esa-sceva/satcom-chunk-collection huggingface dataset:
# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 4 Experiments
### Qualitative Analysis  
The fused images attained by the three state-of-the-art algorithms and our proposed method are shown in Figures 2 and 3. We analyze the relative performance on three images from RoadScene and three from our private dataset using the new loss in discriminator \(L^{*}_{D_{i}}\) as well as generator \(L^{adv*}_{D_{i}}\).  
We can see that images created by our method preserve the thermal targets from the infrared images as in the third example of Figure 2. It can also preserve more textural information from visible images such as sky (red box) in the first example of Figure 3. If we look at the overall images created by these methods, we would observe that the DDcGAN creates blurry images. NestFuse gives better results in first example of Figure 3, but some salient features are not clear such as in the red box in second image of Figure 3. Generally our proposed method tries to preserve as much information from both the infrared as well as the visible images and tries to maintain the overall good quality of images, simultaneously visible in the third and the fourth images.
# Artificial Neural Networks for a Semantic Map of Variables in a Music Listening-Based Study
## 4 Discussion  
The study focused on identifying the characteristics of the target population that would most likely benefit from listening to music. The motivation for this study arises from the considerable subjectivity underlying different responses to music listening [29] and the introduction of a new music listening approach: algorithmic music. This type of music listening aims to understand the musical factors (music features and structures) that underlie the potential effects of music listening [17; 18], in addition to subjective factors (listening pleasure and familiarity/predictability of the song and its reward mechanism) [30]. Therefore, it is crucial to determine which type of listener is most responsive to algorithmic music and whether there is a significant difference compared to self-selected music. The absence of such a difference might suggest the presence of underlying factors that are not influenced by the kind of music used in listening, implying that non-responders remain as such most of the time, regardless of the choice of music (self-selected or algorithmic).  
The results of the study regarding the effects of algorithmic music listening show that certain characteristics, mainly related to age, gender, and education, are associated with positive outcomes (relaxation/deactivation effects). The greatest benefit was observed in women over 49 years of age with a high level of education and musical training. For these individuals, algorithmic songs were perceived as relaxing and evocative. Conversely, \"non-responders\" tended to be male with a low level of education, and they also perceived the music as peaceful but monotonous. Factors such as a high education level and musical training seem to have a greater impact, likely because they facilitate a cognitive predisposition to novelty and innovation. Moreover, factors like age and musical genre may also play a role in this predisposition.  
Regarding conventional music, the desired relaxation effect was correlated with individuals under 50 years old, with a high level of education and musical training, and with a preference for ethnic/traditional genres. The evocation of images and emotions also appeared to be a common element among \"responders,\" while the perception of monotony remained an obstacle to achieving the relaxation/deactivation state. This finding was consistent for both conventional and algorithmic music, suggesting that certain factors are independent of the type of music used in the listening experience.  
This approach provides an interesting perspective to support the music therapist in making music choices by identifying a possible \"responder\" profile.
## Case Study
### Applying Stpafet
#### 5.2.1 System Engineering Foundations Establishment  
As mentioned in Section 4, the first step begins with determining vehicle-level accidents, associated hazards, corresponding system-level SCs, and drawing a safety control structure. An example of vehicle-level accidents related to the FLEDS could be defined as AC1: The vehicle has a rear-end collision. One of the system-level hazards that could result in AC1 is determined as H1 (an example): The vehicle has an unintended deceleration or stop because no more fuel could be collected from the tank when driving on a highway with wet roads. The safety constraint for H1 is SC-1: The vehicle must always have enough fuel to avoid unintended deceleration or stop when driving on a highway with wet road. In STPAFT, system-level SCs as top-level safety requirements are used to mitigate hazards, which is similar to the role of SGs in ISO26262. Therefore, system-level SCs in STPAFT could correspond to SGs in ISO26262.  
The vehicle-level hazard VH1 for ISO26262 could be derived as: The vehicle has an unintended deceleration or stop because no more fuel could be collected in the tank. The particular worst-case environmental condition here is \"driving on a highway with wet roads\" which is represented in S2 in Figure 6. Therefore, the operating mode and operational situation can be determined as O1: Driving on a highway with wet roads. More possible operational situations are represented in Section 5.2.3. Then the corresponding hazardous event HE1 could be determined by combining VH1 with O1: The vehicle has an unintended deceleration or stop because no more fuel could be collected in the tank when driving on a highway with wet roads and the consequence of hazardous events could also be derived as CHE1: The vehicle has a rear-end collision with the following vehicle travelling at high speed when driving on a highway with wet roads.  
