esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
### Theory
#### 1.2.4 Regularization of the Pseudoinverse  
As can be noted, when singular values are very small or null, their reciprocals are not well defined. Small singular values correspond to high frequency elements, which contain the finer details of the image (interesting to keep) as well as most of the noise of the data [15; 16; 49] (which amplifies the noise in the reconstructed image). To deal with this problem, we used regularization methods [50]. A common regularization approach is truncated singular value decomposition (TSVD), which is obtained by setting to 0 the reciprocal singular values larger than a chosen threshold [51].  
\[s^{\dagger}=\frac{1}{s}ifs>\varepsilon\ ;else\,s^{\dagger}=0, \tag{7}\]  
A less extreme approach is Tikhonov regularization [50], which replaces the reciprocal singular values 1/s by:  
\[s^{\dagger}_{k}=\frac{s}{\left(s+k\right)^{2}}, \tag{8}\]  
where \(k\) is a regularization parameter [52; 53].  
Very often in iterative reconstruction methods, the number of iterations serves as a regularization parameter [54]. A small number of iterations corresponds to large regularizations. In fact, it was found that a PINV regularization scheme such as the resulting images coincided with the ones of the iterative Landweber algorithm [55]. The Landweber regularization would replace the reciprocal values by (see Appendix A) [56]:  
\[s^{\dagger}_{n}=\frac{1-\left(1-s^{2}\right)^{n}}{s}, \tag{9}\]  
where \(n\) is the number of Landweber iterations. This implies that through the PINV, an iterative Landweber reconstruction can be performed in one single step.  
Furthermore, the number of iterations in a Landweber regularization (Figure 2) can be related with the regularization parameter \(k\) in Tikhonov regularization as:  
\[Tikh(k=1/n)\simeq Landweber(n), \tag{10}\]  
We present our results in terms of equivalent Landweber iterations, allowing us to study resolution recovery and noise in the image in terms of the number of equivalent iterations, as is usually done for iterative methods.
## 4 Discussion  
### Rationality of Choosing SST as the Only Environmental Input Variable of the Model  
The present study employed the AlexNetMini model, utilizing SST, longitude, latitude, and temporal data to forecast the fishing grounds of purpleback flying squid in the Northwest Indian Ocean. Previous investigations have clearly demonstrated a significant correlation between the distribution of purpleback flying squid fishing grounds and SST in both the Northwest Indian Ocean and the South China Sea [6,7]. This correlation provides a strong rationale for incorporating SST as an input variable in relevant models. However, it is important to note that the scientific community has also recognized that the distribution of the purpleback flying squid is not solely determined by SST. Some studies, such as those cited in [6,8], have suggested that other environmental factors, such as sea surface salinity and chlorophyll-a concentration, also play significant roles. However, a trade-off emerges when considering integrating multiple environmental variables into the model. Incorporating additional variables will increase the number of channels, which, in turn, will lead to an increase in the number of parameters in the training results. This can potentially endow the model with a higher level of accuracy. Conversely, using fewer variables will reduce the number of model parameters. Although this simplifies the model structure to some extent and cuts down on computational costs, it may also limit the amount of information the model can learn, ultimately resulting in a decrease in model accuracy.  
The results of our study appeared to be quite promising. They indicated that by utilizing only SST data, we could achieve satisfactory predictive outcomes for forecasting the fishing grounds of the purpleback flying squid. This finding not only validated our approach but also offered a potential pathway for more efficient and targeted research in this area. We may further explore the combination of SST with other key environmental factors, potentially discovering a more balanced method to incorporate multiple variables without significantly increasing training time and model complexity in the future.
## 3 Fuzzy Set Theory-Based Risk Assessment System for Navigational Obstacles
### Collision Risk Assessment Model
#### 3.3.2 Calculation of Collision Probability  
Seven input variables were used for calculating the probability of collision: number of simulations (num_trials, \(N\)), size of the four-level grid (grid_size_level_4, \(L\)), number of drifting obstacles (num_dirp_objects, \(D\)), size of the drifting obstacles (drip_level_size, \(S\)), speed of the drifting obstacles (\(V\)), number of moving ships (num_moving_objects, \(M\)), and whether the direction of movement of the ships is fixed (direction_fixed, \(F\)).  
