esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
### Privacy Attacks via Wireless Sensing  
CSI-based sensing also introduces privacy risks. Prior work demonstrates that wireless signals can be exploited to infer sensitive user behaviour. [PERSON] et al. [10] revealed password inference using public WiFi CSI. WiKI-Eve [26] used beamforming feedback for keystroke monitoring, and WiPass [28] employed CNNs for smartphone keypad recognition. Systems like WiKey [11] and Wi-Crack [27] show that small signal perturbations can leak keystroke information, even in cluttered environments. These techniques achieve high accuracy in ideal settings but degrade with real-world variability. Our study contributes by showing a significant drop in keystroke sensing accuracy under realistic two-hand typing, revealing a natural privacy safeguard and informing more responsible sensing system design.
## 1 Introduction
This study proposes an innovative high-order receptive field network (HoRFNet) focused on the efficient classification of BCHI. The core design concept of HoRFNet lies in integrating multi-branch receptive field expansion with high-order statistical modeling streams, thereby overcoming the limitations of traditional CNNs in spatial dependency modeling and feature representation. HoRFNet introduces multi-branch convolutions and dilated convolutional layers to replace the square-kernel convolutional layers in traditional backbone networks. This design breaks the limitations of fixed receptive fields in traditional CNNs by employing a multi-scale perception strategy, capturing local details while integrating global contextual information, thereby achieving a more precise understanding of complex breast cancer tumor morphologies. Moreover, the introduction of dilated convolutions effectively avoids information loss caused by downsampling, preserves high-resolution feature representation, and significantly enhances the network's discriminative power and detail depiction capabilities. To capture the global interactions between convolutional features, HoRFNet introduces covariance pooling based on matrix power normalization (MPN) after the final convolutional layer. This design overcomes the limitations of traditional CNNs that rely solely on first-order feature aggregation, by explicitly modeling high-order statistical information to more accurately represent the structural characteristics of pathological tissues. This enables histopathological images to obtain more informative global feature representations. Figure 1 depicts the overall structure of HoRFNet, and the three areas listed below sum up as the key contributions of this study. To categorize breast cancer histopathological image, a high-order receptive field network (HoRFNet) from end to end is proposed. To the best of our knowledge, this is the first instance in which the medical image classification problem simultaneously incorporates second-order statistical characteristics and receptive field block (RFB). * HoRFNet replaces the third layer with a multi-branch convolution layer using various convolution kernels, followed by an expansion convolution layer. This successfully enhances receptive fields and the discriminality of the deep model. To further enhance performance, covariance statistics of deep features computed using matrix power normalization (MPN) are used to collect more discriminant information from breast cancer histopathological image. * HoRFNet demonstrates superior performance on the open-source BreakHis dataset, achieving 99.50% and 99.23% classification accuracy at the image level and patient level, respectively, and surpassing existing approaches.
# Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System
###### Abstract  
A frequency hopping orthogonal frequency division multiple access (FH-OFDMA) can provide low probability of detection (LPD) and anti-jamming capabilities to users against adversary detectors. To obtain an extreme LPD capability that cannot be provided by the basic symbol-by-symbol (SBS)-based FH pattern, we proposed two FH patterns, namely chaotic standard map (CSM) and cat map for FH-OFDMA systems. In our previous work, through analysis of complexity to regenerate the transmitted symbol sequence, at the point of adversary detectors, we found that the CSM had a lower probability of intercept than the cat map and SBS. It is possible when a detector already knows symbol and frame structures, and the detector has been synchronized to the FH-OFDMA system. Unlike the previous work, here, we analyze whether the CSM provides greater LPD capability than the cat map and SBS by detection probability using spectrum sensing technique. We analyze the detection probability of the CSM and provide detection probabilities of the cat map and SBS compared to the CSM. Based on our analysis of the detection probability and numerical results, it is evident that the CSM provides greater LPD capability than both the cat map and SBS-based FH-OFDMA systems.  
FH-OFDMA; frequency hopping; LPD; chaotic standard map +
Footnote †: journal: Applied  
0000-0001-6998-3300][PERSON]
# Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study
## 8 Conclusions  
Two scenarios are introduced to build the industrial IoT environment. The first scenario employs the MODBUS TCP only for synchronous polling communications; this solution complies with most industrial control systems and SCADA-like applications. However, if asynchronous event-based communications are required, MQTT complements MODBUS TCP operations. In this particular case, an industrial IoT environment requires the employment of at least two protocols--one for the IoT functions, mainly for M2M communications, and another for the industrial functions. MODBUS fulfills the industrial requirements, mainly the telecontrol, monitoring, and automation functions. MQTT works in parallel with the MODBUS TCP and complements its functions, but cannot replace MODBUS.  
