esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Literature Review Data Analysis  
Initially, a bibliometric analysis was conducted to identify research trends and determine the most frequently studied emerging drying technologies. Subsequently, separate literature searches were performed for each selected technology. The included studies were then grouped according to the type of drying technology to guide the synthesis process. Only studies reporting outcomes related to the retention of bioactive compounds were considered for qualitative synthesis. Data preparation involved extracting results when available; missing data were not imputed. Results were organized in tables using Excel. No formal heterogeneity analysis was conducted due to the qualitative nature of this review, and sensitivity analyses to assess the robustness of the synthesized results were not performed due to the characteristics of the data and the scope of this study.  
The Scopus database was used to retrieve publications related to emerging technologies in food drying and their implications for bioactive compounds. The search was conducted from 17 January to 2 April 2025, using the terms \"Food\" AND \"Drying\" AND \"microwaves\" OR \"radio frequency\" OR \"ultrasound\" OR \"infrared\" OR \"freeze-drying\" OR \"cold plasma\" AND \"bioactive compounds\" in the fields \"title\", \"keywords\", and \"abstract\". The search was limited to the period from 2014 to 2024. To further narrow the scope to relevant scientific areas, the following subject categories were selected: Agricultural and Biological Sciences, Chemistry, Chemical Engineering, and Engineering. The search returned 111 results for microwaves, 5 for radiofrequency, 76 for ultrasound, 93 for infrared, 297 for freeze-drying, and 8 for cold plasma. However, for comparative analysis, only the 10 most relevant articles were selected for microwave, ultrasound, infrared, and freeze-drying. The total number of articles found was retained for radiofrequency and cold plasma.  
The primary outcomes sought in this review included measures related to the preservation of bioactive compounds, physical and sensory quality attributes, and drying process parameters, when available. Additional variables collected comprised the type of drying technology and characteristics of the food matrices.  
As this review focused primarily on technological applications and bibliometric data rather than clinical outcomes, a formal risk of bias assessment was not performed. As this review did not perform a meta-analysis or quantitative synthesis, no formal effect measures were calculated. Results are presented qualitatively based on reported outcomes.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 5 Introductory Synthetic Tests
### Experiments
#### 5.2.2 Details for the Gaussian Initial Condition  
We want to discuss the jigged curves in Figure 4 now. Every time a numerical solver comes across a discontinuity, over- and undershooting is supposed to occur. This is the so-called Gibbs phenomenon. For more details on this topic, we refer the reader to [24].  
To illustrate the Gibbs phenomenon, we will solve the \(\delta\)-peak (cf. Figure 7) and Gaussian initial conditions (cf. Figure 8) with the undamped wave equation and our three numerical methods.  
In Figure 7, we computed up to 50 iterations (rows) with our three numerical solvers (columns). For all three methods, we observe two waves moving away from each other. Additionally, we notice over- and undershoots occurring in the centre of the plot, exactly where the discontinuities were. Through the inherent damping of the IE scheme, the Gibbs phenomenon is less distinct and is vanishing faster than in the second-order methods.  
In Figure 8, we used a Gaussian curve with \(\sigma=\frac{10}{\sqrt{2}}\) as the initial condition. Again, we solved the wave equation with our models. This time we did not observe any over- and undershoots. We only notice the two waves propagating away from each other.  
Since we started with a curve with a height of one, both second-order schemes produce two waves with a height of a half. The first-order method shows some loss in the height of the two waves, which indicates again the presence of a numerical damping mechanism.  
Figure 7: **(First row)** We see a delta peak as the initial condition. **(Columns)** The solutions of different numerical schemes (left to right) for the IE, CN and \(\text{SOI}_{0}\) schemes are presented. **(Rows 2–4)** We see the solution after different iterations. In the second row, we calculated 10 iterations. The third row shows us the solution after 20 iterations, and in the fourth row, the 50 th iteration is presented. All numerical schemes show these oscillations, called the Gibbs phenomenon. This is more visible in the second-order schemes than in the first-order method. The time increment was set to \(\tau=0.01\).  
