esa-sceva/satcom-chunk-collection huggingface dataset:
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 4 Field Experiment Results
### Comparison of the IoT Device and AWAC--Waves  
In Experiment 2, the IoT tide gauge was set up to return sea-level data. However, the recovered data (i.e., 1-Hz data and 40-s period statistics) also demonstrated the capability to capture higher frequency water level variability due to waves. This is explored in this section to illustrate the wider application of the IoT sensor network for monitoring waves.  
At this stage, no attempt is made to calculate the pressure attenuation factor, which is necessary to determine actual wave heights in meters of water (this type of processing would be beyond the Arduino micro-controller). However, postprocessing of the internally logged 10-Hz data using open source \"R\" and Matlab algorithms developed by [PERSON] and [PERSON] [25, 26] (based on methods outlined by [PERSON] and [PERSON] [27]) could be applied to generate the wave height and wave period statistics. For example, [PERSON] et al. [14] used similar pressure sensors deployed in water depths of 9 m and 18 m and were able to successfully calculate the wave statistics. The AWAC uses a different set of post-recovery algorithms [28] utilizing both the pressure and current to derive the wave statistics.  
Figure 9 shows the hourly wave statistics from the AWAC, measured during the 1000-s measurement time together with the corresponding differences between maximum and minimum pressure recorded during the 40-s bursts. Although the units of the \(y\)-axis scales are different, these IoT pressure statistics do align themselves closely with the maximum wave heights from the AWAC.  
Figure 9: Time-series comparisons left \(y\)-axis: significant and maximum wave heights from the AWAC ADCP right \(y\)-axis: differences between maximum and minimum pressures recorded during the 40-s burst.  
It is likely that the IoT pressure sensor is not resolving some of the larger waves since it is only recording for 40 s every 15 min, whereas the AWAC, setup in burst mode to record over 1000 s every hour, can measure the larger waves. While this Configuration 2 did not send these pressure differences via the SigFox network, the software could be modified easily to return them instead. In practice, one IoT tide gauge could send the sea-level data and another the 40-s period burst pressure statistics.
# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
## 2 Methods  
### Autoregressive Data Generation Using GCC-FLOW  
We assume that the sea clutter data generation is a stationary random process, as assumed in most of the literature [3; 4; 6; 7; 31]. Existing generative methods based on the GAN sample sea clutter sequence \(\mathbf{x}=[x_{1},\cdots,x_{L}]\) follow the joint distribution \(p^{*}(\mathbf{x})\) learned from the train data. In order to increase \(L\), the data size should be increased. The concatenation of two data \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\) by generated GAN cannot be used as a sequence of the sampled random process of length \(2L\), since \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\) are independent due to the block-by-block data generation of GAN.  
Figure 1 illustrates the autoregressive use of the proposed globally conditioned conditional FLOW (GCC-FLOW) in order to generate long data sequences. GCC-FLOW  
generates a sequence \(\mathbf{x}_{o}=[x_{o1},\cdots,x_{oL}]\) given a conditional sequence \(\mathbf{x}_{c}=[x_{c1},\cdots,x_{cL}]\) following a conditional distribution \(p^{*}(\mathbf{x}_{o}|\mathbf{x}_{c};\alpha)\) parameterized by a global conditional variable \(\alpha\in[0,1]\). The conditional pdf \(p^{*}(\mathbf{x}_{o}|\mathbf{x}_{c};\alpha)\) is learned by GCC-FLOW to approximate the conditional distribution of the successive data \([x_{L+1+k},\cdots x_{2L+k}]\) given \([x_{1+k},\cdots x_{L+k}]\) for \(k=0\cdots\). Given the initial sequence \(\mathbf{x}_{c}^{(0)}\) from the data, \(\mathbf{x}_{o}^{(0)}\) is generated by the GCC-FLOW, and \(\mathbf{x}_{o}^{(k)}\) is used as the condition sequence for \(\mathbf{x}_{o}^{(k+1)}\) for \(k=0,1,\cdots\). The joint distribution of the concatenated sample sequence \(\mathbf{x}=[\mathbf{x}_{o}^{(0)},\mathbf{x}_{o}^{(1)},\cdots,\mathbf{x}_{o}^ {(T)}]\) satisfies  
