esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
All tibias with the implants (Figure 5) were removed and immediately immersed in a 4% formaldehyde solution. _Implant stability measurement:_ The stability of all implants was measured using the Oostell (Oostell AB, Gothenburg, Sweden) device. A smartphone sensor was installed in each implant and a controlled torque of 10 Ncm was applied, as recommended by a recent previous study [24]. Measurements were performed in two directions (Figure 6): Proximo-distal and antero-posterior; and, a mean was made for each implant sample. Analysis was conducted three times: Immediately after the implant installation (Time 1), in the first animal lot euthanized after 15 days (Time 2) and, in the second animal lot euthanized after 30 days (Time 3). Figure 4: Representative schematic image of the drill sequence used for the osteotomy in all groups. Figure 5: Representative image of both tibias after the soft tissue was retrieved and removed. _Removal torque measurement:_ Five samples of each group and each time (15 and 30 days) were used to measure the removal torque value (RTv). The analysis was performed in a computed torquimeter machine (Torque BioPDI, Sao Paulo, Brazil). The blocks (bone and implant) were fixed in the apparatus and the maximum value of removal torque was measured and tabulated. Figure 7 shows the machine during the assay realization. _Histomorphemetric and histological analysis:_ Three days after fixation in formaldehyde solution, the samples were washed in running tap water per 12 hours and then gradually dehydrated in a progressive series of ethanol solution (60% to 100%). After the dehydration, the blocks (bone with the implant) were embedded in historesin (Technovit 7200 VLC, Kultzer & Co, Wehrbein, Germany), polymerized, and cut in the central region of the implants using a metallographic cutter machine (Isomet 1000; Buehler, Germany). Then, the samples were polished using a sequence of abrasive paper (180 to 1200 mesh) in a polishing machine (Polipan-U, Panambra Zwick, Sao Paulo, Brazil). The samples were stained using the picosirus hematoxylin staining technique. Images using an optical microscopy (Nykon E200, Tokyo, Japan) were obtained around all samples and, the percentage of bone-to-implant contact (BIC%) and bone area fraction occupancy (BAFO%) inside of the threads were measured using the Image] program (National Institute of Health, Bethesda, MD, USA). For the BIC% calculation, the total perimeter around the implant was considered 100% and, then, the areas where the bone is in contact with the implant surface were measured. Whereas, for the BAFO% calculation, the total area of threads was measured for the implant model used, and, then, the percentage of this area of threads occupied by the bone. _Statistical analysis:_ The ANOVA one-way statistical test was used following [PERSON]'s multiple comparison test to determine individual difference among groups. All analyses were performed using GraphPad Prism version 5.01 for Windows (GraphPad Software, San Diego, CA, USA). When \(p<0.05\), the differences were considered significant.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 3 Proposed Low-Complexity Multibeam Wideband Transmit Beamformer
### Minimax Design of the 2D Sparse FIR Filter  
The design of \(H(z_{x},z_{ct})\) is presented in this subsection, adapting the 2-D sparse FIR filter design method proposed in [52]. We choose the filter design method in the minimax sense to reduce the side-lobe levels. To this end, we express the frequency response of \(H(z_{x},z_{ct})\) as  
\[H(e^{i\omega_{x}},e^{i\omega_{ct}})=\underset{n_{x}=\frac{-N_{x}}{2}}{\sum} \underset{n_{c}=\frac{-N_{c}}{2}}{\sum}h(n_{x},n_{c})e^{-j(n_{x}\omega_{x}+n_ {c}\omega_{ct})}, \tag{11}\]  
where \(h(n_{x},n_{c})\) is the impulse response of size \((N_{x}+1)\times(N_{c}+1)\) (order \(=N_{x}\times N_{c}\)). The order of the filter depends on the number of broadband antennas in the ULA and the expected accuracy of the beams produced by the transmit beamformer. \(H(z_{x},z_{ct})\) is designed as a zero-phase filter ([53], Chapter 3). The impulse response of the filter is centrosymmetric in this case, i.e., \(h(n_{x},n_{c})=h(-n_{x},-n_{c})\). Therefore, considering the centrosymmetric property, \(H(e^{i\omega_{x}},e^{i\omega_{ct}})\) can be simplified as  
\[H(e^{i\omega_{x}},e^{i\omega_{ct}})=h(0,0)+\sum_{n_{x}=1}^{N_{x}} 2h(n_{x},0)\cos(n_{x}\omega_{x})\] \[+\sum_{n_{x}=\frac{-N_{x}}{2}}^{N_{x}}\sum_{n_{c}=1}^{N_{c}}2h(n _{x},n_{c})\cos(n_{x}\omega_{x}+n_{c}\omega_{ct}), \tag{12}\]  
