esa-sceva/satcom-chunk-collection huggingface dataset:
# MA-Xnet: Mobile-Attention X-Network for Crack Detection
## 2 Proposed Methodology
### PA Module  
The PA module is designed to focus information on the fractured part of the image area that contributes to the task. Figure 4 shows the PA module diagram; given a local feature, we first feed it into two parallel convolutional layers so that they generate two new feature maps, V and Q, respectively, where, \(\{\text{V},\text{Q}\}\in\text{R}^{\text{C}\times\text{H}\times\text{W}}\). Then, the features V generated by the pooling operation of features V are cut into K features \(\text{M}\in\text{R}^{\text{P}\times\text{H}^{\prime}\times\text{W}}\), and reshaped into \(\text{R}^{\text{P}\times\text{N}^{\prime}}\) (N\({}^{\prime}\) = H\({}^{\prime}\) \(\times\) W\({}^{\prime}\)). The feature Q is sliced into K features \(\text{Z}\in\text{R}^{\text{P}\times\text{H}\times\text{W}}\) (C = P \(\times\) K) and reshaped into \(\text{R}^{\text{P}\times\text{N}}\) (N = W \(\times\) H). After that, we multiply matrices by the transposition of M and Z and apply the softmax layer to compute the spatial attention map \(\text{S}\in\text{R}^{\text{N}^{\prime}\times\text{N}}\):  
\[S_{ji}=\frac{\exp(M_{i}^{\text{T}}\cdot Z_{j}^{\text{T}})}{\sum_{i=1}^{N}{(M_{ i}^{\text{T}}\cdot Z_{j}^{\text{T}})}} \tag{5}\]  
where \(S_{ji}\) measures the effect of the \(i\)th position on the \(j\)th position. The more similar the feature representations of two locations are, the greater the correlation between them. After computing the spatial attention map, we perform matrix multiplication between the transpose of M and S and reshape the results into \(\text{R}^{\text{P}\times\text{H}\times\text{W}}\). The results of the K-group features are then combined into \(\text{L}\in\text{R}^{\text{C}\times\text{H}\times\text{W}}\):  
\[L_{j}=\sum_{i=1}^{N^{\prime}}S_{ji}\cdot M_{i} \tag{6}\]  
Finally, we multiply it by a scaling parameter \(\alpha\) and perform an element-by-element summation operation on feature A to obtain the following final output \(\text{G}\in\text{R}^{\text{C}\times\text{H}\times\text{W}}\):  
\[G_{j}=\alpha\cdot L_{j}+A_{j} \tag{7}\]  
where \(\alpha\) is initialized to 0 and gradually learns to assign more weights. Equation (7) shows that the resulting feature G for each location is a weighted sum of all location features and original features. Thus, it utilizes the spatial attention network to selectively gather contextual semantic data and has a global contextual view.
## 3 Discussion
coli_ ATCC 25922 (MIC = 28.1 \(\upmu\)g/mL) and antibiofilm effect on _S. epidermidis_ 756 (MBIC = 112.5 \(\upmu\)g/mL), _B. subtilis_ ATCC 6683, _E. coli_ ATCC 25922 (MBIC = 56.2 \(\upmu\)g/mL), and _P. aeruginosa_ ATCC 27853 (MBIC = 14 \(\upmu\)g/mL). In addition, 2-[4-[(4-bromophenyl)sulfonyl]benzamideid]-3-phenylpropanoic acid **1f** had an inhibitory action on _S. epidermidis_ 756 (MIC = 56.2 \(\upmu\)g/mL) and inhibited biofilm formation of _S. epidermidis_ 756 (MBIC = 56.2 \(\upmu\)g/mL) and _P. aeruginosa_ ATCC 27853 (MBIC = 28.1 \(\upmu\)g/mL). By intramolecular cyclization of this \(N\)-acyl-\(\upalpha\)-amino acid **(1f)** to the isosteric 4_H_-1,3-oxazol-5-one analogue **2f**, the antibacterial effect on _S. epidermidis_ 756 disappeared, but the obtained compound inhibited the growth of _E. coli_ ATCC 25922 (MIC = 28.1 \(\upmu\)g/mL). Saturated azlactone **2f** also had antibiofilm activity against _S. epidermidis_ 756 (MBIC = 225 \(\upmu\)g/mL), _E. coli_ ATCC 25922 (MBIC = 56.2 \(\upmu\)g/mL), and _P. aeruginosa_ ATCC 27853 (MBIC = 14 \(\upmu\)g/mL). All \(N\)-acyl-\(\upalpha\)-amino ketones **5j-n** obtained by opening the ring of the 4-benzyl-2-[4-(4-X-phenylsulfonyl)phenyl]-1,3-oxazol-5(4_H_)-ones **2d-f** were devoid of antimicrobial action up to a concentration of 225 \(\upmu\)g/mL. By cyclization of \(N\)-(1-aryl-1-oxo-3-phenylpropan-2-yl)-4-(4-X-phenylsulfonyl)benzamideides, the five-membered heterocycles of the 1,3-oxazoles class (**6k-p**) were synthesized, from which only 4-benzyl-2-[4-[(4-bromophenyl)sulfonyl]phenyl]-5-(2,4-dimethylphenyl)-1,3-oxazole **6p** affected adherence and biofilm formation of _S. epidermidis_ 756 on inert surfaces