esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Testing and Evaluation
### Subjective User Evaluation
#### 5.1.2 Field Trials  
Post-development, the complete system has been demonstrated to both drone pilots and non-expert users. While these evaluations are still ongoing, early results are being used to guide adjustments and improvements in the system. In these events, participants are first responders interested in using drones for search-and-rescue operations. Such mission are often stressful, highlighting the need for more comfortable and intuitive drone control and a video feed easily viewed and placed in context. Early demonstration and feedback sessions have been held in Japan (July 2021--9 participants), Greece (October 2021--6 participants), and France (November 2021--12 participants), with additional events planned for Italy, Finland, and Spain in the first half of 2022.  
Initial feedback from these events indicate that gesture control with a real drone is easy to learn for inexpert users, comfortable, and allows full control, including any maneuver possible with a handheld remote. One setback noted concerned the use of the HoloLens in very bright sunlight or a hot day: Both of these conditions degraded the HoloLens's hand-tracking performance, and the heat caused it to shut down after a few minutes. This is expected, as the HoloLens employs passive cooling and is designed primarily for indoor use. However, under non-extreme heat and sunlight conditions, performance has been consistent and robust.  
Figure 10: Overview of the gesture selection user study results.  
Two experienced drone pilots taking part in the demonstrations stated that they would feel comfortable using gesture control, provided conditions did not degrade the robustness of the HoloLens's hand tracking. The periscope mode, which keeps the drone motionless while tying its yaw to that of the HoloLens, has proven particularly popular and useful for scanning an area.  
Regarding drone tracking in AR, feedback was mixed: The manual calibration process, in particular, often proves too difficult and complicated for users. This feedback has driven a turn towards implementing a easier, largely automated, vision-based calibration process, relying on AI visual pose estimation and/or QR codes, as described in Section 4.4. The new calibration process will be tested and evaluated in upcoming demonstration events.  
Concerning visualization, users preferred the FPV mode rather than the contextualized view mode. This has been in part due to the difficult calibration process, which is a prerequisite for drone tracking and hence contextualized display. In addition, the size of the contextualized display was sometimes deemed too small. Based on this feedback, future versions will feature a larger canvas on which to display the video feed, as well as the option to scale this canvas using gestures on the camera-controlling hand.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 3 IoT Devices and Cloud Processing
### Data Transmission Technologies  
Underground geothermal structures are often situated in remote areas where no easy connection to the internet for monitoring purposes is possible (i.e., WiFi or Ethernet). In these areas, monitoring equipment is usually powered by batteries or energy obtained by harvesting (see Section 3.1), and energy consumption must be low. GSM/LTE technology does not satisfy this low-power requirement, but LPWAN technology is ideal. These types of systems were developed for the IoT and feature reliable, long-distance wireless data transmission (1-40 km), with minimal power consumption. The commonly used LPWAN technologies are currently LoRa, Sigfox and NB-IoT [81; 82].  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author, Source** & **Power Supply Type** & **Application** \\ \hline [PERSON] et al. [61] & AC power grid & AC power monitoring module \\ [PERSON] et al. [10] & Primary battery & Environmental monitoring \\ [PERSON] et al. [67] & Solar panel & Air pollution monitoring \\ [PERSON] et al. [62] & Battery & Industrial monitoring \\ [PERSON] et al. [68] & Kinetic energy—vibration & Agricultural industry \\ [PERSON] et al. [69] & Radio frequency & Healthcare, room temperature, tire pressure, implantable devices, environmental monitoring, air quality monitoring \\ [PERSON] et al. [70] & Thermoelectric generator & Industrial utilities, transportation tools, military devices, space applications, medical services \\ [PERSON] et al. [71] & Wind & Environmental monitoring, agricultural applications \\ \hline \hline \end{tabular}
\end{table}
Table 5: SoA of IoT power supply technologies.
## 3 Results
### Antifungal Activity of KCC-37 and KCC-38  
We then determined the antifungal activity of KCC-37 and KCC-38 by pour plate method. Result exhibited that KCC-37 and KCC-38 strongly inhibit the growth of fungi around KCC-37 and KCC-38 colonies in experimental plates. Both bacteria showed maximum inhibitory zones against _A. clavatus_ and _F. oxysporum_. The data suggested that all tested fungal growth was inhibited significantly by KCC-37 and KCC-38 inoculated plates (Table 5).  
