esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Memory Management in MIROS
### Dynamic Memory Allocators in MIROS
#### 3.2.2 MIROS Defragmented Sequential Fit Allocation  
To solve the fragmentation problem in the _SF_ allocation, the defragmentation _mechanism_ is applied in the MIROS. Currently, two defragmentation approaches are realized: the _proactive_ defragmentation and the _reactive_ defragmentation (concept motivated by the _proactive_ routing protocol and the _reactive_ routing protocol).  
_MIROS proactive defragmented SF allocator:_ With this mechanism, the memory fragments will be assembled proactively once they appear, and no fragments will exist in the heap (Figure 8). The advantage of this approach is that the new allocation can be performed immediately from the starting address of the free space and the allocation time is short and deterministic. However, the addresses of some objects will change after the memory is coalesced, thus each allocated object needs to be accessed indirectly by means of the reference pointer. After an object has moved to a new address, moreover, the update to its reference pointer can help it remain accessible.  
Figure 7: Heap-extendable segregated free list (SFL) allocation in the MIROS. After the partitions are pre-reserved, the left memory space will be utilized both for the extended heap and the run-time stack.  
MIROS proactive defragmentation _SF_ allocation. After \(B\) is de-allocated in Figure 8a, the \(C\) will be assembled with \(A\) proactively. Thus, no fragments will appear and the free memory space is continuous.  
MIROS reactive defragmented _SF allocator:_ The _reactive_ mechanism is different from the _proactive_ mechanism in that the fragments are not assembled every time an object is released. Instead, they are assembled only when the first time allocation is failed, that is, when there is not enough continuous free memory existing in the heap (Figure 9). In order to avoid the redundant assembling operations, not all the fragments are assembled during the defragmentation process. Instead, only parts of the fragments are assembled and the assembling operation will stop once enough continuous memory has been produced for the current allocation. The same as the _proactive_ mechanism, the addresses of some objects will change after the fragments are assembled, thus the reference pointers need also to be used in this _reactive_ mechanism.
# Numerical Investigation of a Novel Grinding Device
## 2 Materials and Methods  
### Experimental Setup  
The scheme of the novel custom-made wet milling device is reported in Figure 1a. As in [24], the main components are the following:  
* A cylindrical container (height 120 mm, internal diameter 31 mm, glass);
* A cylindrical magnetic bar (hemispherical ends, length 30 mm, diameter 5 mm, PTFE coating);
* An L-bent rotating shaft (hemispherical end, diameter 5 mm, L-arm length 11 mm, glass);
* 4 metal precursor beads (diameter 3 mm, Ag 99.9 % (American Elements, Los Angeles, CA, USA));
* 40 grinding beads (diameter 3 mm, ZrO2 95 %, Y2O3 5 % (MSE Supplies, Tucson, AZ, USA), materials known for their high surface hardness and anti-scratch properties).  
The position of the magnetic bar represents the fundamental innovation compared to previous configuration [23]. Since the bar is kept outside the liquid containing the beads, this apparatus is known as outer-bar wet miller (OWM) as opposed to the original inner-bar wet miller (IWM) [16]. The aforementioned magnetic bar drives an L-bent glass shaft, coated at the bottom with PTFE to minimize attrition, which keeps the grinding and precursor beads in motion. The rotation causes the beads to collide and the metal precursor to wear off, releasing NPs. A detailed description of the aforementioned experimental setup is available elsewhere [16].  
Note that when the torque applied by the magnetic drive is overcome by the resisting torque of the beads, the process stops abruptly. This occurs, for example, when the number of beads in the vessel exceeds a certain critical threshold, which is determined by system geometric parameters such as bead size, container shape, and impeller shape and position. In this regard, the OWM has a lower tendency to jam because it is able to exploit all of the available torque with a magnetic bar that rotates parallel to the motor. Conversely, the IWM wastes part of the torque due to the inclined magnetic bar. This advantage makes the OWM configuration more suitable for autogenous grinding, i.e., when only metal precursor beads are used (which generally have a higher density and therefore a higher inertia). On the other hand, to prevent shaft blockage and breakage, the OWM requires careful design and control of the impeller. This constraint adds complexity to the design but is more than compensated by the benefits it provides, resulting in a much higher hourly output of metallic NPs [16].  
Most importantly, the ability to abrade ferromagnetic materials to produce the corresponding NPs is a unique advantage of the OWM setup. Indeed, the IWM is unable to achieve this result since the precursor beads and produced NPs would be attracted by the magnetic field generated by both the stirring bar and the external motor, resulting in magnetic-induced NP aggregation and a severe reduction in grinding efficiency.