The positions and movements of the obstacles and ships are defined as follows. First, the initial position of each drifting obstacle is given by:  
\[O_{0}=L\times rand(D,2), \tag{1}\]  
where \(L\times rand\)(\(D\),2) generates the \(x\), \(y\) coordinates for \(D\) obstacles, ensuring they are randomly distributed within a grid of size \(L\).  
Next, the path of each ship, denoted as \(P_{i}\), is defined by random start and end points given by:  
\[P_{i}=L\times rand(M,2,2), \tag{2}\]  
where \(L\times rand\)(\(M\),2,2) generates random \(x\), \(y\) coordinates for the start and end points of \(M\) ships within a grid of size \(L\).  
The drift direction of each obstacle is expressed as:  
\[\psi_{i}=rand(1,2)-0.5, \tag{3}\]  
where \(rand\)(1,2) \(-0.5\) generates random values between \(-0.5\) and \(0.5\) for the \(x\) and \(y\) components of the direction, which are then normalized to a unit vector.  
Assuming constant direction and speed, the position of an obstacle at each new step (\(O_{i+1}\)) is updated by adding the product between its movement direction (\(\psi_{i}\)) and speed (\(V\)) to its current position (\(O_{i}\)), as follows:  
\[O_{i+1}=O_{i}+\psi_{i}\times V. \tag{4}\]  
Subsequently, the shortest distance between the path of a moving ship (passing through the start and end points) and a drifting obstacle is calculated as:  
\[distance=\frac{\left|det\left[P_{j,end}-P_{j,start},\ P_{j,start}-O_{i}\right] \right|}{\left\|P_{j,end}-P_{j,start}\right\|}, \tag{5}\]  
where _distance_ is the perpendicular distance between the obstacle and the ship's path. The determinant is used to compute the area of the parallelogram formed by two vectors: the vector representing the direction of the ship's movement, \(P_{j,\ end}-P_{j,\ start}\), and the vector traced from the starting point of the ship's path to the obstacle's position, \(P_{j,start}-O_{i}\). This area is then divided by the length of the path vector, \(\left\|P_{j,\ end}-P_{j,\ start}\right\|\).  
Finally, a collision condition is considered to occur if the _distance_ is less than or equal to the obstacle size \(S\):  
\[distance\ \leq\ S. \tag{6}\]  
The collision probability is calculated as the number of collisions that occur over \(N\) simulation runs:  
\[P=\frac{collisions}{N}. \tag{7}\]
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 4 Electrochemical Biofilm Control
### Electrochemical Approaches to Biofilm Removal  
Up until now, a few electrochemical approaches for the removal of biofilm from conductive surfaces have been designed and tested [185]. Two of them utilize the cathodic evolution of hydrogen [186, 187] and the potential pulse/reverse pulse technique [188]. The principles of these two approaches are described below and demonstrated in Figure 6.  
Figure 5: Schematic representation of biofilm impedance chamber with incorporated MEA chip.  
Figure 6: Principles of electrochemical methods developed for biofilm removal from stainless-steel substrates.  
Cathodic hydrogen evolution can be considered as one of the possible ways to effectively remove biofilm from various metal surfaces, such as titanium dental implants [186] and stainless-steel substrates [187]. It has been shown that the application of potentials lower than \(-1.5\) V vs. Ag/AgCl within seconds is sufficient for complete removal of 10-day old _Pseudomonas aeruginosa_ biofilm formed on a 316L stainless-steel substrate [187].  
Besides this, the bacterial biofilm removal can be carried out using a potential pulse/reverse pulse technique employing a periodic waveform that consists of anodic and cathodic pulses, usually lower than 30 s. A regular alternation of pulses then results in an effective stripping of thin metal layers with adhered biofilm. In addition, this regenerates the original mirror-like shiny surface of stainless-steel substrates [188].  
Although these approaches are very effective, they have not found a wider use. From a practical point of view, serious technological problems can be expected for biofilm removal from large areas, especially from pipelines. Thus, electrochemical approaches should at least be verified for the smaller components of equipment used in the food industry.
