esa-sceva/satcom-chunk-collection huggingface dataset:
# On Transient Stability Problems in DC Microgrids
###### Abstract  
Transient stability, i.e., the ability of power systems to maintain synchronism when subjected to a severe disturbance, has extensively been investigated in AC grids. This article reveals that similar transient stability problems (but with different features) also exist in DC microgrids, as caused by the violation of power-transfer limitations between DC generators and grids. Through rigorous theoretical analysis and experimental validation, this article advances the field by establishing a comprehensive analytical framework that derives the equilibrium points, power-absorption limitations, and stable conditions of proportional-integral (PI) power-controlled and droop-controlled DC generators. The methodology combines small-signal stability analysis with large-signal nonlinear analysis to characterize the system dynamics under different control strategies. Our key findings demonstrate that droop-controlled generators benefit from better transient stability due to their enlarged stable operating region and enhanced robustness against voltage disturbances, which is particularly valuable for improving the reliability of renewable energy integration in DC microgrids. The theoretical analysis is comprehensively verified through experimental results using a prototype DC microgrid test platform.  
DC microgrids; droop control; power converters; renewable generation; transient stability +
Footnote †: journal: Applied  
Article  
On Transient Stability Problems in DC Microgrids
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review  
We aimed to understand the challenges in RSA from two different perspectives, namely, the agents' and users' perspectives. This section presents a literature review that produces a list of such challenges from the agents' perspective. Please refer to our IUI conference paper [14] for the detailed steps of our literature review methodology. The left part of Table 1 summarizes the identified challenges. Subsequently, we elaborate on the pertinent literature sources from which each individual challenge is derived.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline  & **Challenges** & **CV Problems** \\ \hline
**G1.** & **Localization and Orientation** & \\ \hline (1) & Scarcity of indoor map [1; 13; 19; 85] & Visual SLAM [86] \\ (2) & Unable to localize the user in the map in real time [1; 10; 42; 85] & Camera localization [87] \\ (3) & Difficulty in orienting the user in his or her current surroundings [9; 19; 42] & Camera localization [87] \\ (4) & Lack of landmarks or annotations on the map [1; 62] & Landmark recognition [88] \\ (5) & Outdated landmarks on the map [1; 62] & Landmark recognition [88] \\ (6) & Unable to change scale or resolution in indoor maps [1] & \\ (7) & Last-few-meters navigation (e.g., guiding the user to the final destination) [26; 85] & Visual navigation [89] \\ \hline
**G2.** & **Obstacle and Surrounding Information Requirement and Detection** & \\ \hline (1) & Difficulty in reading signages and texts in the user’s camera feed [43] & Scene text recognition [90] \\ (2) & Difficulty in estimating the depth from the user’s camera feed and in conveying distance information [13] & Depth estimation [91]; scale estimation [92; 93] \\ (3) & Difficulty in detecting and tracking moving objects (e.g., cars and pedestrians) [43; 94] & Multiple object tracking [95]; human trajectory prediction [96; 97] \\ (4) & Unable to project or estimate out-of-frame objects, people, or obstacles from the user’s camera feed [8; 9; 10; 11; 13; 42; 94] & Human trajectory prediction [96; 97]; FOV extrapolation [98; 99] \\ (5) & Motion sickness due to unstable camera feed [43] & Video stabilization [100] \\ \hline
**G3.** & **Delivering Information and Understanding User-Specific Situation** & \\ \hline (1) & Difficulty in proving various information (direction, obstacle, and surroundings) in a timely manner [1; 101] & Object detection [102]; visual search [103; 104] \\ (2) & Adjusting the pace and level of detail in description provision through communication [1; 43] & Video captioning [105] \\ (3) & Cognitive overload & \\ \hline
**G4.** & **Network and External Issues** & \\ \hline (1) & Losing connection and low quality of video feed [1; 13; 42; 43; 45; 94] & Online visual SLAM [86] \\ (2) & Poor quality of the video feed & Video super-resolution [106]; video relighting [107] \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of challenges in RSA service and related CV problems, presented in four groups (G1, G2, G3, and G4).