\[p(\mathbf{x}_{o}^{(0)},\mathbf{x}_{o}^{(1)},\cdots,\mathbf{x}_{o }^{(T)}) =\prod_{K=0}^{T}p(\mathbf{x}_{o}^{(k)}|\mathbf{x}_{o}^{(k-1)}, \cdots,\mathbf{x}_{o}^{(1)}) \tag{1}\] \[\approx\prod_{K=0}^{T}p(\mathbf{x}_{o}^{(k)}|\mathbf{x}_{o}^{(k-1 )}), \tag{2}\]  
assuming the block Markov property,  
\[p(x_{N+L+1},\cdots,x_{N+2L}|x_{0},\cdots,x_{N+L})\approx p(x_{N+L+1},\cdots,x_ {N+2L}|x_{N+1},\cdots x_{N+L}). \tag{3}\]  
Since each conditional distribution \(p(\mathbf{x}_{o}^{(k)}|\mathbf{x}_{o}^{(k-1)})\) is approximated by \(p^{*}(\mathbf{x}_{o}^{(k)}|\mathbf{x}_{o}^{(k-1)})\) learned from GCC-FLOW, the joint distribution \(\mathbf{x}=[\mathbf{x}_{o}^{(0)},\mathbf{x}_{o}^{(1)},\cdots,\mathbf{x}_{o}^ {(T)}]\) generated by the GCC-FLOW autoregressively approximates the true data distribution as desired.
## 4 Experimental Results and Discussions
### Performance Comparison on the LFW Dataset  
The LFW database comprises 13,233 images of 5749 individuals taken in various environments. It has become a de-facto evaluation benchmark dataset for face recognition. In this work, we evaluated the performance under the unrestricted, labeled outside data protocol, which aims to measure how many image pairs can be recognized correctly. It provides 6000 mixed pair of images, of which 3000 are genuine pairs and others are imposters. These groups of pairs are further segregated into 10 subsets. The matching performance is determined by the mean matching accuracy \(\hat{\mu}\) and the standard deviation of accuracies \(S_{E}\) based on 10 cross validations. These are defined in (2) and (3) respectively, where \(p_{i}\) denotes the recognition rate of each test set. In this work, we computed the measurement of similarity between each image pair based on their Euclidean distance.  
\[\hat{\mu}=\frac{\sum_{i=1}^{10}p_{i}}{10} \tag{2}\]  
\[S_{E}= \frac{\hat{\sigma}}{\sqrt{10}},\hat{\sigma}=\sqrt{\frac{\sum_{i=1}^{10}( p_{i}-\hat{\mu})^{2}}{9}} \tag{3}\]  
In order to extract feature vectors from input images, each image was resized to \(256\times 256\), and total of 10 patches of size \(224\times 224\) were set on the original image and the flipped image (i.e., five patches per image). On each image, one patch was placed at the center of each image and the other four were located near the image corners. We extracted feature vectors from these patches and averaged them to generated the final feature vector.  
For the first experiment, we compared the matching accuracy of each \"_Conv stage_\"-extended model against the baseline. As shown in Figure 3 and Table 3, the performance is incrementally enhanced by adding more convolutional stages. The proposed model with five convolutional stages showed matching accuracy of 98.87%. Note that this score is achieved just by adopting the PSI-CNN architecture and without incorporating additional learning framework such as triplet loss.  
The experimental results demonstrate the effectiveness of _untrained_ patterns in improving the matching accuracy for face recognition.
# Multiple Facies Metamorphic Rocks: A Rare Example from the East Qinling Orogen, China
## 1 Introduction  
Metamorphic rutile deposits are an important source for Ti, a critical metal supporting the world's increasing demand for various commodities [1, 2]. Previous studies have shown that the source rocks for metamorphic and placer rutile deposits are those which have undergone high-pressure and high-temperature metamorphism such as eclogite, retrograde eclogite, and garnet amphibolite [3, 4, 5]. Although greenschist- to amphibolite-facies metamorphism plays a major role in Ti enrichment at relatively low pressures and temperatures, the relationship between these lower metamorphic grades and formation of rutile is poorly investigated [6, 7, 8]. In this study, we investigate a rare example of rutile deposits hosted within upper greeenschist- to lower amphibolite-facies metamorphic rocks in the Baishugang-Wujianfang deposits of East Qinling Orogen in central China [9].  
