esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
###### Abstract
However, the problem remains that the KRF may be forged or altered by a malicious user. To solve this problem, we propose our first scheme, which efficiently provides integrity and non-repudiation functions for the KRF and enhances the security of a multi-agents key recovery system. The main contributions of our proposed scheme-I as follows:  
* It provided a key recovery system based on secure encapsulation against various types of attacks and provides the ability to securely recover a lost or corrupted key. * It uses signcryption to ensure KRF integrity and non-repudiation. In addition, it provides both digital signing and encryption at the same time to increase computational efficiency. * It uses values that only authorized KRAs hold to prevent unauthorized KRAs and group-based authentication attacks. If some KRAs do not perform the key recovery properly, key recovery may be performed by other authenticated KRAs to prevent a single point of failure. Furthermore, the Med, KRA, and KRC may collude and behave maliciously. To solve this problem, we propose scheme 2, which provides a proxy re-encryption function and enhances the security of a key recovery system against various types of attacks such as collusion attacks and the key escrow problem. The main contributions of our proposed scheme-II as follows:  
* It prevents the Med, KRC, and KRA from behaving maliciously to recover keys without authorization and prevents unauthorized entities from obtaining keys. * It uses a partial private key generation scheme to prevent the KGC from generating private keys for all participants. The remaining parts of the paper are organized as follows. Section 2 describes related work, and Section 3 describes system model for the proposed schemes. Section 4 describes scenarios and detailed protocols for the proposed scheme-I, and Section 5 describes scenarios and detailed protocols for the proposed scheme-II. Section 6 analyzes whether the proposed schemes satisfy the security requirements. Finally, Section 7 discusses our conclusions.
# A New Methodological Approach for the Assessment of the \({}^{238}\)U Content in Drinking Water
## 2 Materials and Methods  
### Geological Notes, Sampling and Treatment  
A representative sample of drinking water was collected, in three different aliquots, in a water tank at Paola, a selected location of the Calabria region, southern Italy. The GPS coordinates of the sampling point, indicated in the map reported in Figure 1, are 39.351132 (latitude) and 16.038091 (longitude).  
The geology of the area around Paola (Figure 2) is mainly composed of heterogeneous crystalline-metamorphic rocks and their related weathering products [24]. Crystalline-metamorphic rocks are indeed affected by weathering processes, which are responsible for their chemical and mechanical transformation when interacting with the atmosphere, the hydrosphere and the biosphere [25].  
As reported in the published cartography [24] and several research studies [24, 25], marble, phyllarenites, muscovite-biotite gneiss, garnet and epidote micaschists, foliated and laminated granites, conglomerates, arenites and alluvial sediments crop out in the area surrounding the city of Paola. The high uranium concentration in the local drinking water can be explained according to the mineralogical composition of the surrounding area. In particular, with biotite having a relevant role in the sorption of radionuclides in granitic rocks, the high content of biotite can cause uranium enrichment [26]. Moreover, epidote minerals, with their structure composed of endless chains of edge-sharing octahedra that are crosslinked by isolated SiO\({}_{4}\) tetrahedra and Si\({}_{2}\)O\({}_{7}\) groups, can incorporate significant amounts of geochemically important trace elements such as uranium [27].  
Each aliquot of drinking water was collected into a 750 mL acidified polyethylene bin, in order to avoid radionuclide precipitation and adsorption on the container walls, and then stored in the laboratory for the sample preparation and analysis. Before use, each beaker was first soaked with diluted nitric acid, then washed, rinsed with distilled water  
Figure 1: The map of the Calabria region, southern Italy, with the sampling point indicated.  
Figure 2: The geological map of the area around Paola, Calabria region, southern Italy.  
and finally left to dry in the oven to prevent contamination [28]. In the laboratory, the first aliquot was taken as it was, while the other two were evaporated on a plate in order to reach: (i) a final volume of 250 mL, i.e., factor of preconcentration equal to 3, for the second aliquot and (ii) a final volume of 20 mL, i.e., factor of preconcentration equal to 37.5, for the third aliquot. The volumes of 250 mL and 20 mL correspond to the two sample holder geometries available for the gamma spectrometry analyses [29].
