esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Location  
There has been considerable past work on location with respect to a database. For example, [PERSON] and [PERSON] [26] use a search-tree method to match a query image to its nearest match in the set of Google Street View images, [PERSON] and [PERSON] [27] use a voting scheme to do an initial matching between the query image and the database followed by motion estimation to determine the exact location of the query image, and [PERSON] _et al._[28] describe a dataset of accurately geo-referenced images including camera calibration, taken on part of the MIT campus. We address a somewhat different model where we begin with approximate knowledge of our location from the most recently available GPS data to determine which images of the database are most relevant. Google Street View is perhaps the most well-known and well-developed database of this type. It provides 360-degree view images taken from a vehicle at intervals while traveling along city streets, along with location coordinates of where an image was acquired (given as [longitude, latitude]).  
We have developed two location tools, the first being the tagging of database images. In a preprocessing phase (that is done only once at image acquisition time) a set of standard SIFT key points (e.g., Lowe [29]) is calculated for each relevant database image, and then a small set of corresponding points in two overlapping database images (e.g., successive images in Google Street View) are identified. The intersection of the two lines of sight from the acquisition coordinates to a pair of corresponding key points provides their coordinates. Appropriate ranking and weighted averaging of matching pairs can be used to refine accuracy and confidence.  
Figure 12: Location with respect to _Google Street View_ database.  
The preprocessing step creates geo-located key points along with their associated SIFT descriptors. When a user image has at least three points that can be matched with these database key points, a resection procedure like that used in land surveying can be employed: that is, from the apparent angles between these three points, as seen by the user, and the (already computed) locations of the points, we compute the location of the user. In Figure 12, the top and bottom images are two images from the Google Street View database, and the middle image is the user image. Figure 13 contains three images taken with a cell phone camera in a crowded area of buildings, where the two images on the left are database images, and the image on the right is the user's view. Using the minimum number of three matched key points, we can predict the location of the user to within a couple of feet. When there are more matches available, there is an opportunity to improve accuracy by weighting and averaging multiple predictions.
## 3 Dynamic Compressive Mechanical Properties of Sandstone after Corrosion by Strong Alkali Solution
### Variation of the Dynamic Elastic Modulus  
The dynamic elastic modulus of sandstone specimens corroded by strong alkali solution at different times is shown in Figure 8.  
Figure 8: Variation curve of dynamic elastic modulus with the solution corrosion time.  
Figure 7: Variation curve of dynamic peak strain with the solution corrosion time.  
It can be seen from Figure 8 that the dynamic elastic modulus of sandstone specimens corroded by the strong alkali solution is smaller than that of non-corroded specimens. The \(E_{\text{d}}\) of sandstone specimens shows an overall decreasing trend with the extension of corrosion time, which is consistent with the dynamic compressive strength and reaches the minimum at 28 d of corrosion. The dynamic elastic modulus of the sandstone specimen decreases rapidly with the extension of corrosion time from 1 d to 14 d, while at 14 d-28 d, the rate of decrease in dynamic elastic modulus with the extension of corrosion time becomes smaller and gradually becomes stable, which is similar to the findings of [PERSON] et al. [22], but in contrast, under the same corrosion time conditions, the pre-decrease in dynamic elastic modulus of the sandstone specimens under the strong alkali solution corrosion was significantly greater than that of the limestone specimens. By fitting the variation trend of the dynamic elastic modulus of the sandstone, it is found that the dynamic elastic modulus of the sandstone specimen has an exponential term relationship with corrosion time, as shown in Equation (4).  
\[E_{\text{d}}=14.6e^{-0.032t}\ \ \left(R^{2}\!=0.9922\right) \tag{4}\]  
where \(E_{\text{d}}\) is the sandstone dynamic elastic modulus after different times of corrosion by the strong alkali solution, GPa.  
The \(E_{\text{d}}\) value loss percentage of corroded specimens was 6.21%, 11.36%, 23.58%, 41.19%, and 59.34%, respectively, compared with that of non-corroded specimens. This indicates that the corrosion treatment of the strong alkali solution results in the obvious deterioration of the dynamic mechanical properties of the sandstone, and it shows a time effect.
# The Campus as a Smart City: University of Malaga Environmental, Learning, and Research Approaches
###### Abstract  
For the past few years, the concept of the Internet of Things (IoT) has been a recurrent view of the technological environment where nearly every object is expected to be connected to the network. This infrastructure will progressively allow one to monitor and efficiently manage the environment. Until recent years, the IoT applications have been constrained by the limited computational capacity and especially by efficient communications, but the emergence of new communication technologies allows us to overcome most of these issues. This situation paves the way for the fulfillment of the Smart-City concept, where the cities become a fully efficient, monitored, and managed environment able to sustain the increasing needs of its citizens and achieve environmental goals and challenges. However, many Smart-City approaches still require testing and study for their full development and adoption. To facilitate this, the university of Malaga made the commitment to investigate and innovate the concept of Smart-Campus. The goal is to transform university campuses into \"small\" smart cities able to support efficient management of their area as well as innovative educational and research activities, which would be key factors to the proper development of the smart-cities of the future. This paper presents the University of Malaga long-term commitment to the development of its Smart-Campus in the fields of its infrastructure, management, research support, and learning activities. In this way, the adopted IoT and telecommunication architecture is presented, detailing the schemes and initiatives defined for its use in learning activities. This approach is then assessed, establishing the principles for its general application.  