which is represented in vector form by  
\[H(e^{i\omega_{x}},e^{i\omega_{ct}})=\mathbf{c}(\omega_{x},\omega_{ct})^{\text{ T}}\mathbf{h}. \tag{13}\]  
where,  
\[\mathbf{h}=\left[h(0,0),2h(1,0),\ldots,2h\left(\frac{N_{x}}{2},0 \right),2h\left(\frac{-N_{x}}{2},1\right),\right.\] \[\left.\ldots,2h\left(\frac{N_{x}}{2},\frac{N_{c}}{2}\right) \right]^{\text{T}}, \tag{14}\]  
and  
\[\mathbf{c}(\omega_{x},\omega_{ct})=\left[1,\cos(\omega_{x}), \ldots,\cos\left(\frac{N_{x}}{2}\omega_{x}\right),\right.\] \[\left.\cos\left(\frac{-N_{x}}{2}\omega_{x}+\omega_{ct}\right), \ldots,\cos\left(\frac{N_{x}}{2}\omega_{x}+\frac{N_{c}}{2}\omega_{ct}\right) \right]^{\text{T}}. \tag{15}\]  
In [52], a two-phase design method for 2D FIR digital filters in weighted minimax design applicable for filters having quadrantally symmetric impulse responses was presented. With (13), this approach can be used to design \(H(z_{x},z_{ct})\) despite its centro-symmetric impulse response.
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 5 Results and Discussion
### Ablation Analysis
#### 5.2.2 The Influence of Word Order Information  
The parameter alpha is the proportion of word order information \(P_{ij}\) in the multi-layer semantic network. Figure 8 shows the learning curve of the sentence representation method in this chapter with alpha change on the SNLI dataset. When the number of iterations is equal to 1, the smaller the alpha value, the higher the corresponding SNLI test data set's accuracy. With the increase of the number of iterations, the model's overall learning rate curve first increases and decreases with the increase of alpha. When the learning curve tends to be stable, and the alpha value is equal to 1.5, the model's performance on the SNLI test set is the best.  
From the above analysis, we can see that alpha has a great influence on sentence representation. When alpha is equal to 2, the accuracy rate is 85.4%, which is 0.7% lower than the best performance of 86.1%. It also shows that the word order information is of great significance to the semantic representation.  
Figure 7: Accuracy of different semantic levels on SNLI dataset.
## 2 Simulation-Based Digital Twin Framework
### Implementation
#### 2.2.4 The Multi-Agent System  
The original version of the MAS was developed using Java Agent DEvelopment Framework (JADE) but no communications with external applications were allowed. Furthermore, products used to be manually added, which did not match the desired working mode for this project, where they ought to be inserted periodically. Consequently, in order to ensure the synchronisation between the manufacturing unit and the newly-created DT, a new Java class and a GUI were added to the initial project.  
The _restClient_ class contains auxiliary data structures used to store the system's current status, the remaining production plan and statistics data. Aside from that, it includes methods which interact with the aforementioned variables, keep the database file up to date, using the API, and compute the real KPIs. Furthermore, a _Ticker Behaviour_, from JADE, was used to ensure that an attempt to add a product to the network is made every five seconds.  
In order to provide an intuitive way of describing the production plan to the MAS, a GUI with two sections was created. The first section, dedicated to the production pattern, is where the products' types are inserted and sorted into the desired order, whereas the second one is where the plan size is defined and the instruction to create the plan is given. This graphical interface is represented in Figure 18.  
Figure 17: _getProducts_ method’s algorithm.  
In order to add a new product type, the amount of Brush Up and Brush Down skills has to be written in the respective text fields and the \"+\" button has to be pressed. In case the user mistakenly inserts a product type, it can be removed using the \"-\" button. Moreover, there are two buttons which move the selected product type up or down in the displayed pattern.  
After describing the intended production pattern, the total amount of products shall be written into the respective text field and the \"Create Plan\" button must be pressed. If the content of the text field is numeric and the pattern does not contain any invalid product types, according to the ontology, the plan is created and the button is disabled so as to avoid a new attempted creation. In the example from the previous figure, the production plan would consist of a complete pattern, i.e., ProductType1 \(\rightarrow\) ProductType2 \(\rightarrow\) ProductType3, followed by a product of type one.  