## 4 Optimization of Balanced Partition Routing Algorithm
### Computational Complexity Analysis under Two Partitioning Methods  
**Method 1:** Method 1 here is the balanced partition routing algorithm mentioned above, and its total computational complexity and computation pressure of a single node are shown in Equations (6) and (12). **Method 2:** As shown on the left side of Figure 5, all nodes are divided into three large groups, with two small groups inside each large group. To calculate the routing path from star node numbered i to star node numbered j, there may be three cases to consider. 1. In the first case, the two star nodes are in the same small group and the routing path is calculated directly in the group. 2. In the second case, the two nodes are not in the same small group, but in the same large group. Assuming that the two communication nodes belong to small groups,  
Figure 5: Two kinds of optimized partitioning (method 2 is left, method 3 is right). these two nodes are numbered i + x and j + y, respectively. The communication nodes of each group should be calculated first, and then the paths of these two nodes to their communication nodes should be calculated. Therefore, the total routing path is going to be: i -> i+x -> j+y -> j. 3. In the third case, the two nodes are not in the same large group. For node i and node j, the first step is to calculate the communication nodes of large groups that these two nodes i, j, respectively, belong to. Assuming that two large communication nodes are numbered i + m and j + n. Assuming that the two small communication nodes in the large group where node i is located are i + m\({}_{1}\) and i + m\({}_{2}\). In the same way, we can obtain j + m\({}_{1}\) and j + m\({}_{2}\) and then we have to think about whether i + m and i are in the same small group. We also need to consider whether j + n and j are in the same group. There are four possibilities: (3.1) The first possibility is that node i + m and i are in the same small group and node j + n and j are also in the same small group. The path in this possibility is going to be: i\(->\) i + m\(->\) j + n\(->\) j. (3.2) The second possibility is that node i + m and i are not in the same group while node j + n and j are in the same group. In this possibility, we need to calculate the routing path from i to i + m in the large group firstly and the total routing path is going to be: i\(->\) i + m\({}_{1}->\) i + m\({}_{2}->\) i + m\(->\) j + n\(->\) j. (3.3) The third possibility is that node i + m and i are in the same group while j + n and j are not in the same group. The total routing path is: i\(->\) i + m\(->\) j + n\(->\) j + m\({}_{1}->\) j + m\({}_{2}->\) j. (3.4) The last possibility is the exact opposite of the first and its routing path is: i\(->\) i + m\({}_{1}->\) i + m\({}_{2}->\) i + m\(->\) j + n\(->\) j + m\({}_{1}->\) j + m\({}_{2}->\) j.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 4 Looking Forward: LLM Focus  
The emerging wave of GPU workloads already surpasses the capabilities of most contemporary schedulers. Training and serving billion-parameter models introduce scale, structural diversity, and dynamism that challenge classical algorithms. LLMs are a canonical stress test: they demand aggressive data, model, and pipeline parallelism, and exhibit highly variable resource footprints across execution. Yet these same characteristics--massive parallelism, bursty arrivals, and mutable throughput-latency trade-offs--also define modern HPC simulations, multi-modal generative models, and real-time analytics pipelines.  
Recent systems such as Sia, Frenzy, Lluminx, Crius, Harmony, Varuna, Alpa, and TRAIL point toward four key research directions that may shape future schedulers. First, schedulers are becoming increasingly _resource-aware and learning-augmented_: analytical models are now combined with online learning to capture non-linear speedups and interference effects. Second, to close the cold-start gap we discussed for pure RL policies in Section 2.5--where new workloads suffered minutes of sub-optimal placement--emerging designs embrace _hybrid strategies_ that pair fast heuristics with ML-driven micro-decisions, enabling adaptation to workload drift while maintaining worst-case performance guarantees. Third, there is a growing focus on explicitly managing _hardware heterogeneity_, as clusters mix GPU generations, interconnect fabrics, and specialized accelerators--making bandwidth, memory, and reliability as critical as raw compute. These bandwidth-aware placement schemes explicitly fill the \"unsolved-constraint\" column in Table 2, turning the earlier red-flagged inter-device tiering blind spot into a first-class scheduling dimension. Finally, fault tolerance and risk are being addressed as first-class optimization goals, as schedulers balance checkpointing, replication, and migration overheads in volatile environments such as spot markets or geo-distributed training --preemptively amortizing the 10-30 ms kernel-drain penalty highlighted in Section 3.5 through proactive snapshotting and live migration.  