with an MBIC of 112.5 \(\upmu\)g/mL. In the case of this compound, we hypothesized that the antibiofilm activity may be correlated to the substitution with bromine in the _para_ position of the C-2-linked arylsulfonylphenyl fragment, and with the presence of the benzyl substituent bonded to the C-4, and the _m_-xylyl group grafted at position 5 of the 1,3-oxazole nucleus. In the tested concentration range, {4-(benzyl/methyl)-2-[4-(4-X-phenylsulfonyl)phenyl]-1,3-oxazol-5-yl} ethyl carbonates **4b-f** were proved to be inactive on the studied strains. The results were confirmed by PASS analysis with small probabilities to produce anti-infective, antimycobacterial, or antibacterial effects. Taken together, the antimicrobial activity results indicate that compounds **1d,e,f**; **2d,e,f**; **3a**; **4a**; and **6i,j** are the most promising candidates for further biological investigations and structural optimization as potential new anti-infective agents, as revealed by the lowest MIC and even MBIC values obtained. From these compounds, the in silico assays predicted the anti-infective potential for **1e**, also exhibiting the broadest antimicrobial spectrum, and **2f**, which proved to successfully inhibit _P. aeruginosa_ biofilm development. These compounds have also been predicted to have drug-like properties.
# PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
## 4 Functional Requirements for PDCA 4.0
### Digital Support for CI Documentation (R8)  
As it enables easy access to information regardless of the users' location, the digital support for CI documentation is also a requirement for PDCA 4.0, along with a database for its storage and future access. This requirement is patent in the conceptual approach by [PERSON] et al. [10], in which the authors mention the advantages of a digital CI system in terms of data storage and access through document management systems, digital communication that is independent of space and time, and data visualization. For this, cloud-based data storage is essential in order to allow for more effective data sharing within departments [48], a concept that is aligned with the life cycle of technical documentation in the context of I4.0 [88]. Data exchange through IoT is therefore also fundamental for this requirement, being supported by authors who argue that technical documentation should be connected to IoT in order to facilitate communication between machines and humans [89]. Related to this requirement, a database for CI documentation storage is necessary. This way, through a library search, knowledge from past situations (state of the project, diagnostic, root causes, countermeasures, and their degree of success) can be accessed and used for present CI projects. This is present in conceptual approaches that mention the need for problem-solving databases to aid future CI projects [10; 26]. In fact, [PERSON] et al. [11] propose that successful improvement initiatives should be filed as standards in a database that contains problem information; namely its root causes and countermeasures. Once again, this is supported through the use of Cloud data storage [12; 48], as well as IoT, to ensure data exchange between different devices [9], making documentation accessible everywhere.
# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 3 Experimental Preparation and Framework  
### Experimental Preparation  
This paper applies the GCN-Informer model to the prediction of solar power generation. The study utilizes solar power data sampled every 5 min over the past decade in Australia, which is a publicly available dataset consisting of 966,771 time-series data. In addition, the dataset encompasses 12 feature values, including temporal characteristics, and one target value of active power generation. The feature values comprise Active Energy Delivered Received, Current Phase Average, Wind Speed, Weather Temperature Celsius, Weather Relative Humidity, Global Horizontal Radiation, Diffuse Horizontal Radiation, Wind Direction, Weather Daily Rainfall, Radiation Global Tilted, and Radiation Diffuse Tilted. Notably, the dataset contains a significant amount of missing values, and the specific handling methods will be detailed in the subsequent paragraphs.