## 2 Related Works
### Summary  
Summarizing the various applications of deep learning and GAN technologies in investigating IoT attack classification, as shown in Table 1. Among them, \"binary classification\" refers to methods that classify attacks into only two categories: \"attack\" and \"non-attack\". On the other hand, \"multivariate classification\" signifies approaches that classify attacks into various distinct attack categories. Most methods achieve an accuracy of over 80% in binary classification, some even reaching 99%. However, there is more variability in the classification accuracy of these methods in multivariate classification. A method is considered superior and practical if it can achieve an accuracy of 95% or higher in multivariate classification, as it demonstrates feasibility and real-world utility.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Dataset** & **Method** & **Accuracy** & **Remark** \\ \hline
[7] & CIC DDoS 2019 & DNN & 94.57\% (M) & Analyze the statistical characteristics of \\  & UNSW-NB15 & & UNSW-NB15: 97.48\% (M) & packet traffic \\
[8] & BoT-IoT & DNN & BoT-IoT: 83.82\% (M) & Multivariate classification of four datasets \\  & ToN-IoT & & ToN-IoT: 69.53\% (M) & based on the DNN model \\  & CSE-CIC-IDS2018 & & CSE-CIC-IDS2018: 97.21\% (M) & \\  & & & KDD Test+: 81.33\% (M) & The analysis is mainly based on four parts: \\  & & & KDD Test-21: 64.81\% (M) & Basic packet information \\
[9] & NSL-KDD & CNN & KDD Test+: 86.95.33\% (B) & Packet connection information \\  & & & KDD Test-21: 76.37\% (B) & Time-based network traffic statistics \\  & & & KDD Test-21: 76.37\% (B) & Host-based network traffic statistics \\  & & & Use CNN to capture local features and \\
[10] & CSE-CIC-IDS2018 & CNN + RNN & 97.75\% (B) & RNN to capture temporal features of \\  & & & & packets. \\  & BoT-IoT & & & All datasets are integrated into two new \\  & IOT Network Intrusion & & & datasets, transfer learning concepts are \\
[11] & MQTT-IOT-IDS2020 & CNN & 2D CNN: 99.42\% (M) & used to change the network architecture of \\  & IOT-23 & & 3D CNN: 99.03\% (M) & different CNNs, and detailed category \\  & & & & analysis is performed on each dataset. \\  & & & ToN-IoT: 99.65 (M) & Based on the one-dimensional 1-D CNN in \\
[12] & Edge-IIoT & DenseNet & Edge-IIoT: 94.94\% (M) & the DenseNet architecture and setting \\  & UNSW-NB15 & & UNSW-NB15: 98.4\% (M) & different window sizes for multivariate \\
[13] & NSL-KDD & RNN & 83.28\% (B) & Set different layers based on the RNN \\  & & & & model to evaluate the effectiveness of the \\  & & & & model in classification. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the attack classification method using the deep learning model.
# Extending Appearance Based Gait Recognition with Depth Data
###### Abstract  
Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.  
Keywords: gait recognition; depth camera; RGB camera; 3D sensor; people identification; GEI +
Footnote †: journal: Applied Sciences
# Parallel Seasonal Patterns of Photosynthesis, Fluorescence, and Reflectance Indices in Boreal Trees
## 4 Discussion
### Seasonal Kinetics
The increases in F\({}_{\text{S}}\) during the spring and decreases during the fall observed in our three evergreen species were similar to seasonal patterns reported for _Pinus sylvestris_[45]. Maximum F\({}_{\text{S}}\) values seen in the early spring for _L. laricina_, _P. mariana_, and _P. glauca_, and seen later in the season for _P. banksiana_, coincided with the emergence of new leaf tissues. As young leaves are less productive, with lower chlorophyll concentrations than mature leaves [40], they have more excess absorbed light to dissipate [46], and require greater photoproduction [47]. This excess light may result in greater dissipation through fluorescence. These leaf-level phenomena may not be representative of the whole canopy, and further studies at different spatial scales would be required to test this. The seasonal changes observed in evergreens were likely related to changing NPQ with the adjustment of pigmentation (changing pigment pools and xanthophyll pigment activity) during the spring and fall transitions [7]. During the fall transition, pigmentation changes function to dissipate excess light energy through NPQ [7; 9], which has been reported to be substantially greater in the winter months than summer months [45]; this increase in NPQ results in a quenching of the fluorescence signal [48]. The opposite is likely to have occurred during the growing season, with the increase in light availability reaching potentially saturating levels for photochemistry, combined with the reversal of cold-hardening pigment changes [7] and a reduction of NPQ with increasing temperature [45], all potentially resulting in increased dissipation of absorbed light through fluorescence during the spring transition. In deciduous species, the greater variability in F\({}_{\text{S}}\) seen during the summer for broad-leaf species (_P. tremuloides_ and _P. basamifera_) compared to _L. laricina_ was possibly due to the differential irradiance of individual leaves associated with more dynamic orientation of the broad-leaf species during sampling [49], with fluorescence adjusting quickly with changing light [11]. Further studies are warranted to evaluate the variability in the leaf-level fluorescence signals in relation to photosynthetic phenology, and to compare F\({}_{\text{S}}\) to SIF in response to seasonally changing conditions. Here we used F\({}_{\text{S}}\) as an indicator of likely SIF signals, but key differences in instrumentation and measurement protocols exist between these two methods [10].
