esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Protocol
### Packet Holding Time  
Upon the reception of a data packet by an intended suitable forwarder \(j\) from a sender node \(i\), the node holds it for a certain duration of time called the packet holding time \(\tau_{h}\). This time depends upon the number of neighbors \(N_{j}\) of the forwarder, the pressure difference between the sender and forwarder \(p_{i}-p_{j}\), the speed of the acoustic wave \(c\) and the ratio of the initial energy level \(E_{0}\) to the current energy level \(E_{c}\). Mathematically, it is written as  
\[\tau_{h}=\frac{N_{j}(p_{i}-p_{j})}{c(\frac{E_{0}}{E_{c}})} \tag{15}\]  
The packet holding time modeled above ensures that a packet is reached from a source to a destination with a small delay, a low interference and a low probability of loss. Its dependency on the pressure difference ensures that low-depth nodes close to the surface of the water hold packets for shorter time, as these nodes are often overburdened by the nodes in the bottom. This increases the probability of successful packet transmission towards the sink by reducing the overloading of these nodes by data packets. If such nodes hold the packets for a long period of time, this results in overloading and congestion that finally will result in packet loss. Additionally, a forwarder node having a lower number of neighbors will hold the packet for a shorter time than a node with a greater number of neighbors. This is because the former faces less interference (less neighbors) than the latter (more neighbors). Furthermore, the ratio \(\frac{E_{0}}{E_{c}}\) is smaller for a node with a higher current energy level than a node with a smaller value of its current energy level. Therefore, nodes with greater values of current energy levels hold the packets for a longer time, as these nodes have enough energy to remain alive in the network. A node holds a packet and senses the channel to become free to transmit the packet.  
If the channel is not free, the node backs off. The packet is ultimately dropped when the maximum back-off time is reached. Figure 3 shows the flow chart of the proposed scheme as described above.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 1 Introduction
By combining the vast amounts of new EO data from the Sentinel-1 (S-1), Sentinel-2 (S-2), and Landsat 8 (L8) missions, it is possible to strongly increase the temporal density of available EO data and additionally exploit multi-sensor information for forest monitoring. The Sentinel-2 constellation and Landsat 8 have repeat cycles of five days and 16 days, respectively. Together, these two missions provide up to eight images per month for tropical regions and strongly increase the temporal density of image time series. However, the number of usable cloud-free observations is site specific as it strongly depends on regional cloud cover. An assessment of available Landsat 7 and 8 images for Peru showed that, on average, less than 50% of all potential observations are cloud-free observations and cloud cover has significant intra-annual and regional variation [8]. The combination of all available cloud-free optical observations and SAR data allows to develop near real-time forest disturbance mapping systems at high spatial resolution, which can be used to more accurately detect small scale forest disturbances. The aim of this study is to analyze if the joint use of SAR (S-1) and optical (S-2 and L8) time series data in forest monitoring approaches can increase forest disturbance detection accuracies in the humid tropics. Forest disturbance maps from SAR and optical data are highly complementary, in that they detect different disturbed forest areas. Therefore, the assumption was that higher accuracy values can be obtained by merging detections from both sensor types. In this study, we demonstrate this at two humid tropical forest test sites located in Peru and in Gabon. Our approach includes the generation of a benchmark forest/non-forest mask, which serves as the starting point for the forest disturbance mapping. Separate forest disturbance maps are then calculated from the SAR and optical time series. The final forest disturbance maps combine the forest disturbance results from SAR and optical time series by a simple union process. The benchmark forest/non-forest masks and all the forest disturbance maps (SAR, optical, combined) are validated with a set of sample plots that were visually interpreted in VHR and HR imagery.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 4 Experimental Setup
### Entities and Relations  
Figure 6 shows a UML class diagram illustrating the primary entities used in the simulation. The diagram captures the key data structures--such as consumers, providers, and their associated services and offers--and the fundamental relationships between them. Following the diagram, each entity is described in more detail, outlining its general purpose and role within the system.  