Over 50 Mt of potentially extractable titanium metal has been estimated in the Baishugang-Wujianfang districts, making the region the most endowed with rutile in China [10, 11]. Previous studies of rutile deposits in this orogen focused on their geology and petrography [12, 13, 14], but little is known about the genesis of the deposits. Thus, a more integrated study is necessary for a better understanding of the lithological, geochemical, and geochronological controls.  
This paper reports new petrographic data of the mineralisation, LA-ICP-MS rutile and zircon U-Pb ages, and Zr-in-rutile thermometry of the Baishugang-Wujianfang districts.  
The genesis of the deposits is discussed with reference to lower amphibolite-facies metamorphism, ore-forming processes, and lithological controls on the mineralisation. We also evaluate our results in the light of mineral exploration aiming to discover large-scale rutile deposits hosted by greenschist- to amphibolite-facies rocks elsewhere in the EQO.
## 5 Conclusions  
A THz thermal sensor, based on a defect-containing periodic waveguide, has been proposed in the present investigation. This periodic THz waveguide is found to exhibit a non-Bragg band gap in a specific frequency range. When a defect is introduced in the structure, a defect state can be formed in the non-Bragg band gap (called NBDS). The spectrum of a waveguide, with a defect in the THz frequency range, is also simulated. As a result, it is found that the resonance frequency of the NBDS can be controlled by varying the temperature, which, in turn, is due to the thermal expansion of the metal structure in the waveguide. It is found that the NBDS will move to lower frequencies as the temperature rises. In addition, the Drude model has to be modified since the temperature is varied. It is found that not only the thermal expansion, but also the dielectric constant, will influence the THz thermal sensing. The temperature-modified Drude model is observed to efficiently estimate the THz response of metals at various ambient temperatures. Furthermore, the geometry of the implemented defect is also investigated in the study of the sensing performances. It is thereby found that when the defect length increases, the transmission peak will gradually shift to lower frequencies. On the contrary, the transmission peak will shift to higher frequencies when the radius is increased. In summary, the combination of a tunable defect size, high sensitivity, and ease in operation, of the here proposed structure in the THz regime, makes this temperature sensor very promising for applications based on high-precision temperature measurements and control.  
Conceptualization, Y.-X.F. and Z.-Y.T.; validation, J.-L.X., L.-L.X., and T.-T.W.; formal analysis, L.-L.X., Y.-X.F., and Z.-Y.T.; writing--original draft preparation, J.-L.X., L.-L.X., and T.-T.W.; writing--review and editing, Y.-X.F. and Z.-Y.T.; visualization, L.-L.X., and T.-T.W.; supervision, Y.-X.F. and Z.-Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Dean Project of Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing, the National Natural Science Foundation of China (11374071), and the Natural Science Foundation of Heilongjiang Province, China (QC2018001).  
The authors declare no conflict of interest.
