esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Result
### Result and Comparison
#### 4.3.1 Train and Test with General datasets  
Table 6 is the classification result of training and testing the proposed model and the existing 1D-CNN Hu model and 3D-CNN on the Indian Pines dataset. In the case of Indian Pines, the performance is severely problematic due to class imbalance. However, the [PERSON] model and the proposed model show high performance compared to other models with an OA of about 82%. In view of this, the proposed model is robust against class imbalance. Figure 13 shows the resulting images. Table 7 shows the classification results of the proposed model and the existing 1D-CNN Hu model and 3D-CNNs trained and tested on the Salinas dataset. The Salinas dataset does not have severe class imbalance compared to the Indian Pines dataset. The proposed  
Figure 13: Classification results of Indian Pines: (**a**) ground truth, (**b**) [PERSON] model, (**c**) Li model, (**d**) Hamida model, (**e**) [PERSON] model, (**f**) Hu model, (**g**) proposed ODPA-CNN. model shows overall higher performance than other models except for the Hamida model. This is because the training data is less than 10% of the total data. Hamida model and the proposed model showed high performance with OA of 96% and 92%, respectively. Figure 14 shows the resulting images. Table 8 shows the classification results of the proposed model and the existing 1D-CNN Hu model and 3D-CNNs trained and tested on the Pavia University dataset. This dataset learns quickly and well because there are few classes. Therefore, high performance was obtained except for models that require a lot of training data. Among them, the Li model, Hamida model, and proposed model are the models with an OA of more than 90%. Figure 15 shows the resulting images. Through the above data, it can be confirmed that our model is superior to the existing 1D-CNN in comparison with public HSI data. Also The proposed model is strong against class imbalance and less training data. The Table 9 shows that YU Paint was compared to several 3D-CNN models. Among them, the Luo model, Li model, and Chen model showed low performance. Even looking at all the performance indicators, they came out very low. This comes from the problem of the 3D-CNN. In a situation where there is very little spatial information, 3D-Convolution, which includes spatial feature extraction in addition to spectral information feature extraction, cannot properly extract features. However, among the models based on 3D-CNN, the hamida model showed a very high performance. The reason is that this model contains 1D-Convolution in the middle, so it extracts features to some extent from spectral information. The Hu model is 1D-CNN, but shows low performance. This seems to be from few data. On the other hand, the proposed model shows very high performance with few data. Figure 16 shows the resulting images. Figure 14: Classification results of Salinas: (**a**) ground truth, (**b**) [PERSON] model, (**c**) [PERSON] model, (**d**) [PERSON] model, (**e**) [PERSON] model, (**f**) [PERSON] model, (**g**) proposed ODPA-CNN.
## 2 Description and Challenges of a Customized Biomedical Implantable Device
### State-of-the-Art of the Multipurpose Diagnosis Implantable Devices
A versatile theranostic system was developed by [PERSON] _et al._[33] for the early detection, targeted therapy, and therapeutic monitoring of colon cancer, by using poly(ethylene glycol)-conjugated hyaluronic acid nanoparticles (P-HA-NPs) which can selectively accumulate in tumor tissue. For the diagnostic application, a near-infrared fluorescence (NIRF) imaging dye (Cy 5.5) was chemically conjugated onto the HA backbone of P-HA-NPs. [PERSON] has worked in the areas of biosensors and bioelectronics, biomedical electronics, and integrated circuit design. His company, InSilixa, is working on a multi-diagnostic system, using semiconductor-integrated DNA-sequencing technologies to create point-of-care diagnostics devices. The idea is to take advantage of \"large-volume semiconductor technology,\" manufacturing systems that are widely available and well established, to gain economies of scale [34]. An RF-powered wireless three-channel implantable bio-sensing microsystem has been developed with blood pressure, EKG, and core body temperature sensing capability for untethered genetic tests. A flat silicone blood pressure sensing cuff with a MEMS capacitive pressure sensor is employed to form a novel less-invasive blood pressure sensor, which avoids vessel occlusion, bleeding, and blood clotting associated with the conventional catheter-based sensors. The implantable microsystem can be powered by an adaptively controlled external RF energy source at 4 MHz to ensure a stable on-chip power supply. On-going research efforts are devoted to demonstrating _in vivo_ performance in laboratory animals [35]. Finally, a patent (US 8750961 B1) with a multi-axis, multi-purpose sensor for use with implantable medical devices, and for simultaneously detecting the patient's posture and activity level has been developed [36]. The sensor includes a hermetically sealed, fluid-tight, bio-compatible housing. The housing is formed of a plurality of adjacently secured sides, and a plurality of side electrodes coupled to the sides. A central electrode is disposed at the geometric center of symmetry of the housing, to allow measurement of voltage changes between the central electrode and the side electrodes. A non-toxic electrically conductive electrolyte fills about half the housing, and immerses part of the central electrode and the side electrodes. The sensor further includes a low frequency bandpass filter for passing low frequency signals indicative of the patient's posture, and a high frequency bandpass filter for passing high frequency signals indicative of the patient's activity.
