esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Needs and Requirements Arising from Connected and Automated Driving
## 4 Technical Requirements: from Automotive to Network Perspective
### Relationship between Automotive and Communications Network KPIs  
Each of the automotive KPIs has an impact on one or more network KPIs, depending on the underlying vehicular application. Automotive KPIs are often distinguished for safety and non-safety applications. Localization, completion time, minimum inter-vehicle distance, and mobility have more impact on safety, whereas relevance area and completion time are more relevant for infotainment services. Safety applications often impose requirements on wireless networks in terms of communication range, reliability, and end-to-end latency. Similarly, localization is a key enabler for important safety applications and is thus considered to be an automotive KPI. On the other hand, the infotainment applications often impose requirements in terms of data rate, SDU size, and network availability for seamless connectivity. In many cases, multiple automotive KPIs affect the network KPIs. For example, in the lane merge use case, both the mobility (e.g., acceleration/deceleration) and the relevance area will have an impact on the amount of time that is allowed between subsequent message transmissions, since higher mobility and larger relevance area require higher message periodicity. Therefore, both of these automotive KPIs have an impact on the data rate. Similarly, in the case of the see-through use case, the mobility and the relevance area will directly impact the required communication range. In the next subsection, we focus on one representative example for each use case class to elaborate on how the automotive KPIs impact the corresponding network KPIs to elaborate further on these relationships.
## 2 Methodology
Both bacteria types can form a capsule, which contributes to the overall thickness of bacteria. Capsules can be up to 10 \(\upmu\)m thick and are composed of high molecular-weight viscous polysaccharides forming a thick gel outside the cell wall [18]. Bacteria come in distinctive shapes, including coccus, bacillus, coccobacillus, spiral, and filamentous. For airborne bacteria, the most common forms encountered are cocci, bacilli, and coccobacil. Bacteria also come in a multitude of arrangements, including mono-, diplo-, tetrad-, strepto-, stephlo-, and pleomorphic (multiple arrangements) forms [19]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scientific Name** & **Gram Stain** & **Presence of Capsule** & **Shape** & **Arrangement** & **Flagella or Cilia** & **Type of Cilia** & **Type of Flagella** \\ \hline Acintobacter baumanni & Negative & Capsule & Bacillus & Mono & Cilia & Type IV Pili & N/A \\ \hline Acitronomyces stazelii & Positive & No Capsule & Bacillus & Staph & None & N/A & N/A \\ \hline Acialigenes faecalis & Negative & Capsule & Bacillus & Mono & Flagella & N/A & Perittichious \\ \hline Alidoococcus otitis & Positive & Capsule & Bacillus & Pleo & None & N/A & N/A \\ \hline Bacillus anthracids & Positive & Capsule & Bacillus & Strep & None & N/A & N/A \\ \hline Bacillus cereus & Positive & No Capsule & Bacillus & Strep & Flagella & N/A & Perittichious \\ \hline Bacillus subtilis & Positive & Capsule & Bacillus & Strep & Flagella & N/A & Perittichious \\ \hline Bacillus thuringiensis & Positive & Capsule & Bacillus & Strep & Flagella & N/A & Perittichious \\ \hline Bordetella pertussis & Negative & Capsule & Cocobacillus & Mono & None & N/A & N/A \\ \hline Burkholderia capacia & Negative & Capsule & Bacillus & Mono & Flagella & N/A & Monotrichious \\ \hline Burkholderia mallie & Negative & Capsule & Cocobacillus & Pleo & None & N/A & N/A \\ \hline Cardiobacterium & Negative & No Capsule & Bacillus & Pleo & None & N/A & N/A \\ \hline Holmitis & Negative & No Capsule & Bacillus & Pleo & None & N/A & N/A \\ \hline Chlamydia pneumoniae & Negative & No Capsule & Bacillus & Pico & None & N/A & N/A \\ \hline Chlamydia gutricai & Negative & No Capsule & Cocus & Pico & None & N/A & N/A \\ \hline Clostridium temai & Positive & No Capsule & Bacillus & Mono & Flagella & N/A & Perittichious \\ \hline Coryobacteria & Positive & No Capsule & Bacillus & Pico & None & N/A & N/A \\ \hline Cosiella burneiti & Negative & No Capsule & Cocobacillus & Mono & None & N/A & N/A \\ \hline Eikkenella corrodenes & Negative & No Capsule & Bacillus & Mono & None & N/A & N/A \\ \hline Enterobacter cloace & Negative & No Capsule & Bacillus & Pico & Flagella & N/A & Perittichious \\ \hline Enterococcus faecalis & Positive & Capsule & Cocus & Strep & None & N/A & N/A \\ \hline Francisella tulareis & Negative & Capsule & Cocobacillus & Mono & None & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bacterial data set with seven categorical analyzed features, placed in a specific and logical order for decision tree analysis.