Figure 13: **(a)** MODBUS message formatted via UTF-8 in the MQTT payload to solve the interoperability problem in the second scenario; **(b)** message as it appeared on hivermq.com consumer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Character** & **UTF-8 (Decimal)** & **UTF-8 (Hex)** & **Meaning** \\ \hline
0 & 48 & 0x30 & Digit Zero \\
1 & 49 & 0x31 & Digit One \\
2 & 50 & 0x32 & Digit Two \\
3 & 51 & 0x33 & Digit Three \\
4 & 52 & 0x34 & Digit Four \\
5 & 53 & 0x35 & Digit Five \\
6 & 54 & 0x36 & Digit Six \\
7 & 55 & 0x37 & Digit Seven \\
8 & 56 & 0x38 & Digit Eight \\
9 & 57 & 0x39 & Digit Nine \\ A & 65 & 0x41 & Capital Letter A \\ B & 66 & 0x42 & Capital Letter B \\ C & 67 & 0x43 & Capital Letter C \\ D & 68 & 0x44 & Capital Letter D \\ E & 69 & 0x45 & Capital Letter E \\ F & 70 & 0x46 & Capital Letter F \\ \hline \hline \end{tabular}
\end{table}
Table 3: UTF-8 encoding characters.  
Since MQTT lacks interoperability, the industrial data are formatted using the structure of MODBUS messages, and then transferred in the payload of the MQTT publish message, which uses the format of the UTF-8. Thus, the message structure of the MODBUS TCP is maintained throughout the entire environment. This solution provides interoperability, but doubles the payload of the industrial data.  
The simulation results and measurements, presented in Sections 6 and 7, show that the concurrent execution of MQTT in parallel with the MODBUS TCP within the same platform does not severely influence the performance of the MODBUS TCP.  
Security is a fundamental issue in IoT, which was solved in [32] for MODBUS TCP using TLS via TCP port 802. MQTT may also employ TLS for encryption via port 8883. More information on the security of industrial IoT systems can be found in [53; 54; 55], on intrusion detection in [56; 57; 58], on encryption in [59], and on black-hole detection in [60].  
The author declares no conflict of interest.
## 5 Simulation Results and Discussion
### Mitigation and Recovery Strategies for Cyber-Physical IoT Systems  
Cyber-physical IoT systems need DDoS detection, mitigation, and recovery to ensure important service dependability and availability. Once a DDoS assault is discovered, the system must quickly identify rogue hosts that create enormous anomalous traffic. Dynamically updating router and gateway Access Control Lists (ACLs) isolates these hosts to filter malicious transmissions. Rate-limiting rules may also reduce system impact by reducing packet flow from certain hosts. Sinkholing--rerouting malicious traffic to a server that isolates and analyses attack traffic without disrupting core network services--is another important approach. Dynamic resource allocation and load balancing help cyber-physical IoT devices resist DDoS assaults. Load balancers reduce localized congestion and maintain important services by dispersing incoming traffic across different network nodes. Resource reallocation solutions prioritize bandwidth, CPU, and memory for critical operations, including real-time safety monitoring, healthcare, and industrial automation.  
Another important measure for system operation under an assault is adaptive priority scheduling. This preserves system integrity by prioritizing life-safety duties above non-essential services. By using threat intelligence and working with ISPs to prevent malicious traffic at upstream network points, pinpointing the attack source may improve mitigation efforts. IP traceback and BGP flow specification help detect and stop attack origins. The system restores regular operations using a defined recovery timetable after the assault. This involves redistributing resources to pre-attack levels, checking data integrity to verify no tampering happened, and examining system logs to uncover vulnerabilities and improve detection. Decentralized traffic monitoring helps cyber-physical IoT networks identify suspicious behavior among nodes early, improving long-term robustness. Machine-learning-based self-healing techniques may automate network reconfiguration and respond to changing threats.  
