esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimental Results
### Realtime Implementation
#### 7.1.1 Vision Node  
Moreover, we present the results obtained through the vision node. The main function of the vision node is to enhance fire detection accurately. Here, to obtain real-time data from vision nodes we built a web application that was hosted on the local server. The login window of the application is shown in Figure 22. Only authorized personnel with proper credentials can access the entire system. To trigger the vision node, we need to log on to the web portal. A successful log-on will then trigger all the vision nodes deployed inside the building connected through the internet. Once they connect successfully, the vision nodes start streaming live visuals along with estimated data such as fire detection status and the people count, as shown in Figure 23.  
Figure 24 illustrates the working of the vision node with the web application. After completion of login, a request is sent to the vision node. The vision node starts streaming the visual data, where the visuals are captured through the input camera. The input camera frame can evaluate whether the fire is detected or not. Moreover, the people count is also processed and the result is communicated to the web application through internet protocol (IP). Finally, the result is visualized on the web application.  
Figure 21: Blynk Dashboard Visualizing Sensor Data.  
Figure 23: Realtime Visualization on Website-based Application.  
Figure 24: Stages Involved in End-end Working.  
Figure 22: Admin Panel to Log in to the Portal.
## 4 Results and Discussion
### Performance Evaluation with Various Datasets
It is mainly because the reduction of arching training samples leads to the insufficient learning of arching features. At the same pattern ratio, with the increase of training size, the performance of the imbalanced set improves. However, its performance is still not as good as the balanced set, and the increase in training size means that more time is required for model training. For the detection of the pattern \"Normal\", since there are more normal samples in the imbalanced sets, the performance on the validation set is slightly improved from the balanced set to the imbalanced sets. Nevertheless, the performance on the testing set decreases to some extent, which may be due to the different pattern ratio of the validation and testing set, as shown in Table 5. In the imbalanced set, the number of arching samples of the validation set is far less than that of the testing set. Therefore, despite the high proportion of TN (i.e., True Negative, represents the number of the normal samples that are correctly detected as \"Normal\") in the testing result, the FN increases sharply compared to the validation result, and its influence is non-negligible. Thus, the testing results and validation results of \"Normal\" present different rules. Figure 11: Model performance evaluation using different datasets. (**a**) Arching detection performance on the validation set; (**b**) Arching detection performance on the testing set; (**c**) Normal detection performance on the validation set; (**d**) Normal detection performance on the testing set. Overall, the balanced set (Subset 2) has the best performance and can achieve accurate detection for both \"Arching\" and \"Normal patterns. Therefore, in practice, we should construct balanced datasets to detect slab arching.
## Appendix A Linearization  
The electrical efficiency curve of the micro-CHP system can be found in [PERSON] et al. (2015), Figure 2. This curve is given by the following expressions:  
\[re_{SYS_{n}}=1.183-\frac{0.1756}{OP}\] (A1)  
\[OP=\frac{Pe_{SYS_{nf}}}{Pe_{SYS_{N}}}\] (A2)  
\[re_{SYS}=re_{SYS_{N}}re_{SYS_{n}}\] (A3)  
The first term of the operating cost, Equation (7), represents the cost of the natural gas consumption of the stack. This consumption, logically, depends on the operating point at which the stack is set (\(Pe_{SYS_{nf}}\)). To arrive at this term, therefore, it is first necessary to relate \(Png_{SYS}\) to \(Pe_{SYS_{nf}}\) through a mathematical expression, that is:  
\[Png_{SYS}=g(Pe_{SYS_{nf}})\] (A4)  
