esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The CSP is an optimisation problem that basically consists of cutting larger parts (objects) available in stock in order to produce smaller parts (items) to meet a given demand, optimising a certain objective function, for example, the loss of material or the cost of the objects to be cut. This problem belongs to the NP-Hard class [1], that is, there is no deterministic algorithm that can solve it in polynomial time with certificate of optimality, unless P = NP. Thus, for large sized instances of the problem to be tackled efficiently, it is necessary to resort to approximation algorithms, such as heuristics and metaheuristics, and, therefore, the guarantee of optimality is lost in order to obtain good solutions in a reasonable time.  
Applications of this problem are commonly found in a wide variety of industries in which the waste of materials is a major concern, such as in manufacturing processes of steel, textile, paper, and glass. It is important to note that, in real world applications, different constraints may be considered due to the particularities of the manufacturing processes involved in each scenario. Given its wide applicability, in addition to its theoretical relevance, the search for increasingly promising solutions to this problem is still very relevant [2].  
In this work, we propose a procedure for generating cutting patterns for the 1D-CSP and a strategy for reducing the number of patterns generated, in order to deal with large scale instances of the problem, even if the guarantee of obtaining an optimal solution is lost. Using benchmark instances from the literature [3], computational experiments were performed for two ILP models, an implementation of the classical CG technique [4; 5; 6], and an application of the G&S framework [7; 8; 9; 10].  
We employed CPLEX [11] for solving the ILP models. We resorted to Coluna [12] for running the CG technique and Java Concert for implementing the G&S method to tackle the problem. The exact method was able to solve only small and medium sized instances. None of the CG and G&S algorithms stood out from the other for all instances. In fact, the effectiveness varied according to the characteristics of the instances, such as the number of item types, the size of the items in relation to the size of the object, and the demand for each item type. Roughly, G&S performed well for all classes, obtaining quasi-optimal solutions for the majority of the instances.  
The remainder of this article is structured as it follows: in Section 2, we formally state the 1D-CSP, discuss the approaches commonly used to solve it, and introduce the G&S framework. In Section 3, we present a procedure for generating cutting patterns for the 1D-CSP and propose a strategy for artificially reducing the number of patterns to be considered into the formulation. In addition, we explain in detail the application of this methodology to the 1D-CSP. Section 4 is dedicated to present and analyse the computational results obtained from the different approaches. In Section 5, final remarks and perspectives on future work conclude the article.
## 2 Materials and Methods
### Signal Pre-Processing  
For the self-acquired dataset: Firstly, artifact removal is done to raw EEG data by eliminating any trial if its amplitude exceeds 100 \(\upmu\)V. A 50 Hz notch filter is then applied to remove electrical interference. Thirdly, a 6 th order Butterworth filter is applied to the EEG data for 4-40 Hz band-pass filtering with a time window of 6 s, starting from the beginning of the MI cue until the end.  
For the BCI Competition IV dataset IIa: Firstly, trials affected by artifacts were marked previously by experts and removed subsequently in this study. Next, a 50 Hz notch filter is then applied to remove electrical interference. Lastly, clean trials with a time window of 3 s, from the beginning of the MI cue until the end, traverse through a 6 th order Butterworth filter for 4-40 Hz band-pass filtering.  
Figure 2: Self-acquired data paradigm. A full trial consists of a 6 s pre-task where the subject was asked to relax, 6 s of cue appearance and performing an indicated motor imagery task, and 6 s of post-task where the subject returned to the relaxing state. The cue appeared and disappeared by two beeps.