The next step in STPAFT is to establish a safety control structure to identify potentially hazardous control actions that could violate the SCs and lead to H1. The information from safety control structure could be taken to conduct the item definition. So, S4 in STPAFT is mapped to II in ISO26262 in Figure 6. Figure 7 shows the control structure diagram of the FLEDS at the architectural design level. In this structure, FLEDS is considered as a controller, the fuel tank is a controlled process and the driver is treated as an actuator to refuel the tank according to the FLEDS's commands.  
**Figure 6. The analysis process of STPAFT and the corresponding relationship between the analysis results and the work products required by ISO26262-Part 3: Concept phase.**
## 3 Results and Discussion
### I/O Values  
This study pursued to discover the chemical phases contained in indoor PM\({}_{2.5}\) relative to outdoor PM\({}_{2.5}\). Since a large proportion of PM\({}_{2.5}\) and EC found in classrooms come mainly from outside, it was expected that the chemical composition including EC would not exhibit very differently between indoors and outdoors. A similar tendency of chemical  
\begin{table}
\begin{tabular}{c c c c} \hline  & **PM\({}_{2.5}\)** & **OC** & **EC** \\ \hline PM\({}_{2.5}\) & 1.0 & & \\ OC & 0.37 & 1.0 & \\ EC & 0.26 & 0.60 & 1.0 \\ CI\({}^{-}\) & 0.12 & 0.29 & 0.09 \\ SO\({}_{4}\)\({}^{2-}\) & 0.34 & 0.41 & 0.55 \\ NH\({}_{4}\)\({}^{*}\) & 0.49 & 0.26 & 0.48 \\ K\({}^{*}\) & 0.13 & 0.49 & 0.65 \\ NO\({}_{3}\)\({}^{-}\) & 0.33 & 0.21 & 0.54 \\ \hline \end{tabular}
\end{table}
Table 4: [PERSON]’s correlation coefficients for typical species in indoor PM\({}_{2.5}\).  
Figure 4: Distribution of ions with OC and EC in indoor PM\({}_{2.5}\).  
composition appeared in indoor and outdoor PM\({}_{2.5}\) was shown except for a few species with indoor generation sources, as previously summarized in Table 2, and Figure 4 also indicated a similar pattern for EC.  
Although consistently higher amounts were found outdoors than indoors in all schools, the EC concentration accounted for 2.4% of the outdoor PM\({}_{2.5}\), and 3.7% of the indoor. However, I/O ratio of EC based on the absolute amount was 0.84. In other words, although a greater amount of elemental carbon was present in the outdoor particulate matters, the relative amount of EC occupying PM\({}_{2.5}\) was higher indoors. It implies that the infiltrated elemental carbon tends to remain in the particulate matters inside more than other ions, metal or any inorganic species.  
On the other hand, the average I/O values of OC and EC in PM\({}_{2.5}\) for the five test schools were 1.64 and 0.85, respectively, as summarized in Table 5. A fair amount of outdoor PM\({}_{2.5}\) is filtered through windows or walls when there is infiltration, and removed by air purifiers or ventilators inside the class. However, a relative fraction of carbon species was high in indoor PM\({}_{2.5}\) at all the schools, indicating that there are more significant emission sources of OC comparing to EC. The school (E-4), which is the oldest school built in 1972 and located at the junction of heavy traffic roads, showed higher EC in the indoor than the outdoor despite low I/O value of PM\({}_{2.5}\) (0.56). It implies that there were inflows of carbons from kitchen next to the classrooms in addition to outside roads. Although it is very various depending on classroom situation, it could be concluded that the I/O ratio of carbons was high comparing to the I/O ratio of PM\({}_{2.5}\).  
The proportional increase of indoor carbon to the outdoor as seen in Figure 5 was more obvious in OC than EC. In other words, it can be explained that most of EC which is found in classrooms come from the outside, while a significant amount of OC occurs indoors.