By definition:  
\[Png_{SYS}=\frac{Pe_{SYS_{nf}}}{re_{SYS}}\] (A5)  
Substituting (A2) into (A1), (A1) into (A3), and (A3) into (A5) leads to:\[Png_{SYS}=\frac{1}{re_{SYS_{N}}}\frac{Pe_{SYS_{ref}}^{2}}{1.183\,Pe_{SYS_{ref}}-0.1756 \,Pe_{SYS_{N}}} \tag{10}\]  
Finally, if we include the degradation phenomenon (\(D\)), we obtain:  
\[Png_{SYS}=\frac{1}{re_{SYS_{N}}(1-D)}\underbrace{\frac{Pe_{SYS_{ref}}^{2}}{ 1.183\,Pe_{SYS_{ref}}-0.1756\,Pe_{SYS_{N}}}}_{Png_{max}} \tag{11}\]  
In Figure 11, the non-linear expression \(Png_{aux}\) in (11) is represented, as well as its piecewise linearization. The mathematical expression of this linearization is:  
\[\begin{split} Png_{aux_{l_{k}}}=\underbrace{\frac{Png_{1}}{Pe_{1 }}}_{\kappa_{ng_{0}}}Pe_{T_{0}}+\underbrace{\frac{Png_{2}-Png_{1}}{Pe_{2}-Pe_ {1}}}_{\kappa_{ng_{1}}}Pe_{T_{1}k}+\underbrace{\frac{Png_{3}-Png_{2}}{Pe_{3}- Pe_{2}}}_{\kappa_{ng_{2}}}Pe_{T_{2}k}+\\ +\underbrace{\frac{Png_{4}-Png_{3}}{Pe_{4}-Pe_{3}}}_{\kappa_{ng_ {3}}}Pe_{T_{3}k}+\underbrace{\frac{Png_{5}-Png_{4}}{Pe_{5}-Pe_{4}}}_{\kappa_{ ng_{4}}}Pe_{T_{4}k}\end{split} \tag{12}\]  
The five points of the piecewise linearization, Figure 11, were calculated using the least squares method.  
Let us recap: An expression of the form (10) was needed. It was deduced from the model: (11). This expression contains a non-linear element (\(Png_{aux}\)). That element was linearized, resulting (12). Thus, the final linearized expression of (10) is:  
\[Png_{SYS_{k}}=\frac{1}{re_{SYS_{N}}(1-D)}Png_{aux_{l_{k}}} \tag{13}\]  
From (13), the first term of the operating cost (Equation (7)) is derived.  
Additionally, from (13), the nine constraints of the piecewise linearization are defined: Equations (14)-(18).
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 2 Conventional 3 GPP Enhanced Voice Services
### Context-Based Method  
The GMM-based speech/music classification responds very rapidly to change-over from speech to music and vice versa. To effectively utilize the discrimination potential of the GMM-based approach, \(y\) is sharpened and smoothed by the following adaptive auto-regressive filter:  
\[\overline{y}=\gamma_{c}y+(1-\gamma_{c})\overline{y}^{[-1]} \tag{4}\]  
where \([-1]\) denotes the previous frame and \(\gamma_{c}\) is a filter factor.  
If the segment is energetically significant, the scaled relevant frame energy value will be close to 1, and for background noise, will be close to 0.01. Accordingly, if the SNR is high, more weight is given to the current frame, whereas if the SNR is low, the classifier has more dependency on the past data because it is difficult to make accurate short-term decisions. This situation potentially occurs when \(y\) is smaller than 0, and is smaller than the value of the previous frame. In this case:  
\[g=g^{[-1]}+\frac{y^{[-1]}-y}{20},0.1<g<1 \tag{5}\]  
where \(g\) is the gradient of the GMM approach, and \(g^{[-1]}\) is initialized to the value of \(-y\) each frame. Finally, previous frames of varying sizes (0-7) are combined according to the characteristics of the signal to determine speech/music [10].
## 6 Results and Discussion
### Overall Discussion
Since the tests were performed using a small dimensions boat with not perfect wave conditions, as stated in Section 5.4, a certain degree of motion of the vessel was present: nevertheless this did not significantly affect the performances of the link. While no analytical study was carried out due to the random nature of the movement of the boat, a set of tests with different angles of orientation may be performed in future works.
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### Measures on the Protocol  
xAAL is deployed in our laboratory [34].  
* There are 121 xAAL devices.
* An average of 4.82 messages per second, that is to say about one message every 25 s for a device.
* An average of 378 bytes per message with xAAL 0.5 (former version), and an average of 128 bytes with xAAL 0.7 (new version), in other words: an average bandwidth of 5 bytes per second for a device.  
Note that moving from JSON to CBOR serialization just changes message size, there is no impact on the global behavior nor on the number of messages.  
