esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 6 In-Context Learning  
Finetuning is task-agnostic, but it uses a supervised approach during transfer learning and hence requires access to a large amount of labeled datasets for every downstream task. Furthermore, having such a task-specific dataset leads to finetuning the model on a very narrow distribution, which might potentially yield poor generalization on out-ofdistribution datasets. It might also be overly specific to the distribution, exploiting spurious correlations and features of the training data. The need for such labeled datasets limits the applicability of language models.  
To overcome these limitations, in-context learning (ICL) was proposed in GPT-3 [29], where the language model uses in-context information for inference. The main benefits of ICL are the minimal need for task-specific data and the fact that it does not go through any parameter updates or architectural modifications. In ICL, a prompt feeds the model with input-label pair examples, avoiding the need for large labeled datasets. Unlike finetuning, ICL has no gradient updates, so the weights of the model parameters are not updated. In ICL, the abilities that are developed by LLMs during pretraining are applied to adapt to or recognize the task at inference time, enabling the model to easily switch between many tasks.  
As experimented in GPT-3, the larger model with 175B parameters outperformed the smaller models by efficiently using in-context information. Based on the experiments conducted in GPT-3, ICL showed initial promise and improved out-of-domain generalization. However, the results are far inferior to those of the finetuning technique. ICL helps to analyze whether the model rapidly adapts to the tasks that are unlikely to be directly contained in the training set. In ICL, the model is conditioned on task instruction and a couple of task demonstrations as a context and is expected to complete the target instance of the task. As transformer-based models are conditioned by a bounded-length context (e.g., 2048 tokens in GPT-3), ICL cannot fully exploit data longer than the context window. Based on the number of demonstrations provided for inference in the context window, ICAL can be categorized as few-shot, one-shot, and zero-shot. We describe each of them below.
## 2 Experimental Setup
### Test Facilities  
For the Single Event Effect, the experiment was conducted using the pulsed laser single event effect simulation test facility of the National Space Science Center of the Chinese Academy of Sciences, and the main technical parameters of which are shown in Table 1.  
Figure 3 illustrates the block diagram of the experimental setup. The experimental circuit board with the experimental device was mounted on a three-dimensional moving table, and the movement and position of the three-dimensional moving table were controlled by computer programming. The laser generated by the pulsed laser irradiated the experimental sample, after the optics had been adjusted and the objective lens had been focused. A CCD camera provided images of the sample's surface and the laser spot on the computer display. A DC power was supplied to the sample, and the output of the supporting circuit was continuously monitored.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Parameter** \\ \hline Irradiation Source & Nd: YAG laser \\ Laser Wavelength & 1.064 \(\mu\)m \\ Spot Diameter & 3\(\sim\)4 \(\mu\)m \\ Pulse Repetition Frequency & 1\(\sim\)50 kHz \\ Minimum Scanning Step & 0.6 \(\mu\)m \\ Equivalent LET & 0.1\(\sim\)200 MeV\(\cdot\)cm\({}^{2}\)/mg \\ \hline \end{tabular}
\end{table}
Table 1: Main technical parameters of pulsed laser single event effect test facility.  
Figure 3: Schematic block diagram of pulsed laser irradiation test facility.  
Figure 2: Data description chart on PC software.  
For the Space Electrostatic Discharge Effect, an ESD generator manufactured by SANKI, model SKS-0220G, was used, which fully complied with the requirements of the IEC61000-4-2 standard in terms of performance. With an adjustable voltage range of \(\pm\)500 V to \(\pm\)20 kV, energy storage capacitance of 150 pF and discharge resistance of 330 \(\Omega\), the ESD generator met the most severe electrostatic discharge voltage requirement in the standard. Figure 4 shows the current waveform of this ESD generator, which can be compared to the typical space discharge current waveform.  
The experimental setup is depicted in Figure 5. The device under test (DUT) was placed horizontally on an insulated table, powered by a DC regulated power supply. Data was written and received through the serial port by a host computer. The radiation field method recommended by NASA-HDBK-4002A [5] was used for this experiment, and the ESD generator was used to discharge on a 10 cm \(\times\) 10 cm aluminum plate grounded by a cable at one end.  
Figure 4: Current pulse waveform of ESD generator.  
Figure 5: Schematic diagram of space electrostatic discharge test setting.
# Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
## 3 Results and Discussion
### Validation  
Reflectance and two sets of fluorescence hyperspectral data cubes were acquired of a non-reflective board that had three baby spinach leaves and three dollops of dairy manure on it. Figure 10 shows a photograph of the target. Figures 11-13 show an image at a selected wavelength and representative spectra for the leaves and for the manure. The banding in the reflectance images is due to changes in ambient illumination over time. For one fluorescence acquisition (Figure 12), the gate delay and width were set to capture the full fluorescence response of both the leaves and the manure. For the other fluorescence acquisition (Figure 13), the delay was extended to allow the response from the leaves, which has a faster decay rate, to die out while still capturing the tail of the response from the manure. This result suggests that detection of fecal materials in fields could be accomplished using a single wavelength by taking into account relative fluorescence decay characteristics of fecal materials and intact plant materials. The ability to differentiate the fluorescence responses from intact plant materials and from manure using differences in fluorescence decay rates has been previously documented in laboratory settings [7; 9; 10].  
Figure 10: Photograph of the target with three spinach leaves and three dollops of dairy manure.  
Figure 9: The bottom figure shows a false color image of the illumination profile for a single laser pulse using the Powell lens. The five-point average across the center of the profile that is displayed immediately above the false color image demonstrates the uniformity of the profile.  
Figure 11: Reflectance image at 673 nm. The graph shows the representative spectra for spinach leaves and dairy manure.  
Figure 12: Fluorescence image at 673 nm using a gate delay of 5 ns and a gate width of 25 ns. The graph shows the representative spectra for spinach leaves and dairy manure. The peaks around 532 nm are spillover from the doublet frequency of the frequency-tripled laser.
## 7 Conclusions and Future Work  
In this paper, we have discussed the potential of NDN to support fog computing in smart environments. After scanning the relevant literature, our proposal has been presented that is tailored without loss of generality to the representative IoT smart campus scenario. We have proposed a service orchestration mechanism which is fully aligned with the fog computing concept. Indeed, it supports the decision of whether the IoT data processing has to be performed at the edge or remotely in the cloud in a fully distributed manner and targeting the minimization of the service provisioning time.  
Achieved simulation results confirm the supremacy of our proposal against literature solutions under different workload settings (i.e., input content size and arrival rate of service requests). Improvements are significant in terms of reduction of the service provisioning time. As a drawback, the proposal incurs a slightly higher amount of traffic exchanged in the edge domain. Such a condition holds when comparing NDN-fog against the conventional cloud solution only for small content sizes. The larger traffic exchanged compared to the CE strategy is widely compensated by the achieved reduction of the service provisioning time, which is well below 10 s for NDN-fog under all the considered settings.  
All in all, the proposal adequately targets a user-centric policy which values more how the consumers perceive the services in terms of service provisioning time, in our case, than the incurred traffic in the edge domain.  
Future works will be devoted to the design of service executor strategies with different objectives, e.g., targeting computing load balancing and reducing the amount of exchanged traffic while also considering services with different priorities. Moreover, we plan to investigate the integration of the NDN fog computing paradigm with other emerging techniques, such as software-defined networking (SDN) and network function virtualization (NFV).  
In this paper, [PERSON], [PERSON], [PERSON], and [PERSON] conceived the idea. [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments. All the authors ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]) organized the work; analyzed the experiments; and reviewed the writing of the paper, its structure, and its intellectual content.  
This research was partially funded by the Italian Government under grant PON ARS01_00836 for the COGITO (A COGIntive dynamic sysTem to allOw buildings to learn and adapt) PON Project.  
Figure 8: Offloading-to-the cloud percentage for the NDN-fog solution when varying the service request arrival rate (\(\lambda\)) for different content size (\(\sigma\)) and round-trip-time (RTT) settings, \(\alpha=0.4\).  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# High-Speed Photodetectors for Microwave Photonics
## 4 High-Power Photodiodes on Si  
The development of photonic integrated circuits on silicon has large technological and commercial significance, since it can leverage the mature Si CMOS technology to reduce the manufacturing costs.  
Figure 11: Radiation power of integrated photonic emitters reported in the literature.  
Figure 10: (**a**) Effective isotropic radiated power (EIRP) of the integrated photonic emitters with 5-, 6-, and 14-\(\upmu\)m-diameter PDs at 10-mA photocurrent from 95 to 110 GHz. (**b**) EIRP at 100 GHz as a function of average photocurrent.  
However, in order to achieve optoelectronic functionality at 1.55 \(\upmu\)m wavelength, typically other materials have to be integrated on silicon. For PDs, the candidates include group III-V semiconductors and Germanium.
