esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Key Lines' Identification of Subway Network
### Experimental Result Analysis  
#### 4.3.1 Global Influence Analysis  
Based on Space L and Space P, the key lines' identification experiment of the Beijing subway network is conducted, and the aggregation influence coefficient \(I_{C}\), path length influence coefficient \(I_{D}\), network efficiency influence coefficient \(I_{E}\) and connectivity influence coefficient \(I_{r}\) of each Beijing subway network are calculated as shown in Table 10. Compared with the complete subway network, the clustering coefficient, path length, connectivity and network efficiency of the Beijing subway complex network under two spaces have different changes. The comparison of global impact indexes is shown in Figure 7. As shown in Figure 7a, under Space L, Line 9, Line 10, Line 14 West, Line 4, and Line 7 are the five lines with the largest aggregation influence coefficient. The five subway lines at the edge of the network, Daxing International Airport Express, Capital Airport Express, S1 Line, Xijiao Line, and Yanfang Line have the smallest aggregation influence coefficient. Under Space P, Line 1, Line 2, Line 5, Line 9, and Line 10 are the five lines with the largest aggregation influence coefficient. The five subway lines at the edge of the network, Changping Line, Daxing International Airport Express, Capital Airport Express, Line 8 North, and Line 14 West have the smallest aggregation influence coefficient. Combining the comprehensive effect of the two spaces, the aggregation influence of Daxing International Airport Express and Capital Airport Express is poor, and the aggregation influence of Line 9 and Line 10 is the most significant.
## 2 Materials and Methods
### Prediction Assessment  
We performed a linear regression analysis for each phenotypic trait to extract the residuals adjusted for fixed effects (sex and batch) as the corrected phenotypes (for reproductive traits and egg quality traits in female ducks, the fixed effects include only batch effects). The definition of the corrected phenotype is as follows:  
\[\hat{\epsilon}_{i}=P_{i}-\big{(}\hat{\beta}_{0}+\hat{\beta}_{1}\cdot Sex+\hat{ \beta}_{2}\cdot Batch\big{)}, \tag{6}\]  
where \(\hat{\epsilon}_{i}\) is the residual, representing the corrected phenotype; \(P_{i}\) is the observed phenotypic value; \(Sex\) is the fixed effect of sex; \(Batch\) is the fixed effect of batch; and \(\hat{\beta}_{0}\), \(\hat{\beta}_{1}\), \(\hat{\beta}_{2}\) are the regression coefficients estimated through linear regression.  
To estimate the model's predictive performance based on 10-fold cross-validation, we randomly divided the animals with both phenotypic and genotypic data into 10 subsets. In each iteration, the phenotypic values of one subset were set to \"NA\", and the remaining 9 subsets were used as a reference group to predict the GEBVs of the subset. The accuracy of genomic prediction was defined as the Pearson correlation coefficient between the corrected phenotypic values and the GEBVs, expressed as  
\[Accuracy=r=\frac{Cov\big{(}Y,\ \hat{Y}\big{)}}{\sigma_{Y}\sigma_{\hat{Y}}}, \tag{7}\]  
where \(r\) is the Pearson correlation coefficient; \(Y\) is the corrected phenotypic value; \(\hat{Y}\) is the GEBVs predicted by the model; \(Cov\big{(}Y,\ \hat{Y}\big{)}\) is the covariance between \(Y\) and \(\hat{Y}\); \(\sigma_{Y}\) is the standard deviation of \(Y\); and \(\sigma_{\hat{Y}}\) is the standard deviation of \(\hat{Y}\).  
The unbiasedness of genomic prediction was defined as the regression coefficient between the GEBVs and the corrected phenotypic values, obtained from the linear regression model described as  
\[Y=\beta_{0}+\beta_{1}\hat{Y}+\epsilon, \tag{8}\]  
where \(Y\) is the corrected phenotypic value; \(\hat{Y}\) is the GEBVs predicted by the model; \(\beta_{0}\) is the intercept; \(\beta_{1}\) is the regression coefficient; and \(\epsilon\) is the error term. The measure of unbiasedness is that the regression coefficient \(\beta_{1}\) should be close to 1, i.e.,  
\[Unbiasedness=\beta_{1}. \tag{9}\]
###### Abstract  
General studies examining reservoir bank deformation during its impoundment primarily consider the coupling effect between the seepage field and the stress field, but thermal field variation in the bedrock and its effect are rarely considered. In this paper, a case study concerning a 285.5 m high arch dam project, where a valley narrowing deformation occurs after the initial impoundment, is implemented. An analysis of in situ measurement is given to interpret the causes of the unique hydro-thermal phenomenon of the project. Possible reasons for the valley narrowing deformation pattern are discussed. A numerical model based on the thermo-hydro-mechanical (THM) coupling theory of porous medium is used to calculate the evolution processes of the thermal, seepage, and stress fields of the area after impoundment of the reservoir. The simulated deformation trend and pattern of the river valley are consistent with the monitoring data. The results demonstrate that water infiltration after impounding cools the bedrock and the temperature decrease makes the bedrock contract, which induces the narrowing deformation of the valley. Factor analysis of the hydrothermal field shows that temperature variation is the main cause of long-term deformation. Thus, it shall be considered as a key factor in terms of structural safety assessment. Furthermore, sensitivity analysis of the hydraulic conductivities of rock strata suggests that future development of the deformation can be eased off if the anti-seepage method is adopted on the bedrock.  