# Planning of Optical Connections in 5G Packet-Optical xHaul Access Network
## 4 Optimization Problem
### Notation  
The xHaul transport network is modeled by a graph \(\mathcal{G}=(\mathcal{V},\mathcal{E})\), where \(\mathcal{V}\) denotes the set of network nodes, representing remote and hub sites, and \(\mathcal{E}\) is the set of links representing fiber connectivity between the nodes. We assume that the capacity of each link in terms of available fibers is not a bottleneck and is sufficient to carry the traffic between remote and hub nodes. Let \(W\) denote the capacity of the WDM system, i.e., the maximum number of wavelengths carried in a fiber.  
Let \(\mathcal{D}\) be the set of demands. Each demand \(d\in\mathcal{D}\) represents a number of wavelength connections to be established in the xHaul network between a remote node and a hub node. We assume that each remote node generates a demand. Let \(W(d)\) be the number of requested wavelengths of demand \(d\).  
Let \(\mathcal{P}(d)\) denote the set of candidate transmission paths of demand \(d\in\mathcal{D}\). Transmission path \(p\in\mathcal{P}(d)\) is a fiber route through the network between the remote node corresponding to demand \(d\) and a hub node; in particular, \(p\) is a subset of network links (\(p\subseteq\mathcal{E}\)). Let \(D(p)\) denote the physical length of transmission path \(p\). We assume that path \(p\) may either: (a) realize a direct fiber connection between the end nodes of a demand or (b) be a fiber connection traversing OADM devices installed at some intermediate remote nodes. Let \(N(p)\) denote the number of OADMs allowable on path \(p\). \(N(p)\) depends on path length \(D(p)\) and is determined by the transmission distance values presented in Table 2, namely \(N(p)=\max\{N:D(p)\leq L(N)\}\), where \(L(N)\) is the value of \(L\) for given \(N\) in Table 2. Let \(\mathcal{Q}(d,d)\) be the set of candidate transmission paths of demand \(d\) (i.e., \(\mathcal{Q}(d,d)\subseteq\mathcal{P}(d)\)) which may go through an OADM installed at the remote node of demand \(d\). The notation is summarized in Table 3.
## 3 Results
### Simulations  
Over the course of the work, more than 700 individual simulations were used to replicate over 50 different track environments under various conditions. Figure 13 compares the B-scan of a section from the real dataset with the corresponding simulation result. Visible in this section are three sleepers (narrow hyperbolas in the upper area) and a buried metal container, which appears as a hyperbola between the second and third sleepers.  
A mere examination of the B-scans already reveals a high degree of similarity, although the sleepers in the simulated data appear somewhat clearer. This can be attributed to slight differences in the assumed and actual material properties. When comparing the normalized deviation, shown in Figure 14b, the reflection components of the sleepers are also overemphasized. This effect suggests a deviation of the antenna model from the real antenna.  
In the examination of the C-scan at the sleeper level (see Figure 15), it is evident that channels 4 and 8 are attenuated in the simulated data. These channels are those cross-track channels where the transmitting and receiving antennas are located in different housings. Even though this configuration was not explicitly replicated during the development of the digital twin and thus slight deviations from the real setup have to be expected, the effect is observed in both the real and simulated data.  
Traditional similarity criteria, such as the mean square deviation, are unsuitable in this case, as the absolute position of the objects significantly influences the result. However, this is of secondary importance for the present problem.  
Figure 13: A comparison of the B-Scans of a section from real data (**a**) and simulated data (**b**).  
A more suitable method for evaluating similarity is the structural similarity (SSIM) described by [PERSON] et al. (2018), which is widely used in image processing. In addition to contrast and brightness, the structure of the image is also evaluated. For each image area, a normalized value between \(-1\) and \(1\) is determined, with a value of \(0\) indicating no similarity and \(+1\) absolute equality. A value of \(-1\) would correspond to the inverted image. Applied to the results in Figure 13, this yields an average SSIM value of \(0.95\), indicating a high degree of similarity. When only the image section in the area of the sleepers and the buried object is considered, the average SSIM value is slightly lower, \(0.84\), but also confirms very high similarity here. Applied to the normalized deviation, visible in Figure 14, SSIM values of \(0.95\) for the entire image or, likewise, \(0.84\) for the relevant section are obtained.  
