esa-sceva/satcom-chunk-collection huggingface dataset:
# Congestion-Free Ant Traffic: Jam Absorption Mechanism in Multiple Platoons
## 5 Analysis of Jam-Free Ant Traffic
### Analysis of Platoon Headway and Density  
The inter-platoon analysis indicated that an ant colony maintains \(f\) on a trail by implementing a jam absorption mechanism, for which the platoon headway (i.e., the JAB) plays an important role. Therefore, in this part of the paper, we present further analysis of the JAB. Similar to the inter-platoon analysis, we selected a pair of adjacent platoons with a sufficient inter-platoon distance (i.e., to avoid merging). The simulation was carried out with a heterogeneous trail, and we took care to ensure that the given JAB was undisturbed (i.e., no new ants were introduced into it). Changes in the JAB were observed, and the number of platoons was counted for each observed density.  
Figure 7: (**a**) Instantaneous velocities of ants plotted against the positions of the same ants for prototypical inter-platoon analysis. (**b**) The positions of the ants plotted against time for prototypical inter-platoon analysis. Data were obtained from an ATM computer simulation of a heterogeneous trail. Ants observed: last ant in leading platoon (red); leader of the following platoon (black). Simulation scenarios: \(er=0.02\), high-resistance section \((\Omega=0.1)\) of trail = \(cell_{400}\)–\(cell_{800}\), \(L=1000\) cells, \(\sigma_{sat}=80\), \(\upsilon_{min}=0.15\).  
The results of analyzing the relationship between JAB and \(d\) are presented in Figure 8 and Table 3. As shown in Figure 8, in the free-flow phase (up to \(d\approx 0.75\)), first, a lengthening of the JAB was observed until \(d\approx 0.5\). Then, after that, a slight decrease in the JAB length with a rising \(d\) was observed. It is important to note that the decrease was small when compared against the initial lengthening. At the same time, as shown in Table 1, the number of platoons on the trail decreased.  
An increase in \(d\) led to a larger platoon size, meaning that adjoining platoons that were close to each other came closer and finally merged to form a single platoon, leading to fewer platoons overall. Meanwhile, the platoon mergers provided more space for the remaining platoons to create larger JABs. This JAB lengthening played an important role in managing the \(f\) on a trail. The platoon size increased with \(d\), leading to longer queues. However, the aforementioned JAB lengthening canceled this queue lengthening, thereby maintaining an approximately constant \(v_{avg}\). Finally, near a critical density of \(d\approx 0.75\), any further increase in \(d\) led to the collapse of the multi-platoon structure to form a single platoon (infinite cluster) on a circular trail. Because multiple platoons no longer exist, the stop-and-go motion of one ant propagated to the following ant without being absorbed. This was amplified at each ant and traveled backward as a shock wave. Therefore, jams were never absorbed and were amplified further with \(d\). Consequently, the \(v_{avg}\) decreased with \(d\), triggering the jamming of the fundamental diagrams.
## 8 Application Example: 2023 Bangladesh Flooding Season
### 2023 Bangladesh Flood Progression  
Figure 16 shows the results of a time series analysis of the surface water extent throughout the 2023 Bangladesh flood season, as derived from all available HYDRO30 data. The results show that the surface water extent slowly decreased early in the year as residual water from the previous monsoon season dried up during the dry post-monsoon winter season. A minimum surface water extent of 6549.5 km\({}^{2}\) or 4.42% of the Bangladesh land area was reached on 2 April 2023 near the end of the dry pre-monsoon season. We interpreted this extent as the perennial water extent in Bangladesh.  
Figure 15: Left: Area of interest (AOI) for countrywide analysis of surface water extent for the flood year 2023. (right) Map of Bangladesh’s administrative divisions. These divisions are further analyzed in Section 8.2.  
A small uptick in water extent can be observed for the time step centered on 14 May 2023. This peak is coincident with the landfall of cyclone Mocha, which caused increased water coverage in Bangladesh's southern divisions of Khulna (southwest) and Chittagong (souteast).  
