esa-sceva/satcom-chunk-collection huggingface dataset:
# Bayesian Optimization for Instruction Generation
## 2 Related Works
### Hard Prompt Tuning via (Vanilla) Bayesian Optimization  
The authors of [16] proposed a method for hard prompt tuning (HPT) of LLMs via BO. The method addresses the challenge of finding optimal prompts in the vast combinatorial space of possible n-gram sequences, where the objective is to maximize a task-specific scoring function over a dataset. The key innovation is the adoption of a continuous relaxation of the discrete n-grams, specifically mapping the discrete n-grams into integer-valued vectors belonging to a continuous space whose components are the indices of the tokens in the dictionary. The next solution (i.e., a new hard prompt) to evaluate is obtained by optimizing an acquisition function over the continuous space, balancing between exploitation and exploration; then, the continuous components of the obtained solution are rounded to the closest integer values to retrieve the associated tokens in the vocabulary. This mechanism allows us to leverage BO for HPT.  
More specifically, to solve problem (1), BO approximates the scoring function over the _continuously relaxed_ prompt space. After having evaluated \(n\) prompts, a Gaussian process (GP) regression model is fitted to approximate the posterior mean (\(\mu(\mathbf{p})\)) and variance (\(\sigma^{2}(\mathbf{p})\)) for any continuously relaxed prompt (\(\mathbf{p}\)) according to the following two equations:  
\[\mu(\mathbf{p})=\mathrm{k}(\mathbf{p},\mathbf{P}_{1:n})\Big{[}\mathbf{K}+ \lambda^{2}\mathbf{I}\Big{]}^{-1}\mathbf{h} \tag{2}\]  
\[\sigma^{2}(\mathbf{p})=k(\mathbf{p},\mathbf{p})-\mathrm{k}(\mathbf{p}, \mathbf{P}_{1:n})\Big{[}\mathbf{K}+\lambda^{2}\mathbf{I}\Big{]}^{-1}\mathrm{ k}(\mathbf{P}_{1:n},\mathbf{p}) \tag{3}\]  
where \(\mathbf{P}_{1:n}=\Big{\{}\mathbf{p}^{(i)}\Big{\}}_{i=1,\ldots,n}\) is the set of evaluated prompts, \(\mathbf{h}=\Big{\{}h^{(i)}\Big{\}}_{i=1,\ldots,n}\) are the associated scores, \(k(.,.)\) is the kernel function, \(\mathbf{K}\) is the \(n\times n\) kernel matrix with its entries computed as \(\mathbf{K}_{ij}=k\Big{(}\mathbf{p}^{(i)},\ \mathbf{p}^{(i)}\Big{)}\), \(\mathbf{I}\) is the identity matrix, and \(\lambda^{2}\) is the noise variance.  
The next prompt (\(\mathbf{p}^{(n+1)}\)) is chosen by optimizing an _acquisition function_ balancing between exploration and exploitation. In this paper, we use the well-known Upper Confidence Bound (UCB), which is defined as  
\[UCB(\mathbf{p})=\mu(\mathbf{p})+\beta\sigma(\mathbf{p}) \tag{4}\]  
and represents the most optimistic estimate of the score for any prompt (\(\mathbf{p}\)) depending on the current GP model. Then, the actual score associated to the suggested prompt (\(\mathbf{p}^{(n+1)}\)) is evaluated, and the GP model is updated according to the new observation. The BO algorithm continues iteratively until a maximum number of prompts has been suggested and evaluated.  
A graphical representation of the proposed approach is reported in Figure 2, providing more details on the BO components and their roles.
## 2 Results and Discussion  
### Drying Kinetics  
Figure 1a shows the change of moisture ratio (MR) with time, Figure 1b shows the relationship between the drying rate and MR and Figure 1c shows the change of moisture content with time of _C. alata_ dried using CD at 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, VMD at 6 W/g, 9 W/g and 12 W/g and CPD-VMFD at 50 \({}^{\circ}\)C and 9 W/g. As shown in Figure 1a,c, the drying duration of CD is the longest as convective method uses surface heating, and surface moisture is removed efficiently; however, the slow diffusion of moisture from the internal region is rate limiting. A rapid moisture removal in the initial stage of CD is typical, and moisture removal will progressively reduce with time. With VMD, the volumetric heating of microwaves addresses the limitation of CD because the water removal from the centre region is fast, thereby resulting in higher drying rates than CD methods. A higher drying rate during the initial period of VMD is often observed as a larger microwave radiation is absorbed due to a higher moisture level in leaves. As drying progresses, lower moisture content in leaves corresponds to the lower microwave absorption, thereby lowering the drying rate. In the combined method CPD-VMFD, CPD is effective in removing moisture during the initial drying process whilst VMFD assists in the removal of residual moisture, which is strongly bound to the plant cellular structure, thereby shortening CD from 150 min to 105 min.  
