esa-sceva/satcom-chunk-collection huggingface dataset:
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 6 Simulation Experiments and Performance Evaluation
### Quality and Performance Metrics  
To evaluate the performance of tested routing algorithms we used the energy costs of data center network operation. However, in practice, this is not the only, or even the most important, measure. Network stability and reliability are key to assessing network quality. Therefore, we defined the following additional measures.  
* _Packet loss rate._ Activation of insufficient number of devices may lead to packet losses that significantly influence the QoS. The total packet loss \(F_{loss}\) can be calculated as the proportion of lost data to the total source flow size. \[F_{loss}=\frac{\sum_{m\in M}u_{m}p_{m}}{\sum_{m\in M}w_{m}},\] (11) where \(M\) denotes the set of flows to be transmitted, \(w_{m}\) is size of the \(m\)-th flow, \(u_{m}\) is number of lost packets of \(m\), and \(p_{m}\) is average packet size of flow \(m\).
* _Average port utilization._ High utilization of ports and nodes may lead to difficulties in handling the traffic when unexpected growth occurs. It may be caused by using too-long routing paths or defining very high utilization threshold. Average port utilization can be calculated as the average of utilization of all ports existing in all activated devices in the network: \[F_{avg\_utilization}=\frac{1}{\sum_{n\in N}\sum_{p\in P_{n}}\beta_{p}}\sum_{n \in N}\sum_{p\in P_{n}}z_{p}\] (12) where \(P_{n}\) denotes a set of ports of the \(n\)-th node, and \(z_{p}\) is the utilization of port \(p\in P_{n}\) (in %), i.e., amount of its bandwidth used through the analyzed period. For each port \(p\in P_{n}\)\(\beta_{p}=1\) if the port \(p\) belongs to the active node (0 otherwise). We assume that the analyzed network transmits any traffic, i.e., at least two ports are active.
* _Standard deviation of a port utilization._ Unequal port and node utilization may result in the necessity of significant traffic rearrangement in the case of single node failure. Additionally, highly utilized nodes are more likely to cause packet loss in the case of traffic changes. An appropriate metric of utilization uniformity may be a standard deviation of loads of all active ports. \[F_{std\_dec\_utilization}=\sqrt{\frac{1}{\sum_{n\in N}\sum_{p\in P_{n}}\beta_{p} }\sum_{n\in N}\sum_{p\in P_{n}}\left(z_{p}-F_{avg\_utilization}\right)^{2}}\] (13)
## 3 Results and Discussion
### Electrochemical Analysis of Cu (II) Complex  
To explore the electrochemical properties (Figure 6) of the Cu (II) complex, cyclic voltammetry was employed in a three-electrode system. The cyclic voltammograms of Cu (II) complex were recorded at a glassy carbon electrode in CH\({}_{3}\)CN containing 0.1 M TBAPF\({}_{6}\) as a supporting electrolyte in the potential range from \(-\)1.6 V to +1.6 V with a scan rate of 100 mV s\({}^{-1}\). The Cu (II) complex shows three reduction peaks with Epa values of +0.412, \(-\)0.96, and \(-\)1.36 V vs. SCE. Hence, the irreversible reduction process of the Cu (II) complex leads to the deposition of Cu (0) on the electrode surface [35]. The peak potential (+0.412 V) might be attributed to the reduction of Fc\({}^{+}\)/Fc, whereas the peak potential (\(-\)1.00 V) can be assigned to the reduction of Cu\({}^{+}\)/Cu. Additionally, the third reduction potential (\(-\)1.36 V) might be ascribed to the Cu(II)/Cu(I) reduction process. During the reverse scan, three oxidation peaks of  
Figure 4: **(a)** Thermogravimetric analysis of Cu (II) complex with the scan rate of 10 \({}^{\circ}\)C/min under nitrogen, **(b)** percent derivative curve of the Cu (II) complex.  
\(-\)0.64, +0.38, +0.47 V vs. Ag/AgCl were observed. The first peak (Epc \(-\)0.64 V) is associated with the oxidation peak of Cu (I)/Cu (II) while the second peak (+0.38 V) is associated with the ligand L1. In addition, the third peak is assigned to the reference redox couple of Fe/Fe\({}^{+}\)[36].
# LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move
## 6 Outdoor Testing and Analysis of the Prototypes
### Power and Energy Analysis
The table shows that none of the literature addressed all these functionalities presented. For example, some of the works focused on architectural reliability and latency, whereas they do not account for vital aspects such as evaluation under practical scenarios or statistical analysis which would provide a significant, insightful, and complete analysis. In comparison to these LoRa V2X models, the proposed architecture provides more detailed works which are also evaluated under different practical scenarios and with statistical analysis. Advantages of the proposed LoRa V2X in comparison with the state of the literature include:  
* more functionalities than any other LoRa V2X architecture. * achieved higher reliability with lower latency under practical scenarios. * energy-efficient and compact, and has the potential to integrate with legacy vehicles. * extensively tested under practical scenarios with vehicles on the move at different speeds and the test data shows statistical significance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit** & **Unencrypted Data (hours)** & **Encrypted Data (hours)** \\ \hline OBU & 131.89 & 130.00 \\ RSU & 273.89 & 265.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Running time of OBU and RSU with 3.7 V, 1000 mAh battery. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name of the Model**} & \multicolumn{8}{c}{**Functionalities**} \\ \cline{2-9}  & **Reliability** & **Latency** & **Energy** & **Security** & **Prototype** & **Designing** & **Evaluation** & **Evaluation with** & **Statistical** \\ \hline Evaluation of LoRa & & & & & & & **Under Practical** & **Different** & **Analysis** \\ in V2X [58] & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ \\ \hline LoRa and eMTC- & & & & & & & & & \\ based [59] & ✓ & ✗ & ✗ & ✓ & ✗ & ✗ & ✓ & ✗ \\ \hline Vehicular eco- & & & & & & & & & \\ system [60] & & & & & & & & & \\ \hline Secure V2V/V2I [61] & ✗ & ✗ & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ \\ \hline V2X network [12] & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ \\ \hline Vehicle charging & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ & ✗ & ✗ \\ architecture [62] & & & & & & & & \\ \hline Software defined & ✓ & ✗ & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ \\ LoRa V2X [63] & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline V2V and V2V with & & & & & & & & \\ LoRaWaN and & ✓ & ✗ & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ \\ BLE [65] & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the proposed architecture with state-of-the art LoRa V2X models.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimension** & **Method** & **Precision** & **Recall** & **F1** \\ \hline \multirow{4}{*}{Superpixel-based} & Random forest & 0.9343 & 0.8586 & 0.8948 \\  & XGBoost & 0.9250 & 0.8437 & 0.8825 \\  & GAT & 0.9690 & 0.8827 & 0.9238 \\  & Ours & 0.9882 & 0.9785 & 0.9833 \\ \hline \multirow{4}{*}{Pixel-based} & Random forest & 0.8758 & 0.7647 & 0.8165 \\  & XGBoost & 0.8818 & 0.7799 & 0.8277 \\ \cline{1-1}  & Ours (transformed from & & & \\ \cline{1-1}  & superpixel level to pixel level) & 0.98925 & 0.9808 & 0.9850 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation for study area 2. The proposed method is suitable for all kinds of water-related scenes, such as rivers, lakes, ponds, coastlines, floods, and probably paddies. It has advantages in uncommon and complicated scenes, where large samples are hard to collect, and trained DL models cannot be directly used. High precision can be expected, while for an extremely large area, the computation time is alarming compared to other simple TS methods. It is important to note that the proposed method can also be sped up by simplifying the steps of superpixel segmentation and feature selection. On the other hand, the use of superpixels leads to a problem: how to express the features of each superpixel reasonably. Manually designed SAR image features are shallowly expressed and can affect classification accuracy. Missing any key features likely leads to bad results. A simple operation involves designing and choosing all available features as much as possible, but this makes the method cumbersome and inefficient.
