esa-sceva/satcom-chunk-collection huggingface dataset:
# Motion Response of the Submersible Underwater Towed System as Cable Length Changes
## 2 Computational Theory
### Solution Methods  
For towed cables with changing lengths, the number of discrete segments of the towed cable and the number of effective equations of motion need to be dynamically adjusted during the calculation process. Cable segments and nodes are inserted during a cable deployment maneuver and removed during a cable retraction maneuver. The cable length \(s\) is determined by the following equation at any given time:  
\[s(t)=s_{0}-\int_{0}^{t}\frac{v_{\mathrm{d}\tau}}{(1+\varepsilon)d\tau} \tag{7}\]  
where \(s_{0}\) represents the initial length of the towed cable, and \(\varepsilon\) represents the local strain.  
When \(s_{j-1}+\Delta s<s\leq s_{j}+\Delta s\), the node ordinal number \(i\) at the towed cable node \(N\) can be calculated using the following equation:  
\[N\Leftarrow j \tag{8}\]where \(j\) ranges from 1 to \(N\), \(\Delta s=\frac{l_{N-1/2}}{3}\sim\frac{l_{N-1/2}}{2}\). When \(i=N,s_{N}=s(t)\).  
As the flexible towed cable does not account for bending and torsional stiffness effects, Equation (6) can be simplified to:  
\[s(t)\approx s_{0}-\int_{0}^{t}v_{\rm dr}d\tau \tag{9}\]  
After each time step, the values of s and \(N\) can be determined for the current length of the towed cable. The current s and \(N\) values are used in Equations (8) and (9) to calculate the arc length and node sequence number at the tow cable position. Continue performing the calculations mentioned above until the towed cable is fully retracted or deployed.  
Combining the differential definition of velocity \(\mathbf{v}=dx/dt\) and associating it with Equation (3) allows the equations of motion of the towed cable to be controlled.  
\[\left\{\begin{array}{l}\frac{d\dot{\mathbf{x}}_{i}}{dt}=\mathbf{M}_{i} \ ^{-1}\cdot\mathbf{F}_{i}\\ \frac{d\dot{\mathbf{x}}_{i}}{dt}=\dot{\mathbf{x}}_{i}\end{array}\right.\ \left(i=0,\cdot\cdot \cdot,N-1\right) \tag{10}\]  
The equations of motion control for the underwater towed system of a submersible with varying cable length can be obtained by coupling Equations (3), (5) and (10), which can be solved by using the Runge-Kutta method under the given initial value conditions.
## 4 Narrowband Interference Suppression Algorithm Based on SVD with Guidance Signal  
The first noise reduction obtains the constituent elements of the guidance signal through FSWT and the improved Rife method. Through the SVD algorithm with the guidance signal, the noisy PD signal after suppressing the narrowband interference will be obtained.
# Assessment of Novel Modal Testing Methods for Structures Rotating in Water
## 4 Results
### Comparison of the Natural Frequencies and Mode Shapes of the Non-Rotating Disk in Air and Water  
Figure 17 shows the spectra of the structural responses measured by different sensors mounted at different locations when the disk was not rotating in air and water. All the spectra were normalized using their maximum values in the range of 0 to 100 Hz. All sensors detected the frequency peak associated with the 1 ND at 39.8 Hz in air and the corresponding frequency peak at 13.1 Hz in water. Among all sensors, the 2 ND frequencies at 69.1 Hz in air and at 22 Hz in water were only detected by SG2_6. Both natural frequencies decreased in value when the disk was submerged in water, which was due to the added mass effect. The peak corresponding to the 1 ND at 39.8 Hz was lower in amplitude than the peak corresponding to the 2 ND at 69.1 Hz measured by SG2_6 in air. Nonetheless, the peak corresponding to the 1 ND at 13.1 Hz was higher in amplitude than the peak corresponding to the 2 ND at 22 Hz measured by SG2_6 in water. This may indicate that the mode shape in water had rotated relative to that in air. Consequently, SG2_6 was close to a nodal diameter of the 1 ND in air, whereas it was close to a nodal diameter of 2 ND in water. [PERSON] et al. [36] also observed that the mode shapes in water may change with respect to those in air.  