## 1 Smoothing paths for 2D planning.
Another aspect to be analyzed is whether this reduction in path points results in a reduction in the total distance traveled by the swarm and that, assuming stable dynamic behavior in which UAVs navigate at a virtually constant speed, implies a reduction in response time. In this case, for the proposed 2D planning, the reduction is virtually negligible, as shown in Figure 7 for all experiments. This is due to several reasons: the first is that the total travelled distance in Figure 7 considers only the distances and movement in the \(XY\) plane and therefore does not consider aspects such as the distance traveled during the take-off phase, nor possible variations in height, constituting this aspect as the key to this lack of distance reduction; and, secondly, the density of the constructed graph and the parameters of creating it are well adjusted since the number of path points originally entered is not too high, so the initial paths generated are approaching or are the optimal solution in terms of minimum distance traveled and dynamic behavior. Finally, the other parameter analyzed with respect to the implementation of this algorithm is the additional computation time generated by its use to obtain an optimal solution, not only in distance traveled but in dynamic system behavior. For the cases analyzed, it is noted in Figure 8 that the use of this algorithm leads to an increase in computational time of 6.62% on average, which leads to the conclusion that, given the reduction of the percentage of total paths points and, with it, the dynamic improvement of swarm movement and the possible reduction of navigation time towards the target, this algorithm could be employed at a post-processing stage to obtain a more optimal and complete solution at the cost of a slight increase in computing time spent obtaining such a solution. Figure 7: Comparison of the total travelled distance by a swarm of 5 UAVs for original trajectories and smoothed trajectories in 2D.
## 2 Spatial Domain Chaotic Watermarking
### Parametric Modulation for Chaotic Logistic Map  
The chaotic behavior arises from typical nonlinear dynamical mapping of the logistic polynomial equation. Its driving parameter \(\mu\) is also called the bifurcation parameter as it could exhibit a bifurcation diagram. This simple study was conducted to examine the best parameter value of \(\mu\) that could speedup the watermarking process. Based on its initial interval of [0, 4], by numerical simulations, when the driving parameter \(\mu\) is located within [3.6, 4.0], the turnaround time for image watermarking was within a reasonable range. However, when the driving parameter \(\mu\) is less than 3.575, turnaround time for watermarking increases dramatically, which is no longer feasible for the watermarking scheme. Meanwhile, within [3.6, 4.0], it is found out that the greater the value of \(\mu\), the shorter the turnaround time (Figure 1). Thus, the driving parameter \(\mu\) is chosen to be 4.0 for watermarking. However, when merely the single parameter (\(\mu\)) is used in the logistics map, security vulnerabilities still exist. Rather than adopting logistics map modification, the _introduction_ of dual chaotic scrambling turns out to be more powerful.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Transformer-Based Approaches
#### 3.3.4 Single-Stream Transformer Models  
To tackle the challenges of video captioning, single-stream transformer models have emerged, harnessing the potent self-attention mechanism inherent in transformers. These models strive to integrate both visual and textual information within a cohesive framework. [PERSON] et al. [38] presented the Two-View Transformer (TVT) network, which employs a transformer encoder to capture temporal information and a fusion decoder to merge visual and textual modalities. TVT exhibits enhanced performance over RNN-based approaches, underscoring the efficiency and efficacy of transformers in the realm of video captioning.  
Building on this, [PERSON] et al. [39] introduced MART, which enhances the transformer architecture by incorporating an external memory module to facilitate sentence-level recurrence. This memory module aids in modeling the context of preceding video segments and sentences, thereby producing more coherent paragraph captions. In MART, notably, the encoder and decoder are shared, as illustrated in Figure 10 (left). Initially, video and text inputs are encoded and normalized separately. Figure 10 (right) showcases a modified version of Transformer-XL tailored for video paragraph captioning. Nonetheless, these models encounter difficulties with complex temporal dynamics and multi-modal interactions, and they demand substantial amounts of paired data, which is both costly and time-consuming to acquire.  
Figure 10: The figure illustrates the proposed Memory-Augmented Recurrent Transformer (MART) for video paragraph captioning (redrawn by the authors, adapted from [39]). On the right, the Transformer-XL model for the same task is depicted. Relative PE stands for Relative Positional Encoding, and SG(\(\cdot\)) denotes stop-gradient [40].