Fungal biomass inhibitory property of cell-free metabolites (CFM) produced by KCC-37 and KCC-38 at different temperatures and pH against _S. brevicaulis_, _A. clavatus_, _P. chrysogenum_, _A. flavus_, and _F. oxysporum_ was determined. Cell-free metabolites of KCC-37 and KCC-38 significantly reduced biomass of fungi in PD broth supplemented with 10% CFM. Cell-free supernatant produced at 35 \({}^{\circ}\)C exhibited strong antifungal activity against all tested fungi than the metabolites produced at 25 and 45 \({}^{\circ}\)C (Figure 2).  
Also, we analyzed fungal biomass inhibition study for CFM produced at different pH environments. Results indicated that CFM produced at pH6.5 and pH7.5 showed strong biomass reduction against all tested fungi than the other pH. This result suggested that KCC-37 and KCC-38 might be produced higher amounts of antifungal metabolites at optimum temperature 35 \({}^{\circ}\)C and optimum pH ranges between 6.5 and 7.5 (Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Organic Acids (mg/mL)** & **KCC-37** & **KCC-38** \\ \hline Lactic acid & 56.52 \(\pm\) 1.65 \({}^{\text{b}}\) & 72.32 \(\pm\) 3.52 \({}^{\text{a}}\) \\ Acetic acid & 17.14 \(\pm\) 3.25 \({}^{\text{c}}\) & 17.32 \(\pm\) 1.02 \({}^{\text{c}}\) \\ Succinic acid & 2.523 \(\pm\) 0.325 \({}^{\text{d}}\) & 2.56 \(\pm\) 0.152 \({}^{\text{d}}\) \\ \hline \hline \end{tabular} Data represent mean \(\pm\) STD of three replicates. Different letter a, b, c, and d within a column indicates significances differences between KCC-37 and KCC-38 (\(p\) < 0.05).  
\end{table}
Table 4: Fermentative acids production by _L. plantarum_ KCC-37 and _L. plantarum_ KCC-38 in MRS broth.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tested Fungi** & **KCC-37** & **KCC-38** \\ \hline _S. brevicaulis_ & 15.46 \(\pm\) 1.51 \({}^{\text{c}}\) & 14.92 \(\pm\) 1.36 \({}^{\text{c}}\) \\ _A. clavatus_ & 36.21 \(\pm\) 0.58 \({}^{\text{a}}\) & 30.37 \(\pm\) 1.07 \({}^{\text{a}}\) \\ _P. chrysogenum_ & 24.67 \(\pm\) 1.35 \({}^{\text{b}}\) & 21.51 \(\pm\) 0.54 \({}^{\text{b}}\) \\ _A. flavus_ & 24.94 \(\pm\) 1.48 \({}^{\text{b}}\) & 22.47 \(\pm\) 0.66 \({}^{\text{b}}\) \\ _F. oxysporum_ & 35.03 \(\pm\) 0.33 \({}^{\text{a}}\) & 30.72 \(\pm\) 1.28 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} Data represent mean \(\pm\) STD of three replicates. Different letter a, b, and c between columns indicates significances differences between fungi (\(p\) < 0.05). Zone formation around LAB as mentioned as millimeter (mm).  
\end{table}
Table 5: Antifungal activities of _L. plantarum_ KCC-37 and _L. plantarum_ KCC-38 against different fungi, screened by pour plate method.
# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
## 2 Materials and Method
### Evaluation Method  
In order to avoid the subjectivity of the evaluation method and the complexity of the semi-quantitative method, we adopt the logistic regression method to establish the prediction model.  
The logistic regression model is a multivariate nonlinear statistical analysis model, \(\beta\), which is suitable for the multivariable control dichotomy problem, and is a typical supervised learning model. In the logistic analysis, the binary variable Y is used to describe the state of the moraine lake. Y = 1 indicates the occurrence of GLOF, and Y = 0 indicates no GLOF. The specific principles of logistic regression can be found in the literature [14,25].  
The logistic regression formula is:  
\[\mathrm{Y}=\mathrm{Logis}(\mathrm{P})=\frac{1}{1+\mathrm{e}^{-(\alpha+\beta x _{1}+\beta z_{2}x_{2}+.....+\beta_{n}x_{n}+L)}}\]  
In the formula, P is the probability value of the occurrence of the dependent variable calculated from the independent variable, that is, the outburst probability of the moraine lake, and its range is 0-1. X\({}_{1}\), X\({}_{2}\), X\({}_{3}\),..., X\({}_{n}\) are various evaluation factors, namely, independent variables; n is the number of evaluation factors; and a is the intercept.  
