esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Soil Microbial Community Structure Based on FAME Analysis  
A higher abundance of total abundance of FAME indicators was detected in the 2019 (\(p<0.05\)) samples collected at 0-15 cm depth (Table 1). The addition of biochar resulted in 21%, 23%, and 36% increases in total microbial FAME contents, bacteria (total bacteria, GramPos, GramNeg, and Actino), and fungi indicators (total fungi, SF, and AMF) from 2018 to 2019 in the 5%-Inc, 5%-Sur, and 10%-Inc soils. However, compared to the control, bacteria (total bacteria, GramPos, GramNeg, and Actino) and fungi indicators (total fungi, SF, and AMF) were detected at 6% and 18% higher concentrations in the 2018 5%-Sur and 10%-Inc treatments, respectively. FAME group comparisons of the 2018 and 2019 samples taken at 15-30 cm revealed no significant differences between biochar treatments (Table 1). However, FAME indicators reflecting microbial community size and bacteria and fungi community structure increased from 2018 to 2019 (\(p<0.01\)). The highest total fungi and SF were detected in the control soil for both years, except for total fungi abundance in the 2018 5%-Sur soils. Total FAMEs and bacteria indicators (total bacteria, GramPos, GramNeg, and Actino) were detected at 15% and 29% lower concentrations in the 2018 collection of the 5%-Sur and 5%-Inc samples compared to 2019 (Table 1). Compared to the 2018 soils, the total FAMEs, and bacteria indicators (total bacteria, GramPos, GramNeg, and Actino) were at least 20% higher in the 2019 biochar added treatments. Significant differences in the F:B ratio was detected between years at both depths (0-15: \(p=0.0232\) and 15-30: \(p=0.0298\)) and between treatments at 15-30 cm (\(p=0.0282\)). The highest F:B ratios were detected in the 5%-Sur sample at 0-15 cm and in the control at 15-30 cm (Table 2). The G+/G- ratio increased with biochar's addition in 2018 and decreased in 2019, but these differences between treatments were not significant (Table 2). At 15-30 cm, the G+/G- ratio showed negligible differences after the addition of biochar. AMF/SF and AMF/TB were significantly different between years (\(p<0.01\)). Although the differences are trivial, AMF/SF and AMF/TB ratios were lower in the control, than the biochar-applied treatments at 0-15 cm, while an opposite trend occurred at 15-30 cm. No differences were detected for SF/TB at 0-15 cm, but at 15-30 cm, the control was significantly greater than the biochar-applied treatments (\(p=0.0429\)). Significant differences in the cy/pre ratio in the 0-15 cm soils were also found between biochar application rates and years (\(p=0.0001\) and \(p=0.0001\)); the control was significantly higher than the biochar treated soils. At soil depths of 15-30 cm, the cy/pre ratio was significantly greater during 2018 (\(p=0.0001\)). Using ANOVA negligible differences were detected by Shannon diversity index (SD) comparisons. However, at 0-15 cm, the SD values increased in the 10%-Inc and 5%-Sur soils for both years. While at 15-30 cm, 5%-Sur decreased, and 10%-Inc increased from 2018 to 2019.
# A Path Recommendation Method Considering Individual Driving Preferences
## 4 Personalized Path Recommendation Method
### Secondary Path Recommendation Threshold  
During a driver's journey, unforeseen changes in the road network and shifts in driver preferences can occur, leading to potential changes in driver satisfaction to the remaining paths. Therefore, it becomes necessary to provide secondary recommendations for unfinished paths between OD pair while the driver is en route. However, it is important to note that the driver's perceived path satisfaction may not be highly accurate or continuous, meaning that minor changes in satisfaction may not be sufficient to prompt a decision to change the established path.  
Psychology-related studies have shown that an individual's response to stimulus changes is not solely determined by the magnitude of the change itself, but also by the intensity of the stimulus; that is, the amount of change will only trigger a response from the individual when it reaches a certain ratio in relation to the initial stimulus. In the context of path recommendations, this suggests that a significant change in satisfaction should be required before suggesting an alternative path to the driver.  
Figure 5: Personalized path recommendation process.  