## 2 System Overview
### The System State Diagram  
The TcES system state diagram is shown in Figure 3b. The shaded parts deal solely with the data link; and the non-shaded parts deal with the power link that also depends on the data link. The startup phase of the system will be discussed in Section 2.5.  
Let us consider the system operation after startup during the non-stimulation time, that is, during the power-regulation phase. The external control unit and the internal implant unit exchange roles as master and salve every 500 us. Consider that the configuration data is inputted (through programming the external control unit), and when the external control unit resumes the master role it is then sent across the link. If the internal implant unit receives the data, it will be used to configure the DAC controller and the acknowledgement bit \(\mathrm{A_{C}}\) will be set active low to be sent back to the external control unit in the next data package. After the data package is sent, \(\mathrm{A_{C}}\) will then be reset. If there is no configuration data received within this period, the internal implant unit just sends back the uplink package that consists of the digitized rectifier output voltage \(\mathrm{V_{DC}}\), so that the external control unit could adjust the transmitting power to regulate \(\mathrm{V_{DC}}\). After receiving the data package, the external control unit becomes the master. If there is a new configuration data needed to be transmitted and there is no acknowledgement from the internal unit, the data will be sent and then the external unit shifts to be slave again waiting to receive data. If the above condition is not satisfied, no data will be sent and the external unit becomes slave after 500 us.  
For a certain stimulation profile, the internal implant unit knows the period of stimulation and knows exactly when the next stimulation slot will arrive. Just before the next scheduled stimulation time slot, the power-boosting phase has to be activated. Hence, when the internal implant unit becomes the master, the power require bit \(\mathrm{P_{R}}\) is set active low and sent to the external control unit to increase the transmitting power according to the look-up table. During the stimulation time, the data telemetry block of the internal implant unit is disabled. Note that the data telemetry block of the external control unit is disabled when the instruction for power boosting is received; and it is enabled after power boosting is completed.
# LTmatch: A Method to Abstract Pattern from Unstructured Log
## 4 Experiments
### Efficiency Analysis  
In order to analyze the efficiency of the algorithm, this article uses the same dataset as the previous section and uses the same method to classify patterns and record the time on the logs that are split into the same scale. The final experimental results are shown in Figure 5. It should be noted that in order to make the result clearer, the maximum time period is set to 145 s in Figure 5a, so the LenMa algorithm is not fully displayed, but the trend of increasing time is already obvious.  
First, the improved algorithm LTmatch in this paper is compared with the original algorithm Lmatch. It can be seen that the LTmatch algorithm has improved efficiency in both GBL data and HDFS data, which shows that the optimized algorithm introduces a tree structure for log type storage increases the complexity of the container of the log templates, while it has a better advantage of efficiency.  
Compared with all other algorithms, on the GBL dataset, except for the LenMa algorithm, which has a faster increase in time consumption, other algorithms have linear growth. The efficiency of the LTmatch algorithm we proposed can reach the top three. On the HDFS data set, although the algorithm we proposed is relatively backward, it has always been relatively stable and increases linearly. This is because the time complexity of the LTmatch algorithm is \(O((d+cm_{1}m_{2})n)\), where \(d\) is representing the depth of the parse tree, and \(c\) is the number of log templates contained in the leaf nodes currently searched. \(m_{1}\) and \(m_{2}\) are the number of words in the two logs that were matched separately. Obviously,  
Figure 5: Time of different log parsers over different sizes of GBL/HDFS log data sets.  
\(d,m_{1},m_{2}\) are constants. \(c\) is also a constant compared to the increase in the number of logs when the overall algorithm is in progress. Moreover, the number of templates itself is much smaller than the total number of logs, and at the same time, after the tree is divided, the number of templates for each node is even less. Through the above discussion, it can be seen that the LTmatch algorithm has a level of \(O(n)\) time complexity. The actual situation of time increase in Figure 5 is also consistent with it. Therefore, the analysis efficiency of LTmatch is acceptable under the condition of ensuring the highest accuracy.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 6 Conclusions  
In this study, we introduced a novel CBIR method for collaborative AUV search missions, effectively addressing the challenges posed by underwater environments. According to the authors' research, this is the first study to explore intent recognition in the AUV domain. By combining fuzzy inference for behavior modeling with a deep learning-based intent inference phase, the CBIR approach ensures accurate intent recognition with minimal data exchange, even under communication constraints.  
