esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Interactive Decision Trees and Applications with Multi-Bit TFHE
## 2 Related Work  
Earlier works [8; 9] focus on securely constructing the decision tree using data mining techniques on joint data without sharing the participant's data. With the advent of cloud computing, privacy-preserving decision tree evaluation has become increasingly important. Generic secure multiparty computation protocols, such as [PERSON]'s garbled circuits [10], were proposed for this problem. However, this approach requires one party to create the circuit and the other to evaluate it, resulting in the client participating in the computation. It also incurs high communication costs.  
Specialized protocols for the private evaluation of decision trees were subsequently developed that only used generic methods as and when needed. [PERSON] et al. [11] and [PERSON] et al. [12] proposed a secure protocol for linear branching programs that utilizes both garbled circuits and homomorphic encryption. After that, new interactive protocols were built for decision tree evaluation using a combination of fully homomorphic encryption (FHE), oblivious transfer (OT), and somewhat homomorphic encryption (SHE), requiring multiple rounds of communication. All suffer from high communication and computation overhead. These protocols also do not allow the model to be shared with an evaluator without leaking the model.  
Order-preserving encryption (OPE) was proposed by [PERSON] et al. [13] and is a type of encryption scheme that preserves the order relationship between plaintexts. [PERSON] et al. [14] proposed the order-revealing encryption (ORE) scheme, which uses multilinear maps to generalize OPE. Even though these schemes look appealing for integer comparison operation, their order-revealing nature results in the leakage of plaintext from ciphertext using frequency analysis and is vulnerable to inference attacks [15].  
Efficient homomorphic encryption-based integer comparison methods, such as the DGK protocol [16; 17; 18], compare an encrypted integer with a plaintext integer. Multiple rounds of interaction during protocol execution are required for methods such as that of [PERSON] et al. [19]. The comparison operation from [PERSON] et al. [7] does not support bootstrapping and is unsuitable for our purpose. The current state-of-the-art non-interactive integer comparison protocol XCMP [6] has a limitation on the bit length of the inputs.
# The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins
## 3 The Relationship of Chemical Structure and the Antioxidant Ability of Peptides
### The Relationship of Peptide Structure and Its Antioxidant Activity
#### 3.2.1 Molecular Weight  
The antioxidant peptides listed in Table 1 mainly have more than three amino acid residues, and are dominantly composed of 3-6 amino acids with molecular weights lower than 1000 Dalton (Da). The molecular weight distribution of those peptides is illustrated in Figure 2. The antioxidant peptides range from 400 to 650 Da, accounting for 70% of the 42 identified peptides (Figure 2). Several researchers have tried to show and explain the relationship between molecular weight and the antioxidant activity. The extraction of bioactive peptides from alcalase hydrolyzed residual materials from olive oil production yielded short chain peptides that exhibited significantly higher antioxidant capacities than their higher molecular weight counterparts [39]. Moreover, in the fractions of pinto bean protein hydrolysates using membrane ultrafiltration with molecular weight cutoffs of 100, 50, 30, 10 and 3 kDa, the peptide fraction <3 kDa exhibited the highest antioxidant activities [40]. The peptides below 1000 Da exhibit the best initial and sustained antioxidant activities in 2,2\({}^{\prime}\)-azinobis (3-ethylbenzothisazoline-6-sulphonic acid) diammonium salt (ABTS. +), hydroxyl radical scavenging and ORAC value tests [41]. In addition, one fraction of the alcalase hydrolysates of egg white protein, with M\({}_{\rm W}\) < 1 kDa possessed the strongest antioxidant ability compared with other ultrafiltration fractions [42]. The high activity against lipid peroxidation in a linoleic acid model system of BNH-P7 was due to the small size of peptides, because the more peptide sequences (282 peptides) will lead to a dilution effect of the peptides exhibiting hydroxyl radical scavenging activity [12]. Therefore, appropriately low molecular weight can exert a significant effect on the antioxidant activities of peptides.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 6 Analysis and Validation of Statistical Model for AMQP IoT Attack Detection
### Detecting the Brute Force Attack
#### 6.4.3 Detection Results for Malicious Dataset in Brute Force Packets  
Figure 20 depicts the detection results when the AMQP brute force detection script was applied to a malicious dataset. The evaluation revealed the following:  
* A total of 90.42% of the packets were classified as malicious traffic.
