esa-sceva/satcom-chunk-collection huggingface dataset:
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 5 In-pond WaterMon Sensor Design and Feasibility
#### 4.2.2 Overview Design
Figure 6: Three-dimensional Representation of PCB. With respect to the software governing the behaviour of the WaterMon board, there is no open-source initiative which could be used to have a starting point. Therefore, an implementation of the software has been carried out until a fully functional prototype is achieved. This prototype allows us to:  
1. Perform the continuous monitoring of the 11 metrics supported in the board;
2. Perform the continuous reporting of the values retrieved from the probe using the NB-IoT, LTE-M, LoRa/LoRaWAN wireless connection;
3. Perform the calibration of the water monitoring probes in a very intuitive way. The PCB has been designed with flexibility and extensibility in mind. Therefore, it allows for the attachment of more sensors, and this would propel further research in this field. The PCB also allows for the extension of capabilities such as controlling for the sensor's energy consumption rate to enable future developments of battery-powered or solar-powered versions; however, software has not been developed to support such capabilities since this is out of the scope of this project. Finally, an enclosure has been designed to achieve a device ready to be deployed in an industrially relevant scenario. Figures 7 and 8 show the whole sensor device, fully operative to be used in a fish farm, with proper communications.
## 3 Results and Discussion
### Sensory Evaluation  
The sensory analysis at the visual, olfactory, and gustatory phases of the final herbal tea was performed. The mean scores revealed that samples with higher quantities of tea and longer infusion times received the highest scores during the visual phase (color intensity) in both herbal teas (\(p<0.05\)). Importantly, none of the samples exhibited turbidity. Among the EBF tea samples, those containing 3 g of tea infused for 5 min achieved the highest scores for vegetable and herbal aroma during the gustatory phase (\(p<0.05\)). Whereas, in the olfactory phase, significant differences (\(p<0.05\)) were observed in the scores for floral flavor in EPF tea, with 2 g tea and infused time 2 min. However, no significant differences (\(p<0.05\)) were noted in the scores for other sensory properties. It should be mentioned that panelists identified a balsamic aroma in the EBF tea and a honey-like scent in the EPF tea during the olfactory phase. Furthermore, in the gustatory phase, the EBF teas were characterized by balsamic, honey, irritating, and astringent qualities, while the EPF teas exhibited a slight bitterness.  
Figure 2: Color parameters of the produced herbal teas. (**A**) _E. planum_ herbal teas; (**B**) _E. billardieri_ herbal teas.  
Previous studies have shown that specific aroma compounds in herbal teas contribute to distinct types of aroma. For instance, \(\beta\)-elemene, \(\beta\)-cadinene, and \(\beta\)-selinene are typically associated with herbal stents [37], while phenylacetaldehyde is known for imparting a honey odor [38]. Moreover, \(\alpha\)-cubebene is recognized for its woody, fruity, and floral notes [39] and \(\alpha\)-farnesene, a key stress-related compound, contributes a floral and woody aroma [40]. These aromatic compounds are present in _E. billardieri_ and _E. planum_, as detailed in Table 5. Astringency is typically attributed to the presence of tannins or polyphenols, and it can be considered a desirable characteristic in tea [41].