Therefore, the personalized path recommendation method takes into account this psychological aspect and ensures that a substantial change in satisfaction (i.e., beyond a certain threshold) is required before offering secondary recommendations. This approach balances the need for responding to significant changes in driver satisfaction while avoiding unnecessary path changes triggered by minor variations.  
\[\frac{\Delta\phi}{\phi}=C, \tag{29}\]  
where \(\Delta\phi\) is the amount of stimulus change, \(\phi\) is the current stimulus size, and \(C\) is the Weber ratio constant. Studies related to the path choice behavior of travelers [29] have shown that the size of this Weber constant is distributed from 5 to 20%. In particular, 10% was chosen as the threshold in this paper, such that only when the change rate of path satisfaction exceeded 10% was a new path recommended to the driver; namely, the threshold of path change was  
\[\frac{\Delta S}{S}=0.1. \tag{30}\]
## 3 Results and Discussions
### Alfacrux Digital Twin Pilot
#### 3.2.4 Data Management and Telemetry Viewer  
The ground station information technology team is responsible for keeping the data management system and online open access database. Currently, the data are updated after a set of operations, and they are included as a procedure to be executed after a passage. The current architecture can be seen in Figure 12.
## 1 Introduction
These features are frequently used in the literature. Firstly, the features are evaluated separately, and then the performance evaluations of the hybrid features created by stacking them in different orders are conducted. In the study, four different methods are used for classification, namely KNN, LDA, MLP, and CNN. Among these, KNN and LDA are traditional machine learning approaches. KNN performs classification based on the distance between features, while LDA performs classification based on the linear combination of features. On the other hand, MLP and CNN are deep learning approaches. MLP is considered the fundamental architecture of deep neural networks (DNN) and does not require as much computing power as modern deep learning architectures. CNN is the most known and widely used algorithm in the field of deep learning. CNN is highly effective at capturing complex patterns and relationships within a variety of data types, such as images, audio, and sensor data, offering state-of-the-art accuracy across a variety of tasks. In the study, 20 models are created using individual features, and 48 models are created using hybrid features. From the results obtained in the experiments, it is seen that hybrid features created by stacking different acoustic features increase gender recognition accuracy. The accuracy achieved through this method is competitive with similar studies. In the literature review, no other study was found that used a similar approach as proposed in this study for gender recognition. In this respect, this study will contribute to the literature. The rest of the paper is organized as follows: Section 2 introduces similar studies on gender recognition from speech signals. In Section 3, the materials and methods used in the study are explained in detail. Section 4 presents the experimental results, ending with the conclusion and future works.
## 1 Introduction  
Beam-monitoring detectors play a crucial role in both commissioning and operations of particle accelerators [1]. They allow the measurement and control of several beam parameters such as beam current, position, size, and shape. They are usually located at fixed positions along the beam path. Destructive detectors are the most common, leading to the impossibility of monitoring and using the beam at the same time. Non-destructive beam monitors are more challenging [2]. Both kinds of devices often feature a graphical output providing mono-dimensional projections or 2-dimensional images of the beam. Several options are available and a remarkable progress has been done in the recent years [3], in particular in the field medical applications [4] as proton-therapy [5]. Cyclotrons for the production of medical radioisotopes are presently common in hospitals and are usually equipped with minimal beam diagnostics devices. This is enough for routine production but specific detectors are needed for novel scientific and clinical developments.  
Along this line, at the medical cyclotron in operation at the Bern University Hospital (Inselspital) [6], three different beam monitors have been developed so far by the Laboratory for High Energy Physics (LHEP) of the University of Bern, which are regularly used for multidisciplinary research. The first one is a Faraday cup to measure proton beams down to the pA range [7]. The second one is named UniBEaM and measures the vertical and horizontal beam profiles by means of moving scintillating fibers [8]. This device is commercialized by the Canadian company D-Pace [9]. The third one is named\(\pi^{2}\), it is based on a fixed scintillating foil and a camera, and allows imaging of the beam spot in 2D and calculation of the beam position and size [10].  
This paper describes the development of a novel beam-monitoring detector, named \(\pi^{3}\), which represents an evolution of the \(\pi^{2}\). The main difference is that in the \(\pi^{3}\) both the scintillating foil and the camera are mounted on a moving support located inside the beam pipe in vacuum, allowing assessment of the evolution of the beam envelope along the beam path.  