* A total of 9.56% of the packets were identified as brute force attack packets.
* A total of 0.02% of the packets were classified as normal traffic.  
These results demonstrate the model's high precision in detecting malicious events and brute force attacks within the dataset, while maintaining a low false positive rate for normal traffic.
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 3 Characteristic Analysis of Deformation
### Factors Influencing Maximum Surface Deformation  
#### 3.3.1 Excavation Depth  
The influence of the excavation depth (\(H\)) on the maximum ground surface settlement (\(\delta\)_vm_) is assessed in Figure 8. The \(\delta\)_vm_ shows a positive correlation with \(H\). Most of the data concerning \(\delta\)_vm_ fall into the region within the boundaries of \(\delta\)_vm_ = 0.12%_H_, and \(\delta\)_vm_ = 0.04%_H_, with an average value of 0.089%_H_. Table 3 compares the average values of \(\delta\)_vm_, which have been normalized by depth in previous studies, and this comparison shows that settlements vary significantly between different sites in different ground conditions. The magnitude resembles that which has been recorded in cases involving residual soils, stiff clays, and sands [7] in Hong Kong's mixed ground [2] and in stiff strata [15; 16; 20], which mostly range from 0.02 to 0.2%_H_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Case** & **Ground Condition** & **Support** & \(\delta\)_vm_/**H** & **EMR** \\  & **Number** & **Ground Condition** & **System** & **(\%)** & **(\%)** \\ \hline Clough, O’Rourke & 22 & Sand, Stiff clay & Multiprop & 0.15 & - \\ \hline \multirow{3}{*}{Carder} & \multirow{3}{*}{-} & \multirow{3}{*}{UK largely stiffer soil} & Multiprop & \multirow{3}{*}{0.125, 0.2,} \\  & & & & & \\  & & & & & \\ \hline Fernie & - & UK stiffer soil & - & 0.15 & - \\ \hline Hashash & 3 & Fill, Clay, Glacial soil & Internal struts & 0.05–0.15 & - \\ \hline Leung & 14 & Hong Kong Mixed soil & Internal strut & 0.02–0.12 & \multirow{3}{*}{0.28–1.6,} \\  & & & & & \\ \hline Long & 41 & Soft clay \textless{} 0.6H & Internal strut, Anchor & 0.12, 0.15 & - \\ \hline Long & 21 & Soft clay \textgreater{} 0.6H & Internal strut, Anchor & 0.5, 0.14 & - \\ \hline Long & 24 & Soft clay \textgreater{} 0.6H & Internal strut, Anchor & 0.8, 0.25 & - \\ \hline Moormann & 512 & Soft clay & Multiprop & 1.07 & - \\ \hline \multirow{3}{*}{Masuda} & \multirow{3}{*}{52} & Sand & Multiprop & \multirow{3}{*}{0.47} \\  & & & Clay & & \\ \hline Ou & 10 & Taipei Soft clay & Internal strut, Anchor & 0.5–0.7 & 0.65 \\ \hline Peck & - & UK soil & Multiprop & 1–2 & \\ \hline Wang, Xu & 300 & Soft clay & Internal struts & 0.42 & \multirow{3}{*}{0.45–1.52,} \\  & & & & & \\ \hline Wong & - & Soft soil \textless{} 0.9H Overlay & Struts or anchors & 0.5 & - \\ \hline Wong & - & Soft soil \textless{} 0.6H Overlay & Struts or anchors & 0.2 & - \\ \hline This study & 88 & Beijing Mixed soil & Internal Strut, Soil mail, & \multirow{3}{*}{0.089} & - \\  & & & Anchor & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of maximum ground settlements worldwide.  
The \(\delta vm\) is smaller than that recorded in cases in UK soil [4], in soft soils of a significant thickness (>0.6\(H\)), overlaying stiffer soil or soft materials at the dredge level [8,15], in soft clay [9], and soft soils [1]; the values for these soils are 0.5%\(H\), 0.8%\(H\), 1.07%\(H\) and 0.42%\(H\), respectively.