# Sound Localization Framework for Construction Site Monitoring
## 7 Results and Discussion
The detection radius may be wider depending on the location being estimated (even within a 10 m radius in outdoor environments), and a certain space could be estimated outside the detection radius. Therefore, this technique can also be used to consider installation intervals at construction sites. Verification test 3 provided the test results for the actual construction site (Table 7). Compared to the interior environment test, which had a monitoring radius of approximately 5.8 m, the task radius was expanded to 10 m. Nonetheless, the average difference for each radius was lower than that in the interior test, thereby indicating that the effect of reverberation and reflected sounds was smaller in the exterior environment. As a result, the exterior validation test showed more accurate localization values. Additionally, these results were obtained with the consideration that the framework performs localization using the arrival delay time, which is a very small value, and the difference between arrival delay times becomes clearer as the distance between microphones increases. According to the overall test results, approximately 16.8% of the data were not received (Table 5), with the majority of errors related to hardware overload. Over a few hundred instances of experimentation resulted in errors in the receiving data owing to overload and unstable power supply issues with the hardware equipment. Upgrading the hardware specifications and supporting a stable power supply can presumably solve this issue. Additionally, the framework in this study comprised hardwired connections; therefore, the longer the LAN and hardwired lengths are when calculating the dt values (arrival time delays), the more likely a data delay will occur, possibly resulting in errors in the estimated \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sound-Source** & **Test** & **X Error** & **Y Error** & **Z Error** \\
**Point** & **Repetitions** & **Mean (m)** & **Mean (m)** & **Mean (m)** & **Mean (m)** \\ \hline Point 1 & 18 & 0.39 & 1.35 & 0.39 & 1.27 \\ Point 2 & 12 & 0.37 & 1.09 & 0.56 & 1.08 \\ Point 3 & 6 & 0.80 & 2.82 & 1.17 & 2.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the interior validation test 2. Figure 15: Results of validation test 2 in the section view. sound-source location values. Therefore, wireless connectivity and data transmission can significantly reduce the estimated error values of such sound-source locations, while increasing the installation radius can maximize the on-site utilization.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Countermeasures
#### 4.2.3 Improved Sensitivity  
While efforts to improve the sensitivity and efficiency of QKD systems will increase their effective secure bit rate, requiring a greater increase in QBER to achieve substantial disruption, many potential methods to increase the sensitivity of the QKD receivers to the signal from the QKD transmitter will also increase their sensitivity (and so, vulnerability) to photons from the laser terminal.
## 2 Investigating In Vivo Stroke Model Using Photochemical Thrombosis
### Photochemical Thrombosis-Based Stroke Animal Model Establishments
Additionally, it provided a function to link indicators of the stereotaxic frame and coil to provide support for image acquisitions, so that it could be held firmly, ensuring the least possible movement. Various optical imaging modalities were utilized to analyze stroke and its resultant brain damages in cell tissue. The preclinical animal studies were discussed in a previous section. Relevant studies have also been conducted extensively in which these optical imaging techniques are applied to methods used for verifying stroke-inducing animal models or for analyzing changes in lesions or treatments. [PERSON] et al. used a real-time and high-resolution laser speckle brain imaging system to confirm a blockage of blood vessels and vascular changes in the brain of a rodent model using photochemical thrombosis [34]. In this study, changes in a specific vessel over time were quantitatively explored through high-speed imaging of brain vessels. [PERSON] and [PERSON] employed in vivo two-photon fluorescence microscopy to observe changes in dendrites as well as a distribution of blood vessels in a photothrombotic animal (mice) model [35]. In addition, it was confirmed that changes in neurons and biomolecules could also be observed using in vivo two-photon  
Figure 1: **(a)** Generation of photothrombotic stroke on a single brain arteriole using a photothrombosis inducing module, which was employed using a conventional upright microscope. Images acquired while blocking a single arteriole through photochemical thrombosis and light irradiation for 6, 20, and 120 s are depicted on the right. Reprinted with permission from Ref. [29]. Copyright 2008 Elsevier B.V. **(b)** A fiber-optic light irradiation probe used to induce photochemical thrombosis in a minor stroke, and magnetic resonance T2- and diffusion-weighted images of a brain lesion confirmed that the investigated probe can induce minor stroke. Reprinted with permission from Ref. [31]. Copyright 2016 Elsevier B.V.  
imaging, and precise measurements were achieved for damage and changes in dendrites through photochemical thrombosis, as illustrated in Figure 2. [PERSON] et al. investigated a miniaturized and lightened infrared (780 nm) vessel imaging device, which was equipped with a combination of fiber-coupled blue and green lasers. The device is designed to generate photochemical thrombosis and acquire a brain vessel image of a freely moving animal [36]. The miniaturized imaging device is significant in that it can immediately monitor effects of photochemical thrombosis and changes in cerebral blood vessels without a need for location-limited noninvasive brain imaging techniques such as an MRI.