One may conjecture that using the Gaussian initial conditions together with the second-order methods could lead to more information stored in the feature descriptor and therefore to better matching results, as more spatial information might have been collected by the spread initial function compared to the \(\delta\) peak.
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## Anomaly Detection Method
### Autoencoder-Based IDS  
An autoencoder is trained by means of a given set of points, i.e., the typical _training set_ of a machine learning experiment. Each point **x** of the training set is fed to the autoencoder, and weight matrices and bias vectors are progressively adjusted in order to minimize the difference between **x** and its reconstruction **z**. After training, the autoencoder will  
Figure 1: Representation of an autoencoder (three hidden layers).  
reconstruct accurately, i.e., low RE, future points \"similar\" to those used for training. Based on this principle, in order to pursue an IDS, we train the autoencoder solely by means of **normal** data points, i.e., records related to network traffic generated by the IoT devices under regular operations, which means the autoencoder learns a latent subspace of normal data points [33]. After training, the autoencoder--embedding a model of the \"normal profile\"--can identify any instance not conforming to the model as a potential intrusion.  
The reconstruction error (RE) is a viable indicator to detect intrusions. Since the autoencoder is trained using only normal data points, it will generate (i) _low_ RE (good reconstructions) for future normal inputs, and (ii) _high_ RE (bad reconstructions) for intrusions. In fact, when the autoencoder attempts to process a data point, i.e., an IoT traffic record, that deviates from the norm, it will generate a high RE because it was never trained to reconstruct intrusions. The approach adopted in this paper falls within the larger scope of **semi-supervised** anomaly detection [34]. Moreover, as for any anomaly detection technique assigning a score to data points (RE in this study), we need a cut-off **detection threshold** to discriminate normal points from intrusions. In particular, intrusion detection is based on the use of the threshold value: the data points producing RE values under the threshold are considered normal, and those with REs above the threshold are marked as intrusions. The value of the detection threshold is an outcome of the training phase. As such, it is inferred from normal data points as described in the following.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 7 Conclusions
### Links  
From the user's perspective, a _link_ between the nodes \(n\) and \(n^{\prime}\) is a promise, or a commitment, that the filesystem at and below \(n\) is exactly the same as at and below \(n^{\prime}\). Executing a command below \(n\) automatically executes the same command on the corresponding node below \(n^{\prime}\). A link system must be _loopless_ meaning that any node has only finitely many other nodes _equivalent_ to it. In particular, \(n\) and \(n^{\prime}\) must be uncomparable nodes.  
If the original filesystem \(\Phi\) contains links, but the users do not have tools or permissions to manipulate them, the following synchronization method works. The update detector, knowing the link structure, unfolds it, and replaces each user command by the collection of all \"hidden\" or \"implicit\" commands on the equivalent nodes. After creating the canonical command set \(A\), it marks the commands which are on equivalent nodes. For the merger \(M\) to be applicable to \(\Phi\) it must also contain, with each command in \(M\), all of its equivalents. This can be achieved by the following modification to the _Iterate_ part of Algorithm 2: if \(\sigma\) is chosen as a winner, then all commands equivalent to it will be winners as well.  
It is an open problem to introduce filesystem commands which manipulate the link structure and can be incorporated into our synchronization process.  
All authors contributed equally to this word. All authors have read and agreed to the published version of the manuscript.  
The research of the second author (L.C.) has been supported by the GACR project number 19-04579S and by the Lendulet Program of the HAS, which is thankfully acknowledged.  