high arch dam; thermo-hydro-mechanical (THM) coupling method; in situ measurement interpretation; numerical analysis; valley narrowing deformation mechanism +
Footnote †: journal: Applied Physics Letters
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 4 Related Datasets
### Datasets for Grocery Product Detection and Recognition
**Store shelf images and product images for retail**[52] is a public dataset available on the Kaggle website. It represents 100 categories of retail products. For each one, 3 photos with different perspectives were taken with a product placed on the supermarket floor, which makes a total of 300 photos. Another 3153 images were captured from supermarket shelves, taking into account the lighting conditions and visibility to provide legible photos. No data annotations were made available. **Open Food Facts dataset**[16] is the base of the mobile application with the same name. The users can add photos and information about food products. Therefore, it is a dataset which is continually being updated. To date, there are approximately two and a half million photos captured under real or ideal conditions. There are products from almost every corner of the globe, but they are predominantly from France, the United States and Spain. For each item, it tries to provide as much information as possible, which includes brand, category, quantity, packaging, ingredients, origins of ingredients, allergens, traces, additives, added vitamins, minerals, amino acids and others, as well as the Nutrition Score, Nova classification that categorises the degree of processing of foods, and Eco Score. **SKU-110K**[3] is a dataset designed to support product detection in a densely packed setting. The images contain thick retail shelves with identical items. The location of items is labelled by bounding boxes. Wanting to represent a variety of situations, the images contain thousands of supermarkets, which are located in the United States, Europe and East Asia. The dataset is divided into training, validation and test set, with 8232, 587 and 2940 images, respectively. As the photographers are not subject to any viewing settings, the images present many scales, viewing angles, lighting conditions and noise levels. **WebMarket**[53] dataset is available on Kaggle and contains 300 images of retail racks. It was designed for object detection; therefore, only the products' location is labelled. Outside the supermarket context, there are also logo-oriented databases that can help train the model to be more robust in the presence of small variations of those logos. Most of these datasets are divided into categories, food brands being the obvious option. Some that might be interesting are Logo-2K+ [54], LogoDet-3K [10], FoodLogoDet-1500 [55] and WebLogo-2M dataset [56].
## 4 Control Design
### Stability Analysis  
We define a Lyapunov function which is positive definite as:  
\[V(\mathbf{e},\dot{\mathbf{e}},\mathbf{e}_{E},\mathbf{e}_{\delta})=\frac{1}{2}\mathbf{e}^{T}\mathbf{k}_ {1}\mathbf{k}_{2}\mathbf{e}+\frac{1}{2}\mathbf{e}^{T}\mathbf{k}_{2}\dot{\mathbf{e}}+\frac{1}{2} \mathbf{e}_{\delta}^{T}\mathbf{e}_{\delta}+\frac{1}{2}\mathbf{e}_{E}^{T}\mathbf{k}_{2}(\mathbf{M} ^{*})^{-1}\mathbf{k}_{E}\mathbf{e}_{E} \tag{33}\]  
Differentiating \(V(\mathbf{x}_{1},\mathbf{x}_{2},\mathbf{e}_{E})\) yields  
Figure 4: Block diagram of the proposed controller.  
Figure 3: Input and output membership functions.  
\[\dot{V}(\mathbf{e},\dot{\mathbf{e}},\mathbf{e}_{E},\mathbf{e}_{S})\!=\!e^{T}k_{1}k_{2}\dot{\mathbf{e}}\!+ \!\dot{e}^{T}k_{2}\ddot{\mathbf{e}}\!+\!e_{S}{}^{T}\dot{e}_{\delta}\!+\!e_{E}^{T}k_{ 2}(\mathbf{M}^{*})^{-1}k_{E}\dot{e}_{E} \tag{34}\]  
where \(\mathbf{M}^{*}\) is assumed to be constant.  