Once the plan has been defined, the MAS becomes autonomous and, inclusively, generates a _csv_ file where the entry and exit timestamps of each processed product are stored. These can be used in case the user wants to calculate KPIs that are not automatically computed.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Reduced Overhead and Delay  
After clustering, not every node needs to forward data to the BS, which helps to mitigate network traffic overload, data packet collisions, and retransmissions. It also reduces frequent channel switching, and hence, reduces delay. Frequent channel switching in a CR network was described by [PERSON] et al. [46]. Clustering stabilizes the network topology at the sensor level, and reduces topology maintenance overhead.  
Besides the applications of clustering mentioned above, clustering can be employed to perform attacker detection [47]. Because CR-WSN utilizes idle spectrum resources, it is vulnerable to selfish attack. In the attack, one or more selfish CR-WS nodes occupy spectrum that they are not using [48; 49; 50].
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 6 Existing Methods and Proposed Solution
### Tabular RL  
Tabular Q-learning is a solution used for interference mitigation. In this method, the state-action value function is denoted by \(Q_{\pi}(s_{t},a_{t})\). The Q-learning update process is defined by the learning rate. This is due to the fact that, in the tabular setting, the Q-table must store the Q-values for every possible state-action pair. In practical applications, the state and action spaces can be very large, resulting in a large and possibly infinite  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Discount Factor \(\gamma\) & 0.995 \\ Initial exploration rate \(\epsilon\) & 1.000 \\ Number of States \(\mathcal{S}\) & 8 \\ Deep Q-Network width H & 24 \\ Exploration rate & 0.995 \\ Minimum exploration rate \(\epsilon_{min},\epsilon_{min}^{thresh}\) & (0.15, 0.10) \\ No.of actions \(\mathcal{A}\) & 16 \\ DQN Depth & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RL parameters.  
size of the Q-table. To ensure convergence, the Q-table must be updated continuously with new experiences, and all state-action pairs must be visited and updated an infinite number of times. However, in practice, this is not feasible, and convergence may be slow or never reached. This is why the tabular setting of Q-learning is more suited to problems with smaller state spaces. Additionally, it also requires a non-trivial initialisation of the \(Q\varepsilon\mathbb{R}^{|S|\times|A|}\) table to avoid longer convergence times. Therefore, computationally, it suits problems with smaller state spaces. Q-learning has no neural network involvement. The Q-learning update analog of (1) is defined as  
\[Q_{\pi}(s_{t},a_{t}):=(1-\alpha)Q_{\pi}(s_{t},a_{t})+\alpha\bigg{(}w_{s,s^{ \prime},a}+\delta\max_{a^{\prime}}Q_{\pi}(s^{\prime},a^{\prime})\bigg{)} \tag{7}\]  
In this context, \(Q_{\pi}(s_{t},a_{t}):=[Q]_{s_{t},a_{t}}\). The learning rate, \(\alpha>0\) determines the level of aggressiveness of the Q-learning update and sets how much weight is given to prior experience. The table-based approach is suitable for problems with small state spaces, allowing for the storage of the table Q.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 2 Literature Review
### Fast and Effective CDC Techniques  
After evaluating the drawbacks of CDC techniques, Fast and Efficient CDC (FastCDC) [24] employs gear hash-based CDC, improving the hash judgment while simultaneously simplifying the process. To improve its effectiveness, the sub-lowest data chunk points are ignored. The function of normalized distribution is used to overcome the proportion of deduplication at a particular area. To deal with the lowest data chunks, the chunk size in the normal case is kept large. In the literature, two main categories are highlighted: algorithmic and hardware-based content-defined chunking methods. The two most important phases of the CDC technique are declared to be hashing and hashing judgment. Hashing involves allocating a hash value to a data chunk, while hashing judgment denotes the contrasting of data chunk points. The choice to leverage gear hashing is determined based on the contrast between the Rabin and Adler methods. The gear hash approach needs fewer computations in comparison with the Rabin method. The choice to leverage gear hash over the Rabin and Adler methods is supported by experimental data and a detailed comparative analysis presented in a previous paper. The key advantages of gear hash are highlighted by the experimental results. Firstly, gear hash uses fewer calculation operations compared with the Rabin and Adler methods, which enhances its performance. Gear hash relies on simpler operations, including a left shift, an addition, and an array lookup to generate hash values. In contrast, the Rabin and Adler methods need more complex and time-consuming operations. This simplicity of gear hash results in faster computation times, making it more suitable for content-defined chunking.  