## 4 Review: Slant Links Rain Fade Models
### Statistical Models
#### 4.2.3 ITU-R P.618 Model  
In the ITU-R P.618 model [45] the rain attenuation calculation procedure is complicated as several factors need to be estimated before determining the rain attenuation. The factors that need to be calculated are effective path length, vertical adjustment factor, estimation of the rain height, and calculation of the slant path length and its horizontal projection. A detailed procedure of the ITU-R P.618 model is given in Algorithm 12.  
Advantages  
ITU-R P.618 showed good performance in a tropical Malaysian experiment [8, 111], Italy [112], and a site diversity study in Japan showed good performance [113].  
Limitations  
It is reported that ITU-R P.618 overestimates the CCDF of rain attenuation with version P.618-11 in France [114].
# MIMO Signal Detection Based on IM-LSTMNet Model
## 2 Methods
### MIMO-OFDM Signal Detection Model Based on FrFT
#### 2.2.3 Long Short-Term Memory Module  
LSTM is a kind of long short-term memory network and a special recurrent neural network. Its structure is shown in Figure 7. Compared with traditional RNN, LSTM is more suitable for processing and predicting important events with long intervals in time series. In this paper, an LSTM neural network with a parameter value of 64 is used to capture the relationship between MIMO-OFDM signal sequences and detect signals.  
As shown in Figure 7, LSTM can effectively solve the long sequence problem by introducing the concepts of memory cells, input gates, output gates, and forgetting gates [20]. The control of these gates can effectively capture important long-term dependencies in the sequence and can solve the gradient problem.  
The memory cell is responsible for preserving important information. By manipulating the three gates and linear operation, it determines which information should be forgotten and discarded and which information should be retained or unchanged, so as to generate the cell state \(C_{t}\) at the current moment and then output to the next moment, as well as to update the memory. The forgetting gate's object is the cell state \(C_{t-1}\), whose function is to commmand the information in the cell state for selective forgetting. It resolves which parts need to be discarded and which need to be retained. The object of the input gate is also the cell state, and the role is to decide what new information to store in the cell state, that is, what new memories the cell state selectively adds. Construct an input gate \(i_{t}\) that decides what information is added to the cell state as a new memory. Construct candidate cell states \(\vec{C}_{t}\), determine which memories are useful, and retain information as new memories to add to the new cell states. The role of the output gate is to determine the final output, which is  
Figure 6: Squeeze-and-Excitation Network.  
Figure 7: LSTM structure diagram.  
\(h_{t}\). \(o_{t}\) is obtained using the sigmoid function, which determines which part of the cell state needs to be output. The new cell state is processed by the tanh function, which changes the output value to \([-1,1]\) and is dotted with \(o_{t}\) to control which part needs to be output.
