esa-sceva/satcom-chunk-collection huggingface dataset:
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
## 2 Related Work
In recent years, some research has been done on the development of multiple user encryption key search schemes in the cloud environment [16] that can support multi-user encryption keyword search through using linear pair and proxy re-encryption technologies. [PERSON] et al. introduced the idea of proxy re-encryption into public key searchable encryption and used query trapdoor conversion on the server side to allow multiple users to perform keyword query through query trapdoor generated by different keys [17]. A user authorization and revocation system was also designed. The multiple keyword search was mostly focused on the logic with connectives support. [PERSON] et al. proposed a ranking search algorithm that can also protect privacy through encrypting the frequency of keywords using an order preserving encryption algorithm [18]. To improve the utilization of data, [PERSON] et al. proposed a fuzzy keyword search scheme that can still return relevant data in case that the user input had minor errors or format inconsistencies [19]. The scheme makes use of two fuzzy set construction methods based on grams and wildcards, respectively, and defines the similarity between keywords based on the edit distance. [PERSON] et al. performed further research on fuzzy keyword search with formal proof of security [20]. Due to security and performance considerations, it has become increasingly likely that the owner of big data wants to spread massive amount of data across multiple cloud servers, making it necessary to develop solutions for the multi-server model. This paper will propose a fuzzy keyword search scheme for multiple servers and multiple users that can preserve the security and privacy and, at the same time, improve the efficiency of data retrieval.
# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 3 GIS Defect PD Pattern Recognition Based on YOLOv5 Algorithm
### Model Training Method  
In this paper, the GIS partial discharge map defective target detection model is trained using a migration learning [25] approach. To ensure the training efficiency of the model and avoid the problem of overfitting [26] due to insufficient training samples, the weight parameters are obtained by training on the migrated Pascal VOC public dataset. The model training process is divided into two stages: freeze training and unfreeze training. Freeze training can speed up the training speed and also prevent the weights from being destroyed at the early stage of training. Training starts from pre-training weights of the whole model with epoch set to 100 and freeze epoch set to 50. Freeze training is performed on the first 50 epochs of model training, and thaw training is performed on the last 50 epochs. The freeze training phase batch_size is set to 16, the thaw training phase batch_size is set to 8, and the maximum learning rate of the model is set to 1 \(\times\) 10\({}^{-3}\). In addition, three training methods, namely Mosaic data augmentation, cosine annealing learning rate decay, and smooth labeling, are combined to improve and enhance the model training effect. YOLOv5 follows the Mosaic data enhancement approach of YOLOv4 [27]. The principle is to randomly read four images containing label annotations, and then stitch them to get one image by image broadening, which can effectively solve the situation of small target detection leakage in model training.  
The learning rate decrease method used in this paper is the cosine annealing decay method, which is used to prevent the model from falling into local minima during training. The expression of the cosine annealing function is shown in Equation (7).  
\[\eta_{t}=\eta_{\text{min}}^{i}+\frac{1}{2}(\eta_{\text{max}}^{i}-\eta_{\text{ min}}^{i})[1+\cos(\frac{T_{\text{cur}}}{T_{i}}\pi)] \tag{7}\]  
where \(\eta_{t}\) is the learning rate at moment \(t\), \(\eta_{\text{max}}^{i}\) and \(\eta_{\text{min}}^{i}\) are the maximum and minimum values of the learning rate, respectively, \(T_{\text{cur}}\) and \(T_{i}\) are the current number of iterations and the total number of iterations of the ith training round.  
For the multi-classification problem of GIS partial discharge defects, one-hot coding is usually used. When the predicted probability is used to fit the one-hot true probability, the generalization ability of the model may not be guaranteed to cause overfitting. Therefore, introducing label smoothing [28] training can better calibrate the network model, better generalize the network, and improve the model prediction accuracy.  
\[q(K|x)=(1-\varepsilon)q(x)+\frac{\varepsilon}{K} \tag{8}\]  
where \(q(x)\) is the one-hot label, \(q(K|x)\) is the smoothed label, \(K\) is the number of categories and \(\varepsilon\) is the label smoothing value, which is generally a small constant and is set to 0.01 in this paper.