# BIM-GIS-Based Approach for Quality Management Aligned with ISO 9001
## 4 Method
### Stage C: Device Selection and On-Site Inspection  
The on-site inspection process is carried out using a mobile device with the QField application, which accesses the synchronized inspection form from QFieldCloud. To ensure proper operation, the selected device must meet the following minimum requirements:  
* Stable internet connection for uploading data and maintaining synchronization with QFieldCloud;
* Operating system compatible with the QField application (Android(r), iOS(r), or Windows(r))
* Built-in GPS for real-time geolocation during inspection;
* A screen large enough to display both the project floor plan and the inspection form (a tablet is recommended).  
Once the inspector logs in to the QField application and synchronizes the project, the inspection begins by activating the editing mode of the inspection layer. The operator selects the elements to be inspected directly from the floor plan view and adds new control points at their physical location. The GPS continuously updates the inspector's position, allowing accurate spatial association between the control point and the actual element. The inspector completes the form for each control point by filling out the fields grouped under the relevant quality attributes. The system also allows the inspector to return to previously created control points to make necessary corrections or updates. After completing the inspection of all required elements, the operator selects the \"upload changes\" option, which updates the QGIS file with the collected data. This information can then be accessed from a computer to continue the quality control process. Figure 5 summarizes the overall workflow for field inspection using QField.  
Figure 4: Reference for the creation of fields and attributes within a QGIS geopackage layer.
## 3 Methodology
### Automatic Dialect Identification
#### 3.2.2 Feature Extraction  
The structures are mined using Mel-Frequency Cepstral Coefficient (MFCC) extraction. MFCC features capture the spectral and temporal characteristics of the audio wave. The Librosa Python v3.11.13 library is used for feature extraction. Triangular filters are used to extract MFCC features that are necessary for understanding speech content. Human hearing works on a Mel scale, but standard FFTs analyze sound frequencies linearly. Triangle-shaped filters are applied to the FFT's output. By adding the contributions of each filter, with each concentrating on a specific frequency range, we can capture the parts of the spectrum that are most relevant to human hearing. This model-focused representation yields the MFCC. The distribution of filters is equal, and the filters are spaced logarithmically apart above 1000 Hz. Mel frequency is calculated using the following equation:  
\[\text{mel(f)}=1127\text{In}(1+\text{f}/700) \tag{1}\]  
Mel(f): Mel units.  
f: actual frequency in Hertz.  
In: Natural Logarithm.  
The first and second MFCCs can be used to compute the changes in speech from frame to frame. The Arabic audio dataset is used to generate 13 MFCC features. The block diagram for MFCC feature extraction is shown in Figure 4.  
Figure 3: Region-wise dialect classification.  
Once the audio is in time domain format, we use the Fast Fourier Transform to convert the audio into a periodogram. We do this to identify the sound by downsampling the audio to obtain a Nyquist frequency.  
\[\text{f\_(Nyquist)}=1/2\text{nu} \tag{2}\]  
The Fast Fourier Transform (FFT) algorithm can be used to determine a sequence's discrete Fourier transform (DFT) or its inverse discrete Fourier transform (IDFT). Fourier scrutiny converts a signal from its original domain, usually time or space, to a representation in the frequency domain and vice versa. A spectrogram is created from a periodogram using the Short-time Fourier Transform. This is conducted to facilitate the study of brief, presumably steady audio intervals. The Fast Fourier Transform is then used to translate amplitude to frequency, and the frequency is then converted to the Mel scale. Next, the logarithm of each filter bank energy is calculated. Finally, the log filter bank energies are computed using IDCT, resulting in thirteen features.
# Humans and Automation: Augmenting Security Operation Centers
## 3 Results and Findings
### SOC Automation Human Factor Sentiment  
Security analysts are important because of their decision-making skills and tacit domain knowledge. One way to partially mitigate automation is to limit where automation is applied. One study participant mentioned that \"automation recommendations are fine, but the final decision should be on the incident handler,\" advocating for limited (if any) automation in the response phase [61] (p. 16). Another study credited security analysts for their ability to handle intuitive tasks, with automation better catered to resource-intensive repetitive tasks [62]. Studies suggested that the SOC marks a prime environment for human-AI collaboration whereby analysts can train automation and automation can improve efficiency for analysts [37, 39].  
