esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 4 Algorithm  
The goal is to optimize multi-path selection while minimizing the bottleneck weight of paths, with the frequency of bottleneck path occurrences used as a weight. We adopted a custom approach that covetously iterates over the culling of edges.  
We cluster the frequency distribution of statistically identified bottleneck edges within the network to establish systematical evaluation. Each bottleneck edge is assigned a vulnerability level through a nonlinear classification methodology that employs exponential scaling to amplify inter-category distinctions. This approach heightens sensitivity to high-risk scenarios during decision-making processes while quantitatively reflecting the cumulative risk impact of bottleneck edges on network-wide stability.  
The goal is to extract up to k edge-disjoint paths from source \(s_{d}\) to destination \(t_{d}\) that minimize bottleneck weight, subject to latency and iteration constraints. Under no circumstances should the latency of any chosen path exceed that of an equivalent terrestrial fiber-optic link. As shown in Algorithm 1, we propose a K-Bottleneck Minimize algorithm to avoid the bottleneck risk path in LSN. The algorithm proceeds as follows:  
Shortest-Path Search: Run a weighted Dijkstra on the current graph \(G\) (\(V,E\)). Find the shortest path from source satellite \(s_{d}\) to target satellite \(t_{d}\). If no path exists, terminate.  
Length Check and Edge Pruning: Measure the end-to-end path length of the candidate path. If it exceeds the fiber-equivalent bound, identify the single link with the greatest propagation delay, and mark it as unavailable.  
Accept and Disjoint Enforcement: Once a path meets the length constraint, record it in the solution set, then remove its ISLs while permitting node reuse to ensure edge disjoint in subsequent iterations. Then, continue to calculate until paths are found or the attempt limit is reached.  
Our network uses the Starlink S1 constellation, which means it has 1584 satellites, each with four ISLs, which means a total of 6336 ISLs. The number of k paths can be customized as needed. In our experiment, we fix the number of paths to 5. Following prior work [53], we first compute the fiber length by multiplying the great-circle distance between endpoints by a path-stretch factor of 1.53. We then estimate the one-way latency by dividing this length by the speed of light in fiber, which is approximately two-thirds of its vacuum value. We limit the maximum number of attempts to k + 3, and the majority of candidate paths are either pruned or accepted on the first search invocation, making secondary computation infrequent. By imposing a retry cap, the algorithm ensures sufficient flexibility to handle the pathological cases while bounding any excessive overhead from redundant re-search operations.
###### Abstract  
Legacy IEEE 802.11 Medium Access Control (MAC) adopts the Distributed Coordination Function (DCF) mechanism, which provides the same access opportunity for all contenders. However, in dense multi-rate Wireless Local Area Networks (WLANs), the pure distributed control mechanism will cause high collision rate and performance anomaly, which results in low network utilization and wasting valuable channel resources. In this paper, we present a decoupling MAC mechanism (DMAC) based on the idea of contention/reservation to reduce collision and realize collision free data transmission. In proposed mechanism, the channel access time is partitioned into channel contention process and data transmission process. The proposed algorithm makes full use of the distributed random channel access mechanism and performs a centralized collision-free data transmission. Wherein, we also design an adaptive algorithm to adjust the length of the contention period to improve the channel utilization. Furthermore, we further propose two airtime fairness algorithms Improve-DMAC1 (I-DMAC1) and Improve-DMAC2 (I-DMAC2) for delay sensitive network and high throughput network scenarios, respectively, to solve the performance anomaly in multi-rate WLANs, based on DMAC. We verify the effectiveness of these decoupling algorithms through extensive simulations. Moreover, the simulation results show that the proposed algorithms achieve better performance than the 802.11 standard and other protocols.  