These mitigation and recovery mechanisms and the suggested federated-learning architecture protect cyber-physical IoT systems comprehensively. They improve real-time DDoS attack detection and essential infrastructure operation under unfavourable circumstances, making IoT-driven ecosystems more resilient and reliable.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model Variant** & **Accuracy (\%)** & **AUC (\%)** & **F1-Score (\%)** & **MS1 (\%)** & **Inference Time (ms)** \\ \hline ResNet-only & 92.5 & 93.2 & 91.8 & 88.9 & 3.2 \\ \hline ResNet + VGGNet & 95.4 & 95.9 & 94.6 & 92.5 & 3.6 \\ \hline ResNet + Swin-Transformer & 96.1 & 97.0 & 96.0 & 94.2 & 3.8 \\ \hline Full ResVGG-SwinNet & 99.0 & 99.3 & 98.5 & 97.5 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Component-wise performance comparison
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 3 Preliminaries of Hypergraph Matching
The initial embeddings at k = 0 are scalar, i.e., \(l_{k}=1\), taken from the diagonal of \(\mathbf{K}\). \begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Definition** \\ \hline \(\mathcal{G}\) & Graph/Hypergraph \\ \(\mathcal{V}\) & The set of vertices of \(\mathcal{G}\) \\ \(\epsilon\) & The set of hyperedges of \(\mathcal{G}\) \\ \(N\) & The number of vertices on \(\mathcal{G}\). \\ \(M\) & The number of hyperedges on \(\mathcal{G}\). \\ \(\mathbf{A}\) & The adjacency matrix \(\mathbf{A}=\overline{\mathbf{P}\mathbf{Q}}^{\text{T}}\), where \(\overline{\mathbf{P}},\overline{\mathbf{Q}}\) are the connectivity matrices. \\ \(\mathbf{D}_{\epsilon}\) & The diagonal matrix of vertex degrees. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations and definitions. By outputting a doubly stochastic matrix through the Sinkhorn network, the node embeddings generated by GCNs are transformed into representations that satisfy matching constraints. By introducing matching constraints in the embedding process, the matching-aware embedding not only utilizes the topological structure information of the nodes but also more directly learns node representations that meet the matching conditions, thereby enhancing the performance of the graph matching task. The hypergraph is a generalization of the graph. A hypergraph, \(\mathcal{G}=(\mathcal{V},\varepsilon)\), encompasses a collection of vertices and a set of hyperedges, where \(\mathcal{V}\) denotes the set of vertices, with the number of vertices in \(\mathcal{V}\) represented as \(N\). Each vertex possesses certain semantic information. \(\varepsilon\) represents the set of hyperedges. Unlike edges in a simple graph that can only connect two vertices, each hyperedge can connect any number of vertices, and the number of hyperedges in \(\varepsilon\) is denoted as \(M\). The above QAP models can also be generalized to the higher-order case. One line of work adopted a tensor-marginalization-based model for \(p\)-order (\(p\geq 3\)) hypergraph matching, resulting in a higher-order assignment problem [51].
# Removal of Sodium Dodecylbenzenesulfonate by Macroporous Adsorbent Resins
## 3 Results
### Adsorption Isotherm  
To elucidate the adsorption behavior of SDBS on MARs, Langmuir and Freundlich isotherm models were investigated [28; 29]. Langmuir isotherm model is for a type of chemical process, in which the target molecules are adsorbed as a monolayer onto the surface of adsorbents. Langmuir isotherm equation is described below:  
Figure 8: Removal efficiency of Cs ions with SDBS by HP20 and HP2 MGL from SDBS/Cs at various initial concentrations of SDBS. [adsorbents] = 5 mg/mL; adsorption time 7 h.  
Figure 6: Changes in SDBS adsorption efficiency of (**a**) HP20 and (**b**) HP2 MGL in SDBS/Cs with adsorption time at various concentrations of adsorbents. [SDBS] = 0.1 mg/mL.  
Figure 7: Effect of initial SDBS concentration for the removal of SDBS by (**a**) HP20 and (**b**) HP2 MGL from SDBS/Cs. [adsorbents] = 5 mg/mL; adsorption time = 7 h.  
\[\frac{C_{\rm e}}{q_{\rm e}}=\frac{1}{Kq_{\rm max}}+\frac{C_{\rm e}}{q_{\rm max}} \tag{2}\]  
where \(C_{\rm e}\) (mg/mL) and \(q_{\rm e}\) (mg/g) are the equilibrium concentration of SDBS and the adsorption capacity at equilibrium, respectively. \(q_{\rm max}\) and \(K\) are the maximum adsorption capacity (mg/g) and the equilibrium adsorption constant (mL/mg), respectively, calculated by the fitting curve.  