Each entity in the system defines a set of parameters that characterize its role and relationships. The Account entity holds a numeric balance, tracking available credit. The Consumer references one Account and maintains an array of services (identifiers to Service entities). Similarly, the Provider references an Account, stores an array of its services, and records a servicesLimit that constrains the maximum number of active services it can manage at once.  
A Service specifies its state from a predefined set of lifecycle stages, stores arrays of offers (links to OfferDirect objects), and includes time-related parameters: startTimestamp, endTimestamp, and duration. It also holds references to the consumer and provider involved in that particular service and a count value for indexing or enumerating services.  
OfferDirect defines commercial proposals by specifying seller and buyer accounts, a price, and an expiryTimestamp. Its state parameter indicates whether the offer is active, accepted, rejected, or expired, while the service link ties it to the corresponding Service.  
If not accepted directly, an offer may be converted into an OfferCapacity, which introduces a fee parameter and can be placed in a capacity pool. OfferCapacity maintains a reference to the original offerDirect, along with its own seller, buyer, price, expiryTimestamp, and state parameters.  
Lastly, PoolCapacity aggregates multiple offers (now OfferCapacity objects) and tracks which providers are associated with the pool. In this manner, the parameters of each entity define their basic structure, their links to other entities, and the essential timing, pricing, and state conditions that govern their participation in the system.
## 5 Discussion
### Classifying Challenges  
Figure 3 categorizes the challenges into four distinct groups: critical challenges, connector challenges, localized influence challenges, and low-connectivity challenges. Each group reflects a different significance level of criticality and influence on the overall network.  
Critical challenges demand immediate attention and priority in resolution, as they significantly affect the government entity's service innovation. In contrast, low-connectivity challenges are the least critical, exerting minimal influence and requiring the least priority. Thus, they can be addressed later when resources permit. This prioritization ensures a structured and efficient approach to tackling challenges, focusing first on those with the most significant potential to disrupt innovation.
# Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
## 2 Related Works  
### Concept Bottleneck Models (CBMs)  
In tasks such as facial recognition [8] and animal identification [6], CBMs initially deviated from the conventional end-to-end network structure. These networks generally performed less well than standard networks, revealing a trade-off between accuracy and interpretability within concepts. However, the emergence of CBMs [4] introduced an intervenable approach comparable to standard networks based on end-to-end structures. CBMs were designed with three types of bottleneck structures: independent, sequential, and joint bottlenecks. These designs enable interventions that are not feasible in standard networks, facilitating updates to standard end-to-end networks' predictive performance.  
In a critical examination of CBMs, [PERSON], [PERSON], et al. [9] questioned whether a CBM truly learns and operates as intended. They thoroughly analyzed the three structures of CBM using post hoc methods, assessing the concepts' relevance and impact. [PERSON] et al. [10] presented the first case study that thoroughly analyzed interventions in CBMs. They introduced various methods for selecting intervention concepts and the effects of interventions, performing a deep analysis of how these evolve across different scenarios. Ref. [11] focused on the locality of concept-based learning within CBMs, pointing out the challenges of localized learning.  
Alternative modifications of CBMs have been proposed [12; 13; 14; 15; 16; 17]. Notably, [PERSON] et al. [12] suggested a method to convert any pretrained model into a data-efficient CBM to address performance issues arising from limited concept-annotated training datasets. Additionally, [PERSON] et al. [13] introduce ProbCBM, an interpretable network that utilizes probabilistic embeddings to provide explanations based on concepts and their corresponding uncertainties. In contrast to these new approaches to modifying CBMs, [PERSON] et al. [18] implemented these modifications in actual applications.  
As shown above, previous studies have mainly focused on analyzing the operational aspects of CBMs and providing solutions to the need for concept annotations. However, our study takes a different approach by viewing CBMs from a model compression perspective, aiming to leverage KD as a practical methodology. Similar attempts have been made by [PERSON] et al. [19], who investigated concept distillation in graph neural networks (GNNs), and [PERSON] et al. [20], who proposed a model-agnostic distillation technique. However, these KD approaches were limited to graph-structured data and tabular data. Therefore, we propose VICO-KD, which targets CNN-based networks, as the most commonly used architecture, achieving both model efficiency and interpretability in visual tasks such as image classification.
# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 3 Minimally Invasive Methods
### Raman Spectroscopy
**(b)** The graphical depiction of the process shows the incorporation of SERS-gNPS into the 3D tumor spheroid, followed by the recording of the Raman signal under a confocal Raman spectrometer. The Raman image also shows that a lower Raman intensity towards the central region of the tumor spheroid. **(c)** The quantification of Raman data to analyze the glucose level in the intratumor region. **(i)** Glucose concentration of co-cultured (color cancer cell (HCT-8), fibroblast cells (NIH3T3)) tumour spheroid as measured after 3 days and 5 days respectively. _p_-values are calculated using one-way ANOVA test with Bonferroni  
corrections, ***\(p<0.0001\). **(ii)** Glucose concentration of mono-cultured fibroblast tumour spheroid as measured after 3 days and 5 days, respectively. p-values are calculated using a one-way ANOVA test with Bonferroni corrections, ***\(p<0.0001\). The error bars illustrate the relative standard deviation (RSD) for three replicates [9]. Although Raman spectroscopy opens new pathways for the continuous monitoring process, there are several hurdles associated with this process, such as high equipment cost, requirement of professional operators, precise calibration methods, and so on. Continued advancements in Raman sensor technology, including improved sensitivity and calibration methods, could overcome these limitations. Overall, Raman sensors hold significant potential for revolutionizing glucose monitoring, particularly in minimally invasive and continuous applications.
## 3 Results  
### GPP Product Accuracy Validation  
By comparing the observations from 11 flux sites with the average GPP values within a 3 \(\times\) 3 grid of the adjacent pixels centered around the geographic location of the flux station (Figure 3). It can be observed that, on an 8-day scale, the \(R^{2}\) values for GPP_PML and GPP_MODIS are calculated as 0.86 and 0.70, respectively, with \(RMSE\) values of 8.41 \(\mathrm{gc}\cdot\mathrm{m}^{-2}\) and 10.07 \(\mathrm{gc}\cdot\mathrm{m}^{-2}\). On an annual scale, the \(R^{2}\) values for GPP_PML and GPP_MODIS are 0.92 and 0.84, respectively, with \(RMSE\) values of 190.26 \(\mathrm{gc}\cdot\mathrm{m}^{-2}\) and 230.09 \(\mathrm{gc}\cdot\mathrm{m}^{-2}\). Compared to GPP_PML, GPP_MODIS products exhibit lower \(R^{2}\) and \(RMSE\) values, with noticeable underestimation at sites like Changbai Mountain and Yucheng. The GPP_PML performs slightly better than GPP_MODIS at the 8-day scale, especially with estimates closer to the measured values at sites like Xiaolangdi, Qianyanzhou, and Inner Mongolia. Therefore, the subsequent analysis of the spatial and temporal distribution characteristics of the GPP in China and its response to climate factors was based on the GPP_PML product.  
Figure 3: \(Cont.\)
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 4 Results  
### Datasets  
To assess the effectiveness of DBCAN, we conducted experiments on two publicly available RGB-D datasets:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & **Output Size** & **DBCAN-T** & **DBCAN-S** & **DBCAN-B** & **DBCAN-L** \\ \hline Stem & \(\frac{H}{4}\times\frac{W}{4}\) & C = (16, 8) & (32, 16) & (32, 16) & (48, 24) \\
1 & \(\frac{H}{4}\times\frac{W}{4}\) & C = (32, 16), \(N_{1}=3\), \(E_{1}=8\) & (64, 32), 2, 8 & (64, 32), 3, 8 & (96, 48), 3, 8 \\
2 & \(\frac{H}{8}\times\frac{W}{8}\) & C = (64, 32), \(N_{2}=3\), \(E_{2}=8\) & (128, 64), 2, 8 & (128, 64), 3, 8 & (129, 96), 3, 8 \\
3 & \(\frac{H}{16}\times\frac{W}{16}\) & C = (128, 64), \(N_{3}=5\), \(E_{3}=4\) & (256, 128), 4, 4 & (256, 128), 12, 4 & (288, 144), 12, 4 \\
4 & \(\frac{H}{32}\times\frac{W}{32}\) & C = (256, 128), \(N_{4}=2\), \(E_{4}=4\) & (512, 256), 2, 4 & (512, 256), 2, 4 & (276, 288), 3, 4 \\ \hline \multicolumn{2}{c}{Decoder channels} & C = (512, 256) & (512, 256) & (512, 256) & (512, 256) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model architecture and variants.  