Figure 1: Schematic map of southeastern Tibet.  
\(\beta_{1},\beta_{2},\beta_{3}.\ldots.\ \beta_{n}\) represent the independent variable regression coefficients computed by the logistic regression model.
## 5 Discussion  
In this study, the length of warming-up time is a significant element that affects the performance of the FCNS algorithm in opportunistic social networks. In real life, during a day, the user is more likely to repeat the same mobile route, such as from places of residence to workplaces, and there is a high probability of them meeting the same person in the day. Therefore, the activity cycle of the user is usually one day, and the optimal value of the warming-up time in the real social scene should be set to 24 h.  
The second issue to be discussed is the weight setting of social attributes of nodes. In the FCNS algorithm, the information entropy is used to determine the weight of social similarities of each node in the network, and this method also effectively considers the historical experience of the message carrier. Therefore, the FCNS algorithm can reasonably and accurately evaluate the similarity degrees between nodes in opportunistic social networks.  
At last, in opportunistic social networks, although many routing-forwarding algorithms are based on the node similarity, there is no one focusing on mobile similarity. The higher the mobile similarity between a pair of nodes, the higher the probability of them meeting in the future will be. Consequently, the FCNS algorithm creatively pays attention to the mobile information of nodes in the network, and evaluates the comprehensive impact of the mobile and social similarities on data transmission through the fuzzy inference system. Meanwhile, the experimental results show the feasibility and improvement of this algorithm in the transmission environment.  
Figure 11: Overhead on average with various cache spaces.
## 3 Distributed Memory Architectures  
DM is a type of parallel system that is inherently different to SM. In this environment, the memory is distributed over the cores and each core can only directly access its own private memory. The exchange of information stored in the memory of the other cores is achieved  
Figure 1: S-R—example for \(N=8\). Each \(\mathbf{x}^{i}\) is actually a real vector, but is marked with a capital letter for brevity.  
by sending/receiving explicit messages through a common communication network (see Figure 2).  
DM provides several advantages over SM, such as: scalable and larger DOP; scalable and larger memory; memory contention (and other issues that stem from multiple cores accessing the same addresses in memory) not being a concern on DM. The main disadvantage of DM is the cost of communication and the consequent data movement. This can affect the speed-up relative to a single core implementation.  
Any Application Programming Interface (API) for DM could be used. We choose MPI, since its intuitive syntax means it is arguably the most popular API for DM. MPI is also used in several referenced works on parallel redistribution for DM [7; 17; 22; 23; 25]. In MPI, the \(P\) cores are uniquely identified by a rank \(p=0,1,...,P-1\), connected via communicators, and use explicit communication routines, e.g., MPI_Sendrecv, to exchange messages of arbitrary size.  
Most algorithms that we implement in this paper use the divide-and-conquer paradigm. Therefore, we recommend using a power-of-two number of cores to balance the communication between them. For the same reason, the number of particles \(N\) should also be a power-of-two. In this case, the particles \(\mathbf{x}\) and every array related to them, e.g., \(\mathbf{w}\), \(\mathbf{n}\mathbf{c}\mathbf{opies}\) etc., can then be evenly split between the cores. Every core then always owns exactly \(n=\frac{N}{P}\) elements of all arrays, whose global indexes are spread over the cores in increasing order. This means that, given a certain \(N\), \(P\) pair, the \(i\)-th particle (where \(i\in[0,N-1]\)) will always belong to the same core with rank \(p=\left\lfloor\frac{i}{n}\right\rfloor\). More precisely, while all equations in this paper use global indexes for simplicity, each core \(p\) actually uses a local index \(j\in[0,n-1]\) to address arrays and achieve parallelism, knowing that each \(j\) corresponds to global index \(i=j+pn\). The space complexity is then \(O(\frac{N}{P})\) for any \(P\leq N\).