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 2 Image Registration
Usually, it is considered a 3D rigid transformation composed by seven real-coded parameters: A rotation \(R=(\theta,\ A\ axis_{x},\ A\ axis_{y},\ A\ axis_{z})\) and a translation \(\vec{t}=(t_{x},t_{y},t_{z})\), with \(\theta\) and \(\vec{A\ axis}\) being the angle and axis of rotation, respectively. Then, the transformed image points of the scene view are noted by  
\[f(\vec{p}_{i})=R(\vec{p}_{i})+\vec{t},\ \ i=1\cdots N_{I_{s}} \tag{1}\]  
Then, the main goal of the Optimizer is to search for the optimal IR transformation \(f^{*}\) which provides the best alignment according to the Similarity metric \(F()\):  
\[f^{*}=\underset{f}{\arg\min}\ F(I_{s},I_{m};f)\ \ s.t.:\ f^{*}(I_{s})\cong I_{m} \tag{2}\]  
Specifically, the median square error (MedSE) is the Similarity metric considered in 3D modeling [4]:  
\[F(I_{s},I_{m};f)=MedSE(d_{i}),\ \ \forall i\in\{1,\ldots,N_{I_{s}}\} \tag{3}\]  
where \(MedSE()\) corresponds to the median \(d_{i}\) value. We define \(d_{i}=\|f(\vec{p}_{i})-\vec{q}_{cl}\|^{2}\) as the squared Euclidean distance between the transformed scene point, \(f(\vec{p}_{i})\), and its corresponding closest point, \(\vec{q}_{cl}\), in the model view \(I_{m}\). Moreover, it has been used ([3; 15]) indexing structures such as the kd-tree [16] and the grid closest point (GCP [17]) in order to speed-up the computation of the closest point \(q_{cl}\) of \(I_{m}\). Figure 2: The 3D model reconstruction pipeline.
## 2 Materials and Methods
### Feature Extraction
#### 2.2.1 Bert  
The training of the BERT model can be divided into two steps: pre-training and fine-tuning. Pre-training is performed on unlabeled datasets to obtain initial parameters, while fine-tuning is achieved by reassessing the initial parameters through labeled downstream tasks. Originally developed to process natural language, BERT can be trained to treat the amino acid sequences of proteins as sentences. In this context, the concept of a 'word' can be either a single amino acid, several adjacent amino acids [44], or a functional unit [45]. In this paper, we used the assumption that a single amino acid in protein was a 'word' in a sentence.  
There are several distinct versions of the BERT that differ in their pre-training methods. In order to select the most suitable version for thermophilic protein identification, we tested BERT, BERT-bfd, and TAPE-BERT. During pre-training, BERT uses UniRef100 [46], BERT-bfd uses the Big Fantastic Database (BFD) [47], and TAPE-BERT uses the protein families database (Pfam) [48]. Of these, BFD is the most extensive collection, containing 2122 million proteins, while UniRef100 contains 216 million, and Pfam 31 million sequences.  
BERT, BERT-bfd, and TAPE-BERT have similar structures. As an example, BERT-bfd is shown in Figure 1B. As illustrated, BERT-bfd first encodes and tokenizes an amino acid sequence of length L into a matrix. This process generates the input of a transformer function that produces a hidden state of the last self-attention layer as the output. This output is a 1024 \(\times\)\(L\) matrix for BERT-bfd and BERT, and a 768 \(\times\)\(L\) matrix for TAPE-BERT. In the final step, this matrix is transformed into a feature vector through mean-pooling, to facilitate downstream machine learning. The final feature vector is 1024-dimensional for BERT and BERT-bfd, and 768-dimensional for TAPE-BERT.
## 6 Convergence Speed of the Algorithm and the Localization Error of Unknown Nodes
### Convergence Speed of the Algorithm  
In this section, the percentage of anchor nodes is 20% and the number of deployment nodes is 100. The communication radius is 20 m. The number of iterations of the algorithm is 1-200. All experimental results are averaged by executing 100 times independently. Experimental results are shown in Figure 10.  
According to the results of Figure 10, three meta-heuristic algorithms have faster convergence speeds yet better effects in the square topology. It can be clearly seen that the convergence speed of the three meta-heuristic algorithms is relatively fast; notably, the algorithm DEIDV-Hop proposed in this paper has a fast convergence speed and small localization error.  