Although there is no relevant reference for intent recognition in the field of underwater collaborative search missions, we conducted a comparative study from two perspectives: the accuracy of the intent classification model and its performance compared to traditional collaborative search methods. The experimental results demonstrate that the proposed method outperforms in both intent classification accuracy and collaborative search mission efficiency.  
While there are no comparable studies on intent recognition in AUVs to evaluate the types of data used, our simulation analysis of the impact of data packet size on underwater acoustic communication indicates that selecting key information through behavior modeling is far more effective than using more extensive data for decision making. Additionally, under more extreme communication topology conditions, the proposed method maintains high efficiency and success rates in collaborative search missions by predicting the intent of AUVs within the swarm--something traditional methods cannot achieve.  
This study introduces a novel approach to underwater collaborative operations, providing valuable insights and a practical foundation for future research in multi-AUV systems.  
Future efforts will focus on refining the model to handle more diverse mission scenarios and optimizing its performance in practical underwater operations.  
Conceptualization, Y.W.; methodology, Y.W.; software, Y.W.; validation, Y.W.; formal analysis, Y.W.; investigation, Y.W.; resources, K.L.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, K.L., L.G. and S.Z.; visualization, Y.W.; supervision, K.L., L.G. and S.Z.; project administration, K.L.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 61821005 and No. 61973297) and the Liaoning Revitalization Talents Program (No. XLYC1902032).  
Not applicable.  
The data presented in this study are available on request from the authors.  
The authors declare no conflicts of interest.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 2 Proposed Approach  
Our approach is based on a hierarchical correlation of events. In the reviewed literature, some works have used similar approaches but with fundamental differences from our work. In the table below (Table 2), we highlight some of the works worth noting. The fundamental difference between all these works from the approach proposed in this research is that they perform correlation after intrusion detection by sensors such as IDSs. While this prevailing approach will still reduce the alerts in the end, the approach taken in this research is to correlate before the alerts are produced by the sensor. Incidental to the above fact, all the reviewed systems operate on alerts, while in this approach the correlation is performed on raw events. The significance of this is that the volume of alerts is reduced at the IDS level itself since the IDS operates on a reduced data set. Additionally, the IDS can now detect some complex and multistep attacks since it analyzes already correlated hypererevents. This capability is not possible when analyzing singular events. This is particularly beneficial for security operations that do not have SIEM systems and other third-party correlation systems since the correlation component becomes integral to the IDS itself. The other difference is in the techniques used and how they affect the overall performance of the system. Both [7] and [33] use attribute-based correlation as a first stage to produce hyperalerts, then they cluster the hyperalerts further by correlating them using pre-post conditions. Pre-post conditions require a knowledge base of such rules to capture every possible attack. If the pre-post conditions do not cover all possible attack scenarios, unknown attacks cannot be discovered. In the case of the research by [PERSON] and [PERSON] [6], they also used pre-post conditions; however, they used the same technique at both levels of the hierarchy. Using the same correlation technique at different levels of a hierarchy might enhance the strength of the technique without necessarily eliminating its weaknesses. In the case of [8], they rely on a third-party system, Splunk, to do the aggregation and then use a custom automated tool to cluster the aggregated alerts using text analysis. This approach increases operational complexity as the security team must master the operations of the IDS, the SIEM system (Splunk), and the custom correlation tool. Operational complexity is undesirable, even more so in information security, where pragmatism, nimbleness, and agility must be the operational ethos. In addition to the above differences, our approach offers the benefit of visualization due to the graph correlation at the second level of the hierarchy [34; 35; 36].