The experimental data include a detailed comparison of the hashing stages of the Rabin, Adler, and gear methods, highlighting the fundamental operations involved. The analysis demonstrates that gear hash is faster, providing a pseudocode for each method. It advocates for the use of gear hash for improved performance.
## 4 Experiments  
The training of a 1D-CNN model requires low computational resources, and hence, a laptop computer (HP ZBook 15 Mobile Workstation) with an Intel(r) Core(tm) i7-10750H processor at 2.6 GHz, 16 GB of RAM, without any graphical processing units (GPU), and Ubuntu as the operating system, was used to train the proposed model. The open-source TensorFlow machine learning library written in Python was used as a coding platform. Implementation consisted of four stages. The first stage chunked the data into training and test sets: 70% for training and the rest for testing, applied normalisation techniques from'sklearn.preprocessing', and converted the training and test data into single-length vectors, each with a time step of 12. Defining the model took place in the second step. The TensorFlow 'Sequential' class and 'keras.layers' modules were used to build the model function. The third phase included defining the model training function. The TensorFlow 'Model Training' application programming interface (API) was employed to achieve this.  
The mean squared logarithmic error function from 'Keras Losses' API and adaptive moment estimation (Adam) function from 'Keras Optimizers' API were used as the loss function and optimiser, respectively. The final phase evaluated the proposed model and plotted the obtained results. Two separate predefined functions helped with this. The'sklearn.metrics' API eased import of the evaluation metrics, and 'Matplotlib' with 'NumPy'  
Figure 4: Schematic representation of the architecture of the proposed model.  
libraries helped to plot the results to allow graphical comparisons. The quantity of trainable parameters of the proposed model was 564,377, and it took 112 min to train it for 100 epochs. In this study, support vector regression, LSTM, gated recurrent unit, and CNN-GRU-based models were also trained in the same environment with identical parameters in order to make comprehensive comparisons. The total training time for the LSTM, GRU, and CNN-GRU models was 85, 78, and 108 min, respectively.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Computational Resources
#### 6.2.2 Distributed Systems and Edge Computing  
Distributed multi-robot clustering systems provide scalability and faster processing speed, making them well-suited for tasks like collaborative mapping and cooperative navigation [95]. One approach by Qi et al. [104] introduces a homogeneous distributed collaborative mapping system using bathymetric cooperative active SLAM, where a server vehicle optimizes positioning accuracy through online path planning based on Fisher information matrix (FIM) metrics. A novel prediction method for inter-vehicle loop closure factors and an augmented matrix determinant lemma reduce computational overhead, improving both accuracy and efficiency in semi-physical simulations. Similarly, to address trajectory drift in AUVs caused by ocean currents, a multi-AUV cooperative navigation algorithm based on a factor graph with stretching nodes' strategy has been developed by Ben et al. [105]. By introducing ocean current velocities as variable nodes and transforming the FG into a cycle-free structure, this method enhances localization accuracy and stability while maintaining computational feasibility.  
Beyond navigation, distributed systems also play a crucial role in edge computing within underwater environments. Underwater IoT relies on AUVs to supplement the limited computational resources of sensor cluster heads. A proposed AUV-aided offloading framework by Chen et al. [106] integrates multiple AUVs, buoys, and low Earth orbit satellites under an edge intelligence service platform, which manages computational resources dynamically.
## 3 Results and Discussion
### Simulations
#### 3.1.3 Effect of Lateral Distance  
In addition, we evaluated the tomographic capabilities of the system simulating different positions of the inclusion with \(\Delta\mu_{a}\) of 0.017 mm\({}^{-1}\) along the \(y\) axis, from 15 to 75 mm, with respect to the lateral side of the phantom (y = 0 mm) with steps of 10 mm and at 15 mm of depth (Figure 5). Regarding the distance, we can state that the inclusion is precisely detected until it remains within the superficial octagonal perimeter defined by the detectors (see Figure 2). Instead, as long as it is placed outside this boundary (i.e., a few mm), it is not properly detected anymore, as visible in the \(y=75\) mm position.  
