esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 3 Methodology
### Classification Algorithms
#### 3.2.2 Attention Mechanism  
We evaluated the performance of an attention-based method in comparison with the baseline model MLP. In this study, we customized a class called _AttentionClassifier_, which is a classifier with an attention mechanism that was inherited from _m.Module_, consisting of two linear layers called _attention_ and _classifier_. The _attention_ layer calculates the weights  
Figure 4: Architecture diagram of MLP.  
of each input feature, while the _classifier_ layer generates predicted categories based on the weighted sum of all input features. During the forward function, the _input_ids_ are propagated through the _attention_ layer, resulting in a tensor that represents the attention weights assigned to each input feature in the sequence. Subsequently, the _squeeze_ function is applied to compress the last dimension of the tensor, and the _softmax_ function is used to normalize the weights, yielding the weights of each input feature in the original sequence. Once the _attention_weights_ are acquired, we utilize the broadcast mechanism to multiply the _input_ids_ and _attention_weights_ to calculate the importance or relevance of each feature within the context embeddings. By multiplying the input features with their associated attention weights, this model effectively highlights significant features while attenuating less important ones, enabling it to focus on the most relevant information within the input sequence for improving prediction accuracy. The _attention_output_ is a weighted representation of the input features and is obtained by multiplying each feature with its corresponding attention weight. The weighted sum of _attention_output_ integrates the importance assigned to each feature by the attention mechanism, efficiently capturing the most salient features and enhancing prediction accuracy. The resulting feature representation emphasizes crucial information of the original sequence, which is subsequently fed into the _classifier_ layer to make the final predictions. In summary, this model utilizes an attention mechanism to assign weights to the input features, enabling the classifier to effectively comprehend the essential information contained within the _input_ids_, thereby enhancing classification performance.
# Early-Age Cracking in Concrete: Causes, Consequences, Remedial Measures, and Recommendations
## 7 Remedial Measures for Early-Age Cracking
### Materials and Design Based Remedial Measures
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Reference**} & \multicolumn{3}{c}{**Designing**} & \multicolumn{3}{c}{**Construction**} \\ \cline{3-8}  & & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** \\ \hline
1 & [PERSON] [24] & Y & Y & - & - & - & - & - & - \\
2 & Klemczak and Knoppli-Wrobel [10] & Y & Y & - & Y & Y & - & Y & - \\
3 & Krauss and Rogalla [28] & - & Y & - & Y & Y & - & - & - \\
4 & [PERSON] and Bernarder [9] & - & Y & - & - & - & - & Y & - \\
5 & [PERSON] and Abu-Hejleh [105] & Y & Y & - & Y & Y & - & - & - \\
6 & [PERSON] and Purvis [106] & Y & Y & - & - & Y & - & - & - \\
7 & [PERSON] et al. [107] & Y & Y & Y & - & Y & - & - & - \\
8 & [PERSON] [37] & Y & - & Y & Y & - & Y & - & Y \\
9 & Combrinck and Boshoff [37] & - & Y & - & - & - & - & - & - \\
10 & [PERSON] and Ha [108] & - & - & - & - & Y & Y & - & - \\
11 & [PERSON], [PERSON], and [PERSON] [36] & - & - & - & - & Y & - & - & - \\ \hline \hline \end{tabular} Legends: A: Concrete constituent materials, B: Concrete mix design, C: Structural design, D: Curing, E: Placing sequence and environment conditions, F: Vibration, G: Cooling method, and H: Formwork.  
\end{table} 
Table 3: Classification of remedial measures to reduce early-age cracking in concrete. are the two main reason of reducing the early-age autogenous shrinkage cracking susceptibility of concrete. [PERSON] et al. [111] studied the potential of using phase change materials (PCMs) to diminish the early-age thermal cracking in cement-based materials. PCMs are latent energy/heat storage media that absorb heat when temperature is high and release it when temperature is low. The utilization of PCMs in concrete helps keeping the heat of hydration within the acceptable limit and thus reduces the rate of deformation and stress development, decreasing the risk of early-age concrete cracking. PCMs were reported to be the governing factor in reducing thermal cracks in early-age concrete [111]. In addition, [PERSON] and [PERSON] [112] and [PERSON] et al. [113] studied the application of PCMs in mitigating thermal cracking in early-age concrete. They also reported the potential of PCMs in reducing temperature and stresses during the hardening of concrete and cement paste. Some design characteristics of concrete structures may also contribute to the early-age cracking in concrete. To minimize the cracking risk of concrete structures, the following directions are given by several researchers. 1. Increase the girder spacing to decrease girder restraint [107]. 2. Avoid specifying the decks with a thickness of 165 mm (6 \(\frac{1}{2}\) in.) and less [107]. 3. Limit the transverse reinforcement steel size to No. 5 at 140 mm spacing (5 \(\frac{1}{2}\) in.) and/or No. 6 spaced at 178 mm (7 in.) [107]. 4. Increase the ratio of concrete cover to reinforcement bar diameter [53].
