esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
### High-Power Laser Diode Driver Performance in CW and QCW Modes  
The laser driver produced a current waveform that could be either CW, modulated, or pulsed, up to a current of 200 A. In our experiments, we used diode currents up to 12 A in the CW mode, which is the rated maximum current of the pump laser diode in the CW mode. At this power, the diode produced an output power of 60 W (see Figure 2). The pump diodes were very stable, and wavelength-stabilized.  
However, in the QCW mode, a 100-\(\upmu\)s pulse duration and a duty cycle of 1% were used, corresponding to a pulse repetition rate of 0.1 kHz. The maximum current that could be used was then more than twice the manufacturer's CW rating. Specifically, and as shown in Figure 2, we were able to drive the laser diode up to 25.5 A and produce a peak power of 123 W. The diode efficiency was nominally the same in the QCW and CW modes: operating it in the pulsed mode enabled us to extract twice as much power from it. The laser diode's output power was monitored for several hours at these settings and showed no sign of power degradation or thermal damage to the pump source, indicating that potentially higher diode currents or longer pulse durations could have been used. Nevertheless, this pump current was deemed sufficient for the purpose of this study.
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 2 Materials and Methods  
### Site Description and Equipment  
The Finnish Antarctic research station (Aboa) was established in 1989 [8]. The station is located in the western part of the Dronning Maud Land, approx. 130 km from the coast, on Basen nunatak in the northern part of the Kraul mountain range at 73.0437 S, 13.4071 W, and 485 m elevation above sea level. Aboa is occupied only during the Antarctic summers. The GPS permanent station was setup during the 2002/2003 Antarctic expedition. It became operational on 31 January 2003 at 10:10 UT [3].  
Figure 1: Location of the Finnish research station Aboa within the Antarctic continent. The red triangles show the location of the research stations visited by the Finnish researchers during the Antarctic summers. The blue squares denote the IGS GPS stations for which the time series are longer than 15 years [40]. The grey dots show the location of other episodic and/or continuous GPS stations established in Antarctica and reported by the Nevada State Laboratory [41].  
The Aboa station tracks only GPS satellite signals. The station is equipped with an Ashtech choke ring antenna (ASH701945 CM) covered with an Ashtech conical SNOW antenna dome. The antenna has remained unchanged since the beginning of the operation. It has been installed on bedrock on the top of a 1.5 m tall steel mast (Figure 2). Installation on the top of the mast is necessary since the snow environment changes constantly at the site. The height of snow has been close to the antenna height during several seasons, whereas during other seasons the bedrock below the mast appears visible. The GPS receiver is a Javad EURO80 GDA card (JPS HE GDA) custom-fitted into a metal enclosure. The choice of the receiver was made based on the low power usage (1.8-2.4 W) of the Javad receiver since the battery power is limited at Aboa during the winter months. For the same reason, the data is obtained only once per year during the FINNARP expeditions to Aboa. Actually, two identical receivers are used. The FINNARP expedition changes receivers at Aboa and returns the one from the previous year to FGI for data download and maintenance. This strategy allows for the FINNARP expedition to always have spare receiver in case of instrument failure during the previous winter season. Further technical information is given in [3].
## 3 Results and Discussion
### Uniformity of Flow Distribution  
The uniformity of flow distribution is closely related to the freezing completion effect in aquatic products. To investigate the uniformity of flow distribution, a coefficient of velocity non-uniformity (\(k_{v}\)) was considered [41]. Lower \(k_{v}\) means that the uniformity of flow distribution is better. The calculation formula for \(k_{v}\) is:  
\[k_{v}=\frac{1}{v_{ave-t}}\sqrt{\frac{1}{n}\sum_{i=1}^{n}(v_{i-t}-v_{ave-t})^{2}} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stacking Modes** & \(\mathbf{a_{1}}\) & **Stacking Modes** & \(\mathbf{a_{1}}\) \\ \hline NSMs with one-pile & 0.200 & NSMs with three-piles & 0.310 \\ UPF-PSMs with one-pile & 0.025 & UPT-PSMs with three-piles & 0.315 \\ NSMs with two-piles & 0.190 & NSMs with six-piles & 0.200 \\ UPF-PSMs with two-piles & 0.040 & UPT-PSMs with six-piles & 0.355 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absolute values of slope \(\mathbf{a_{1}}\) in Equation (9).  