With regard to CD and VMD, high drying intensities resulted in reduced drying durations and high drying rates. Increasing hot-air temperature for CD from 40 \({}^{\circ}\)C to 60 \({}^{\circ}\)C reduced drying time from 180 min to 120 min. Meanwhile, for VMD, increasing microwave wattage from 6 W/g to 12 W/g reduced drying time from 32 min to 16 min. The trend of MR with time of CD and VMD at different drying intensities is similar (Figure 1a). Drying kinetics of _C. alata_ dried using CD, VMD and CPD-VMFD are best described by an exponential function (modified Page model).  
\[MR=a\cdot\exp{(-k\cdot t^{n})}. \tag{1}\]Figure 1: (**a**) Relationship between moisture ration (MR) and time. (**b**) Drying rate against MR. (**c**) Relationship between moisture content and time for the drying of _C. alata_ at convective drying (CDs) at 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, vacuum microwave drying (VMD) at 6, 9 and 12 W/g and convective pre-drying followed by vacuum microwave finish drying (CPD-VMFD) at 50 \({}^{\circ}\)C and 9 W/g.
## 5 Discussion  
### The Performance of the Higher-Level Gaussian Pyramid  
In the proposed method, the Gaussian pyramid can provide a multi-level of sub-images. However, the high-level images are obtained through several low pass filtering and downsampling processes, which implies that the higher level contains more information loss. To investigate whether the model could extract important features from the higher level, this section will discuss the performance of each model and fusion network. Here, the level 1 structure was adopted as the level 2 and level 3 structures, which removed the last two inception modules from the pre-trained GoogLeNet and applied a higher learning rate factor for the shallower layers. According to Figure 10a, the accuracies of the individual networks decreased as higher levels of the Gaussian pyramid were applied, the accuracy gaps between level 1, level 2, and level 3 increased significantly, which indicates that some important features were removed in the high level of the Gaussian pyramid. While comparing the performance between individual networks and fused two networks, the fused 0-1 network achieved the highest accuracy (99.61%) among the fused two networks based on the results in Figure 10b. In contrast, the fused 0-2 and fused 0-3 networks were 0.05% and 0.37% lower than the level 0 network, and the fused 1-2 and fused 1-3 networks were 0.24% and 0.64% lower that the level 1 network. In Figure 10c, the overall performance of the fused three networks was lower than the level 0 network, and the accuracies dropped significantly while applying level 2 and level 3 as the main proportions. These results indicate that the level 2 and level 3 networks were misleading the prediction of the fusion network. Lastly, the greatest performances of the individual network, fused two networks, fused three networks, and fused all networks are compared in Figure 10d. It shows a noticeable peak at the fused 0-1 network, which illustrates that the fused 0-1 yielded the highest accuracy among all of the networks. Besides, the results of the fused three networks, which contained larger training parameters, were not as good as the fused 0-1 network. These results indicate that the fused 0-1 network is able to promote the performance of the individual network, simultaneously surpassing the other large-scale fusion network. As a result, this study utilized level 0 and level 1 of the Gaussian pyramid as the proposed method.
