esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The presence of SHB in hives was investigated over a six-month period, including three months in late winter and three months in autumn. A total of 431 inspections were carried out: 197 inspections of up to 67 beehives across six apiaries in late winter, and 234 inspections of up to 81 beehives across five apiaries in autumn (Table 1). Some colonies could not be inspected three times during the same season due to colony death. A total of 379 SHBs were collected and killed during the inspections. No larvae have been detected. Table 2 provides a detailed breakdown of the number of SHBs detected both behind the MD and in the remaining volume of the hive during each inspection across all six apiaries. Figure 3 shows the trend in the total number of SHBs detected throughout the sampling period, without distinguishing between each apiary. It also illustrates the distribution of SHBs found behind the MD and in the remaining volume of the hive. A peak in SHB presence--with 1.8 SHBs detected per hive--was observed during the fourth inspection (September 30-October 2), and the majority of the SHBs detected (99 out of 146; 68%) were located behind the MD. Both the total number of SHBs found within the beehive--including those found behind the MD and in the remaining volume of the hive--and the number of SHBs found exclusively behind the MD exhibited a positive asymmetric distribution with high variability, deviating from a normal distribution (Figure 4). \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & \multicolumn{3}{c}{**Late Winter**} & \multicolumn{3}{c}{**Autumnumnumn{3}{c}} \\ \hline  & \multicolumn{1}{p{56.9 pt}}{**First Inspection (January 21–23)**} & \multicolumn{1}{p{56.9 pt}}{**Second Inspection (February 11–13)**} & \multicolumn{3}{c}{**Third Inspection (March 4–6)**} & \multicolumn{3}{c}{**Fourth Inspection (September 30–October 2)**} & \multicolumn{3}{c}{**Fifth Inspection (October 21–23)**} & \multicolumn{3}{c}{**Sixth Inspection (November 4–6)**} \\ \hline Total number of SHBs detected & 31 & 18 & 32 & 146 & 92 & 60 \\ Number of hives inspected & 67 & 65 & 65 & 81 & 79 & 74 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of SHBs detected and number of hives inspected during each inspection. Figure 2: Descriptive diagram of the MD method used in each inspection of this study. Considering all the inspections of the study across all the apiaries, we found a total of 238 SHBs (63%) behind the MD device and 141 SHBs (37%) in the remaining volume of the hive (Figure 5). Chi-square analysis revealed a statistically significant difference in SHB presence behind the MD and in the remaining volume of the hive (x\({}^{2}\) = 24.83, df = 1, \(p\) < 0.0005), confirming a non-random distribution of SHBs. A total of 238 out of 379 SHBs were found behind the MD. This value is well beyond the upper limit of the acceptance interval of 3 \(\sigma\) (160.3 \(\leq n\leq\) 218.7), allowing a stricter degree of reliability of 99.7%.
# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 4 Model and Algorithm
### Domain Knowledge Classification Algorithm Based on Ullman Subgraph Matching
#### 4.2.1 Improved Ullman-Subgraph-Matching Algorithm
 \tag{3}\]where \(n(s_{i}[i])\) is the number of related attributes of the i-th element in the subgraph template \(Q_{i}\), and \(\alpha\) is the matching relaxation coefficient, which is used to relax the matching conditions and match the subgraphs of domain knowledge with related attributes in the subgraph template \(Q_{i}\) by default so as to improve the knowledge coverage of the matching results of the matching algorithm; \(n(S[j])\) is the number of related attributes of the \(j\)-th element in the business data graph; \(d_{+}(s_{i}[i])\) is the out-degree of the \(i\)-th element in the subgraph template \(Q_{i}\), and \(d_{+}(S[i])\) is the out-degree of the \(j\)-th element in the business data