WLANs; performance anomaly; decoupling MAC; airtime fairness +
Footnote †: journal: Future Internet
## 4 Discussion
Therefore, we have the opinion that the motor response to reflex locomotion stimulation is not a condition to induce changes in the activity of the motor cortex, and even more, motor reaction should not be the only aim of therapy. A recent study [41] described changes in the cortisol levels in the saliva of infants after reflex locomotion stimulation. These findings could fundamentally affect the use of the reflex locomotion method. In case the cortisol levels are affected by the reflex locomotion stimulation, is it possible to observe this correlation in the cerebral cortex? The topic of the humoral response of the human body to reflex locomotion stimulation should be a research subject in this field. We are also aware of the limitations of this study: First, in the present study, we used the method of standard low-resolution brain electromagnetic tomography (sLORETA) to evaluate changes in intracerebral electrical activity. Although sLORETA allowed us to monitor changes in the electrical brain activity with zero localization error, our observation was bound only to the cortical areas of the cerebral cortex. MRI should be used to observe subcortical structures as in the above-mentioned research. Second, the sample size needs to be increased in order to generalize our data.
# Improvement of Surface Roughness and Cracking Study of Nb\({}_{3}\)Sn SRF Films
## 2 Standard Coating Procedure for Nb\({}_{3}\)Sn Cavities  
A high-temperature furnace was designed and fabricated for Nb\({}_{3}\)Sn coating on niobium cavities by the tin vapor method. The typical temperature profile for cavity and the vacuum measurements are shown in Figure 1. A complete coating process includes several stages: degassing, nucleation, coating, and annealing. In the nucleation stage, the SnCl\({}_{2}\) crucible is heated rapidly to 800 \({}^{\circ}\)C and kept for 30 min. The vacuum valve of the molecular pump is closed, and the vacuum is measured during the whole coating process by a film gauge with a measurement range of 0.01 Pa-10 Pa. Then, the SnCl\({}_{2}\) crucible cools down to 550 \({}^{\circ}\)C for 4.5 h. After nucleation, the cavity temperature is ramped up to 1200 \({}^{\circ}\)C and kept for 1.5-3 h, followed by an annealing at 1100 \({}^{\circ}\)C for 1 h. After the above procedure, the system cools down naturally.  
This process was proved to coating a uniform Nb\({}_{3}\)Sn film with 25-26% Sn content in previous experiments [6].  
We compared those BCP, EP, BEP pre-treated samples coated with slightly different coating parameters to study the influence of polishing method. The samples for roughness study were hung inside the cavity as witness sample. The inner space of cavity cannot support many large samples coating. Thus, samples for the tension and compression were coated in a niobium tube, which is about 12 cm in height and 5 cm in radius, with a tungsten cover, offering a similar reaction environment. The amounts of tin and tin chloride are 0.5 g and 1.5 g, stored in a tungsten boat at the bottom of the furnace. We used the abovementioned typical parameters for coating without annealing, as these parameters are proved to fabricate Nb\({}_{3}\)Sn film of grain size about 2000 nm.
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 4 Standards Evaluation Results
### Evaluation of the Standards
#### Data Model Architecture  
From an architectural perspective, STIX is the language with the most holistic architecture and it is applicable in different use cases. The four entities considered essential to delineate a holistic contextualization of the cyber threat intelligence scenario can be fully represented and characterized by the classes that compose the STIX schema. IODEF and OpenIOC also have a satisfactory architecture but with some flaw points. Both standards do not have the necessary resources for an adequate definition of defense mechanisms or courses of action. Besides, OpenIOC has some shortcomings in the process of characterizing a threat actor in a more specific way.
# Asymptotic Performance of GNSS Positioning Approaches under Cross-Correlation Effects
## 1 Introduction
This study shows that the 2 SP cannot reach the same asymptotic performance as that of the DPE due to the presence of cross-correlation. It is demonstrated that the optimization problems for the DPE and 2 SP are maximization in the projection space and direct maximization with the received signal, respectively. The latter is a transformation of the approximation trick for the former without considering the cross-correlation effects; therefore, a performance degradation exists. The derivation of the CRBs for the 2 SP and DPE is presented, and the performance degradation of the 2 SP compared to the DPE is evaluated by considering the cross-correlation errors. Furthermore, a more strict result is proved: the 2 SP is not asymptotically efficient in GNSSs, and the DPE outperforms the 2 SP in all scenarios. The remainder of this paper is organized as follows. Section 2 describes the signal model. Sections 3 and 4 present an analysis of the asymptotic performance of the DPE and 2 SP, respectively. In Section 5, the numerical results are reported, and they are discussed in Section 6. Finally, Section 7 concludes the paper.