# Thermal Energy Storage Performance of Tetrabutylammonium Acrylate Hydrate as Phase Change Materials
## 3 Results and Discussion
### In the System with Mechanical Agitation
Regarding the thermal energy storage rate, \(\dot{q}\) is proportional to the difference between the inflow temperature \(\mathrm{T_{in}}\) and the outflow temperature \(\mathrm{T_{out}}\). This was why \(\dot{q}\) had almost the same trend as the \(\mathrm{T_{out}}\) change in the static system. As \(\mathrm{T_{out}}\) increased rapidly more than the static system, \(\dot{q}\) increased rapidly more than the static system as well, rising almost vertically. The maximum thermal energy storage rate at that time was about 6 to 8 times faster than that of the static system. The larger the rotation rate of the agitation increased the value of \(\dot{q}\). After a rapid increase, the change in \(\dot{q}\) suddenly turned into a downward movement and gradually descended to 0. This was because the hydrate had grown, and the viscosity had increased, which resulted in less convection. As shown in Figure 8b, \(\overline{q}\) in the agitated system increased earlier than that in the static system, and the higher rotation rate of the agitation increased \(\overline{q}\) earlier. This was because the temperature of the thermal energy storage medium was lowered quickly by forced convection, and thus hydrate nucleation also occurred quickly. The thermal energy storage density \(\overline{q}\) was larger than that of the static system and increased by a higher rotation rate. This was because the forced convection caused by the agitation allowed hydrate to be formed in the entire bulk of the solution. In the agitated system with the rotation rates of 100 rpm, 300 rpm and 600 rpm, thermal energy density reached 121 MJ/m\({}^{3}\), 159 MJ/m\({}^{3}\) and 183 MJ/m\({}^{3}\) in 10 h, respectively. Similarly, assuming the TBAAc hydrate has 200 MJ/m\({}^{3}\) of heat in hydrate formation and decomposition, the thermal energy storage density corresponds to a solid phase fraction of 61%, 80% and 92%, respectively.
## 2 Model of Dynamic Imaging
### Curvature Distortion Mapping Function of Ground Pixels  
For explaining the curvature mapping relationship between the ground target image and camera focal plane image, the local frame-by-frame imaging model between the object and the image is established, as shown in Figure 4.  
Firstly, the phase coordinate system is set, in which the center of the focal plane of the CMOS camera is the coordinate origin \(O\). There are \(i\times j\) imaging units on the phase surface (the number of imaging units in the X direction is \(i\), and the number of imaging units in the Y direction is \(j\)). Secondly, the object plane coordinate system is set, in which the center point of the ground imaging area is the coordinate origin \(Q\). Then, the field angle \(\delta\) and \(\mu\) of any pixel point in the X direction and the Y direction on the camera focal plane can be expressed as:  
\[\tan\delta=\frac{2p_{x}-i}{2f}\cdot a \tag{11}\]  
Figure 4: Schematic diagram of local dynamic frame-by-frame imaging.  
Figure 3: Schematic diagram of overall dynamic frame by frame imaging.  
\[\tan\mu=\frac{2p_{y}-j}{2f}\cdot a \tag{12}\]  
In the formula, \(f\) is the focal length of the camera, \(a\) is the pixel size, while \(p_{x}\) and \(p_{y}\) are the X and Y components of the target pixel in the phase coordinate system, respectively. Then, any pixel point in the object plane coordinate system can be expressed as:  
\[g_{x}=\frac{H\cdot\tan\delta}{\cos(\theta+\mu)} \tag{13}\]  
\[g_{y}=H\cdot\tan(\theta+\mu) \tag{14}\]  
In the formula, \(g_{x}\) and \(g_{y}\) are X and Y components of target pixel point in the object plane, respectively. Combining Formulas (13) and (14), the curvature mapping function \(\xi\{g(x,y)\}\), which represents the relationship between the ground target image and the phase surface distortion image, can be expressed as:  
\[\xi\{g(x,y)\}=\left\{\begin{array}{l}g_{x}=\frac{Ha(2p_{y}-i)}{2f\cos\left\{ \theta+\arctan\left[a\left(2p_{y}-j\right)/2f\right]\right\}}\\ g_{y}=H\tan\left\{\theta+\arctan\left[a\left(2p_{y}-j\right)/2f\right]\right\} \end{array}\right. \tag{15}\]
# Optic Disc Detection from Fundus Photography via Best-Buddies Similarity
## 3 Results  
### Data and Parameter Settings  
Algorithm performance was tested and comprehensively analyzed on the DRIVE and DIARETDB1 database, respectively. The DRIVE database contains 40 images obtained from a diabetic retinopathy screening program in The Netherlands. The images were acquired using a Canon CR5 non-mydriatic 3 CCD camera (Canon, Tochigiken, Japan) with a 45-degree field of view (FOV). Each image is 565 \(\times\) 584 pixel and 8 bits per color channel. The set of 40 images was divided into a training and a test set, both containing 20 images [27]. The DIARETDB1 database consists of 89 retinal images, of which 84 contain at least mild diabetic retinopathy and 5 are considered normal. Images were captured using the same 50-degree field-of-view digital fundus camera with varying imaging settings, and each image is 1500 \(\times\) 1152 pixel. To test our method, the OD boundary and center of each image from both databases were manually marked by a trained ophthalmologist, which were used as the ground truth for constructing the template image and evaluating the performance of OD detection, respectively.  