## 3 UAV Path Planning Method Based Modified Fruit Fly Optimization Algorithm
### Msfoa
#### 3.1.2 Multi-Swarm with Multi-Tasks Strategy  
The searching process can be separated by global searching and local searching. Global searching occurs in the early stage of searching, while local searching occurs in the end stage of searching. In this paper, global searching and local searching are considered as different tasks, which should employ different searching strategies. This paper uses a threshold to confirm the two different stages. When the cost is larger than the threshold value, the algorithm is in the global searching period, when the cost is smaller than the threshold value, the algorithm is in the local searching period.  
In the global searching period, it is important to enhance the searching space to improve global searching ability since the size of the searching space has a significant impact on the results. FOA only uses the single swarm approach to search for all fruit flies from a single location, thus when the searching space is small, the searching capacity is insufficient. In MSFOA, the searching space of the algorithm can be expanded linearly by using multi-swarm fruit fly at the same time and starting from many different positions. The fruit fly population \(M_{pop}\) is divided equally into \(G\) swarms. \(G\) is the total amount of swarms. Every fruit fly intends to have a big step in searching as shown in Equations (10) and (11). \(\sin((\pi/2)*\text{random}(-1,1))\) is introduced to map \(\text{random}(-1,1)\) near \(-1\) and \(1\).  
\[X_{i}^{(g)} =x_{\text{best}}^{(g)}+\sin((\pi/2)*\text{random}(-1,1)) \tag{7}\] \[Y_{i}^{(g)} =y_{\text{best}}^{(g)}+\sin((\pi/2)*\text{random}(-1,1)) \tag{8}\]  
where \(X_{i}^{(g)}\) and \(Y_{i}^{(g)}\), \(i=1\), \(2\), \(\ldots\), \(M_{pop}\)/\(G\), \(g=1\), \(2\), \(\ldots\), \(G\) denote the position of the \(i\)-th fruit fly in the \(g\)-th swarm, while \(x_{\text{best}}^{(g)}\) and \(y_{\text{best}}^{(g)}\) are the concentration position of the \(g\)-th swarm.  
In the local searching period when fruit fly swarms are near the global optimum, it is important to reduce searching space for further convergence; fruit flies should pay attention to the points near themselves to have a better result. The step of searching is dwindled.  
\[X_{i}^{(g)} =x_{\text{best}}^{(g)}+R*\text{random}(-1,1) \tag{9}\] \[Y_{i}^{(g)} =y_{\text{best}}^{(g)}+R*\text{random}(-1,1) \tag{10}\]  
where \(R\) is the searching step dwindled parameter.
## 3 On-Site Investigation and Specimen Design
### Results of the On-Site Investigation
The Schmidt hammer test is one of the prominent non-destructive testing methods used to estimate the compressive strength of concrete, by measuring its surface hardness. This test relies on experimental observations indicating a specific correlation between the rebound value (R) obtained by impacting the hardened concrete surface with a Schmidt hammer and the compressive strength of the concrete. When striking the hardened surface of concrete with a Schmidt hammer, the resulting rebound value represents the internal rebound energy, and the compressive strength of the concrete is estimated based on the magnitude of this rebound value. For the selected measurement locations determined by visual inspection results, surface smoothing operations were performed. The impact points were standardized at 3 cm intervals, forming a grid of 20 points where lines were drawn at intervals of five rows and four columns. The impact tests were conducted with an N-type Schmidt hammer in a vertically downward direction (\(\alpha=-90\) degree), and adjustments for the slope angle were considered when estimating the strength. According to the RILEM Recommendations [31], for the determination of the rebound hardness (R), values outside the range of each rebound hardness measurement were excluded. The remaining values were then used to calculate the arithmetic mean rebound hardness value (R). If four or more rebound values were excluded, the rebound hardness values (R) for that measurement point were discarded. A measured rebound value showing significant variability is generally indicative of non-uniformity in the quality of the concrete. To correct for the impact direction, measurements were conducted in the horizontal and vertical direction to ensure the most stable readings of the rebound hardness. Meanwhile, in Korea, a proposed formula for estimating the compressive strength (\(F_{c}\)) of concrete from the corrected rebound hardness (\(R_{0}\)) is widely utilized, referencing Equation (1) proposed by the Architectural Institute of Japan [32]:  
\[F_{c}=(7.3R_{0}+100)\times 0.098(\text{Mpa}) \tag{1}\]  
The compiled data for each region are presented in Table 1. The measurement results indicated that the average strength of the concrete in Samcheok was 19-23% lower than in other surveyed regions.