Substituting the proposed control law (31) in (22), and taking into account (29), (34) can be rewritten as  
\[\dot{V} = \mathbf{e}^{T}k_{1}k_{2}\dot{\mathbf{e}}\!+\!e_{E}^{T}k_{2}(\mathbf{M}^{*})^{-1 }k_{E}\dot{e}_{E}\!+\!e_{\delta}{}^{T}(-\mathbf{T}^{-1}\mathbf{e}_{\delta}+\dot{\delta}) \tag{35}\] \[+\!\dot{e}^{T}k_{2}(\!-\!k_{2}\dot{\mathbf{e}}\!-\!k_{1}\mathbf{e}\!-\!( \mathbf{M}^{*})^{-1}J^{T}k_{E}\mathbf{e}_{E}\!+\!(\mathbf{M}^{*})^{-1}(\mathbf{e}_{\delta}-\bm {\rho}\dot{\delta}-\mathbf{\epsilon}))\] \[= -\!e^{T}\|k_{2}\|^{2}\dot{\mathbf{e}}\!-\!e_{\delta}{}^{T}\mathbf{T}^{-1} \mathbf{e}_{\delta}\!+\!e_{\delta}{}^{T}\dot{\delta}\!+\!\dot{e}^{T}k_{2}(\mathbf{M}^ {*})^{-1}((1+\rho)\mathbf{e}_{\delta}-\mathbf{\rho}\delta-\mathbf{\epsilon})\] \[= -\!e^{T}k_{2}[k_{2}\dot{\mathbf{e}}-(\mathbf{M}^{*})^{-1}((1+\rho)\mathbf{e} _{\delta}-\rho\delta-\mathbf{\epsilon})]\!-\!e_{\delta}(\mathbf{T}^{-1}\mathbf{e}_{\delta }\!-\dot{\delta})\]  
By choosing large enough values of \(\mathbf{k}_{2}\), small values of \(\mathbf{\rho}\) and small enough values of \(\mathbf{T}\) such that  
\[k_{2}\dot{\mathbf{e}}\geq(\mathbf{M}^{*})^{-1}((1+\rho)\mathbf{e}_{\delta}-\mathbf{\rho}\delta -\mathbf{\epsilon}),\|\mathbf{\rho}\delta\|\leq\mathbf{\sigma},\ \ \mathbf{T}^{-1}\mathbf{e}_{\delta}\!\geq\!\dot{\delta}, \tag{36}\]  
\(\dot{V}(\mathbf{e},\dot{\mathbf{e}},\mathbf{e}_{E},\mathbf{e}_{\delta})\) is negative semi-definite, where \(\mathbf{\sigma}\to\mathbf{0}\) is a vector with smaller positive values. Consequently, the tracking errors and estimated errors of the system all converge to zero asymptotically; i.e.,  
\[\lim_{t\to\infty}\mathbf{e}\to\mathbf{0},\lim_{t\to\infty}\dot{\mathbf{e}}\to\mathbf{0},\lim_ {t\to\infty}\mathbf{e}_{E}\to\mathbf{0},\lim_{t\to\infty}\mathbf{e}_{\delta}\to\mathbf{0} \tag{37}\]  
Therefore, the closed-loop system is asymptotically stable in the entire state space.
## 4 Results and Discussion
### Intensity-Dependent Photomechanical Measurements
#### 4.2.2 Separating Photothermal Heating and Angular Hole Burning Using the Tensor Response
\begin{table}
\begin{tabular}{c c c c c c} \hline
**CTA Volume** & \(\kappa_{//}^{(1)}(I)/\kappa_{\perp}^{(1)}(I)\) & \(R^{(re)}(0)\) & \(\kappa_{//}^{(heat)}(I)=\kappa_{\perp}^{(heat)}(I)(s/m)\) & \(\kappa_{//}^{(re)}(I)(s/m)\) & \(\kappa_{\perp}^{(re)}(I)(s/m)\) \\ \hline \(1.33\times 10^{-3}\) & 0.96 \(\pm\) 0.28 & \(-0.02\pm 0.19\) & 246 \(\pm\) 80 & \(-6\pm 46\) & \(3\pm 23\) \\ \(2\times 10^{-3}\) & 0.91 \(\pm\) 0.08 & \(-0.06\pm 0.05\) & 259 \(\pm\) 22 & \(-15\pm 14\) & \(8\pm 7\) \\ \(2.66\times 10^{-3}\) & 0.94 \(\pm\) 0.23 & \(-0.04\pm 0.16\) & 231 \(\pm\) 49 & \(-10\pm 36\) & \(5\pm 18\) \\ \hline \end{tabular}
\end{table} 
Table 6: Contribution of photothermal heating and angular hole burning to the photomechanical response of DR1/PMMA fibers pumped with 488-nm light for different CTA volume fractions. Figure 20: A plot of the ratio of molecular reorientation to heating along the sample’s axis when pumped with 488-nm light for three different CTA concentrations. negative value. A possible explanation of the observation is as follows. For long chain lengths, high background irradiation softens the material, making the molecules more mobile and hence increasing the molecular reorientational contribution. For short chain lengths, the molecules are more mobile when softened by the background light. The light also induces molecular reorientation through light-induced torque on the induced dipole moment. Such effects are the topic of future research.
