esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
In this section, we compare the training strategy proposed in this paper with cross-entropy Loss and the most advanced AM-softmax Loss, PEDCC Loss on three widely used data sets with many categories to show the advantages of our method. We also cover several different network structures which prevalently used recently to demonstrate the robustness of our method. All experiments are built on CNN tools framework: PyTorch [14]. The computing server is equipped with a single Nvidia GTX 1080 Ti GPU. In the training phase of all experiments, we set the batch size to 128, and adopt SGD as the optimization algorithm. The momentum is set to 0.9, and the weight decay is 0.0005. The initial learning rate is set to 0.1, which will be multiplied by 0.1 at the 30 th, 60 th, and 90 th epoch. The maximum number of epochs is 100.  
In order to prove the effectiveness of our method, we choose the most widely used framework VGG and ResNet as the basic network structure. Three data sets, namely CIFAR100, TinyImagenet and Facescrub, which require high generalization performance were evaluated. CIFAR100 has 100 classes, image resolution is \(32\times 32\), and there are 500 training images and 100 test images for each class. TinyImagenet has 200 classes, image resolution is \(64\times 64\), and the number of training and test images for each class is 500 and 50, respectively. In the Facescrub dataset, we eliminated categories with less than 70 samples. For each category, 10% of the images are randomly selected as the test set and the image are resized to \(64\times 64\). Finally, we have 361 categories, each one has 90 images to train and 10 images to test. The final results are shown in Tables 1-3, respectively. All experimental results show that our method has better classification accuracy than classic Softmax loss, AM-Softmax, and PEDCC-loss. Here we set two sets hyperparameter \(s=7.5\), \(m=0.35\), and \(s=10\), \(m=0.5\), which is following the original PEDCC-loss [9]. The hyperparameter \(s\) is used to let the network be trainable. As the features and every column of weight are normalized, the output range is only [-1, 1], which is not suitable for Softmax loss [15]. Another hyperparameter \(m\) represents the expected cosine margin between each class after training [8]. For each run, we repeat 3 times and report the \"mean(\(\pm\)std)\" values.  
Figure 3: Modulus comparison of the first ten classes of linear layer corresponding to weights.
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 3 Gain Localized in Space
### Linear Gain  
Consider a linear gain function localized in space but independent of time as described by a Gaussian function in \(x\), i.e.,  
\[\gamma(x,t)=\frac{\gamma_{1}}{\epsilon\sqrt{\pi}}\exp\left(-\frac{x^{2}}{ \epsilon^{2}}\right) \tag{4}\]  
where \(\epsilon\) represents the 'width' of the Gaussian function and the parameter \(\gamma_{1}\) measures the maximum for a fixed \(\epsilon\). This gain will not lead to indefinite growth as it is balanced by the background loss \(\beta<0\).  
This localized gain can compensate for the loss in the bulk of the medium and shorten the delay in reaching the maximum due to the background dissipation. For the numerical example as shown  
Figure 2: (**a**) Effect of cubic background gain at \(\sigma=1\), \(\beta=-0.1\), \(\gamma\equiv 0\) and \(\alpha_{0}=0.01\). (**b**) Modulation instability gain spectrum at \(\sigma=1\), \(\beta=-0.01\), \(\gamma\equiv 0\) and \(\alpha_{0}=0.01\) (red dashed line); \(\alpha_{0}=0.002\) (blue solid line). (**c**) Rogue wave generated from the plane wave by a baseband disturbance at \(\sigma=1\), \(\beta=-0.01\), \(\gamma\equiv 0\), \(\alpha_{0}=0.01\), \(K=0.1\), \(b_{0}=0.05\). (**d**) For a system with a larger baseband increment, rogue-wave-like objects appear sooner. Parameters are the same as (**c**) except \(\alpha_{0}=0.002\).  
in Figure 3, this maximum is attained at \(t\approx 0.4\) (Figure 3a), which is sooner than the time necessary (at \(t\approx 0.8\) in Figure 1a) for the case without the Gaussian gain. Obviously, a greater maximum can be attained as compared to the case with background loss only (Figure 1a). With a fixed total gain but a greater maximum net gain, i.e., an amplification function corresponding to a smaller \(\varepsilon\), the peak of the rogue wave increases.