The first prototype of the \(\pi^{3}\) has been designed and constructed in the framework of the commissioning of a new irradiation system for the production of non-standard medical radioisotopes using solid target stations [11]. In particular, it was used to study a novel compact magnet embedding two focusing quadruples and two steering magnets into a single device. This detector represents a step forward towards 3D imaging for beam diagnostics. In the following sections, the conceptual design, the experimental setup and the results of the first beam tests are presented and discussed.
## 4 Simulation Results and Performance Analysis
### Simulation Parameters Setup  
The process of applying GC with an adaptive scaling scheme to signal acquisition is shown in Figure 1. For each parameter, all individuals of the current population are evaluated and a local  
Figure 1: Flowchart of GC acquisition procedure with adaptive scaling scheme.  
optimal value is selected. This operation is repeated as many times as required with a diversified population. This diversity is assured by the application of GC operators in binary coding. Once a set of local optimal values is obtained, a global one is selected. Similarly, this process is carried out for all search values to produce the solution per iteration. At this stage, if the acquisition process is not terminated, the maximum search space boundaries (including Doppler frequency and code delay) are adjusted by subjecting them to an evolution cadence (variation of fitness function). This process is repeatedly carried out in this manner as long as it has not entered into offspring stagnation; otherwise, the program is interrupted by a fixed maximum iteration number. The initial population size of C/A-code is set at 1500, P-code, and BOC is set at 1500, 7000, and 5750, respectively, with carrier to noise density ratio (C/No) is 45 dB-Hz, crossover probability 0.85, mutation probability 0.008  and false alarm rate \(P_{FA}=0.01\) in control process of proposed scheme. The selection criterion of initial population size is calculated as follows:  
\[Z=2\upmu\times\left(\text{chip }\text{ length}\right) \tag{22}\]  
where \(\upmu\) is a scale factor with a value between 0.8 and 1. The chip length of C/A-code is 1023. Figure 2a illustrates the detection probability as a function of the C/No. It is shown that the adoption of GC under an adaptive and without an adaptive scaling scheme improves roughly 0.2 dB and 0.8 dB, respectively, in detection performance, in contrast to the adoption of the traditional method. Figure 2b demonstrates a receiver operating characteristics (ROC) curve with C/No = 38 dB-Hz. It shows that the proposed method improves the detection performance with adaptive scaling scheme.  
Figure 2: Performance of signal detection; (**a**) Probability of detection _versus_ C/No for different acquisition methods (\(T_{c}=1\text{ms}\), \(K=4\), \(P_{FA}=0.1\%\) ); (**b**) Probability of detection _versus_ false alarm rate (C/No = 38 dB-Hz).
## 6 Conclusions  
This paper introduced F&C, a flooding algorithm for ad hoc and sensor networks that alleviates the adverse effects of traditional flooding as well as other repeal-based protocols. First, we showed that repeal-based protocols are effective only when source-target pairs are located nearby and can generate even more signaling packets compared with traditional flooding for longer routes to the resource. F&C, on the other hand, has a behavior similar to repeal-based protocols for short-route resources and similar to traditional flooding for long-route resources. F&C adopts a repeal-flooding boundary strategy that signals whether nodes should chase the initial flooding or not. This paper presented a derivation of the optimized repeal-flooding boundary and a method to estimate it in practice accurately. Various experiments were conducted in ns-3 and Simpy simulators to test F&C performance under diverse network conditions and against other repeal-based flooding protocols. The results showed that nodes in F&C can accurately estimate their optimized repeal-flooding boundary with minimal network knowledge. Overall, the results show F&C outperformed traditional flooding and other repeal-based protocols in signaling overhead and delay metrics. Finally, this paper showed that even with diverse  
Figure 13: Comparison of flooding contention protocols. (**a**) Signaling overhead; (**b**) Route discovery delay.  
network conditions, F&C remains a robust and efficient alternative to resource discovery protocols in wireless ad hoc and sensor networks.  