## 2 Materials and Methods
### Electronic Systems  
The complete diagram of all electronic components installed in the drone and wirings among them are presented in Figure 18. We chose the Holybro Kakute F7 AIO [39] as the central Flight Controller (FC) board--mainly due to its compact size and efficient primary microcontroller (STM32F745). The FC board was attached to the fuselage through vibration dampers--which is crucial for correct onboard Inertial Measurement Unit (IMU) readings.  
The wiring diagram (Figure 18) reveals many connections across far drone sections. To simplify the maintenance of the electronic components, we designed a dedicated connector board (Figure 19). The board exposes all signal sockets and separates the voltage supply for the servos. A separate DC/DC converter provides a 5 V supply for the servos and video camera. The servos could generate dangerous voltage spikes that could interfere, e.g., with the FC or other crucial components. The FC board supplies other components through a built-in 5 V DC/DC converter, which should be free of any voltage spikes. The diagram shows that all power lines go through the FC board because the FC has a built-in current sensor (up to 120 A). It is worth mentioning that the FC has only 6 Pulse Width Modulation (PWM) output channels: four of them are used by the four main quadcopter motor ESCs, and the two elevon servos use the remaining two. Because of the lack of another PWM output, the pusher motor ESC is connected directly to the Radio Control (RC) receiver. We used a Futaba T14 SG transmitter and a Futaba R7008 SB receiver to pilot the drone. The receiver has the S.Bus output--a single connector to send all 14 channels to the FC board. A 3 DR radio transceiver (433 MHz) was used for telemetry and autopilot commands from the Ground Station (GS). A GPS and compass were placed in a compact module mounted outside the fuselage--to improve GPS signal reception and move the compass away from substantial magnetic field interference induced by the power wires. The FC can communicate its status via programmable LEDs (WS2812)--two such LEDs are mounted in the front section, on both sides of the fuselage. The FC has a built-in barometer for altitude reading. However, the barometer's accuracy is limited. We planned to install a down-facing rangefinder for a smooth auto-landing feature. Initially, an ultrasonic sensor was tested, but we decided to replace it with a tiny, affordable, and very reliable Time-of-Flight (ToF) laser rangefinder (Pololu VL53L1X [40]). With its range up to 400 cm, the auto-landing mode works perfectly smoothly.  
Figure 17: The final design of Elka1Q fuselage—a PVC tube with internal plywood structure.  
We used good-quality 18 AWG power wires with XT60 and XT30 connectors. For the signal connectors, we used Ninigi NXG-02 [41] (2 mm raster connectors), 2-6 pins, depending on a particular component to connect.  
Figure 18: A diagram explaining the wiring of all electronic components of the Elka1Q drone.
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 3 Methods
### The Final Framework  
We apply the idea of re-weighting to develop the final framework, which makes relatively clean samples contribute more to the training and compresses the influence of relatively noisy samples. We construct our total loss consisting of two parts. The first one \(L_{seg}\) is the cross-entropy loss, which is calculated with prediction maps and the original labels. The second one \(L_{R}\) is defined as the shape-awareness regularization loss. The final re-weighted loss \(L_{final}\) is:  
\[L_{final}=Q_{LQAM}(L_{seg}+\lambda L_{R}), \tag{4}\]  
where \(Q_{LQAM}\) are the quality weights originating from the LQAM. \(\lambda\) is the hyper-parameter. Our method is independent of an auxiliary clean dataset or prior information about noise distribution, which is more applicable for 3D biomedical segmentation tasks.
# Influence of the Analytical Segment Length on the Tram Track Quality Assessment
## 4 Discussion
The same analysis shows that decreasing the analytical segment length causes the average and minimum J index values to decrease while the maximum value of the J index stays the same relative to the value of J for the corresponding, more significant 200m segment. Calculated J index values for all analyzed analytical segments did not exceed 5 mm. By comparing the calculated to the prescribed allowable J value of 12 mm for a speed of 30 km/h, it could be concluded that the tram tracks in the City of Osijek are in good condition. However, considering that the tram track was measured in unloaded conditions and that the tram tracks constructed on the concrete slab showed very small deviations of horizontal alignment and longitudinal level, the correctness of applying the synthetic J index (or its allowable values) in assessing the tram track geometry quality is questionable. Future research should explore the possibility of modifying the J index calculation, as well as re-defining the allowable values of horizontal alignment and longitudinal level for tram track geometry measured in unloaded track conditions.