## 3 Data Analysis and Forecasting Process
### Data Pre-Processing
#### 3.2.2 Time Interval Processing  
The Python Data Analysis Library e.g., pandas is a NumPy-based tool created for solving data analysis tasks. Pandas incorporates a large number of libraries and standard data models to provide the tools for efficiently processing large scale data sets. In our case, pandas was used to integrate all the charging pile data according to the time point and split them into 24 time points every single day. To get the charging station load data 24 h a day, 10944 rows of data are generated in final.
## 2 Materials and Methods
### Dual-Choice Test on Single Volatile Compounds  
Using the same experimental unit as above, dual-choice bioassays were used to evaluate LEM attraction to the six major volatile compounds identified in potted plants and field-collected tissues (nonanal, decanal, sabinene, limonene, \(\beta\)-caryophyllene and _ar_-curcumene; see Result Section 3.1). Eight concentrations were tested for each compound: 3%, 5%, 7%, 10%, 25%, 50%, 75% and 100%. Compounds were diluted in absolute EtOH, which was also used as a control. All dual-choice tests were made by offering a single compound concentration against the control (EtOH). To test whether LEM was able to make reliable choices we offered the clean filter paper or ethanol on both sides, as controls. Forty-five minutes prior to testing, 5 \(\mu\)L of each compound was applied to a circular filter paper (d = 1 cm), allowing enough time for the EtOH to evaporate. Then, the filter paper containing the test compound was placed on one end of the slide, and the filter paper containing EtOH (control) was placed on the other end (Figure 1a). The assessment of LEM choice between the two odor sources was performed as above. Replicates in which LEM made no choice (remained in the middle of the arena, Sections 1 and 2) were discarded. To correct for the potential positional bias, the treatment and the control sources were switched from left to right in all choice experiments. The number of replicates per compound concentration are shown in their respective figures (see Section 3). The same analysis described above was performed separately for each compound, as well as each concentration.  
Figure 1: Schematic drawing of the bioassay used for assessing LEM preferences. Panel **(a)** shows the experimental arena used in the dual-choice bioassay. The drawing represents a microscope slide (75 mm \(\times\) 25 mm) divided into Sections 1–4 and TAPE. Panel **(b)** shows the white Styrofoam board (20 cm \(\times\) 10 cm) used to hold four slides and two Eppendorf® tubes (1.5 mL) per slide. Only LEM crossing half of the slide (Sections 3 and 4 and TAPE) were considered to make a choice between the two sources offered. The number of mites in each section was assessed after 24 h.
## 3 Results and Discussion
### Quasi-Static Operative Mode of the Test Rig  
#### 3.5.1 Expressing the Coordinates of \(CoG^{\prime}\) in the Support Plane Frame of Reference  
Any consideration regarding this mode of operation of the test system implies the knowledge of the position of the \(CoG\) in the reference system fixed to the turntable, i.e., \(x_{SP}\)-\(y_{SP}\)-\(z_{SP}\)-\(CoR\).  
If \((x_{SP}(O_{t});y_{SP}(O_{t});0)\) are the coordinates of \(O_{t}\) (origin of the vehicle frame of reference \(x_{t}\)-\(y_{t}\)-\(z_{t}\)-\(O_{t}\)) in the coordinate system of the support plane, and \(\delta\) is the angle (positive if counter-clockwise) between the abscissa/ordinate axis of the coordinate system of the support plane (axis \(x_{SP}\) or \(y_{SP}\)) and the abscissa/ordinate axis of the coordinate system of the vehicle (axis \(x_{t}\) or \(y_{t}\) see Figure 10), it is possible to locate the \(CoG\) in the support plane frame of reference by using the following system of equation (in matrix form):  
\[\left\{\begin{array}{l}x_{SP}(CoG)\\ y_{SP}(CoG)\\ z_{SP}(CoG)\end{array}\right\}=\left[\begin{array}{ccc}\cos\delta&\sin \delta&0\\ -\sin\delta&\cos\delta&0\\ 0&0&1\end{array}\right]\cdot\left\{\begin{array}{l}x_{t}(CoG)\\ y_{t}(CoG)\\ z_{t}(CoG)\end{array}\right\}-\left\{\begin{array}{l}x_{SP}(O_{t})\\ y_{SP}(O_{t})\\ 0\end{array}\right\}= \tag{13}\] \[=\left[\begin{array}{ccc}\cos\delta&\sin\delta&0\\ -\sin\delta&\cos\delta&0\\ 0&0&1\end{array}\right]\cdot\left\{\begin{array}{l}a\\ b\\ h\end{array}\right\}-\left\{\begin{array}{l}x_{SP}(O_{t})\\ y_{SP}(O_{t})\\ 0\end{array}\right\}\]  
Figure 10: Procedure for positioning the \(CoG\)/\(CoG^{\prime}\) on the support plane frame of reference; (**a**) starting from the \(CoG\)/\(CoG^{\prime}\) position on the vehicle, (**b**) the vehicle reference frame (in red) is placed and (**c**) the coordinates of the \(CoG\) are then written in the support plane reference frame.  