Also note that the number of messages is impacted by timeout value for _alive_ notifications and for _get_description_ requests (usually performed by HMI applications). However, thanks to bus communication principles, a reply message to an application is used by other applications. Having multiple applications on the bus does not increase so much the number of messages.
## 5 Experimental Results and Discussion
### Discussions  
In Table 7 we have summarized some of the performances of various schemes from the literature related to reliability of LoRaWAN networks. Even though there is a huge gap between these papers and ours, we did our best to compare the performance levels with the ones achieved in this work.  
In particular, the achieved reliability values are reported with the corresponding number of nodes used in the simulations. As mentioned in Section 2, the closely paper to ours is [6] which provides significant results in terms of reliability with the accommodation of nearly 2000 users. However, this scheme doesn't provide any specific solution to address the problem of urgent traffic delivery, i.e., the latency problem is not specifically addressed. As a matter of fact, the authors just speculate that the proposed solution can handle urgent traffic if the number of EDs is small without actually simulating this scenario. Moreover, the nodes are considered to be fixed so that it is possible to design a fixed frequency/time scheduling procedure in which all the nodes are perfectly synchronized with the GW, a situation which is not verified in the FMAR time varying scenario considered in our paper. Finally, the solution provided in [6,7] deviates from standard LoRaWAN and requires modification of the MAC layer. To sum up, the results obtained through our scheme not only validate the proposed algorithm but also provide important intuitions in enabling a real FMAR scenario where both reliability and latency requirements are very critical.
# The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review
## 2 Materials, Processes and Methods
### Processes  
The newly increasing interest in WAAM is based on the advances in robotic control and modern electronic control circuits for welding machines. They made modern controlled short arc welding processes with reduced energy input possible and very stable. The most known modified short arc process, \"cold metal transfer\" (CMT) [66], has been developed by the company \"Fronius\" and is the most used process for WAAM based on gas metal arc welding (GMAW) in the literature. Furthermore, for controlled short arcs, the intention to lower the heat input is the focus of some research, increasing the potential applications [67]. Aside from the common gas metal arc welding processes, tungsten inert gas (TIG) welding processes or plasma welding processes have been used due to the separation of energy and mass input. Aside from standard welding processes, the combination of welding processes with others (e.g., laser-assisted welding) or with forming processes have been attempted as well. In the following, each of the most commonly used welding processes and their process behavior in welding arc additive manufacturing will be described. The basic literature about welding (e.g., [11] and [PERSON] et al. [68]) gives a good overview of the used welding processes.
## 4 Discussion
Photographs of the upper hemisphere, e.g., from fisheye-camera-based systems, are mostly utilized in astronomy (for instance, in optimization of closing/opening a telescope depending on the cloudiness, [10; 11; 12]) and the energy industry (particularly at solar plantations [13; 19]). In our case, the stitched images of the full hemisphere will be used to estimate the sky index [20] and the greyness rate index [21], which again, will be further used to segment the image, based on the [PERSON]'s method of automatic thresholding [22], i.e., to decide which pixels belong to the foreground (clouds) and which ones represent the background (clear sky). Methods for automated recognition of cloud types from camera images require more sophisticated statistical methods compared to the detection of cloud coverage; these are at research level, however, they generally lack real implementation in practice [23; 24; 25].