graph. Step 6: Generate the mapping matrix set \(M_{i}^{\prime}\) of the search space matrix \(M_{i}\). Select any subgraph template from the set to obtain the search space matrix \(M_{i}\). Check \(M_{i}\) row by row, replacing some non-zero elements with zero so that the matrix satisfies the condition that each row has exactly one non-zero element and each column has at most one non-zero element. Sum up all matrices that satisfy the above conditions to obtain the set of mapping matrices \(M_{i}^{\prime}\) for the search space matrix \(M_{i}\). Step 7: Obtain a collection \(SG_{i}\) of subgraphs with isomorphic domain knowledge to the subgraph template \(Q_{i}\). For each mapping matrix \(M_{i}^{\prime}\) in the set, perform the matrix operation in Equation (4):  
\[MC_{i}=M^{\prime}MB_{i}\big{(}M^{\prime}\big{)}^{T} \tag{4}\]  
If \(\forall i,j:MA_{i}[i][j]=1\), \(MC_{i}[i][j]=1\), then the mapping matrix satisfies the subgraph isomorphism theorem. Enumerate all mapping matrices that satisfy the subgraph isomorphism theorem and obtain the set of domain knowledge subgraphs corresponding to the subgraph template \(Q_{i}\). Step 8: Iterate Steps 6 and Step 7 to solve for all element domain knowledge subgraphs in the set of search space matrices \(M\). Step 9: Output the result \(Result=\{SG_{1},SG_{2},\ldots,SG_{n}\}\) of the subgraph matching for domain knowledge. It is worth mentioning that the performance of the improved Ullman-subgraph-matching algorithm mentioned above is related to the number of nodes. Here, we analyze the complexity of this algorithm, specifically, the complexity can be expressed as O(n! *m), where n denotes the number of nodes in the subgraph, and m denotes the number of nodes in the target graph, from which it can be seen that the computational complexity of this algorithm is exponential, and the computational cost becomes very large, especially when the number of nodes in both the subgraph and the target graph is high.
## 4 Analysis Model
### Analysis Setting
#### 5.1.4 Earthquake Records
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**No.**} & \multicolumn{1}{c}{**Earthquake**} & \multicolumn{1}{c}{**Year**} & \multicolumn{1}{c}{**Station**} & \multicolumn{1}{c}{**Epi. Dist. (km)**} & \multicolumn{1}{c}{**Dir.**} & \multicolumn{1}{c}{**PGA (g)**} \\ \hline
1 & Kobe, Japan & 1995 & KJMA & 1 & NS & 8.21 \\ \hline
2 & Northridge, USA & 1994 & Tarzana & 4 & EW & 17.45 \\ \hline
3 & Santa Barbara, USA & 1978 & UCSB Goleta & 14 & NS & 3.40 \\ \hline
4 & Chi-Chi, Taiwan & 1999 & TCU075 & 18 & NS & 2.57 \\ \hline
5 & Northridge, USA & 1994 & Newhall & 19 & EW & 5.72 \\ \hline
6 & Northridge, USA & 1994 & S\_Monica & 23 & EW & 8.66 \\ \hline
7 & Norcialtaly, Italy & 2016 & St\_Angelo & 28 & - & 2.06 \\ \hline
8 & Northridge, USA & 1994 & LA & 38 & EW & 3.48 \\ \hline
9 & Northridge, USA & 1994 & SF bay & 40 & - & 1.64 \\ \hline
10 & Norcialtaly, Italy & 2016 & Colfoliito & 40 & - & 2.55 \\ \hline
11 & Loma Prieta, USA & 1989 & S\_Cruz & 48 & NS & 3.62 \\ \hline
12 & Calexico, USA & 2010 & Sierra & 77 & EW & 4.91 \\ \hline
13 & Chile & 2010 & CCSP & 109 & EW & 5.94 \\ \hline
14 & Sumatra, Indonesia & 2007 & PSKI & 125 & EW & 1.24 \\ \hline
15 & Chile & 2016 & LL06 & 136 & - & 2.24 \\ \hline
16 & Chile & 2010 & VA03 & 168 & - & 2.68 \\ \hline
17 & Chile & 2010 & GO01 & 170 & EW & 2.32 \\ \hline
18 & Chile & 2010 & CUR & 170 & EW & 4.66 \\ \hline
19 & Chile & 2010 & GO04 & 175 & EW & 2.34 \\ \hline
20 & Chile & 1980 & ANGO & 209 & NS & 6.84 \\ \hline
21 & Chile & 1980 & MAT & 230 & NS & 3.37 \\ \hline
22 & Alaska, USA & 2016 & HNE & 254 & EW & 2.07 \\ \hline
23 & Chile & 1980 & LLO & 274 & NS & 3.19 \\ \hline
24 & Chile & 2010 & S\_Jose & 333 & NS & 4.61 \\ \hline
25 & Chile & 2010 & S\_Lucia & 334 & NS & 2.39 \\ \hline
26 & Chile & 2010 & ColegioLasAmericas & 339 & NS & 3.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Earthquake records for analytical use.