## 2 Materials and Methods
### Spectral-Domain Optical Coherence Tomography (Sd-OCT)  
Cross-sectional analysis was performed with a commercial OCT device, Thorlabs Telesto-II, using a superluminescent diode (central wavelength: 1300 nm, bandwidth: about 100 nm) with axial resolution of 5.5 \(\upmu\)m in air, and lateral resolution of 13 \(\upmu\)m. The maximum field of view (FOV) is 10.0 \(\times\) 10.0 mm\({}^{2}\), with 3.5 mm imaging depth. The detector consists of a spectrograph made of a diffraction grating and a fast camera. The system is controlled via a 64-bit software preinstalled on a high-performance computer. The 3D scanning path probe with integrated video camera performs high-speed imaging (76 kHz) for rapid volume acquisition and live display. The sample stage provides XY translation and rotation of the sample along with axial travel of the probe.
## 2 Theoretical Background
### Influence of Sociodemographic Variables  
The relevance of variables such as age, work experience (which is related to age), and gender in the adoption and use of technology has been widely acknowledged in adoption models, such as UTAUT and its extensions [27; 70]. This study included four sociodemographic variables: gender (SEX), generation (GEN), educational level (ED), and income level (IN).  
Sex has strong potential to influence the perception of GAI. Women tend to express higher levels of ethical and privacy concerns regarding these technologies, which may translate into a lower willingness to adopt them than men [71]. This difference is also reflected in self-perceptions of technological competence: men tend to assess their AI skills more positively and trust the technology more, whereas women demand more transparency, understanding, and practical examples before adopting it [72].  
Age is another potentially relevant factor for GAI acceptance. Younger individuals are more likely to adopt these technologies, showing more positive attitudes, confidence in their use, and willingness to integrate them into educational or professional environments. By contrast, older individuals tend to express more concern, particularly regarding privacy, transparency, and potential loss of control over automated processes [71]. Trust in AI varies across generations. Digital natives tend to perceive AI as a useful and trustworthy tool, whereas older adults are more cautious or skeptical [73].  
Individuals with higher education levels typically demonstrate more favorable attitudes toward GAI, partly because they have more prior technological knowledge and are better equipped to understand how it works and its potential. This positive relationship between education and acceptance has been observed in various academic contexts, where students with advanced training show greater interest in integrating GAI into their work, particularly valuing its practical utility and ability to streamline processes [72]. Moreover, prior training in technology and AI, often included in university curricula, directly influences a more confident and positive perception of these tools [74].  