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 4 Results and Discussion
### The Robustness of the MentorNet Classifier
#### 4.2.2 The Impact of Carrier Frequency Offset and Phase Offset  
As shown in Equation (1), the carrier frequency offset and phase offset induced by the drift of the clock could also increase the difficulty of modulation classification. In this subsection, we would explore the impact of carrier frequency offset and phase offset on the classification accuracy of the MentorNet classifier. Firstly, the ratio of carrier frequency deviation to sampling frequency \(\Delta f/f_{\mathrm{s}}\) was set within \(1\times 10^{-4}\) to \(2\times 10^{-4}\) to investigate the anti-interference ability of the MentorNet classifier to carrier frequency offset. For a fair comparison, the Inception classifier, Baseline classifier and MentorNet classifier were trained by a training set with \(-20\)-\(18\) dB SNR, and then they were tested in a test set with an SNR of 10 dB. The experimental results are reported in Figure 12a. We could find the accuracy of all classifier decreased monotonously with \(\Delta f/f_{\mathrm{s}}\), but the reduction of Inception classifier was the smallest (around 5%), due to its simple network structure [28] Meanwhile the reductions of the Baseline classifier and MentorNet classifier were around 13% and 12% separately. Although the accuracy of the Baseline classifier and MentorNet classifier was significantly disturbed by the carrier frequency offset, their accuracy was still 14% and 18% higher than the Inception classifier, respectively. Hence, it was obtained that in the presence of frequency offset, the performance of the MentorNet classifier was still the best, so that has actual importance in the field of communication.  
Figure 11: Classification accuracy of the MentorNet classifier under the interference of Rayleigh fading.  
Then, the impact of the phase offset \(\Delta\theta\) on the accuracy of the classifier was studied and discussed. The experimental parameters are the same as above, except the carrier frequency offset and phase offset. The phase offset was set within 0-10\({}^{\circ}\), while the carrier frequency offset was set to 0 Hz. The results are shown in Figure 12b. It is obvious that the phase offset had little effect on the accuracy of classifier, which suggests the strong robustness to phase offset. Moreover, the accuracy of the MentorNet classifier could maintain at 99% regardless of the phase offset, while the Inception classifier and Baseline classifier could only achieve a classification accuracy of 96% and 83%, respectively. This phenomenon reveals that among these three classifiers, the designed MentorNet classifier obtained a better performance.
## 4 Results
### Simulation Analysis at Different Market Penetration Rates  
To further investigate the model's stability in a mixed traffic flow environment, this study investigates the impact of CAV penetration rates on mixed traffic flow stability. The scenario simulates car-following behaviour in a single-lane mixed traffic flow, focusing on a situation with 100 uniformly distributed vehicles on a road. By varying the proportions of human-driven vehicles (IDM) and CAVs (MIDM-SPF) in the scenario, the study examines the model's resilience to disturbances in large-scale mixed traffic flows. The initial vehicle speeds were set in the range 7-9 m/s.  
Numerical simulations were conducted for CAV market penetration rates (MPRs) of 0.2, 0.4, 0.6, and 0.8. At \(t=0\), a disturbance was introduced at the leading vehicle's position to simulate emergency braking behaviour. The resulting velocity and position curves for different MPRs are displayed in Figure 9. In these figures, grey lines represent HDVs, and blue lines represent CAVs. Figure 9a reveals that at a 20% MPR, the disturbance causes significant velocity fluctuations, characterised by an oscillatory behaviour. As the MPR increases, as seen in Figure 9b-d, the extent of velocity fluctuations gradually reduces, the overall speed oscillation trend in the traffic flow diminishes, and the duration of fluctuation decreases. At an 80% MPR, the range of velocity fluctuations and the fluctuation duration are noticeably shorter. These observations suggest that a higher proportion of CAVs in mixed traffic flows can significantly enhance traffic flow stability.