## 4 The Algorithms
### The DSR-Fle Variant Algorithm
#### 4.2.1 Problem Formulation
For each element \(k\in K\), we create an order \(r_{k}\) with the fixed drop-off window \(t_{k}\). For each tuple \((i,j,k)\in T\), we create a directed edge from the order \(r_{i}\) to the order \(r_{j}\), and a directed edge from \(r_{j}\) to the order \(r_{k}\). The directed edge from \(r_{i}\) to \(r_{j}\) indicates that it is feasible to travel from \(r_{i}\)'s drop-off region (at the period \(t_{i}\)) to \(r_{j}\)'s pickup and drop-off region before the period \(t(i,j,k)\). The edge directed from \(r_{j}\) to \(r_{k}\) indicates that it is feasible to travel from \(r_{j}\)'s drop-off region (at the period \(t(i,j,k)\)) to \(r_{k}\)'s pickup and drop-off region before the period \(t_{k}\). The region of the orders \(r_{i},r_{j}\) and \(r_{k}\) and time distance among orders can be easily constructed to satisfy the shareability requirements. Moreover, there are \(n\) couriers in the DSR-Fle problem. The construction can be done in the polynomial time. We will show that the DSR-Fle can serve \(3n\) orders iff 3 DM has a perfect matching. **The 'if' direction**: Assume that 3 DM has a perfect matching \(M\subseteq T\) with \(n\) triples. In the DSR-Fle, for each courier, it can serve three orders \(r_{i}\), \(r_{j}\), and \(r_{k}\) consecutively, where \((i,j,k)\in M\). For each \(r_{i}\) and \(r_{k}\), it is only served exactly once. This is because the drop-off windows of \(r_{i}\) and \(r_{k}\) are fixed, and, for \(r_{j}\), it is only served once since element \(j\) occurs exactly in one triple of \(M\). **The 'only if' direction**: Assume that these \(n\) couriers can serve \(3n\) orders in the DSR-Fle problem, where each courier's delivery plan is \(\langle r_{i},r_{j},r_{k}\rangle\). Then, in 3 DM, the \(n\) delivery plans correspond to the \(n\) triples \((i,j,k)\), which is a perfect matching. This is because each element in \(i\in I,j\in J\), and \(k\in K\) occurs exactly once.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 6 Conclusive Observations
### Selection of Vehicles  
For the distribution task, the selection of vehicles has been one of the key components in enhancing the effectiveness of the distribution task. Many of the distribution models consider one kind of vehicle only, which is more often not the case of vehicle availability at the supply points after any disaster. Besides this, the road conditions and constraints, and relief demands also vary from one affected region to another which makes the single-type vehicles distribution plan less effective. Because of these, for the effective implementation of the relief distribution, the RID model must include the selection of vehicles from the heterogeneous vehicles fleet. The dynamic model used in this paper includes heterogeneous vehicles, which makes it a practically highly feasible option to distribute relief items. Moreover, the model applies an optimum feasible selection of vehicles based on demands across disaster regions that optimise vehicle use.
## 3 Results
### Evaluation of TSNP Application in Biosensing and Diagnostics
#### 3.4.4 Determination of Adequate Fn Concentration  
Several concentrations of Fn were added to aliquots of PEGAuTSNPs to determine the minimum amount of the protein to successfully cover the nanoplates and observe differences in the spectrum. One \(\mu\)g of anti-Fn was added afterwards to analyze binding. As reflected in Figure 12, changes in the spectra were observed with low concentrations of Fn. Results show that when higher concentrations of the protein were added to PEGAuTSNPs, longer red shifts were recorded as expected (Figure 12a). For all added concentrations of Fn, a red shift in the \(\lambda_{\text{max}}\) was observed upon addition of anti-Fn antibody (Figure 12b). In Figure 12c, expanded results show that shorter red shifts are recorded in higher Fn concentration samples once the anti-Fn antibody is added. This could be attributed to the anti-Fn binding to the excess of free unattached Fn within the nanoplate solution.  
Figure 10: UV-Vis spectra of AuTSNPs suspended in a range of water–sucrose solutions.
## 2 Emergency Representation Model
### Geographical Event Analysis  
For the geographical analysis, the information required in the \(\vec{r}\) argument consists of longitude and latitude, and these correspond to the geographical coordinates taken from the alert phone call by one of the following methods: (1) Automatic number identification and automatic location identification for land lines; (2) GPS-based positioning for smart phones; (3) Calculated geolocation for low-end cellphones. This initial location may vary from the real coordinates, and when the FRIs arrive at the event site, they can verify to keep or update it. All this information is saved in the corresponding PSAP's DBs.  