## 3 Results
### Statistical Significance Analysis  
In order to determine and further validate the superiority of the Late Fusion model over the Early Fusion model, a paired _t_-test statistical analysis was conducted on the main evaluation scores, as shown in Table 4. This provides a better insight into the model's strengths and weaknesses and sheds light on its observed performance.  
The results shown in Figure 5 indicate that there are no statistically significant differences in the two models, apart from precision, which suggests that Early Fusion outperforms the Late Fusion model. Late Fusion shows better overall performance in metrics; however, this could also indicate that the higher scores could stem from random sampling variations. The two models show no statistical significance; therefore, other factors should be taken into consideration, such as computational cost and robustness. To further test this, the Inference Speed Performance test was performed. The **degrees of freedom (df)** for each test were calculated as \(n-1\), where \(n\) = 10 test runs (df = 9). **a 95% confidence interval** (CI) was computed for the mean difference between the Early Fusion and Late Fusion models on each metric.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **T-Stat** & _p-Value_ & **Degrees of Freedom** & **95\% CI (Mean Diff)** & **Significant Difference** \\ \hline Accuracy & 7.249 & 0.5086 & 9 & [\(-\)0.015, 0.036] & No \\ \hline Precision & 3.891 & 0.0177 & 9 & [0.003, 0.019] & Yes (Early Fusion is better) \\ \hline Recall & \(-\)11.882 & 0.3005 & 9 & [\(-\)0.067, 0.022] & No \\ \hline F1-Score & 1.414 & 0.2304 & 9 & [\(-\)0.007, 0.025] & No \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical Significance Analysis (T-tests).  
Figure 4: Overall performance comparison between Early and Late Fusion models across all metrics.  
modern laptop configuration, commensurate with deployment environments in the real world and without requirements for special hardware.  
Comparative analysis of Early and Late Fusion models shows that both models have strengths and weaknesses. Overall, Late Fusion outperforms Early Fusion with higher evaluation metrics, faster inference times, and no statistically significant differences, making it the superior model. This provides a perfect example for a strong candidate in applications where sensitivity in detecting and predicting aggressive behaviors is highly sensitive and critical. Early Fusion still has some strong points in cases when precision is the major priority, since it provides fewer false positives, making it useful in applications where false alarms are costly and must be prevented, such as automated monitoring systems in health clinics without supervision from staff.
## 2 Materials and Methods
### Microalgal Growth Monitoring and Kinetic Growth Parameters  
Operational parameters, such as pH and temperature, were daily monitored using a SympHony SB90M5 pH-meter (VWR, Portugal). Microalgal growth was also daily assessed through optical density measurements at 680 nm (OD\({}_{680}\)) using a UV-6300 PC spectrophotometer (VWR, United States). To eliminate the interference of the effluent color on OD\({}_{680}\) measurements, the cells were separated from the culture medium by centrifugation (at 4000 rpm, for 10 min), the supernatant was discarded, and the cells were resuspended in an equal volume of distilled water, as described by [PERSON], et al. [18]. This procedure was repeated twice. The relationship between OD\({}_{680}\) and biomass concentration (\(X\), mg\({}_{\text{dw}}\) L\({}^{-1}\)) for _C. vulgaris_ was previously established by linear regression, according to Equation (1):  
\[X=0.0024\times\text{OD}_{680}+0.0030\text{ R}^{2}=0.9999, \tag{1}\]  
Biomass concentrations were used to determine the kinetic growth parameters: (i) specific growth rates (\(\mu\), d\({}^{-1}\)); and (ii) maximum and average biomass productivities (\(P_{max}\) and \(P_{ave}\), mg\({}_{\text{dw}}\) L\({}^{-1}\) d\({}^{-1}\)). The specific growth rates were determined from the first-order kinetic model, according to Equation (2):  
\[\frac{\text{d}X}{\text{dt}}=\mu X\leftrightarrow\mu=\frac{\ln X_{1}-\ln X_{0}} {\text{t}_{1}-\text{t}_{0}}, \tag{2}\]  
Figure 1: Schematic representation of the experimental setup.  