Figure 9: Shift of \(CoG^{\prime}\) downstream as a consequence of the inclination of the support plane; in the represented case the transversal axis of the tractor (\(y_{t}\)) is parallel and opposite to \(y_{SP}\).
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Sample Value Restoration  
Finally, each sample undergoes a downward shift of 384 to restore the original sample values. The resulting mixed audio values are [52, 28, \(-\)67], which can then be played back as the mixed audio result.  
This process demonstrates how homomorphic encryption and the big integer mixing method securely and efficiently mix audio data while preserving its integrity. In network communication, the commonly accepted standard for audio quality is based on the G711 codec, which uses a sample rate of 8 kHz and a bit depth of 8 bits. The sample rate represents the number of audio samples transmitted per second, while the bit depth indicates the number of bits per sample. Consequently, the bitrate, which is the amount of data transmitted per second, is calculated as Bitrate = 8000 (samples/second) \(\times\) 8 (bits/sample) = 64 Kbps.  
In this study, we used a homomorphic encryption mixing algorithm on three iPhones (iPhone 11, 13, and 14) to encrypt and mix audio calls. By observing the relationship between the length of the homomorphic encryption key and the resulting bitrate of the mixed plaintext, the following was observed.  
* When the encryption key length is 512 bits, the bitrate reaches its maximum value of 36.8 Kbps.
* Although a longer encryption key allows for the encryption of longer plaintexts, it also requires more time to complete the encryption process, resulting in a lower bitrate.
* Conversely, shorter encryption keys take less time to encrypt but also encrypt shorter plaintexts, leading to a decrease in bitrate.  
Thus, under different key lengths of 256, 512, and 1024 bits, the 512-bit key length enables the highest bit rate. However, the experiment's results did not meet the commonly accepted network communication quality standard, where a bitrate of 64 Kbps is typical. This difference indicates that the audio cannot be played back correctly at this bitrate (Table 1).  
To address this, we reduced the sample rate to 2.66 kHz to play the audio back. However, lowering the sample rate results in a significant loss of audio quality, leading to distortion. The results of this adjustment were insufficient and were not appropriate for the multi-bandwidth end-to-end encrypted mixing conference scenarios described in this particle.
## 2 Methodology
### Implementation of Softening Effect  
In this study, the mudstone samples were completely immersed in the water tank. At different immersion time (1 day, 4 days, 8 days, and 15 days), the samples were taken out to conduct the uniaxial compression and Brazilian splitting tests. From the perspective of the diagenetic process of soft rock, the cementation process is one where the microstructural bonding strength increases. The softening process during water immersion means that the bonding capability of the microstructure is weakened. Therefore, for numerical models, the softening process corresponds to the attenuation of contact cohesion (_jcoh_) and contact tensile strength (_iten_) of contact elements. Unlike the existing literature that uses macroscopic elastic damage factors to indirectly characterize the damage of microstructures [43], a damage variable to describe the water-induced weakening effect of ICB structure directly from the mesoscale is provided by the author as follows [32]:  
\[d(t)=1-\frac{\sum\limits_{n=0}^{\infty}\frac{r^{2n+1}}{n!(2n+1)(2n+2)}(- \frac{1}{4})}{n}^{D-\frac{2n+1}{2}}t^{-\frac{2n+1}{2}}, \tag{6}\]  
where \(d(t)\) is the meso-scale softening damage factor, \(D\) is the diffusion coefficient, and \(t\) denotes the time of water-rock interaction. The innovative grain-based discrete element model considering the heterogeneity and the softening effect is illustrated in Figure 4. Therefore, the water-induced softening effect under water-rock interaction can be expressed as follows:  
\[jcoh =jcoh_{ini}\cdot(1-d(t)), \tag{7}\] \[jten =jten_{ini}\cdot(1-d(t)), \tag{8}\]
## 3 Results
### Comparative Analysis of Proposed CMSS with Other Related Works/Existing DS System Methodologies
Incorrect AIS data may cause the system to select unsuitable vessels, resulting in delays or failures in SAR missions, potentially leading to severe consequences including loss of life. Last, but not least, the main weakness of CMSS 2 is its reliance on particular communication protocols and hardware configurations. This dependency could limit the system's ability to evolve and expand, diminishing its adaptability and raising implementation expenses. An extensive examination of the limitations of three systems--CMSS, CMSS 1, and CMSS 2--and their potentially disastrous consequences in specific situations demonstrates a comprehensive understanding. The CMSS