esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates
## 5 Experimental Results and Analysis
VT only showed the impact damage areas, although additional information of resin-rich areas and fibers can be visualized. Figure 11: Front-side inspection for the BFRP laminate subjected to 15 J impact loading after PPT (**e**–**g**) and PCT processing. Figure 13 shows the thermographic results of SCB. The images from the depths of 1.65 mm (0.066 Hz) and 1.94 mm (0.048 Hz) were obtained. The resin-rich areas were detected, and the fibers can be identified. The fiber orientation evaluation is more difficult in the case of SCB due to the thermal characteristics. The impact damage areas A and B in Figure 13a,b,e,f were not detected at the depths of 1.65 mm and 1.94 mm because they are too close to the surface. The impact damage area C was detected at the depth of 1.65 mm in Figure 13c, and the impact damage area D was detected at depths of both 1.65 mm and 1.94 mm in Figure 13d,h. The depth information is a little bit deeper than the mechanical measurement because the thermal diffusivity \(\alpha\) value was calculated using similar and reasonable data present in the scientific literature. Nevertheless, the growing trend of the impact damage areas coincides with the theoretical estimation. The impact damage areas in the four specimens can be detected by PCT. Of interest, VT showed the clearest results for the impact damage inspection of SCB. Figure 12: IRT for the jute/hemp fiber hybrid laminates after PPT and PCT processing. Table 7 shows the relationship between modulated frequency \(f_{b}\) and depth \(z\) according to Equation (1) for phase-based LT. Figure 14 shows the LT (phase) results of SCB at depths of 0.95 mm, 1.9 mm and 4.25 mm. The impact damage area A was not detected at the depths of 0.95 mm and 1.9 mm in Figure 14a,e because it is located so close to the surface. The impact damage area B was detected at the depth of 0.95 mm in Figure 14b, but not at the depth of 1.9 mm in Figure 14f, which coincides with the mechanical measurement. The impact damage areas C and D were detected at the depths of 0.95 mm (Figure 14c,d) and 1.9 mm (Figure 14g,h) which is due to the calculation of the thermal diffusivity \(\alpha\) from literature data. All the impact damage areas were not detected at the depth of 4.25 mm. The growing trend of impact damage in SCB coincides with the theoretical estimation. The resin-rich areas were also detected by phase-based LT, which showed clearer results, but the longer inspection time is the disadvantage. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Frequency \(f_{b}\) (Hz)**} & **0.2** & **0.1** & **0.08** & **0.05** & **0.02** & **0.01** \\ \hline \multirow{3}{*}{Depth \(z\) (mm)} & BFRP & 0.49 & 0.69 & 0.77 & 0.98 & 1.55 & 2.19 \\  & Jute/hemp hybrid & 0.99 & 1.39 & 1.56 & 1.97 & 3.12 & 4.41 \\ \cline{1-1}  & SCB & 0.95 & 1.34 & 1.50 & 1.90 & 3.00 & 4.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Relationship between modulated frequency \(f_{b}\) and depth \(z\). Figure 13: IRT for the SCB laminates after PPT and PCT processing.
## 5 Unbalanced PSI-CA Protocol Based on Cuckoo Filter
### Security Analysis
#### 5.4.3 Proof  
If the Hellman encryption scheme is secure, then the simulators for sender and receiver are guaranteed to exist; we can use them as subroutines when constructing our simulators.  
* **Sender's View:** We start from the case where the sender is corrupted. We construct a simulator \(Sim_{S}\), which receives the sender's private input and output and generates the view of the sender \(S\) in the protocol. We want to show that this view is indistinguishable from the view of \(S\) in an execution of the unbalanced PSI-CA protocol based on Cuckoo filter. That is, \(\{\text{Sim}_{S}(S,f_{S}(X,Y))\}_{S,R}\overset{c}{=}\left\{\text{view}_{S}^{ \pi CC}(X,Y)\right\}_{S,R}\). We first sketch the algorithm of \(Sim_{S}\). In the pre-process step, \(Sim_{S}\) determines a prime number \(q\) and chooses a public key with the same security parameter. It constructs a Cuckoo filter in the presence of the ciphertexts, which is all the same as the real execution. Then, \(Sim_{S}\) fills a set \(Y_{R}\) with the same size as \(Y\) randomly and determines a key to encrypt each element in \(Y_{R}\) utilizing the Hellman encryption scheme randomly. We denote the encryption set result as \(Y\). Last but not least, \(Sim_{S}\) attempts to obtain the cardinality of the set intersection by querying the Cuckoo filter. From the view of the sender, given the secure Hellman encryption scheme, the distribution of \(Y_{R}\) produced by \(Sim_{S}\) is indistinguishable from the one produced by the real execution. Thus, we can conclude that the simulated view is indistinguishable from the real view.
