esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background  
### User-Based Recommender Systems  
The fundamental concept is to identify users who share similar preferences with the target user \(u\), and utilize this information to predict ratings for the unrated items by \(u\).  
The cosine distance is a measure of similarity between two vectors, often used in recommender systems to calculate the similarity between items or between users. In the context of recommender systems, the cosine distance is often used with the cosine similarity measure to compute the similarity between items or between users based on their rating patterns.  
The cosine similarity between two vectors \(u\) and \(v\) is calculated as the cosine of the angle between them, which can be expressed as the dot product of the two vectors divided by the product of their magnitudes. The cosine distance is simply the complement of the cosine similarity, i.e., 1 minus the cosine similarity. The cosine distance between two vectors \(\mathrm{u}\) and \(\mathrm{v}\) is therefore calculated as 1 minus the cosine of the angle between them. The cosine distance ranges from 0 (indicating perfect similarity) to 2 (indicating maximum dissimilarity). In recommender systems, the cosine distance is often used as a dissimilarity measure, so items or users with a smaller cosine distance are considered more similar.  
The cosine similarity between two vectors \(u\) and \(v\) is defined as the cosine of the angle between the two vectors. It is a measure of similarity between two non-zero vectors in an inner product space and can be calculated using Equation (1).  
\[sim(u,v)=\frac{u.v}{||u||||v||} \tag{1}\]  
where:  
* \(u.v\) is the dot product of vectors \(u\) and \(v\);
* \(||u||\) and \(||v||\) are the Euclidean norms of vectors \(u\) and \(v\), respectively.  
By computing the dot product of two vectors and dividing it by the product of their magnitudes, we can determine the cosine similarity between them. This similarity score falls between \(-1\) and \(1\), where 1 signifies complete similarity, 0 indicates orthogonality, and \(-1\) represents complete dissimilarity. Equation (2) is utilized to predict the rating that a user \(u\) is likely to assign to an item \(j\)[45].  
\[\bar{R}_{u,j}=\bar{K}_{u}+\frac{\sum_{v\in KNN}(R_{v,j}-\bar{R}_{v}).sim(u,v) }{\sum_{v\in KNN}|sim(u,v)|} \tag{2}\]  
where \(KNN\) is a set of k nearest neighbors of user \(u\), \(R_{v,j}\) is the rating of user \(v\) on item \(j\), \(\bar{R}_{u}\) and \(\bar{R}_{v}\) are the average ratings of users \(u\) and \(v\) respectively, and \(sim(u,v)\) denotes the similarity between users \(u\) and \(v\).
## 4 Simulation Results
### Results of FS-ANVPC  
While we have conducted many simulations for FS-ANVPC, the scenarios we present in this paper are the ones shown in Figure 9. The results show the relation between QoS and NiPC for different PCD values. The effect of ACP or AS on NiPC can be seen when a pair of subfigures are selected for comparison, provided that one of the parameters has the same value.  
In Figure 9a, we show the results for ACP = 0.1 and AS = 0.1. The results indicate that no neighbor vehicle can be of much help for _the vehicle_ in these conditions. In Figure 9b, we see that there is some improvement in NiPC values when the neighbor vehicles have large amounts of storage resources to share. Nevertheless, the probability _the vehicle_ uses the resources of these neighbors remains very low. This is can be attributed to the low ACP, which denotes that there will be no guarantee that these neighbors can run any additional applications successfully.  
In Figure 9c,d, we see how an increase in ACP, however small, affects NiCP. A neighbor vehicle offering a medium amount of computing power has more chances of being selected to help _the vehicle_, provided that their connection is of high quality and they stay connected for a long time.  
When the neighboring vehicles have much more to offer in terms of ACP, their NiPC values are improved even more. We can see this effect in Figure 9e,f. Nevertheless, if they have almost no storage to offer, the neighbors should be within the communication range of _the vehicle_ for a long time and the QoS must be above the moderate level to enable quick data transmission all the time. On the other hand, when the neighbor vehicles are willing to and can share abundant resources in terms of both storage and computing power, we see that the system decides that they are capable of helping even if they do not stay connected with _the vehicle_ for a long time. These neighbors are sometimes chosen even when the PCD is short or the QoS is not in high values, since letting their resources unexploited would lead to a huge amount of resources in the edge layer being unused.
## 4 Experimental Results and Discussion
### The Visualization Heat Map  
In order to understand whether the self-attention mechanism in the encoder produces adequate effects in the model, we applied the visualization heat map in the last self-attention mechanism in the encoder. A brighter color shows more attention, while a darker color represents less attention.  
We randomly selected a sentence (\"World currency standards have enormous inertia\") from the WMT 2014 English-German corpus to show the heat maps.  