## 2 Methodological Background
### PID Control
#### 2.2.2 Adjustment of PID Gain Parameters  
An excellent PID controller also needs to be matched with suitable PID gain parameters. Although it seems easy to tune these three variables, they correspond to different PID gain parameters for different applications and system transfer functions, and may even be in conflict with each other. Thus, the PID gain parameters require tradeoffs to achieve the best overall control results. There are many methods to find suitable PID gain parameters under this type of control problem, for example, the Ziegler-Nichols [8] and Cohen-Coon [9] tests.  
The most famous PID gain parameter adjustment method is the Ziegler-Nichols test, which belongs to the empirical formula proposed by [PERSON] and [PERSON] in 1912. The PID controller adjusted by the Ziegler-Nichols test is called the Z-N tuning method. The Z-N tuning method is proposed for a general system and a first-order transfer function model with delay. The Z-N tuning method is one of our strategies for modifying the PID controller. Its model can be expressed as  
\[G(s)=\frac{Ke^{-Ls}}{Ts+1} \tag{5}\]
## 5 Stability Analysis and Augmentation Adjustment of UAV
### Influence Analysis of Dynamic Stability  
According to the configuration and flight conditions of the multi-section variable-sweep wing UAV studied in this research, the flight quality of the targeted UAV can be preliminarily evaluated according to the requirements of type I aircraft in flight phase B [47].  
For the longitudinal pitch motion, the long-period modal of the UAV reaches level 1 quality, but the damping ratio of the short-period modal is insufficient. The CAP range for level 1 flight quality requirements is 0.085-0.36, and the damping ratio is required to be 0.3-2. Therefore, it is necessary to introduce the angle of attack and the pitch rate for feedback compensation. For the lateral/directional motion, the roll modal achieves  
\begin{table}
\begin{tabular}{c c} \hline
**Pre-Selected Model** & **Correction Function of Centroid Offset** \\ \hline Model2 & \(\triangle y_{\text{in}}=2.31\times 10^{-5}\Lambda^{2}-1.86\times 10^{-5} \Lambda+0.2226\) \\  & \(\triangle x_{\text{out}}=1.52\times 10^{-5}\Lambda^{2}+0.0035\Lambda+0.1040\) \\  & \(\triangle y_{\text{out}}=1.39\times 10^{-5}\Lambda^{2}-2.29\times 10^{-5} \Lambda-0.2086\) \\  & \(\triangle x_{\text{in}}=7.76\times 10^{-6}\Lambda^{2}+0.0046\Lambda+0.1503\) \\ Model3 & \(\triangle y_{\text{in}}=2.70\times 10^{-5}\Lambda^{2}-2.14\times 10^{-5} \Lambda-0.2627\) \\  & \(\triangle x_{\text{out}}=1.41\times 10^{-5}\Lambda^{2}+0.0027\Lambda+0.0997\) \\  & \(\triangle y_{\text{out}}=1.07\times 10^{-5}\Lambda^{2}-2.29\times 10^{-5} \Lambda-0.1632\) \\  & \(\triangle x_{\text{in}}=8.48\times 10^{-6}\Lambda^{2}+0.0054\Lambda+0.1486\) \\ Model4 & \(\triangle y_{\text{in}}=3.16\times 10^{-5}\Lambda^{2}-3.21\times 10^{-5} \Lambda-0.3095\) \\  & \(\triangle x_{\text{out}}=1.28\times 10^{-5}\Lambda^{2}+0.0020\Lambda+0.0948\) \\  & \(\triangle y_{\text{out}}=7.91\times 10^{-6}\Lambda^{2}-1.79\times 10^{-5} \Lambda-0.1196\) \\  & \(\triangle x_{\text{in}}=9.19\times 10^{-6}\Lambda^{2}+0.0063\Lambda+0.1467\) \\ Model5 & \(\triangle y_{\text{in}}=3.61\times 10^{-5}\Lambda^{2}-2.279\times 10^{-5} \Lambda-0.3632\) \\  & \(\triangle x_{\text{out}}=1.20\times 10^{-5}\Lambda^{2}+0.0013\Lambda+0.0901\) \\  & \(\triangle y_{\text{out}}=-9.05\times 10^{-7}\Lambda^{2}+1.08\times 10^{-4} \Lambda-0.080\) \\ \hline \end{tabular}
\end{table}
Table 7: The modification functions of the centroid offset of pre-selected model sections considering the actual structure.  