A rapid increase in the water extent commenced around 28 May, marking the onset of the monsoon season. The maximum water extent of 17,138.4 km\({}^{2}\) or 11.6% of the land area was reached near 6 August 2023, marking 2023 as a below-average monsoon season for Bangladesh. After 6 August, the surface water started slowly and progressively receding through the rest of the year.  
Banglades is divided into eight administrative divisions (Figure 15) that are differently affected by monsoon-related flooding. A division-by-division assessment of the surface water extent (Figure 17) showed that a vast majority of the inundation occurred in the northeastern Sylhet division of Bangladesh. Sylhet is a low-lying area that often acts as a natural \"reservoir\" for monsoon-related floodwaters. At the peak of the 2023 monsoon season, 31.1% of Sylhet was covered in water. The Chittagong division, located in southeast Bangladesh, showed the lowest surface water area relative to its area (6.7%). This is due to the lack of major rivers draining into this division and due to its higher average topography.  
The available data also allowed determining the timing of the peak water extent for each division (see the text in Figure 17). Our data show that the peak flooding dates were reasonably consistent across Bangladesh, with flood waters pe  
Figure 16: Time series of surface water extent in Bangladesh throughout 2023 as derived from HYDRO30 products. (**left**) Minimum water extent occurring on 2 April 2023. (**middle**) Surface water extent time series. (**right**) Maximum Water extent on 6 August 2023.  
Figure 17: Maximum surface water extent for different divisions of Bangladesh as a percent of the overall division area as calculated from HydroSAR HYDRO30 mosaics.  
in most divisions. The maximum water extent occurred slightly earlier in the Sylhet and Khulna divisions (23 July 2023) and slightly later in Barisal (3 September 2023).
## Appendix A IPv6 Neighbor Discovery  
If IPv6 ND is used (optional), each node (conceptually) keeps a single neighbor cache, a single default router list, and a single routing table, as in every other IPv6 ND implementation. From the neighbor cache, we consider only the neighbors not labeled UNREACHABLE or INCOMPLETE to be part of the candidate neighbor set. Note that Neighbor Unreachability Detection (NUD) tracks the reachability of neighbors, not interfaces. A separate conceptual data structure keeps track of virtual/inferred metrics (see Appendix B) for every (link-layer) neighbor and its interfaces. Contrary to the neighbor cache, this structure is (indirectly) managed by the link-layer.  
If IPv6 ND is indeed used, all multicast ND messages not sent in response to a Neighbor Solicitation (NS) [28] (Section 4.3) or Router Solicitation (RS) [28] (Section 4.1) must be sent over all interfaces (with a different MAC sequence number). Unsolicited unicast ND messages are sent over a neighbor's preferred interface, except for unicast NSs, which are sent over all known interfaces of a neighbor (with the same MAC sequence number). Solicited multicast Router Advertisements (RAs) [28] (Section 4.2) must be sent on the same interface as the one on which the RS (to which it is replying) was received. The same goes for multicast Neighbor Advertisements (NAs) [28] (Section 4.4) sent (to FF02::1) in response to an NS with an unspecified (::) source and for solicited unicast NAs and RAs. We do not consider redirect messages. A duplicate unicast NS provokes a link-layer ACK to the sender, thereby updating the inferred metric of the corresponding interface, but it does not provoke an NA because it is dropped before routing layer processing.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues
#### 3.2.13. Poor Bridging or Ugly Overhangs  
Bridges are stretches of plastic extruded between two raised points. Poor overhangs occur when the printer is printing material in thin air due to lack of support, or there is nothing to deposit above. Solutions include the following:  
1. Using supports for overhangs angle greater than 45\({}^{\circ}\) and bridges over 10 mm [2]. The bridging gaps should not exceed 5 mm to ensure minimal sagging of parts.
2. Design according to the guidelines and rules for ME to avoid geometry that is difficult to print, particularly for models with large overhangs that tend to warp during mid-print.