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 3 Smart Meter Technological Solutions in Europe and Projects for Their Implementation
### Spain  
In Spain, the energy provider Iberdrola has chosen to use smart meters based on PRIME technology. By the end of 2014, circa 4.2 million smart meters had been installed, as part of the Sistemas de Telegestion y Automatizacion de la Red (STAR) or 'Network Remote Management and Automation Systems' project [39]. By 2018 the company intends to update the whole distribution grid by installing 10.3 million smart meters and updating 80,000 transformer units, which will cost around 2000 million \(\mathbf{\upxi}\)[40]. Smart meters used by another Spanish energy provider, Gas Natural Fenosa, are also based on the PRIME technology. By 2014 the company had already installed 1.26 million smart meters [39]. On the other hand, Endesa, which belongs to the ENEL group, has already installed 7 million smart meters. This number represents 60% of the company's current fleet. The company has also announced that will complete the massive replacement procedure within 2017, one year ahead of the deadline. So far, data of more than 6.4 million smart meters are being managed, whereas a total of 600 million euros will be invested for the implementation of the new remote management system between 2015 and 2017. The Meters and More technology is used, meaning that NB-PLC is the key technology for the first transmission link, whereas wireless solutions, like GPRS are preferred for the second transmission link [41].
## 4 Results
### Classification
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Word-Embedding**} & \multicolumn{5}{c}{**F1-Score Macro**} \\  & & **LR [\%]** & **MLP [\%]** & **SVM [\%]** & **RF [\%]** & **k-NN [\%]** \\ \hline \multirow{4}{*}{Average} & FastText & 49.01 & 44.32 & 48.71 & 37.42 & 38.08 \\  & GloVe No Correction & 48.68 & 47.21 & 50.03 & 39.81 & 42.41 \\  & W2V No Correction & 49.08 & 49.36 & 49.23 & 38.75 & 41.30 \\  & GloVe Correction & 51.18 & 48.88 & 50.76 & 42.98 & 43.42 \\  & W2V Correction & 47.97 & 50.37 & 50.19 & 39.81 & 42.16 \\  & W2V Trained & 26.10 & 40.17 & 47.84 & 40.17 & 42.14 \\ \hline \multirow{4}{*}{Clustering} & FastText & 44.89 & 49.10 & 48.26 & 43.59 & 40.54 \\  & GloVe No Correction & 45.06 & 47.94 & 48.36 & 43.09 & 37.20 \\  & W2V No Correction & 46.32 & 47.46 & 47.06 & 47.07 & 38.05 \\  & GloVe Correction & 46.93 & 50.06 & 49.29 & 46.44 & 39.95 \\  & W2V Correction & 48.16 & 49.63 & 50.02 & 45.03 & 41.66 \\  & W2V Trained & 46.75 & 49.79 & 48.61 & 44.51 & 40.31 \\ \hline \multirow{4}{*}{PCA} & FastText & 35.14 & 34.92 & 36.38 & 28.40 & 23.59 \\  & GloVe No Correction & 40.44 & 44.92 & 48.32 & 37.35 & 37.42 \\  & W2V No Correction & 36.73 & 48.44 & 47.73 & 40.38 & 39.03 \\  & GloVe Correction & 42.93 & 48.31 & 50.03 & 38.86 & 41.25 \\  & W2V Correction & 48.48 & 51.47 & 52.54 & 43.39 & 42.88 \\  & W2V Trained & 27.99 & 38.91 & 40.40 & 35.85 & 36.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different models applied in the test set (20% of documents)  
Figure 2: Confusion matrix normalized for the best models in the test set. (a) SVM—Word2 Vec Correction—PCA. (b) LogReg—GloVe Correction—Average. which results in markedly higher true negative values compared to false negative values. We can highlight that, further to WS, the classes MC and HD have the best performance in terms of TPR.