In the C-shaped topology, it is easy to see that the DV-Hop algorithm is very unstable. Because of changes in the deployment environment, there will be some isolated single-hop nodes, so the localization effect of the algorithm is very unstable. Looking at the curve in the Figure 10, the localization error values of the PSODV-Hop algorithm and GSODV-Hop algorithm continue to fluctuate with the increase of the number of iterations; in contrast, the DEIDV-Hop algorithm tends to have a stable value, which demonstrates the robustness of the algorithm.  
The relationship between the number of iterations of the simulation algorithm and ALE in four topological environments proves that the DEIDV-Hop algorithm has fast convergence speed, robustness, and stability.
## 3 Background
### Merkle Tree  
A Merkle tree [60] is a tree-like data structure known as a hash tree. Every leaf node in a Merkle tree has a hash value. The hashes of the non-leaf nodes in the tree are computed by adding the hashes of the node's two children. When traveling one layer above the tree, the number of hashes will be reduced by half. Moreover, because the hash value of the root node depends on every other node in the tree, the hash value can be used to describe the complete tree [61].
# A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities
## 9 Conclusions  
The paper presents a novel solution supporting the development and dynamic deployment of applications in infrastructure-less wireless environments, specifically considering sparse Smart City environments where mobile devices carried by users collaborate to support the remote monitoring and control of sensor nodes. In particular, the proposed RAMP-WIA middleware allows dynamic management of mobile nodes, not only at the communication level, by creating new single-hop links and opportunistically dispatching packets at multi-hop distance, but also at the application level, by allowing software components on mobile nodes to be deployed and upgraded on-the-fly. The RAMP-WIA middleware also takes into consideration that the greatly dynamic nature of sparse Smart City scenarios may not allow to dispatch commands to sensor nodes in a timely manner. For this reason, it dispatches commands only if a quota of sensor nodes is currently reachable and supports a resiliency policy that allows sensor nodes detached from the network to properly reconfigure themselves while waiting to restore connectivity with a controller.  
The encouraging results achieved based on the working implementation of the RAMP-WIA middleware are stimulating our ongoing research work. We are mainly working on the containerization of the implemented middleware as well as of specific sensor/controller components. In addition, we are investigating the adoption of Software Defined Networking (SDN) based approaches to improve the QoS by appropriately managing different traffic flows, by avoiding interferences, and by considering network capabilities/conditions combined with application-level requirements. Finally, we intend to perform wider and more in-depth performance analyses based on a real-world testbed with the scale expected for the targeted smart city execution environments, that is, including thousands of off-the-shelf devices.  
Figure 7: PresenceSensor application testing deployment.  
**Author Contributions:** All the authors have equivalently contributed to the manuscript, with a strong focus of [PERSON], [PERSON], [PERSON] and [PERSON] on architectural and design choices, and a prevalent contribution of [PERSON] and [PERSON] in terms of experimental evaluation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Remaining Useful Life (RUL) of Lithium-Ion Battery (LiB)  
While product lifetime can be predicted through reliability tests in most cases, field reliability is likely different. A typical rule of thumb is to expect the unreliability in the field to be twice what was observed in the lab. This is because product will usually receive harsher treatment in the field than in the lab. Units being tested in the labs are often carefully set up and adjusted by engineers prior to the beginning of the test. The tests are performed by trained technicians who are adept at operating the product being tested. Most end-use customers do not have the training and experience in its operation, thus leading to many more operator-induced failures than would be experienced during in-house testing [8].  
In some cases, accelerated life test cannot be performed such as LiB and other highly reliable products. This is because accelerated test condition for LiB can affect the chemistry in LiB and the degradation mechanisms will be completely different from that operate at normal condition. For highly reliable products, the test time is simply too high and accelerated degradation tests usually employed.  
Regardless of the situations, ability to estimate the remaining useful life of a product, as it might have been through various operating conditions, will be useful. [PERSON] et al. developed a methodology to estimate the RUL for LiB. Their method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a LiB for the estimation of its maximum stored charge capacity, and thus its state of health (SoH). The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of LiB for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.
