esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Relationship between the Surface Tension and Contact Angle  
According to the characterization of the balanced relationship between the three-phase interfacial tension (\(\gamma_{sl}\), \(\gamma_{lg}\), \(\gamma_{sg}\)) and the cosine value of contact angle (cos\(\theta\)) in [PERSON]'s equation [40]:  
\[\gamma_{sg}-\gamma_{sl}=\gamma_{lg}\cos\theta \tag{1}\]  
the cos\(\theta\) formed by the deposition of droplets on tea leaves has a certain relationship with the surface tension of liquid. Here, the surface tension of the pesticide mixture (x value variable) and the cos \(\theta\) (y value variable) of droplets with four adjuvants deposited stably (60 s) on the surface of tea leaves at different concentrations were analyzed by regression. The fitted images and regression equations are shown in Figure 6.  
There was a negative linear correlation between the two variables at the \(p\) = 0.05 level after adding the four adjuvants regardless of the growth stage of tea. The cos \(\theta\) value decreased with the increasing surface tension of the mixed solution. Moreover, the contact angle of adjuvant droplets on the fresh buds was larger than that on the young leaves under the same concentration, making the fitting straight lines representing the fresh buds below those representing the young leaves. The larger the absolute value of the slope of the fitted straight line, the more sensitive the cos \(\theta\) value changes with the surface tension of the mixed solution. Theoretically, the corresponding surface tension would be the minimum value required for the mixed droplets to spread on the surface of tea leaves if the straight line is extended to cos \(\theta\) = 1.  
Among the four graphs in Figure 6, the slope of the (b) line had the largest absolute value, indicating that the pesticide with nonionic surfactant 6501 can achieve the maximum reduction of the contact angle within the minimum change in surface tension. Compared with the other three adjuvants, the continuous extension of the straight red line (leaf) corresponds to the maximum surface tension value at the intersection of \(\cos\theta=1\) (26.183 mN/m). It is predicted that when the surface tension is reduced to 26.183 mN/m, the droplets will completely spread on young leaves, and a smaller value (25.84 mN/m) is required for fresh buds.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 3 Proposed Routing Protocol
### Multi-Decision Dynamic Intelligent Routing Protocol
```
Input: Node \(a\) connects to node \(b\) Output: Update \(Q_{table},C(a,b)\)
1 forThe message \(m\) destined for node \(d\) stored in node \(a\)do
2 ifnode \(b\) is the destination node for message \(m\)then
3 node \(a\) forwards \(m\) to node \(b\)
4 end if
5 else
6 if\(max\ Q_{a}(x,m)=Q_{a}(b,m)\)then
7 node \(a\) forwards \(m\) to node \(b\)
8 end if
9 end if
10 else
11 ifrel(\(b,d\)) = friendthen
12 Compare \(C(a,d)\), \(C(b,d)\)
13 if\(C(a,d)<C(b,d)\)then
14 node \(a\) forwards \(m\) to node \(b\)
15 end if
16 end if
17
18 end if
19
20 else
21 ifrel(\(b,d\)) = colleaguethen
22 ifrel(\(a,d\)) = colleaguethen
23 if\(Hops^{m}<3\) and \(avgNT_{T_{a}}^{d}<avgNT_{T_{b}}^{d}<TTL_{m}\)then
24 Compare \(C(a,d)\), \(C(b,d)\)
25 if\(C(a,d)<C(b,d)\)then
26 node \(a\) forwards \(m\) to node \(b\)
27 end if
28 end if
29
30 end if
31
32 end if
33
34 end if
35
36 end if
```  
**Algorithm 1**Actions when node \(a\) establishes a connection with node \(b\)  
If both nodes have a friend relationship with the destination node, only the relationship values are compared, and the message is forwarded to the node with the higher relationship value. If node \(b\) has a colleague relationship with the destination node, and node \(a\) also has a colleague relationship with the destination node, then their contact with the destination node is not frequent enough, so decisions cannot be based solely on encounter information. The forwarding hop count and remaining message time are compared. If the forwarding count is less than three and the remaining message time is greater than the average encounter interval between the two nodes and the destination node, then further relationship value comparisons are performed. If node \(a\) and the destination node are strangers, the message is forwarded to node \(b\). If both node \(a\) and node \(b\) are strangers to the message destination node, meaning that the probability of both node \(a\) and node \(b\) transmitting the message to the destination node is low, then the message is not forwarded and remains in node \(a\), awaiting the next connection.