The locations of measurement points can be seen in Figure 10a. Three measurement X-Z planes (y = 0.35, 1.05 and 1.75 m) were selected with six measurement points on each plane. The lengths L33-38 are: L33 = 1.1 m, L34 = L35 = 2.175 m, L36 = 0.2 m, L37 = 2.95 m, L38 = 0.15 m. The measurement points are symmetrical with respect to X-Z plane (y = 0).  
As shown in Figure 10b, the four \(k_{v}\) curves are increasing first, and then decreasing with time. All maximums of \(k_{v}\) occur at a residence time of 2 h. The maximums are larger at UPF-PSMs and UPT-PSMs and amount to 1.67 and 1.23, respectively. The maximums of the corresponding NSMs are smaller and equal to 0.84 and 0.77, respectively. First, the low-temperature space in the refrigerated room is larger when using PSMs at constant air supply temperature and speed; more time is necessary for the flow distribution in a larger low-temperature space to become uniform. Second, the geometry of low temperature environment is more regular at NSMs, which decreases the differen  
Figure 10: (**a**) Locations of measurement points (top view), (**b**) Variation of \(k_{v}\) with time.  
Figure 9: Temperature coefficient of variation with time.  
various locations. Furthermore, after the peak time of 2 h, \(k_{p}\) is gradually improved and equalised, but a little difference still exists. For UPF-PSMs, UPT-PSMs and their corresponding NSMs \(k_{p}\) reaches stable values with time at 0.66, 0.64, 0.60 and 0.53, respectively. Such a stabilisation is important, given the frozen storage takes about or over 6 months [1; 7; 8]. It was found out that the uniformity of flow distribution during frozen storage is best at NSMs.
# Storage and Processing of Big Data for Geomagnetic Support of Directional Drilling
## 2 Geomagnetic Data and Data Management
The initial architecture of the system is described in detail in [31]. Thanks to the open data policy of GC RAS, the system has become a convenient tool for researchers from various organizations. In the period from 2016 to 2024, the system was constantly being developed, and new functionality was consistently being added to it. In addition to the data from magnetic observatories, geomagnetic measurements from Swarm satellites from 2013 to the present were loaded into the database (DB) [32]. With the increase in requirements for the measurement frequencies of observatory magnetometers, an active transition to a 1 s measurement standard began, and data from new observatories were added. All this led to a significant increase in the load on the system. For its further operation, a decision was made to transfer MAGNUS to Big Data (BD) technology [33]. Information systems built on Big Data technologies meet the requirements of simplicity of horizontal scaling and query processing speed [34; 35]. The ultimate goal is to create a single scalable system of satellite and ground-based geomagnetic data, MAGNUS BD. In the future, such a system can be used both to solve fundamental geophysical problems and to provide high-quality data to oilfield service companies in real time for the purpose of supporting DD in Russia. This article is devoted to the process of such a transition and the first results obtained.
