ba/TiO\({}_{2}\) photoanodes.  
The Ba/TiO\({}_{2}\) photoanode yielded higher PCE (3.4%) compared to the TiO\({}_{2}\) photoanode (2.1%). The calculated photovoltaic parameters are summarized in Table 3. The higher PCE of the Ba/TiO\({}_{2}\) photoanode (3.4%) corresponds to a maximum output power density (P\({}_{\rm max}\)) of approximately 34.4 \(\mu W\)/cm\({}^{2}\) as compared to the TiO\({}_{2}\) photoanode's P\({}_{\rm max}\) of 26.5 \(\mu W\)/cm\({}^{2}\). These findings suggest that DSSCs can be produced with the appropriate power rating to operate electronic devices such as tablets and Internet-of-Things gadgets that require low power P\({}_{\rm max}\) < 100 \(\mu W\)/cm\({}^{2}\)[56].
## 2 Materials and Methods
### Methods  
#### 2.2.1 Chemical Flocculation Experiments  
Flocculation assays were performed in batch mode using Jar test equipment equipped with six stirrers and a fluorescent lamp to facilitate floc formation observation. In all experiments, 800 mL beakers containing 500 mL of microalgal culture were utilized. Preliminary assays were conducted to ascertain the optimal pH for flocculation. Flocculants were incrementally added to the 500 mL microalgal culture at pH levels of 5, 6, 7, 8, and 9 until primary flocs formed. Following each addition, the solutions were stirred at 150 rpm for 3 min (RM) and 20 rpm for 15 min (SM). Visual observation assessed floc formation. The assays, performed in duplicate, proceeded with different flocculant dosages ranging from 10 to 200 mg\(\cdot\)L\({}^{-1}\) at the determined optimal pH for each flocculant. The experiments included the RM and SM stages, followed by a 15 min sedimentation period. Harvesting efficiency (n) was calculated using Equation (2), with absorbance measured at 680 nm. This wavelength was selected through wavelength scanning, indicating maximum absorbance at this value.  
ODi corresponds to the optical density of the microalgal culture before any flocculant addition, and ODf is the optical density after the settling period. Samples for ODf determination were taken from the supernatant approximately 2 cm below the surface. Absorbance was measured using a UV-vis spectrophotometer (UV-6300 PC double beam, VWR).  
\[\eta(\%)=\frac{\text{ODi}-\text{ODf}}{\text{ODi}}\times 100 \tag{2}\]
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
###### Abstract
A fundamental tool for carrying out cache attacks is an _eviction set_: a set of addresses that fall into a specific cache set. When an attacker cannot reliably build an eviction set, many cache attacks are not feasible. Therefore, this study analyzed the behavior of state-of-the-art eviction set construction algorithms with randomized L1 caches. For this, we simulated a noise-free cache hierarchy to avoid any kind of unwanted noise (e.g., buses, MMU or other applications running concurrently). This environment modelled an \"ideal\" attacker with much stronger capabilities than a real-world attacker. Our experiments showed that L1 address randomization prevented some, but not all, eviction set construction algorithms. This means that randomization would still be required on higher-level caches in order to mitigate all known algorithms. Nevertheless, we showed that L1 randomization combined with a lightweight randomization approach in the last level would protect against any eviction set construction algorithms; namely, we designed a random eviction scheme to be used in the last-level cache and demonstrated its effectiveness against eviction set construction algorithms. To summarize, in this paper, we present the following contributions:  
* We performed a systematic behavioral study of algorithms for constructing eviction sets in a noise-free environment. Using this approach, we showed that the Prime-Prune-Probe algorithm is not efficient when it uses a large number of addresses to find an eviction set;
* We proposed to dynamically randomize the first-level cache mapping. The experimental results showed that dynamic randomization in the L1 cache effectively mitigates some (but not all) eviction set construction algorithms;
* To protect the last-level cache, we proposed to randomly evict the last-level cache sets. We showed that our solution reduces the success rate of the Prime-Prune-Probe algorithm to below 0.1%. The rest of this paper is organized as follows. In Section 2, we present the technical background. Then, in Section 3, we introduce our threat model and the hypothesis regarding the attacker's capabilities. In Section 4, we provide a description of eviction construction algorithms. In Section 5, we present our main security analysis, including the complexity of building an eviction set in a noise-free environment with unprotected caches. We show how we used our simulation framework to analyze the side effects of these algorithms and to evaluate the security of lower-level randomized caches in Section 6. In Section 7, we first consider a random eviction cache as a countermeasure for the last-level cache and we then discuss security parameters used to mitigate the construction of eviction sets through so-called random eviction. Finally, we conclude with some extensions and comments for future research.