## 5 Conclusions  
The difficulties of segmentation are effectively mitigated by the research method described in this paper. The proposed MFPN structure aims at the different performance of edge features in different channels. By controlling scale and fusing multi-level feature maps we can obtain feature fusion modules with rich semantic features, thus, solving the problem of weak edge features of TRUS images. A method of accurately positioning glandular tissue in a TRUS image and generating a segmentation target region is used, effectively avoiding the problem of interference of the artifact region on the prostate segmentation. Therefore, the algorithm proposed in this research can be effectively applied to clinical medicine.  
Conceptualization, L.G.; validation, L.G. and S.L.; investigation, Z.X.; resources, F.Z.; writing--original draft preparation, S.L.; writing--review and editing, L.G.; supervision, F.Z.; project administration, F.Z. All authors have read and agreed to the published version of the manuscript. This study was supported by the Program for Innovative Research Team in University of Tianjin (grand No.TD13-5034) and the Tianjin Science and Technology Major Projects and Engineering under grant No.17 ZXSCSY00090, and the Tianjin Natural Science Foundation under grant No.18 JCYBJC15300. The authors declare no conflict of interest.
## 2 Localization in IIoTs
### Communication Technologies for Indoor Positioning System
* **Ultra-WideBand, (UWB):** UWB technology has gained popularity in precision indoor positioning systems due to its advantages over narrowband-based technologies, such as Bluetooth and Wi-Fi. Some factors of UWB include a very large bandwidth, very high data rate, short signal transmission length, low transmission energy, and high penetration capability [62; 63; 64; 65]. These characteristics are also very important for high precision indoor localization accuracy. Currently, UWB technology has already received significantly attention in industry, as many companies have started to adopt it for precise tracking and navigation systems. For example, the iPhone-13 from Apple contains UWB for precise location tracking, and the Samsung Galaxy Note 20 Ultra uses UWB as a digital key for doors and cars. The structure of a UWB signal is based on the IEEE 802.15.4_a_-2011 standard, which involves the signal transmission of extremely short pulses within a very large bandwidth, specifically from 3.1 to 10.6 GHz [65; 66; 67], rather than broadcasting on separate frequencies. Due to its large bandwidth and short pulses, UWB systems are highly precise and secure, and are less susceptible to multipath interference and fading. * **Evaluation Metrics of different technologies:** Evaluation metrics can explain the parameters which affect the performance of a technology. The metrics of different wireless indoor positioning technologies are summarized in Figure 2. The technologies are compared in terms of accuracy, energy efficiency, range coverage, and cost. The maximum metric achievable by a technology is 9. From the figure, it can be observed that UWB is highly accurate as compared to BLE, Wi-Fi, RFID and ZigBEE. However, the lowest power consumed is by BLE followed by RFID, ZigBEE, UWB, and Wi-Fi. Finally, it can be concluded that there is a trade-off when selecting an appropriate technology, and depending upon the application, the most suitable technology should be chosen.
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 4 Results and Evaluation
### Quantitative Analysis  
In the quantitative analysis, four distinct datasets with different distance distribution of data objects are used. The **wdbc** is a breast cancer dataset obtained from digitized images of breast masses [38]. In this dataset, most data objects are similar between themselves with a few dissimilar ones. The **twonorm** and **simplex** are artificial datasets generated using the _mlbench_ package [39]. The first one is composed of multidimensional points from two Gaussian distributions with a unit covariance matrix. The second one consists of \(m\)-dimensional spherical Gaussian points with a predefined standard deviation and mean at the corners of a \(m\)-dimensional simplex. There is a balanced distribution between similar and dissimilar objects in both cases, composing well-separated groups of data objects. The **text** dataset is a vector space model representation of scientific papers from four distinct areas [40]. Most data objects are dissimilar with few similar objects in this dataset, a feature normally found on high-dimensional sparse spaces.
