esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Zero-Shot Approaches  
Zero-shot stance detection addresses the challenge of generalizing to unseen targets without requiring target-specific labeled training data [26]. The early zero-shot approaches mainly rely on domain transfer methods [9; 27; 28; 29; 30], contrastive learning techniques [31], and the integration of external knowledge [32; 33] to learn generalizable stance representations.  
For example, TOAD [9] formulates stance detection as a domain-adaptive learning problem, employing task-oriented adversarial learning to mitigate distribution shifts between seen and unseen targets. JointCL [10] employs contrastive learning to enhance target-agnostic representations by training on multiple datasets and minimizing task-specific biases.  
Recently, LLMs, such as GPT-3.5, have emerged as powerful tools for zero-shot stance detection. Rather than task-specific training, in-context learning and prompt engineering enable LLMs to perform stance classification without fine-tuning. For example, based on GPT-3.5, COLA [13] introduces a collaborative, role-infused framework where multiple LLM-based agents interact to infer stance by modeling discourse context and user interactions. MB-Cal [34] focuses on reducing biases in LLMs for stance detection, using calibration techniques to enhance model robustness and fairness.  
Although these methods have demonstrated effectiveness, both supervised and zero-shot approaches still face significant challenges in accurately detecting stance when the target is implicit as they often struggle to capture the nuanced relationships and indirect associations that arise in real-world scenarios.
## 1 Introduction
### Motivation  
Cell densification of HetNet enhances the spectrum efficiency in an energy-efficient way. On the other hand, cell densification potentially increases the inter-cell interference, especially at the cell edges, which deteriorates the QoS of user equipments (UEs).  
Interference coordination schemes by means of radio resource allocation and power control can be implemented to achieve higher energy efficiency and spectral efficiency [2]. However, the growing complexity of wireless networks due to increased links and heterogeneous network structure create tremendous challenges for system designs, thus calling for more intelligent techniques for effective yet efficient resource management strategies. In this perspective, data-driven machine learning techniques have been regarded as viable new approaches to dealing with complex network dynamics [5; 6; 7; 8; 9; 10]. Compared with traditional model-based algorithms [11; 12; 13; 14], deep reinforcement learning (DRL), leveraging recent advances in deep neural networks with reinforcement learning [15; 16; 17], can autonomously extract features from the raw data with different formats and complex correlations experienced by the mobile environments. This potentially reduces the cost of data pre-processing [11; 18]. In view of this, in this paper, we provide a reinforcement-learning-based solution for power allocation and radio device association with the objective of maximizing energy efficiency while satisfying the required QoS and wireless backhaul capacity constraints.
## 4 Discussion
While the manufacturer recommends placing the near segment inferiorly and slightly nasally displaced, they also assert that the near segment can be positioned in different orientations without any adverse effects. However, it cannot be overlooked that the asymmetrical design of the multifocal AN6V and AN6 VM can produce changes on the aberrations due to the position of the IOL, while the position of a symmetrical IOL does not affect aberrations. In fact, significant differences were noted in total vertical coma aberration between the IOL placed vertically with the near add inferior and the near add superior. The differences varied in magnitude, and the inferonasal group showed negative values [4,5]. The position of the near segment in eyes with rotationally asymmetric multifocal IOLs has been evaluated. [PERSON] et al. [5] and [PERSON] et al. [4] demonstrated that the position had no significant effect on visual performance. This design has also prompted some authors to suggest that positioning the near segment differently could enhance visual outcomes in cases of irregular astigmatism. [PERSON] and [PERSON] [21] published a case in which rotating the Mplus lens resulted in a noticeable improvement in near vision for a patient with irregular astigmatism. In this regard, for a 4-mm optical zone, our results for primary vertical coma were 0.00 microns for the +30.00 D monofocal IOL, +0.17 microns for the +30.00 D AN6V IOL (with +1.50 D addition), and +0.35 microns for the +30.00 D AN6 VM IOL (with +3.00 D addition). This could suggest that an eye implanted with a +30.00 D Acunex AN6 VM with a cornea not inducing coma would have similar vertical coma values to an eye with a mild keratoconus. Our findings, coupled with those reported by other authors employing similar lens designs, demonstrate the capacity of asymmetric multifocal near segment lenses to function effectively in orientations beyond the vertical placement with the near segment positioned inferiorly. Additionally, they highlight the lens's resilience against less-than-ideal corneal topography. Comparing the lens-induced coma with the patient's corneal coma could assist ophthalmologists in determining the optimal near segment positioning. The main limitation in our study is the impossibility of comparing results with other works, since this is the first article reporting power and aberrometric profiles for the AN6V and AN6 VM IOLs. Another limitation relates to the fact that we have measured optical quality of the IOLs when isolated, and not inserted in an eye model, so our results for the AN6 are not directly comparable to other works, such as [PERSON] et al. [14], who describe a total SA of an eye model implanted with a +22.00 D AN6 IOL to be about \(-0.03\)\(\upmu\)m, which could provide a very good image contrast.