faces challenges in assessing the precision of input data, leading to the Garbage In, Garbage Out (GIGO) problem. This limitation is particularly worrisome in scenarios where decision-making accuracy is vital, such as in military operations, healthcare systems, or nuclear plant management. CMSS 1's vulnerability mostly stems from its heavy reliance on the precision and thoroughness of AIS data. During critical conditions, any interruption or inadequacy in SAR operations could result in fatalities, especially in significant occurrences like natural disasters or marine incidents. CMSS 2 is constrained by its dependence on specific communication protocols and hardware configurations. This limitation is particularly detrimental in time-sensitive scenarios such as handling natural catastrophes or carrying out military field operations, where the prompt deployment of appropriate communication equipment is crucial. In contrast to CMSS 1, the proposed CMSS has been designed by the principle of a human-centric approach and the participant actionable actors play the role of the feedback loop with the system partially mitigating the consequences of the GIGO phenomenon. Regarding the comparison with CMSS 2, our system is generic, thus it gives the designer engineer freedom in implementation choices, without being constrained by particular protocol and configuration requirements.
# Drought: A Common Environmental Disaster
## 7 Impact of Drought in South Africa

Table 1: Cont. As highlighted in Table 1, a seasonal-scale drought evaluation and forecasting system can potentially support an early warning system, as it can provide updated drought-related events and its potential impacts on a timely basis by accounting for the drought conditions as of the forecast release date and climate outlook over the forecast period [126; 145; 160]. However, thus far, the application of space-based information for food insecurity early warnings in South Africa and other African nations has been limited at best, with the only other main example being the African Flood and Drought Monitor [161]. Droughts, unlike other weather-related disasters that make headlines because of immediate obvious devastation, are slow-burning calamities with long-term consequences. Severe drought, on the other hand, has far-reaching consequences for society and ecosystems: Agriculture: Increased temperatures and water stress cause agricultural losses, which can devastate farmers who rely on crop yields for their livelihood. Increasingly dry range-land has a negative influence on cattle output; feeding and hydrating huge herds becomes more challenging. Ecosystems: As bodies of water dry up due to drought, huge fish deaths can occur. Droughts can also cause pest outbreaks, wildlife losses and forest diebacks, all of which threaten the viability of the important ecological services that humans rely on. Infrastructure: Drought may cause reductions in outdoor recreation sectors, and shifting soil and moisture levels can destroy physical infrastructure. Drought conditions will undoubtedly increase the likelihood and severity of devastating wildfires. Economy: Droughts have cost billions of dollars in damages since 1980, with an average annual cost of $9 billion since 2010 [162]. Therefore, droughts are likely to last longer and become more severe in the coming years. This is because continuous emissions of heat-trapping gases are expected to result of even more warming. While we can expect more rainfall around the world, experts estimate that evaporation may surpass precipitation, resulting in more frequent and extended drought periods [162].
## 6 Conclusions  
This paper presents a comprehensive investigation on survivable RBMSA, focusing on fragmentation and ISRS awareness in C+L-EONs. The proposed algorithm incorporates the SBPP to ensure network survivability for requests. To mitigate the impact of ISRS on the OSNR of services, the primary and backup paths are selected using the minimum congestion and maximum sharing algorithms, respectively. In the spectrum allocation phase, the algorithm takes into account both free fragmentation on the primary path and shared fragmentation on the backup path to minimize spectrum fragmentation. The simulation results demonstrate that the proposed algorithm outperforms other algorithms in terms of reducing the spectrum fragmentation rate and bandwidth blocking rate while simultaneously ensuring network survivability and request OSNR.  
Conceptualization, Y.L. and L.S.; methodology, Y.L. and J.L.; software, L.S.; validation, Y.L.; formal analysis, Y.L. and N.F.; investigation, Y.L., L.S. and D.Y.; resources, Y.L.; data curation, Y.L.; writing\(-\)original draft preparation, Y.L. and N.F.; writing\(-\)review and editing, Y.L. and N.F.; visualization, Y.L. and L.S.; supervision, J.Z.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure 6: The fragmentation ratio in (**a**) the NSFNET network and (**b**) the COST239 network.