Table 3 shows the numerical and experimental natural frequencies of the 1 ND, 0 NC, 2 ND, 3 ND, 4 ND, 1 NC and 1 NC+1 ND in air and water and the corresponding frequency reduction ratios (FRRs), calculated as:  
\[FRR=(f_{air}-f_{water})/f_{air} \tag{3}\]  
where \(f_{air}\) and \(f_{water}\) are the natural frequencies in air and water, respectively. The circular mode shapes always presented higher FRRs than the diametral mode shapes. Additionally, those with a lower number of NCs or NDs also presented higher FRRs. For this reason, the 1 ND presented a lower frequency than the 0 NC in air but a higher frequency in water. Therefore, the NC modes, especially those with few nodal circles, accelerated more water resulting in higher added masses. Finally, we can also see that the numerical and experimental FRRs were in close agreement.  
Figure 16: Maximum normalized spectra of the STFT measured from several sensors when the disk (non-rotating and rotating) was excited with a chirp in the range of 0–100 Hz in water.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 1 Introduction
### Opportunistic Routing  
The rationale behind opportunistic routing [12; 13] is to exploit the broadcast nature. Indeed, when nodes which are closer to the destination overhear a transmission, they are allowed to participate in forwarding the packet. A side effect is that energy consumption may be spread more evenly over multiple paths, making also possible a forwarding strategy avoiding intermediate forwards with low battery levels. Moreover, latency is improved, along with capacity and fault tolerance.  
Using an opportunistic routing strategy, one critical question is which neighboring node forwards a given packet. The answer to this question is not straightforward because: (i) nodes have asynchronous sleep schedules, (ii) data arrival time is not necessarily predefined; when multicast is used, packet duplication occurs, and (iii) finally, complex methods do not fit low capacity sensors and delay-sensitive applications. Solutions vary from selecting the first awake node [14], or conducting a more or less complex selection process [15]. For our case, our proposal ODYSSE can wait for a specific duration for potential forwarders and can select the best one with respect to many parameters such as residual energy. We will also prove through theoretical modelling that selecting the first available forwarder independently of the provided progress towards the source is the optimal choice from delay perspective under some conditions.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Multi-Speaker Scenario
#### 4.2.4 Enhanced Adaptation Techniques
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adaptation**} & \multirow{2}{*}{**Dropout**} & \multicolumn{2}{c}{**Speaker Count**} & \multicolumn{2}{c}{**Speaker Turn Detection**} & \multicolumn{4}{c}{**Overlap Speech Detection**} \\ \cline{3-10}  & & **SCE** & **Acc.** & **P** & **R** & **F1** & **P** & **R** & **F1** & **Acc.** \\ \hline \multirow{3}{*}{CH1 all} & 0.1 & 0.348 & 73.6 & 62.41 & 46.31 & 53.16 & 64.9 & 42.4 & 51.3 & 87.95 \\  & 0.3 & 0.332 & 73.2 & 61.94 & 48.07 & 54.13 & 65.6 & 42.0 & 51.2 & 88.03 \\  & 0.5 & 0.436 & 66.0 & 60.20 & 45.06 & 51.57 & 69.4 & 19.4 & 30.4 & 86.67 \\ \hline \multirow{3}{*}{CH1 2–4 spk} & 0.1 & 0.320 & 74.4 & 62.63 & 47.51 & 53.97 & 64.4 & 44.5 & 52.6 & 88.02 \\  & 0.3 & 0.312 & 74.8 & 62.68 & 49.41 & 55.26 & 66.5 & 42.0 & 51.5 & 88.16 \\ \cline{1-1}  & 0.5 & 0.364 & 68.0 & 59.36 & 47.70 & 52.85 & 61.8 & 25.9 & 36.5 & 86.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison of different dropout rates across multiple metrics for MFB features. Results are shown for both complete (CH1 all) and restricted (CH1 2–4 spk) adaptation sets. P indicates Precision and R indicates Recall. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adaptation**} & \multirow{2}{*}{**Dropout**} & \multicolumn{2}{c}{**Speaker Count**} & \multicolumn{2}{c}{**Speaker Turn Detection**} & \multicolumn{4}{c}{**Overlap Speech Detection**} \\ \cline{3-10}  & & **SCE** & **Acc.** & **P** & **R** & **F1** & **P** & **R** & **F1** & **Acc.** \\ \hline \multirow{3}{*}{CH1 all} & 0.1 & 0.444 & 64.0 & 56.20 & 37.97 & 45.32 & 42.5 & 45.6 & 44.0 & 82.64 \\  & 0.3 & 0.384 & 70.0 & 58.86 & 38.69 & 46.69 & 43.3 & 45.1 & 44.2 & 82.96 \\  & 0.5 & 0.592 & 57.2 & 55.33 & 36.10 & 43.69 & 41.2 & 44.3 & 42.7 & 81.32 \\ \hline \multirow{3}{*}{CH1 2–4 spk} & 0.1 & 0.376 & 68.4 & 59.84 & 40.54 & 48.33 & 45.2 & 47.0 & 46.1 & 83.55 \\  & 0.3 & 0.384 & 68.8 & 60.41 & 41.06 & 48.89 & 50.5 & 41.3 & 45.4 & 85.16 \\ \cline{1-1}  & 0.5 & 0.464 & 62.8 & 57.06 & 38.17 & 45.74 & 44.3 & 37.4 & 40.6 & 83.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of different dropout rates across multiple metrics for ECAPAP-TDNN \(\oplus\) MFB features. Results are shown for both complete (CH1 all) and restricted (CH1 2–4 spk) adaptation sets. P indicates Precision and R indicates Recall.