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 4 Results
### Analysis of Geographical-Optimal-Similarity Model Prediction Results
#### 4.2.3 Uncertainty Analysis of Prediction Results  
Figure 8 illustrates the uncertainty analysis findings of the prediction under various confidence levels. The results indicated that when different confidence levels were selected (\(\zeta\) = 0.9, 0.95, 0.99, 0.995, 0.999, and 1), the uncertainty levels of the prediction results were spatially consistent. As the \(\zeta\) value decreased, the uncertainty value increased. When the confidence level was 0.9, the uncertainty value reached its maximum, and most areas had uncertainty values less than 0.001. High values (\(\Delta>0.01\)) were concentrated primarily in the northern part of the study area in the Taiping Lake water region, as shown in the red box in Figure 8. Because the central areas of water bodies do not provide conditions for landslides, they did not affect the landslide susceptibility assessment, and the prediction findings were valid and reliable, with strong stability in the GOS model predictions.
## 3 Experimental Results and Analysis
### Security Analysis  
This section evaluates the performance of image encryption when we encrypt the stego HDR RGBE image by our scheme to produce a stego encrypted HDR RGBE image, \(I_{SE}\). To the best of our knowledge, no existing security metrics are available to evaluate an encrypted HDR image. Since the interest of our study focuses on the RGBE format with four channels, we instead modify the LDR security evaluation metrics extending them to four channels in an HDR RGBE image. In this study, we use six categories to analyze the security of the encrypted images: the visual perception, histogram, correlation, entropy, image sensitivity, and key security.  
Figure 4: An exhibition of 20 stego tone-mapped images before the encryption process.  
Figure 5: Stego HDR images “dani_cathedral”: (**a**) direct display of an HDR image; (**b**) and (**c**) using different tone-mapped algorithms [37] and [2], respectively, and then displaying the stego low dynamic range TM images.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 3 The Sensor Layer
These stations measure various data for precision agriculture such as airflow, seasonal rainfall, speed of wind, humidity level, direction of wind, atmospheric pressure and solar radiation, etc. Weather stations are an important component of EO technologies since they can provide daily agro-meteorological information regarding the cultivating fields. Summarizing the above, Table 1 presents the most frequently acquired data by sensors in the agricultural domain. A sensor node consists of a radio transceiver with an internal antenna or a connection to an external antenna, a micro-controller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or a built-in energy harvested form. There are numerous commercial models of micro-controllers to be used in precision agriculture applications. The most popular ones are the Arduino, the Raspberry Pi, the Atmega328 and the LPC2148 boards. Accordingly, commonly used wireless communication modules used are the XBee module, the WSN802G module and the NRF24L01 module. A sensor node can vary in size and cost, depending on the complexity of its capabilities. Size and cost constraints result in corresponding limitations on resources such as energy, memory, computing speed, and bandwidth of communications. The types of sensors that are mostly used in Smart farming monitoring systems are summarized in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Type** & **Sensor Type** \\ \hline Soil moisture and temperature & Ground sensors \\ \hline Soil color & UAV sensors \\ \hline Environmental humidity and temperature & Ground sensors or UAV sensors \\ \hline Leaf-wetness & Ground sensors or UAV sensors \\ \hline Electric conductivity & Electrochemical sensors \\ \hline Wind speed and direction & Weather stations \\ \hline Barometric pressure & Weather stations \\ \hline Carbon dioxide & Electrochemical sensors \\ \hline Ph value & Electrochemical sensors \\ \hline Light intensity & Weather stations or Ground sensors \\ \hline Solar radiation & Weather stations or Ground sensors \\ \hline Rainfall & Weather stations \\ \hline Size of crops & UAV sensors \\ \hline Shape of crops & UAV sensors \\ \hline Thickness of plant stem & UAV sensors \\ \hline Latitude, longitude and altitude of the plants & Location sensors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Precision agriculture sensor data types.