## 2 Materials and Methods
### Data Processing  
As the objective of this proof-of-concept study was to screen various sample types, tentative compound identifications were performed based on the National Institute of Standards and Technology (NIST) 2014 and Wiley 10 libraries with a match factor threshold \(>\) 700. Peaks identified below this threshold were labelled \"unknown\" and those above this threshold were labelled with tentative names. Sample acquisition and peak finding was performed using ChromaTOF v. 4.50.8.0 (LECO(r) Corporation). A baseline offset of 0.6 was used, with a 12 s \({}^{1}\)D peak width and a 0.1 s \({}^{2}\)D peak width. A signal-to-noise (\(S/N\)) ratio of 50 was used and unique mass was used for area calculations. Statistical Compare was used by importing the samples into two classes (_i.e._, blanks and samples) in order to perform peak alignment and filtering of non-specific peaks (e.g., column bleed, background compounds, _etc._). The analyte spectral match required a mass threshold of 10 and a minimum similarity match of 600. Peaks were searched for a second time if not identified by the initial peak find algorithm down to a \(S/N\) of 20. A Fisher Ratio (FR) was calculated for each compound and compounds with undefined FRs (_i.e._, only present in one class) or those exceeding the calculated critical F-value (\(F_{\text{crit}}\)) (_i.e._, present in both classes but highly variable) were kept for further processing. This approach has already been well-established in several volatile profiling studies [27; 28; 29].
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area
## 2 A New Mapping Scheme of Rmap
### The Framework of Rmap
If the robot has acquired a new area, the Rmap process starts from the MBR creation step, as shown in Figure 3d after combining the previously acquired area with the new area. In the case of not acquiring a new map, the Rmap process returns to the R1 selection step and selects R1 from the previously collected area, except the updated area in the _Rmap_. If R1 is no longer created after this iteration process, it means that the mapping is completed. ```
1 Begin
2 Let \(R=\{r_{1},r_{2},\ldots,r_{n}\}\) is a set of rectangles
3 Let \(E=\{e_{1},e_{2},\ldots,e_{n}\}\) is a set of edges of adjacent rectangles
4 Let \(Map=<\)R,E\(>\) be a map generated by Rmap algorithm
5 Let MBR be the Minimum Bounding Rectangle
6 InitialMapSetting with robot rotation
7 Loop until there is no more unkown area :
8 MBR \(\leftarrow\) MBR Creation(sensed data)
9 Rectangle list \(\leftarrow\) RectangleTiling (MBR)
10 R1 \(\leftarrow\) R1 Selection (Rectangle list)
11 Map \(\leftarrow\) MapUpdate(\(R1\))
12 sensed data \(\leftarrow\) Exploration with following the borderline of R1
13 Map Extension Check(sensed data)
14 If there is no new sensed data outside MBR area
15 Goto line 11 R1 Selection step
16 Else
17 Goto line 9 MBR Creation step
18 EndIf
19 EndOfLoop
20 End
```  
**Algorithm 1**Rmap Algorithm  
Algorithm 1 shows the entire procedure of the Rmap scheme described so far. The Rmap algorithm works until there is no more unknown area after initialization. The _Map_, generated by the Rmap algorithm, consists of R and E, where R is a set of rectangles, the E is a set of edges of adjacent rectangles. In the initialize stage, the initial map setting step initializes the _Map_ using sensed data. After the initialization, the map building process repeats until the map is completed. The MBR is generated in the MBR creation step based on the sensed data. Next, the rectangle list is created using the rectangle tiling algorithm on the generated MBR. The R1 selection step selects R1, the largest rectangle in this rectangle list. The selected R1 is updated to the _Map_ through the map update step. The exploration step explores the updated R1, where the default value is R1's borderline following policy. The map extension check step checks the existence of the newly acquired information by comparing the sensed data. If there is new information, this process goes to line 9 MBR creation step. Otherwise, this process goes to line 11 R1 selection process. The Rmap can complete the map building through iteration of this process.