esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 6 Conclusions  
In this paper, we put forward an integrated air-space-sky network architecture for UAV-assisted task offloading to provide more available computational resources for ground devices and to ensure computational requirements in emergency scenarios. To minimise the delay and energy consumed by offloading tasks, described the problem as MDP. We further develop an algorithm to solve it. The solution enables the UAV controller to determine the best unloading decision based on dynamic channel conditions and the satellite position, including task offloading scenarios and resource allocation strategies. Finally, a series of trials are conducted to validate the validity and superiority of our proposed unloading scheme.  
In the future, we need to consider more of drones' auxiliary access LEO mobile Internet of things in the edge of the network equipment. In some real-world situations, IoT devices are mobile at high speed; the approach we have proposed may not be appropriate for such scenarios. In the case of mobile IoT devices, by using satellite switching to overcome this problem, in this paper, IoT devices can only perform task offloading. When the number  
Figure 10: Distributions of offloading task and computing resource.  
Figure 9: Relationship between cost and \(f_{j}\).  
of IoT devices or tasks increases, this offloading strategy puts a lot of bandwidth pressure on the satellite network and increases the energy consumption for task transmission. Therefore, a partial offload strategy can be explored for future work associated with air-based edge computing.  
Conceptualization, H.Z. and B.S.; methodology, S.X.; software, S.X.; validation, H.J., Q.S. and J.W.; formal analysis, B.S.; investigation, H.J.; resources, H.Z.; data curation, S.X.; writing--original draft preparation, S.X.; writing--review and editing, H.Z.; visualization, B.S.; supervision, H.Z.; project administration, S.X. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2020 MF006 and ZR2022 LZH015, R&D Program of Beijing Municipal Education Commission under Grant KM202211417014, Academic Research Projects of Beijing Union University under Grant ZK20202215 and Open Foundation of State Key Laboratory of Integrated Services Networks (Xidian University) under Grant ISN23-09.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Proposed Real-Time Shipping Container Accident Inference Edge System
### Service Flow  
The RSCAIES is divided into three parts in the service flow: the data acquaintance and preprocessing step, the YOLO-based safety level estimation inference step, and the transmission and management step. Figure 3 shows the overall service flow of the RSCAIES.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Device** & **Role** \\ \hline Rolling shutter camera & Shipping container photograph data acquainting \\ \hline Edge server & A server running an inference model for a safety level estimation based on the shipping container alignment image \\ \hline GPS Receiver & A device that receives the current latitude, longitude, and azimuth to transmit a shipping container location with a high accident rate to the manager \\ \hline \end{tabular}
\end{table}
Table 2: The role of each component of the edge system.  
Figure 3: Overall service flow of the RSCAIES.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Device** & **Role** \\ \hline DB Server & Store image and JSON format-based information of the detected classification results \\ \hline Operation and management server & Display and monitor the result to the user and take action on the results \\ \hline \end{tabular}
\end{table}
Table 3: The role of each component of the operation and management system.  
Figure 2: Overall structure of a real-time shipping container accident inference edge system.  
First, data acquisition and the preprocessing step involve obtaining the shipping container image from the rolling shutter camera and dividing it into frames. The location information of the image is also obtained from the GPS receiver. The next step is the YOLO-based safety level estimation inference step. This is the process of inferring the safety level-based shipping container alignment status using the YOLO algorithm. This process sequentially receives the divided shipping container alignment status images, converts the 1920 \(\times\) 1080 images to a size of 416 \(\times\) 416, and divides it into 7 \(\times\) 7 grids. For each grid, a bounding box is generated, and the NMS algorithm is applied to generate a bounding box for shipping container corner casting. And for each grid, the conditional class probability is obtained to infer the safety level class in each bounding box. The next step is the transmission step. This is the process of transmitting the result for classifying the safety level to the management and DB server. This process generates a JSON file and an image file based on the inference result for each frame. The generated files are converted to a binary form and transmitted via HTTP. The last step is the management step. The management step displays the results to the user with the transmitted JSON file and image file and monitors the result for the user. The operator then takes action on the results.