# Co-Operatively Increasing Smoothing and Mapping Based on Switching Function
## 6 Conclusions  
This paper proposes a collaborative localization method based on the fusion of IMU-camera and UWB data, which can use the co-operation information between drones to improve the localization accuracy and robustness. We designed a switching function to deal with the outliers and wild values of the IMU-camera data and UWB data, thus suppressing or eliminating their influence. Furthermore, a series of simulation experiments were conducted to verify the effectiveness and superiority of the proposed method.  
Our study also has the following shortcomings and directions for improvement: This paper assumes that each drone is in a two-dimensional plane, and further innovative calculations can be made for three-dimensional space in the future. In addition, we used a switching function based on MD to deal with IMU-camera data outliers and a switching function based on the distance sum to deal with UWB data outliers, which may have required more memory consumption and communication bandwidth. Moreover, a graph optimization algorithm based on the Gauss-Newton method was used to solve the collaborative localization problem, which may have required more iteration time and convergence time.  
Future work can be improved and extended as follows: The use of different types and configurations of sensors, such as lidar, can be considered in order to increase the diversity and reliability of the data. Different types and configurations of communication modules can be used to reduce the cost and complexity of communication. Finally, the use of different types and forms of switching functions, such as Kalman filters, particle filters, neural networks, etc., can be considered in order to improve the detection and processing ability of data outliers and wild values.  
Conceptualization, R.W. and Z.D.; Investigation, R.W.; Methodology, R.W.; Validation, R.W.; Visualization, R.W.; Writing--original draft, R.W.; Writing--review and editing, R.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 4 Methods
### Deep Learning  
Deep learning is a subfield of machine learning that focuses on using neural networks with multiple layers to model complex patterns in data. Recurrent Neural Networks (RNNs) are a specific type of neural network used within deep learning, particularly suited for handling sequential data. By maintaining an internal state that encodes the setting of previous input data, RNNs can be used when temporal relationships within the data are important [29]. However, despite their advantages, RNNs usually cannot handle long-term dependencies due to problems like vanishing and exploding gradients. As the sequences are longer, the RNNs can lose important information presented earlier in the input, which limits their performance in deeper sequence contexts [30].  
Starting from [31], many theoretical and experimental studies have been conducted on the Long Short-Term Memory (LSTM) network in order to address the limitations of RNNs. LSTM architecture is specifically designed to learn long-term dependencies in sequence data [32]. It consists of the memory cell, input gate, forget gate, and output gate, which work together, selectively maintain, or discard information. This approach to control how much of the past information should be forgotten improves the performance of predictions with respect to the broader context of the data [33]. Due to their properties, LSTMs have been successfully applied to solve problems in Natural Language Processing [33], finance [34], healthcare [35], or in navigation in complex and dynamic environments [36, 37, 38].
## 5 Open Research Issues and Challenges
### Power Consumption and Energy Harvesting  
In CRBANs, the replacement of batteries is not practical. CR sensors are power-constrained devices. In addition to the energy needed for normal network operations (such as route discovery, transmission/reception of data packets and data processing), CR sensors also require power for spectrum sensing, channel negotiation and frequent spectrum handoffs. Energy harvesting will have a significant impact on the lifetime of future CRBANs. Common sources of energy harvesting include mechanical, thermal, electromagnetic, natural energy and energy from the human body. Energy harvesting devices available nowadays can efficiently capture, accumulate and store energy to power sensor nodes [30]. The source of energy can be utilized by means of an energy-efficient protocol to extend network life.