3. Consider changing the build orientation to avoid the need for an overhang.
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
###### Abstract  
Predictive business process monitoring is predicting the next stage of the business process based on the sequence of events that have occurred in the business process instance, which is positive for promoting the rational allocation of resources and the improvement of execution efficiency. There are drawbacks in modeling business process instances, such as conceptual drift phenomenon and long event sequences. Therefore, we propose a hierarchical Transformer-based business process prediction model to improve the performance of the Transformer-based predictive business process monitoring model. We encode the event features using two different encoding methods to obtain the relationship between activities and attributes. A drift detection algorithm is proposed to segment the business process and calculate the correlation between activities and segments by using cross-attention. Furthermore, learnable position encoding is designed to capture the relative position information of subsequences. Finally, the information of different granularity, such as event attributes, event subsequences, and complete instances, is fused by different weights. Experiments were run on seven real event logs for the next activity prediction and remaining time prediction, and the next activity prediction accuracy improved by 6.32% on average, and the mean absolute error of remaining time prediction reduces by 21% on average.  
business process; predictive business process monitoring; Transformer; concept drift detection Article
## 3 Trends in Operational USVs
### Communication Technologies  
The communication technologies used in USVs shows a clear preference for certain widely available and reliable communication methods (Figure 8). Wi-Fi, 4G LTE, and satellite communication stand out as the most frequently utilized technologies, emphasizing the importance of stable and long-range connectivity for USV operations. Wi-Fi, being widely accessible, is likely used for short-range applications, whereas 4G provides broader coverage where terrestrial networks are available. Also, the prominence of satellite communication suggests that many USVs operate in remote or offshore environments where terrestrial networks are insufficient. Low Earth Orbit (LEO) satellite technologies such as Starlink and Iridium are also used in some USVs. Traditional radio-based communication methods such as VHF and UHF also have significant usage, particularly for maritime applications where established radio networks provide reliable and regulated communication. Emerging technologies like 5G, though less frequent, hint at a gradual shift towards high-bandwidth, low-latency solutions that could enhance real-time data transmission in future USV designs.  
To give an overview of the communications technologies implemented in operative USVs, Table 1 lists the solutions given by manufacturers. Also, a high-level category of radio, satellite, or cellular is presented together with the type of technology and approximated ranges that are achievable by each one.  
Figure 8: Bar charts illustrating the frequency of different communication technologies used in USVs.
# Blockchain-Based Multistage Continuous Authentication for Smart Devices
## 4 System Model
### Signal denoising  
The noise generated by built-in sensors is high-frequency random noise, while the gait signal belongs to a low communication signal. Walking frequency is usually below 20 Hz, and most of the energy of the signal is mainly concentrated in the frequency range of 1-5 Hz. Therefore, a Butterworth filter with maximum flatness was chosen in this paper to allow the low-frequency signal in the signal to pass through, while filtering or suppressing the high-frequency signal, thus retaining the appropriate step signal. The principle is as follows: the amplitude frequency mode-squared characteristic \(H(u,v)\) of a Butterworth low-pass filter is given in Equation (2).  
\[|H_{a}(j\Omega)|^{2}=(\frac{1}{\sqrt{1+(\Omega/\Omega_{c})^{2N}}})^{2}, \tag{2}\]  
where \(N\) is the order of the filter, and \(\Omega_{c}\) is the cut-off frequency of the low-pass filter. In this paper, the cut-off frequency of the 3 rd-order Butterworth filter was set to 5 Hz. Figure 10 shows a comparison of the waveforms before and after denoising.
# An Introduction of a Modular Framework for Securing 5G Networks and Beyond
## 3 Proposed Network Service Security (NSS) Framework
### Security Modelling
#### 3.2.4 Device-to-Device Security  
D2D communications' security was addressed to some extent in 4G and has to be explored more in 5G as it uses D2D communications as an underlay technology critical to its functionality and attaining its key objectives [1, 3, 47, 48, 32]. This study is concerned with service security and how the existing D2D security can be improved. How will the UE deal with the data accessed after being granted access to the network and service? To date, D2D authentication and authorisation have been dependent on various security procedures, requiring it to be authenticated and authorised each time it disconnects from the network. Furthermore, in out-of-coverage conditions, the UE is currently not able to share restricted data with another UE. What happens to data on the UE and how can it be securely exchanged with or without network support is the subject addressed in this study. As a result, the DDS security level will attempt to address these concerns [2, 26].