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 4 Related Work
### Hybrid
The key schedule, involving circular shifts, S-box substitutions, and round-dependent constants, is more intricate, which may challenge implementation on ultra-constrained microcontrollers with very limited code space. Additionally, although GFSPX strikes a balance between security and efficiency, its GE count (1715), while lower than many SPN ciphers, is still slightly higher than ARX-based or simplified GFN ciphers such as GFRX (1496 GE) or SPNRX (1357 GE), which may matter in ultra-minimalist chip designs. Thus, while GFSPX is highly optimized, there's still room for further simplification or specialization in extreme low-power IoTs contexts. QLW [68] is a 64-bit LWBC with a 128-bit key, designed to achieve high diffusion using a hybrid structure that combines a type-III generalized Feistel structure (GFS) with Lai-Massey elements. Its round function incorporates a double half-round mechanism, dynamic round constants, and a genetic algorithm-optimized 4 \(\times\) 4 S-box with an area of only 15.01 GE. The entire cipher achieves full diffusion in just two rounds, enhancing resistance to differential and linear attacks. Hardware implementation on the SMIC 0.18 \(\upmu\)m process shows that QLW requires 1655.26 GE and consumes only 7.37 \(\upmu\)J/bit, making it more area- and energy-efficient than similar ciphers such as PRINCE, CRAFT, and SKINNY. Despite its strong security margins and hardware efficiency, QLW's evaluation lacks independent third-party cryptanalysis or open-source implementations. The complexity introduced by the dynamic round constant and non-linear key schedule, while enhancing diffusion and unpredictability, may increase implementation difficulty in constrained environments. Additionally, no detailed analysis is provided for side-channel or fault attack resistance, which may limit adoption in high-assurance or critical IoTs applications.
## 5 Damage Characterization, Objective Functions, and Optimization Algorithm
### Optimization Algorithms
#### 5.4.4 Guideline for Algorithm Selection  
In summary, no single optimization algorithm can solve all optimization problems. A basic knowledge of different algorithms helps the selection of the appropriate algorithm for the problem at hand. The guideline for the selection of optimization algorithms is listed in Table 2 below. As for damage detection problems that always result in numerous local extrema, convergence to the correct estimates (not known a priori) will be strongly dependent on a good initial guess. Therefore, the evolutionary algorithm is the better option unless a good initial value is available.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Methods** & **Guidelines** \\ \hline Gradient-based & suited for problems with many design variables (more than 50) \\ Algorithms & where the analysis is computationally expensive \\  & when numerical noise is not severe \\  & when gradients are available and local extrema is not an issue \\ \hline Evolutionary & suited for problems with fewer design variables (less than 50) \\ Algorithms & where the analysis is computationally inexpensive \\  & when numerical noise is severe \\  & when the gradient does not exists and a global optimum is required \\  & for discrete and combinatorial optimization problems when the objective \\  & and/or constraint functions are discontinuous \\ \hline Multi-objective & for problems with more than one conflict objective functions \\ Algorithms & gradient based Pareto algorithm for differentiable problems \\  & evolutionary based Pareto algorithm for all other cases \\ \hline \hline \end{tabular}
\end{table}
Table 2: Guidelines of Optimization Algorithms [175].
## 3 Results
### Results: Participants' Demographics
#### 3.1.2 Spatial Abilities  
The PSVT: R and Cube Comparison test data were coded as correct or incorrect. The PTA scores were calculated based on the degrees of deviation from the correct response. The smaller deviations showed better performance. Table 3 shows the results of participants' performance in each spatial ability test in terms of means and standard deviation. The table also shows the difference in spatial ability scores between the two cognitive styles (FD vs. FI). In spatial visualization, the mean score of FI (M = 59) is higher than that of FD (M = 33.07) participants. The same trend was observed in spatial orientation: FI participants showed less angular error (M = 36.94) than FD (M = 56.8). In the spatial relation test, the mean score of FI participants (M = 53.25) is slightly higher than FD (M = 45).  