# Blockchain Technology and Artificial Intelligence Together: A Critical Review on Applications
## 5 AI and Blockchain Use Cases
### Life Sciences  
The use of AI and blockchains in the pharmaceutical business has the potential to greatly increase the success rate of clinical trials while also boosting transparency and traceability across the medication supply chain. The integrity of data, automation of trial participation and data collecting, monitoring of patients, management of permission, and openness of data are all made possible by combining powerful data analysis with a decentralized framework for clinical trials.
# Evaluation and Economics of Shale Gas Reserves in the Flysch-Eocene Formation of the Jaca Basin
## 4 Discussion
### Environmental Challenges
#### 4.3.2 Potential Water Contamination  
Although some of the chemicals used are harmless to the environment [44,45], others are classified as toxic and carcinogenic and are more or less likely to affect human health. [6,11,46]. Safety measures must be taken during transportation, storage, and disposal at the drilling sites [11,43], and the volumes of liquid and solid wastes generated must be properly treated to avoid contaminating the soil and surface water.  
The production of shale gas without applying good practices can lead to environmental contamination, for example, the propagation of fractures can lead to the discharge of contaminated water into surrounding aquifers [47,48,49,50,51,52]. For direct contamination of underground drinking water aquifers to occur, it is necessary for the hydrofractures to propagate several thousand feet beyond the upper boundary of the target formation through many rock layers.  
Another factor to consider and assess is the risk of well integrity failure, with particular attention to the surrounding cement and casing [43,53]. If the annulus is poorly sealed, natural gas, fracturing fluids, and formation water with high TDS concentrations can communicate directly from outside the target formation wellbore with intermediate rock layers and drinking water aquifers [11,43].  
Several tools are available to help producers and regulators minimize the risk of cement and casing failures. The American Petroleum Institute (API) develops and updates standards and recommended practices for oil and gas exploration and production activities.
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 4 Experiments
### Parameter Setting  
Experiments were carried out to compare the behaviour of two clustering algorithms: Quickbundles and spectral clustering.  
In order to validate results, ten different experiments with batches of 2000 trajectories were conducted for each algorithm and distance metric. A total of 80 experiments were run (10 per pair algorithm-distance metric). The mean Silhouette Coefficients of each set of the 10 experiments carried out was analyzed.  
To build each experimentation batch, a different approach was set depending on the data source:  
* Kaggle data: since there were 1,704,757 trajectories, each experiment run for an algorithm-distance metric pair comprised 2000 different trajectories each time, guaranteeing no repetition of trajectories in each experiment.  
* Cross-CPP data: data from only 5852 remained for experimentation after filtering, and each experiment was run over a subset of 2000 randomly selected trajectories, unavoidably repeating trajectories between experiments.  
In each experiment, the hyperparameters of QuickBundles were adjusted via a 10-fold cross-validation approach. The adjusted hyperparameters were:  
* Resampling factor: number of coordinates to which each trajectory will be resampled (as the algorithm requires all trajectories to have the same number of coordinates).
* Neighboring threshold: distance at which two trajectory points are considered neighbors.  
The spectral clustering implementation used the radial basis function (RBF) kernel for its construction. It is to note that, for the Spectral Clustering algorithm, only the number of clusters hyperparameter was checked. Results are displayed for its maximum silhouette coefficient value.
## 1 Introduction  
Denial of service (DoS) is a cyber-attack that an adversary launches on a network service by sending a huge number of service requests, in various shapes and forms, beyond what the server can handle. The attack disrupts the normal functioning of the server and, depending on the severity, can result in a slowdown of performance or, ultimately, a complete shutdown. Distributed denial of service (DDoS), a more dangerous variant of DoS, attacks are often carried out by utilizing botnets from many different compromised devices that send simultaneous and coordinated service requests to the targeted server. DDoS, which was initially a tool used by hacktivists, has effectively been weaponized by cyber criminals to disrupt and export organizations in ways that shut down operations and reduce accessibility to existing networking infrastructure capability.  
The first reported DoS attack was perpetrated against Panix, the third-oldest internet service provider (ISP), in September of 1996 [1]. The first reported DDoS attack brought down e-commerce sites of large companies like Amazon, Yahoo!, eBay, Dell, and others in February of 2000 [2]. Despite numerous technological innovations devised to prevent and countermeasure DDoS attacks, according to NETSCOUT's DDoS Threat Intelligence Report, there were approximately 7.9 million DDoS attacks in the first half of 2023, which is a 31% year-over-year increase.  