Conceptualization, J.G. and M.M.-d.-O.; methodology, J.G. and J.J.C.-E.; software, M.M.-d.-O. and J.J.C.-E.; validation, M.M.-d.-O, and J.J.C.-E.; formal analysis, J.J.C.-E. and M.M.-d.-O.; investigation, M.M.-d.-O and J.J.; writing--original draft, J.J.C.-E.; writing--review and editing, J.J.C.-E.; visualization, J.J.C.-E.; funding acquisition, J.G. All authors read and agreed to publish to the published version of the manuscript.  
This research was funded in part by UNAM-PAPIIT grant number IN119820.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} F\&C & Flood and Contain \\ IoT & Internet of Things \\ ID & Identificator \\ BRIS & Broadcast-Repealing-Initiated in Source \\ BRID & Broadcast-Repealing-Initiated in Destination \\ TTL & Time to Live \\ RQ & Resource Request \\ RP & Resource Reply \\ MPR & Multipoint Relay \\ OLSR & Optimized Link State Routing \\ \end{tabular}
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 6 Conclusions  
In this paper, we propose a novel reliable data transfer protocol based on network coding and hybrid ARQ (NCHARQ) for underwater acoustic sensor networks. The protocol combines the coding efficiency of network coding and reliability of hybrid ARQ and achieves the asymptotic optimal reliable data transmission in underwater networks. We design a 3D dynamic underwater network model to verify our protocol. Simulation and analysis results show that NCHARQ can save the energy by decreasing the number of sent packets while reducing the average delay. The window estimation algorithm can adaptively change the number of sent packets at each node and thus the protocol can be insensitive to the environment change. For future research, we suggest that a more complex network model and the corresponding relay selection and routing algorithm for our protocol should be investigated. We will also conduct some tests in the real underwater environment in the future.  
This work is supported in part by the National Science foundation of China No. 61401476.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
###### Abstract  
Histogram shifting (HS) has been proved to be a great success in reversible data hiding (RDH). To reduce the quality loss of marked media and the increase in file size, several two-dimensional (2D) HS schemes based on the characteristics of cover media have been proposed recently. However, our analysis shows that the embedding strategies used in these methods can be further optimized. In this paper, two new 2D HS schemes for RDH in H.264/AVC video are developed, one of which uses the DCT coefficient pairs with both values 0 and the other does not. The embedding efficiency of a DCT coefficient pair in different embedding modes is firstly calculated. Then, based on the obtained embedding efficiency along with the statistical distribution of DCT coefficient pairs, two better embedding strategies are proposed. The secret data is finally embedded into the pairs of DCT coefficients of the middle and high frequencies using our proposed strategies. The comparison experiment results demonstrate that our schemes can achieve enhanced visual quality in terms of PSNR, SSIM, and entropy in most cases, and the increase in file size is smaller.  
Keywords:two-dimensional histogram shifting; reversible data hiding; H.264/AVC video +
Footnote †: journal: Applied Sciences
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 4 Application of Swarm Intelligence Algorithms in UAV-Aided Wireless Networks
The convergence speed of the algorithms for different building are considered as the evaluation criterion. However, this algorithm is only based on equally distributed users. Reference [50] proposed an SI-based 3D positioning algorithm (SIL) and SI-based energy saving clustering algorithm (SIC). SIL algorithm follows the boundary-based optimization method and uses the PSO algorithm to detect the location of target UAV nodes. The use of boundary box technology reduces the initial search space and speeds up the convergence rate; bounding box technology was used to refine the position estimation further. The SIC adopts an improved PSO to select CH. The SIL algorithm improves convergence speed and positioning accuracy at a lower computational cost. Comparably, the SIC algorithm is superior to typical routing protocols in packet transfer rate, average end-to-end delay, routing overhead, and energy consumption. The UAV-assisted wireless network is a direction of future network and IoT. As the DBS and UAV can assist network coverage and act as temporary networks in disaster situations. they can be used as  
Figure 6: UAV-aided wireless networks. relay, auxiliary information collection, and so on in many kinds of IoTs. The key issues of UAV-aided wireless network are deployment and path optimization. For deployment issues, it consists: (1) joint optimization of deployment and bandwidth allocation to reduce communication delay; (2) deployment of perceived obstacles to improve network coverage; (3) association between users and DBSs. Such issues should be evaluated with regard to user coverage, robustness, and power consumption. With respect to path optimization, energy constraints, obstacles, and perceived information value maximization should be considered, as well as collaboration issues for multiple UAVs. With the development of edge computing, future UAVs should also take into account the data analysis.