###### Abstract  
During pipe-roof construction using pipe-jacking technology, lubricants are injected into the tail void to reduce pipe-soil friction and minimize soil loss. However, research on ground settlement caused by multiple adjacent pipe jackings remains limited, and the influence of lubricant Young's modulus on ground settlement control is not clear. To address these gaps, this study conducts a comprehensive investigation using a Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC3D). Initially, the research model is validated against a pipe-roof case in Japan. Subsequently, ground response characteristics are simulated under lubricants with different Young's moduli, considering four burial depths, two pipe-roof arrangements (\"gate-shaped\" and \"horseshoe-shaped\"), and two tail voids. The results indicate that low-stiffness lubricants mobilize greater surface settlement, while increasing the lubricant Young's modulus more markedly optimizes the interaction among adjacent pipelines, thereby greatly alleviating the settlement. Nonetheless, the control effectiveness of lubricant on the settlement is influenced by other factors. Increasing burial depths and tail voids weaken the lubricant's capacity to mitigate surface settlement. In contrast, the horseshoe-type arrangement is more conducive to the lubricant's control effect on surface settlement than the gate-type system. Moreover, under these three cases, an increase in the lubricant Young's modulus can more substantially reduce surface settlement. These findings provide valuable insights for controlling ground settlement during pipe-roof construction.  
surface settlement; pipe roof; lubricant Young's modulus; burial depth; pipe-roof arrangement; tail void Article
## 2 Materials and Methods
### Sampling Site  
The experimental soil was taken from a rainfed agricultural field located in the municipality of Zempoala, Hidalgo, Mexico, at 19\({}^{\circ}\)48\({}^{\prime}\)20\({}^{\circ}\)03\({}^{\prime}\) N and 98\({}^{\circ}\)31\({}^{\prime}\)98\({}^{\circ}\)50\({}^{\prime}\) W, at an altitude between 2400 and 2900 m.a.s.l. Its climate is predominantly dry temperate, with 14.3 \({}^{\circ}\)C as annual average temperature and 480 mm of annual precipitation [55]. According to [PERSON]'s aridity index (I\({}_{\text{DM}}\) = 19.75), this zone is classified as semi-arid [56]. For almost 20 years, barley had been grown in this soil, with periodical sheep droppings additions, but it has remained uncultivated for the last 5 years.  
Preparation and Characterization of Soil  
Before taking soil samples, a hand weeding and a 0-40 cm subsoiling using a walking tractor (Honda, Tokyo, Japan) were carried out. A part of the soil was added with biochar at a rate of 8 t/ha (0.2%). Once prepared, the soil and soil with biochar were packed into plastic bags (3 kg/bag), and characterized according to the methods established by the NOM-021-SEMARNAT-2000 [47] (the method number is in parentheses): pH (AS-02), bulk density (BD, AS-03), content of organic matter (OM, AS-07), inorganic nitrogen (N\({}_{\text{inorg}}\), AS-08), available phosphorus (P\({}_{\text{av}}\), AS-10, AS-11), cation exchange capacity (CEC, AS-12), DTPA-extractable Zn, Fe, Cu, Ni, Pb, Mn, and Cd (AS-14); exchangeable Ca, Mg, and K (AS-13), electric conductivity (EC, AS-18). The water-holding capacity (WHC) was measured gravimetrically placing 10 g of soil into a funnel containing a folded filter paper, then 50 mL of distilled water was added to saturate the soil, and the funnel was covered with an aluminum cap for 12 h to avoid evaporation.  