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 1 Introduction
As shown with HDT [12], combining these features from different layers improves the performance of the tracker. HDT uses an improved hedge algorithm to hedge weak trackers from each layer into a strong tracker. In this work, we consider feature fusion by using a \(1\times 1\) convolution to concatenate and fuse features from different stages in the Siamese backbone network, which can improve the algorithm accuracy. Meanwhile, in order to improve the detection speed, we use a group convolution for the dimensionality reduction. A group convolution [13] can exponentially reduce the number of parameters compared with a normal convolution, which can speed up the operation. In the correlation stage, we use a new matching method, namely a pixel-level correlation operation, in the network, which is able to obtain a correlation feature map with a smaller kernel size and a more diverse target representation, reducing the interference of background clutter and preserving the target boundary and scale information, which is beneficial to the subsequent prediction. The main contributions of this work are as follows:  
1. Feature fusion: we use not only the last layer output feature map for prediction but also the feature map of layers 3, 4, and 5 for feature fusion to output the prediction;
2. Pixel-level correlation: the template features are decomposed into spatial features and channel features, which are matched with the search features, instead of correlating channel-by-channel;
3. Speed improvement: we use a group convolution for the dimensionality reduction, which reduces the number of parameters and the use of activation functions and normalization in the backbone to speed up the detection;
4. New attention module: we designed two new attention modules, namely, a multi-scale feature aggregated channel attention block (MCA) and a global-to-local-information-fused spatial attention block (GSA), enabling the network to focus on certain parts of the features and reduce the attention on useless parts, thus improving the performance and accuracy of the model. The rest of this paper is organized as follows. In Section 2, we present research on object tracking based on Siamese networks published in recent years. Section 3 outlines the core of our tracker, including four parts to improve accuracy, from the lightness to the robustness of the algorithm. Section 4 is the experimental section, which presents an ablation study and a comparison of the results of different trackers on different datasets to analyze the validity of our work. Finally, we conclude the paper in Section 5.
## 3 CCUS3O Model
### Objective Values
#### 3.2.2 Coverage Time  
In addition to covering as much area as possible, the time for the UAVs to return to their base must be minimised. In the CCUS3O model, UAVs are considered to fly at a constant speed. The fact that the speed is constant shows that the time spent and the distance travelled are proportional, regardless of the value of the speed. Therefore, the coverage time can be calculated from the distance travelled by the UAVs. During a coverage mission, the UAVs do not return to their base at the same moment. Therefore, the total coverage time corresponds to the time needed by the UAV that finished last, that is, with the longest trip. For any solution \(S\) (including nonfeasible ones), \(O^{(time)}(S)\) is defined as:  
\[O^{(time)}(S)=\max_{u\in U}l_{u}\]  
where \(l_{u}\) is the length of the path made by UAV \(u\) at the current time and the path from the current position to the starting vertex (i.e., base station). Thus, it represents the length of the cycle made by the UAV \(u\) if the latter comes back to its initial vertex from the current position.  
\[l_{u}=\underbrace{dist(pos(u),P_{u}[1])}_{\text{distance from the initial vertex}}+\underbrace{\sum_{i=1}^{\left|P_{u}\right|-1}dist(P_{u}[i],P_{u}[i+1])}_{\text{distance travelled so far}}\]  
Since the objective is to cover the area as fast as possible, the coverage time objective should be minimised. Furthermore, minimising the longest cycle (compared to minimising the average, for instance) prevents the situation where some UAVs finish their tour much earlier than the other ones.
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## Evaluation
### Result and Analysis  
The results of MAPE are listed in Table 3. As for the baselines, SARIMA achieves the best performance. Nevertheless, LS6 performs the best, while SARIMA ranks second among all approaches. For both datasets, the results clearly corroborate the superiority of the proposed LS6 in IPv6 traffic prediction. Specifically, the MAPE value of LS6 in DHU is 2.65% lower than that of SARIMA, and this number is 0.29% lower in ECNU.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### Power Optimization Process
#### 3.3.3 Encoding of Voltage  
The encoding of chromosomes is the initial step in solving a problem through genetic algorithms. There are various encoding methods for GA algorithms, such as binary, permutation, value, and tree encoding. However, the encoding process is often problem-dependent, and in this work, binary encoding is used as the appropriate method. In essence, during binary encoding, each chromosome is represented by a string of bits, 0 or 1, where each chromosome bit represents a specific characteristic of the problem.  
