esa-sceva/satcom-chunk-collection huggingface dataset:
# Future of IoT Networks: A Survey
## Future IoT Network
### Supporting Heterogeneity of Network Technologies  
Many network protocols, including radio-frequency identification (RFID), Wi-Fi, Bluetooth, ZigBee, 3G, and LTE, work independently. A unifying architecture that can support heterogeneity of networking protocols, interoperability among network protocols and the devices is needed to enablethe IoT network [86]. In [58], the authors proposed six layers for the IoT network, which are listed as follows : physical layer, link layer, ID layer, network layer, end-to-end layer, and data layer. This is different from the legacy networking stack, but the main problem with this protocol stack is that it is focused only on the LoWPAN networking technology. In terms of wide-range communications, a large proportion of the communication features of IoT is concentrated on wide-range coverage as stated in the Weightless open standard [87]. It is true that IoT devices should support wide coverage; however, the current commodity network protocol requires additional technical features and chips that can support low power and yet increase communication coverage to support this feature. This will represent an additional cost for the manufacturers. To reduce the additional cost to enable IoT, the future IoT network should incorporate the idea of supporting interoperability among various network protocols. To encompass the feature to support different network protocols, the IoT network should be able to acquire the data from different networking protocols. Furthermore, the gateway for the IoT network should also support acquisition of various network protocols and connect the devices to the IoT network.
## 5 Finite Element (FE) Analysis Models
The transition temperature range (TTR) for each superelastic (pseudoelastic) SMA depends on its composition, which causes considerable variability. The SMA specimens used for pull-out tests were composed of 56% nickel by weight and 44% titanium. This composition has 1% more nickel content as compared to nominal Nitinol (nickel-titanium) alloys (55% nickel by weight and 45% titanium), and thus these SMA specimens show low austenite start temperature (\(A_{\text{s}}\)), which is \(-11\)\({}^{\circ}\)C [23]. The SMA specimens were heated at 450 \({}^{\circ}\)C (annealing temperature) for one hour before water quenching. In fact, the type of such superelastic SMA materials can be limitedly implemented in the warm area because austenite phase transformation without heating occurs at room temperature. For such reason, the behavior of the superelastic SMA was only taken into consideration for FE analyses regardless of austenite finishing temperature after completing austenite phase transformation. Figure 7: 3D finite element (FE) analysis models for the recentering slit damper system, (**a**): Element Mesh; (**b**): Loading and BC’s.
## 3 Results
### Temporal Variation
#### 3.3.4 October 2019  
The level of 40 umol/m\({}^{2}\) was exceeded in 10 national parks, and it was the highest in Ujscie Wurty NP (60 umol/m\({}^{2}\)), Wolinski NP (56 umol/m\({}^{2}\)), and Wielkopolski NP (52 umol/m\({}^{2}\)). The lowest levels were recorded in Bieszczadzki NP (34 umol/m\({}^{2}\)), Tatraznski NP (30 umol/m\({}^{2}\)), and Gorczanski NP (31 umol/m\({}^{2}\)) (Figure 6). The mean monthly NO\({}_{2}\) content in the air was greater than 40 umol/m\({}^{2}\) in 105 landscape parks, with the highest level being 85 umol/m\({}^{2}\), and the lowest being 26 umol/m\({}^{2}\). The level of 40 umol/m\({}^{2}\) was exceeded in 15 tourist regions: it was the highest in the region of Krakow-Czestochowa (60 umol/m\({}^{2}\)) and Swietokrzyskie (55 umol/m\({}^{2}\)), while the lowest level occurred in the region of Romanow-Zdroj (28 umol/m\({}^{2}\)). The level of 40 umol/m\({}^{2}\) was exceeded in 26 out of 46 health resorts (Figure 6). It was the highest in Swoszowice (87 umol/m\({}^{2}\)), Goczalkowice-Zdroj (73 umol/m\({}^{2}\)), and Sopot (70 umol/m\({}^{2}\)), and it was the lowest in Rymanow-Zdroj (28 umol/m\({}^{2}\)) and Polanczyk (29 umol/m\({}^{2}\)). The level of 40 umol/m\({}^{2}\) was exceeded in all provincial capitals. It was the highest in Katowice (92 umol/m\({}^{2}\)), Warsaw (84 umol/m\({}^{2}\)), and Krakow (78 umol/m\({}^{2}\)), and it was the lowest in Bialystok (45 umol/m\({}^{2}\)).  