## 2 Design Consideration
### Proposed Continuous-Time Linear Equalizer  
Figure 4 presents a schematic of the proposed CTLE comprising two differential pairs, \(M_{1}\) with \(M_{2}\) and \(M_{3}\) with \(M_{4}\). The differential pair comprising \(M_{3}\) and \(M_{4}\) is degenerated by the parallel resistor \(R_{\rm S}\) and the capacitor \(C_{\rm S}\). \(M_{5}\), \(M_{6}\), and \(M_{7}\) are the current sinks and are controlled \(V_{bias1}\) and \(V_{bias2}\).  
Figure 3: Single-ended Bode Plot of the CTLE.  
The left-hand side of Figure 4 presents the bias circuit for the CTLE. The CTLE gain can be varied by adjusting the differential voltages of \(V_{\mathit{bissn}}\) and \(V_{\mathit{bissp}}\). The differential voltages of \(V_{\mathit{bissn}}\) and \(V_{\mathit{bissp}}\) can be expressed as follows:  
\[V_{\mathit{bissn}} =V_{\mathit{cnt}}+/-V_{\mathit{adjust}} \tag{5}\] \[V_{\mathit{bissp}} =V_{\mathit{cnt}}-/+V_{\mathit{adjust}} \tag{6}\]  
where \(V_{\mathit{cnt}}\) is the common-mode voltage, and \(V_{\mathit{adjust}}\) is the adjusting voltage for varying the bias currents of the differential pairs of the CTLE. If \(V_{\mathit{bissp}}>V_{\mathit{bissn}}\), the current \(I_{2}=2I_{1}\) flows through M\({}_{5}\), passes to \(M_{1}\) and \(M_{2}\), and the current \(I_{1}\) flowing through \(M_{6}\) and \(M_{7}\) passes to \(M_{3}\) and \(M_{4}\), as presented in Figure 4. The output current at nodes \(V_{\mathit{out1}}\) and \(V_{\mathit{out2}}\) is \(I_{\mathrm{tot}}=2I_{1}\). Similarly, \(V_{\mathit{bissp}}<V_{\mathit{bissn}}\); the output current at nodes \(V_{\mathit{out1}}\) and \(V_{\mathit{out2}}\) is \(I_{\mathrm{tot}}=2I_{1}\). This design also boosts the DC gain at high frequency and slightly shifts the zero (\(\omega_{2}\)) and poles (\(\omega_{\mathrm{p1}}\) and \(\omega_{\mathrm{p2}}\)). The zero \(\omega_{\mathrm{z}}\) of the proposed CTLE is dependent on \(g_{\mathrm{m1}}\) and \(g_{\mathrm{m3}}\) and on \(R_{\mathrm{S}}\) and \(C_{\mathrm{S}}\). The transfer function of the single-ended proposed CTLE is presented in Equation (8).  
\[\mathrm{H}\left(\mathrm{S}\right)=\frac{\left(G_{\mathrm{M1}}+G_{\mathrm{M3}} \right)R_{L}}{1+\frac{\left(G_{\mathrm{M1}}G_{\mathrm{M3}}\right)R_{\mathrm{ S}}}{2}}\times\frac{1+\frac{S}{\omega_{\mathrm{z}}}}{\left(1+\frac{S}{ \omega_{\mathrm{p1}}}\right)\left(1+\frac{S}{\omega_{\mathrm{p2}}}\right)} \tag{7}\]  
where \(\omega_{\mathrm{z}}\)=\(\frac{1}{R_{\mathrm{L}}C_{\mathrm{L}}}\), \(\omega_{\mathrm{p1}}\)=\(\frac{1+\frac{\left(G_{\mathrm{M1}}G_{\mathrm{M3}}\right)R_{\mathrm{S}}}{R_{ \mathrm{S}}C_{\mathrm{S}}}}{R_{\mathrm{S}}C_{\mathrm{S}}}\), and \(\omega_{The Bode plot of the single-ended proposed CTLE with zeroes \(\omega_{Z}\) and \(\omega_{Z1}\) that shift due to variations in the DC gain is presented in Figure 5.