## 4 Description of the Proposed Workflows
### Workflow for EnOcean Components on the Room Level  
The workflow to install EnOcean devices is shown in Figure 6. As a large part of the installation can be completed without the need for access to electrical installations, the majority of the required steps are taken by the research institute. Steps requiring close connection to the existing infrastructure must be carried out by a qualified electrician. In this project, such services must be put out to tender.  
The first step in the procedure is to collect all the necessary information about the building and the rooms. After determining the building to be equipped, it is important to obtain an overview of the rooms to be equipped based on existing floor plans. The next step is to visit and assess the rooms on site to capture any details that are not included in the accessible plans, such as the position and number of the heating elements in the room, the nature of windows and doors, etc. With this information available, the selection of technologies and the planning of execution steps can be performed. Finally, it is confirmed with the responsible infrastructure manager whether the envisaged measures can be carried out.  
Afterwards, the installation of backbone components (including power lines, IPC, signal cables, and transceivers), the assigning of specific devices (meaning not only the type but the exact IDs) to their planned installation positions, as well as the preparation of the IPC program can be executed in parallel. Within the task of assigning devices, the steps \"Select Devices\", \"Prepare Devices\", and \"Register Devices\" inside Figure 6 are implied. To store the decisions taken in these steps, we use a management web application with an underlying database, which was developed internally and is called WALDO (WALDO stands for _Web AppLication for Device Organization_). Processes such as ordering and storing hardware or the assembly of the IPC components are assumed to be executed up front and, thus, are not discussed further at this point.  
Figure 6: Proposed workflow for the selection, provisioning, and installation of devices that use the EnOcean protocol.  
With a basic IPC program available and the backbone components being installed, a test of the backbone is executed. Moreover, the IPC program can be configured. This puts the logic into place, which allows the IPC to communicate with the devices prepared before.  
After these two steps are successfully carried out, all devices can then be installed. For actuators that require a teach-in process, this can now be carried out with the program specially parameterized for the building while the IPC is running. If no such teach-in procedure is required, the device installation can, of course, be carried out independently of the IPC program or the backbone installation.  
Before the final operation, a short test of the total system is performed.
## 2 Materials and Methods
### Characterization of the Materials  
The crystal structure and crystallite sizes of Co\({}_{1-x}\)Mn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles were studied by X-ray diffraction performed at room temperature, using a Bruker D8 Advance diffractometer. The lattice parameters were obtained by Rietveld refinement of XRD patterns, using FullProf Suite Software (Windows (64 bits)) [46]. The crystallite sizes of the synthesized nanoparticles were estimated using the Debye-Scherrer equation:  
\[D=\frac{\mathrm{k}\lambda}{\beta\cos\theta}\]  
where by \(\beta\) we denoted the peak full width at half maximum (in radians) at the observed peak angle \(\theta\), k is the crystallite shape factor (was considered 0.9), and \(\lambda\) is the X-ray wavelength.  
Additionally, the morphologies of the prepared nanoparticles were investigated by transmission electron microscopy (TEM) and Scanning Electron Microscopy (SEM) using an Hitachi HD2700 CFEG STEM(Hitachi High-Technologies Corporation, Tokyo, Japan) at 200 kV with secondary electron imaging capability. To analyze the composition of the synthesized nanoparticles, Energy Dispersive X-ray Spectroscopy (EDS) measurements were performed.  