## 2 Materials and Methods
### Mid-Infrared (MIR) Spectra Acquisition  
The mid-infrared spectroscopy analysis of the samples was carried out using an attenuated total reflection-Fourier transform infrared spectroscopy spectrometer equipped with a deuterated triglycine sulfate detector (Perkin-Elmer company, Waltham, MA, USA). The crystal material of the attenuated total reflection attachment was diamond. Each sample was scanned 64 times, the spectral resolution was set as 16 cm\({}^{-1}\), and the scan range was set as 4000-400 cm\({}^{-1}\). Each sample was collected repeatedly 3 times, and the average spectrum was taken for analysis.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 2 The Dual-Platform GMTI Method against Nonuniform Environment
### Analysis of the Influence of Heterogeneous Environment in STAP
#### 2.2.3 Power Nonuniformity  
Figure 2(a) shows the background of the land-water junction, with the land on the left and the water on the right. The simulated echo is shown in Figure 2(b). We investigated the statistical characteristics of the power nonuniformity. Two areas were selected in the water area and the land area, respectively, the histogram of the obtained statistical characteristics is shown in Figure 4, and the specific statistical analysis is shown in Table 1. It can be seen that there are great differences in the statistical characteristics of the nonuniform environment, especially in the mean value, due to the nonuniform power. When the target is at the land-water junction with nonuniform power, it is assumed that the target is in the water area, close to the land. When the sample estimation method is used to estimate CCM, the land training samples are selected, which will lead to an inaccurate detection threshold, thus affecting the detection performance.  
Figure 4: Statistical characteristics of different areas: (**a**) area 1, (**b**) area 2, (**c**) area 3, (**d**) area 4.  
Figure 3: Background: (**a**) the background image, (**b**) the echo of background.
###### Abstract  
This paper describes the principles behind the radio-frequency (RF) sensing of bacterial biofilms in pipes and heat exchangers in a dairy processing plant using an electromagnetic simulation. Biofilm formation in dairy processing plants is a common issue where the absence of timely detection and subsequent cleaning can cause serious illness. Biofilms are known for causing health issues and cleaning requires a large volume of water and harsh chemicals. In this work, milk transportation pipes are considered circular waveguides, and pasteurizers/heat exchangers are considered resonant cavities. Simulations were carried out using the CST studio suite high-frequency solver to determine the effectiveness of the real-time RF sensing. The respective dielectric constants and loss tangents were applied to milk and biofilm. In our simulation, it was observed that a 1 um thick layer of biofilm in a milk-filled pipe shifted the reflection coefficient of a 10.16 cm diameter stainless steel circular waveguide from 0.229 GHz to 0.19 GHz. Further sensitivity analysis revealed a shift in frequency from 0.8 GHz to 1.2 GHz for a film thickness of 5 um to 10 um with the highest wave reflection (S11) peak of \(\approx\)\(-\)120 dB for a 6 um thick biofilm. A dielectric patch antenna to launch the waves into the waveguide through a dielectric window was also designed and simulated. Simulation using the antenna demonstrated a similar S11 response, where a shift in reflection coefficient from 0.229 GHz to 0.19 GHz was observed for a 1 um thick biofilm. For the case of the resonant cavity, the same antenna approach was used to excite the modes in a 0.751 m \(\times\) 0.321 m \(\times\) 170 m rectangular cavity with heat exchange fins and filled with milk and biofilm. The simulated resonance frequency shifted from 1.52 GHz to 1.54 GHz, for a film thickness varying from 1 um to 10 um. This result demonstrated the sensitivity of the microwave detection method. Overall, these results suggest that microwave sensing has promise in the rapid, non-invasive, and real-time detection of biofilm formation in dairy processing plants.  