# Novel Microfluidics Device for Rapid Antibiotics Susceptibility Screening
## 2 Device Description  
As mentioned before, our device utilizes the gas permeability of PDMS [25]. The main goal was to develop a microfluidic device in which the influence of antibiotics on the bacterial cell growth rate can be visually observed. The working principle of the designed microfluidic chip was inspired by [26], where the presented technique successfully captured mammalian cells in isolated regions, protecting the cells from potential flow-damaging effects. The design of the device is shown in Figure 1a. The chip consists of three channels: two cultivation channels (220 \(\upmu\)m \(\times\) 220 \(\upmu\)m, 200 \(\upmu\)m height), each having 69 individual culture chambers (300 \(\upmu\)m \(\times\) 300 \(\upmu\)m, 200 \(\upmu\)m), and one vacuum channel, running parallel to the column of chambers at a distance of 300 \(\upmu\)m, as shown in Figure 1b. The cell traps are connected to the flow in the cultivation channels through narrow channels (80 \(\upmu\)m \(\times\) 150 \(\upmu\)m, 200 \(\upmu\)m), which ensures that mass transport mostly occurs through diffusion. The main idea of the chambers is to allow the control of cell loading at low velocities without the need for high external pressure. The application of a vacuum in the middle microchannel allows for air evacuation from the chambers, followed by chamber cell loading. This way, the chambers protect the cells from the shear forces in the main flow channel and, most importantly, serve as micro incubators, in which cells could develop without any disruption. According to our knowledge, we are the first to report the vacuum loading of bacterial cells in a microfluidic device. In contrast to [26], we utilized a vacuum pump for the air evacuation (described in Section 4), allowing for a higher degree of automation in the process.  
In order to choose the optimal length of the narrow channel, we made a finite volume analysis, comparing the mass transport in the culture chambers for four different lengths. The results are described in the section below. After cells are loaded in the channel's chambers through the vacuum, an antibiotic is introduced in Cultivation Channel 1 and nutrient-rich media in Cultivation Channel 2. This way, the effect of the antibiotic on the cell culture growth rate compared to pure medium could be inspected through direct observation.
# Shear Strength and Durability of Expansive Soil Treated with Recycled Gypsum and Rice Husk Ash
## 2 Materials and Methods
### Sample Preparation  
Clay was crushed and oven-dried before being mixed with recycled gypsum and rice husk ash. The direct shear tests used proportions of 0%, 5%, 10%, and 15% gypsum and 10% RHA. The admixture proportion of 15% gypsum + 10% rice husk ash was selected for the durability tests, as [PERSON] and [PERSON] [37] reported that this ratio resulted in the most significant maximum dry density, least optimum moisture content, and lowest expansion index, which rendered the sample non-expansive.  
The soil was mixed with water to reach the optimum moisture content of 31.395%. Recycled gypsum and rice husk ash were added to the soil-water mixture to prevent the crumbling observed during dry mixing before adding water. The mixed samples were cured inside airtight containers for at least 16 h, as prescribed by ASTM D4829-11. Relative compaction of 90% was targeted to achieve minimum compaction requirements, as suggested by [PERSON] et al. [50]. This was achieved across all trials using manual tamping.  
Variations in curing periods (7, 14, 21, 28, and 35 days) were applied to all untreated and treated samples to observe the strength development in each sample as curing days increased. Air-drying was done for the specified curing period before the unconfined compression strength test, modified durability test, and modified water immersion test. After curing, the soil mixtures were tamped in a mold with an approximate diameter of 50 mm and height of 100 mm, considering the targeted 90% relative compaction. The entire compacted cylindrical sample was used for the unconfined compression test, while smaller portions were cut from the molded sample for the expansion index test.
## 3 Simulation Results
### Simulation Results According to the Utilized Matrices  
Figure 4 shows the results of applying our techniques to both an EDM and a GM, where a GM has a sharper condition in terms of its low-rank property. Under this setting, the EDM error and the localization error are investigated with respect to the size of the training dataset. A GM is created by multiplying the centering matrix on both sides of the EDM, and it is clear that it has a sharper condition than that of the EDM. In other words, \(\text{rank}(\text{GM})\leq k\) where \(k\) is the dimension of the Euclidean space. As can be seen in Figure 4, both tests show a mostly similar performance when the size of the training dataset is large enough since they contain essentially the same information. However, in cases where the training dataset is insufficient, a slight deterioration in performance is observed when the GM is applied. This can be inferred because when the GM is created through the multiplication of the EDM and the centering matrix, then some useful information is contaminated during this procedure, as the centering matrix is a singular matrix.