Not Applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CRDT & Conflict-free Replicated Data Type \\ CSCW & Computer Supported Collaborative Work \\ OT & Operational Transformation \\ \(\mathbb{O},\mathbb{F},\mathbb{D}\) & empty, file, and directory content \\ \(\Phi,\mathbb{Y}\) & filesystem \\ \end{tabular}
## 2 Related Works
### Rule-Based and Statistical Methods  
Early studies relied on handcrafted linguistic rules and statistical models. Frequency-based methods prioritized high-occurrence homophones (e.g., \"\(\cancel{R}\)\" over \"\(\cancel{R}\)\") but ignored contextual semantics. Hidden Markov Models (HMMs) utilized phonetic confusion matrices to model contextual transitions (e.g., decoding \"shi\" as \"\(\cancel{I}\)\" in \"\(\cancel{R}\)\" vs. \"\(\cancel{R}\)\" in \"\(\cancel{R}\)\".\") [3]. While effective for common phrases, these methods struggled with dynamic environments and low-frequency homophones.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 5 Evaluation and Analysis
### Diversity of Multipath Generating  
In this section, we evaluate the performance of MDSM in improving multipath diversity and we define the _Multipath Stretch_ to evaluate the diversity among multiple paths. _Multipath Stretch_ could indicate the correlation between paths by reflecting the joint ratio of nodes between multiple paths. The definition of _Multipath Stretch_ of k paths is shown as follows: _Multipath Stretch_ = \(\sum_{1\leq m,n\leq k,m\
eq n}\frac{1}{2c_{k}^{2}}\big{(}\frac{I_{mn}}{N_{m}}+ \frac{I_{mn}}{N_{n}}\big{)}\), where \(J_{mn}\) denotes the quantity of joint router nodes between the mth and nth paths, respectively. \(N_{m}\) and \(N_{n}\) denote the quantity of router nodes on the mth and nth paths. _Multipath Stretch_ indicates that, when the correlation between multiple paths is higher, i.e., they have larger ratio of joint nodes, then _Multipath Stretch_ becomes higher, and it indicates lower multipath diversity. In order to obtain statistics of packets on the router-level, the router in OMNeT++ platform is modified with a module to parse packets so that packets on multiple paths could be traced.  
The evaluation scenario is set as, the video stream would be delivered between origin-destination end hosts using a real-time protocol. In order to get extensive evaluation results, 200 pairs of origin-destination end hosts are selected, and using the statistics of traced packets we calculate the _Multipath Stretch_. We compare _Multipath Stretch_ among the proposed MDSM and other multipath transmission methods. In the work of [16], DR-MP generates multiple paths by deflecting packets from the shortest path. In the work of [15], GA and KSP-MP generates multiple overlay paths with by combining genetic and k-shortest algorithm. In the work of [7], GMS-MP selects the multihomed paths group that has the least correlation. In MDSM, we set the _Deviation Angles_ with two values, \(10^{\circ}\) and \(20^{\circ}\).  
We calculate the CDF (cumulative distribution function) of _Multipath Stretch_ for different multipath generating methods. The CDF is shown in Figure 7 and it could be observed that the ratio of relatively lower _Multipath Stretch_ of MDSM with \(20^{\circ}\)_Deviation Angle_ is larger than other methods; this means that MDSM could obtain higher multipath diversity. This is because MDSM directly uses some objective factors of underlay networks to characterize multipath diversity; in contrast with other methods, they adopt some indirectly inferring approaches. For instance, DR-MP uses deflection that could not directly discover end-to-end disjoint paths, GA and KSP-MP might discover path sharing links, and GMS-MP indirectly infers the correlation using packet transmission features and could not probe path diversity when the features do not appear.
## 2 Materials and Methods
### Architecture and Component Layout  
Figure 3, which is derived from our implementation, shows the overall architecture of a single Data Collaborative competition, showing the placement of the components we designed. Each team's notebook shares the same public IP address, which is an address on an application gateway. The gateway has multiple functions, such as load balancing traffic to the proxy servers, running a Web Application Firewall, and being a TLS endpoint.  
We use reverse proxies on VMs to balance and filter traffic to the notebook containers, restrict the kind of operations permitted on data (requirement #5), and provide additional layers of isolation from the Internet. We made the choice of VMs rather than containers as we ran into issues containerizing the proxy application. In addition, as we were developing the application, we needed to log into the proxy periodically and make changes to configurations--this was much easier to do when they were in VMs rather than containers. Once we had the working configuration, we deployed using proxies in VMs.  