level 1 quality, but the Dutch roll modal does not meet the requirements for level 1 quality (\(\xi_{RS}\omega_{RS}>0.5\) rad/s), and feedback compensation is also required.  
In this section, stability augmentation adjustment is performed for the longitudinal and lateral/directional motions separately.
###### Abstract  
This paper examines hybrid threats to maritime transportation systems and their governance responses; focusing on the congested Straits of Malacca and Singapore (SOMS) as an illustrative case study. The methodology combines secondary sources with primary data from 42 expert interviews, a 28 respondent survey, and two maritime security roundtables. Key findings were that ships' critical systems are increasingly interconnected, yet aging IT infrastructure and minimal cybersecurity awareness among crews heighten risks. Meanwhile, regional terrorist groups have previously targeted shipping and shown considerable skill in exploiting online tools, aligning with broader calls for jihadist violence. Furthermore, opportunistic piracy persists in the SOMS with the potential to disrupt shipping. Experts confirmed that maritime cybersecurity lags behind other critical infrastructure sectors and needs updated governance. Initial International Maritime Organization (IMO) guidelines lack specificity but revisions and updated IMO guidance are in process, while Port state implementation of maritime cybersecurity standards varies. Crucially, information sharing remains inadequate, even as recorded attacks increase. Findings underscore that although major hybrid incidents have not occurred, simulations and threat actors' capabilities demonstrate potential for catastrophic collisions or cascading disruption in congested waterways. Mitigating factors like redundancy and crew training are deficient currently. Some alignment between SOMS states on maritime security cooperation exists, but not on cyber threats specifically. Key recommendations include an anonymous cyber attack reporting system, reinforced training and shipboard systems, and consolidated regional frameworks. Until these priorities are addressed, the analysis concludes that hybrid vulnerabilities in this vital global chokepoint remain a serious concern.  
maritime security; cybersecurity; maritime governance; Southeast Asia; cybercrime
## 4 Benchmark Databases and Performance Evaluation Metrics
### Dataset Used
#### 4.1.15 UAV-IDS-2020  
This is a recently compiled dataset utilized in UAV communication networks to detect or predict attacks. It contains updated and non-redundant encrypted Wi-Fi traffic logs, with binary output labels listed for each record as 0 for UAV normal and 1 for anomalous [177]. The dataset has several interpretations of the data logs as having genuine and anomalous networked UAV communication traffic. The original dataset is available for the bidirectional-flow mode, which includes the uplink flow, downlink flow, and total traffic flow, as well as the unidirectional-flow mode for UAV communication. In total, there are 55 attributes and 17,256 instances in the dataset. UAV-IDS-2020 is publicly available in flow-based format.  
Table 11 provides an overview and comparison of the well-known datasets utilized in studies on attack detection. In this table, the details of the various attacks within the dataset are provided. Additionally, details regarding the type of attack, file format, volume and duration of the traffic, and test networks are shown. It has been noted that most of the datasets are freely accessible in both packet and flow formats, provide information about legitimate and malicious traffic, and are, thus, helpful for attack detection. It is also observed that CICDDoS2019 and IoTID20 are the most recent datasets for DDoS attack detection. Of note, the most recent types of attacks are included in the CICDDoS2019 dataset.  