These trends surface several cross-cutting open questions: how to schedule under workload and model uncertainty; how to scale to tens of thousands of devices in multi-tenant environments; how to support emerging architectures like state-space models and mixture-of-experts; how to minimize energy and cost under power constraints; and how to decentralize the scheduling logic itself to avoid central bottlenecks. Addressing these challenges will require tighter integration of scheduling theory, performance modeling, and systems engineering.
###### Abstract  
To effectively separate strong cultural noise in Magnetotelluric (MT) signals under strong interference conditions and restore the true forms of apparent resistivity and phase curves, this paper proposes an improved method for suppressing strong cultural noise based on Particle Swarm Optimization (PSO) and Variational Mode Decomposition (VMD). First, the effects of two initial parameters, the decomposition scale K and penalty factor \(\alpha\), on the performance of variational mode decomposition are studied. Subsequently, using the PSO algorithm, the optimal combination of influential parameters in the VMD is determined. This optimal parameter set is applied to decompose electromagnetic signals, and Intrinsic Mode Functions (IMFs) are selected for signal reconstruction based on correlation coefficients, resulting in denoised electromagnetic signals. The simulation results show that, compared to traditional algorithms such as Empirical Mode Decomposition (EMD), Intrinsic Time Decomposition (ITD), and VMD, the Normalized Cross-Correlation (NCC) and signal-to-noise ratio (SNR) of the PSO-optimized VMD method for suppressing strong cultural noise increased by 0.024, 0.035, 0.019, and 2.225, 2.446, 1.964, respectively. The processing of field data confirms that this method effectively suppresses strong cultural noise in strongly interfering environments, leading to significant improvements in the apparent resistivity and phase curve data, thereby enhancing the authenticity and reliability of underground electrical structure interpretations.  
magnetotelluric; strong cultural noise; variational mode decomposition; particle swarm optimization
## 3 A Steffensen-Type Family of Super Convergence with Memory
** Since  
\[\alpha_{n}=f[x_{n-1},x_{n},z_{n},y_{n}]-\frac{f[x_{n},z_{n},y_{n}]^{2}}{f[x_{n},y_{n}]} \tag{11}\]0 \[\alpha_{n}=f[x_{n-1},x_{n},z_{n},y_{n}]-\frac{f[x_{n},z_{n},y_{n}]^{2}}{f[x_{n},y_{n}]} \tag{11}\]1  
we obtain Equation (15) by Theorem 2 from  
\[\alpha_{n}=f[x_{n-1},x_{n},z_{n},y_{n}]-\frac{f[x_{n},z_{n},y_{n}]^{2}}{f[x_{n},y_{n}]} \tag{11}\]2  
Furthermore, we propose two doubly-accelerated Steffensen-type methods:  
compute Equation (10) with \(\gamma_{n}=-\frac{1}{f[x_{n},z_{n-1}]}\) (16)compute Equation (11) with \(\gamma_{n}=-\dfrac{1}{f[x_{n},z_{n-1}]}\) (17)  
**Theorem 5**. : _Let \(f:D\rightarrow\Re\) be a sufficiently differentiable function with a simple root \(a\in D\), \(D\subset\Re\) be an open set, \(x_{0}\) be close enough to \(a\), then the doubly-accelerated Steffensen-type methods Equations (16) and (17) achieve third-order and 4.74483 order convergence, respectively._  
**Proof. ** Denoting \(e_{n}^{z}:=z_{n}-a\) and \(e_{n}:=x_{n}-a\), if \(z_{n}\) converges to \(a\) with order \(p>1\) and satisfies the error equation  
**Example 2. ** The numerical results of self-acceleration of [PERSON]'s method (SASM), Equations (16) and (17), DPM1(3), Equations (16) and (17) and DPM2(3) are in Table 2 for the following nonlinear functions:  