where \(X_{1}\) and \(X_{0}\) correspond, respectively, to biomass concentration (mg\({}_{\rm dw}\) L\({}^{-1}\)) in the end (\(t_{1}\), d) and beginning (\(t_{0}\), d) of the exponential growth phase. Biomass productivities (\(P\), mg\({}_{\rm dw}\) L\({}^{-1}\) d\({}^{-1}\)) were calculated for each pair of consecutive points, through Equation (3):  
\[P=\frac{X_{x+1}-X_{x}}{t_{x+1}-t_{x}}, \tag{3}\]  
where \(X_{x}\) represents the biomass concentration (mg\({}_{\rm dw}\) L\({}^{-1}\)) at time \(t_{z}\) (d) and \(X_{z+1}\) corresponds to the biomass concentration (mg\({}_{\rm dw}\) L\({}^{-1}\)) at time \(t_{z+1}\) (d). The maximum productivity was determined from the maximum value obtained from Equation (3). On the other hand, average biomass productivities were determined according to Equation (4):  
\[P_{\rm aver}=\frac{X_{t}-X_{i}}{t_{f}-t_{i}}, \tag{4}\]  
where \(X_{f}\) and \(X_{i}\) correspond, respectively, to biomass concentration (mg\({}_{\rm dw}\) L\({}^{-1}\)) in the end (\(t_{f}\), d) and beginning (\(t_{i}\), d) of the cultivation period.
## 2 Model Description
### The Isolated String  
Consider a linear, stiff, and lossy string, of length \(L\). The string displacement in the _vertical_ or _normal_ polarisation is denoted by \(w(x,t)\), while \(u(x,t)\) is the string displacement in the _horizontal_ or _tangential_ polarisation. Both are defined for position \(x\in\mathcal{D}=[0,L]\) and time \(t\in\mathbb{R}^{+}=[0,+\infty]\) (see Figure 1).  
The partial differential equations governing the time evolution of \(u(x,t)\) and \(w(x,t)\) can be written as:  
\[\mathcal{L}w=0\qquad\text{(1a)}\qquad\qquad\mathcal{L}u=0\qquad\text{(1b)}\]  
Figure 1: String displacement in two polarisations. The horizontal polarisation (\(u(x,t)\)) corresponds to the plane containing the bow and the string; the vertical polarisation (\(w(x,t)\)) is orthogonal to this plane.  
\(\mathcal{L}\) is the partial differential operator defined as [20]:  
\[\mathcal{L}=\rho\partial_{t}^{2}-T\partial_{x}^{2}+EI_{0}\partial_{x}^{4}+\lambda _{1}\rho\partial_{t}-\lambda_{2}\rho\partial_{t}\partial_{x}^{2} \tag{2}\]  
where \(\partial_{t}^{i}\) is equivalent to \(\frac{\partial_{x}^{i}}{\partial_{x}^{i}}\). \(\rho\) is the linear mass density of the string, in kg/m; \(T\) is the tension of the string, in N; \(EI_{0}\) is the bending stiffness, where \(E\) is [PERSON]'s modulus in Pa, and \(I_{0}=\frac{\pi r^{4}}{4}\) is the area moment of inertia of the circular cross-section of the string, with \(r\) the string radius in m. Some typical parameters for a violin, a viola, and a cello, can be found in the literature, notably those measured by [PERSON] [44] and [PERSON] [45].  
\(\lambda_{1}\) (1/s) and \(\lambda_{2}\) (m\({}^{2}\)/s) are positive damping coefficients, that empirically account for frequency independent and dependent losses in the string, respectively. With more refinement, they could be replaced by a full model accounting for energy dissipation through air viscosity, acoustic radiation, and internal friction, each of these three mechanisms having a more or less pronounced impact across the spectrum [46]. In accordance with the statements in Section 2.1 however, this empirical, simplified loss model is deemed sufficient for  
have purposely been excluded from this work; as the primary aim is sound synthesis, it is found here that a dynamic, nonlinear, albeit lumped bow is a satisfying compromise between computational cost and gestural versatility.  
The second aspect of the contact interaction lies in the capture of the string between a left hand finger and the fingerboard. The use of the damped impact law allows for realistic simulation of the fingertip reacting against the tension of the string, while significantly absorbing vibrations. The distributed fingerboard acts as a continuous barrier for fingers to slide along, allowing for _glissando_ and _vibrato_ gestures on the fly. Again, the built-in impact model allows for string rattling effects [39], used, for instance, in jazz double bass playing.