Figure 14: A comparison of the normalized deviation between channels of a section from real data (**a**) and simulated data (**b**).  
Figure 15: Comparison of the C-scans from real data (**a**) and simulated data (**b**).
# A Hybrid Deep Learning Model for Protein-Protein Interactions Extraction from Biomedical Literature
## 4 Datasets and Experimental Setup  
### Datasets and Preprocessing  
Two benchmarking corpora, Aimed [39] and BioInfer [40], were used for the experiments and evaluation. The Aimed dataset was manually tagged, which includes 1955 sentences, and was considered as the standard dataset for the PPI extraction tasks. BioInfer was developed by the Turku BioNLP group [41], and contains 1100 sentences.  
To ensure the generalization of features, we use a similar data preprocessing method to the authors of [9], by replacing two target entities with special symbols \"Entity 1\" and \"Entity 2\", separately, and entities that are not target entities are all represented as \"Entity\". Text preprocessing includes dependency parsing and shortest dependency paths (SDPs) extraction. If there is an interaction between the two entities, we consider this instance as a positive one; otherwise, we consider it as a negative one. Table 1 shows the statistics for the PPIs corpora.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Sentence Num.** & **Positive Num.** & **Negative Num.** \\ \hline BioInfer & 1100 & 2534 & 7132 \\ AIMed & 1955 & 1000 & 4834 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics for protein–protein interactions (PPIs) corpora.  
Figure 4: Illustration of the long short-term memory (LSTM) cell.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 5 Discussion
### Ablation Study
#### 5.1.1 Impact of CFBA  
In this part, we examine the impact of CFBA, which comprises channel attention and sparse attention, on the performance of carrier detection in microscopic images. To conduct a thorough evaluation, we perform three sets of comparative experiments on Carrier600 and present the results in Table 2. The experiments involve the following: (a) only utilizing channel attention for carrier detection, (b) only utilizing sparse attention for carrier detection, and (c) employing both channel attention and sparse attention for carrier detection.  
The results demonstrate that using channel attention alone improves mAP by 4.18% compared to not using it. Similarly, only using sparse attention improves the mAP by 4.65% compared to not using it. Furthermore, when both channel attention and sparse attention are combined, it outperforms the two individual experiments, and its mAP reached 92.93%.  
In the absence of CFBA, general object detection methods may encounter challenges in accurately distinguishing carriers with shallow edges from the background, as their colors often bear a resemblance. Consequently, the detection of such carriers may be missed. However, by leveraging both channel attention and sparse attention, CFBA achieves a more precise focus on the channel information at each pixel and the spatial locations of carriers' edges. These complimentary attention mechanisms contribute to effective background suppression while highlighting carriers' edge features. As a result, CFBA significantly  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **CFBA** \\ **(Channel Attention)** \\ \end{tabular} & \begin{tabular}{c} **CFBA** \\ **(Channel Attention)** \\ \end{tabular} & \begin{tabular}{c} **SDCD** \\ \end{tabular} & \begin{tabular}{c} **mAP(\%)** \\ \end{tabular} \\ \hline \hline \
\begin{tabular}{c} \(\checkmark\) \\ \(\checkmark\) \\ \(\checkmark\) \\ \(\checkmark\) \\ \(\checkmark\) \\ \end{tabular} &
\begin{tabular}{c} 84.72 \\ 88.90 \\ 89.37 \\ 92.93 \\ 89.04 \\ **94.32** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection results on Carrier600.  
Figure 4: (**a,c**) are the detection results of the general object detection methods on the Carrier600 dataset, and (**b,d**) are the detection results of CarrDet. The red bounding boxes denote carriers with shallow edges, and the green bounding boxes denote normal carriers.  
amplifies the contrast between carriers and the background, thereby reducing unrecognized carriers with shallow edges.
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 7 Evaluation
### Varying Transaction Arrival Rates  
To evaluate the peak performance improvement brought by sharding to NeuChain under the same number of nodes, we observe changes in the throughput and latency by varying the transaction arrival rate under the SmallBank workload.  