## 3 Materials and Methods
### Testbed  
The simulations included in this research paper have been conducted with a publicly available tool, which was modified according to predefined requirements. All test results presented within this research have been obtained using a personal computer with an Intel Core i5-1335U CPU, 16 GB of RAM memory, and Windows 11 Pro 64-bit operating system. All scripts have been executed using the Python programming language, version 3.11.  
To perform the tests, the authors used a _NeuralKey_ tool [41]. This allows the synchronization process between two TPMs with specified values of K, N, and L to be simulated. The update rule (referred to as the update algorithm from now on) can also be selected-all of the algorithms covered in Section 2.2.2 (hebbian, anti-hebbian, or random walk) are available. During synchronization, an attack attempt is carried out-the attacker's machine tries to synchronize with both the sender's and receiver's machines (referred to as victims from now on). In other words, this program allows simulation of the neural cryptography process. The output of _NeuralKey_ consists of the following:  
* The number of updates between synchronizing TPMs,
* The percentage synchronization value between the attacker's and victims' machines,
* The number of attacker's TPM updates.  
To simulate quantum cryptography, the tool was modified. The modified version (called _QuantumKey_ from now on) allows setting a specific QBER for the victims' machines, as well as allows one to choose new, aforementioned update algorithms. Additionally, the modifications enable to perform multiple iterations at once, ensuring that the results are not merely anomalies. _QuantumKey_ is executed with parameters corresponding to the following variables:  
* K value,
* N value,
* L value,
* QBER value in %,
* Chosen update algorithm,
* Attacker's update algorithm (optional, used in case of new, dynamic update algorithms).
## 4 Results
### Assessment of Flood-Affected Zones  
Figure 10 illustrates the flood frequency maps for peak monsoon rainfall and cyclonic events, respectively. Across the coastal region, the western coast shows the most severe impact from monsoon rainfall. According to the classification, large areas in the west--including Morrelganj, Kaliganj, and nearby low-lying regions fall into the high flood-affected category, likely due to their relatively low elevation. In contrast, the central coastal region is predominantly characterized by low to moderate flood vulnerability, with some high-impact zones near Kalapara. The eastern coastal region generally falls within low to moderate flood-affected categories, although Chakaria and surrounding low-lying areas stand out as zones of high flood exposure.  
Figure 9: Inundation in the coastal districts due to the cyclones (**a**) Mora, (**b**) Titli, (**c**) Fani, (**d**) Amphan, (**e**) Yaas, (**f**) Sitrang, (**g**) Midhili, and (**h**) Renal.  
The flood frequency map for cyclonic events, based on inundation data from eight selected cyclones, reveals that Morrelganj and its surrounding areas are classified as highly flood-affected due to repeated cyclone-induced inundation. The western coastline is consistently impacted, leading to a designation of moderate cyclone flood risk, with some localized areas facing high impacts. Portions of the Sundarbans are also affected by cyclone-driven flooding and are marked as low to moderate flood-affected zones. In the central coastal area, particularly near Kalapara, frequent inundation occurs due to its proximity to the shoreline, placing it in the moderately flood-affected category. Meanwhile, in the eastern coastal region, the higher topographic elevation helps limit flood exposure. Flooding here is generally localized, affecting low-lying areas near Chakaria and Cox's Bazar, which fall mostly within low to moderate impact categories.  
Table 3 shows the comparison of flood-affected areas due to monsoon and cyclone events. In the low frequency inundation category, the area affected due to cyclones is quite large (19,085 sq. km) compared to the area of inundation due to monsoonal rainfall (13,829 sq. km). The areas moderately affected by flooding are relatively similar for both events, with 4054 sq. km under monsoon and 3459 sq. km under cyclones. High-impact  
Figure 10: Flood zoning map for (**a**) monsoon rainfall and (**b**) cyclone.  
inundation (i.e., high frequency of inundation) is more extensive during monsoonal rainfall (1626 sq. km) than during cyclonic events (255 sq. km).