\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} p{56.9 pt} p{56.9 pt}} \hline \hline
**Dataset** & **Year** & **Publicly Available?** & **Traffic Category** & **Format** & **Traffic Volume** & **Span** & **Traffic Present** & **Attack Type** \\ \hline DARPA’98/99 & 1998/1999 & Yes & Simulated &.pcap, logs & n/a & 7.5 weeks & Normal and attack traffic & DoS, privilege escalation, probing \\ \hline KDD Cup’99 & 1999 & Yes & Simulated & - & 5 M points & - & Normal and attack traffic & TCP, DoS, privilege escalation, probing \\ \hline NSL–KDD & 1999 & Yes & Simulated & - & 150 k points & - & Normal and attack traffic & DoS, probing \\ \hline SSENet-11 & 2011 & No & Simulated & - & n/a & 4 h & Normal and attack traffic & DoS, port scan \\ \hline ISCX2012 & 2012 & Yes & Real &.pcap, csv & 2 M flows & 7 days & Normal and attack traffic & Infiltration, DDoS, SSH brute force, HTTP DoS \\ \hline CIC DoS & 2012 & Yes & Simulated &.pcap & 4.6 GB packets & 24 h & Normal and attack traffic & Slowloris, slowbody, slaveed, Hulk, app. layer DoS \\ \hline SSENet-14 & 2014 & No & Simulated & - & 200 K points & 4 h & Normal and attack traffic & Botnet, flooding, port scan \\ \hline \hline \end{tabular}
\end{table}
Table 11: Overview of some of the frequently used datasets.
# New Generation Compact Linear Accelerator for Low-Current, Low-Energy Multiple Applications
## 2 LINAC 7 Accelerator Component Descriptions
### Radio Frequency Quadrupole (RFQ)  
The particle beam generated in the source will be accelerated using a radio frequency quadrupole. This element is still in the design phase. In order to reduce the size of the RFQ its operating frequency was chosen to be 750 MHz. To further reduce the length, the choice was made to dispense with the gentle buncher section of the RFQ. This comes at the cost of a lower particle transmission, but this is not an issue since the goal is a low current accelerator. The particles will be lost at the entrance of the RFQ, meaning they will not have enough energy to generate any activation of the copper. The final RFQ is expected to achieve an output energy of 4 MeV at the end of 1.5 m, and capture between 15% and 30% of the particles at the entrance. The input parameters of the RFQ were set by the measured values of the beam in the LEBT. The design of the cavity has been completed and a cold model was manufactured by Eglie out of ETP copper (electrolyte tough pitch copper, UNS C11000) to validate the design process, as well as the ability to machine the RFQ with the required precision, that is, tolerances lower than 0.1 mm. Figure 5 shows the RFQ cold model under measurement. ETP copper was used because the cold model is not intended for a vacuum, but it still provides information about the achievable tolerances and surface finishes during machining, and has roughly the same conductivity as oxygen-free high-conductivity copper for the electromagnetic testing.  
A key part of the machining process of the RFQ is the modulation. For this reason the cold model includes a dummy modulation that is representative of what can be expected in the final RFQ. Figure 6 shows one of the minor segments of the RFQ cold model where the dummy modulation is clearly visible.  
Geometric tolerances were verified using a Zeiss CMM (coordinate measuring machine, Carl Zeiss Iberia, S.L.--Division Metrologia Industrial, Tres Cantos, Espana) at TEKNIKER. All general dimensions were found to be within less than 0.1 mm of the design values and below 0.05 mm in the modulation. Design of the final modulation is in process; the main goal is to achieve the highest acceleration in the shortest distance at the expense of particle transmission.  
Figure 4: On the (**left**), a photograph of the LEBT as built. On the (**right**), a cross-section of the 3D CAD model, 1 is the ion source, 2 is the solenoids, 3 is the pepperpot and 4 is the Faraday cup.
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 5 Simulation Analysis
### Performance Overhead and Reliability  
In this research, we focus on the environmental identification capabilities of autonomous vehicles under rainy conditions, particularly in scenarios where rapid response is crucial. The time sensitivity of autonomous driving systems when performing tasks necessitates that prediction times be optimized to the utmost. In such cases, the delay in prediction time is typically strictly limited to below the millisecond level to ensure that vehicles can operate safely and effectively execute tasks under varying and adverse weather conditions.  
To meet this requirement, we propose a vehicle environment identification method based on deep learning, which uses Channel State Information (CSI) from vehicle-to-vehicle communication as input. Our CNN model demonstrates high-accuracy environmental identification capabilities within microsecond-level prediction times, far surpassing the limitations of traditional sensing systems under adverse weather conditions. This ensures that even in complex climatic conditions such as rain, intelligent vehicles can quickly and accurately adjust driving parameters, significantly enhancing the safety and reliability of the autonomous driving system. Moreover, the efficient performance of the CNN model means that in resource-constrained situations, it can minimize the consumption of energy and computational resources while ensuring task efficiency.