## 4 Experiments and Discussion  
### The TWVS Platform  
In this study, an experimental TWVS was developed as the experimental platform for gathering data to simulate and verify the proposed controller design. A picture of the TWVS is shown in Figure 13. The TWVS was equipped with a microchip (PIC 18F258)-based onboard computer module which was responsible for the data processing and the autonomous control. The main onboard electronic sensor was the self-developed IMU which consisted of the gyroscope and the accelerometer circuit. They were used to collect data from system identification and to output commands for automatic balance. The main control system was composed of the Borland C++ Builder.
## 2 Results
### Change in Biofilm Formation Activity through Quantitative and Qualitative Assay  
The anti-biofilm formation activity of the test compounds was determined by using Crystal violet assay. Biofilm decreased by \(97\pm 7\%\), \(87\pm 6\%\) and \(82\pm 7\%\) with \(10\)\(\mu\)g/mL of CHX, MF-1 and MD-1, respectively (Figure 7). Interestingly, clove oil also shows significant antib biofilm activity in CV assay with a decrease of \(64\pm 5\%\) at the highest test concentration. For qualitative assessment, _S. mutans_ biofilms were grown in 48-well polystyrene plates with the test compounds and were subsequently visualized under a scanning electron microscope (Figure 8). Biofilm formation was not inhibited significantly by clove oil, as evident by the presence of well grown uniform biofilm with densely arranged bunches of cells, while, CHX, MF-1 and MD-1 inhibited the biofilm significantly, as evident from the presence of a few short chains of the cells (Figure 8). Furthermore, cells treated with MF-1, MD-1 and CHX appeared deformed and lysed and are indicated with the arrows in Figure 8.  
Figure 6: Decrease in the viability of _S. mutans_ when grown with \(5\)\(\mu\)g/mL of Chlorhexiidine digluconate (D; CHX), clove oil (CO; B) and essential oils (MD-1; C and MF-1; E) as determined through Live and Dead staining. Panel A1 and A2 show untreated control.  
Figure 8: Reduction in the biofilm formation activity of _S. mutans_ by chlorhexidine digluconate (CHX), and essential oils (MD-1 and MF-1) as visualized under SEM. The exposure to these test compounds results in lysed and deformed cells, as shown by arrows. While the cells in untreated control (CONT) appeared intact and dense.  
Figure 7: **(A)** Decrease in the _S. mutans_ biofilm formation activity by various concentrations of the test compounds as determined by crystal violet assay. Change in the expression of _AtI_E, _gtf_B, _sod_A and _ymc_A genes of _S. mutans_ when grown with the test compounds. Significant values (_p_-value \(>\) 0.05) are marked by asterisk. **(B)** The test compounds induced the expression of _AtI_E, _ymc_A and _sod_A genes, while _gtf_B gene involved in biofilm formation was downregulated.
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
## 6 Conclusions  
1. Graphene was added to a hydrogel coupling agent and a glycerin coupling agent to prepare a hydrogel-graphene composite coupling agent and a glycerin-graphene composite coupling agent, which can change the acoustic impedance value, so that the coupling agent can better realize the acoustic impedance match between the sensor and the tested specimens, and improve the propagation performance of the ultrasonic wave.
2. Adding an appropriate amount of graphene to the coupling agent has a positive effect on the coupling effect and the acoustic propagation performance of the composite coupling agent will be improved. The properties of the composite coupling agents are arranged according to the following mass fraction: 0.75%, 0.5%, and 0.25%, graphene free and 1%. Graphene can effectively change the acoustic impedance of polymer hydrogels and glycerol, while the viscosity of glycerol effectively increases, so that the parameters of both composite coupling agents have good improvement. However, when the mass fraction of graphene is greater than 0.75%, the sound absorption effect of graphene itself will gradually become obvious, leading to the decline of three parameters.
3. At different test frequencies, when the mass fraction of graphene added in hydrogels coupling agent and glycerin ultrasonic coupling agent was 0.75%, the peak-to-peak value, attenuation coefficient, and energy value of ultrasonic echo showed the best effect, and the effect of composite coupling agent would not change with the change in signal frequency.  