## 4 Simulation
### SOC Balancing Control for Supercapacitor with Different Capacitance  
When supercapacitor capacity varies across ESMs, the power of each arm should be adjusted proportionally to its supercapacitor's remaining capacity. Considering a scenario where the supercapacitor capacity in phase A differs, Figure 12 presents a comparative analysis of SOC balancing performance under two operating conditions: fixed power allocation and capacity-based power allocation.  
Without this adjustment, the SOC of ESMs can still be balanced due to the intrinsic correlation between SOC and submodule capacitor voltage, but an imbalance in SOC persists between arms. This is consistent with theoretical analysis. As observed, after reallocating arm power according to the remaining capacity, the proposed control strategy effectively maintained SOC balance. This confirmed that while the intrinsic voltage-SOC  
Figure 11: Circulating current for different proportional coefficients. (**a**) Circulating current with adaptive proportional coefficients. (**b**) Circulating current with constant proportional coefficients.  
Figure 12: SOC with different capacitance. (**a**) Capacity-based power allocation. (**b**) Fixed power allocation.  
dynamics facilitated SOC balancing among ESMs, capacity-based power allocation was essential to achieve SOC balance across phases and arms.  
Table 3 presents a comparative analysis of control architectures between the proposed enhanced SOC balancing strategy and the traditional SOC balancing strategy. The enhanced SOC balancing strategy significantly simplified the control architecture. It also improved balancing performance through adaptive coefficients, achieving a 15% faster balancing speed while reducing initial circulating currents by 25%. Additionally, it maintained balancing capability across varying supercapacitor capacitance.
# Integrating Interactive Detection of Code Smells into Scrum: Feasibility, Benefits, and Challenges
## 2 Background
### Code Smells  
During software development, strict deadlines and evolving requirements can often result in the accumulation of technical debt [1]. Technical debt refers to a collection of sub-optimal decisions made during development that may impede the maintainability of a system in the future. One of the primary indicators of technical debt found within the source code is code smells. Code smells serve as warning signs that indicate areas where the source code may require refactoring or improvement [1]. Researchers such as [PERSON] [1], [PERSON] [17], and [PERSON] [3] have proposed and cataloged various code smells. Two common examples of code smell, along with their consequences, include:  
* God Class: this code smell refers to excessively large classes, possess poor cohesion, and have multiple dependencies on other data classes within the system [1]. Previous studies have demonstrated that God Classes can hinder program comprehension and adversely impact software maintainability [18; 19].
* Long Method: this code smell arises when methods encompass multiple functionalities or address more than one concern [1]. Research has shown that Long Methods can diminish program understanding and make the source code more prone to changes and faults [18; 19].  
Early detection of code smells holds substantial potential for promoting the longevity and sustainability of software systems [10; 20]. It involves promptly identifying opportunities for refactoring [1; 21; 22] as soon as code smells emerge in the source code during development. The longer the code smells persist in the codebase, the more challenging they become to eliminate. Refactoring, a behavior-preserving change in the program's structure, is pivotal in enhancing software maintainability [1; 23]. This paper delves into the significance of early code smell detection in software development, highlighting the risks associated with delayed detection and addressing code smells. Drawing insights from studies by [PERSON] [10], [PERSON] [6], [PERSON] [1], [PERSON] et al. [21], and [PERSON] et al. [22], we underscore the benefits of early detection and emphasize the importance of refactoring to ensure software quality and foster long-term success.
## 3 Hybrid Service Placement Based on a Recommender System
### Proposed Architecture (System Model)  
In this section, we propose a three-tier architecture (Cloud, Fog, and IoT), as illustrated in Figure 2, to describe the interactions between system components and address the SPP. This framework is based on the autonomous MADE-k model and a recommendation system. The SPP determines which IoT services are deployed on which Fog nodes for execution. Consider that there are \(m\) applications, represented by \(A=\{a_{1},a_{2},\ldots,a_{i},\ldots,a_{m}\}\), and \(a_{i}\) refers to the i-th application. Each IoT application comprises multiple tasks, with each task acting as an IoT service request generated by IoT devices. The whole set of services from m IoT applications includes \(r\) service, denoted as \(S=\{s_{1},s_{2},\ldots,s_{j},\ldots,s_{r}\}\), where \(s_{j}\) corresponds to the \(j\)-th service.  