Intending to specify the length of the chromosome or the number of bits the string consists of, the domain of the objective function (i.e., the set of possible values of the independent variable) has to be defined as well as the desired decimal precision for the problem. Then, the number of bits can be calculated from the following equation [33]:  
\[2^{l}-1 \geq(ub-lb)*10^{Decimals}\implies\] \[2^{l} \geq(ub-lb)*10^{Decimals}+1\implies\] \[l \geq\log_{2}[(ub-lb)*10^{Decimals}+1],\]  
where \(l\) is the number of bits required for the representation, \(Decimals\) are the decimals of precision, and \(ub\) and \(lb\) represent the upper bound and lower bound of the possible permitted values, respectively. Assuming the voltage (\(V\)) value lies in the range of [0, 20] volts and one decimal of precision is required, the number of bits to represent a chromosome can be computed according to the previous equation as follows:  
\[l \geq\log_{2}[(20-0)*10^{1}+1]\implies\] \[l \geq 7.65\implies\] \[l =8\]Taking into consideration the constraints in terms of voltage range and decimals of precision, each chromosome can be represented by a string of bits, as depicted in Figure 4. This string of bits is also called a genotype.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Sample Set Division  
In this study, a full 105 images were obtained, with 4 samples on each image and for a total of 420 samples. White shrimps with different refrigeration days were labeled as 1, 2, 3, 4, 5, 6, 7 as their respective sample labels. Before performing data analysis, the samples were divided into a calibration set and a prediction set. The model was trained by using calibration set data with known sample labels and followed by testing of model performance with prediction set data. Sample set partitioning based on be x-y distances (SPXY) was developed on the basis of the Euclidean distance method, which regards the spectral data as the independent variables and the sample labels as the dependent variables when calculating the distance between samples. Therefore, SPXY can be used to effectively establish the NIR quantitative model [20]. In this study, the average spectral data of white shrimps after being extracted the region of interest were divided into a 3:1 ratio by SPXY, that is, 315 samples were taken as the calibration set, and 105 samples were taken as the prediction set.
# Mechanical Properties Analysis of WAAM Produced Wall Made from 6063 Alloy Using AC MIG Process
## 3 Results and Discussion
### Residual Stresses  
After the additive manufacturing of the samples was completed and cooled to ambient temperature, residual stresses were measured using X-ray diffraction based on the cos\(\alpha\) method. The measurements were performed using the Pulstec u-X360 device (Pulstec Industrial Co., Ltd.), as shown in Figure 8. The measurements of longitudinal residual stresses (i.e., in the direction of welding) were conducted on the last layer at the top of the samples, as well as along line A-B (Figure 8). The measurements on the last layer were taken immediately after sample preparation, while the measurements along line A-B were performed after cutting the substrate from the sample due to the geometrical limitations of the measurement equipment. For the last layer, the distance between measurement points was 20 mm, while on the centerline, the distance was 5 mm.  
For the last layer, the highest normal residual stresses were observed in sample 14, produced with DC-MIG, amounting to \(-110\) MPa in compression and 80 MPa in tension. The residual stresses in the samples obtained with AC-MIG were slightly lower, with maximum tensile stresses of 55 MPa in sample 12 and compressive stresses of approximately \(-80\) MPa in sample 13. The obtained results correlate with findings from the literature [PERSON] et al. (2019), showing slightly higher tensile stresses but significantly lower compressive stresses. This discrepancy is attributed to the fact that in the referenced literature, the stresses were measured after 20 layers, while in our study, they were measured after 50 layers.  
Figure 8: X-ray residual stress measurement.  
The distribution of residual stress along line A-B, as presented in Figure 9, reveals a tensile-compressive-tensile pattern extending from the top layers of the deposit down to the substrate. This pattern results from the complex thermal cycling and mechanical constraints inherent in the WAAM process.  
The overall distribution indicates that tensile residual stresses occur in the middle of the deposition layers and near the top, while compressive stresses are intermittently present in certain areas, particularly at the ends of the last deposition layer in samples 11 and 12. Notably, sample 11 exhibits the highest tensile residual stress of approximately 40 MPa, located several layers below the top surface.  