From the heat maps shown in Figures 7 and 8, we found that the self-attention mechanism in the original Transformer can only understand which token is important to the whole sentence, but X-Transformer upgrades to a higher precision level. The X-Transformer can understand the influence relation between tokens, which also demonstrates a better comprehension ability. These findings also support that the X-Transformer significantly outperforms the Transformer and BERT-fused Transformer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**BLEU (EN–DE)**} & \multirow{2}{*}{**BLEU (EN–FR)**} & \multicolumn{2}{c}{**Training Time**} \\  & & & **(under the Same Env.)** \\ \hline Transformer [4] & 27.3 & 38.1 & 1\(\times\) \\ Transformer + Large Batch [14] & 29.3 & 43.2 & \\ BERT-fused Transformer [18] & 30.75 & 43.78 & 2.42\(\times\) \\ X-Transformer & 46.63 & 55.63 & 0.35\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: BLEU scores on EN–DE and EN–FR newstest 2014 corpus according to model.  
Figure 7: Heat maps of the sentence “World currency standards have enormous inertia” obtained with the Transformer.  
Figure 8: Heat maps of the sentence “World currency standards have enormous inertia” obtained with the X-Transformer.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 3 The System Model and Problem Statement
### Problem Statement  
In this part, some definitions are given firstly to ensure clear statements. They are as follows:  
**Definition 1**.: _Transmission Reliability. It represents the statistical probability of packets successfully forwarded to sink from a node in QoS level. Let \(\delta_{i}\) denote the transmission reliability of packets transmitted from node \(i\) to a sink._  
**Definition 2**.: _Network Reliability. It represents the statistical probability of packets successfully forwarded to the sink from all nodes in QoS level. Each sensed data is received by the sink with a probability not less than \(\delta\), where \(\delta\) represents the network transmission success rate constraint._  
As stated in the system model, the information sensed by an individual non-collecting node is collected by some special colleting nodes, and then these collecting nodes are responsible for data aggregation and information forwarding to the sink. Each collecting node at depth \(d\) or tier \(d\) has at least \(x\) (\(x\geq 1\)) collecting parents at depth or tier \(d-1\) (except the hotspots and there is only one parent collecting node in hotspots). It transmits the packet through the wireless broadcast channel to its parents (\(1+\mu\)) times, where, \(\mu\) denotes the number of retransmission times. We say that a node successfully transmits a packet if the broadcast packet is successfully received by one of \(x\) parents. In the meantime, we try to minimize network delay.  
The focus of the paper is to improve the network delay performance while achieving the same level of network reliability. That is \(\delta_{i}\geq\delta\). To sum up the above, the goal can be expressed as the following expression, that is, as for any node \(i\) in the network it meets the following formula:  
\[\left\{\begin{array}{c}\min(D)=\min\max_{0<i\leq n}(\Gamma_{i})\\ \Gamma_{i}=\sum\limits_{k=0}^{h}\tau_{k}\\ s.t.\quad\delta_{i}\geq\delta\end{array}\right. \tag{3}\]  
Hence, the target is minimizing the maximum transport E2E delay under the guarantee of transport service quality, e.g., \(\delta_{i}\geq\delta\).
## 3 Results and Discussion
### Material Properties
#### 3.4.3 Surface Testing Properties  
Figure 6D describes the percentage distribution of surface properties. The predominant investigations involve quantifying wear loss (60%) and changes in the surface roughness (40%) of CAD-CAM RCBs.  
The durability and clinical success of CAD-CAM resin blocks is significantly influenced by their resistance to wear, which is a critical aspect of material surface analysis [45; 46]. Studies such as those by [PERSON] et al. [47] and [PERSON] et al. [30] have focused on comparing the wear resistance of CAD-CAM resin composite blocks to traditional ceramic materials, finding varied results in terms of durability and efficacy of surface treatments like sandblasting and silane application. [PERSON] et al. [48] further emphasized that the type of surface treatment and adhesive used can dramatically affect the retention strength and overall wear resistance of these materials, underlining the importance of material handling and preparatory processes in extending the longevity of dental restorations.  
Surface roughness and biofilm formation are additional concerns with CAD-CAM resin blocks, as they can influence the material's aesthetic and hygienic properties. Studies indicate that resin composites might promote bacterial growth more than other restorative materials, leading to increased biofilm accumulation, as noted by researchers [49; 50]. The choice of polishing procedures significantly affects the surface topography, with techniques like those evaluated by [PERSON] et al. [51] and [PERSON] et al. [52] showing that certain procedures can lead to smoother surfaces, thereby potentially reducing biofilm adherence and improving the material's clinical performance.  