[PERSON] isotherm model suggests that the multilayer-adsorption process of target molecules on the adsorbent surface. The Freundlich isotherm equation is represented by Equation (3):  
\[log\;q_{\rm e}=log\;K_{F}+\frac{1}{n}log\;C_{\rm e} \tag{3}\]  
where \(C_{\rm e}\) (mg/mL) and \(q_{\rm e}\) (mg/g) are the equilibrium concentration of SDBS and the adsorption capacity at equilibrium. \(K_{F}\) and \(1/n\) are the adsorption constants of Freundlich isotherm model that mean the adsorption capacity (for \(K_{F}\)) and the adsorption intensity (for \(1/n\)).  
The adsorption behavior of SDBS on MARs and the adsorption capacities of MARs were explored using Langmuir and Freundlich isotherm models. Figure 9 shows the fitting curves for both isotherm models, and the obtained values are tabulated in Table 2. The correlation value (\(R^{2}\)) shows that the Freundlich model is good for explanation of adsorption with HP20, whereas, in the case of HP2 MGL, the \(R^{2}\) showed that Langmuir model is suitable. The adsorption capacity of SDBS was calculated to be 57.9 (for HP20) and 15.5 mg/g (for HP2 MGL), indicating that HP20 had better adsorption capability for SDBS than HP2 MGL. Moreover, such an adsorption capacity of HP20 for removal of SDBS is higher than that used with other materials, such as activated carbon (29.4 mg/g), alumina (19.8 mg/g), and zeolite (30.7 mg/g) [12; 30; 31; 32].  
Figure 9: [PERSON] (**a**,**c**) and Freundlich isotherm models (**b**,**d**) for the adsorption of SDBS onto MARs, HP20 (**a**,**b**) and HP2 MGL (**c**,**d**). [adsorbents] = 5 mg/mL; [SDBS] = 0.1–5 mg/mL; adsorption time 7 h.
## 4 Before and After Travel Characteristic Analysis
### Shopping Destination Change  
Two heat maps are drawn to show the changes in shopping destinations (Figure 1). As shown in the black circles in Figure 1, the hottest spot before the opening of Wanda Shopping Plaza was located at Jiangqiao Business District (JBD). After the opening of Wanda, the hottest spot moved to Wanda.  
The detailed sample sizes and percentages of the shopping destinations are shown at the bottom of Figure 1. We focus on shopping-related trips in this table, which include the travel purposes of shopping for necessaries, dining out, social or recreational events, family or personal errands, and entertainment.  
Before the opening of Wanda Shopping Plaza, 25% of shopping-related trips' destinations are located at Jiangqiao Business District, including Jiangqiao RT-Mart, Jiangqiao Fabric Market, and Jiangqiao Wal-Mart. After the opening of Wanda Shopping Plaza, 21% of shopping-related trips' destinations are Wanda Shopping Plaza and the visit percentage of Jiangqiao Business District declined sharply to 3%.
## 3 Results
### Results of Experiment 2  
Twenty-two participants were initially enrolled in Experiment 2. Four participants were excluded from the final analysis due to COVID-19 infection (\(n\) = 2) or malfunction of the isCGM device (\(n\) = 2). As a result, data from eighteen participants were included in the final analysis (age: 21.5 \(\pm\) 0.6 years, BMI: 20.3 \(\pm\) 2.3 kg/m\({}^{2}\), HbA1c: 5.2 \(\pm\) 0.2%, FPG: 94.6 \(\pm\) 12.3 mg/dL; mean \(\pm\) SD). The mean blood glucose profiles in the young, healthy women under the two different eating manners were analyzed (Figure 4). Key parameters, including the SD, MAGE, MAX, LAGE, %CV, GP during lunch and dinner, and IAUC for glucose at 30, 60, 120, and 180 min, were all significantly lower for the full-course meal manner compared to the fast eating manner (Table 2). Despite the significant reduction in glycemic variability and related parameters, no significant differences were observed in the mean blood glucose and the 24 h IAUC between the two eating manners.  
Figure 4: The mean plasma glucose profiles of the young healthy women (\(n\) = 18) under the fast eating manner and the full-course meal manner in Experiment 2. Each participant consumed the same meals in the fast eating manner (10 min) or the full-course meal manner (60 min) on the fourth and the fifth day in a randomized crossover study. Solid green line: full-course meal manner; red dotted line: fast eating manner.  