## 5 Results and Discussion  
In order to facilitate the discussion, the experimental data of Table 3, Table 6 and Table 7 are all shown in Figure 6. As seen in Figure 6, it might be concluded that the prediction equation of \"Model-B + Model-B\" and the irregular polygon model has higher accuracy because the broken lines of \"Model-B + Model-B\", simulation and experiment match well with each other. For \"Model-A + Model-A\", \"Model-A + Model-B\" and \"Model-B + Model-A\", there are obvious deviations due to neglecting interactions between components. The addition of ZrW\({}_{2}\)Os can significantly reduce the CTE of resin concrete. Sample No.3 has lower CTE than the other two samples. The volume ratio of aggregate to ZrW\({}_{2}\)Os\({}_{8}\) powders is an important factor which influences the concrete CTE.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 4 Security Scheme
### Group Key Construction Phase
#### 4.3.1 Multicast Approach  
Using LI through the \(n\) points \((x_{s},y_{s})=(B_{s},k_{ps})\) with \(s=1,\ldots,n\), together with the point containing the group key \((0,k_{g})\), a polynomial of degree \(n\) can be constructed. Next, \(n\) other points \((v,V_{v})\) with \(v=1,\ldots,n\) on this polynomial are derived. Finally, \(P\) sends the following message using the multicast address \(ID_{g}\):  
\[T_{g},h_{w},V_{1}\oplus H(x),\ldots,V_{n}\oplus H(x),\] \[H(ID_{G}\|T_{g}\|V_{1}\|\ldots\|V_{n}\|k_{g}\|h_{w})\]  
with \(T_{g}\) the current timestamp.  
When this message arrives to all subscribers of the multicast group \(ID_{G}\), each subscriber \(S_{s},s=1,\ldots,n\) constructs the polynomial through the \(n+1\) points with coordinates \((v,V_{v})\), (for \(v=1,\ldots,n\)) and its own point \((B_{s},H(ID_{G}\|k_{s}))\). The group key \(k_{g}\) is derived as the intersection point of this polynomial with the X-axis. Its integrity is confirmed using the hash value of the message.  
Figure 2: Summary of the registration phase.
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.2.3 Problem Description  
We take the system EE as the optimization objective, and the EE is defined as the ratio of the system communication rate to the total power consumption, which can be modeled as:  
\[\mathrm{EE}=\frac{\sum\limits_{l=1}^{L}\min\left(\left\{R_{k,l}\right\}_{k=1}^ {\left|\mathbf{l}_{l}\right|}\right)}{P_{total}}=\frac{B\sum\limits_{l=1}^{L}\log _{2}\left(1+\min\left\{\frac{\left|\mathbf{h}_{k,l}^{H}\mathbf{f}_{l}\right|^{2}}{ \sum\limits_{j=1,j\
eq l}^{L}\left|\mathbf{h}_{k,l}^{H}\mathbf{f}_{j}\right|^{2}+\sigma ^{2}}\right\}_{k=1}^{L_{s}}\right)}{P_{l}+P_{0}}, \tag{19}\]  
where \(P_{total}\) represents the total power consumption, \(P_{t}=\frac{L}{l}|f_{l}f_{l}^{H}|\) denotes the transmission power of the LEO satellite, and \(P_{0}\) denotes the inherent power consumption of the communication system.  
Let the Boolean variable \(\eta_{k,l}\in\{0,1\}\) indicate whether the \(k\)th user in the \(l\)th multicast group is served, \(\eta_{k,l}=1\) and \(\eta_{k,l}=0\) indicate that the user can be served and not served, respectively, and \(\mathbf{\eta}=\left[\mathbf{\eta}_{1},\mathbf{\eta}_{2},\ldots,\eta_{L}\right]\), \(\mathbf{\eta}_{l}=\left[\eta_{1,l},\eta_{2,l},\ldots,\eta_{\left|\mathbf{U}_{l},L \right|}\right]^{T}\). In conclusion, under the constraints of the transmission power and QoS, the problem of maximizing system EE can be modeled as:  
\[Q_{1}:\max_{\mathbf{\eta}_{d},\mathrm{SINR}_{l}^{\min}}\mathrm{EE}=\frac{B\sum \limits_{l=1}^{L}\log_{2}\left(1+\mathrm{SINR}_{l}^{\min}\right)}{\sum \limits_{l}^{L}\left|\mathbf{f}_{l}\mathbf{f}_{l}^{H}\right|^{2}+P_{0}}, \tag{20}\]  
\[s.t.\ C_{1}:\eta_{k,l}\in\{0,1\},\forall k,l, \tag{21}\]  
\[C_{2}:\mathrm{SINR}_{k,l}\geq\eta_{k,l}\mathrm{SINR}_{l}^{\min},\forall k,l, \tag{22}\]  
\[C_{3}:\mathrm{SINR}_{l}^{\min}\geq\mathrm{SINR}_{0},\forall l, \tag{23}\]  
\[C_{4}:\sum\limits_{k=1}^{\left(U_{l}\right)}\eta_{k,l}=U_{s},\forall l, \tag{24}\]  
\[C_{5}:\sum\limits_{l=1}^{L}\left|\mathbf{f}_{l}\mathbf{f}_{l}^{H}\right|^{2}\leq P_{T}, \tag{25}\]  
where \(\mathrm{SINR}_{l}^{\min}\) represents the minimum SINR of the \(l\)th multicast group and constraint \(C_{3}\) represents that \(\mathrm{SINR}_{l}^{\min}\) should be greater than the minimum SINR constraint \(\mathrm{SINR}_{0}\). Constraint \(C_{4}\) limits the number of scheduled users in each multicast group to \(U_{s}\).