dairy facilities; biofilm detection; RF-based method; real-time monitoring Article  
[PERSON], [PERSON], [PERSON], [PERSON] Simulation of a Radio-Frequency Wave Based Bacterial Biofilm Detection Method in Diary Processing Facilities  
10390/app14114342
## 2 Edge Computing
### Security and Privacy Challenges
#### 2.1.1 Data Security and Privacy Threats Classification  
As an extension of cloud computing, EC inherits several security challenges commonly associated with cloud environments. In addition, EC faces a unique set of security and privacy risks, such as man-in-the-middle attacks, Distributed Denial of Service (DDoS) attacks, and various other forms of intrusion attempts [33]. Adversaries frequently target EC servers during three key stages: communication, computation, and storage [31; 37]. Traditional cloud-based security approaches are often unsuitable for EC due to its limited resources. As a result, specialized security solutions are necessary to ensure the reliability and efficiency of IoT applications in EC environments [5]. The associated data security threats and challenges in EC are summarized in Table 1.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Type of Threat** & **Description** \\ \hline Hardware or software malware & Software malware exploits vulnerabilities in the operating systems, applications, or network protocols of EC systems. In contrast, hardware malware intrudes at the physical or firmware level, leveraging vulnerabilities in the hardware or its microcode. Malware such as Trojans, worms, and viruses can cause privacy leakage, power depletion, and degraded system performance [38]. \\ \hline Physical tampering and attacks & Physical attacks attempt to gain unauthorized access to and manipulate IoT devices or EC infrastructure. Adversaries might alter hardware characteristics, extract sensitive information, or insert malicious components [39]. \\ \hline Routing information attacks & The routing of data packets in a network can be maliciously manipulated by redirecting or dropping packets [31]. Such attacks include routing loops, false error messages, blackhole attacks (diverting data to incorrect destinations), and grayhole attacks (selectively dropping packets), as well as wormhole attacks, hello flood attacks, and Sybil attacks, which introduce nodes with fake identities [40]. \\ \hline DDoS attacks & Edge servers can be overwhelmed by massive data volumes that exceed network capacity during DDoS attacks, thereby disrupting their responses to legitimate users and posing ransom threats to service providers [41]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of edge security threats.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 2 Description of the Mechanism  
As mentioned above, the reference scenario considered during the mechanism design is the steps required for berthing docking when a service satellite has captured a client satellite by a robotic manipulator and is ready to dock. The primary function of the docking mechanism is to establish a fluid transfer pipeline between itself and another satellite. In the design of the mechanism, simplicity and low weight are strongly preferred qualities to decrease the driving force during docking.  
The application of the docking mechanism proposed in this paper is not restricted to only one type of satellite. The mechanism can be scaled up or down to adapt to the requirements of different missions. In this study, the design of the docking mechanism aims at the on-orbit refueling by berthing for microsatellites. According to the analyses of Section 1, the design requirements of the docking mechanism can be summarized as follows and the subsequent design need to meet the requirements:  
R1--the mechanism shall have five degrees of freedom (DOF) (except in the docking di-rection) alignment abilities to adapt to initial deviations.  
R2--the mechanism shall have self-resetting ability for reuse.  
R3--the additional force of the refueling pipe needs to be decreased as much as possible to decrease the influences for the docking operation (mainly driving force).
# Chinese News Text Classification Method via Key Feature Enhancement
## 3 Key Feature-Enhanced Chinese News Text Classification Method
### KFE-CNN Text Classification Method  
Considering the advantages of convolutional networks in classification problems, CNN is selected as the skeleton network for the entire Chinese news text classification, which contains a total of seven layers from the input text to the end of the output category probability. The relevant process of the entire KFE-CNN text classification method is shown in Figure 4.  
According to Figure 4, it can be seen that the input layer is mainly used to input expanded text content. Then, in the vector representation layer, all the text content is represented as a zero-one binary distributed vector form. Next, the distributed vector is input into the convolution layer to realize the extraction of characteristic features. The character feature vector is input into the pooling layer and then the maximum pooling method is applied to complete the pooling operation for all feature maps. The processed feature vectors are then fed into a fully connected layer to map them to the sample label  
Figure 4: The structure of the KFE-CNN classification model.  