Additionally, to determine if the change in BD y WHC is due only to the dilution effect by the biochar addition, theoretical bulk density and WHC were also evaluated applying Formulas (1) y (2), respectively.  
\[\rho_{BT}=\frac{1}{\left(\frac{1}{\rho_{BS}}*A_{S}\right)+\left(\frac{1}{\rho_ {BB}}*A_{Bc}\right)} \tag{1}\]  
\[WHC_{T}=\left(WHC_{s}*A_{s}\right)+\left(WHC_{Bc}*A_{Bc}\right) \tag{2}\]  
where \(\rho_{BT}\) is the theoretical bulk density, \(WHC_{T}\) is theoretical water holding capacity, subscript \(S\) stands for soil, subscript \(Bc\) stands for biochar, and \(A\) is the application rate by weight (99.8% for soil and 0.2% for biochar).
# FREDY: Federated Resilience Enhanced with Differential Privacy
###### Abstract
FREDY applies differential privacy during the aggregation of teachers' outputs, thereby safeguarding the privacy of individual contributions while still enabling knowledge transfer to the student model. This ensures that the sensitive information of each participant remains protected and contributes to a collective resilient model. Together, these two techniques create a synergistic effect. Federated learning's collaborative nature distributes the training process, while differential privacy adds a layer of noise that prevents adversaries from extracting sensitive information. This combination not only enhances the system's overall resilience by diversifying data sources, but also fortifies the privacy of participant data. FREDY thus offers a robust and privacy-conscious framework that strengthens resilience through the seamless integration of federated learning and differential privacy. The contributions of this paper are twofold:  
* We introduce FREDY, a knowledge transfer federated learning framework which offers PATE's privacy-preserving technique in a federated learning setting. From a federated learning standpoint, multiple teachers are trained in a federated manner and then the FL server queries the teachers and aggregates their outputs in order to label public data and thus train a student model on these data. We evaluate FREDY on two benchmark datasets, namely CIFAR10 and MNIST. * We implement a membership inference attack model and perform inference attacks to several student models that are trained with different noise addition values, showing its robustness and the privacy preservation ability of our proposed method. The remainder of the paper is organized as follows. Section 2 describes the related work in the field of federated learning and knowledge transfer frameworks from private data. Section 3 provides the architecture of FREDY following with experimental results in Section 4 and discussion in Section 5. Finally, the paper concludes in Section 6.
# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
## 2 Materials and Methods
### Feature Enhancement  
The feature enhancement module is designed to enhance the object cues and weaken the background noise, as shown in Figure 3. The enhancement module first divides the given feature map into groups along the channel dimension, infers channel and spatial attention maps for each sub-feature in parallel, and then generates refined sub-feature by multiplying the attention map with the input feature. Finally, all the refined sub-features are concatenated as the new feature map for fine-grained classification.  
The feature enhancement module first divides the feature map \(F\) into \(m\) groups along the channel dimension, where each sub-feature \(F_{k}\) captures a specific semantic response during training. Each sub-feature \(F_{k}\) is then split into two branches, as shown in Figure 3. One branch is used to create a channel attention map by leveraging the inter-relationship of channels, while the other branch generates a spatial attention map by utilizing the inter-spatial relationship of features.  
By applying global average pooling operation [33] to the feature map \(F_{k1}\) for feature compression, a feature vector containing global information is obtained. Then, the vector is passed through a fully connected layer and a sigmoid activation function to calculate the channel weight coefficients. Finally, the channel weight coefficients are multiplied with the feature map \(F_{k1}\) to generate a feature map \(A_{k1}\) embedded with channel attention mechanism [34].  
\[A_{k1}=\sigma(W_{1}\cdot GP(F_{k1})+b_{1})\cdot F_{k1} \tag{1}\]  
where \(W_{1},b_{1}\) represent the parameters to be learned, \(GP\) represents global pooling operation, and \(\sigma(\cdot)\) represents the sigmoid activation function.  