The analysis of the relationship between the level of nitrogen dioxide air pollution and the number of deaths demonstrated a clear correlation for cancer and, to a slightly lesser extent, for the total deaths. Such a correlation did not occur for deaths from respiratory diseases (Table 3). This may indicate an indirect effect of the pandemic on deaths related to more difficult access to health care and not directly deaths from respiratory diseases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total Deaths per 100,000** & **Total Deaths from Cancer** & **Total Deaths from Respiratory Diseases per 100,000 Inhabitants** \\ \hline Mean 2020 (all districts, n = 380) & 0.18 & 0.32 & \(-\)0.19 \\ Mean 2020 (districts with \textgreater{}150,000 inhabitants, n = 50) & 0.44 & 0.54 & \(-\)0.12 \\ December 2020 (all districts, n = 380) & 0.09 & 0.25 & \(-\)0.19 \\ December 2020 (districts with \textgreater{}150,000 inhabitants, n = 50) & 0.38 & 0.51 & \(-\)0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pearson correlation coefficients between the level of NO\({}_{2}\) pollution and the number of deaths in the districts’ demographic data [31].
# A Lifestyle-Based Perspective on the Mobility Choices and Preferences of Urban Travelers
## 4 Results  
### Attitudinal Clustering  
Segmentation analysis was applied to 12 variables, expressing lifestyles and general attitudes. These variables were selected for decoupling the segmentation analysis from sociodemographic and mode-choice characteristics as well as from attitudes toward the mode choices or the trip itself, which are typically featured in previous studies [1; 5; 9; 16; 18]. First, hierarchical clustering was applied, setting the number of clusters to four. The optimal number of clusters was determined by examining the dendrogram produced during hierarchical clustering using [PERSON]'s linkage method. The dendrogram revealed a significant increase in the rescaled distance when merging beyond four clusters, indicating that forming more than four groups would combine distinct clusters. Similarly, fewer than four clusters would result in a loss of detail, merging groups with meaningful differences. Thus, the dendrogram supports the selection of four clusters as an optimal balance between interpretability and statistical robustness. Once the number of clusters was defined, k-means clustering followed, resulting in 12 statistically significant variables (\(p<0.05\)). The results of the clustering analysis are presented in Table 1, which displays the cluster centers (means), cluster size, F-ratio, and level of significance for the included variables. Each cluster represents a distinct group of individuals with unique lifestyle characteristics, which are described in detail below. Individuals in cluster 1 show significant trust in products promoted through media and social media as well as in the opinions of close acquaintances. They score highly in their recycling habits and care for energy savings, and they express strong trust in state-executed recycling processes. They place high importance on adapting to social developments but also feel the need to pursue social distancing. This cluster values exercise and has the highest score among all clusters from excluding meat from their diet. However, they have the lowest average in terms of both the number of electronic devices owned and the number of electronic activities performed. Cluster 1 can be labeled as \"Eco-Conscious Social Adapters\". The individuals belonging to cluster 2 have the highest trust among all clusters in products promoted through media and social media as well as in state-executed recycling processes. They place significant value on the opinions of close acquaintances and consider it important to adjust to social developments. They also score highly in recycling habits and care for energy savings. This cluster has the highest score in the pursuit of social distancing but the lowest score in considering exercise as a contributor to health. They have the highest average for the number of electronic devices owned and perform the greatest number of electronic activities. Cluster 2 can be labeled \"Digitally Engaged Conventionalists\".
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 3 Naming Scheme of SWIM Data
### Algorithm Implementation of Hybrid Naming Scheme  
In the process of SWIM providing or obtaining services, the realization process of the content name generated by SWIM service resource information is shown in Algorithm 1. The SWIM service resource first obtains the type, length, quantity, service quality, and other information of the service theme provided in the SWIM network according to the label attribute theme, and generates a summary value through the Bloom filter on the TLC element with the same name prefix. Determine the specific content suffix of the given information. Next, according to the TLC structure of the given format, the hierarchical components are divided according to the standard top-level string set. The flattened and attributed components are divided through the calculation of the hash algorithm and the identification of special attributes.  