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 5 Learning with Errors (LWE)
### Attacks and Threats
Researchers are working on refining parameter selection guidelines to ensure robustness against known attacks and provide higher levels of security assurance. LWE is considered one of the leading candidates for post-quantum cryptography, so preventing attacks on learning with errors and its variations involves implementing robust cryptographic schemes and making informed choices in parameters and implementations. Some of the well-studied and recommended strategies are careful parameter selection, such as using larger parameters, randomness in error generation vectors, the implementation of appropriate techniques to resist lattice reduction attacks, and, as expected, continuous evaluation and updates.
# On a Framework for Federated Cluster Analysis
## 2 Materials and Methods
### Background: Related Work
#### 2.1.2 Nonfederated Fuzzy \(c\)-Means Clustering  
Fuzzy \(c\)-means is a well-known soft clustering method that assigns a membership index \(u_{ij}\) for clusters \(j=1,\ldots,K\) to data points \(x_{i}\in X\) such that \(\sum_{j}^{K}u_{ij}=1\ \forall i=1,\ldots,N\). The term soft clustering refers to the fact that points are allowed to belong to more than one cluster. In contrast, a hard clustering method such as \(k\)-means assigns each point to exactly one cluster.  
For given data \(X\), the objective is to find cluster centers \(c_{j}\) and membership matrix \(U=[u_{ij}]\) such that the following expression is minimized [26]:  
\[J_{m}(U,c) =\sum_{i=1}^{N}\sum_{j=1}^{K}(u_{ij})^{m}||x_{i}-c_{j}||^{2}, \tag{4}\] \[u_{ij} :=\frac{1}{\sum_{k=1}^{K}(\frac{||x_{i}-c_{j}||^{2}}{||x_{i}-c_{k }||^{2}})^{\frac{2}{m-1}}}, \tag{5}\]  
where \(||y||:=\sqrt{\sum_{l=1}^{n}y_{l}^{2}}\). It is closely related to the \(k\)-means clustering, and the main difference lies in the assignment matrices \(A\) in \(k\)-means versus \(U\) in fuzzy \(c\)-means.  
Parameter \(m>1\) controls how fuzzy the cluster assignments should be. The greater the \(m\), the more fuzziness in the assignment, i.e., points are assigned to more clusters with smaller values. A common choice that we also employ in all of our experiments is \(m=2\).  
A widely used algorithm to find a solution to the optimization problem was introduced by [26] and follows four basic steps:  
1. Initialize matrix \(U:=U^{0}\).
2. In iteration \(t\), (re)calculate cluster centers \(c_{j}\) according to: \[c_{j}=\frac{\sum_{i}u_{ij}^{m}x_{i}}{\sum_{i}u_{ij}^{m}}\] (6)
3. Update membership matrix \(U^{t+1}\) according to Equation (5).
4. Check if the convergence criterion is met: \(||U^{t+1}-U^{t}||\leq\epsilon\) for some predefined \(\epsilon\), i.e., did the memberships change by at most \(\epsilon^{2}\) If it was not met, return to step 2 after setting \(U^{t}=U^{t+1}\). Terminate if it was met.  
The time-complexity of the algorithm is quadratic in the number of clusters \(K\), and methods to reduce the complexity have been proposed [27]. Similar to \(k\)-means, other shortcomings of the algorithm are sensitivity to the cluster initialization and sensitivity to noise, as noted in [28]. Those challenges have been addressed by subsequent works, but each auxiliary method comes with its own shortcomings [28]. Clustering in high-dimensional spaces is another well-known challenge for clustering algorithms in general [29], and fuzzy \(c\)-means in particular [30], due to high sparsity in high-dimensional spaces. Authors of [30] show that fuzzy \(c\)-means centers are likely to converge to the center of mass of the whole dataset in high-dimensional spaces. It remains up to the practitioners to decide on a suitable method for their specific problems.  
For the introduction of federated fuzzy \(c\)-means, we focus on the original formulation and extend it to the federated setting.