* **Receiver's View:** In this case, the receiver is corrupted. We construct a simulator \(Sim_{R}\) that is given the private input \(Y\) and the output \(|X\cap Y|\). Similarly, we will prove this view is indistinguishable from the view of the receiver \(R\) in the real execution. That is, \(\{\text{Sim}_{R}(R,f_{R}(X,Y))\}_{S,R}\overset{c}{=}\left\{\text{view}_{R}^{ \pi CC}(X,Y)\right\}_{R,S}\).  
We first sketch the algorithm of \(Sim_{R}\). In the pre-process step, \(Sim_{R}\) determines a prime number \(q\) and chooses a public key with the same security parameter to encrypt its own set \(Y\). Then, \(Sim_{R}\) randomly chooses \(|X|-|X\cap Y|\) elements from a predefined universe and \(|X\cap Y|\) elements from \(Y\). We denote the new dataset as \(X_{new}\). Afterward, \(Sim_{R}\) determines a sender's key to encrypt the dataset and constructs the Cuckoo filter in the presence of the new dataset's ciphertext using the parameters determined in the pre-process step. Since the distribution of \(X_{new}\) produced by \(Sim_{R}\) is indistinguishable from the distribution of \(X\) produced by the real execution, we conclude the simulated view is indistinguishable from a real view.  
Combining both of the above, we finish our proof.
## 3 Cellular Network Modeling
### UMTS Networks  
Other than the entity PLMN, UMTS networks have several differences with respect to GSM ones, as reported in Figure 4.  
Administratively, considering the registration service, the cells are grouped in LAs, each identified by the _lai_, which is the combination of a PLMN identifier and _lac_. In a  
Figure 4: Conceptual schema for UMTS networks.  
Figure 3: Conceptual schema for GSM networks.  
similar way, focusing on data routing, cells within an LA are grouped in RAs. Each RA is identified by the attribute _rai_, which is the combination of _tai_ and _rac_. Each cell is globally identified by the _ci_ within its LA (which is also the option we consider in the picture, for a more uniform description with GSM and LTE).  
From the physical perspective, an UMTS PLMN deals with a set of radio network controllers (RNCs), weakly identified by _rnc-id_. Each RNC controls one or more base station (here called NodeB). Due to an absence of data about _NodeB_ in positioning systems and a reference standard definition, we define it as a weak entity whose internal identifier (_nb-id_) can be operator-defined within the connected _RNC_. Each base station manages one or more _cell_. Finally, at the physical level, each cell can be locally identified by the combination of _psc_ and the _uarfcn_. Note that the cycle given by the relationships among the entities _cell_, _NodeB_, and _RNC_ is redundant. Nevertheless, we prefer to maintain it, since it gives us the added possibility to uniquely identify a _cell_ with _ci_ within its _RNC_.
## 5 Experiments and Results
### Comparison of SC-M* to Baselines
#### 5.3.2 Run Time  
Table 4 shows the average run time of the three SC-based MAPP solvers and we observe that both SC-M* and SC-CBS are significantly faster than SC-A* in terms of run time. This is reasonable because SC-A* always searches the global high-dimensional joint space, which is expensive. SC-CBS is faster than SC-M* because it always searches in one individual dimension at a time, whereas the SC-M* needs to occasionally deal with high-dimensional space when collisions occur.