## LSTMCaps Autoencoder Network
### Capsule Network  
The Capsule network (CapsNet) [25], is a novel neural network architecture designed to address the issues the convolutional neural network has with spatial context. It does this by \"encapsulating\" the spatial information between the variables using vectors, which allows the neural network to learn the distances between the identified features as well as the classification of the features. A capsule differs from the traditional artificial neuron in various ways: A traditional neuron receives scalar inputs; performs the weighted sum of the aforementioned scalars; applies an activation function and outputs a scalar dependant on the weights and biases that it has adopted through training. A capsule on the other hand, whilst operating in a similar fashion, slightly differs from the internal operation and the representation of the values that it receives. A capsule receives a vector input, where the input denotes the probability of occurrence as well as orientation and other spatial features not captured by a scalar value. It applies an \"affine transformation\" which is essentially a transformation matrix weight that replaces the traditional scalar weight; this operation is formally defined in Equation (7) [25]:  
\[\hat{u}_{j|i}=W_{ij}u_{i} \tag{7}\]  
This transformation matrix is used to represent the spatial context that is missing from the traditional method of weight application. The weighted sum of these vectors is calculated using Equation (8) [25]:  
\[s_{j}=\sum_{i}c_{ij}\hat{u}_{j|i} \tag{8}\]  
In order to preserve the vector information that is input to the capsule, a new type of activation is proposed, known as the non-linear \"squashing\" function. This operates similar to the normal activation functions discussed previously by squashing the output between 0 and 1 but does so in a way that is able to preserve the length and spatial information of the input values, so that a long vector will shrink to a value just below 1 and shorter vectors are shrunk to near 0. Equation (9) [25] formally defines this operation:  
\[v_{j}=\frac{||s_{j}||^{2}}{1+||s_{j}||^{2}}\times\frac{s_{j}}{||s_{j}||} \tag{9}\]
## 4 GPU Performance Issues
### Bank Conflict  
As with global memory accesses, access to shared memory also occurs at the warp level. In other words, every access request made by the warp to shared memory takes place in a single operation. In the worst case, each access request to shared memory is executed sequentially in 32 separate processes. Shared memory is divided into 32 equal-sized memory modules (banks) having a simultaneous access pattern to achieve high memory bandwidth. There are 32 threads in a warp, thus 32 banks. When multiple addresses in the shared memory request indicate the same bank of memory, a bank conflict occurs, causing a re-execution of the request. The system divides the memory request with a bank conflict into multiple non-conflicting processes. This situation reduces the effective use of memory bandwidth. For example, if all 32 threads in a warp access different memory locations of a single bank, this memory request is handled by 32 separate memory operations. When all addresses in the shared memory request point to different memory banks, a single memory operation executes this request. This access with no bank conflicts is a parallel access to shared memory. When all threads in a warp request access to the same memory bank, a single memory operation executes this access, and, in this case, bandwidth usage is low.
## 3 Implementation and Experimental Results
### Experiments on Automatic Template Extraction and Die Detection  
The proposed method for template extraction was verified using images _img01_, _img02_, and _img03_. The parameters used in this experiment are as follows:  
* The size of the original SAT image is: \(w_{\text{Orig}}=\) 30,000 and \(h_{\text{Orig}}=\) 30,000.