The experimental results are shown in Figure 7. With the increase in the transaction arrival rate, the throughput of the two systems increases linearly. Due to the deterministic parallel execution, the latency only increases slowly with the increase in the transaction arrival rate before reaching the peak performance. NeuChain first reached its peak performance at 87.35 kTPS, corresponding to a throughput of 85.7 kTPS. It can be observed that as the transaction arrival rate continues to increase, transaction accumulation occurs due to slow execution, resulting in a significant increase in latency, with throughput almost plateauing. This is because after reaching the peak performance, a higher transaction arrival rate will cause transactions that exceed the system's processing capacity to accumulate in the pending queue, significantly increasing the latency.  
NeuChain+ processes different transaction sets, respectively, through two shards, theoretically improving transaction processing efficiency by 2\(\times\) compared to NeuChain. The actual peak performance appears at the transaction arrival rate of 160.1 kTPS, and the corresponding throughput is 146.7 kTPS. Due to the overhead of cross-shard transaction processing, the actual peak performance corresponds to approximately 1.8\(\times\) that of NeuChain at a transaction arrival rate. Before transaction accumulation, NeuChain+ exhibits a latency improvement of approximately 0.04 s compared to NeuChain. We speculate that this is caused by cross-shard transaction processing, which is evaluated and analyzed in detail in Section 7.5.  
Figure 7: Performance of NeuChain+ and NeuChain under a changing transaction arrival rate (SmallBank). (**a**) Throughput performance when changing the transaction arrival rate. (**b**) Latency performance when changing the transaction arrival rate.
# A Scalogram-Based CNN Approach for Audio Classification in Construction Sites
## 6 Implementation Aspects  
In this section, we provide some important remarks about the implementation aspects of the proposed idea.  
\begin{table}
\begin{tabular}{l l r} \hline \hline
**Layer (Type)** & **Output Shape** & **Number of Parameters** \\ \hline Rescaling & (None, 227, 227, 1) & 0 \\ Conv2D & (None, 55, 55, 96) & 11,712 \\ BatchNormalization & (None, 55, 55, 96) & 384 \\ MaxPooling2D & (None, 27, 27, 96) & 0 \\ Conv2D & (None, 27, 27, 256) & 614,656 \\ BatchNormalization & (None, 27, 27, 256) & 1024 \\ MaxPooling2D & (None, 13, 13, 256) & 0 \\ Conv2D & (None, 13, 13, 384) & 885,120 \\ BatchNormalization & (None, 13, 13, 384) & 1536 \\ Conv2D & (None, 13, 13, 384) & 1,327,488 \\ BatchNormalization & (None, 13, 13, 384) & 1536 \\ Conv2D & (None, 13, 13, 256) & 884,992 \\ BatchNormalization & (None, 13, 13, 256) & 1024 \\ MaxPooling2D & (None, 6, 256) & 0 \\ Flatten & (None, 9216) & 0 \\ Dense & (None, 4096) & 37,752,832 \\ Dropout & (None, 4096) & 0 \\ Dense & (None, 4096) & 16,781,312 \\ Dropout & (None, 4096) & 0 \\ Dense & (None, 5) & 20,485 \\ \hline \hline  & Total parameters: & 58,284,101 \\  & Trainable parameters: & 58,281,349 \\  & Non-trainable parameters: & 2752 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layers and number of parameters of the customized AlexNet.  
The computation of the CWT can be memory and computationally demanding. For this reason, we recommend not exceeding the chunk size; 30 ms or 50 ms represents a good compromise between the efficiency and tracking performance of the classifier due to the intrinsic non-stationarity of audio signals.  
The CWT applied to a 30 ms chunk returns a matrix of the size \(230\times 662\). In view of using such data as the input to a state-of-the-art CNN, it is convenient to resize the matrix to a commonly used size. Generally, \(227\times 227\) (for AlexNet) or \(224\times 224\) (for GoogLeNet, ResNet, and similar architectures) are adequate choices.  
However, saving more than 150,000 (see Table 1) floating-point matrices of \(227\times 227\) entries requires a large amount of disk space and a consistent quantity of RAM memory to load and process the dataset. For this purpose, after the resize, these matrices have been scaled to the interval \([0,\,255]\), converted to integer numbers, and saved as images. In this way, it is possible to work with this dataset on a normal office PC while avoiding memory explosion.  