## 2 Related Work
### Quantum Key Distribution (QKD)
Experimental results showed that the proposed scheme exhibited robust resistance to various attack vectors, including brute force, statistical, differential, and quantum attacks. They also demonstrated the applicability of the scheme to a range of applications, such as smart healthcare, smart grid, and smart city domains. In addition, [PERSON] et al. [12] used artificial neural network and deep learning techniques to detect the presence of an attacker during QKD without disrupting the key distribution process. They also evaluated the detection accuracy of the proposed techniques and compared them with other state-of-the-art techniques. Experimental results showed that the proposed techniques can achieve 99% accuracy in detecting attackers. The application of QKD in mobile networks is also a research hotspot. [PERSON] et al. [13] designed and implemented a compact acquisition, pointing, and tracking (APT) system and QKD modules that are loaded on a home-made octocopter. They demonstrated the establishment of a robust QKD link between a flying octocopter and a ground station that were separated by a distance of 200 m. Additionally, real-time QKD was performed continuously for a duration of 400 s. Furthermore, [PERSON] et al. [14] proposed a high rate Continuous-Variable Quantum Key Distribution (CV-QKD) system for securing mobile Wavelength Division Multiplexing (WDM) fronthaul for 5G networks. Experimental results showed that quantum keys are effectively distributed via a 13.2 km fiber, in the presence of 11 carrier-grade classical signals within the C-band. The CV-QKD system can generate quantum keys with rates of 18 and 10 Mb/s. [PERSON] et al. [15] identified several challenges for QKD network research, such as improving the key rate and distance, enhancing the network security and robustness, developing efficient network protocols and algorithms, and integrating QKD networks with classical networks. QKD can provide unconditional security against eavesdropping attacks, which is a desirable feature for many communication scenarios. [PERSON] et al. [16] described a simple and robust experiment and theory for an upper-level undergraduate investigation of QKD using the BB84 protocol. They independently emulated the key principles of QKD using an optical apparatus and computational scripts, and demonstrated the security of the key exchange against eavesdropping attacks. They also provided a simulation of the experiment using MATLAB and Python, and discussed the advantages and disadvantages of QKD compared to classical cryptography. [PERSON] et al. [17] proposed a hierarchical architecture and an adaptive resource allocation and routing model for Quantum Key Distribution secured Federated Learning (QKD-FL) systems. They aimed to minimize the deployment cost of QKD nodes under various uncertainties, such as security requirements, by jointly optimising the QKD resources (i.e., wavelengths) and routing for FL applications. They showed that their proposed model can reduce the deployment cost by 7.72% compared to the CO-QBN algorithm.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Initial Parameter Variation
#### 3.2.3 Algorithm Implementation  
This procedure summarizes the proposed method, where \(e\) is the sequence of observations and \(\theta=\theta_{\gamma}\), and solves the recursive Equation (8).  
As shown in Algorithm 2, once the Initial Vector is decomposed into the parts, \(\bar{\Gamma}\) independent of \(\theta_{\gamma}\) and \(\bar{\Gamma}\) dependent on \(\theta_{\gamma}\) and the Observation Matrix is represented in the diagonal matrices, the sensitivity coefficients are computed by filling the forward matrices \(F^{k}\) for \(k=1\cdots t\). The details of filling the forward matrices in Algorithm 2 are discussed as follows. \(F^{1}\) is initialized as a sum of two temporary matrices \(Ftmp_{1}\) and \(Ftmp_{2}\) which are used to compute the coefficients for the parts independent of and dependent on \(\theta_{\gamma}\), respectively. \(Ftmp_{1}\) is computed as the matrix multiplication of \(O^{1}\) and \(\bar{\Gamma}^{\prime}\), and a zero vector of size \(n\) is appended at its end to represent the zero coefficients of degree 1. In the same way, \(Ftmp_{2}\) is computed as the matrix multiplication of \(O^{1}\) and \(\bar{\Gamma}^{\prime}\), and a zero vector of size \(n\) is appended in front of it to represent the zero coefficients of degree 0. Then  
```
0:\(A,O,\Gamma,\mathbf{e}\), and \(\theta_{\gamma}\)
0:\(F^{1},\cdots,F^{t}\)
1:\(\bar{\Gamma}\leftarrow\Gamma\)
2:\(\bar{\Gamma}_{r}\gets 0\)
3:\(\bar{\Gamma}_{\vdots}\leftarrow\bar{\Gamma}_{\vdots}/(1-\gamma_{r})\)
4:\(\bar{\Gamma}\leftarrow\Gamma-\bar{\Gamma}\)
5:\(\bar{\Gamma}_{\vdots}\leftarrow\bar{\Gamma}_{\vdots}/\gamma_{r}\)
6:\(O^{1}\gets diag(O_{,\mathbf{e}^{1}})\)
7:\(Ftmp_{1}\leftarrow[O^{1}*\bar{\Gamma}^{\prime},zeros(n,1)]\)
8:\(Ftmp_{2}\leftarrow[zeros(n,1),O^{1}*\bar{\Gamma}^{\prime}]\)
9:\(F^{1}\gets Ftmp_{1}+Ftmp_{2}\)
10:for\(k=2\) to \(t\)do
11:\(O^{k}\gets diag(O_{,\mathbf{e}^{k}})\)
12:\(F^{k}\leftarrow[O^{k}*A^{\prime}*F^{k-1}]\)
13:Return\(F^{1},\cdots,F^{t}\)
```  
**Algorithm 2** This computes the coefficients of the forward (filtering) probability sensitivity function \(p(x_{v}^{t},\mathbf{y_{e}^{1:t}})(\theta_{\gamma})\) with \(\theta_{\gamma}=\gamma_{r}\) in forward matrices \(F^{k}\) for \(k=1,\cdots,t\) using SMF.  
\[F^{1}=Ftmp_{1}+Ftmp_{2}\]  
The remaining matrices \(F^{k}\) of size \(n\times k,2\leq k\leq t\), are computed using recursion. They are computed as the matrix multiplication of \(O^{k}\), \(A^{\prime}\) and \(F^{k-1}\).