Conceptualization, S.L. and H.M. ([PERSON]); methodology, H.M. ([PERSON]); validation, Z.H. and X.L.; formal analysis, H.M. ([PERSON]); investigation, J.R. and X.Y.; resources, H.M. ([PERSON]); data curation, S.L.; writing--original draft preparation, S.L. and X.Y.; writing--review and editing, [PERSON] ([PERSON]) and S.L.; supervision, Z.H. and X.L.; project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funding by the Science and Technology Base and Talents Special Project of Guangxi Province (Grant No. AD19259002), the Natural Science Foundation of Guangxi Province (Grant No. 2018 GXNSFAA180206, Grant No. 2018 GXNSFAA180116), and the National Natural Science Foundation of China (Grant No.513650060). The authors gratefully acknowledge the financial support.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available because the data is company confidential and should not be shared with unauthorized individuals.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### Data Minimization and Purpose Limitation  
GDPR mandates that personal data collected must be adequate, relevant, and limited to what is strictly necessary for the intended purposes [5]. Moreover, data should be collected for specified, explicit, and legitimate purposes, and should not be further processed in a manner incompatible with those purposes [1].  
LLMs, however, require vast amounts of data covering a wide array of topics and sources to achieve high performance [22; 25]. This broad data collection can conflict with the principles of data minimization and purpose limitation, as the data used for training may extend far beyond what is strictly necessary for a specific application [39]. In practice, the challenge lies in balancing the need for large, diverse datasets against the GDPR's requirements. Organizations are addressing this by curating training data selectively, developing models tailored for specific purposes, and implementing robust data governance frameworks that closely monitor data collection, usage, and retention [1; 19; 39].
## 3 Results
### Identification Key Based on Seed Morphology and Micromorphology  
Based on our morphological and micromorphological analysis of Lepidii seu Descurainiae Semen and adulterant seeds, we designed a key to accurately identify seeds. According to this key, seeds are identified according to shape, cross-sectional shape, appearance, mucilage reaction, wing-like appendage, and combining micromorphological characteristics, such as ornamentation, epidermal cell pattern, and shape of the anticlinal and periclinal cell walls, as described below:  
1. Seeds void, obovate-to-oblong in shape; linear-elliptic to linear-triangular in cross-section; dull.
2. Seeds 0.57-0.86 \(\times\) 0.32-0.55 mm; no mucilage reaction; simple reticulate ornamentation; concave, smooth periclinal cell wall _Draba memorosa_
2. Seeds 1.25-2.21 \(\times\) 0.56-1.48 mm; moderate mucilage reaction; reticulate-papillate ornamentation; coarse folded central papillae on periclinal cell wall.
3. Seeds 1.25-1.67 \(\times\) 0.56-1.00 mm; wing-like appendage absent or narrow; nearly indistinct and convex anticlinal cell wall; narrow papillae (9.4-16.0 um in diameter)  
_Lepidium apetalum_
3. Seeds 1.65-2.21 \(\times\) 0.92-1.48 mm; wing-like appendage wide; slightly raised and strongly convex anticlinal cell wall; wide papillae (24.6-36.5 um in diameter)  
_Lepidium virginicum_1. Seeds oblong or obliquely oblong-to-obovate pyriform in shape; not linear-elliptic in cross-section; somewhat shiny.
4. Seeds circular-to-elliptic in cross-section; weak mucilage reaction; simple reticulate ornamentation; smooth periclinal cell wall _Descurainia sophia_
4. Seeds triangular-to-trapezoid in cross-section; no mucilage reaction; reticulate-tuberculate ornamentation; globular central tuberculae on periclinal cell wall.