Finally, to deal with such data, an additional Rescaling layer has been used in the customized version of AlexNet. This layer converts the integer input data back into the float interval \([0,\,1]\).
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 3 Network Architecture Model
### Network Architecture Model  
In this section, we propose a solution by seamlessly integrating the Institute of Electrical and Electronics Engineers standard (IEEE 802.15.6) with other wireless networks. This standard is specially designed for WBAN to gain the benefits of low power, a short range and high reliability while supporting a wide range of data rates for various applications [26].  
All the sensor nodes that were mentioned previously (ECG, EEG, EMG, heartbeat, blood pressure and body temperature) are placed on the patient to collect sensory data and are then ubiquitously disseminated to the healthcare staff via WBAN coordinator devices such as PDAs, laptops, Smartphone or robot-assisted devices. These devices will be equipped not only for IEEE 802.16 interfaces (for intra-body communication) but also for other wireless interfaces such as WiFi, UMTS, and LTE. Normally, a coordinator device always connects to the best available network, mainly in overlap coverage due to the popularity, flexibility or cost effectiveness. Nevertheless, in this paper, the QoS application requirements will be accounted to select the best connection for normal traffic, especially for emergency traffic types that are strictly required for accurate and timely transmission.  
Furthermore, to evaluate the performance of the proposed solution, we have also integrated CCN processing modules into all the network elements in extra-body communications, such as WiFi-access points, UMTS base stations, LTE eNodeB and edge routers. Figure 1 illustrates our network model architecture.
# Groundwater Mixing Process Identification in Deep Mines Based on Hydrogeochemical Property Analysis
###### Abstract  
Karst collapse columns, as a potential water passageway for mine water inrush, are always considered a critical problem for the development of deep mining techniques. This study aims to identify the mixing process of groundwater deriving two different limestone karst-fissure aquifer systems. Based on analysis of mining groundwater hydrogeochemical properties, hydraulic connection between the karst-fissure objective aquifer systems was revealed. In this paper, piper diagram was used to calculate the mixing ratios at different sampling points in the aquifer systems, and PHREEQC Interactive model (Version 2.5, USGS, Reston, VA, USA, 2001) was applied to modify the mixing ratios and model the water-rock interactions during the mixing processes. The analysis results show that the highest mixing ratio is 0.905 in the C12 borehole that is located nearest to the #2 karst collapse column, and the mixing ratio decreases with the increase of the distance from the #2 karst collapse column. It demonstrated that groundwater of the two aquifers mixed through the passage of #2 karst collapse column. As a result, the proposed Piper-PHREEQC based method can provide accurate identification of karst collapse columns' water conductivity, and can be applied to practical applications.  
Keywords:mixing process; mining groundwater; hydrogeochemical properties; PHREEQC; piper diagram +
Footnote †: journal: Applied Sciences
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.5. Slip Mode Frequency Shift Method (SMFS)  
In this method, the voltage phase of the DG terminal is perturbed with positive feedback, and the frequency deviation is monitored to detect the islanding condition. The voltage and current phase angle of the DG which changes with the frequency in a sinusoidal form is presented as [21]:  
\[\theta_{\mathrm{SMFS}}=\theta_{\mathrm{m}}\sin((\pi/2)(\mathrm{f}^{k-1}-\mathrm{ f}_{\mathrm{n}}/\mathrm{f}_{\mathrm{m}}-\mathrm{f}_{\mathrm{n}})), \tag{7}\]  
where \(\mathrm{f}_{\mathrm{n}}\) is the nominal frequency, \(\mathrm{f}^{k-1}\) is the earlier cycle frequency, and \(\theta_{\mathrm{m}}\) is the highest phase angle at the frequency \(\mathrm{f}_{\mathrm{m}}\).  
When the main grid is connected, the DG operates at the frequency with zero phase angle. However, when the main grid is disconnected, the DG voltage and frequency will be varied, and the error of phase angle will be increased. Therefore, the DG will be unstable to operate at the nominal frequency and the frequency disturbance will be further enhanced by the uncertainty which moves the system to operate in another operation condition either over or under the frequency range.  
The SMFS is simple to implement and it has a smaller NDZ compared to other active methods. Moreover, in the case of multiple inverter systems, the effectiveness of this method is high. On the other hand, when SMFS is applied, the power quality of the grid can be reduced, and the system's transient stability can be affected.