```
Input: Service resource call information Output: content name
1: Procedure SHash(Prefix)
2:\(W_{S}\leftarrow\text{strlen}(\text{prefix})\)
3:Hash [0...k] \(\leftarrow\) 0;
4:Word \(\leftarrow\) 0;
5:Hash \(\leftarrow\) DJB(Prefix)
6:For i \(\leftarrow\) 1 to \(k\) do
7:Location \(\leftarrow\) hash[\(i\) - 1] % w\({}_{-}\);
8:Hash[\(i\)] \(\leftarrow\) (Prefix[location] + hash[\(i\) - 1]) % w;
9:Word \(\leftarrow\) word[(i\(\text{ }<\) - hash[\(i\)]);
10:Prefix\(\left(x^{T},x^{B}\right)\) \(\leftarrow\) \(x\);
11:If x \(<\) div &! match(x^{T})then;
12:Update(x^{T});
13:Or else, if x \(>\) div &! match(x^{T})then;
14:Positioning in \"BF\" := \(j\gets h_{k}\)(x^{B});
15:Increase j calculator value : \(C_{j}\) \(\leftarrow\) C\({}_{j}\) + 1;
16:Or else x \(>\) div
17:Positioning in \"BF\" := \(j\gets h_{k}\)(x^{B});
18:Increase j calculator value : \(C_{j}\) \(\leftarrow\) C\({}_{j}\) + 1;
19:End
```  
**Algorithm 1** Flow of hybrid naming scheme.
## 3 Numerical Simulation  
Various numerical simulations are performed to verify the feasibility and effectiveness of the optical signature and authentication cryptosystem by using a triangular association encryption algorithm (TAEA) and 3D Arnold transform in the Gyrator domains. Numerical experiments consider a hyperspectral image 'airport', which has 256 \(\times\) 256 \(\times\) 189 pixels as the secret information needed to be encrypted in the following experiments. A grayscale image of the color image 'Lena' having 256 \(\times\) 256 \(\times\) 3 pixels is considered as the image signature. The pseudo color composites combined of the 30 th, 70 th and 100 th band and the image signature 'Lena' are depicted in Figure 5a,b.  
In the following tests, matrix B (see (1)) is used for the transformation, and we set \(a=b=c=d=e=f=1\). The number of 3D Arnold transformation is \(k=3\), and the number of triangular association encryptions is \(a_{0}=3\). The parameters \(\theta\) in the Gyrator transform is set as 0.5. What is more, in the block mobile method of 3D Arnold, set \"upper left\" as \"1\", \"lower left\" as \"2\", \"upper right\" as \"3\" and \"lower right\" as \"4\". In the experiment, the normal order of movement is \"1234\".  
Figure 5: Experimental data: (**a**) pseudo color composites combined of 30 th, 70 th and 100 th band; (**b**) ‘Lena’ test image.  
The computer environment for the experiment is the Windows 10 system, Intel (R) Core (TM) i7-10700 CPU @ 2.90 GHz, and 8.00 GB of RAM. Then, we obtain the final encrypted and decrypted images shown as Figure 6a,b. The time required for the encryption and decryption is respectively 15.391 s and 13.283 s.  
The decrypted image (see Figure 6b) and the original image (see Figure 5a) are visually similar. This paper introduced a structural similarity index (SSIM) function in order to numerically measure the difference between the original image and the decrypted image. The SSIM value of two same images is equal to 1. If two images are very different, then the SSIM value is near to 0. The mathematical expression of the SSIM [40] is as follows:  
\[\text{SSIM}(A,B)=\frac{(2v_{A}v_{B}+r_{1})(2\partial_{AB}+r_{2})}{\big{(}v_{A }^{2}+v_{B}^{2}+r_{1}\big{)}\big{(}\partial_{A}^{2}+\partial_{B}^{2}+r_{2} \big{)}} \tag{13}\]  
where, \(v_{A}\) and \(v_{B}\) are the average of \(A\) and \(B\); \(\partial_{A}^{2}\) is the variance of \(A\); \(\partial_{B}^{2}\) is the variance of \(B\); \(\partial_{AB}\) is the covariance of \(A\) and \(B\); \(N\) is the value variation range of pixels, \(r_{1}=\big{(}i_{1}L\big{)}^{2}\), \(r_{2}=\big{(}i_{2}L\big{)}^{2}\), \(i_{1}=0.01\), \(i_{2}=0.03\).  
The following experiments were quantitatively analyzed in detail. Note that, since the output of the Gyrator transform is complex, the simulation appears to be a real function.