Performing the independent sample \(t\)-test was considered to examine the effect of cognitive style on the three dimensions of spatial ability, i.e., spatial visualization, relations, and orientation. The Shapiro-Wilk normality test and [PERSON]'s test of homogeneity of variance were also performed. [PERSON]'s test result of no spatial ability data was statistically significant (\(p>0.05\)), indicating that the assumption of equal variance was met. However, the Shapiro-Wilk test results showed a deviation from normality (\(p<0.05\)) in spatial visualization and spatial orientation data. Therefore, the Mann-Whitney U test was performed for spatial visualization and spatial orientation. As Table S1 in the Supplementary Materials shows, there was a significant difference between cognitive style (FD vs. FI) for spatial visualization ability (U =38.5, \(p<0.001\)) and spatial orientation (U = 172, \(p=0.04\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cognitive Style**} & \multicolumn{3}{c}{**Frequency**} \\ \cline{2-4}  & **Male** & **Female** & **Total** \\ \hline FD & 6 & 9 & 15 \\ \hline FI & 17 & 8 & 25 \\ \hline Total & 23 & 17 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Participants’ cognitive style by gender.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Spatial Ability Dimension**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Female**} & \multicolumn{2}{c}{**FD**} & \multicolumn{2}{c}{**FI**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-10}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Visualization & 54.21 & 21.43 & 41.43 & 21.43 & 33.07 & 11.1 & 59.00 & 21.5 & 44.79 & 22.05 \\ \hline Orientation & 33.23 & 18.19 & 60.42 & 29.67 & 56.8 & 27.89 & 36.94 & 23.96 & 44.77 & 27.01 \\ \hline Relation & 50.00 & 15.9 & 50.00 & 17.87 & 45.00 & 16.07 & 53.25 & 16.33 & 50.00 & 16.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Means and standard deviation of spatial ability test scores by gender and cognitive style.  
However, there was no significant difference in mean scores between FD and FI for spatial relation: t (38) = \(-1.531\), \(p\) = 0.134.
## 4 Lessons Learned from Norwegian Case Studies
### Embodied and Operational GHG Emissions  
Figure 2 shows the embodied and operational GHG emission results from the four case studies. The results show that the operational GHG emissions of the refurbishment range from 50 to 91%, whilst the operational GHG emissions from new buildings range between 55 and 79%. For the refurbishment, the lowest value (50%) is from Powerhouse Kjorbo, where the ZEB factor is considered. The highest value (91%) is from Bergen city hall, where the EU factor is used. For the new building, the lowest operational GHG emission (55%) is from Statens hus Vadso, where the EU factor and the impact from the technical installations are considered. Meanwhile, the highest operational GHG emission (79%) is from Bergen city hall.  
The embodied GHG emissions of the refurbishment range from 8 to 50%, whilst the embodied GHG emissions from new buildings range between 21 and 45%. For the refurbishment, the lowest value (8%) is from Bergen city hall, where the operational GHG emissions dominated due to the use of the EU factor. The highest value for the refurbishment (50%) is from Powerhouse Kjorbo, where the energy efficiency measures were aiming at refurbishing the building to ZEB-COME. For the new building, the lowest embodied GHG emissions (21%) are from Bergen city hall, where the EU factor is considered. Meanwhile, the highest embodied GHG emissions (45%) are from Statens hus Vadso, where the EU emission factor and impact from technical installations are considered.  
The overall results show that the lowest embodied GHG emissions originate from the refurbishment of existing buildings compared to new buildings. A Historic London study [50] showed that the embodied GHG emissions from a new building accounted for 30% of emissions in comparison to 2% from the refurbishment of a Victorian terraced house from 1891 (which is representative of a large number of British homes). Thus, by conserving existing buildings, the embodied GHG emissions that are inherent in the generation of waste during the demolition of old buildings can be reduced. Furthermore, the materials used and waste generated in the construction of new buildings and other  
Figure 2: Embodied and operational GHG emission results.  
emissions associated with transport and energy consumption linked to the construction of new buildings can be reduced.