The FC layer consists of \(n\) decentralized Fog nodes, expressed as \(F=\{f_{1},f_{2},\ldots,f_{k},\)\(\ldots,f_{n}\}\), with \(f_{k}\) representing the \(k\)-th Fog node. The service \(s_{j}\in a_{i}\) can be divided into subtasks, each assigned to a specific Fog node for processing. As a result, the node \(f_{k}\) can execute a subset of parsed tasks from multiple services. Each node includes a set of virtual machines (VMs) capable of hosting these task subsets.  
For simplicity, we assume that services are indivisible and that each Fog node executes all tasks associated with a given service independently. Additionally, each service remains hosted by a single Fog node until the completion of the execution process.  
As shown in Figure 2, the proposed architecture consists of four main layers each with specific responsibilities divided into three tiers: the Cloud, the Fog, and the Edge. The Cloud layer centrally places services that are not sensitive to delay, do not require an ultra-fast response, and services that do not stand a chance of being placed in the Fog or Edge layers. The Fog layer manages the decentralized processing of services and is positioned closer to IoT devices than the Cloud layer, thereby improving the response time for time-sensitive applications and reducing the services that need to be transmitted to the Cloud after filtering and aggregation. The Edge layer is located closest to end user devices and acts as an intermediary between the Fog and IoT devices, enabling faster response times for time-sensitive applications and facilitating communication between IoT devices and Fog nodes for dynamic interaction between connected objects and distributed computing resources. Finally, the IoT layer comprises connected objects, sensors, and actuators and is tasked with collecting data from the environment.  
Our contribution is made within the Fog layer, so we will focus particularly on this layer. Figure 3 shows the hierarchical structure of the proposed architecture, which consists of a group of colonies and a hybrid recommendation system (RS).  
Figure 2: Detailed architecture to solve the SPP based on the FOCN and the RS, where the numbers represent the steps of the service placement process.
## 4 Maintenance Improvements Bring by DT into O&M Phase
### Improvements for Planning  
Once inspection and damage assessment have been performed by the operators, it is necessary to plan the further maintenance procedures that must be done. We observed that many researchers have highlighted the improvements that specific and BIM-based DT can bring to such planning. Some authors have used prediction algorithms (e.g., machine learning, neural networks and linear regression) to visualise the later degradation and remaining life of equipment [15; 22; 39; 44; 46; 48; 53; 57; 58; 60; 61; 62; 63; 64]. [PERSON] et al. use such algorithms to estimate the impact of long-term dead load creep and shrinkage on the Sydney Opera House [61]. With such prediction capabilities, the operators and managers can use the results to plan the maintenance early enough to prevent any equipment failure, helping them to move from _reactive maintenance_ to _proactive maintenance_, such as _preventive maintenance_ (Table 5). Some authors have also decided to use knowledge from previous incidents and maintenance reports to propose the optimised period in which the maintenance should be performed. This last improvement can be seen as _predictive maintenance_, where the maintenance planning is automatically performed and proposed to the operator. In Table 5, we can see which type of maintenance is the focus in the 68 papers we found. However, even with the term _\"Maintenance\"_ included in all our searches, only 30 papers were found to provide this information.  
Another advantage brought about by these algorithms is that they can be used to perform maintenance simulations to choose the most suitable one for the situation [18; 63] or to compare the reparation cost with the equipment replacement cost [37; 38; 54; 65] to propose the cheapest maintenance solution. Combined with predictive maintenance to perform automatic planning, these algorithms can be used to propose the best maintenance procedures to be performed to the operator, providing a _prescriptive_ version of the maintenance [35; 53]. The occupancy of the building is another type of data that can be used to optimise the proposed procedures [35; 54], which can help with cleaning and maintenance procedures by preventing any interruption of tasks, or even to optimise energy consumption for unused rooms. The algorithms included in the centralised DT environment can be used by all authorised stakeholders to plan the maintenance procedures in a collaborative manner between the different professions [51; 65].