Figure 3: Spatial-Semantic Fusion Module.  
* NYUDv2 [12]: The NYUDv2 dataset consists of 1449 RGB-depth images with corresponding pixel-wise labels. The images have a unified resolution of \(480\times 640\). The dataset is divided into a training set and a testing set, with 795 image-depth pairs used for training and the remaining 654 pairs utilized for testing. It encompasses 40 object categories, thereby providing a diverse range of scenes and objects for evaluation.
* SUN-RGBD [13]: The SUN-RGBD dataset is a large-scale RGB-D dataset comprising 10,335 RGB-depth images. The images have a resolution of \(530\times 730\) pixels. The dataset encompasses a wide variety of indoor scenes and objects, with objects categorized into 37 classes. For our experiments, we split the dataset into a training set of 5285 images and a testing set of 5050 images.  
By using these two publicly available datasets, our objective is to evaluate the performance of our model on different scenes and object categories. The NYUDv2 dataset offers a relatively smaller yet more diverse set of scenes, while the SUN-RGBD dataset provides a larger-scale dataset with a wide range of object categories. The combination of these datasets enables a comprehensive assessment of the generalization and effectiveness of our proposed approach in various real-world scenarios.  
In the experimental section, we will present comprehensive statistics about the datasets used, including the number of images, resolution, and the number of object categories. Furthermore, we will elaborate on the data preprocessing steps and augmentation techniques employed to enhance the training process and improve the model's performance.
# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 3 Simulations and Dataset Generation
### Simulation Environment  
To create the realization of the bottleneck scenario presented in the previous section, we performed a simulation for the four bottleneck topologies in ndnSIM (v2.7) [26]. There are alternatives available for NDN simulations such as NS2, ccnSim, Omnet++, and CCNPL-Sim. The motivation for choosing the ndnSIM simulation software was that ndnSIM is an open source and an official simulator for NDN experimentation and a widely-used network simulator tool by the NDN research community. It is based on the NS-3 simulation framework, and currently, it has become a popular platform used by hundreds of researchers around the world (i.e., about 116 public forks, 35 contributors, and 500 papers). Moreover, it offers support for diverse technologies, popular programming languages (C++ and Python), large-scale NDN experiments, extensive documentation, and active support. Specifically, the simulator provides the implementation of the data structure such as PIT, FIB, and CS.  
The network topologies employed in the simulations are presented in Figure 3. The orange-colored nodes show the WAPs deployed in an ultra-dense network to give Internet access to the end users. The WAPs are connected to the high-power cloud devices (red color) via a high-powered edge device (green color). The edge device is connected at a one-hop distance from the end-users and the cloud devices. It is to be noted that cloud devices are always located at multiple hops from the edge devices. However, for the sake of simplicity, we placed cloud devices at a one-hop distance from the edge device in our simulations and left the multihop statistical network analysis for future work.  
We varied the number of WAPs and cloud devices and limited the edge device to one to create a bottleneck situation. These WAPs generated a large number of interest packets at a high frequency, i.e., 1000 interest packets per second. We chose to set the interest rate at a higher frequency to mimic massive traffic behavior in crowded areas.  
To analyze the performance of the network, we calculated the ISR at each WAP. The ISR in a network is the ratio of the total number of satisfied interests to the total number of transmitted interests [27] and can be calculated using Equation (1), which is defined as follows:  
\[ISR=\frac{\sum_{1}^{m}\text{satisfied interest}}{\sum_{1}^{n}\text{ transmitted interest}}\times 100 \tag{1}\]  
where \(m\) is the total number of satisfied interests and \(n\) is the total number of transmitted interests.  