Figure 4: Effect of inclusion depth on tomographic reconstructions: two dimensional planes taken at specific \(z\) (**rows**) show the perturbations arising from an inclusion of \(\Delta\mu_{a}\) = 0.017 mm\({}^{-1}\) placed at four increasing depths (**columns**), from 5 to 20 mm with steps of 5 mm. Two white circles of radius = 10 mm are placed in the layers just adjacent the depth at which the center of the inclusion should be retrieved.  
As a general overview about the results, we can affirm that the simulations are effective in showing perturbations of strength down to 0.017 mm\({}^{-1}\), with a satisfactory \(x\)-\(y\) localization and until the perturbation is under the superficial optodes coverage. In general, the perturbation depth is slightly underestimated (at most by 5 mm). These results can be considered quite encouraging for the proposed approach. In fact, the intersection of the light paths of several source-detector pairs are usually employed to obtain a precise localization, while here, despite that we only employed eight pairs, we can still attribute a specific position to the perturbation. Furthermore, the reduced number of optodes is probably the origin of the superficial artifacts that we have always observed on the reconstructed images at shallow depths. In the first reconstructed layers, the traces of the eight \"banana shapes\", which are the most probable regions of each photon path, are evident; they could probably result from the downsampling of the volume under the probe. In order to verify whether this effect was seen only in the presence of the inclusion, we tested the system without it and leaving the Poisson noise as the sole perturbation: the artifact is visible also in this case, but very similar superficial effects have been already reported in several works [1,45].  
Figure 5: Effect of lateral position of the perturbation on tomographic reconstructions: \(x\)-\(y\) reconstructed images at increasing \(z\) values (**rows**) of an inclusion of \(\Delta\mu_{a}=0.017\) mm\({}^{-1}\) placed at 15 mm of depth and at different positions along the \(y\) axis (**columns**). The two dashed horizontal lines in each slice correspond to the edges of the superficial coverage of the probe. The white circles indicate the first neighbor planes of the inclusion and trace its path along y axis.
# Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher
## 3 System Model and Feature Extraction
### Receive Packet Time Interval Feature  
The RPTI is the time between the arrival of successive packets and is approximately equal to the minimum transmission time \(\text{T}_{min}\) on the wireless link. Assume that \(p_{new}\) is the out-of-order packet received by the receiver, \(p_{last}\) is the last packet arriving in sequence before \(p_{new}\), and \(\text{T}_{loss}\) is the RPTI of the \(p_{new}\) and \(p_{last}\). If the packet loss occurs on the wireless link and the number of consecutive packet losses is \(x\), the minimum of \(\text{T}_{loss}\) is \((x+1)\text{T}_{min}\). When the packet has been lost before arriving at the gateway, which connect the wired and wireless network, the out-of-order packet is queued with other packets for forwarding, so the minimum of \(\text{T}_{loss}\) is \(\text{T}_{min}\). Therefore, the RPTI feature value can be given by  
\[F_{RPTI}=\text{T}_{loss}-(x+1)\text{T}_{min}, \tag{2}\]  
As shown in Figure 1(c), after packets are forwarded by the gateway, the wireless link randomly dropped \(p_{3}^{high}\), \(p_{6}^{high}\) and \(p_{7}^{high}\). The RPTI between \(p_{2}\) and \(p_{4}\) is 2T, \(p_{5}\) and \(p_{8}\) is 3T. The value of \(\text{F}_{RPTI}\) is greater than zero. As shown in Figure 1(b), \(p_{4}^{low}\) and \(p_{8}^{low}\) are dropped due to the overflow of router  
Figure 2: The different characteristics of packet loss in wired scene and wireless scene.  
queue. Other packets are forwarded by the gateway and then reach the receiver and the RPTI are T. So the value of F\({}_{RPTI}\) is less than zero.  
In addition, according to the above definition, the format of the training sample item A\({}_{n}\) (\(n\in N\), \(N\) is the number of training sample item) is \([a_{pri},a_{RPTI},\text{C}(A_{n})]\). The value of \(a_{pri}\) and \(a_{RPTI}\) can be given by  
\[a_{pri}=\begin{cases}\text{congestion loss},&\text{if F}_{pri}\leq 0\\ \text{wireless random loss},&\text{if F}_{pri}>0\end{cases}, \tag{3}\]  
\[a_{RPTI}=\begin{cases}\text{congestion loss},&\text{if F}_{RPTI}\leq 0\\ \text{wireless random loss},&\text{if F}_{RPTI}>0\end{cases}, \tag{4}\]  
C\((A_{n})\) is the classification result of A\({}_{n}\), where C is the training sample classification set.