## 2 Methods
### Characterization of Aw-AgNPs  
UV-Vis spectrophotometers characterization: The preparation of Aw-AgNPs was observed and the reduction of Ag\({}^{+}\) ions was monitored by using a UV-Vis spectrophotometer (Thermo-Scientific Evolution 201) operating at wavelengths of 300-700 nm. To obtain the UV-visible spectra of the Aw-AgNPs sample, 2 mL of the diluted supernatant was loaded in a quartz cuvette with a 1 cm path length and inserted in a UV-Vis spectrophotometer.  
FTIR characterization: Functional compounds associated with seed extract are involved in the reduction of silver ions into silver nanoparticles, which was confirmed by FTIR spectrometer ([PERSON], UK). The spectra were recorded in the wavenumber range of 4000 to 500 cm\({}^{-1}\) by crushing the small amount of sample with the KBr powder.  
SEM-EDAX characterization: The morphology and size of the Aw-AgNPs was examined by Scanning electron microscopy using a FESEM, Supra 55--Carl Zeiss, Germany. The conductive resin tape was used to cover the SEM copper plate, and particles were dispersed and gold-coated on the tape. The elemental analysis of Aw-AgNPs was carried out by Energy-dispersive X-ray spectroscopy (EDAX). The EDAX attachment on the SEM provided chemical analysis of the minute particles and confirmed the presence of specific elements.  
TEM characterization: The morphology and size of the Aw-AgNPs particles were further confirmed by Transmission Electron Microscopy (JEOL JEM-10100). Aw-AgNPs samples were drop-coated on carbon-coated copper grids mounted on blotting paper and left to dry in the air for five minutes.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 2 Background
### Sparse Matrix Storage Format
Table 1 illustrates an example matrix represented in CSR5, where \(\omega=\sigma=2\). **ELL** The ELLPACK (ELL) format is suitable for the vector architectures [20]. For an \(M\times N\) matrix with a maximum of **K** non-zero elements per row, ELL stores the sparse matrix in a dense \(M\times K\) array (data), where the rows with fewer than **K** are padded. Another data structure, indices, stores the column indices and is zero-padded in the same way as that of data. Table 1 shows the ELL representation of the example sparse matrix, where \(\textbf{K}=3\) and the data structures are padded with \(\star\). The ELL format would waste a sizable amount of storage. To mitigate this issue, we can combine ELL with another general-purpose format, such as CSR or COO. **SELL and SELL-C-\(\sigma\)**_Sliced_ ELL (SELL) is an extension of the ELL by partitioning the input matrix into strips of **C** adjacent rows [22]. Each strip is stored in the ELL format, and the number of non-zero elements stored in ELL may differ over strips. Thus, a data structure slice is used to keep the strip information. Table 1 demonstrates a matrix represented in the SELL format when \(\textbf{C}=2\). A variant to SELL is the SELL-C-\(\sigma\) format, which introduces sorting to save storage overhead [9]. That is, they choose to sort the matrix rows not globally but within \(\sigma\) consecutive rows. Typically, the sorting scope \(\sigma\) is selected to be a multiple of **C**. The effect of local sorting is shown in Table 1 with \(\textbf{C}=2\) and \(\sigma=4\). **HYB** The HYB format [11] is a combination of ELL and COO, and it stores the majority of matrix non-zero elements in ELL and the remaining entries in COO [4]. Typically, HYB stores the _typical_ number of non-zero elements per row in the ELL format and the _exceptionally_ long rows in the COO format. In the general case, this _typical_ number (**K**) can be calculated directly from the input matrix. Table 1 shows an example matrix in this hybrid format with **K** = 2. **CVR** The CVR format is an SpMV representation targeting the efficient vectorization developed especially for the Intel KNL processor [21]. The number of columns in CVR formats equals the number of SIMD lanes. It stores the matrix switching from row order to column order, while trying to fill the \"data\" with non-zero elements. Both the format conversion and the SpMV implementation are SIMD friendly. Table 1 shows an example matrix in CVR format, with two SIMD lanes.