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 2 GNSS Processing  
### Data Collection  
China has launched the International GNSS Monitoring and Assessment System (iGMAS) [23] to monitor and assess the performance as well as operational status of BDS satellites and to promote compatibility and interoperability among different GNSS systems. BDS-3 observations for the present study are collected by a total of eleven reference stations, among which eight stations are from the iGMAS tracking network, and the other three stations in Australia are from the GA (Geoscience Australia) network. In addition, BDS-2 observations from twenty-six MGEX (Multi-GNSS Experiment) [24] and eight CMONOC (Crustal Movement Observation Network of China) stations are selected for joint POD processing. The orbit and clock results discussed in this paper are based on data for the time period 22 July (DOY 204) until 10 August 2016 (DOY 223). Figure 1 shows the geographic distribution of all GNSS stations and the ground tracks of four BDS-3 satellites. Table 2 lists the information of stations capable of tracking BDS-3 satellites. Since these stations provide only BDS-3 B1 and B3 observations in common with BDS-2, we assess B1 and B3 signal characteristics and use B1 and B3 observations for POD in this paper.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Abb.** & **Location** & **Country** & **Receiver** & **Antenna** \\ \hline BjF1 & Beijing & China & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ BRCH & Braunschweig & Germany & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ CLGY & Calgary & Canada & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ GUA1 & Drumqi & China & GNSS\_GGK & RINT-8 CH CETD \\ LHA1 & Lhasa & China & CETC-54 GMR-4011 & NOV50.R4 NOVS \\ TAHT & Tahiti & France & GNSS\_GGR & RINT-8 CH CETD \\ WUH1 & Wuhan & China & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ ZHON & Antarctica & United Nations & CETC-54 GMR-4011 & LEIAR25.R4 LEIT \\ FROY & Fitzroy Crossing & Australia & SEPT POLARX5 & LEIAR25.R3 LEIT \\ KUNU & Kununurra & Australia & SEPT POLARX5 & JAVRINGANT\_DM SCIS \\ WALAL & Wallal & Australia & SEPT POLARX5 & LEIAR25.R3 LEIT \\ \hline \hline \end{tabular}
\end{table}
Table 2: GNSS monitoring stations with BDS-3 tracking capability.  
Figure 1: Ground traces of BDS-3 satellites and distribution of the tracking stations used in this study. Stations capable of tracking BDS-3 satellites are marked in blue. The other stations are marked in red.
## 2 Potential Application Areas of CR-WSNs
### Home Appliances and Indoor Applications  
Many potential and emerging indoor applications require a dense WSNs environment to achieve an adequate QoS. Conventional WSNs experience significant challenges in achieving reliable communication because ISM bands in indoor areas are extremely crowded [13]. Some examples of the indoor applications of WSNs are intelligent buildings, home monitoring systems, factory automation, personal entertainment, _etc._ CR-WSNs can mitigate the challenges faced by conventional indoor WSNs applications.
# Design and Verification of a Large-Scaled Flapping-Wing Aircraft Named \"Cloud Owl\"
## Materials and Methods  
### Overall Layout and Weight Estimation  
The Cloud Owl uses an up-and-down flapping mode and a flexible wing structure to achieve passive deformation of the chord and spreading directions, simulating the active and passive deformation of the wing during the bird's flapping motion. The lifting ailerons are placed on the trailing edge of the inverted t-shaped tail, which was chosen as the tail configuration. The benefit of this configuration is that it has high static stability characteristics for the heading and longitudinal separation, making it easy to realize attitude control. The mechanism is in front of the flapping-wing aircraft, the battery is on the trailing edge of the wing, the electronic equipment is fixed in the middle of the fuselage, and the payload is in the head of the fuselage, as shown in Figure 3.  
Figure 2: The design flow chart of the Cloud Owl.  
The takeoff weight of the aircraft can be roughly divided into five parts: the fuselage structure weight \(\mathrm{W_{S1}}\), which includes the fuselage shell and internal structure; the aerodynamic structure weight \(\mathrm{W_{S2}}\), which includes flapping wings and tail wings; the drive system weight \(\mathrm{W_{P}}\), which includes a reduction gear set, a brushless DC motor, a crank rocker, as well as other transmission parts and batteries; the electronic equipment weight \(\mathrm{W_{E}}\) (including the flight control system, GPS, data transmission, image transmission, receiver, airspeed meter, pitot tube, governor, etc.); and the payload weight \(\mathrm{W_{M}}\), namely \(\mathrm{W_{TO}}=\mathrm{W_{S1}}+\mathrm{W_{S2}}+\mathrm{W_{P}}+\mathrm{W_{E}}+ \mathrm{W_{M}}\).  
The chosen materials and an approximate weight estimate are provided in Table 2 with reference to the team's mature aircraft, \"Dove\" [14].