The appearance of OD such as the shape, color, and size showed large variance, especially in the presence of retinopathies. Choosing different fundus images to construct templates and a different number of templates will correspondingly affect the final results. We selected fundus images withrepresentative features of ODs from the DRIVE and DIARETDB1 databases at random, and we constructed minimum bounding rectangles containing the entire OD region on the basis of these images as template images. The different results using different numbers of templates are shown in the Results section. Because the proposed method has strong robustness, we only need a small number of images with complete features of ODs as template images. During the experiment, the target image selected as the template was matched using other templates without matching it to itself.  
To obtain the specific performance indicators of our method, we defined criteria for correctly detecting ODs, whereby the estimated OD center was inside the contour of the OD in the fundus photograph, and the distance between the estimated OD center and its manually identified OD center was within 60 pixel [5].
## 5 Half-Duplex
### Optimal Time Allocation Scheme  
The energy efficiency optimization problem is converted to  
\[max_{\alpha}\xi(\alpha)=\frac{(1-\alpha)log_{2}\Bigg{(}1+min\Bigg{\{}\frac{|h _{1}|^{2}P_{s}}{N_{0}},\frac{2\eta P_{s}|h_{1}|^{2}|g|^{2}\alpha}{N_{0}(1- \alpha)}\Bigg{\}}\Bigg{)}}{2(\beta P_{s}+P_{c})},\text{s.t.}0<\alpha<1. \tag{53}\]  
The time switch threshold is \(\alpha_{3}=\frac{1}{1+2|g|^{2}}\).  
Therefore, the optimal time allocation scheme for maximizing energy efficiency can be transformed into two processes: \((1)1(1)0<\alpha_{3}<\frac{1}{1+2|g|^{2}}\) and \((2)\frac{1}{1+2|g|^{2}}<\alpha_{3}<1\).  
For the first case, the time allocation problem of maximizing energy efficiency can be translated into  
\[\begin{array}{c}max_{\alpha}\xi(\alpha)=\frac{(1-\alpha)log_{2}\Bigg{(}1+ 2\eta P_{s}|h_{1}|^{2}|g|^{2}\alpha\Bigg{)}}{N_{0}(1-\alpha)},\\ s.t:0<\alpha<\frac{1}{1+2|g|^{2}}.\end{array} \tag{54}\]  
Therefore, we can get the optimal time allocation factor \(\alpha^{*}\) in the range of \(0<\alpha<\frac{1}{1+2|g|^{2}}\), which is expressed as  
\[\alpha^{*}=\frac{exp\Big{(}W\Big{(}\frac{\alpha_{4}-1}{c}\Big{)}+1\Big{)}-1}{ a_{4}-1+exp\Big{(}W\Big{(}\frac{\alpha_{4}-1}{c}\Big{)}+1\Big{)}}, \tag{55}\]  
where \(a_{4}=\frac{2\eta|g|^{2}P_{s}|h_{1}|^{2}}{N_{0}}\).  