## 3 Results  
### Dynamics of GPP, R\({}_{e}\), and NEE in Dryland Ecosystems  
Firstly, the seasonal dynamics of carbon fluxes (GPP, \(R_{e}\), and NEE) are shown in Figure 2. As illustrated in Figure 2, the GPP, \(R_{e}\), and NEE of all flux sites showed clear seasonal patternsfrom 2014 to 2016. Particularly, the GPP and \(R_{e}\) exhibited consistent trends. During the dormant seasons, the vegetation GPP was nearly zero because photosynthesis stopped. With the increase of temperature during springtime, the GPP and \(R_{e}\) increased gradually and reached a peak in July or August. Inversely, the NEE had deep valleys in the growing seasons. It decreased gradually after approximately 18 th week of year (WOY 18), to the valley around in July or August, and then gradually increased. In addition, the S_Dm site had the highest GPP at the peak period among all sites. Although consistent seasonal dynamics exist, different ecosystems still had their own characteristics, especially in forest ecosystems (the S_Sdq and S_Hhl sites). The time when GPP began to grow rapidly was earlier at S_Hhl than the rest of the other sites, and the period of carbon sequestration was the longest in every year among all ecosystems. In the S_Sdq site, we found that the peak periods came later year after year from 2014 to 2016.  
To analyze the differences in carbon fluxes between different ecosystems, we conducted a statistical analysis of carbon fluxes (GPP, \(R_{e}\), and NEE) from 2014 to 2016. The annual mean carbon fluxes of different ecosystems were statistically calculated, presented in Table 2. Overall, the average annual GPP in all flux sites was greater than the values of \(R_{e}\), and according to the NEE annual mean value, it implied that all ecosystems also behaved as carbon sinks. In particular, the S_Dm site had the largest annual mean GPP and NEE, which was near double the GPP and NEE values of other ecosystems. Furthermore, the lowest annual mean GPP value appeared in the S_Dsl site, in which the annual mean \(R_{e}\) value was also the lowest, reaching up to \(208\pm 49\) g C m\({}^{-2}\) y\({}^{-1}\). However, although both the annual GPP and \(R_{e}\) in the S_Dsl site were far lower than those in the S_Ar site, and there was no obvious difference in the annual mean NEE between the two sites. The largest annual mean NEE was found at the S_Sdq site, and it implied that the lowest carbon sequestration was there, about \(208\pm 56\) g C m\({}^{-2}\) y\({}^{-1}\).  
Figure 2: Seasonal variations in carbon fluxes (GPP, \(R_{e}\), and NEE) at the typical flux sites between 2014 and 2016 at the eight-day period. WOY means week of year.
## 3 The Main Parameters and Indices of MOT
### Application Requirement Analysis
#### 3.3.2 Reduce Measurement Noise  
In high-precision measuring instruments, environmental noise can significantly impact device stability and reliability, as well as measurement resolution. The noise in an atomic gravimeter comprises Raman laser phase noise, Raman laser intensity noise, vibration noise, detection noise, and quantum projection noise. Among these methods, the use of mirror vibration noise poses the greatest challenge [64; 65]. A gravity gradiometer employs a differential measurement mechanism to effectively suppress Raman laser phase noise, Raman laser intensity noise, and mirror vibration noise, but it cannot suppress Raman laser frequency noise and magnetic field noise [66; 67; 68]. In an atomic clock, mechanical vibration can introduce noise into the laser and atomic system, leading to inaccuracies in the frequency locking of the spectral line and reduced output signal stability [58]. Similarly, in a continuous cold atomic beam interference gyroscope, interference fringes may be affected by atomic beam flux fluctuations, Raman optical power jitter, Raman optical phase jitter, and other factors, resulting in measurement noise [69].
# Exploring Graph and Digraph Persistence
## 3 Graph-Theoretical Persistence
### Categorical Persistence Functions  
**Definition 1**.: [6] (Def. 3.2) _Let \(\mathbf{C}\) be a category. We say that a lower-bounded function \(p\): \(\operatorname{Morph}(\mathbf{C})\to\mathbb{Z}\) is a categorical persistence function if, for all \(u_{1}\to u_{2}\to v_{1}\to v_{2}\), the following inequalities hold:_  
* \(p(u1\to v1)\leq p(u2\to v1)\) _and_ \(p(u2\to v2)\leq p(u2\to v1)\)_;_
* \(p(u2\to v1)-p(u1\to v1)\geq p(u2\to v2)-p(u1\to v2)\)_._  
With \(\mathbf{C}=(\mathbb{R},\leq)\), the graphs of such functions have the appearance of superimposed triangles typical of persistent Betti number functions [37] (Sect. 2), so they can be condensed into _persistence diagrams_[6] (Def. 3.16).  