For Raman spectroscopy analysis, dry samples in powder form were suspended in distilled water and drop-coated onto a quartz plate. Raman micro-spectroscopy analysis was conducted after evaporation of water droplets in ambient conditions. The Renishaw spectrometer model InVia Reflex (Renishaw, Wotton-under-Edge, UK) was used, equipped with a 632.8 nm laser and a 100\(\times\) objective (NA 0.9). Acquisition parameters comprised 10 acquisitions per spectrum of 60 s integration time under 0.85 mW laser power. Background was subtracted from all spectra and 5-point Sawitzky-Golay smoothening was applied. Accurate band positions were obtained by Lorentzian profile fitting.  
Magnetic measurements were performed using a vibrating sample magnetometer, 12 T VSM from Cryogenic Limited London, in the 4.2-300 K temperature range, and external magnetic fields up to 12 T. The saturation magnetizations, \(M_{s}\), were determined from magnetization isotherms according to the approach to saturation law:  
\[M=M_{s}(1-b/H)+\chi_{o}H \tag{1}\]  
where by \(b\) we denoted the coefficient of magnetic hardness and by \(\chi_{o}\) a Pauli-type contribution.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.8 Generation of Digital Tree Height and Canopy Models  
In the field of forestry, the distribution of vegetation in general is known as digital height model (DHM). The distribution of tree heights is also known as digital canopy model (DCM). The DHM was calculated for each of the priority monitoring sites in the Bossematie classified forest based on two other landscape indicators: the DSM and the DTM according to Equation (1).  
\[\text{DHM}=\text{DSM}-\text{DTM} \tag{1}\]  
The DTM produced may have heights that do not always correspond to tree heights but rather to low vegetation heights (grass or cocoa crop) (Figure 6). Therefore, the DCM was calculated from the DHM based on the threshold value of minimum forest tree height excluding perennial cocoa-coffee crops. This threshold value will be defined from the statistical analysis of the vegetation height distribution:  
\[\text{DCM}=\text{DHM}-\rho \tag{2}\]  
where \(\rho\) is the height threshold value obtained with the drone to ensure that only forest trees are detected.  
Figure 5: Delineation and extraction of a portion of Site 1 from the DSM (**left**) and orthomosaic (**right**) of 1 November 2018.  
Figure 6: Illustration of the principle of creating the MNH from the MNS and MNT (modified by [35]).
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 3 Code-Frequency Two-Stage Compression Acquisition
### Frequency Domain Compression Preprocessing  
The incoming signal is multiplied with \(\exp[j2\pi(f_{\text{o}}^{i}+f_{\text{c}}^{l})]\) to produce the mapped signal \(r_{1}^{\prime}(n)\)  
\[r_{1}^{\prime}(n)=r(n)\exp[j2\pi(f_{\text{o}}^{i}+f_{\text{c}}^{l})nT_{\text{ s}}] \tag{17}\]where \(f_{\rm c}^{l}\) is the multiple carrier frequency bins, \(l\in\{0,1,2,\ldots,L-1\}\), \(L\) is the frequency compression ratio (FCR), the frequency search step \(f_{\rm step}{=}f_{\rm c}^{l+1}{-}f_{\rm c}^{l}\), \(f_{\rm o}^{i}\) is the starting frequency in once frequency compression search, \(i\in\{0,1,2,\ldots,I-1\}\), \(I\) is the number of sub-carrier bins, the frequency compression steps \(f_{\rm o}^{i+1}{-}f_{\rm o}^{i}{=}L\cdot f_{\rm step}\).  
Hence the Doppler frequency offset can be expressed as  
\[f_{\rm d}=I\cdot L\cdot f_{\rm step} \tag{18}\]  
Figure 2 shows the process of the mapping and overlapping-based carrier frequency searching technique. We divide the Doppler frequency offset \(f_{\rm d}\) into \(l\) sub-carrier frequency bins and search for \(L\) sub-frequency points separated by \(f_{\rm step}\) simultaneously.  