The containers containing teams' notebooks are also on a separate segment. This segment has access lists that restrict where on the Internet the notebooks can connect. Teams can download software to their notebooks from a limited number of software repositories. Another firewall controls access from the containers to the datasets used as the basis for competition. These datasets are also stored in the Cloud on SDI.  
The infrastructure in Figure 3 will be duplicated for each competition. This minimizes the opportunity for datasets from one competition leaking into another. It also reduces the effects of any single configuration mistake from affecting all competitions. This architectural decision matches the Cloud design pattern called the Bulkhead pattern [41].
# Flexural Strength and Acoustic Damage Characteristics of Steel Bar Reactive Powder Concrete
## 4 Conclusions  
In this paper, the steel bar was adopted to prepare SBRPC, and the effect of the number of steel bars on the flexural strength of SBRPC was assessed. Simultaneously, the failure of the SBRPC specimen was real-time monitored by AE technology during its three-point bending test. The AE parameters direct and indirect analysis methods were adopted to investigate the fracture process and failure mode for SBRPC.  
1. The flexural strength of SBRPC improves as the steel bar number increases. The flexural strength of SBRPC-5 is 37.32 MPa, which is 33.2% higher than that of RPC. The cooperation of steel bars and RPC can effectively improve the performance of RPC against bending stress;
2. The AE parameters can be used to describe the failure procedures of SBRPC. The failure stage of SBRPC could be delimited accurately, considering hits and cumulative counts;
3. AE amplitude will suddenly change at the critical diacritical point in the failure period, which indicates that AE amplitude could be applied for an early warning of damage;
4. The addition of steel bars would affect the distributions for shear and tensile cracks in SBRPC. In addition, the damage mode of the structure could be judged through RA, AF and RF, which provide a reference for practical engineering applications;  
Figure 10: The variation curve of RF: (**a**) RPC; (**b**) SBRPC-1; (**c**) SBRPC-3; (**d**) SBRPC-5.  
The acoustic damage characteristics of a fully reinforced RPC beam will be investigated in future studies.  
Conceptualization, H.W. and W.W.; methodology, H.W. and W.W.; validation, J.X.; formal analysis, T.Y., W.W.; investigation, T.Y. and J.X.; writing--original draft preparation, W.W.; writing--review and editing, and H.W.; project administration, H.W. and W.W.; funding acquisition, H.W. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by China Postdoctoral Science Foundation (grant number: 2021T140262). This research was also funded by Nanning Excellent Young Scientist Program (grant number: RC20180108), Nanning Excellent Young Scientist Program and Guangxi Beibu Gulf Economic Zone Major Talent Program (grant number: RC20190206), Science & Technology Base & Talent Special Project of Guangxi Province (grant number: AD19245152), \"Yongjiang plan\" of Nanning leading talents in innovation and Entrepreneurship (grant number: 2018-01-04).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to appreciate anonymous reviewers for their constructive suggestions and comments to improve the quality of the paper. Thanks to [PERSON] for technical support in the testing procedure and data processing.  
The authors declare no conflict of interest.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 3 Field Experiment Locations and Configurations  
Experiments were carried out to ascertain the accuracy of the IoT tide gauges compared with established tide gauges and also to demonstrate the versatility of an IoT tide gauge network with its different and complementary hardware and software configurations.  
Two different locations were chosen (see Figure 3):  
* Experiment 1--a sheltered location beside the dock wall at Liverpool (near to the Sandon Half-tide Dock entrance) on the tidal River Mersey is typical of most tide gauge installations;
* Experiment 2--a beach location at Blundellsands, Crosby, where there is a wide intertidal beach and sea defenses to protect residential areas, with more exposure to waves.  
In this macro-tidal region, the tidal range can exceed 10 m at spring tides; at Liverpool, the largest tidal range is 10.35 m, mean high water spring (MHWS) is 9.39 m above local chart datum (CD) while mean low water spring (MLWS) is 1.12 m CD [21]. The local chart datum is 4.93 m below the land-based ordance datum Newlyn.