5. Seeds obliquely oblong-to-obovate, pyriform in shape, circular or ovate in cross-section; slightly raised and convex anticlinal cell wall; narrow tuberculae (5.0-9.2 um in diameter) _Erysimum cheiranthoides_
5. Seeds oblong in shape, triangular-to-trapezoid in cross-section; raised and strongly convex anticlinal cell wall; wide tuberculae (9.1-17.2 um in diameter) _Erysimum macilentum_
# Fatigue Performance and Model of Polyacrylonitrile Fiber Reinforced Asphalt Mixture
###### Abstract  
Fatigue is considered a major pavement structural distress and an important part of a performance-based mix design. Currently, the fatigue model of asphalt mixture, especially the mixture incorporated with fibers, is not perfect. In this paper, the central-point bending fatigue test was conducted by constant strain mode with MTS apparatus. The fatigue performance and model of polyacrylamide(PAN) fiber-reinforced asphalt mixture produced with different fiber contents and asphalt contents were reported. The results indicated that the fatigue life of fiber reinforced mixture was higher than the reference one. The effects of fiber contents and asphalt contents on fatigue life were discussed. The mechanism of an optimum fiber content for the fatigue life in the fiber reinforced asphalt mixture was explained. The statistical analysis of variance (ANOVA) method and regression method were used to evaluate the effects of fiber content, strain level, and volumetric parameters, etc. on the fatigue life of an asphalt mixture. This paper presents a new fatigue performance model of a PAN fiber-reinforced asphalt mixture, including the fiber content, tensile strain, mixture initial flexural stiffness, and voids filled with asphalt (VFA). Compared to the earlier fatigue equations, the accuracy of the new fatigue model with the fiber content is improved significantly according to the statistical analysis results. Meanwhile, the model can preferably reveal the effect of fiber content, strain level, initial stiffness, and VFA on fatigue life.  
sphal mixture; PAN fiber; fatigue performance; fatigue model; loading condition Article  
Fatigue Performance and Model of Polyacrylonitrile Fiber Reinforced Asphalt Mixture  
[PERSON], [PERSON], [PERSON], and [PERSON]
## 3 Zero-Trust Architecture Implementation
### Dynamic Policy Implementation Scheme  
As described in Figure 1, one of the crucial components of zero-trust architecture is its policy engine which controls access to resources based on dynamic policies. Every enterprise has some form of monitoring system and also SIEM (Security Information and Event Management) which collects the analytics and security events from multiple sources such as Firewalls, IDS, IPS, endpoint security, antivirus agents, etc., and analyses them to detect abnormal behavior [16]. Based on this abnormality and its criticality, a risk score is assigned to the users and/or to their devices. Table 1 shows an example of a series of suspicious events detected in SIEM and the corresponding risk scores assigned to each of them.  
These risk scores for each user and device are fed into the policy engine to decide whether to allow or deny an access request for a resource. Depending on the criticality and sensitivity of the resource, there can be a threshold set for the risk score, beyond which access to that resource will be denied by the policy engine. For example, if a resource is set to bare a maximum risk score of 50 to be able to access, then the user with the above series  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Events** & **Risk Score** \\ \hline Malware detected on the device & +10 \\ Connect to the corporate network remotely from an unknown location & +5 \\ Access an unusual internet site that has been reported as malicious & +5 \\ Execution of a file downloaded from the internet that deletes shadow copies & +20 \\ Account switch from user to a service account & +40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of risk score based on security events.  
of security events will be denied access to this resource because the total risk score is, e.g., 80, which is higher than the threshold for the requested resource.  
Another classification of policy is based on the job function of the requester. The level of access granted to the resource will be determined by the role of the requester and is also known as RBAC (Role Based Access Control). Each enterprise resource is assigned different types of access roles, for example, an incident management application will have an incident reporter role, a service requester role, a technical support role, an auditor role, a report manager role, and an administrator role. Now, a user who just wants to report an incident should not be able to view/modify incidents reported by other users. Similarly, a reporting manager is only responsible for generating reports from the application and should not have access to create a user. Whereas an administrator role will have complete access to the application. The policy engine validates the user account and authorizes the least privileged access to the resource depending on the user's role. This can also be tied up with the risk score assessment such that a user with a moderate risk score device connecting to an application will get less privileged access than what their account is entitled to rather than completely denying the access.