Figure 3: The postprandial blood glucose (**A**), insulin (**B**), IAUC for glucose (**C**), and IAUC for insulin (**D**) in young healthy women (\(n\) = 15) for the full-course meal manner and fast eating manner in Experiment 1. The data are the mean \(\pm\) SEM. Full-course meal manner vs. fast eating manner; \(p\) < 0.05 *, \(p\) < 0.01 **, \(p\) < 0.001 *** by a paired \(t\)-test.
# Double-Constrained Consensus Clustering with Application to Online Anti-Counterfeiting
## 5 Experimenting with Counterfeit Web Shops
### Results
In particular, _CCC_ with zero constraints is equivalent to using unconstrained consensus clustering (i.e., CC). We also show, in parenthesis, the percentage improvement of CCC over the average performance of the eight base methods (for each single feature). Finally, the last row reports the mean performance values (averaged over the set of features) of the minimum, maximum, average (and thus means of means), and _CCC_ (relative to the eight base algorithms and for any set of constraints, including unconstrained _CC_). There are two main findings. The first is that _CC_ works well, with improved performance ranging from 8% to 26% compared to the average results of the base clustering methods, depending on which clustering feature we consider. The average improvement over all methods and features was 17% (last row, _CC_ column). Not only were the _CC_ results better than the average results of the base methods for all the features, but very often they were also equal or close to those obtained by the the best base method. In particular, _CC_ matched the best results for the _Header30_, _Header90_, _Privacy_, and _Shipping_ features, while it was only slightly inferior to the best method for the _Header60_, _Header120_, and _Header150_ features. These observations confirm the effectiveness of _CC_ for the domain at hand. The second main finding is that the use of constraints within _CC_ is very effective. Table 3 shows that the results of _CCC_ were better than the average performance of the eight base algorithms for any set of constraints and for any feature, with a peak improvement of 59% over a single feature and an improvement of 45% over the average performance, reached with 80 constraints. Also, and more importantly, _CCC_ outperformed the best base  
Figure 7: Performance of clustering features as a function of clustering algorithms. method. For instance, with 30 constraints, \(CCC\) performed better than the best base method seven times out of eight. When comparing \(CCC\) to \(CC\), we can see that with a small number of constraints, \(CCC\) was slightly worse than \(CC\), whereas with 30+ constraints, CCC was systematically better than \(CC\) both on average and for individual features. In this range of constraints, the improvement of \(CCC\) over \(CC\) grew monotonically, gaining up to 24% with 80 constraints (from 0.7 to 0.87). Before concluding this section, we would like to note that, besides using clustering algorithms with individual features, we also tried to combine the single features into one overall feature by normalizing the distance matrices and taking their mean, as in [55], for instance. The results were unsatisfactory, probably due to the different distribution of the values produced by each feature.
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 2 Materials and Methods
### Knowledge Distillation  
The concept of knowledge distillation was first proposed by [PERSON] [18] et al. However, it is only applicable to the classification task and does not work with the target detection task. Subsequently, [PERSON] [19] et al. proposed a self-attentive distillation algorithm to improve the learning ability of the student network by guiding the shallow network to learn the features of the deep network. [PERSON] [20] et al. proposed a residual knowledge distillation algorithm to introduce a residual module between the teacher network and the student network, but their algorithm is too complex and takes up a lot of computing resources in training. In addition, [PERSON] [21] et al. proposed a data-free knowledge  
Figure 1: The process of scratch sample generation by GAN.  
distillation method for target detection. [PERSON] [22] et al. proposed a cross-level connection path for teacher and student networks to train the model with very little overhead.  
The algorithm flow of knowledge distillation is shown in Figure 2. For the same dataset, the output of the teacher network needed to be calculated first as the soft label, and the soft label contained the implicit relationship between each category. Then, the output of the student network was calculated to generate soft prediction and hard prediction, and distillation loss was calculated between soft prediction and soft label. Hard prediction and true label were calculated as the loss of the student network, and the weighted value of distillation loss and loss of the student network was finally calculated as the final loss function, which is calculated as follows:  
\[L_{\text{soft}}(p_{s},p_{t})=-\sum p_{s}\log p_{t} \tag{2}\]  
\[L_{\text{hard}}(p_{s},p_{t})=-\sum p_{s}\log y \tag{3}\]  
\[L_{\text{final}}=\alpha L_{\text{soft}}(p_{s},p_{t})+(1-\alpha)L_{\text{hard}} (p_{s},y) \tag{4}\]  
where \(p_{t}\) and \(p_{s}\) are the outputs of the teacher network and the student network, \(y\) is the ground truth and \(\alpha\) is the weighting factor.