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues
### Monitoring FPV Systems for Evaluating the Water Environment Impact on Energy Production
#### 3.1.5 Monitoring via UAV  
Unmanned aerial vehicle (UAV) monitoring systems for photovoltaic installations have reached a level of full maturity. However, marine applications [49], particularly floating photovoltaics (FPV) and offshore systems [19] present novel challenges that necessitate further advancements in UAV monitoring techniques. The monitoring of FPV systems by UAVs encounters specific difficulties due to the aquatic environment and the anchoring mechanisms of the infrastructure. Previously unaddressed issues can emerge with FPVs, such as optical disturbances affecting sensors due to sunlight reflections and wave glints. Additionally, controlling the UAV's attitude and stability may be more challenging due to stronger wind gusts. Take-off, landing, and navigation become more difficult because of the random relative motion of the pontoons. These factors may necessitate additional maneuvers, thereby reducing the UAV's operational autonomy.  
Navigation complexity increases with FPV systems, since the floating structure is in constant motion. Even with perfect GPS positioning of the UAV, its relative position to the FPV remains uncertain. This uncertainty requires frequent verification of the GPS position and orientation of the FPV, considering its continuous random rotation along the axes, which imposes a significant real-time computational burden. Nonetheless, non-GPS navigation systems, such as optical navigation and microwave radar sensors, are available.  
Finally, issues related to the quality of the images acquired by UAVs during FPV monitoring may arise. Sunlight reflection and glint can significantly impact vision-based object detection in marine environments. Rapid course corrections can limit the areas affected by reflections.
# A Disposable Soft Magnetic Ribbon Impedance-Based Sensor for Corrosion Monitoring
## 2 Materials and Methods  
### Sensor Design and Fabrication  
Soft ferromagnetic amorphous ribbons of the composition Co\({}_{65}\)Fe\({}_{4}\)Ni\({}_{2}\)Si\({}_{15}\)B\({}_{14}\) (METGLAS 2714A) were cut into 3 mm-wide strips so that the magnetic domains were transverse to their length. The impedance of the strips was measured by a four-point measurement technique over a length of 15 or 20 mm using an HP4192 impedance analyzer at a test current of 1.36 mA. The electric contacts were made using copper wires and silver paint and the whole measurement system was monitored by the LabVIEW program. A diagram of the experimental setup is shown in Figure 1. We note that this measurement set-up is to demonstrate the proof-of-concept of the sensor for chemical sensing. For the practical implementation of the sensor, the sample needs to be changed at each test; therefore, the use of copper wires glued with silver paste directly onto the amorphous strip is not desirable, and a mechanical system for connecting the amorphous strip should be used instead.
## 6 Results and Discussion
### Meta-Classifier  
To achieve the results of our proposed framework, we build a sequential neural network that assembles the results of the utilized methods: LSTM, CNN, CNN feats with SVM, user-based features as presented in Figure 12. As shown in Tables 5 and 6, the proposed model achieved the best results in terms of accuracy, precision, and recall compared to the latest research in this field. Although [41] has the lowest execution time that it take 0.002 for each tweet, this execution time is very small compared to our proposed method as it takes longer, approximately 2 ms for each tweet. That is because of the number of features used to detect the spam and the combination of models that the tweet must pass to get the final result. However, this time can be optimized using clusters of nodes to decrease the time.  
We also found that the results of the meta classifier are not boosted very much as they are too close to the ensemble model, but it is able to preserve the performance by a significant margin for this dataset. So, we can offer robust framework, that can be self  
Figure 11: ROC curve for CNN features with SVM model results for each dataset as a second component in our ensemble method.  
trained with the new words and hashtags, which the spammer can use as Twitter always has new subjects and interests of their users.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 6 Power Transfer
### Laser to Electricity Conversion  
Under laser illumination of incident power \(P_{i}\), the short circuit current \(I_{sc}\) of a photovoltaic cell is given by Equation (11) [40].  
\[I_{sc}=\frac{qP_{i}}{h\
u}QE \tag{11}\]  
where \(QE\) is the external quantum efficiency at the wavelength of interest, and for higher efficiency solar cells, the quantum efficiency is very close to unity. The I-V curve model of solar cells is given by Equation (12).  
\[I=I_{sc}-I_{sat}\left[exp\left(\frac{V+R_{s}I}{V_{T}}\right)-1\right]-\frac{V +R_{s}I}{R_{sh}} \tag{12}\]  
where \(I\) is the operating current, \(V\) is the operating voltage, \(V_{T}=nkT/q\), \(n\) is the diode factor, \(k\) is the Boltzmann constant, \(T\) is the temperature, \(R_{s}\) is the series resistance, and \(R_{sh}\) is the shunt resistance of the solar cell (\(R_{s}\) is assumed to be small and neglected). Thus, the laser to electricity conversion efficiency is defined as Equation (13).  
\[\eta_{L-E}=\frac{IV}{P_{i}} \tag{13}\]