Income level also affects access to and use of GAI. Individuals from higher socioeconomic strata tend to have more resources to access devices, specialized training, and supportive digital environments, which increases their exposure to and familiarity with these technologies [75]. Thus, we propose the following:  
**Hypothesis 8 (H8):**_Men are more likely to use GAI than women._  
**Hypothesis 9 (H9):**_Members of younger generations (Z and Y) are more likely to use GAI than older generations (X and Baby Boomers)._  
**Hypothesis 10 (H10):**_Individuals with higher levels of education are more likely to use GAI._  
**Hypothesis 11 (H11):**_Individuals with higher income levels are more likely to use GAI._
# A Study on Improving the Machining Performance of Scrolls
## 1 Introduction
[20] determined the wearing mechanism that could reduce tool wear, supplied an effective coolant system, and developed an appropriate tool geometry for the milling of scrolls with high precision and efficiency. For the rapid measurement of scroll profiles, [PERSON] et al. [21] proposed a measurement system and analyzed the measurement errors. [PERSON] et al. [22] planned experiments based on the Taguchi L27 orthogonal array to investigate and optimize the effects of control parameters on system performance in waste heat recovery applications using a mechanical heat pump. [PERSON] et al. [23] focused on estimating the performance of a scroll-type hydrogen pump for fuel-cell vehicles and utilized the Taguchi method to determine the optimum conditions for volumetric efficiency and evaluate the effects of design factors. To improve machining efficiency and product precision, this study proposes three adaptive milling processes for scroll manufacturing applications: adaptive feed rate planning, chatter suppression measures, and optimization of milling parameters. For the adaptive feed rate planning implemented with rough machining processes, we computed the cutting area per cutter tooth to simulate the cutting amount and the adjustable feed rate during the milling processes. The results were used to increase the material removal rate (MRR) under a given maximum acceptable cutting load. For the chatter suppression measures considered for the semi-fine and fine machining processes, we developed programs based on MATLAB that can connect a PC and the controller (Delta NC311A) of a milling machine to process the signals of the chatter frequency detected with a microphone. The developed programs can also be used to adjust the real-time spindle speeds to suppress the occurrence of chatter. For the determination of the optimum milling parameters in the fine machining processes, such as the spindle speed, tool feed rate, and tool path, we applied the Taguchi method to implement optimization processes and analysis of variance (ANOVA) to analyze the profile accuracy, such as the profile errors and surface roughness of scrolls. Using a TMV-760 vertical milling machine made by Tongtai Company, we also undertook actual experiments and tests to demonstrate and verify the feasibility of the proposed methods.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 6 Conclusions  
Addressing the void issue in 3D acoustic UWSNs is a challenging task when designing routing protocols. In this paper, it has been shown that including a preventative void-handling technique by utilising soft-state information can significantly increase the routing protocols' performance. We have also investigated opportunistic routing to show how it can overcome the drawback of unreliable acoustic transmission by taking advantage of intermediate nodes' collaboration to relay packets. To this end, we have proposed OVAR, an opportunistic routing protocol, to minimise the number of dropped packets by efficiently bypassing void areas and also to maximise the transmission reliability where there exists significant ambient noises and channel fading. OVAR exploits the local information obtained from the periodic beaconing, constructs the adjacency graph, selects the best forwarding set by removing the possibility of having hidden nodes in the cluster after applying a low-cost heuristic solution and, finally, adjusts the number of forwarding nodes within the cluster based on the energy-reliability trade-off constraints. Finally, different timer values, proportional to the depth difference of members, are assigned to each member of the forwarding set to specify the priority of each node to relay the packet. In contrast to most of the protocols reported in the field, which route packets only toward the surface, OVAR can route packets in any direction to guarantee smooth bypassing of any type of void areas. Our simulation results have demonstrated that OVAR significantly decreases packet loss, energy consumption, end-to-end delay, hop count and traversed distance in sparse to dense scenarios. As future work, we plan to investigate the relationship between the opportunistic data forwarding and network energy balance based on the residual energy distribution in the entire network.  
This work has been supported by the School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK.  
[PERSON] and [PERSON] developed the algorithm, analysed the results and wrote the paper. The simulation model was developed by [PERSON]. [PERSON] assisted with the integrity of the research and also to revise the manuscript.  
The authors declare no conflict of interest.
# Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
## 2 Development of the Algorithm
### YOLOv7 Algorithm
#### 2.3.3 Pruning YOLOv7 Model
The alterations in the channels within each layer following the process of pruning are depicted in Figure 9. The number of reduced channels in each layer ranged from 60 to 1,046,860, with an average reduction of  
Figure 8: Diagram of pruning using the channel pruning algorithm. 158,826 channels per layer. Furthermore, it can be observed from Figure 9 that the majority of the convolutional layers experienced a substantial reduction in the number of channels, suggesting the effectiveness of the pruning algorithm. The findings presented in Table 4 demonstrate that the pruned model exhibited a reduction of 32.92% in the number of parameters compared to the original model. This reduction corresponded to a decrease in size of 24.82 MB. Additionally, the pruned model demonstrated a decrease in the time required for forward inferences by 7.6 ms. Despite these modifications, the pruned model only experienced a marginal decrease of 0.034% in its mAP (mean accuracy), resulting in an accuracy level that closely resembled that of the original model. Channel pruning simplifies the model while preserving its accuracy.