## 3 The Theory of Composite Plate without Holes
### Comparison with the Experimental Results  
In order to verify the theoretical method's results, a comparison with the authors' experimental results on the critical stress of perforated plates strengthened with FRP has been performed. As shown in Figure 6, a test rig capable of applying uniaxial compression on the plate was fabricated in the laboratory. And a classic perforated plate strengthened with FRP is shown in Figure 7. The unidirectional FRP has been used. The plates were constructed as following: Firstly, holes are drilled in plates if needed, then a single-layer of FRP is installed directly on the steel surface in the load direction and then another single-layer of FRP is installed in the direction of vertical load. The aim of this paper is to study the buckling of the plate, and when buckling, no delimitation of FRP has been found, so in the finite element (FE) models, the delimitation of FRP and the influence of manufacturing technology is not taken into account, and the FRP are considered an orthotropic material. The values of the basic geometry and material parameters are in Table 1, and the details of the experiments are in literature [19]. The elastic buckling loads \(P_{\sigma Exp}\) are determined from the experiments by two methods, and averaged; These two methods are the load-axial displacement curve and the load-transverse displacement squared method [14; 20]. The experimental critical stress \(f_{\sigma Exp}\) can be calculated by \(P_{\sigma Exp}/(bt_{t})\), where \(t_{t}\) represents the total thickness of the plate. The comparison of experimental and theoretical results are in Table 2. From this table, it can be found that experimental and theoretical results are consistent when \(d/b\) and \(\gamma\) are equal to 0. It should be noted that the theoretical method cannot consider the influence of hole, which is a very important parameter to the elastic stability of perforated plates strengthened with FRP under uniaxial compression; so in calculating \(f_{\sigma Exp.10}\) and \(f_{\sigma Exp.11}\), \(k\) is taken as \(K_{u}\), which is obtained by the numerical results in the following section. The values calculated by \(f_{\sigma Exp.10}\) and \(f_{\sigma Exp.11}\) are very closed, it proves the feasibility of Equation (11) in Calculating \(D_{t}\). It also can be seen that when \(\beta\
eq 0\), the theoretical results of \(f_{\sigma Exp.10}\) and \(f_{\sigma Exp.11}\) are basically larger than experimental results \(f_{\sigma Exp}\). The reason is that the theory method cannot consider the orthotropic properties of FRP, the effect of FRP's orthotropic properties is more obvious in the following numerical analysis, especially when pasting a lot of FRP. Figure 5: Variation of Stiffness enhancing ratio \(D_{t}/D_{0}\) calculated by Equation (11) as a function of \(\gamma\).
## 3 Results and Discussion
### Effects of 3D Volume Reconstruction Parameters on the Model  
3D volume reconstruction parameters, image Threshold (T) and Surface Quality Factor (SQF), appeared to have relatively significant effects on the reconstructed elementary model. T values calculated with methods detailed by [PERSON] [18] define the grayscale values that are used to generate isosurfaces. SQF, ranging from 0 to 1, defines the image resample value. A smaller SQF generates rougher, coarser surfaces, whereas a larger SQF results in finer, higher-quality surfaces.  
As shown in Figure 2, at a T value of \(-522\), SQF values appeared to have a significant impact on the surfaces and internal structures of the reconstructed model, with larger SQF values resulting in lower porosity, a greater surface area (Figure 3a), and structural complexity. At an SQF value of 0.5, as depicted in Figure 2, from T values of \(-542\) to \(-502\), greater T values corresponded to greater structural complexity in the reconstructed model.  
As shown in Figure 3b, the model porosity was proportional to T values, signifying greater fragmentation at higher T values. For example, at a larger T value of \(-502\), the model was fragmented and broken. It can also be seen in Figure 3b that the surface area of the reconstructed model increased before decreasing as the T value increased from \(-542\) to \(-502\), peaking at a T value of \(-522\). As shown in Figure 3a, for the same T factor, the model's surface area was inversely proportional with its porosity. In Figure 3b, under the same SQF factor, with the increase of porosity, the model's surface area increased before decreasing.  