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
## 2 The Best Mix Ratio Design of Permeable Concrete Piles
### Experiment Method
#### 2.1.2 Experiment Design  
When designing the mix ratio for pervious concrete piles, it is essential to balance mechanical strength and permeability to meet the dual functions of drainage consolidation and load bearing in composite foundations. The key parameters for the pile material mix design are as follows:  
(1) Design Porosity: Porosity directly affects the drainage performance of pervious concrete piles. According to the \"Technical Code for Pervious Concrete Pavements\" (CJ)/T 135-2009) [16], the porosity should be greater than 10%. Therefore, this study sets design porosities of 20%, 25%, 30%, and 35% to balance permeability and strength.  
(2) Water-to-Cement Ratio (\(w/c\)): The \(w/c\) ratio is crucial for the strength and permeability of pervious concrete. The workability of the cement paste directly influences the overall performance of the concrete. Unlike conventional concrete, the workability of pervious concrete is primarily assessed by observing the coverage of the cement paste between aggregates. To prevent paste segregation, this study selects \(w/c\) ratios of 0.25, 0.3, and 0.35 for experimentation. This investigation focuses on exploring the changes in the water-cement ratio and does not consider the influence of the cement-aggregate ratio on the strength of permeable concrete. At the same time, the aggregate dosage amount of each set of test blocks during the test was set to a fixed dosage amount.  
The mix ratio calculations are based on the formulas provided in the \"Technical Code for Pervious Concrete Pavements\" (CJ)/T 135-2009) [16] to ensure the optimization of material properties.
# Radiation-Balanced Lasers: History, Status, Potential
## 4 Development of Radiation-Balanced Lasers
### Athermal Spherical Microlasers  
Microlasers fabricated from upconverting nanoparticles (UCNP) coupled to whispering gallery mode (WGM) microresonators were demonstrated in [61,62]. They can exhibit continuous wave anti-Stokes lasing useful for tracking cells, environmental sensing, and coherent stimulation of biological activity. Continuous-wave anti-Stokes lasing with laser thresholds as low as 1.7 \(\pm\) 0.7 kW/cm\({}^{2}\) was achieved.  
The radiation-balanced version of the spherical microlaser was proposed in [63]. As one can see in Figure 20a, in this athermal microlaser the monolayer of UCNPs is deposited on the surface of the microsphere. This monolayer consists of two different UCNPs: Yb\({}^{3+}\)-doped \(\beta\)-NaYF4 UCNPs, which are responsible for optical refrigeration, and Yb\({}^{3+}\)/Er\({}^{3+}\)/Tm\({}^{3+}\)-codoped \(\beta\)-NaYF4 UCNPs, which serve as solid-state gain media. In this scheme, the cooling power and the stimulated emission are provided by UCNPs with different compositions operating at the same pump wavelength.  
The authors of [63] also proposed to use Yb\({}^{3+}\)-doped UCNPs distributed throughout the entire volume of the microsphere for its optical cooling (Figure 20b). The athermal operation of microsphere lasers has not been demonstrated yet.
## 5 The Algorithm Based on Conformal Antenna  
### The Pre-Processing of Sub-Array Divided Technique  
In order to eliminate the \"shadow effect\" caused by the metal structure, sub-array divided technique is used in this paper. The \"shadow effect\" means that when the incident signal comes from a special direction, not all antenna elements can receive the signal. The principle of sub-array divided technique is to ensure that at least one sub-array can receive the incident signal. For instance, on the cylindrical missile body, the sub-array divided technique should ensure that the coverage of each sub-array is more than \(\pi/2\). We can have an intuitive impression from Figure 8, which shows four elements arranged on the circumference in the first quadrant. The four black points are actual elements, and the twelve shadow points are virtual elements. Assuming there are \(n\) antennas, \(A_{2}^{n}\) virtual elements can be constructed by the virtual baseline method. Both the actual elements and the virtual elements can be used for direction finding. The details for direction finding algorithm were described in Section 4.