By using these geographical coordinates, the emergencies can be plotted over Google Maps in order to observe their distribution in a specific geographical zone. Figure 2b shows an example of the emergencies attended in a single day using blue points to denote their location \(\vec{r}_{j}\) in terms of scatter. It can be seen that the spatial information is richer in regions where the events tend to concentrate. The point representation only provides us with a scatter representation of the geographical dynamics of the events. The notation now for this event series is given by  
\[\Delta_{i}(\vec{r}|mT)=\sum_{j\in I_{i,nT}}\delta(t-t_{j},\vec{r}-\vec{r}_{j}) \tag{8}\]  
where \(j\in I_{i,mT}\) is the subset of indexes for which events take place in the \(i\)th institution during time period \((mT-T/2,mT+T/2)\).  
This empirical function gives us a scatter of points with an approximation of the spatial distribution. However, its scatter plot is not detailed enough to generate heat maps. In other words, we need a representation based on probability density functions (PDFs) maintaining the nonparametric characteristic of the empirical distribution. To yield an easy-to-plot statistical representation, we propose to use Parzen windows in our system.
## 4 Prototype Implementation
### Healthcare Services Prototype
#### 4.2.3 SOS-Social Media Service  
This service is similar to the SOS-SMS service, except that it uses social media as the emergency notification medium. Social media have been used extensively in the last couple of years, and can be extended further to support emergency situations for the patient. People within the patient's social media circle can be the first responders in case of emergency (e.g., due to close proximity to the patient). A prototype that uses a Twitter account to disseminate emergency information has been implemented, utilizing the same SOS information channel used by the SOS-SMS service. The author's Twitter account is used in the current prototype. This service can be enabled or disabled by the patient through a web page similar to the SOS-SMS service (the two services are deployed as different applications). Figure 13a shows a web page confirming that the SOS-Twitter service is enabled, and Figure 13b shows a tweet of an emergency notification from the patient (_i.e._, the author).  
Figure 12: **(a)** Web interface for enabling specific mobile numbers to be notified in the case of emergency; **(b)** Web interface confirming that SOS-short message service (SMS) is enabled.  
Figure 13: **(a)** Web interface confirming that SOS-Twitter service is enabled; **(b)** A tweet of an emergency notification.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 5 Discussion  
### Application Differences in the Pabs Model Across Growth Stages  
The Pabs model developed in this study to estimate maize phosphorus uptake across six growth stages demonstrates distinct advantages over previous approaches by addressing key limitations in existing research. Prior studies primarily relied on critical phosphorus concentration and phosphorus nutrition index [49] to evaluate crop phosphorus status. While these studies provided valuable insights, their methods were inherently qualitative, offering only a broad categorization of phosphorus status (e.g., deficiency or sufficiency) without the ability to quantify actual phosphorus uptake. This qualitative limitation restricts their applicability for precise nutrient management.  
In contrast, our Pabs model provides a quantitative evaluation of maize phosphorus uptake, capturing phosphorus dynamics across different growth stages. The model achieves its highest accuracy during the jointing stage, surpassing the performance of previous methods. Moreover, this study highlights a critical advancement: the recognition that the optimal PNI is not static but varies across growth stages, typically peaking during the tasseling and silking stages and declining with plant maturation. By incorporating this variability, the Pabs model enhances its predictive capability and offers stage-specific insights, which were absent in earlier studies that relied on fixed PNI thresholds. Furthermore, while existing Pc-based methods focused on diagnosing phosphorus nutritional status qualitatively [50], the Pabs model enables precise, quantitative estimations of phosphorus uptake. This capability not only facilitates more accurate nutrient management decisions but also improves the understanding of phosphorus dynamics throughout the growth cycle. Compared to previous models, the Pabs model represents a significant advancement by combining qualitative diagnostic insights with quantitative precision.  
In summary, the Pabs model addresses the limitations of existing methods by offering a more comprehensive and accurate evaluation of maize phosphorus nutrition. Its ability to quantitatively assess phosphorus uptake at a regional scale, coupled with its stage-specific adaptability, makes it a superior tool for improving phosphorus management strategies and optimizing maize production in different agricultural systems.  
Figure 11: The relationship between PNI and maize yield.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 2 Review of Related Works
proposed a clustering algorithm by considering the residual energy, relative velocities difference and connections with neighbor nodes. Furthermore, a novel group mobility metric combining the instantaneous velocities and directions is proposed. To better highlight our contribution, a table-based comparison is presented in Table 1.