These findings collectively suggest that while CAD-CAM resin blocks present a viable option for dental restorations, their success heavily relies on the appropriate selection of materials, meticulous surface treatment, and ongoing maintenance to mitigate wear and biofilm challenges. The evolution of surface treatments and polishing methods, as explored by [PERSON] et al. [51], and the impact of toothbrush abrasion, as studied by [PERSON] et al. [52], underscore the dynamic nature of this field, highlighting the need for continuous research and adaptation of clinical practices to optimize outcomes with CAD-CAM technologies.
# AI in Gravitational Wave Analysis, an Overview
## 8 Summary and Conclusions  
The statistics presented in this analysis indicate a growing interest in utilizing deep learning and machine learning techniques for gravitational wave analysis. These techniques have proven to be useful in detecting and characterizing gravitational waves, resulting in an increasingly intensive use of techniques and algorithms based on AI.  
The use of GPUs and specialized frameworks has played a significant role in facilitating research in this field. GPUs have been instrumental in speeding up the computations and accelerating the analysis process. Specialized frameworks, on the other hand, have made it easier for researchers to develop and implement complex deep learning and machine learning models for gravitational wave analysis.  
Moreover, the increasing interest in deep learning and machine learning techniques in gravitational wave analysis signifies the potential of these techniques in advancing our understanding of the universe. The ability to detect and analyze gravitational waves has opened up new avenues for studying black holes, neutron stars, and other astrophysical phenomena.  
In conclusion, the statistics presented demonstrate the crucial role of deep learning and machine learning techniques in gravitational wave analysis and the significance of using GPUs and specialized frameworks in accelerating research in this field. This area of research is likely to continue to grow and evolve in the future, contributing to our understanding of the universe and its mysterious phenomena.  
Validation, L.T.; Investigation, F.G., V.B. and G.C., Data curation, F.G., V.B. and G.C.; Writing--original draft, F.G., V.B. and G.C.; Writing--review & editing, F.G., V.B. and L.T.; Supervision, L.T.; Funding acquisition, L.T.; Methodology, L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems
## 3 Methodology and System Model
### Network Planning Procedures  
Considering the industrial factory layout, the norm is to have the machines located in predefined positions. Since field devices are connected to machines in an industrial factory, their locations are considered fixed. As a result, the focus in this work is to ensure that all field devices are reliably connected by positioning gateways, access points, and routers in the right places. Without loss of generality, it is assumed that an industrial factory usually has dimensions of W meters by L meters in the form of a square or rectangular layout. The field devices represented by the red squares are arranged within the factory, as shown in Figure 3a.  
The first phase in network planning is gridding the factory environment into multipath triangular cells. If required, a router should be deployed on each vertex edge in Figure 3b. To ensure compatibility, each triangular cell's edge length should not exceed the specific transmission range of the WirelessHART standard, i.e., a single transmission link hop is represented by one vertex edge.  
The second phase is to coordinate the access points. They are placed in a position based on the least total hops to the field devices in the layout of the factory. It is performed by calculating the shortest hops between any field device and any vertex. The position of the access point can be defined in this manner, as shown in Figure 4a. For simplicity, we show one access point at the center of the network layout located at the same distance of each field device. The gateway is connected to the access point through cables; therefore, locating it is relatively less constrained.  
In phase three of network planning, routes between field devices and access points are planned. The routing protocol is known to detect the highly suitable router subset of  
Figure 2: An example of the broadcast graph (\(G_{B}\)) (**a**), uplink graph (\(G_{U}\)) (**b**), and downlink graph (\(G_{d}\)) (**c**).  
all routers in the network to link field devices to access points. In contrast, the process of network planning works differently when determining the required routers is done first and then deploying them wherever needed. This paper considers the principle of multipath routing, which employs the principle of a shortest path with least routers and shortest hops to create a network planning algorithm as shown by the Algorithm of multipath routing protocol. The access points are connected to the gateway on a wired basis, therefore the placement of the gateway in the network is not a constraint. Figure 4b shows the deployment of the routers along the paths extended between the field devices and the access point. Table 2 provides the variables used in this paper along with their notations.  
Figure 4: (**a**) Phase 2: place access point, (**b**) Phase 3: plan routes and place the routers.  
Figure 3: (**a**) Original layout of factory, (**b**) Phase 1: forming multipath triangular cells grid.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 4--Attack on Physical Devices  
As the majority of IIoT devices function in open and unrestricted settings, this poses high-security threats for those devices against physical attacks [89]. The most common damage could be device damage and disconnection, which may cause serious disruption over the network. For instance, an attacker physically disconnects a device (e.g., laptop, smartphone, air-conditioner machine) from the Internet.  