# SIW Cavity-Backed Antenna Array Based on Double Slots for mmWave Communications
## 4 Conclusions  
In this paper, we presented the design, manufacturing, and measurement of a cavity-backed antenna array using substrate integrated waveguide (SIW) technology at millimeter-wave frequency bands. The design is based on double slots as radiating elements, which provide better control in the design of the operating frequency bands, fed by SIW cavities. A study of the impact of introducing a single and a double slot in a cavity-backed antenna was presented. As a proof of concept, a 2 \(\times\) 2 array of cavity-backed antennas was designed, manufactured, and measured, where each cavity-backed antenna contained 2 \(\times\) 2 double slots, which means a final 4 \(\times\) 4 antenna array configuration. The experimental operating frequency band of the proposed antenna array ranges from 35.4 to 37 GHz, and the measured gain is around 17 dBi in the whole operating frequency band. We obtained proper agreement between the simulated and measured results. The radiation outcomes, radiation pattern, and gain values make this antenna suitable for directive communication systems in millimeter-wave bands.  
[PERSON], [PERSON] and [PERSON] designed the antenna; [PERSON], [PERSON] and [PERSON] assembled the prototype and characterized it in the laboratory; [PERSON], [PERSON] and [PERSON] wrote the manuscript; [PERSON] and [PERSON] supervised the work. All the authors participated in revising the article. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Spanish Program of Research, Development and Innovation under Project TIN2016-75097-P, Project RTI2018-102002-A-I00, and Project EQC2018-004988-P; in part by Junta de Andalucia under Project B-TIC-402-UGR18, and Project P18.RT.4830; and in part by the predoctoral grant FPU18/01965.  
Not applicable.  
**Data Availability Statement:** All the data are available upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Setup
#### 4.1.7 SNNL Temperature Selection  
To determine an appropriate value for the SNNL temperature parameter \(T\), we performed a sensitivity analysis using a validation-based approach. Specifically, we split the original NSL-KDD training set into 90% for training and 10% for validation. For each value \(T\in\{0.01,0.1,0.3,0.5,1\}\), we generated synthetic samples using WGAN+SNNL and trained both Random Forest and XGBoost classifiers. The performance was evaluated on the held-out validation set, and each configuration was repeated three times to ensure robustness. The average results are summarized in Table 1.  
As seen in Table 1, all temperature settings yielded high classification accuracy, but \(T=0.1\) provided the best overall trade-off across multiple metrics. Notably, XGBoost achieved the highest macro-F1 score (0.97), the lowest false positive rate (0.03%), and the highest detection rate (99.97%) at \(T=0.1\). Random Forest results remained stable across different temperatures, suggesting robustness. Based on these observations, we fix \(T=0.1\) in all subsequent experiments across all datasets.
# A Metric for Secrecy-Energy Efficiency Tradeoff Evaluation in 3 GPP Cellular Networks
## 2 System Model  
### Cellular Network Model  
The cellular network model that is considered in this paper is compliant with the Evolved UMTS Terrestrial Radio Access (E-UTRA) Radio Frequency System Scenarios as defined in [18]; therefore, it resorts to the same frequency bands specified for UTRA: particularly, the simulation frequencies are assumed to be at 2000 MHz. Moreover, the macro cell propagation model in urban area is taken into account, i.e., the BS antenna gain (including feeder loss) and the BS antenna height are assumed, respectively, equal to 15 dBi and 30 m, whereas the propagation loss \(L\) is equal to \(L=128+37.6 log(R)\), where \(R\) is the distance between the BS and the User Equipment (UE).  
A single operator layout is assumed. Base stations with three sectors per site are placed on a hexagonal grid with distance of \(3\cdot R\), where \(R\) is the cell radius. The sector antennas and the transmit power are assumed to be equal. The number of sites is equal to seven.  