\[\alpha_{n}=f[x_{n-1},x_{n},z_{n},y_{n}]-\frac{f[x_{n},z_{n},y_{n}]^{2}}{f[x_{n},y_{n}]} \tag{11}\]3 \[\alpha_{n}=f[x_{n-1},x_{n},z_{n},y_{n}]-\frac{f[x_{n},z_{n},y_{n}]^{2}}{f[x_{n},y_{n}]} \tag{11}\]4 \[\alpha_{n}=f[x_{n-1},x_{n},z_{n},y_{n}]-\frac{f[x_{n},z_{n},y_{n}]^{2}}{f[x_{n},y_{n}]} \tag{11}\]5 \[\alpha_{n}=f[x_{n-1},x_{n},z_{n},y_{n}]-\frac{f[x_{n},z_{n},y_{n}]^{2}}{f[x_{n},y_{n}]} \tag{11}\]6 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **SASM** & **Equation (16)** & **Equation (16)** & **DPMI(3)** & **Equation (17)** & **Equation (17)** & **DPM2(3)** \\ \hline \(f1:|e_{4}|\) & 0.245e-40 & 0.784e-14 & 0.107e-28 & 0.164e-23 & 0.101e-195 & 0.727e-273 & 0.426e-231 \\ COC & 2.41353 & 2.45350 & 3.00734 & 2.98211 & 4.23599 & 4.74517 & 5.04588 \\ \(f2:|e_{4}|\) & 0.396e-44 & 0.194e-17 & 0.177e-35 & 0.304e-29 & 0.524e-176 & 0.148e-254 & 0.188e-283 \\ COC & 2.41316 & 2.32334 & 3.01791 & 2.94762 & 4.23567 & 4.74606 & 5.04155 \\ \(f3:|e_{4}|\) & 0.380e-49 & 0.346e-14 & 0.300e-38 & 0.172e-32 & 0.168e-168 & 0.689e-258 & 0.618e-265 \\ COC & 2.41295 & 2.51251 & 3.16594 & 3.12621 & 4.23622 & 4.74895 & 5.04542 \\ \(f4:|e_{4}|\) & 0.344e-86 & 0.696e-37 & 0.112e-70 & 0.326e-60 & 0.111e-399 & 0.115e-560 & 0.437e-555 \\ COC & 2.41721 & 2.43146 & 3.00078 & 2.99954 & 4.24283 & 4.7598 & 5.04856 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerical results for \(f_{i}(x),i=1,2,3,4\).
## 4 Conclusions  
The test campaign has brought very promising results. The behavior of the samples, tested in different shapes and configurations, showed that it is possible to determine the attitude in orbit of an object based on its reflection from a radar signal. In fact, the different configurations tested show how a different setup produces a single response, which can be associated with a different position. By associating this system with an optical system, for example, LEDs or light-curve acquisition systems, or magnetometer data to determine the attitude of a satellite, it will be possible to determine its exact position. Scaling the model makes it possible to carry out evaluations even for large satellites and space debris, which are usually identified by radar systems with much lower frequencies than those of the experimental system considered.  
These results, therefore, have a double value: they allow us to identify small satellites in a high-frequency range (12-18 GHz) as well as to have a prediction of visibility of large orbiting systems at the frequencies of the actual present radar systems, based on the scale considered. For example, it will be possible to determine the attitude of a satellite of a certain size by considering the working frequency of the radar and its wavelength and a scale model that is subjected to a measurement frequency at the same scale.  
Figure 11: Parallelepiped with appendices reflection trend.  
**Author Contributions:** Conceptualization, A.D., F.P.; methodology, A.D. and R.P.; software, A.D. and R.P.; validation, F.S., F.P. and M.M.; formal analysis, A.D. and R.P.; investigation, A.D., F.P. and R.P.; resources, F.P.; data curation, A.D. and R.P.; writing--original draft preparation, A.D. and R.P.; writing--review and editing, A.D., F.P., F.S., M.M. and R.P.; visualization, A.D.; supervision, F.P. and F.S.; project administration, F.P.; funding acquisition, F.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Italian Space Agency through the grant agreement n. 2020-6-HH.0 (Detriti Spaziali--Supporto alle attivita IADC e SST 2019-2021).  
The authors declare no conflict of interest.