* The size of the image patch is: \(w_{\text{P}}=w_{\text{Orig}}/5=6000\) and \(h_{\text{P}}=h_{\text{Orig}}/5=6000\). This size is determined to ensure that there are sufficient die patterns in this image patch. If template extraction fails, this size can be increased by \(w_{\text{P}}=w_{\text{Orig}}/4=7500\), \(h_{\text{P}}=h_{\text{Orig}}/4=7500\), and so on.
* The size of the initial template is: \(w_{\text{Tpl}}=w_{\text{P}}/3=2000\) and \(h_{\text{Tpl}}=h_{\text{P}}/3=2000\). The criterion for determining this size is to ensure that there exists one (or more) whole die pattern in this initial template. Generally, this size is big enough to detect and extract a standard template.  
Figure 11: More examples of abnormal patterns: (**a**) crack; (**b**) defect; (**c**) and (**d**) errors caused by voids.  
Figure 12: Deep convolutional neural network (CNN) for die pattern classification.  
* The similarity threshold is the 90 th percentile value of the map \(R(x,y)\), that is, \(T_{\text{SIM}}=0.9\times\underset{x,y}{\max}\{R(x,y)\}\).
* The binarization thresholds are adaptively determined using [PERSON]'s method [20].  
Figure 13 shows the extracted templates, in which subplots (a), (b), and (c) correspond to _img01_, _img02_, and _img03_, respectively, and the estimated template size can be found in Table 1. These are very similar because their original SAT images are from the same batch of wafer products. Next, we apply template matching followed by clustering to obtain an initial wafer map that contains the detected die patterns (marked by the yellow dots) and predicted pattern candidates (marked by the blue dots). Figure 14 shows the results of the initial wafer maps for images _img01_, _img02_, and _img03_. These wafer maps need to be further analyzed by conducting our proposed classification model for every pattern.
## 4 Controller Design
By integrating both sides of (52) simultaneously with respect to \([0,t]\), it can be found that  
\[0\leq V_{n}(t)\leq\frac{D_{n}}{C_{n}}+[V_{n}(0)-\frac{D_{n}}{C_{n}}]e^{-C_{n}t} \tag{53}\]  
Denote the initial conditions are \(\widetilde{w}(0)=[\widetilde{w}_{1}(0),\cdots,\widetilde{w}_{n}(0)]^{T}, \widetilde{\theta}(0)=[\widetilde{\theta}_{1}(0),\cdots,\widetilde{\theta}_{n}(0)] ^{T}\), \(\widetilde{\widetilde{v}}(0)=[\widetilde{\widetilde{v}}(0),\cdots,\widetilde{ \widetilde{v}}(0)]^{T}\), and \(z(0)=[z_{1}(0),\cdots,z_{n}(0)]^{T}\). According to (53), it can be seen that, with the definition of \(V_{n}(z,\widetilde{\theta},\widetilde{w},\widetilde{\widetilde{v}}),\forall t\geq t _{0}+T_{0}\), we can obtain \(\left\|(z,\widetilde{\theta},\widetilde{w},\widetilde{\widetilde{v}})\right\| \leq\sqrt{2C_{n}/D_{n}}\), and the control tracking error satisfy \(\|z\|\leq\sqrt{(2V_{n}(0)e^{-C_{n}t}+C_{n}/D_{n})}\). The proof of Theorem 1 is complete. \(\square\)  
**Remark 4**. : _The time complexity is generally \(O(T\cdot L\cdot n)\), where \(T\) is the number of iterations, \(L\) is the number of layers in the network, and \(n\) is the number of neurons per layer; the time complexity for such methods is generally \(O(m\cdot k)\), where \(m\) is the number of parameters to be optimized and \(k\) is the number of optimization iterations. Although parameterized attack algorithms typically have lower computational complexity, the parameterization assumption they rely on is quite restrictive. This assumption limits their applicability to parameterized attack models exclusively, making them less flexible for general adversarial attack scenarios. In contrast, our approach removes this restrictive assumption, offering a more general and adaptable framework for evaluating a broader variety of adversarial attack methods._  
In this paper, parameter selection guidelines are as follows:  
1. Select design parameters \(q_{i}\) and \(m\) to determine the adversarial attack reconstruction adaptive laws \(\hat{w}_{s}\) and \(\hat{w}_{s}\). 2. Select design parameters such that \(c_{s},\ s=1,\cdots,n-1\), \(p_{s},\sigma_{s},\overline{\sigma}_{s}\) and \(\overline{\sigma}\) to determine the virtual controller \(\beta_{s}\) and the adaptive law \(\dot{\hat{\theta}}_{s}\).