Therefore, it is evident that SQF and T values could be adjusted to create elementary models with varying surface and interior structural properties, which could be used to create an assortment of unique composites upon combination or dilation.  
Figure 2: Effects of Surface Quality Factor (SQF) and image Threshold (T) values on the surface and interior structure of reconstructed models.
# Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition
## 3 Approach
### Embedding Layer  
The work introduced by [21] for word distributed representation has substituted the traditional bag-of-words encoding technique and accomplishes excellent results on many NLP tasks. In distributed embeddings, the model is generalizable because each word gets a map to space. As a result, semantically approximated words can have similar vector representations. However, using word embedding alone as the smallest feature representation unit can result in losing some accurate information. For languages with rich morphology, such as Arabic, we need to capture all morphological and orthographic information. As word embedding encodes semantic and syntactic word relationships, character embedding carries important morphological and shape information. Inspired by this integration as in [22], we acquire the sequence representations from character and word levels.  
* Character Embedding Layer  
Character sequence representations are helpful for morphologically rich languages and for dealing with the OOV problem for tasks, such as POS tagging and language modeling [23] or dependency parsing [24]. The authors in [25] proposed CharCNN, which is a character-aware neural language model that learns character word representations using CNNs. We follow the same technique for generating the character embedding representation. Details about the implementation can be found in [25].  
* Word Embedding  
Word embedding refers to the representation of words as vectors in a continuous space, thereby capturing many syntactic and semantic relations among them. We treat the embeddings as fixed constants as this consideration performs better than treating them as learnable model parameters [26]. We adopt pre-trained word embedding, AraVec 2.0 [27], to obtain the fixed word embedding of each token.
## 5 Modelling Rationale
### Modelling Assumptions
#### 5.3.2 Failure Model and Fault-Tolerance Coverages of the Modelled System
For instance, we can see that the unreliability of a TSN standard talker is expressed using the equation \(1-e^{-(\text{T}\times 1\times 10^{-4})}\), where Tx represents the time required for a port to transmit a frame. We can also see that, whenever a fault  
Figure 5: Inclusion hierarchy diagram that depicts the presented failure modes. Figure based on Figure 2.9 in [32]. affects the talker, there is a 50% probability that this fault affects its output port and a 50% probability that it affects its input port. If the fault affects the output port, we see that it can affect the whole port or just the queues and, regardless of the affected component, the fault manifests as Byzantine and, with a 100% probability it results in the failure of the stream. In the specific case of TSN, when a temporary fault affects the input or the output port of a TSN bridge, the bridge transmits, through one or several output ports, a Byzantine version of the frame it is forwarding. Fortunately, each one of these Byzantine versions is dropped by means of the error-containment mechanisms of the next bridge or listener that receives it; thereby transforming each one of these Byzantine frames into an omitted one. In this sense, the impact of a faulty bridge resembles the one of a faulty link, i.e., it prevents the listener from receiving the affected frames through the corresponding path. In the particular case of using a single path, this results in the failure of the stream. Conversely, the fault can be tolerated when using multiple paths, as long as the Byzantine failure does not compromise the schedule of other streams transmitted or forwarded by the same device, e.g., by starving the bandwidth of the links connected to the output ports through which the talker or bridge sends Byzantine frames. For the sake of simplicity, we have decided to model this scenario by means of a parameterised probability of affecting the schedule when a Byzantine fault affects a bridge. A temporary fault affecting a TSN end-system only results in a frame being incorrectly transmitted or received if the fault affects the components involved in the operation of the end-system. Specifically, if the end-system acts as a talker, then the fault can only lead it to transmit a Byzantine version of the frame through a given output port if the fault affects that port. Likewise, in the case of the listener, a fault can only lead it to receive a Byzantine version of the frame through a given input port, if the fault affects that port. The impact of a Byzantine frame being transmitted by a TSN talker is the same as if it was transmitted by a bridge, i.e., the frame is dropped by the next bridge, causing the stream failure when using a single path or in case the corrupted frame affects the schedule of other streams when using multiple paths. In contrast, we assume that a fault affecting any input port of a TSN listener always results in the failure of the stream. This is
## 2 Materials and Methods
### Numerical Modeling  
#### 2.3.1 Optical Analysis Details  
An optical analysis based on the Monte Carlo ray tracing method was conducted in this study. This analysis was implemented by using Tonatiuh. Tonatiuh is a free and open-source program designed by the National Renewable Energies Center (CENER) that employs Monte Carlo ray tracing methods for simulating concentrating collectors. The software's findings were verified by comparing them with empirical data from various solar installations [32]. Additionally, it has been employed in several scientific articles examining the performance analysis of solar thermal collectors [33; 34].  