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Peptides in Cancer Therapy
#### 3.2.2 Azurin  
Azurin (14 kDa, 128 amino acids) is a copper-containing metalloprotein with redox activity that plays a role in the denitrification process that is produced by _Pseudomonas aeruginosa_. Azurin acts as an electron transfer shuttle in _Pseudomonas aeruginosa_ and other bacteria. It penetrates into the tumor cells, enhances the intracellular levels, and increases the stability of p53 by inhibiting COP1-mediated ubiquitination and proteasomal degradation, and therefore \"p53\" induces cell cycle arrest at G2/M and inhibits the development of cancer [59; 60]. The domain of Azurin that is probably responsible is p28 (50 to 70 amino acids of azurin), containing 28 amino acids. It has a molecular weight of 2.8 kDa. p28, can penetrate human endothelial cells, and inhibit the kinase activity of VEGFR-2 (vascular endothelial growth factor receptor 2) and bFGF (basic fibroblast growth factor) that induce migration, capillary tube formation, and neoangiogenesis [61]. Furthermore, Azurin decreases the hyperphosphorylation of FAK (Focal adhesion kinase) and Src non-receptor tyrosine kinases associated with P-cadherin overexpression. P-cadherin overexpression occurs in 30% of invasive breast carcinomas and is associated with poor patient prognosis [62]. As an anticancer agent, \"p28\" has finished Phase I clinical trial as an investigational new drug application (IND 77,754) approved by the Food and Drug Administration [63]. Azurin has anticancer activity against various cell lines of breast cancer such as MCF7, ZR-75-1, T47D, MDA-MB-157, MDD2, and MDA-MB-231 [51; 52]. Furthermore, different studies have demonstrated the anticancer activity of p28 on breast cancer models. The result of one study reported that p28 was able to significantly reduced the tumor size of MCF-7 xenografts in athymic mice after exposure to 10 mg/kg (3.4 umol/kg) of peptide over the course of a daily dose for 30 days i.p [64]. Moreover, in another study, the MDA-MB-231 xenograft tumor was exposed to 10 mg/kg of p28 which resulted in inhibiting the tumor growth equal to or better than the IC50 dose of paclitaxel in MDA-MB-231 [65].
## 3 Results and Discussion
### Raman Spectra of Colon Tissues and Colorectal Polyps
All of these biological macromolecules mentioned above are sensitive to carcinogenesis to varying degrees, which is manifested in more or less pronounced changes in the position and intensity of characteristic bands in the spectra of colorectal adenomatous polyp and adenocarcinoma. However, at least for portable instruments, these spectral differences cannot always be used as pathology markers due to band overlap. In contrast, Raman spectra measured with the DXR SmartRaman instrument can detect even subtle band position and intensity changes, which can be informative when comparing normal and damaged tissues and assessing molecular changes caused by disease progression. Comparing the Raman spectra of normal colon tissue, adenomatous polyp, and adenocarcinoma, one can note significant differences in the position and intensity of individual bands in some spectral regions. Firstly, the changes occur in the region from 1500 to 1630 cm\({}^{-1}\), where the stretching vibrations of aromatic C=C bonds of aromatic amino acids and nucleic bases are located [29; 30; 31]. Further, a gradual decline in the band intensity at 1303 cm\({}^{-1}\) and a simultaneous increase in the band intensity around 1335 cm\({}^{-1}\) is evident compared to the neighboring band around 1314-1317 cm\({}^{-1}\). The first band refers to the twisting vibration of methylene groups primarily in lipids but also in proteins, including collagen, and the second is associated with the C=N stretching vibrations of purines, as well as C\({}_{\alpha}\)-H bending and triptofan (Trp) vibrations in proteins [5; 29; 30; 47; 49]. The changes also occur in the region from 800 to 900 cm\({}^{-1}\), where the intensity of the 851 cm\({}^{-1}\) band is lower in the case of the polyp and, conversely, higher in adenocarcinoma, compared to the neighboring bands around 832, 865, and 880-882 cm\({}^{-1}\). The band at 851 cm\({}^{-1}\) corresponds mainly to the ring breathing vibration of Tyr [32; 33; 34], but the C1-H bending of glycogen also contributes here [48]. Finally, one can note the increased intensity of the bands at 664 and 726 cm\({}^{-1}\) in adenocarcinoma. These two bands arise mainly from the purine ring breathing vibrations [29; 30], and the C-S stretching vibration of cysteine (Cys) contributes to the first one [31; 32; 33]. Thus, the observed spectral differences between normal colon tissue and benign and malignant polyps, which can be associated with colorectal cancer development, indicate changes in the ratio between such major biochemical tissue components as proteins, nucleic acids, lipids, and polysaccharides [44; 45; 46; 47; 50]. The above bands, showing significant differences between samples, can be offered as potential markers of carcinogenesis in colon tissue.