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 2 Related Work
### Research Related to Small-Sample Object Detection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Methods** & **Performance** & **Disadvantages** \\ \hline \multirow{3}{*}{[PERSON] et al. (2018)} & This research proposed a decoupled two-stage object detection framework based on CNNs. & The method achievs 94.15\% mAP on FFCB-DET, surpassing SOTA object detection methods in accuracy. & This method requires substantial computational resources and storage space. \\ \hline \multirow{3}{*}{[PERSON] et al. (2019)} & This paper proposed a surface defect detection model based on the convolutional block attention module and atrous convolution. & The proposed model performs better than current mainstream defect detection methods for both KolektorSDD and magnetic tile datasets. & This model still requires a small number of images with pixel-level annotations on defective images as training data when training the segmentation network. \\ \hline \multirow{3}{*}{[PERSON] et al. (2020)} & This study introduced a novel task setting that can achieve few-shot defect recognition by transferring knowledge across domains. & The extension of cross-domain experiments from textured to metal surfaces shows the superior performance of the proposed method compared to other mainstream methods. & This method exhibits a large domain shift. \\ \hline \multirow{3}{*}{[PERSON] et al. (2021)} & This research proposed a rapid detection network for strip steel surface defects based on deformable convolution and attention mechanism (DCAM-Net). & The proposed algorithm achieves a detection speed of 100.2 fps and an mAP of 82.6\% on the NEU-DET dataset. & It struggles with complex texture defects like Cr defects due to noise interference, necessitating improved feature extraction. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Nevertheless, few-shot object detection approaches based on transfer learning and meta-learning have significantly fortified the groundwork for the subsequent selection of small-sample learning algorithms and the holistic framework for object detection. Following an in-depth consideration of the aforementioned studies, this research has made the decision to adopt a small-sample object detection framework based on transfer learning. This approach integrates the findings from the aforementioned research, offering the potential to provide effective solutions for the tasks.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 3 Locata Development History and Mathematical Models
### Locata Mathematical Model
#### 3.2.2 Precise Positioning Methods  
The Locata SD carrier phase measurements are used for precise positioning, with the float ambiguities being resolved either by the KPI or OTF techniques. KPI is implemented for LocataLite channel \(i\) by the following set of equations  
\[\lambda_{j}\Delta N_{j}^{ik}=\Delta\Phi_{j}^{ik}-\Delta\rho^{ik}-\Delta\tau_{ \textit{trap}}^{ik}-\Delta\varepsilon_{\varphi,j}^{ik} \tag{3}\]  
The initial position of the Locata Rover is required to calculate the precise \(\Delta\rho^{ik}\). A surveying instrument, such as a \"total station\" or a geodetic-grade GNSS, can be used to compute accurate initial coordinates [35]. Once the floating ambiguities are estimated with a certain level of accuracy, they can be treated as 'fixed' known parameters in Equation (2).  
OTF ambiguity resolution is dependent on transmitter-receiver geometry _change_. Although the LocataLites are fixed on the ground, the trajectory of the Locata Rover provides the spatial diversity in the measurements, which makes the OTF ambiguity resolution possible. OTF ambiguity resolution can be implemented either via an Extended Kalman Filter (EKF) [23; 29; 36] or a nonlinear batch Least Squares (LS) algorithm [37] using SD carrier phase measurements. However, for applications, such as continuous displacement monitoring, the Locata Rover is static, and the KPI method is more useful.
## 6 Simulations and Experiments
### Results  
We ran our simulations on a machine equipped with an Intel Core i7-13700H CPU and 32 GB of RAM, using Docker with Wale [26], using a pre-built simulator image. The simulations were performed based on the parameters described in Table 1. In particular, we used the European Frequency for LoRa communication (868 Mhz) because soon, we will perform a few experiments on the reputation model using real devices. In our laboratory, we hold LoRa modules running at a frequency of 868 Mhz. Moreover, the value of the Hop The limit shown in Table 1 was calculated after several attempts to tune the simulation, as well as the maximum retransmission of messages within the network.  