# 185-215 GHz CMOS Frequency Doubler with a Single Row Staggered Distribution Layout Design
## 2 Circuit Design
### 220 GHz Frequency Doubler
Figure 8: The relationship between the simulated output power and the input power of two kinds of frequency doublers with a different structure at 220 GHz when the bias voltage is set as 0 V. (**a**) Frequency doubler uses a single-row interleaved Poly-Diffusion Contact balancing structure; (**b**) Frequency doubler uses conventional two-line structure. Figure 7: Simulated power contour of the frequency doubler for different input power levels and bias voltages at 220 GHz. (**a**) Single-row interleaving distribution design; (**b**) Conventional two-line design. For a gate bias voltage V\({}_{\text{gs}}\) of 0 V, Figure 9 shows the load pull diagram for a drive power of 11.5 dBm. As can be seen from Figure 9, the output power reached a maximum of 7.8 dBm. The topology of the output impedance matching network was L-C-L.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 4 Handover Management for Drone Networks
### Handover in Drone Networks  
The handover performance is a common assessment in cellular networks since it is a good indicator for demonstrating the efficient mobility techniques. Handover, or handoff, is a key technique in mobile networks that allows a UE to switch its connection across BSs while on the move. Handover with drone networks has become a more significant matter because the connected drones move in the sky faster with different characterizations. Depending on the functionality of drones within the network, one or several drones may be needed to provide network access services to specific terrestrial users. Drones may also serve as UEs and receive service from ground BSs or from satellite networks. Since a drone's operation is restricted by its power, coverage, mobility characterizations, and serving network traffic, handover will be increasingly required. The handover (handoff) process is crucial for the continuation of a connection, imposing only a short delay [58]. Furthermore, the drone network remains highly dynamic since mobile aerial vehicles and the radio environment are different compared to ground users due to several factors, such as high altitude [59; 26]. The traditional handover control systems in MANETs and VANETs must be altered to be suitable for drone networks. In MANETs, the commonly utilized handover techniques lead to constantly separating or merging network nodes [60]. Several architectures for drone traffic control systems have been proposed. For instance, NASA and the Federal Aviation Administration (FAA) proposed the UTM scheme [61]. The European Union is also developing U-space, which contains a set of guidelines and services [62].
# Evaluation of a New-Concept Secure File Server Solution
## 1 Introduction
One such task is to ensure file availability, meaning the capability of restoring files from backups. However, this requires a file system protection solution that provides adequate protection for the file servers. The solution presented in this paper aims to provide both file system protection and backup protection. Our goal is to show that the proposed solution does not significantly degrade the user experience and transfer speed while providing protection. It offers adequate file operation speed and network transfer speed depending on the software configuration and can indeed be an integral part of any IT system where file protection and continuous availability are crucial factors. The structure of the paper is as follows: Section 2 of the paper describes the evolution of ransomware, the changes in their working methods, some previous research results in the international literature focused on file server protection, and some of the advanced technologies in the field of protection solutions that can be applied today. Section 3 presents the authors' previously published complex file server protection solution, whose file server protection module is the subject of the current research. Furthermore, the measurement methodology for the file server protection performance test is also detailed in this section. Section 4 discusses the results of the tests. Section 5 discusses the results and presents the answers to the research questions. Finally, Section 6 summarizes the paper.
## 2 Methodology
### Data Analysis Process  
We started our investigation with 1143 studies from three databases, but we eliminated 8 of them since they were duplicates. Six pieces of research were also disqualified since they were retracted papers. We eliminated a total of 853 papers from our title-abstract screening procedure because they were either not research publications (n = 1) or did not address mitigation, detection, or prevention-related issues (n = 852). Lastly, we removed a total of 24 papers from our full-text analysis since 2 did not fall within the timeframe (2020-2024) that we were interested in, and 22 articles were not available even with institutional access.  