The BS antenna radiation pattern to be used for each sector in three-sector cell sites is also identical to those defined in [18]:\[A(\theta)=-min\left[12\left(\frac{\theta}{\theta_{3 dB}}\right)^{2},A_{m}\right], \tag{1}\]  
where \(-180\leq\theta\leq 180\), \(\theta_{3 dB}\) is the 3 dB beam width which corresponds to 65 degrees and \(A_{m}=20\) dB is the maximum attenuation.  
The BS power consumption model is the same proposed in [15]: particularly, the power consumption at maximum load has been defined as  
\[P_{in}=N_{TRX}\cdot\frac{\frac{p}{\eta_{PA}\cdot\left(1-\sigma_{rad}\right)}+P _{RF}+P_{BB}}{\left(1-\sigma_{DC}\right)\left(1-\sigma_{MS}\right)\left(1- \sigma_{cool}\right)}, \tag{2}\]  
where \(N_{TRX}\) indicates the number of transceiver chains per site, \(P\) is the power level which is radiated by the Antenna of each sector, \(P_{RF}\) and \(P_{BB}\) are the power consumption of the power amplifier and of the baseband block, \(\eta_{PA}\) is the amplifier efficiency and the terms \(\sigma_{fead}\), \(\sigma_{DC}\), \(\sigma_{MS}\) and \(\sigma_{cool}\) account for the loss of the feeder, the converter, the main supply and the cooling, respectively.
## 5 Stability Analysis of TSWs
### Variation in Critical Thickness with TSW Structural Parameters and Lateral Load Conditions  
In practice, ore body thickness varies along both strike and dip, necessitating design adjustments. To ensure TSW stability while maximizing ore recovery, the relationship between critical TSW thickness and its structural parameters was evaluated using the proposed theoretical model. The results are shown in Figure 10.  
As seen in Figure 10, when SH > SL, critical thickness is primarily influenced by SH and increases with height. When SL > SH, it is influenced by SL and decreases with increasing length.  
Further analysis was conducted on the effect of lateral load parameters, holding other conditions constant. For a standard TSW of 50 m \(\times\) 50 m, critical thicknesses were computed for various LP and LW values. The results are shown in Figure 11.  
Figure 11 indicates that critical thickness increases with both LP and LW. When the LP is small, the effect of LW on MPS and thus on thickness is minor. As the LP increases, the stress concentration becomes more pronounced, leading to a more rapid increase in critical thickness.  
Figure 10: Contour image of the critical thickness of the TSW as the structural parameters.
###### Abstract  
With the development of the mobile Internet, location-based services are playing an important role in everyday life. As a new location-based service, Spatial Crowdsourcing (SC) involves collecting and analyzing environmental, social, and other spatiotemporal information of individuals, increasing convenience for users. In SC, users (called requesters) publish tasks and other users (called workers) are required to physically travel to specified locations to perform the tasks. However, with SC services, the workers have to disclose their locations to untrusted third parties, such as the Spatial Crowdsourcing Server (SC-server), which could pose a considerable threat to the privacy of workers. In this paper, we propose a new location privacy protection framework based on local difference privacy for spatial crowdsourcing, which does not require the participation of trusted third parties by adding noises locally to workers' locations. The noisy locations of workers are submitted to the SC-server rather than the real locations. Therefore, the protection of workers' locations is achieved. Experiments showed that this framework not only preserves the privacy of workers in SC, but also has modest overhead performance.  
spatial crowdsourcing; location privacy; local differential privacy +
Footnote †: journal:  
0000-0001-4500-3390/00/10/00003
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 5 Modification Processes of Kaolin: Enhancing Industrial Applicability
### Coating Modification  
Coating modification is an effective technique for enhancing the properties of kaolinite by preventing powder agglomeration and improving fluidity and dispersion characteristics [6]. This process involves applying a coating layer to kaolinite particles, which significantly influences their performance and structural integrity [32; 77; 78].  