First, the feature map \(F_{k2}\) is subjected to group normalization operation. Then, the calculated result is passed through a convolutional layer and a sigmoid function to calculate the spatial weight coefficients. Finally, the spatial weight coefficients are multiplied with the feature map \(F_{k2}\) to generate the feature map \(A_{k2}\) with embedded spatial attention mechanism [35].  
\[A_{k2}=\sigma(W_{2}\cdot GN(F_{k2})+b_{2})\cdot F_{k2} \tag{2}\]  
where \(W_{2}\), \(b_{2}\) represent the parameters to be learned, and \(GN(\cdot)\) represents group normalization operation.  
Merge the outputs \(A_{k1}\), \(A_{k2}\) of two branches, concatenate all grouped features \(A_{k}\) together to generate a feature map \(A\) embedded with attention mechanism. Compared to input features, the output features are enhanced through the attention mechanism.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary  
### Chebyshev Chaotic Mapping  
**Definition 1**.: _Let \(n\) be a positive integer, \(x\in[-1,1]\), and the \(n\) order Chebyshev polynomial \(T_{n}(x):[-1,1]\rightarrow[-1,1]\) is defined as:_  
\[T_{n}(x)=cos(n\text{-}arccos(x)) \tag{1}\]  
\(T_{n}(x)\) _can also be defined using the following recursive relation:_  
\[T_{n}(x)=2 xT_{n-1}(x)-T_{n-1}(x),n\geq 2 \tag{2}\]  
where in \(T_{0}(x)=1,T_{1}(x)=x\). from Equation (2), it can be observed that the first \(n\) terms of the Chebyshev polynomial are:  
\[T_{2}(x)=2x^{2}-1,T_{3}(x)=4x^{3}-3x,T_{4}(x)=8x^{4}-8x^{2}+1,\cdots\]  
Chebyshev polynomials possess two important properties: the semi-group property and the chaotic property [49].
## 2 Materials and Methods
### RoF
An SS-DWDM system with high speed can be achieved with 32 channels a data rate can be reached of up to 3 Gb/s non-return-to-zero (NRZ) and return-to-zero (RZ) using a broadband amplified spontaneous emission (ASE) source. This was demonstrated in a 40 km optical fiber link with bit error rate (BER) \(<\) 10\({}^{-12}\). [PERSON] et al. (2017) [19] proposed and investigated a 10 Gbit/s symmetric bidirectional WDM-PON system with centralized direct and lightwave detection. The transmission of the differential phase-shift keying (DPSK) modulated signal over Single Mode Fibre (SMF) with a length of 25-km can be considered for downstream. While, for upstream link the DPSK modulated signal would be demodulated using RSOA (Reflective Semiconductor Optical Amplifier) that exit at the optical network unit (ONU) side. The corresponding results for a WDM-PON with symmetric 10 Gbit/s were achieved in the proposed design with a small effect of dispersion. The results indicate that the BER curve went down from BER = 1 \(\times\) 10\({}^{-13}\) at Pu = \(-\)13.89 dBm to BER = 1 \(\times\) 10\({}^{-16}\) at Pu = \(-\)13.835 dBm in the downstream signal for the SMF length of 25 km. Optical amplifiers and local networks might make major contributions in the long term. Even optical signals can be transmitted without amplifiers for a long distance. Many techniques can be used for optical amplification, such as regeneration techniques that provide support for powering up the signal and ensuring total data transparency. Another technique is regeneration and reshaping amplification, in which an optical signal can be converted into electrical signals and then be utilised for modulating a laser. There are also regeneration, reshaping, and relocking techniques, which can transform the data stream within an electronic signal. After that, the retransmission of optical signal for amplifying the signal [9]. In this technique, the noise can be removed greatly over the reshaping process. Typically, reshaping tasks are conducted on digital signals; however, in some cases, they are conducted on analog signals. Relocking cannot be done with an analog modulated signal.
## 3 Prescribed Performance Constraint Control of EHS  
### Prescribed Performance Constraint  
The prescribed performance constraint of position tracking error is regulated by a designed weighted performance function, which can guarantee not only the satisfactory dynamic performance but also the stable margin of EHS.  