## 3 Results and Discussion
### Kinetic Modeling of Substrate Consumption
This (BF with MLP mode I = 0.86 for Medium D) is because the predicted data are slightly higher than the experimental data in all phases (Figure 3). Similarly, AF values ranged from 1.02 to 1.16 (Table 1). Therefore, the MLP model was \"good\" based on the BF and AF values. Consequently, the MLP model can describe the experimental sugar consumption data in the biofilm reactor containing different medium compositions. In addition, the kinetic parameters related to sugar consumption were predicted using the data obtained from the MLP model and the results were represented in Table 2. The results indicated that the predicted values of \(\Delta S\) and \(SUY\) with MLP model were slightly higher than its experimental values, except for Media D and E. Besides, the MLP model estimated slightly lower experimental values of \(Y_{S/\Delta X}\). Nevertheless, the maximum volumetric sugar consumption rates in Media A, B, C, D, and E were predicted to be 4.10, 4.40, 3.65, 3.77 and 3.71 g/L/h, which were in good agreement with the experimental results of 4.31, 5.00, 3.69, 3.80 and 3.47 g/L/h, respectively (Table 2). Accordingly, the predicted results were highly compatible with the experimental sugar consumption kinetics. On the other hand, the \(\varPhi\)-values for Media A, B, C, D and E were calculated to be 0.212, 0.078, 0.075, 0.096 and 0.055, respectively (Table 1). Therefore, based on the \(\varPhi\)-values, the order to represent all the experimental data of the model determined for each medium is in the form of Media E, C, B, D and A, respectively. In a study, the \(\varPhi\)-value of the kinetic model related to ethanol production was found to be 0.1 [28]. Therefore, it can be concluded that the overall present models except for that of Medium A better represent all experimental data.
# Analysis of Passive RFID Applicability in a Retail Store: What Can We Expect?
## 3 Influence of Retail Store Products and Propagation Phenomena on a Tag Sensitivity
### Chip's Generation Comparison and Effect of Retail Products on the Shelves  
So far, we have investigated tag antenna performance, but it is also well known that the chip's response depends on frequency and power impinging on the chip [24]. Also, there is a tag activation power threshold needed for the tag to respond at each frequency. The measurement platform for UHF RFID tags characterization [24] was used to determine the \(P_{tag\_sens}\) of three tags from one company (Alien Technology, Morgan Hill, USA) with similar layout but with different chips (Higgs 2: ALN-9540, Higgs 3: ALN-9640 and Higgs 4: ALN-9740) throughout the RFID frequency band.  
The results, given in Figure 6, show that generally the new generation of chip shows better (lower) \(P_{tag\_sens}\) in comparison with the previous ones. Also, it can be noticed that in European UHF RFID frequency band (865-868 MHz) the difference between the Higgs 3 and 4 in relation to the Higgs 2 chip is larger than for US UHF RFID frequency band (902-928 MHz). Therefore, in this band we can expect bigger differences, i.e., better improvement in tag range and readings, when using a new generation of chip instead of the old one.  
In our work, the results are obtained at \(f\) = 866 MHz whereas Higgs 2, Higgs 3, and Higgs 4 achieve the sensitivity level \(P_{tag\_sens}\) of \(-14.3\) dBm, \(-18.4\) dBm, and \(-18.5\) dBm, respectively. As the sensitivity for Higgs 3 and Higgs 4 are very close, in further text, the results and discussion are given only for Higgs 2 and Higgs 4 comparison, but the conclusions are also valid and can be applied to Higgs 3 and the rest of the RFID frequency band.