One of the primary advantages of coating modification is its ability to inhibit powder agglomeration [79]. Uncoated kaolinite particles tend to clump together due to van der Waals forces and other intermolecular interactions, which can lead to reduced processability and performance. By applying a coating, these interactions are minimized as the coating creates a protective layer around the particles. This protective layer effectively prevents clumping, allowing the particles to remain dispersed and resulting in a more homogeneous powder. Consequently, the coated kaolinite demonstrates improved flow properties and is better suited for various applications.  
In addition to addressing agglomeration, the application of a coating layer enhances the fluidity of kaolinite powders [80]. Improved fluidity is crucial for several processes, including mixing, handling, and application in industries in which uniform powder distribution is essential. The coating modification improves the dispersion properties of kaolinite, ensuring that the particles are distributed more evenly when used in composite materials or as fillers. This uniform distribution contributes to the consistency and quality of the final product, making it more effective in diverse formulations.  
Beyond improving fluidity and dispersion, coating modification plays a vital role in reinforcing the structural integrity of kaolinite [81]. The coating layer enhances the mechanical strength of the powder by enveloping the kaolinite particles, which helps to maintain their structural cohesion under mechanical stress or during processing. This reinforcement improves the stability and performance of the kaolinite in various applications, ensuring that the powder remains effective even under challenging conditions.  
The success of coating modification depends on several factors, with reaction conditions and the specific surface area of the kaolinite particles being particularly critical. Reaction conditions, such as temperature, pH, and coating material concentration, significantly impact the quality and uniformity of the coating layer. Additionally, the specific surface area of the kaolinite particles influences how well the coating adheres and covers the particles. Generally, larger surface areas allow for better coating coverage and more effective modification.
###### Abstract  
In this paper, a 4 amplitude shift keying (4-ASK) fast orthogonal frequency division multiplexing (FOFDM) scheme was experimentally investigated over a turbulent air-water channel for optical wireless communications. The experiment results showed that the 4-ASK-FOFDM modulated signals were not sensitive to weak atmospheric turbulence, and the bit-error rate (BER) was lower than the 7% forward error correction (FEC) limit of \(3.8\times 10^{-3}\). Under the condition of the same spectra efficiency, the 4-ASK-FOFDM scheme just had a tiny performance penalty compared to the 16-QAM-OFDM scheme. Consequently, the 4-ASK-FOFDM scheme is a promising alternative to the conventional 16-QAM-OFDM scheme in optical wireless communications.  
4-ASK; fast-OFDM; turbulent air-water channel; OFDM +
Footnote †: journal: _Photonics_
## 5 Discussion
### Scalability Analysis for Large-Scale Deployment  
Client-side. Unlike general FL, FRSs have distinct requirements. For each client, a user model must be constructed, and this user model does not participate in the federated aggregation. Each client is obligated to partake in the federated training at least a few times to build its own user model. Clients cannot remain entirely idle while anticipating obtaining a user model trained by other clients. As a result, on the client side, the local computational overhead of all clients in FRSs is inevitable. As the number of clients increases, new clients directly utilize the computational resources of local devices for training. Put differently, the computational overhead is dispersed by the distributed architecture. In FedRecI2C, for clients with communication constraints, the I2C period parameter \(p(p>1)\) provides them with more time to fetch model parameters from the server and perform local updates. The I2C client ratio parameter \(r\) allows for the existence of clients that are entirely unable to communicate. These further demonstrate the scalability of FedRecI2C.  
Server-side. With the increase in the number of clients and items, the server may encounter challenges in meeting the communication bandwidth requirements for both downloading and uploading, as well as the computational resources necessary for parameter aggregation. In such circumstances, FedRecI2C opts to select a subset of clients for FL and omits the model parameter upload step for I2 CG and I2 CL clients. This approach can effectively alleviate the burden on the server's communication and computational resources.