###### Abstract  
Focused Ion Beam-Induced Deposition (FIBID) is a single-step nanopatterning technique that applies a focused beam of ions to induce the decomposition of a gaseous precursor. The processing rate of FIBID increases by two orders of magnitude when the process is performed at cryogenic temperatures (Cryo-FIBID): the precursor forms a condensed layer on the surface of the cooled substrate, greatly enhancing the amount of material available for decomposition. Cryo-FIBID has been achieved so far by making use of liquid nitrogen-based cooling circuits, which require the passage of a flowing gas as a cooling agent. Here, the Cryo-FIBID of the W(CO)\({}_{6}\) precursor is performed using a coolant-free thermoelectric plate utilizing the Peltier effect. Performed at \(-60\)\({}^{\circ}\)C, the procedure yields a W-C-based material with structural and electrical properties comparable to those of its counterpart grown in coolant-based Cryo-FIBID. The use of the thermoelectric plate significantly reduces the vibrations and sample drift induced by the flow of passing coolant gas and allows for the fabrication of similar nanostructures. In summary, the reported process represents a further step towards the practical implementation of the Cryo-FIBID technique, and it will facilitate its use by a broader research community.  
focused ion beam; nanofabrication; direct-write growth; tungsten-carbon nanostructures; cryogenic nanolithography +
Footnote †: journal: Physics Letters B
## 2 Literature Review
Studies have used grab sampling or data acquisition from government authorities to analyze physico-chemical parameters such as pH [25], conductivity [26], hardness [27], and phosphate [28], and the WQI is calculated to identify the underlying issues. The literature reveals that the traditional indices are based on specific physico-chemical water parameters and thus have limitations that make these indices unsuitable for worldwide use. The uncertainty of the WQIs makes them unpredictable for complex environmental situations [29]. These indices are biased to a set of parameters, place, area and purpose of use. The dynamic nature of the water body can cause certain changes in the physico-chemical properties [30]. Moreover, the influence of air pollutants, meteorological features and hydrological features on the aquatic ecosystems is ignored in the development of the WQI method [31]. These challenges indicate that most WQIs fail to accurately classify a water body. Therefore, there is a need for a universally accepted index that removes the uncertainties and bias in the traditional standards.
# Underwater Object Detection Method Based on Improved Faster RCNN
## 3 Proposed Method
### Improvement of Bounding Box Mechanism
#### 3.5.2 Soft-NMS  
In the process of underwater biological object detection, the two-stage object detection algorithm will generate multiple bounding boxes for the same underwater bio-logical object in the image, and will generate confidence scores. There is overlap between the regions of these bounding boxes. In order to filter out qualified candidate prior boxes and eliminate redundant candidate priori boxes, the traditional NMS algorithm will select the bounding box \(A\) with the highest score. Then, it will calculate the IOU values of other bounding boxes that have significant overlap with \(A\). If the IOU value exceeds the set threshold, it will be deleted. If the IOU value is lower than the set value it will be retained, and the bounding boxes that do not overlap will also be retained. However, according to the operation of the traditional NMS, the confidence score will be low and the bounding box with the IOU value greater than the set value will be deleted, which may not detect underwater objects.  
The Soft-NMS algorithm does not simply remove those bounding boxes whose IOU is greater than the threshold, but only reduces the confidence in them. The Soft-NMS algorithm only needs to make simple modifications to the original NMS algorithm without adding additional parameters, and therefore it is easy to implement and can be easily applied to different object detection algorithms. The calculation formula of the Soft-NMS algorithm is shown below [40]:  
\[S_{i}=\left\{\begin{matrix}S_{i}&iou(M,b_{i})<N_{t}\\ S_{i}(1-iou(M,b_{i}))&iou(M,b_{i})\geq N_{t}\end{matrix}\right. \tag{4}\]  
where \(M\) represents the candidate prior frame with high score, \(b_{i}\) is the candidate prior frame to be processed, \(N_{t}\) is the set IOU threshold, and the IOU function is a weight function for attenuation, which is used to attenuate the scores of the adjacent candidate prior frames overlapping with the candidate prior frame with the high score. The higher the degree of overlap, the greater the attenuation degree of the scores, and \(S_{i}\) is the score of the \(ith\) candidate prior frame in the \(S\) set.  
In the improved Faster RCNN underwater object detection scheme, the original NMS algorithm is replaced by Soft-NMS to improve the detection performance of the model.