space. Finally, the calculation results of the fully-connected layer are input into the normalization layer and the SoftMax function is used to output the normalized categories and their corresponding probability values. The relevant calculation of the normalization functions SoftMax is shown in Formula (2):  
\[P(t_{k})=\frac{e^{t_{k}}}{\sum_{k=1}^{N}(e^{t_{k}})} \tag{2}\]  
where \(P(t_{k})\) represents the probability value corresponding to the \(k\)-th category, \(t_{k}\) represents the \(k\)-th category and \(N\) refers to the total number of categories. After the SoftMax normalization calculation step, the category corresponding to the maximum probability value can be output as the final text classification result.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 4 Promising PLS Solutions for IoT
### Anti-Eavesdropping Signal Design via Constellation Rotation
In order to extract user A's signal, the untrusted relay has to first extract the real component of \(y_{\text{R}}\) and then perform the maximum likelihood (ML) detection to estimate \(\Re\{x_{\text{A}}\}\). Due to the adoption of  
Figure 2: Two-way untrusted relaying system and the constellation rotation based secure transmission method. the constellation rotation, there is a one-to-one mapping between \(\Re\{x_{\rm A}\}\) and \(x_{\rm A}\). Therefore, \(x_{\rm A}\) can be directly derived from \(\Re\{x_{\rm A}\}\). Similarly, to obtain user B's signal, the untrusted relay R extracts the imaginary part of \(y_{\rm R}\) to estimate \(\Re\{x_{\rm B}\}\) and then recover \(x_{\rm B}\). It can be proven that, due to the injection of the artificial noise, the symbol error probability (SEP) at the untrusted relay in decoding user A's information will not decrease with the increase of SNR, which yields an error floor at the untrusted relay. Similar analysis also holds for the detection of user B's message. This result indicates that the proposed constellation-rotation based scheme can effectively deteriorate the SINR at the untrusted relay, thus enhancing the transmission secrecy. During the 2 nd phase, the relay amplifies the received signal and broadcasts it to users A and B. Having received the broadcast signal, user A as well as user B performs the ML signal detection as follows: the user first applies matched filtering to the received signal, and then extracts the real or imaginary component to perform self-interference cancellation, finally applies the ML decision to the processed signal. It has been shown in [70] that the received SINR at either terminal user is a monotonously increasing function of SNR. In other words, the SEP for the detection at the terminal users sharply decreases as the SNR increases, thus guaranteeing the transmission reliability. Therefore, the proposed constellation-rotation based scheme does not heavily affect the detection performance of the legitimate users. From the implementation perspective, the main advantages of the proposed scheme can be summarized from the following aspects. First, through the rotation of signal constellations, the degree of freedom of the signal space can be fully exploited to enhance the data confidentiality, thus avoiding the excessive consumption of the power in transmitting artificial noise. Second, the rotation angles only depend on the adopted modulation format, and real-time calculations are not required to find or update the valued of the rotation angles, which yields a low implementation complexity. Third, compared with the physical-layer encryption methods which are based on secret keys, the proposed scheme does not need CSI-dependent key extraction or sharing between the legitimate users, thereby reducing system overhead significantly.
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology
### Antenna's Performance at Different Distances from the Human Body  
In this section, the impact of the antenna is investigated on different parameters, such as reflection coefficients and peak gain, when it is placed at various distances from the human body. At increasing distance values between the antenna and the human body, all the resonant frequencies shift to the higher side, as shown in Figure 10.  
Because the antenna moves away from the body, the impact of the human body's high dielectric constant on the antenna reduces. Similarly, the high dielectric constant and conductivity of the biological tissues of the human body increase losses, and due to this, the antenna's peak gain is reduced. As shown in Figure 11, as the distance between the antenna and the human body increases, the peak gain increases [33].  
At a 1 mm distance between the antenna and the body, peak gain values at 1.8 GHz, 2.45 GHz, and 5.8 GHz are 1.6 dBi, 3.0 dBi, and 7.6 dBi, respectively. But, at 7 mm distance, peak gain values at 1.8 GHz, 2.45 GHz, and 5.8 GHz are 4.4 dBi, 6.3 dBi, and 10.9 dBi, respectively.  
Figure 10: Comparison of antenna’s \(\mid\)S\({}_{11}\mid\) parameters at different distances from human body.  
Figure 9: Comparison of antenna’s efficiency in the air (without the human body) and on the human body.  
It can be observed that the antenna covers all the reported frequency bands when placed on the different body parts; only a slight detuning is observed and that also does not affect the performance due to wide bandwidths in all the reported frequency bands.  
Also, reflection coefficients in different bending scenarios (as shown in Figure 14) are shown in Figure 16. It can be observed that the proposed antenna shows a lower side shift of resonant frequencies as it experiences more bending. A small bending radius causes a shift of resonant frequencies on the lower side [27]. But, all the reported frequency bands are still covered effectively, owing to the wideband behavior of the proposed wearable antenna.  
Figure 16: Comparison of measured reflection coefficients in different bending scenarios (at 25 mm, 35 mm, and 45 mm).  
Figure 14: Antenna’s measurement: (**a**) On wrist, (**b**) On arm, (**c**) On chest.  
Figure 15: Comparison of simulated and measured reflection coefficients.