**Findings:** To achieve a mutually beneficial relationship between security analysts and automation, involving the analysts during the development and integration of new tools is critical. Automation will only be as skilled as the analysts who program it. Therefore, recognizing the domain knowledge that analysts possess, specifically concerning Tier 3 analysts, will result in more intelligent technical solutions being identified. It is important to note that an organization will derive little benefit from an environment purely analyst or automation-based. Instead, the two entities must complement each other.
# Sand and Dust Storms: Impact Mitigation
## 4 SDS Hazards and Their Mitigation
### SDS Source Mitigation
#### 4.1.5 Integrated SDS Control Strategies  
Effective SDS control should adopt an integrated multi-scale and multi-functional approach [96]. Control measures to protect soil and reduce wind speed at the field level must be combined with wider landscape measures to reduce wind speed, reduce sand and dust mobilization and increase deposition of sand and dust from the atmosphere. Such integrated landscape management must simultaneously identify and manage different landscape components, such as cropland, rangeland, dunefields, mines and building sites. An integrated, landscape level approach is especially important given the transboundary nature of SDS and their impacts.  
A landscape approach is also critical for the management of water resources, which are relevant to SDS emissions in many ways. Water is vitally important for many of the land uses associated with anthropogenic SDS generation (cropland, rangeland, mine tailings), but there are numerous examples of diversion and/or consumptive use of surface or groundwaters that have resulted in new sources of dust storms, blowing sand and sand dune formation in and around desiccated lake beds all over the world [97]. The measures required for mitigating SDS at these sources need to be part of sustainable integrated water management strategies. These plans, at national and international levels, should take into account relevant SDS issues.
# Compressed Communication Complexity of _Hamming Distance_
## 2 Preliminaries  
### Strings  
Let \(\Sigma\) be an _alphabet_ of size \(\sigma\). An element of \(\Sigma^{*}\) is called a _string_. The length of a string \(S\) is denoted by \(|S|\). The empty string \(\varepsilon\) is the string of length 0, namely \(|\varepsilon|=0\). The \(i\)-th character of a string \(S\) is denoted by \(S[i]\) for \(1\leq i\leq|S|\), and the _substring_ of a string \(S\) that begins at position \(i\) and ends at position \(j\) is denoted by \(S[i..j]\) for \(1\leq i\leq j\leq|S|\). For convenience, let \(S[i..j]=\varepsilon\) if \(j<i\). Substrings \(S[1..j]\) and \(S[i..|S|]\) are respectively called a _prefix_ and a _suffix_ of \(S\). For simplicity, let \(S[..j]\) denote the prefix of \(S\) ending at position \(j\) and \(S[i..]\) the suffix \(S[i..|S|]\) of \(S\) beginning at position \(i\). A suffix \(S[j..]\) with \(j>1\) is called a _proper suffix_ of \(S\).  
For string \(X\) and \(Y\), let \(\mathsf{lcp}(X,Y)\) denote the length of the _longest common prefix_ (LCP) of strings \(X,Y\), namely \(\mathsf{lcp}(X,Y)=\max(\{\ell\mid X[..\ell]=Y[..\ell],1\leq\ell\leq\min\{|X|, |Y|\}\}\cup\{0\})\). The _Hamming distance_\(\mathsf{d_{H}}(X,Y)\) of two strings \(X,Y\) of equal length is the number of positions where the underlying characters differ between \(X\) and \(Y\), namely \(\mathsf{d_{H}}(X,Y)=|\{i\mid X[i]\
eq Y[i],1\leq i\leq|X|\}|\).