## 3 Polynomial Time Algorithms for the Bandwidth Problem
### Linear Time Algorithm for a Chain Graph
Repeating this process, the computation of \(\mathsf{LC}_{i}\), \(\mathsf{CC}_{i}\), \(\mathsf{RC}_{i}\), and the best pair of \(H_{i}\) from \(\mathsf{LC}_{i-1}\), \(\mathsf{CC}_{i-1}\), and \(\mathsf{RC}_{i-1}\) with the best pair for \(H_{i-1}\) requires \(O(d(x_{i-1})-d(x_{i+1}))\) time for each \(1<i\leq n\). Hence, by maintaining the differences, the total computation time of Algorithm 3 is the sum of the computations of (1) \(\mathsf{bw}(H_{0})\), (2) \(\mathsf{LC}_{1}\), \(\mathsf{CC}_{1}\), \(\mathsf{RC}_{1}\), and the best pair of \(H_{1}\), and (3) \(\mathsf{LC}_{i}\), \(\mathsf{CC}_{i}\), and \(\mathsf{RC}_{i}\) with the best pair of \(H_{i}\) from \(\mathsf{LC}_{i-1}\), \(\mathsf{CC}_{i-1}\), \(\mathsf{RC}_{i-1}\), and the best pair of \(H_{i-1}\) for \(i=2,3,\ldots,n-1\), which is equal to \(O(n+n^{\prime})+\sum_{i=2}^{n-1}O(d(x_{i-1})-d(x_{i+1}))=O(n+n^{\prime})\). Here we extend Corollary 7 to a chain graph. **Corollary 10**: _For a connected chain graph \(G=(X,Y,E)\), we assume that \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) and \(Y=\{y_{1},y_{2},\ldots,y_{n^{\prime}}\}\) are already ordered by inclusion of neighbors. Then we have an optimal layout that satisfies \(X_{0}<Y_{0}<X_{1}\cup Y_{1}<X_{2}<Y_{2}\) such that (1) \(X_{0}=\{x_{1},\ldots,x_{i}\}\), \(X_{1}=\{x_{i+1},\ldots,x_{j}\}\), and \(X_{2}=\{x_{j+1},\ldots,x_{n}\}\) for some \(1\leq i\leq j\leq n\), and (2) \(Y_{2}=\{y_{1},\ldots,y_{k}\}\), \(Y_{1}=\{y_{k+1},\ldots,y_{\ell}\}\), and \(Y_{0}=\{y_{\ell+1},\ldots,y_{n^{\prime}}\}\) for some \(1\leq k\leq\ell\leq n\). Any arrangement of vertices in \(X_{1}\cup Y_{1}\) gives us an optimal layout. Moreover, the bandwidth is determined by an edge between (1) \(X_{0}\) and \(Y_{0}\), (2) \((X_{1}\cup X_{2})\) and \((Y_{0}\cup Y_{1})\), or (3) \(X_{2}\) and \(Y_{2}\)._  
Proof. For an optimal layout, we have a corresponding wiper. Then the set \(X_{0}\) is determined by \(x_{i}\in X\) which is the maximum vertex in \(X\) not crossing the wiper. Then \(Y_{0}\) is determined by the maximum vertex \(y_{k}\) in \(N(x_{i})\). Similarly, the set \(Y_{2}\) is determined by the minimum vertex \(y_{\ell}\) not crossing the wiper, and \(X_{2}\) is determined by the minimum vertex in \(N(y_{\ell})\). Considering the maximum cliques which can give the bandwidth, the corollary follows.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 5 Software Interface
### High-Level Programming Interface  
The software interface (ISE) presented in the previous section allows controlling, at a low level, the adaptation of the interconnection. A higher level programming interface is further required to ease the exploitation of the massive parallelism offered by our design. The capability of expressing parallelism directly in the program is mandatory. Starting from the observation that most of the ML/DL applications behave in a data-driven fashion, we introduce a high-level programming language interface. We implemented such interface within the Go language. Go provides an inherent concurrent execution model. We introduced a PXM, based on the producer-consumer paradigm which can reduce the effort for the programmer to describe concurrent activities in the application and ease the mapping on the proposed chip architecture. By forcing concurrent Go functions to exchange values only via dedicated data structures used for buffering input data (channels), the user can easily express parallelism in the application following an explicit data-driven scheme.