## 3 Results and Discussion
### Slr1599 Displays Z-ISO Activity in Escherichia coli  
To test the function of Slr1599, we expressed slr1599 in an _E. coli_ strain containing pAC-ZETAipi, which contains the _Pantoea agglomerans_ (previous known as _Erwinia herbicola_) _crtE_ (encodes GGPPS), _crtB_ (encodes PSY), and _idi_ (encodes IPP isomerase, which interconverts IPP and DMAPP) genes and the _Synechococcus_ sp. PCC 7942 _crtP_ gene (encodes PDS) to drive the production of the substrate of Z-ISO, 9,15,9'-tri-_cis-_\(\zeta\)-carotene [27]. In agreement with the previously reported data, _E. coli_ cells containing pAC-ZETAipi and empty pCDFDuetTm-1 accumulated primarily 9,15,9'-tri-_cis-_\(\zeta\)-carotene (Figure 3A, peak 1) when grown in darkness, with small additional peaks that we assigned as 9,9'-di-_cis-_\(\zeta\)-carotene (peak 3) and all-_trans-_\(\zeta\)-carotene_ (peak 4) based on previous analyses [11; 44]. The same was true for pAC-ZETAipi plus pCDFDuetTm-1 with the _Synechocystis zds_ (_crtQ_) gene, consistent with the fact that ZDS cannot act on 9,15,9'-tri-_cis-_\(\zeta\)-carotene [26].  
Upon the co-transformation of _E. coli_ with pAC-ZETAipi and pCDFDuetTm-1 carrying either maize _Z-ISO_ or slr1599, the size of the 9,15,9'-tri-_cis-_\(\zeta\)-carotene peak (no. 1) was significantly decreased and that of the 9,9'-di-_cis-_\(\zeta\)-carotene peak (no. 3) increased correspondingly (Figure 3A), showing that both enzymes can isomerize the 15-15' _cis_ double bond of 9,15,9'-tri-_cis-_\(\zeta\)-carotene. Furthermore, when _zds_ and slr1599 or _zds_ and the maize _Z-ISO_ gene were present in the pCDFDuetTm-1, \(\zeta\)-carotene species were almost completely absent and a variety of new carotenoid species (Figure 3B, peaks 5-7), likely _cis_-isomers of lycopene [23], were produced; these _cis_-lycopene species were absent in the corresponding samples lacking Z-ISO, which accumulated 9,15,9'-tri-_cis-_\(\zeta\)-carotene_ (peak 8, Figure 3B), as in the absence of _zds_. Taken together, these data demonstrate that slr1599 encodes an enzyme with 9,15,9'-tri-_cis-_\(\zeta\)-carotene isomerase activity akin to previously characterized Z-ISO enzymes [5; 7; 8; 11].
## 5 Results and Discussions  
Research question 3 is addressed in this section. In total, 36 publications have been analyzed systematically to perform the review. The articles considered are from familiar publishers such as Elsevier, IEEE, Springer, MDPI, Taylor & Francis, Wiley, etc. Around 30% of the articles are from Elsevier, 24% are from Springer, 26% are from IEEE, and 20% are from other publishers such as MDPI, Taylor & Francis, Emerald, IOP, etc. Figure 4 depicts the percentage of publications from database sources. The publications demonstrate that IoT holds a lot of promise for clinical settings and healthcare. IoT may also be applied to medical applications that can save lives or enhance quality of life through monitoring habits, tracking health-related metrics, assisting with independent living, regulating drug use, etc. Internet-based patient systems have the potential to be helpful tools for public health organisations to maximise their efficacy, similar to IoT healthcare solutions.  
Healthcare organisations supported by IoT operate with big data, which entails data-handling techniques like data gathering, mining, and analysis. Greater threat might result from attackers starting to alter data in order to give feedback to IoT devices. Risks linked with IoT devices used in healthcare management systems include the lack of a strong encryption strategy, a faultless access system, insufficient ability to protect personal privacy, and issues with mobile device security.  
We looked at and analysed the factors that influence IoT-based medical systems. The attributes of IoT-based medical management systems are shown in Table 3. These factors' effects could be advantageous or harmful. Additionally, some of the mentioned properties  
Figure 4: Percentage of publications relying on database sources.  
are reported indirectly. As a result, implicit qualities are identified and scored via a content analysis of the chosen articles that have been discussed with respect to these attributes.
## 6 Performance Evaluation
### Selection of Threshold Value \(th_{cs}\)
When the attacker ratio is 0.5, the probability of a successful attack can be reduced to less than 0.1 with the voting weight continuously increased, which also reflects the robustness of the system. Even if the system is attacked by half of the total users in the domain, it still maintains a 90% probability of correct revocation. At the same time, due to the limitation of crypto puzzles, it is quite difficult for attackers to occupy 50% of total users, which will consume a lot of computing power. Therefore, our scheme can resist collusion attacks very well and ensure system security. Figure 6: Collusion attacks to prevent the revocation of a malicious public key. Panels **(a–d)** show the success probability fluctuation of this kind of collusion attack when user number is 100, 200, 300, and 500.