Control over IIoT devices can be made in two ways when an attacker controls a single device and collectively controls several devices. In the first case, an attacker can insert a user's home network and disconnect a device from the Internet [90]. We argue that this kind of attack is not uncommon to IIoT systems. This can be applied to any networked computing system. However, the resource-limited characteristics of some devices make them further endangered where conventional security mechanisms cannot be embedded properly [61; 91]. This will disrupt the system as data will be coming from various sources.  
In the latter case, an attacker can control the functionality of many IIoT devices and manipulate services by disrupting the operation, e.g., block the service of the monitors in a traffic controlling unit to make unavailable the current traffic transport flows [92]. Furthermore, most of today's televisions (TVs) in the marketplace are embedded with a feature to surf the Internet in real-time, but in a TV, there are no firewall, antivirus protection, or monthly software update features. Thus, it is possible for an attacker to lock and jam the TV screen with unwanted advertisements for extortion of payment by money (e.g., bitcoins) to unlock the screen. Likewise, an attacker can lock the door of a refrigerator and turn down the cooling system, and demand payments to return the refrigerator to its normal operations. It is also possible that having compromised a smart physical device, an attacker can capture and control a remote car or the other IIoT devices that are connected to that device, e.g., taking control over an aircraft operating system [93].  
In addition, attackers can malfunction devices through data exfiltration. In an IIoT system, data exfiltration can be done by an attacker when performing various malicious activities through different techniques. For instance, rotate the service of an electric heater in an unauthorized way, so that it cools during winter and heats during summer, and turn on the lights of a room when a person leaves the house [92; 94].
# A Multi-Carrier Waveform Design for 5G and beyond Communication Systems
## 5 Conclusions  
This paper presents a phase-rotation-based multicarrier waveform for 5G and beyond communication networks. The simulations have been conducted in MATLAB(r) to investigate the PAPR performance of the designed waveform. In this research work, a joint SLM and GCL precoding was applied to improve the PAPR gain. The results of the computer simulations show that the proposed waveform gives better PAPR results when compared to standard NOMA-UFMC waveforms i.e., at clip rate of \(10^{-3}\), approximately 5.4 dB of PAPR gain is obtained once proposed waveform is compared with the standard NOMA-UFMC waveform by using 4-QAM. It is important to mention here that the proposed waveform has a comparatively high implementation complexity. However, the complexity of the proposed waveform can be minimized by using smaller size FFTs and by reducing the number of sub-bands. A significant advantage offered by the proposed waveform over the other waveforms is its enhanced capacity with reduced PAPR. The capacity of the proposed waveform is quite high in comparison to existing waveforms. This high capacity is because of the power domain multiplexing. Therefore, massive numbers of users can be supported under certain conditions. Hence, it can be concluded that the proposed waveform may be one of the best choices amongst the other waveforms proposed for the 5G and beyond communication networks.  
**Author Contributions:** Supervision, I.B.; Writing--original draft, I.B. and U.F.; Writing--review and editing, N.U.H., M.Z. and V.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiment and Analysis
### Producing Spectrograms  
With the script described in Section 3.4.1, 2410 hammering sounds were extracted from the sound files. The parameters used in STFT were as follows: the size of the window function \(R=64\), the time lapse \(L=32\), the sampling frequency \(f\)s = 24 kHz, and the frequency discretization \(N=8192\).  
As shown in Table 2, there are 10 different rebound values in total, each of which corresponded to 200-260 spectrograms. According to the rebound values, all the spectrograms of Batch A were assigned to 10 different file folders, and the folders were named by their rebound values. After that, the modified K-means clustering algorithm described in Section 3.4.3 was used to remove the invalid spectrograms in every folder. Table 3 shows the filtering result of Batch A.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Mean** & **Variance** & **No.** & **Mean** & **Variance** & **No.** & **Mean** & **Variance** \\ \hline A1 & 20 & 1.12 & A6 & 54 & 1.56 & B1 & 61 & 1.88 \\ A2 & 73 & 1.13 & A7 & 67 & 1.18 & B2 & 48 & 2.37 \\ A3 & 48 & 1.50 & A8 & 58 & 1.72 & B3 & 64 & 2.39 \\ A4 & 39 & 1.73 & A9 & 26 & 1.00 & B4 & 72 & 2.43 \\ A5 & 62 & 1.15 &
## 3 Experiment and Results
### Datasets
#### 3.1.2 Objective Model-Eye Dataset  
A system for acquiring eccentric infrared images of model eyes was used to collect images of 20 different diopters, ranging from [\(-6.0\) D to \(+6.0\) D] in 0.5 D intervals. For each diopter, 3300 sets of images were captured, with varying pupil sizes and positions relative to the camera. The dataset was divided into training and testing sets at a ratio of 4:1.