# Addressing ZSM Security Issues with Blockchain Technology
## 4 The Architecture of the Blockchain-Enabled ZSM Approach
NP1.ZSM(search_Mns[network_resources, MD] = true;  
ZSM.returns true; \"This is the result of ZSM that triggers the SC via the oracle mechanism\"  
function requestResources(NP1, NP2, Mns) public returns(  
if NP2 is true:  
NP2[id].lease[Mns] to NP1  
transfer_resources(NP2, NP1): \"This is a transaction stored in the ledger\"  
oracle.transaction(NP2,NP1, Mns): \"Triggers ZSM to implement the agreed Nxs\"  
**Pseudocode 1. ** Example of a Management Function in the form of SC. The consumption of NP2's service by NP1 is registered as a transaction in the blockchain and the details of this transaction are defined by the SC. Finally, since the network is zero-touch, the decision regarding the consumption of a management service by the NP1 provider can be made by an AI/ML mechanism. Blockchain stores the decisions of AI in the form of transactions. (It could additionally be used to check the validity of datasets stored in an Inter Planetary File System (IPFS) structure which were used to train the ML model). Figure 3 illustrates the complete lifecycle of the described resource management operation that follows the blockchain-enabled ZSM approach.
## 4 Experiments and Results
### Detection Examples
#### 4.6.2 SSD with the Different Aspect Ratios of Default Boxes  
In order to handle different target scales, we set default boxes with different aspect ratios to process images with different scales, and combined the results afterwards. We specified different aspect ratios for the models and recorded their evaluated results in Table 4.  
As shown in Table 4, based on the experiments, we gradually increased the features of the different aspect ratios of the default box for detection. We started with a single default box with an aspect ratio of 1 and gradually denoted the default box with different aspect ratios. From the presented results, it can be inferred that the performance impacts of the large-scale different aspect ratios pairs were relatively large. This proved that the default boxes of different aspect ratios were beneficial for the improvement of the detection performance that in turn justified the effectiveness of our model.
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
## 3 Results
### Nanoindentation Analyses  
It becomes clear that different phases can modify both hardness and the average material's modulus of elasticity. Most works only use an average value that is measured using microindentation equipment. The measurement made in instrumented nanoindentation allows the analysis of both the hardness and modulus of elasticity separately and with higher precision.  
Figure 8 shows the results of instrumented nanoindentation analysis performed using the QCSM method, in as-cast samples without a refiner. It was conducted from an average of 10 indentations, carefully positioned in the center of each grain of the corresponding phase with a maximum load of 30 mN. Grains with at least 20 \(\upmu\)m were also chosen to set the nanoindentations, except for the aluminum matrix, which had a larger area, where the positioning was relatively easier. The relatively low load was determined through initial  
Figure 7: Variation of the primary phase grain size (\(\upmu\)m) and microhardness (GPa) in the A332 alloy with different grain refiners.  
testing so that hardness results had a minimal influence from the aluminum matrix or other phases.  
The graphs in Figure 8 show that the highest hardness values (a) (7.8 GPa in the Al\({}_{5}\)FeSi phase and 8.9 GPa in the Al\({}_{15}\)(FeMn)\({}_{3}\)Si\({}_{2}\) phase) and the modulus of elasticity (b) (161 GPa in the Al\({}_{5}\)FeSi phase and 163 GPa in the Al\({}_{15}\)(FeMn)\({}_{3}\)Si\({}_{2}\) phase), appear in the Fe intermetallic phases. These phases are present, as proven, in the A332 alloy in a molten state without the use of refinement. This also demonstrated that the values of hardness and modulus decrease with the increment of indentation penetration, which may be related to the effect of the primary aluminum matrix.  
Figure 9 shows an instrumented nanoindentation map, made at full load of 30 mN, with SEM-SE images (c) e SEM-BSE (d) and EDS maps of the elements Fe (e) and Si (f). In general, as shown, both hardness and elasticity modulus are higher in intermetallic rich Fe phases (Al\({}_{15}\)(FeMn)\({}_{3}\)Si\({}_{2}\): Al\({}_{5}\)FeSi). This fact can be observed in the small nanoindentation formations in the Fe-phases, showed in the Figure 9c,d.  
Figure 8: Instrumented nanoindentation analysis performed using the QCSM method, in A332 as-cast samples. Average of 10 indentations made with an indentation positioned at the center of the phase resulting in (a) hardness; (b) modulus of elasticity.  
Figure 9: Instrumented nanoindentation analysis with a 20 \(\times\) 20 matrix using the fast H-E method. (a) Hardness; (b) elastic modulus maps of a region with different types of phases, where each point represents an identification; (c,d) SEM-SE and SEM-BSE images; (e,f) EDS-Map of Fe and Si.