Extensive research has been carried out to develop methods for detecting and preventing such attacks [3]. Typically, intrusion detection systems (IDSs) are based on known attack signatures as well as significant and unique heuristics. Unfortunately, these techniques are becoming less effective against these attacks, making them inadequate in detecting zero-day (i.e., unseen) attacks that have novel or hidden attack patterns with an unknown signature. Machine learning (ML)-based techniques are being employed to make detection more accurate and effective, thereby improving the reliability and resiliency of our networking infrastructure.  
Three basic machine learning techniques, namely supervised, semisupervised, and unsupervised techniques, are used for differentiating network packets with anomalous content from those with normal content. Supervised methods use labeled datasets to differentiate anomalous instances from normal instances. Unsupervised methods use unlabeled datasets and create different clusters. Novelty and outlier detection strategies create \"inlier\" clusters with normal instances, where anomalous instances stay as outliers [4]. Semisupervised methods use small portions of labeled datasets for initially training the models and then continue with large amounts of unlabeled datasets.
# A Dynamic Decision Support System for Selection of Cloud Storage Provider
## 5 Results and Discussion
### Ranking Validation by Using AHP
Table 24 shows complete results for the criteria and their sub-criteria weights. In order to test the final calculated values of the sub-criteria, sums of values must be equal to the value of the criterion. For example, the value of the operating system criterion is 14.70, its sub-criteria values are 8.90, 2.23, and 3.57, which sum to 14.70 (equal to the criteria weight). This table will be used in all further calculations. The cloud storage provider's comparison provides the values and their ranges (as shown in Table 5). Ranking of cloud service providers will be calculated on the basis of these parameter values and their corresponding weights from Table 25. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Staring** & **Yes** & **No** & **Priority** \\ \hline
**Yes** & 0.90 & 0.9 & 90.5\% \\
**No** & 0.09 & 0.1 & 9.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Values and Priorities for Sharing. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CPU** & **Rep** & **OS** & **Mobile Access** & **Sync** & **Sharing** & **Free St.** & **Trial** \\ \hline
33.00\% & 27.30\% & 14.70\% & 8.70\% & 5.00\% & 6.20\% & 2.80\% & 2.20\% \\
24 Core & 250,000 GB & Linux 61.1\% & Yes 91\% & Yes 90\% & Yes 91\% & 50 GB & 180 Days \\
54.8\% & 39.6\% & & & & & 54.0\% & 54.0\% \\
100,000 GB & Win 13.8\% & & No 9\% & No 10\% & No 9\% & 2 GB & 60 Days \\
8 Core 28.1\% & & & & & & 8.5\% & 25.5\% \\
4 Core & 50,000 GB & Mac 17.5\% & & & & 10 GB & 30 Days \\
11.5\% & 13.1\% & & & & & 12.4\% & 12.4\% \\
1 Core 5.6\% & 25,000 GB & & & & & 25 GB & 15 Days \\
9.1\% & & & & & & 25.2\% & 8.2\% \\
10,000 GB & & & & & & \\
5.6\% & & & & & & & \\
1000 GB 2.7\% & & & & & & \\
500 GB 2.1\% & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Weights of all Parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trial Period** & **180 Days** & **60 Days** & **30 Days** & **15 Days** & **Priority** \\ \hline
**180 Days** & 0.60 & 0.25 & 0.48 & 0.83 & 54.0\% \\
**60 Days** & 0.20 & 0.08 & 0.03 & 0.02 & 8.5\% \\
**30 Days** & 0.12 & 0.25 & 0.10 & 0.03 & 12.4\% \\
**15 Days** & 0.09 & 0.42 & 0.39 & 0.12 & 25.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Priority Values for Trial Period. The above results show that performance attributes are major contributors in this process. All parameter values are taken from the previous section's vendor comparison table. Results are calculated for performance ranking and cost-benefit relationships. Parameters used are number of CPU cores, repository size in gigabytes, sharing (both file sharing and link sharing), operating system (Linux, Windows, or Mac), mobile access (if Yes), free storage in gigabytes (a survey of the internet showed the minimum value is 5 GB), and synchronization (users' device synchronization with their cloud account).