The analysis was performed using a sufficient number of rays, specifically \(10^{6}\), which was determined after conducting a preliminary analysis while maintaining a constant direct solar irradiation of 1000 W/m\({}^{2}\).  
In the simulation, the collector was positioned in a southerly direction in the area of Athens, Greece, with the latitude and longitude parameters configured as 37.98\({}^{\circ}\) and 23.93\({}^{\circ}\), correspondingly. Tonatiuh's internal libraries were utilized to compute the sun's position and determine the resulting orientation of the incident direct irradiation. These libraries considered time, date and coordinates as inputs.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 6 Tests on New Methods for Energy Reconstruction  
### Tests on Energy Reconstruction  
Our main focus is to address the bias, as there is significant room for improvement in this aspect. Bias refers to the systematic deviation or error in the predictions made by our models. Instead of solely prioritizing the optimization of energy resolution, as even significant differences in the ensembles had negligible impact on it, our emphasis is on enhancing the ensembles' performance in terms of reducing bias. We achieve this by carefully selecting the ensembles and their corresponding weights to specifically optimize and minimize bias, as it plays a crucial role in achieving accurate results.  
Figure 9: Comparison of receiver operating characteristic (ROC) curves for gamma/hadron discrimination methods. The first plot shows the full ROC curve, with the x-axis ranging from 0 to 1, while the second plot is a zoomed-in view of the false positive rate (x-axis ranging from 0 to 0.06) to better visualize the performance of the classifiers at low false-positive rates.  
The energy reconstruction code is based on Machine Learning methods for energy regression. In our tests, input data are DL2a files from ASTRI MA Prod2-Teide MC production and output data are DL2b.  
Our benchmark is the method implemented in A-SciSoft based on Random Forest, which makes use as default of the following eight discriminating parameters (\"EN SET 0\" in Figure 2):  
1. \(log_{10}(SIZE)\)
2. \(WIDTH\)
3. \(LENGTH\)
4. \(DENS\)
5. \(CONC\)
6. \(TELIP\)
7. \(STMAXH\)
8. \(LEAKAGE\)  
Random Forest reconstruction filtering for training quality is performed by applying the same conditions as for the gamma/hadron separation.  
By means of the two feature importance methods already described in Section 5.1, we evaluate more parameters set. In order to test our feature importance evaluation (see Figure 10) we add a random control feature whose importance is expected to be equal to zero.  
A second set of eleven parameters (from now dubbed as \"EN SET 1\") has been considered for tests:  
1. \(log_{10}(SIZE)\)
2. \(WIDTH\)
3. \(LENGTH\)
4. \(DENS\)
5. \(CONC\)
6. \(TELIP\)
7. \(STMAXH\)
8. \(LEAKAGE\)
9. \(DELTA\)
10. \(ECCE\)
11. \(ELONG\)  
Figure 10: Feature importances for energy reconstruction computed with two methods: (**Right**) Mean decrease in impurity method. (**Left**) Permutation method.  
For our tests and for the benchmark, the same training samples have been used. All sets of parameters both for energy estimation and for gamma hadron discrimination are summarized in Figure 2.