## 3 Experimental Results  
### Self-Comparison Measurement Error Cancellation using the Drift-Insensitive Self-Comparison Method  
The SCME is evaluated by measuring the frequency difference between the two identical clock loops (the systematic parameters of \(R_{1}\) and \(R_{2}\) are the same) with the TSC and DISC methods, respectively. If there are no drifts at all, the expected frequency difference between \(R_{1}\) and \(R_{2}\) is zero. Figure 2a shows the frequency difference between \(R_{1}\) and \(R_{2}\) by TSC and DISC methods, respectively, without changing any experimental parameters but the detection order. The SCME of the TSC method is (240 \(\pm\) 34) mHz, while the SCME measured using the DISC method is (\(-15\pm 16\)) mHz, where the measurement uncertainty is given by the last point of their respective total Allan deviation of the self-comparison instabilities. With the DISC method, the SCME is consistent with zero, indicating that the DISC method almost completely removes the frequency offset caused by the drifts even if the clock laser frequency drift is nonlinear and dramatically change as shown in the inset of Figure 2a. Furthermore, the comparison data shown in Figure 2a also demonstrate that the magnitude of the frequency difference fluctuation with the DISC method is significantly smaller than the TSC method. Therefore, the self-comparison instability of the DISC method is lower than the one using the TSC method. As shown in Figure 2b, the self-comparison instability of the TSC method is 5.6 \(\times\) 10\({}^{-15}\)\(\tau^{-0.5}\) (\(\tau\) is the averaging time), while the self-comparison instability of the DISC method is 3.1 \(\times\) 10\({}^{-15}\)\(\tau^{-0.5}\), indicating that the DISC method cannot only cancel the SCME, but also improve the measurement accuracy when the averaging time exceeds 100 s. In order to eliminate the frequency offset caused by the drifts, we also try to use the AOM to compensate the frequency drift of the clock laser in the TSC method, where the frequency sweeping rate is calculated every 40 s. However, the frequency compensation method cannot completely remove the frequency offset and the residual frequency offset is more than 30 mHz which is caused by the rapidly changed drift rate in our system.
## 2 Materials and Methods
### Signal Processing
#### 2.5.2 Respiratory Inductance Plethysmography  
RIP is a non-invasive method of monitoring pulmonary function. By placing elastic bands with embedded coils around the RC and ABD walls, the output is proportional to the variation of the cross-sectional area of the surfaces surrounded by those bands [23; 34].  
The respiratory signals were first denoised with a finite response band-pass filter with cut-off frequencies of 0.15 and 0.45 Hz [19]. To be able to separate the actual signal in the low frequency band from the tissue artifacts in the high-frequency band and ambient noise in all frequency bands, the signal was further decomposed with masked sift empirical mode decomposition (EMD). EMD is a processing method that is able to decompose nonlinear signals with multiple components into intrinsic mode functions (IMF's), containing a defined frequency and amplitude through the Hilbert Transform [35; 36]. When signals are very noisy, traditional EMD is not able to correctly separate components of different time scales, leading to mode-mixing. One of the proposed methods to solve this is injecting a masking signal into the original, preventing lower frequency components from contaminating the wrong IMF [36]. The signal was reconstructed with only IMF 4 as it presented the clearest respiratory pattern. Both signals from the RC and ABD belt were subjected to this procedure.  
From the filtered signal, respiratory rate (RR) in breaths per minute was determined through differentiation of the signal and determining its zero-crossings [37]. RC percentage (RC%) is defined as the RC's contribution to tidal volume as a percentage of the sum of both RC and ABD volume variation [38]. The correlation between RC and ABD signalswas determined using a rolling window [39] of 400 samples. Finally, phase synchrony (PS) between the two signals from each belt was determined by applying the Hilbert transform to both signals, extracting the imaginary part (phase) from each of them, and finally, subtracting their imaginary parts [19].