## 2 Method
### Procedure  
Upon arrival, participants provided written informed consent and were then briefed on the experimental procedure. They were informed that the task simulated a street-crossing scenario without crosswalks or traffic signals, and thus no clear right-of-way was established. It was specifically emphasized that the vehicles in the platoon would not slow down or yield, requiring participants to select sufficiently large temporal gaps to ensure safe crossing. This clarification ensured that participants' decisions were based solely on vehicle speed and gap size, eliminating assumptions or expectations about whether the vehicles would yield.  
Participants were then introduced to the eHMI signals: cyan roof lights indicated AVs, while green, yellow, and red lights, positioned in place of fog lights, conveyed low, medium, and high crossing risk, respectively. Familiarizing participants with eHMI meanings was essential, as prior research indicates that once eHMI was internalized, the influence of eHMI presentations on crossing decisions diminishes [51; 52].  
Additionally, participants were instructed to press the '1' key on the keyboard if they decided to cross the street. For each approaching vehicle, an observation was recorded, including the participant's gap acceptance (cross or wait), eHMI penetration condition (partial or full), vehicle type (AV with eHMI [Partial-AVs] or TV without eHMI [Partial-TVs]), vehicle speed (30 km/h or 36 km/h), and gap size (ranging from 2 to 6 s in 0.5 s increments). The vehicle platoon continued to proceed regardless of whether the participant pressed the key.  
For the concurrent oddball task, participants were instructed to count the occurrences of infrequent high-pitch tones embedded within a continuous stream of low-pitch tones. They were then repeatedly exposed to both high-pitch and low-pitch tones until they could reliably distinguish between the two.  
Importantly, participants were explicitly informed that their primary task was to make safe street-crossing decisions, and that their attention should be directed primarily toward that task rather than the secondary oddball task.  
Participants were then seated 60 cm from a screen (see Figure 4), with their right index finger resting on the \"1\" key. A practice phase followed, combining the primary task (under low-medium-high risk condition) and the oddball task to familiarize participants with both.  
Subsequently, participants were fitted with EEG helmets (see Figure 4). Once electrode impedance was reduced below 20 k\(\Omega\), the formal experiment commenced.  
The experiment comprised six eHMI information content blocks under partial or full penetration conditions, each paired with an oddball task. The order of blocks was counterbalanced across participants using a Latin square design to mitigate order effects. After each block, participants rated their SA for the primary task using the 3D-SART.  
Each block (practice and experimental) lasted five minutes, followed by a three-minute rest interval to minimize fatigue. The total experiment duration was approximately 1.5 h, with participants receiving CNV 50 as compensation.
## 4 Discussion
### Critical Reflections and Future Research  
The results from the study generate several questions that need to be addressed in future research. Those questions are: (1) When and in which learning scenarios should digital interventions be implemented?; (2) what would be the best outcome, one where all interventions are digitized or one where only some of them are?; (3) how do we know exactly in which conditions we should exploit the advantages of the digital interventions and in which we should better use the analogous ones?; (4) can we improve digital interventions so that we can overcome their weaknesses?; (5) what type of studies are required to answer these questions? and finally, (6) based on the results and observations obtained from the present study, how could we improve the current digital version of _Terminkalender_ to guarantee better learning outcomes?  
The current research has stressed once again the importance of thoroughly analyzing, prior to any kind of digital intervention, the possible strengths and weaknesses of the targeted learning scenarios and tools. A key factor in this process is to follow an iterative cycle as proposed by action research-based methodologies [48; 57; 58]. An iterative cycle consists in doing research based on different phases that are (1) exploring, (2) identifying, (3) planning, (4) collecting data, (5) analyzing/reflecting, (6) hypothesizing/speculating, (7) intervening, (8) observing, (9) reporting, (10) writing and finally, (11) presenting [57; 58].  
The idea behind this iterative cycle is to explore and analyze both users' needs as well as requirements to meet such needs by means of a user-centered design process.  