## 4 Multi-Objective Optimization Design
### Optimization Algorithm  
As identified in the analysis of Section 3.2, the final performance indicators present potential conflicts in the selection of structural parameters. Therefore, the implementation of MOOP is essential to identify a set of feasible solutions rather than a singular optimal solution. The MOOP algorithm is executed using MATLAB on an Intel(r) Core(tm) i7-12700H CPU with 40 GB RAM. Given its prominence in extensive research literature, the NSGA-II is primarily selected. NSGA-II, an enhanced version of the original NSGA, incorporates a fast nondominated sorting method, an elite retention strategy, and a crowding distance operator. These enhancements reduce computational complexity, improve optimization efficiency, and ensure diversity within the solution population. To validate the results obtained from NSGA-II, the classic MOPSO algorithm is also employed for further verification.
## 3 Results  
### Definitions  
We refer to phases using bold lettering and the phase number subscript (e.g., \(\textbf{Expectation}_{1}\)). For codes within phases, we use italics and the phase number subscript (e.g., _Recognize Disadvantages\({}_{6}\)_).  
Prior literature did not operationally define experiencing a phase based on qualitative data. We propose a set of definitions about experiencing a phase and characteristics of participants' experiences to help guide the reader through our findings. For each interview, we graphed what percent of codes indicated each phase. For example, [PERSON] had 87.5% \(\textbf{Expectation}_{1}\) phase during the house tour, which we calculated by taking the frequency count of her Expectation phase codes indicated during this session (HT) (7 instances) and dividing it by the total of phase codes in the session (8 instances total in HT) and then put it in percent form.  
We defined characteristics of participants' experience of acceptance phases in Table 3--please review this table now. We defined participants as being in a given phase when that participant's codes for that phase had a primary or secondary peak (Appendix D). If multiple phases had peaked at the same time, we described participants as being in multiple phases. We consider peaks as indicative of experiencing a phase, rather than which phase was most commonly discussed in the interview because some topics were more commonly discussed throughout the entire study. For example, the \(\textbf{Identification}_{6}\) phase was the highest-indicated phase for most participants across several months; however, during those months, other phases peaked. If we ignored these peaks, it would seem as though participants did not enter these phases. There are several reasons that certain phases are more common than others. First, following the Phased Framework of Technology Acceptance (PFTA; [9]) coding scheme, the \(\textbf{Identification}_{6}\) phase had eight codes whereas all other phases had between one and four codes [9], which made the most commonly indicated. Second, our interview questions may not have sufficiently encompassed questions about each phase.  
Note that there were no assigned codes to the **Encounter\({}_{2}\)** phase in the PFTA [9], instead indicating that this phase always occurred during participants' first encounter with the technology. Likewise, we assigned no **Encounter\({}_{2}\)** phase codes-but we did code the Bringing Robot interview, which was the participants' first encounter with the robot. In our study, all participants experienced characteristics of the **Expectations\({}_{1}\)**, **Adoption\({}_{3}\)**, and **Identification\({}_{6}\)** phases during this session.  
We also combined the PFTA's [9] \"intermediary\" codes (i.e., **Adoption/Adaptation\({}_{3:5}\)**, **Adaptation/Integration\({}_{4:5}\)**) with the full phases (**Adaptation\({}_{4}\)** and **Integration\({}_{5}\)** respectively). These codes had very few occurrences, and doing so strengthened the appearances of these phases and more accurately represented the flow of phases, rather than having in-between phases that were underrepresented.
###### Abstract  
With the widespread deployment of video surveillance systems, effective access control is essential to enhance the accuracy and security of video anomaly detection. This paper proposes a Searchable and Revocable Attribute-Based Encryption scheme (ABE-RS) that is specifically designed for dynamic video anomaly detection scenarios. By integrating a user management tree structure, attribute-based key distribution, and keyword grouping techniques, the proposed scheme enables efficient user-level revocation along with dynamic updates to ciphertexts and keyword indexes. Furthermore, an inverted index structure is introduced to accelerate keyword search, facilitating the rapid detection and retrieval of anomalous video events. Formal security analysis demonstrates that the scheme is secure against chosen plaintext attacks (CPAs) and chosen keyword attacks (CKAs). The experimental results demonstrate that the scheme maintains millisecond-level revocation efficiency in methodology involving 512 users and either 50 attributes or a thousand keywords.  
video anomaly detection; attribute-based encryption; keyword revocation; event recognition; data privacy protection Article