## 3 Results  
### Preprocessing  
In this study, preprocessing was performed before generating the predictive model. First, the missing values in the data were replaced using the multi-imputation method. To match the different units for each explanatory variable, standardization was performed by setting the mean of each variable to 0 and the standard deviation to 1. In addition, to understand the relationship between the response and the explanatory variables, correlation analysis, and multicollinearity were checked for continuous variables among the explanatory variables, and an ANOVA was performed for categorical variables.
# Seismic Behavior and Modeling of Ductile Composite Steel-Trussed Concrete Beam to Column Joints
## 2 Experimental Tests
### Test Apparatus and Load Pattern  
In order to reproduce the correct stress strain due to gravitational and seismic loads, a dual configuration of constraints and external loads is required. Consequently, two distinct experimental stages of loading have been considered. Stage 1 of loading is intended to reproduce the loading and specimen deformation due to only gravitational loads. A constant axial load of 80 kN is applied to the column, and a shear load with a triangular shape of 34 kN is applied to the beam when it is free to be deformed. The specimen is restrained by the horizontal actuator on the top of the upper column. During stage 2 of loading, a connecting rod with a cylindrical hinge is installed at the end of the beam. In this way, the constraint boundary conditions of the specimen reproduce the inflection points of the elastic deformation in the prototype three-dimensional structure. In this phase, the application of the horizontal action simulating the earthquake occurs. Displacement-controlled  
Figure 3: Test specimens: (**a**) hinged column model and (**b**) fixed column model.  
quasi-static cyclic tests consist of almost twelve consecutive tests, each composed of three displacement cycles imposed on the specimen. Each test corresponds to an increasing value of drift, calculated at the top of the column as the top displacement d divided by the application point height of the horizontal force, according to the test program shown in Figure 5 and Table 1.  
The test equipment consists of horizontal and vertical hydraulic jacks and a contrast structure made of a reinforced concrete reaction wall for the hinged column model (Figure 6a) and a reactive steel structure for the fixed column model (Figure 6b). Potentiometric displacement transducers were installed on the specimen to monitor and record the global behavior (F) and the local deformations (P, T) of the joint panel and the base of the fixed column. Three load cells have been placed to record the force applied at the top of the columns in a vertical and horizontal direction and the reaction force at the end of the vertical rigid rod elements placed at the end of the beams.  
Figure 4: Geometric characteristics of specimens: (**a**) hinged column model; (**b**) fixed column model; and (**c**) designed moment-rotation of section A-A.  
Figure 5: Quasi-static cyclic testing program: (**a**) hinged column model and (**b**) fixed column model.
# Study on New Bolted T-Stub Connection with Inserted Plates under Axial and Cyclic Loads
## 3 Test Specimen, Test Setup and Instrumentation
After the letters TC, WA or WB, the letters P or N denote the existence or absence of inserted plates, respectively, while the following two numbers represent the length of T-stub \(W\) and the width of inserted plate \(L_{p}\), respectively. The coupon tensile tests of steel plates with different thicknesses and grades were performed first. The sample plates were cut from the base metal according to the requirements of specification [31], processed with the test specimens at the same time following the stipulations of each specification [32], then tested in uniaxial tensile loads, to measure the elastic modulus, yield strength, tensile strength, yield strain, strengthening strain and so on to provide a basis for the test results analysis. The basic mechanical properties of both 10.9s and 8.8s bolts were also determined by axial tensile tests. Under the pure tensile force, the failure modes of all bolt assemblies (including bolt head, shank and nut) were featured by shank necking in threaded parts during yielding and fracturing at the same location at the end. This type of failure mode is attributed to a reduced cross-section resulting from threads, after which the bolts can be sorted as high-resistance (HR), according to literature [33]. The average value of yield strength, ultimate strength, percentage elongation and section shrinkage for property classes 10.9s and 8.8s were 1046 and 782 MPa, 1156 and 931 MPa, 12.4% and 16.4%, and 45.1% and 53.1%, respectively. The experimental tests were carried out at the Structures Laboratory at the Fujian Academy of Building Research. In tension and compression tests, specimens were loaded under displacement control through the jaws of the testing machine, the PWS-1000 Servohydraulic Fatigue Testing Machine (maximum test load \(\pm\) 1000 kN, piston stroke \(\pm\) 200 mm). The cyclic tests followed the loading pattern of force-to-displacement control regulated by the Specification of Seismic Test of Buildings (JGJ/T 101-2015) [34]. Specifically, the force-control loading was adopted before the specimen yielded, while after that, the displacement-control loading went ahead until the specimen failed. The loading force amplitude and displacement amplitude circled one time before yielding and three times after yielding, respectively.