###### Abstract  
Refugee-dwelling footprints derived from satellite imagery are beneficial for humanitarian operations. Recently, deep learning approaches have attracted much attention in this domain. However, most refugees are hosted by low- and middle-income countries where accurate label data are often unavailable. The Object-Based Image Analysis (OBIA) approach has been widely applied to this task for humanitarian operations over the last decade. However, the footprints were usually produced urgently, and thus, include delineation errors. Thus far, no research discusses whether these footprints generated by the OBIA approach (OBIA labels) can replace manually annotated labels (Manual labels) for this task. This research compares the performance of OBIA labels and Manual labels under multiple strategies by semantic segmentation. The results reveal that the OBIA labels can produce IoU values greater than 0.5, which can produce applicable results for humanitarian operations. Most falsely predicted pixels source from the boundary of the built-up structures, the occlusion of trees, and the structures with complicated ontology. In addition, we found that using a small number of Manual labels to fine-tune models initially trained with OBIA labels can outperform models trained with purely Manual labels. These findings show high values of the OBIA labels for deep-learning-based refugee-dwelling extraction tasks for future humanitarian operations.  
emote sensing; deep learning; semantic segmentation; label noise; OBIA; refugees +
Footnote †: journal: _sciences_  
_Article_
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 3 Results
### Throughput  
Comparing average throughputs over different load points gives an understanding of how the transport layer impacts the performance. The throughput is calculated such that we take the data size of the payload to be transferred and divide that with the delay measurement from the time the packet is generated to the buffer in the device until the time the full packet is received in the cloud end. As the data sizes are too small to reach the full capacity of the link, the larger data size shows better throughput.  
Figure 2 shows the mean throughput as a function of system load as experienced by a single device (a) and on the cell level (b). As can be seen in Figure 2a, CoAP shows a higher application layer throughput than MQTT. This is due to the TCP three-way handshake contribution to the delay for the data transfer in the beginning of the communication. CoAP confirm performance degrades faster than the CoAP performance without confirm messages for the larger file as the load increases in the system. This is most likely due to the increased load from CoAP retransmissions that load the system further when the packets get delayed due to congestion. A lower throughput may increase the power consumption at the device as the device needs to remain in connected state for a longer period of time.  
Figure 2b shows how the system reaches its saturation point earlier for MQTT. This can be seen as the point where the cell throughput reaches its peak. When the cell throughput starts decreasing under load the air interface is no longer efficiently used. The second curve to start bending downwards is the CoAP confirm case. CoAP with no confirm reaches its saturation point outside of the plot area.
## 5 Conclusions  
In this paper, a novel MFI scheme has been proposed for digital coherent receivers. According to the notable features in the polar coordinate system, the incoming PDM signals of the digital coherent receivers can be identified by the Gaussian weighted KNN algorithm. The proposed MFI scheme is able to achieve 100% correct identification rates for five modulation formats when the values of OSNR are no less than the corresponding theoretical 20% FEC limit. Meanwhile, the proposed MFI scheme is also robust against  
Figure 14: The comparison of the average runtime for identifying a testing sample with two MFI schemes.  
Figure 13: The correct identification rate in long-distance transmission for five modulation formats.  
the effects of residual CD, PMD, and fiber nonlinearities. Furthermore, compared with other relevant MFI schemes, the proposed MFI scheme significantly reduces the average execution time. Therefore, the proposed MFI scheme may be regarded as a good candidate for implementing next-generation EONs.  
Conceptualization, S.L. ([PERSON]) and M.H.; methodology, S.L. ([PERSON]) and M.H.; software, S.L. ([PERSON]), R.X. and S.L. ([PERSON]); validation, S.L. ([PERSON]), R.X. and S.L. ([PERSON]); formal analysis, S.L. ([PERSON]) and M.H.; writing--original draft preparation, M.H. and S.L. ([PERSON]); writing--review and editing, J.T., W.J. and L.C.; supervision, M.H.; project administration, M.H. and J.T.; funding acquisition, M.H. and J.T. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Sichuan Science and Technology Program (2023 YFH0067), partly supported by the North Wales Growth Deal through Ambition North Wales, partly supported by the UK GOV DSIT (FONRC) project REASON, partly supported by Engineering and Physical Sciences Research Council Project TITAN [EP/Y037243/1], and partly supported by the project of Sichuan University of Science and Engineering (2023 RC23).  
Not applicable.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.