At first, the position tracking error is given by  
\[e(t)=x_{1}(t)-y_{d}(t). \tag{4}\]  
If the cylinder position \(x_{1}\) is restricted in \(x_{1\min}<x_{1}(t)<x_{1\max}\), where \(x_{1\min}\) and \(x_{1\max}\) are the maximal and minimal boundary of \(x_{1}\), and the position demand \(y_{d}\) has also two definite boundaries as \(y_{d\min}\leq y_{d}\leq y_{d\max}\), then  
\[e_{\min}<e(t)<e_{\max}, \tag{5}\]  
where \(e_{\min}=x_{1\min}-y_{d\max}\), \(e_{\max}=x_{1\max}-y_{d\min}\).  
**Definition 1**.: _A continuous smooth function [32]\(\rho(t)=(\rho(0)-\rho(\infty))e^{-\lambda t}+\rho(\infty)\) is called a weighted performance function if (1) \(\rho(t)\) is positive and monotonically decreasing; (2) \(\lim\limits_{x\rightarrow\infty}\rho(t)=\rho_{\infty}>0\); (3) \(\rho(\infty)<\rho(0)<1\)._  
**Lemma 1**.: _If a weighted performance function \(\rho(t)\) is designed such that_  
\[e_{\min}<e(t)/\rho(t)<e_{\max}, \tag{6}\]  
_then \(e(t)\) is restricted in \((e_{\min},e_{\max})\)[31]._  
Actually, if \(e(t)\geq 0\), then \(e(t)\leq e(t)/\rho_{i}(t)<e_{\max}\) due to \(0<\rho_{i}(t)<1\). On the other hand, if \(e(t)<0\), then \(e_{\min}<e(t)/\rho(t)<e(t)\). Thus, the position tracking error \(e(t)\) is always restricted in the boundaries \((e_{\min},e_{\max})\).  
Secondly, according to _Lemma 1_, the PPC \(\rho(t)e_{\min}<e(t)<\rho(t)e_{\max}\) can derive a new state errors as follow  
\[z_{1}(t)=T^{-1}(\frac{e(t)}{\rho(t)})=\ln(\frac{e_{\max}(e_{\min}-e(t)/\rho(t) )}{e_{\min}(e_{\max}-e(t)/\rho(t))}), \tag{7}\]where \(T(\cdot)\) is a smooth function, \(T^{-1}(\cdot)\) is its inverse function, \(\ln(\cdot)\) is the natural logarithm function.  
**Theorem 1**: _The smooth function \(T(\cdot)\) is a monotonically increasing function [33], and holds the following properties_  
\[\begin{array}{l}e_{\min}<\!T(z_{1})<e_{\max}\qquad\qquad T(0)=0\\ \lim_{z_{1}\to-\infty}T(z_{1})=e_{\min}\quad\lim_{z_{1}\to+\infty}T(z_{1})=e_{ \max}\end{array}. \tag{8}\]  
From (33), the inverse function of \(z_{1}\) is given by  
\[T(z_{1})=\frac{e(t)}{\rho(t)}=\frac{e_{\min}e_{\max}(e^{z_{1}}-1)}{e_{\min}e^{ z_{1}}-e_{\max}}. \tag{9}\]  
Since \(e_{\min}<0\) and \(e_{\max}>0\), the derivative of \(T(z_{1})\) yields  
\[\frac{dT}{dz_{1}}=\frac{e_{\min}(e_{\min}-e_{\max})e^{z_{1}}}{(e_{\min}e^{z_{1 }}-e_{\max})^{2}}>0. \tag{10}\]  
Hence, \(T(z_{1})\) is a monotonically increasing function. Furthermore, due to \(\rho(t)e_{\min}<e(t)<\rho(t)e_{\max}\) with \(0<\rho(t)<1\), then \(e_{\min}<T(z_{1})<e_{\max}\) is established. When \(z_{1}\to\pm\infty\), \(T(z_{1})\) is close to its up and down boundary \(e_{\max}\) and \(e_{\min}\) respectively. If \(z_{1}=0\) is substituted into (7), then \(T(0)=0\).