## 2 Materials and Methods
### Methodology and Processing Workflow  
The processing workflow used to analyze the impact and effects of scanning quality on AT and DIM accuracy is shown in Figure 7. The experiments were performed using the same image dataset scanned with different technologies (photogrammetric and consumer-grade scanners) and the same image coordinates of the GCPs and CPs, testing two photogrammetric tools: Trimble Inpho (ver. 5.7.2.) and Agisoft Metashape (ver. 1.8.4.) (referred to in the following sections simply as Inpho and Metashape, respectively). We  
Figure 5: Examples of stable-over-time points measured in the field: points identified in the historical dataset (**left panel**) and the corresponding points in the modern orthophoto (**right panel**).  
Figure 6: **(a)** The distribution of CPs and GCPs used in the AT process; (**b**) reference DSM from LiDAR data (2012).  
compared the AT results (Section 3) obtained using the two processing tools, as well as the effect of camera self-calibration.  
In the analog era of aerial photogrammetry, airborne sensors were equipped with at least four fiducial markers that were helpful in determining the relationship between the image and the camera space. Their image coordinates (x, y) were determined and certified in the camera calibration process to unambiguously resolve the interior orientation of each image. An example measurement of the fiducial markers in Inpho is shown in Figure 8.  
The AT in Metashape also included camera self-calibration for the calculation of additional parameters. No self-calibration in Match-AT Inpho was performed due to the limited flexibility in using additional parameters. Therefore, it was more valuable to compare AT results from professional software for large-format aerial photography (Inpho) vs. software dedicated to close-range photogrammetry and low-altitude imaging (Metashape). Once the initial transformation between the pixel and camera coordinate  
Figure 8: Measurement of fiducial marks on historical images within Trimble Inpho.  
Figure 7: Processing workflow used for the experiments.  
systems was computed, GCPs and CPs were measured in Inpho and used for the AT in both tools. However, a manual correction of point projections was necessary in Metashape with the consumer-grade scanner data. Indeed, some offsets were evident due to geometric distortions introduced in the scanning process.  
Finally, the quality of the derived DIM products was evaluated against reference ground truth data (Section 2.1), comparing dense point clouds elaborated with the two different processing parameters.
# Secure and Efficient Authentication Scheme in IoT Environments
## 4 Cryptoanalysis of the Proposed Protocol
### Informal Security Analysis
However, even if the attacker succeeds in extracting the parameters, he/she cannot determine the password \((psw_{i})\) or the server's secret key (s), which is obscure and protected by the concepts of ECDLP and CDHP (which we mentioned earlier). Furthermore, there is no chance to speculate the random numbers \((x_{i1}\) and \(x_{j1})\) because of the protection offered by ECDLP and CDHP (i.e., this has already been explained in the section on known session-specific temporary information attacks). Therefore, the proposed scheme can resist brute-force attacks. Table 2 summarizes the comparison of security attacks on the existing approaches and the proposed scheme. We selected some security attributes (i.e., usually protecting the security of an authentication protocol) for comparison. The comparison demonstrates that the proposed scheme is free from all the shortcomings in the existing schemes. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Attacking Scenarios** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **Proposed** \\  & [16] & [17] & [18] & [20] & [21] & [22] & [23] & [24] & [25] & **Proposed** \\ \hline Reply attack & V & P & P & P & P & P & P & P & P & P \\ Password guessing attack & P & V & V & V & V & P & V & P & P & P \\ Embedded device & V & P & P & V & V & V & P & V & P & P \\ impression attack & V & P & P & V & V & V & P & V & P & P \\ Does attack & V & V & V & V & P & P & V & V & P & P \\ Many logged-in user attack & V & V & V & V & V & V & V & V & V & P \\ Server impersonation attack & V & V & V & V & V & V & V & V & V & P \\ Forward secrecy & V & P & V & P & P & P & V & P & V & P \\ Insider attack & P & V & V & V & P & V & V & P & P & P \\ Known session-specific & V & V & V & P & V & V & V & P & V & P \\ temporary information attack & & & & & & & & & & & \\ Attack on user anonymity & P & P & V & V & P & P & P & P & P & P \\ Database attack & V & V & V & V & V & V & V & V & V & P \\ Cookie theft attack & V & V & P & V & V & V & V & V & P & V & P \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison by types of security attack.