From Equation (13), we can see that if the Doppler frequency is greater than the correlation bandwidth \(B_{\rm c}=2/(KT_{\rm s})\), the correlation value is attenuated seriously. Accordingly, the frequency search step should satisfy \(f_{\rm step}\leq B_{\rm c}/2\), making sure at least two neighboring acquisition frequency points are located within the correlation bandwidth \(B_{\rm c}\), which is beneficial to enhancing the acquisition performance, as shown in Figure 3a. However, if \(f_{\rm step}>B_{\rm c}\), the neighbor acquisition frequency exceeds the correlation bandwidth \(B_{\rm c}\), causing the correlation value decrease and the acquisition failure, as illustrated in Figure 3b.  
Then, the mapped signals which correspond to the same code phase are overlapped together. Consequently, we obtain the preprocessed incoming signal  
\[r_{l}^{\prime\prime}(n)=\sum_{l=0}^{L-1}r_{l}^{\prime}(n) \tag{19}\]  
The correlation bandwidth of the comprssion acquisition is improved by frequency domain preprocessing, which can be expressed as  
\[B_{\rm c}^{\prime}=(L-1)f_{\rm step}+B_{\rm c} \tag{20}\]  
Compared with the traditional Doppler frequency search method, the \(L\) frequency bins are searched simultaneously in this method, which can reduce search number and acquisition time.  
Figure 2: Schematic diagram of frequency domain compression processing.
## 2 Related Work and Comparison
We show the comparisons between SA-UBA and existing systems in Table 1. SA-UBA enables resource access authentication for privileged accounts and achieves unified management. Moreover, the cryptography-based account storage and sharing methods are applied to prevent password leakage even if the system is compromised. As attackers can possibly try to read authentication credentials in the system or application codes, SA-UBA adopts hard-coded elimination and code obfuscation technology to securely store all of the credentials.
## 2 Methods
### Data Analysis  
Inter-rater agreement was analyzed by percentage agreement and [PERSON]'s Kappa. An a priori guide was set to interpret the percentage of inter-rater agreement as acceptable (75% to 80%) or high (>80%) and [PERSON]'s Kappa agreement as poor (<0), slight (0-0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.80), or excellent (0.81-1.00) [34; 35]. A sensitivity analysis was conducted to compare the inter-rater agreement with and without the piloting phase.  
Completeness and consistency of reporting location were analyzed descriptively by percentages. For completeness, the percentage of complete, incomplete, and absent reporting was calculated separately for each TIDieR-Rehab checklist item and across all items. For consistency of reporting location, first, the most common key reporting location for each TIDieR-Rehab item across all interventions was identified. Then, the percentage of intervention groups that used this key location to report on the respective item was calculated. All calculations were performed separately for robotic interventions, conventional interventions, and all interventions combined, both for each individual article and across all articles. An a priori guide was set to separately interpret completeness and consistency of location as poor (<50%), moderate (50-79%), or excellent (\(\geq\)80%) [36]. All data extraction, evaluation, comparisons, and analysis were conducted in Microsoft Excel.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect of Reporting Quality** & **Definition** & **Evaluation** \\ \hline Completeness of reporting & The completeness of reported intervention information according to the TIDieR-Rehab item and table of reference. & Complete = Full and clear description, to an extent which would allow it to be replicated. Incomplete = Partial or ambiguous description. Absent = No description. \\ \hline Comistency of reporting location & The consistency of article location where key intervention information for replication was reported. & After all relevant descriptions pertaining to a TIDieR-Rehab item were extracted, the key location containing the most pertinent information for replication was identified (e.g., Methods). Intervention groups that had ’absent’ reporting of an item were not included in the evaluation of consistency for that item. \\ \hline \hline \end{tabular} Note. Completeness of reporting and consistency of reporting location were evaluated within and across intervention groups and TIDieR-Rehab checklist items.  
\end{table}
Table 2: Definitions and evaluation of intervention reporting quality.