For the second case, the energy efficiency optimization problem can be translated into  
\[\begin{array}{c}\underset{\alpha}{max}\xi(\alpha)=\frac{(1-\alpha) log_{2}\left(1+\frac{|h_{1}|^{2}P_{S}}{N_{0}}\right)}{\beta P_{S}+P_{c}},\\ s.t:\frac{1}{1+2\eta|g|^{2}}<\alpha<1.\end{array} \tag{56}\]  
The energy efficiency derivation function can be obtained as  
\[\frac{\partial\xi(\alpha)}{\partial\alpha}=-\frac{log_{2}\left(1+\frac{|h_{1}| ^{2}P_{S}}{N_{0}}\right)}{\beta P_{S}+P_{c}}. \tag{57}\]  
Since \(\frac{\partial\xi(\alpha)}{\partial\alpha}\) is always less than zero in the range of \(\alpha\in\left(\frac{1}{1+2\eta|g|^{2}},1\right)\), the energy efficiency \(\xi(\alpha)\) is a monotonically decreasing function at \(\alpha\in\left(\frac{1}{1+2\eta|g|^{2}},1\right)\). When \(\alpha=\frac{1}{1+2\eta|g|^{2}}\), the energy efficiency \(\xi(\alpha)\) takes the maximum value in the range of \(\alpha\in\left(\frac{1}{1+2\eta|g|^{2}},1\right)\). Therefore, the above two cases can be represented as  
\[\alpha^{*}=\left\{\begin{array}{cc}\frac{\alpha\
u\left(\left(\frac{\alpha ^{*}}{\tau}\right)+1\right)-1}{\alpha\
u+1-\alpha\
u\left(\left(\frac{\alpha^ {*}}{\tau}\right)+1\right)},&if\left(\
u\left(\frac{\alpha^{*}-1}{\tau}\right)+ 1\right)<ln\left(\frac{\alpha}{2\eta|g|^{2}}+1\right)\\ \frac{1}{1+2\eta|g|^{2}},&otherwise\end{array}\right.\right. \tag{58}\]
# Unsupervised Deep Learning-Based RGB-D Visual Odometry
## 4 Experiments
### Discussion  
Compared to our previous work, the main contribution of this paper is to incorporate an additional depth channel to the unsupervised VO system. The depth information is used for both network training and pose estimation. Experiments have shown that the proposed dual-channel network managed to reduce translation and rotation errors significantly during pose estimation. According to Table 2, the average RMSE of translation estimation (\(t_{rmse}\)) in terms of sequence 00-10 decreases from 4.38% to 1.89%, a drop of 56%, and the average RMSE of rotation (\(r_{rmse}\)) \(\times\) 100 decreases from 1.84\({}^{\circ}\)/m to 1.10\({}^{\circ}\)/m, a drop of 40%.  
Figure 8: Sequence 11–20 trajectories.  
However, we have observed the system suffered additional computational cost at the same time. In order to evaluate time complexity, we tested our VO system, UnMono, SfMLearner, and VISO2-Stereo on a DELL computer with an Nvidia GeForce GTX 980 GPU (Xiamen, China) with 4 GB memory. Figure 9 shows the time required for each camera pose estimation. It should be noticed that methods based on deep learning generally require more time than traditional feature-based methods. Our system, SfMLearner, and UnMono require a GPU for evaluation, whereas VISO2-Stereo relies on a single CPU and achieves a faster speed. After incorporating the depth channel, the time required for each camera pose estimation increased from 87.3 ms to 102.7 ms, an increase of 18%. In some cases, it is acceptable to sacrifice a small amount of computational power in order to effectively improve accuracy. However, computational efficiency is also an essential factor that should not be ignored. In the future, we will adjust our algorithm to further improve it.  
On the other hand, compared to feature-based stereo VO system VISO2-Stereo, the translation error of our system is slightly smaller, but the rotation error is twice as high. This is because compared to translation, rotation is highly nonlinear, thus is more difficult to train. One possible solution is to increase the training dataset size and increase the ratio of rotational scenes.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.9 De-Synchronization Attack Resistance  
Even if the first message flow is intercepted by the adversary during the key agreement phase, DC does not need to update the parameters in the database, which will not affect the consistency of the next communication. Even if the second message flow is intercepted by the adversary, it still cannot affect the consistency of communication between SM and DC. Only when \(M_{8}^{*}=M_{8}\) is established will the communication parties update the data. Therefore, the scheme can resist de-synchronization attack.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology
### Dataset Selection
#### 3.1.1 Materials  
Firstly, the authors designed an eight-channel GPS receiver using a Universal Software Radio Peripheral (USRP) unit, a front-end active GPS antenna, and an open-source Global Navigation Satellite System Software-Defined Receiver (GNSS-SDR). This architecture allowed real-time extraction of GPS signal features at critical points of the processing blocks, including acquisition and tracking. Furthermore, the proposed architecture is capable of tracking diverse satellites simultaneously, providing a more efficient analysis of GPS signals and the various types of spoofing attacks.