As anticipated in the Introduction, we shall write \"(di)graph\" for \"graph (respectively, digraph)\", extending this notation to cases such as sub(di)graphs et similia. Symmetrically and when no confusion arises, we shall denote with \(\mathbf{Gr}\) the category \(\mathbf{Graph}\) or the category \(\mathbf{Digraph}\), with monomorphisms as morphisms. We shall use the noun _edge_ for edges of a graph and arcs of a digraph.  
_Filtered (di)graphs \((G,f)\) are pairs consisting of a (di)graph \(G=(V,E)\) and a filtering function \(f\): \(V\cup E\to\mathbb{R}\) on the edges and extended to each vertex as the minimum of the values of its incident edges. For any \(u\in\mathbb{R}\), the _sublevel (di)graph_\(G_{f,u}\) (briefly, \(G_{u}\), if no confusion occurs) is the sub(di)graph induced by the edges \(e\) such that \(f(e)\leq u\).  
Definition 2: [7] (Def. 5) The _natural pseudodistance_ of filtered (di)graphs \((G,f)\) and \((G^{\prime},f^{\prime})\) is  
\[\delta\big{(}(G,f),(G^{\prime},f^{\prime})\big{)}=\left\{\begin{array}{ll}+ \infty&\text{if}\ \ \mathcal{H}=\emptyset\\ \inf_{\phi\in\mathcal{H}}\sup_{e\in E}|f(e)-f^{\prime}\big{(}\phi(e)\big{)}| &\text{otherwise}\end{array}\right.\]  
where \(\mathcal{H}\) is the set of (di)graph isomorphisms between \(G\) and \(G^{\prime}\).  
Let \(p\) be a categorical persistence function on \(\mathbf{Gr}\); \((G,f)\), \((G^{\prime},f^{\prime})\) any filtered (di)graphs; \(\mathcal{D}\), \(\mathcal{D}^{\prime}\) the respective persistence diagrams; and \(d\) the bottleneck distance [37] (Sect. 6), [6] (Def. 3.24).  
Definition 3: [7] (Sect. 2) The categorical persistence function \(p\) is said to be _stable_ if the inequality  
\[d(\mathcal{D},\mathcal{D}^{\prime})\leq\delta((G,f),(G^{\prime},f^{\prime}))\]  
holds. Moreover, the bottleneck distance is said to be _universal_ with respect to \(p\) if it yields the best possible lower bound for the natural pseudodistance among the possible distances between \(\mathcal{D}\) and \(\mathcal{D}^{\prime}\) for any \((G,f)\), \((G^{\prime},f^{\prime})\).
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Description of GATTRP  
In this section, we propose an improved GA named GATTRP. Through GATTRP, we could encode the routes of the TT flows and seek the optimal solution for this problem in a genetic evolutionary way. In the following content, we describe how the improved GA works, starting with the rules by which the chromosomes are encoded.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 4 Security Analysis of the Proposed Algorithm
### Robustness against Various Attacks
#### 4.3.6 Possible Collusion by Clients  
Corrupted clients can join forces to manipulate the input for the FL process. Hence, the list of clients selected for FL should be kept secret. However, clients must be notified if they are to be selected. To facilitate this, we used an encrypted reply from the server side instead of a list of clients. The decryption key is only available to the receiver, so other clients will not know which other clients are selected for FL training.  
Detecting collusion by clients can be done only on a dynamic basis when they actually collide and pose malicious behavior. Unfortunately, there is no specific measure present in the basic FL setup to handle this issue. However, using group signature, we have made sure that even the client does not have data of another client. Even if a few clients join hands and want to conspire against a client, since they do not have any additional information about the client, their keys, or their data, it will not be possible for the group of conspiring clients to attack the targeted client. It is possible only in one case, where all the clients of the group join hands with the external server to go against a single client of the group, which is a highly unlikely case. The server is an external entity, and clients belong to a group. Until an extremely specific case, it is very unlikely for all the clients of a group to go against a member of their group to join hands with an external entity. Moreover, the list of clients selected for FL for that particular iteration is never revealed in public. Therefore, the group of conspiring clients will never know which client to target. In this way, our scheme prevents the collusion attack by clients on a single client.