## 3 Results and Discussion
### Design of the Energy-Saving Processes
#### 3.2.1 Design of the Energy-Saving Processes by Recovering the Sensible Heat from the Hot Streams
Another configuration for energy saving by using both the latent heat of the overhead vapors and the sensible heat of the hot solvent is shown in Figure 10b. Unlike the previous configuration shown in Figure 10a, the B3 hot extractive solvent at 464.93 K first gave some of its sensible heat in the QR2 reboiler to provide 1810 kW of the 5967 kW required in the reboiler of the EDC; after this, the temperature of the B3 stream decreased at 420.08 K. The B3 stream was still hot and could be used further to ensure 1867 kW in the auxiliary reboiler QR1-2 of the PC. Even after this heat exchange, the temperature of the B3 stream  
Figure 10: The TCED process with two energy-saving configurations, using both the latent heat of the overhead vapor and the sensible heat of the hot solvent: (**a**) LH-SHI; and (**b**) LH-SH2. was still high (i.e., 369.63 K) and could further preheat the EDC feed in Heater 3. Thus, the temperature of the B3 stream, after Heater 3, remained at only 348.15 K, the temperature necessary for the recirculated solvent, removing the necessity to use a supplementary cooler for the extractive solvent. By preheating of the EDC feed with the hot solvent, the EDC reboiler duty was decreased from 6639 kW to 5967 kW. As previously mentioned, B3 hot stream ensures in QR2-1 1810 kW from the total 5967 kW required at the reboiler of the EDC. For the other part of the reboiler duty of the EDC (i.e., 4157 kW), an auxiliary reboiler needs to be installed. This auxiliary reboiler (noted as QR2-2) consumed 7587 kg/h medium-pressure steam. Three benefits are identified in this configuration: the reboiler duty of the PC is completely provided by the energy saving from both the latent heat of the SRC overhead vapor and a part of the sensible heat of the B3 hot solvent; the reboiler duty of the EDC is partially ensured by the other amount of the sensible heat of the B3 hot solvent; and, finally, the extractive solvent cooler is eliminated. Compared with the conventional TCED process, it was found that the energy saving for QR1 was 100%; for the QR2 reboiler, the energy saving was 37.4%, and for the QCooler, 100%.
## 2 Materials and Methods
### Neural Resolution Enhancement  
The last technique that will be discussed is Neural Resolution Enhancement [31], which leverages the properties of any already trained image segmentation CNN to intelligently increase the resolution of the output mask at no cost, even beyond the resolution of the original image.  
To understand how this method operates, let us describe how a threefold increase in the resolution along the z-axis would be performed (refer to Figure 4). First, the resolution of the input image \(X\) is triplicated along the z-axis (by using bicubic interpolation, for instance), therefore becoming \(\hat{X}\). Then, three new images \((X_{0},X_{1},X_{2})\) are built by taking z-slices from \(\hat{X}\) in such a way that they have the same size (dimensions) and spacing (voxel size) as \(X\), but are offset by different sub-voxel amounts along z-axis (in fact, note that \(X_{0}\equiv X\)). Then, \(X_{0},X_{1}\) and \(X_{2}\) are fed through the CNN, and three segmentation masks are obtained \((Y_{0},Y_{1},Y_{2})\). Finally, all three predictions are combined by stacking them in the correct order, hence obtaining \(\hat{Y}\), which is a predicted mask with three times the resolution of \(X\) along the z-axis. This same procedure could be applied to any number of dimensions simultaneously, although the inference cost would scale abruptly, as all the possible sub-voxel displacement combinations would have to be computed.  
This method, albeit simple, is extremely powerful, as it allows to predict (rather than to interpolate) segmentation masks beyond the resolution of the original image. The problem of interpolation is therefore shifted from the mask domain to the image domain, where the conveyed information is still complete and not yet binarized. Furthermore, it can be applied to any already trained segmentation CNN, as a simple post-processing step. Figure 5 shows an example application.  
In the context of our problem, z-axis resolution is triplicated to reduce the impact of the final mask interpolation. This is: once the CNN outputs a segmentation mask, it must be transformed back to the space of the original input image (same resolution, spacing, physical orientation, position, etc.). Although this is necessarily a lossy process, by leveraging this technique, the predicted mask can have a higher resolution, which significantly mitigates the issue.  
Figure 4: Visual representation of the Neural Resolution Enhancement method to triplicate the resolution along the z-axis.