The data analysis process involved categorizing attack types based on their mechanisms and assessing their impact on blockchain security and integrity. We gave specific attention to the evolution of attack techniques and motivations. Early attacks predominantly exploited technical flaws, while contemporary threats have evolved into more sophisticated strategies with financial gain as the primary objective. For instance, Sybil attacks leverage multiple fake identities to compromise network integrity, while advanced exploits target vulnerabilities in consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS).  
To evaluate countermeasures, we analyzed the effectiveness of network security protocols, including firewalls and intrusion detection systems, and cryptographic techniques such as digital signatures and hash functions. Security audits conducted by external experts and consensus mechanisms are critically assessed to determine their resilience against evolving attack methods. The analysis also examined how these measures have adapted over time in response to the growing sophistication of blockchain threats.  
The research further explored the anticipated trajectory of blockchain attacks and corresponding security measures. As blockchain technology evolves, new attack vectors are expected to emerge, necessitating continuous advancements in defense mechanisms.  
Current challenges such as scalability, interoperability, and the trade-offs between decentralization and security are highlighted as critical areas for future investigation.
# Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources
## 3 System Configuration and Procedure
### Vibrators and Vibration Characteristics  
This section describes the structure and vibration characteristics of the vibrator, which is used to convey information about the direction and distance of the sound source to the user. The vibrator is not in direct contact with the skin and is designed to work even when the user wears layers of clothing outdoors, or thick clothing. For the vibrator, we used a speaker driver with a diameter of about 3 cm, from which the frame and cone of the small loudspeaker were removed (Figure 9a). A plastic plate was glued to the speaker driver so that the vibration could be transmitted easily. The speaker drivers were driven by the analog signal output terminal of the FPGA module through an isolation amplifier and a class D power amplifier. Two vibrators were attached on the back of a vest, as shown in Figure 9b. The relationship between the input voltage of the vibrator and the vibration amplitude of the plastic plate is shown in Figure 10. The vibration amplitude of the plastic was measured with a laser displacement meter (LK-G35, Keyence). The resolution of the laser displacement meter was 0.05 \(\upmu\)m. It can be seen that the vibration amplitude increased almost in proportion to the input voltage. The vibration of the vibrators can be easily perceived even when wearing layers of clothing outdoors. The class D amplifier shown in Figures 7 and 8 has an adjustable gain, and if the user feels pain or cannot perceive the vibrations, the gain of the amplifier can be adjusted. Therefore, the user perceives variation of vibration relatively but absolutely, since humans have logarithmic sensitivity.  
Figure 8: Appearance of hearing assist system.  
Figure 7: Configuration of hearing assist system.
## 2 Results and Discussion
### Optimization of the Extraction Procedure
#### 2.2.1 Extraction Time  
The HS-SPME extraction is an equilibrium process, and the extraction time is the major affecting variable on the distribution coefficient of the analyte between the sample/headspace and the headspace/fiber. Therefore, to obtain the minimum time required to set up an equilibrium between the headspace and the fiber coating, the extraction time was investigated in the range of 5-30 min (Figure 3). According to the results, the amount of extracted analyte increased by increasing of extraction time up to 15 min, and then remained almost constant. Hence, 15 min was selected as the optimum extraction time for further studies.
## 1 Introduction
We consider the scenario in which an expert source node already exists, and new data flows are generated from another source node. Simultaneously, a small number of relay nodes change their location, and the network topology has little change compared with the previous network scenario. In this case, DQfD is adopted to learn strategies from the expert source node, which speeds up the learning process and achieves better network performance than that of the expert source node. 2. To resolve the inefficiency in power adaptation when utilizing responsibility rating, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power adaptively by comparing single-hop latency with double thresholds. This enables the transmit power to transfer multiple levels at each time step, until rational transmit power is achieved, which accelerates power assignment and improves the system performance. 3. To save the system memory and further improve the performance, PM-DQfD is proposed. This makes it possible to periodically erase the inessential self-generated data and outdated expert demonstrations to release replay memory. In addition, it reduces data redundancy and optimizes memory structure, which produces better performance. Simulation results show that the proposed method decreases routing delay and enhances energy efficiency, as well as network stability. The rest of this paper is organized as follows: Section 2 describes the system model. The novel metric called dynamic adjustment rating is introduced in Section 3. Section 4 presents the PM-DQfD-based cross-layer routing scheme, which is followed by the simulation results in Section 5. Finally, Section 6 concludes the paper.