Figure 3 shows how the nodes were spatially located in the topology (the plot was generated by the simulator), while Figure 4 depicts the message exchange between nodes.  
Figure 5 shows the results after the first 8 h of simulations (then, eight temperature submissions by the nodes). The reputation score of the malevolent node, which is labelled as node 0, approaches values around 0.1, after a short transitory, period while the reputation of the other nodes increases slowly after 11 h. These fluctuations are generally due to the cost of sending the resource and the number of jumps of each  
Figure 2: Architecture simulated for the experiment. The topology of the simulated network.  
the key point highlighted by this experiment is represented by the fact that the measured reputation quickly assumes values that reflect the real (simulated) behaviour of the nodes.  
Figure 4: Messages of resource from Node 0 (malicious one) to the consumers in the Meshtasticator simulator.  
Figure 3: Architecture simulated for the experiment in the Meshtasticator simulator — spatial distribution of the simulated topology.  
The result clearly shows that the reputation model is effective in identifying a malicious/malfunctioning node. The simulations with Meshtasticator were useful to show that integrating the reputation model is possible, thus providing trustworthiness in a Meshtastic-based IoT network. Nevertheless, results were obtained through simulations in a discrete simulation environment; therefore, we are planning to perform tests in a real-world IoT network.
# Enhanced Control Strategy for Three-Level T-Type Converters in Hybrid Power-to-X Systems
## 4 Proposed Control Design
### DC-Link Capacitors Voltage Balancing Control  
The voltage imbalance of the DC-link capacitors is the main technical challenge of the T-type topology. Imbalanced capacitor voltages can lead to reduced system performance, increased losses, and potential damage to the converter. Effective balancing control is proposed in this paper to ensure operational stability. As shown in Figure 4, \(v_{\text{C1}}\) is the voltage value across the DC link capacitor \(C1\), and \(v_{\text{C2}}\) is the voltage value of capacitor \(C2\). Figure 9 illustrates the proposed control strategy. The control algorithm is implemented in \(\alpha\beta\) stationary frame. The proposed control outputs \(d_{\alpha}\) and \(d_{\beta}\) (cf. Figure 6) transform from the \(\alpha\beta\) coordinate to the \(abc\) coordinate. The difference between capacitors voltages is multiplied by proportional controller, where \(k_{C}\) is the proportional controller coefficient, and the controller output is added to the modulation signal \(v_{mx}\), where \(x\) refers to the phases \(a\), \(b\), and \(c\), to achieve capacitor voltage balance by compensating for the neutral point current, as in the following:  
\[\begin{split} v_{ma1}&=v_{ma}+k_{C}(v_{C1}-v_{C2}) \\ v_{mb1}&=v_{mb}+k_{C}(v_{C1}-v_{C2})\\ v_{mc1}&=v_{mc}+k_{C}(v_{C1}-v_{C2})\end{split} \tag{30}\]  
where \(v_{ma}\), \(v_{mb}\), and \(v_{mc}\) are the modulation signals of phases \(a\), \(b\), and \(c\) before the correction, and \(v_{ma1}\), \(v_{mb1}\), and \(v_{mc1}\) are the modulation signals after the correction as shown in Figure 9. The proportional gain \(k_{C}\) is selected to achieve a balance between response speed and system stability. A higher \(k_{C}\) results in faster voltage correction but may introduce oscillations, while a lower \(k_{C}\) provides a more stable response at the cost of slower balancing. Based on control stability analysis, the gain is tuned to prevent excessive overshoot and ensure smooth voltage regulation.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Radio Resource Management Techniques
#### 3.1.1 Power Control Schemes
Then, the expected number of active BBUs is derived from the BBU aggregation model, which is based on the BPM. Subsequently, the expected total power consumption is determined from the expected number of active or sleeping RRHs and BBUs and their power consumption. Finally, the authors presented a procedure based on the bisection method for determining the optimal threshold.