The simulation was divided into two phases, to analyze the behavior of ISR. A detailed discussion of these two phases is explained in the following subsection.  
Figure 3: Network topologies: (**a**) 3 wireless access points, (**b**) 9 wireless access points, (**c**) 15 wireless access points, and (**d**) 21 wireless access points
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Gemini  
The Gemini (GPS-based Orbit Estimation and Laser Metrology for Intersatellite Navigation) project was initiated by the German Space Operations Processing Center [20] in 2001. Gemini is composed of two satellites with a mass of approximately 80-100 kg and an orbital plane altitude of 500-700 km. The state measurement payloads on each satellite include GPS receivers, pulsed LiDARs, and laser interferometers. The payload weighs 30 kg, and the power is about 70 W. Gemini's first mission was the verification of key technologies of formation flying, including laser measurement tracking technology, autonomous orbit control technology, and a closed-loop formation control strategy; mission two was high-resolution Earth-observation imaging. To accumulate experience to aid in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Item** & **Effective Range** & **Measurement Accuracy** \\ \hline \multirow{2}{*}{GPS} & \multirow{2}{*}{\(Z\) distance} & Perigee & 7.5 cm \\  & & Apogee & 10 cm \\ \hline \multirow{2}{*}{CLS} & \multirow{2}{*}{X-Y plane offset} & 150 m & 1 mm \\ \hline \multirow{2}{*}{FLLS} & \multirow{2}{*}{\(Z\) distance} & 25–250 m & 30 μm \\  & & X-Y plane offset & 20.5 mm & 21 μm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proba-3 measuring system performance indicators.  
the upcoming formation-flying mission, the formation shape would constantly be changed, as shown in Figure 7 and Table 5.
## 7 Conclusions  
In this paper, the formation tracking problem for multiple small spherical robots system was studied. This study aimed at providing a method that is implementable and easily deployed. Therefore, a trajectory tracking-based formation strategy was designed that divides the complex formation problem into trajectory tracking for each of the robots in the formation. Firstly, the cubic spine was utilized to obtain the global reference state information according to the discrete reference point proved by the planner. Secondly, based on the virtual structure, the reference state information of each of the robots in the formation could be acquired. On this basis, a liner MPC combined with the ESO was designed for achieving the trajectory tracking of each robot. At the same time, the inner collision term was considered and added to the cost function of the MPC. The implementation of the proposed formation method only depends on the position and heading angle information of each robot, which guarantees the portability of the method. In addition, the ESO was able to overcome the effect of the uncertainty of the dynamic model of the robot, and the MPC was able to cope with the actuator saturation and safety constraint, which guarantees that the proposed formation method can be deployed to a multi-robot platform. A series of simulations and experiments were conducted to verify the effectiveness of the proposed method. The formation errors converged to 0 in the numerical simulations. The maximum formation error of the physical simulation was 0.05 times the size of the formation shape. The formation error in the real experiment was 0.11 times the size of the formation shape. Although the formation tracking results produced errors in the physical simulation and experiment, the errors were small relative to the size of the formation shape, and they were less than the diameter of the spherical robot. Above all, the results prove that the proposed formation tracking method is feasible and effective.  
However, there are some inadequacies in this paper. Firstly, the position of each robot in the formation was obtained based on a global visual system, which caused the formation experiments to not be able to be conducted in an underwater environment. This is because when the spherical robots dive into deep water, we cannot obtain the position of the robots. In the future, a better location method for small spherical robots will be researched and further underwater formation experiments for multi-spherical robots will be investigated.  
Secondly, the avoidance of dynamic obstacle was not considered here, which deserves further research.  
Methodology, writing, and editing, X.H.; software and visualization, H.X.; formal analysis, H.S.; review and investigation, N.Y.; supervision, S.G.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Project of the Hebei Education Department (BJK2023064)  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.