# Fault Detection Algorithm Based on Dynamic Global-Local Preserving Projection
## 2 Background Techniques  
### PCA Algorithm  
The basic idea of PCA involves retaining most of the maximum variance information via orthogonal transformations to ensure that the data loses the least information after dimensionality reduction [34; 35; 36]. The aim is to include as much information as possible about most changes in the original dataset with minimal combined data. In particular, a set of mapping matrices \(P=(p_{1},p_{2}\cdots p_{t})\) is found for a given dataset \(X=(x_{1},x_{2}\cdots x_{n})\in R^{n\times m}\) and projected onto a low-dimensional space to obtain a mapping matrix \(Y=(y_{1},y_{2}\cdots y_{n})\). Its objective function is as follows:  
\[\begin{array}{l}J_{Global}=\max\limits_{p}\sum\limits_{i=1}^{n}\left(y_{i}- \overline{y}\right)^{2}\\ subject\ to:\ p^{T}p=1\end{array} \tag{1}\]  
where \(\overline{y}=\frac{1}{n}\sum\limits_{i=1}^{n}y_{i}\). The dataset \(X\in R^{n\times m}\) (\(m\) is the number of variables in the sample and \(n\) is the number of samples) is decomposed by PCA into the following:  
\[X=X_{p}+E=TP^{T}+E \tag{2}\]  
where \(X_{p}\) is the projection of the sample vector \(X\) in the principal element subspace, \(T\) is the score matrix, \(P\) is the load matrix, and \(E\) is the residual subspace.
# Dynamic Controllability of Processes without Surprises
## 2 Background and Motivation
### Sudden Start  
We explain the sudden start problem with another small example. Figure 2 depicts a simplified manufacturing process. The process starts with the preprocessing of some raw materials, which takes 20 to 24 h, depending on the quality of the materials. Subsequently, assembly of component A and of component B start, requiring 3 to 5 h, and 1 to 2 h, respectively. The time required for assembling component A cannot be controlled, since it also heavily depends on the quality of the raw material. Afterwards, the two components are assembled (2 to 4 h), and finally the product can be packaged. The final assembly must start at least 1 h and at most 2 h after completion of component A assembly.  
One can observe that, due to the uncontrollable duration of component A assembly, it is not known when final assembly has to start, until the task has to start, which means that it cannot be scheduled beforehand. Similar to the sudden termination, such a behavior may as well be unacceptable, or impossible. Especially for activities that require some preparation, it would be desirable, instead, that the agent receives an advance notice some time \(k\) before the activity has to start.  
An example for an activity whose start time should be known in advance from the administrative workflows is, for an employee, being asked to write a report immediately, interrupting any other task that was being performed before. An example from the aviation industry is boarding a plane: the boarding time should be announced at least 30 min prior to its start, otherwise passengers might not make it in time to reach the gate. Similar and from the same domain, the air traffic control should communicate to the pilot of a plane, flying on hold due to high traffic, the landing time at least 5 min in advance, in order for the plane to perform the necessary maneuvers. Or, before telling the cabin crew to sit for (imminent) landing, the pilot gives them an advance notice 5 min before (_\"prepare for landing\"_), so that the crew will not be put in danger and forced to rush to their seats.  
In Section 4 we will show how to address the sudden start problem, by transforming it into a sudden termination problem, such that the start time of an activity can be scheduled with an advance of \(k\) time units.  
Figure 1: Example process with a potential sudden termination of an activity.