Further steps to evaluate whether a digital intervention and concrete software meet the requirements to trigger better learning outcomes might consist in gathering feedback from the users themselves. A valuable method for such purposes is the one proposed by the Technology Acceptance Model (TAM) [59], which aims to analyze aspects such as the perceived ease of use, the usefulness of a concrete technology, and the intention to use it.  
With regard to _Terminkalender_ and the intention to improve its digital version, we aim to analyze next the results from the TAM handed out to our German language learners once they have participated in the learning experience. This way, we aim to gather valuable feedback on their learning experience and possible difficulties with the activity and the learning tool. At the same time, we intend to identify the need to refine the system and software and thus to ensure that in future interventions, learners might be better supported by the system and feedback provided to develop their language competencies.
## 2 System Design
### GNSS Module  
The GNSS module chosen to be integrated to the camera system is a u-blox Neo M8T chip with a L1 GPS antenna. The GPS time of exposures is provided by the GPS module and is registered in the header file for each image. During the acquisition, the GPS sampling rate was set to 1 Hz.
# A Survey on Hardware Accelerators for Large Language Models
## 2 Computational and Energy Requirements
### Benchmarks for LLMs  
Large language models (LLMs) such as BERT [5], GPT-2, and others are evaluated using well-established benchmarks to assess their performance across various natural language processing tasks. Common benchmarks include GLUE [6] (general language understanding evaluation) for tasks like sentiment analysis and natural language inference, SQuAD [7] (Stanford Question Answering Dataset) for extractive question answering, and SuperGLUE [8] for more complex understanding tasks. Additionally, tasks like text classification, summarization, machine translation, and conversational AI are widely used to test these models' capabilities. These benchmarks and applications provide a standardized way to measure progress and compare different models in diverse language understanding and generation scenarios.
## 4 FE-Based Method
### Static Schemes  
Hereafter, three static schemes are considered. Each static scheme is chosen to analyze the following deformation patterns (also called deformation modes):  
* MODE 1: shear deformation mode;
* MODE 2: extension along \(X\) axis, parallel to joists' direction;
* MODE 3: extension along \(Y\) axis, orthogonal to joists' direction.  
To reproduce the shear deformation pattern (MODE 1), the load and restraint configurations shown in Figure 10a are adopted. Note that a pin support is applied to vertex node A, and a roller support preventing translations along X direction is applied to vertex node D. Displacements of the floor cell middle plane orthogonal to the plane are restrained.  
Upward forces along Y are applied to vertex nodes B and C, while a downward force is applied to node D. Along X, a rightward force is applied to node C while a leftward force is applied to node B.  
Figure 10: Static schemes of (**a**) MODE 1, (**b**) MODE 2 (**c**) and MODE 3.  
In MODE 2 (Figure 10b), two roller supports preventing translations along X are applied to nodes B and C, and a roller support preventing translation along Y is applied at the midpoint of the floor edge \(AD\). This roller guarantees that the deformed shape of the floor cell is symmetric with respect to the axis passing through the midpoints of edges \(AD\) and \(BC\).  
Finally, MODE 3 (Figure 10c) is obtained by applying roller supports to nodes A and B to prevent displacements along Y direction. Similarly to MODE 2, a roller support is also applied to the midpoint of floor edge \(AB\) in order to prevent translations of this point along X direction and guarantee the symmetry with respect to the axis passing through the midpoints of edges \(AB\) and \(DC\).
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 2 Materials and Methods
### Nicotiana Benthamiana Culture and VIGS Incluation  
_N. benthamiana_ plants were grown in pots (120 cm3) containing an Agrolite/Peat moss mixture, cultured at 25 \({}^{\circ}\)C under a 16/8 light/dark photoperiod, and fertilized (Ultra FolTM; 1 mL/L) every two weeks. _N. benthamiana_ is a widely used experimental host in VIGS vectors studies, and the vector pEuMV-YP:\(\Delta\)AV1 works efficiently. Furthermore, the production of leaf area generated under culture conditions is adequate for this type of study [22; 23].  
Plants with four true leaves were inoculated by bombardment with 10 \(\upmu\)L of a prepared mixture (1 \(\upmu\)m gold; 50 \(\upmu\)L 2.5 M CaCl\({}_{2}\); 20 \(\upmu\)L 0.1 M spermidine; 10 \(\upmu\)D DNA from pEuMV-YP:Krt18, or pEuMV-YP:\(\Delta\)AV1:chII) [24]. Using a custom-built device, the bombardment was applied at 30 psi at 2 cm from the plant surface using pressurized He as a propellant.