Among the analyzed samples, sample 11 not only achieved the greatest height with the same number of layers but also exhibited the highest residual stress values among the samples produced by AC-MIG. Furthermore, the frequency of transitions between tensile and compressive stresses becomes more pronounced as the height of the structure increases. Sample 14, produced with DC-MIG, also exhibited tensile stresses of around 40 MPa.  
Overall, the determined residual stress distributions and magnitudes are consistent with the results reported in the literature [28], confirming the expected stress distributions in WAAM-manufactured components of aluminum alloy 6063.
## 5 Distinguishing AI Agents in Autonomous Maritime Systems from Conventional AI Systems
### Operational Context and Decision Constraints  
AI agents deployed in autonomous maritime systems operate under drastically different conditions than their conventional AI counterparts. While traditional AI systems are often developed for predictable and structured environments--such as office automation, industrial robotics, or server-based analytics--maritime AI agents are engineered for vast, dynamic, and often hostile operational environments [70; 77; 80].  
Autonomous ships must traverse open oceans, coastal zones, and congested ports, all of which introduce high variability in traffic patterns, environmental conditions, and risk exposure. Agents must dynamically adapt to shifting sea states, unpredictable weather systems, varying visibility, and maritime hazards (e.g., debris, wildlife, fishing zones), while also interacting with human-operated vessels. This complexity is magnified by the fact that many autonomous voyages may extend over days or even weeks without consistent access to external support systems or high-speed communication links [81].  
In such contexts, real-time decision-making becomes essential. Maritime AI agents must ingest continuous sensor data--including radar, sonar, LIDAR, AIS, and camera feeds--and instantly translate them into safe and efficient navigational actions. These decisions cannot wait for cloud-based inference cycles or human validation. For example, when a sudden obstacle appears--like a drifting buoy, small craft, or storm front--the system must react within milliseconds to avoid collision or reroute the vessel safely.  
This requirement for autonomy at the edge contrasts sharply with conventional AI systems, which typically process data in batch mode, depend on stable infrastructure, and allow for some level of human intervention or retraining. Even in high-stakes domains like healthcare or finance, operational environments are more constrained, and failures--though costly--rarely translate into immediate physical threats.  
Another important distinction lies in sensor integration and interpretation. Maritime AI agents must work with a wide array of domain-specific, heterogenous sensors, unlike general-purpose AI systems, which are usually tailored to a narrower, more standardized input space. Integrating maritime sensor data in real time, especially under degraded or noisy conditions, requires specialized fusion pipelines and uncertainty-aware reasoning mechanisms [70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105].  
Furthermore, mission duration impacts architecture. AI agents on autonomous vessels may have to maintain uptime and operational integrity for weeks at sea, without the benefit of frequent software updates or direct technical support. This contrasts with conventional AI systems, which are often monitored, retrained, or manually adjusted on a frequent basis in controlled environments [77; 78; 79; 80] (Figure 3).
## 2 Related Work
### Attribute/User Revocation  
The concept of attribute groups, introduced by [PERSON] et al. [22], enables fine-grained user revocation through a binary tree structure for managing group keys. Enhancements by [PERSON] et al. [15] and [PERSON] et al. [23] improved efficiency. [PERSON] et al. [24] addressed vulnerabilities in [22] related to collusion attacks and proposed a more secure scheme via binding user key segments. Fine-grained revocation is also supported by ABE with negation clauses, as seen in [25, 26, 27, 28]. According to [5, 29], even a single attribute group's revocation can revoke access to the entire system. This is vital for applications like video anomaly detection and event recognition, where the dynamic control of video feed access is necessary. Efficient revocation that supports both attribute- and user-level revocations is crucial in real-world scenarios, as noted by [PERSON] et al. [5], [PERSON] et al. [27], and [PERSON] et al. [30].