# Analyzing GPU Performance in Virtualized Environments: A Case Study
## 2 Background
### PCI Passthrough  
PCI passthrough utilizes hardware virtualization technologies--specifically, Intel VT-d [16] and AMD-Vi [17]--to allow VMs to access the pGPU of the host directly. While this method offers near-native GPU performance, it limits one GPU to a single VM, leading to potential resource underutilization. Google Cloud is an example of a cloud service employing this technology [18].
## 2 Stability Test of GNSS-RTK Receivers  
To evaluate the accuracy of the GNSS-RTK receivers, a stability test was implemented in a wide and breezeless site, as shown in Figure 1. There were five Hai-Xingda H32 receivers from Hi-target company in Tianjin, China employed in the experiment. Among those five instruments, one was designed as a reference station, while the other four were rover stations. Each of the rover stations not only received information from the reference station through radio-receiving equipment, but also automatically collected three-dimensional information of measuring points together some systematic errors (e.g., the satellite clock error, receiver clock error, ionospheric and tropospheric refraction errors, etc.). Then, the coordinate values of the measuring points could be obtained through real-time differential processing. This experiment was carried out for three hours without interruption. The sampling rate was set to 10 Hz.  
One monitoring point has been selected as an illustrative example; the other points are similar enough to not need repeating. Theoretically, there should not be any amplitude variation of all the monitoring points. However, the monitoring results present violent oscillations due to background noise. Figure 2 presents a captured part of the monitoring data of the vertical displacement. The vibration amplitudes are within \(\pm\)2 cm, which confirm a standard precision of the GNSS-RTK sensors as mentioned above. The root mean square (RMS) was employed for the assessment of the background noise level of the GNSS-RTK receivers. In this experiment, the RMS result was 0.0058 m, which is small and within a reasonable range. Nevertheless, the structural dynamic deformation in this research was also small. Hence, there should be a further step which considers the background noise mixed in with the GNSS-RTK signal. The power spectral density (PSD) of the monitored data was obtained through FFT. As shown in Figure 2, the low-frequency background noise is less than 0.06 Hz. The spectrum distribution characteristics of high-frequency noise above 0.06 Hz are similar to the characteristics of white noise. The PSD values of high-frequency noise range from \(10^{-12}\) m\({}^{2}\) s to \(10^{-7}\) m\({}^{2}\) s. The natural frequency value of the Rainbow Bridge is far greater than 0.06 Hz, and thus the low frequency components less than 0.06 Hz in the measurement signal can be cognized as noise directly. As long as a reasonable threshold value is set, the above cognized noise can be dealt with using traditional denoising algorithms. However, high-frequency random noise, which presents a wide distribution in the frequency domain, is hard to remove. To address this problem, a combined denoising method, which addresses both low-frequency and high-frequency noise, is proposed in the next section.  
Figure 1: Schematic figure for stability experiment.
## 2 Theoretical Method and Physical Model
### Numerical Results  
#### 2.2.1 Oil Film Pressure Distributions  
The dimensionless oil film pressure \(P\) is obtained using the two parameters of width-to-diameter ratio and eccentricity. According to the foregoing, the oil film pressure inside and outside the floating ring bearing is derived.  
Figures 4 and 5 present the selected pressure distribution of the inner and outer oil films during one period of the journal's whirl. After the cylindrical oil cavity is expanded into a plane, the three-dimensional distribution of the non-dimensional oil film pressure calculated is approximately a continuous parabola. In the wedge-shaped convergence zone, the dimensionless oil film pressure gradually increases from zero to the maximum pressure value, and then the oil film pressure begins to drop to zero. Still under the Reynolds boundary condition, the oil film pressure remains at zero in the divergence zone. The oil film pressure value is varied under different eccentricity conditions. Note that the oil film pressure is larger when the eccentricity is greater. The corresponding oil film damping will also increase, which leads to a subsequent reduction in journal vibration.