## 5 State-of-the-Art
### Edge-Cloud Integration
* **Improving Energy Efficiency:** a few articles [111, 116] touch on energy efficiency in running Wasm on IoT devices and computational offloading, respectively. However, more research could be carried out on ways of optimizing energy consumption when executing Wasm in different scenarios. * **Generalization of Deployments:** multiple works [108, 112] suggest a need for broader application of Wasm outside browser environments and across different hardware devices and software environments. This indicates a gap in how Wasm can be generalized and deployed beyond the specific use cases studied to date. Lessons Learned:
* **Wasm is Versatile:** Wasm offers a highly flexible approach for executing code across a variety of environments, from IoT devices to cloud servers and from in-browser to edge computing contexts. * **Performance Gains:** when correctly optimized, Wasm can yield substantial performance improvements in certain contexts even on resource-constrained devices. For example, [111] demonstrates how a correctly optimized Wasm runtime can achieve substantial reductions in RAM usage and energy consumption. * **Benefit for IoT and Edge Computing:** Wasm can be beneficial in the contexts of edge computing and IoT. It can aid in overcoming issues of device heterogeneity, resource constraints, and the need for close-to-source data processing. In addition, it supports the idea of computational offloading, enabling more efficient execution of tasks. * **Potential for Industrial Applications:** there are potential benefits of using Wasm in retrofitting industrial machinery for smart manufacturing (Industry 4.0), as suggested by [113, 114]. It can help to modernize old equipment without substantial upfront investment, making it a cost-effective solution for businesses. * **Migration and Portability Challenges:** despite its versatility, Wasm faces challenges regarding the portability and migration of applications. Efforts are being made to address these challenges, such as the development of solutions that allow Wasm applications to be dynamically deployed, migrated, and partitioned. * **Security and Multi-tenancy:** while implementing secure and isolated execution environments for Wasm in serverless computing contexts can be complex, it is crucial in order to ensure the safety of operations and data. * **Energy Efficiency:** while there is ongoing research into ways of optimizing energy consumption when executing Wasm, it is clear that energy efficiency is an important factor, especially when deploying on IoT devices or in computational offloading scenarios. * **Non-Browser Deployments:** Wasm's use is not limited to the browser environment; it shows promise in various other environments, including the cloud-edge continuum, servers, and IoT devices. Its capabilities in these environments are currently being explored and better understood.
## 3 Methodology
### Model Parameterization
#### 3.3.1 Sensitivity Analysis
In addition, a large difference between the first order sensitivity index and the total order sensitivity index indicates that the parameter mainly affects the model output through interactions. The [PERSON]' method uses the Monte Carlo [59] sampling scheme to generate random parameter samples. To calculate sensitivity metrics, it requires a parameter set with a sample size of \(M\times(2n+2)\), where \(M\) represents the number of base samples and \(n\) represents the number of input parameters. In this study, \(M\) was set to 512. In addition, we used the total order sensitivity index to quantitatively evaluate the effect of each ecophysiological parameter on the GPP simulation and set its threshold to 0.05. That is, parameters with a total order sensitivity index greater than 0.05 were considered as influential parameters and required further calibration.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm
### Main Path Direction Calculation  
The space segment network topology is a mesh network structure. Each satellite in orbit is identified by an orbit number and a phase number. The relative position relationship between any two satellites at any time is determined and will not change, that is, between two satellites the orbital difference and phase difference are always the same. As shown in Figure 3, the current satellite node orbit number \(c_{i}=2\), phase number \(c_{j}=6\), destination node orbit number \(d_{i}=5\), phase number \(d_{j}=4\), the orbit difference between the current node and the destination node \(|d_{i}-c_{i}|=3\), phase difference \(\left|d_{j}-c_{j}\right|=2\). Data packets from the current node to the destination node need to pass through three hops of inter-plane ISLs and two hops of intra-plane ISLs. In this article, we refer to them as horizontal hops and vertical hops for short [18]. For any two source and destination satellites in the entire network, the direction of the horizontal hops and vertical hops can be determined.