smart-campus; smart-city; architecture; IoT; remote sensing; education; research initiatives 1
That test, known as [PERSON]'s \(w\)-test, can also be derived as a particular case of the generalised likelihood ratio test [27]. [PERSON]'s \(w\)-test makes a decision between the null \(\mathcal{H}_{0}\) (model in the absence of an outlier) and a single alternative hypothesis \(\mathcal{H}_{A}\) (model in the presence of an outlier). In that case, rejection of \(\mathcal{H}_{0}\) automatically implies acceptance of \(\mathcal{H}_{A}\), and vice-versa [21; 22; 28]. Here, we restrict ourselves to the only one single alternative hypothesis. In the case where multiple alternative hypotheses are in play, the readers can consult, for example, [22; 26]. Since Data Snooping is based on a hypothesis testing, it may lead to a errors decision. There is the probability of rejecting a _true_ null hypothesis \(\mathcal{H}_{0}\), which is well-known type I decision error (denoted by \"\(\alpha\)\"). The probability level \(\alpha\) (known as significance level of a test) defines the size of a test and is often known as \"false alarm probability\" [29]. On the other hand, there is the probability of rejecting a _true_ alternative hypothesis \(\mathcal{H}_{A}\) (type II decision error--'missed detection', denoted by \"\(\beta\)\"). Based on the probability levels \(\alpha\) and \(\beta\), [PERSON] [3,4] derived the concept of Minimal Detectable Bias (MDB)--the term given [30]. The MDB is the additional bias (or are the additional biases) in the parameters vector that can be detected by the \(w\)-test with a certain probability of \(1-\beta\). The MDB can be computed before actual measurements were carried out, using only a functional model and the expected stochastic properties of the data [5]. In addition, it is possible to describe the influences of the MDBs on the geodetic network coordinates (i.e., on the parameters). The set of MDBs describes the internal reliability, whereas their propagation on the parameters is said to describe the external reliability [31]. The internal and external reliability, therefore, are very useful tool to assess the magnitude of possible errors that can be detected during the pre-processing of the data. For this reason, the concept of the internal (quantified by MDB) and external reliability can be applied during the design stage of geodetic network. In this context, the goal is to apply the reliability theory for designing geodetic network. The quality criterion considered here is based on the external reliability. The position of the control point of geodetic network is selected so that the maximum influence of an MDB on the coordinates of the network points is minimum. A levelling network is used as an example of application of the proposed method. Here, we consider a scenario where the observations of the network have the same uncertainties and another with different uncertainties. We also consider the case of minimally constrained (one control point in the case of levelling network) and over-constrained network (two control points).
# Coordination and Private Information Revelation
## 6 Extensions
### Two-Sided Private Information
The expected payoff of agent \(i\) from not revealing, given that \(j\) has not revealed her type is  
\[\pi_{i}(i=\text{Not Reveal }\theta_{i}|j=\text{Not Reveal }\theta_{j})=\left\{ \begin{array}{l}\theta_{i}+\frac{c-\hat{\theta}}{2},\text{ if }\theta_{i}>\hat{\theta},\\ \frac{c+\hat{\theta}}{2},\text{ otherwise}\end{array} \right.\]  
where, \(F(\hat{\theta})=\frac{c-\hat{\theta}}{2c}\). Now, consider the case where agent \(i\) reveals her information, given that \(j\) does not. From our earlier analysis of one-sided asymmetric information case, the expected mixed strategy payoff for agent \(i\) is \(\frac{c+\hat{\theta}}{2}\). Hence, we have  
\[\pi_{i}(i=\text{Not Reveal }\theta_{i}|j=\text{Not Reveal }\theta_{j})-\pi_{i}(i=\text{Reveal }\theta_{i}|j=\text{Not Reveal }\theta_{j})\] \[= \left\{ \begin{array}{l}\theta_{i}-\hat{\theta}>0,\text{ if } \theta_{i}>\hat{\theta},\\ 0,\text{ otherwise.}\end{array} \right.\]  
Thus, all types with \(\theta\leq\hat{\theta}\) are indifferent about revealing or not revealing, if \(j\) does not reveal. All types strictly greater than \(\hat{\theta}\) prefer non-revelation to revelation. Finally, taking steps (i) and (ii) together, the proposition follows.13\(\qed\)  
Footnote 13: It is straightforward to show that there exists an equilibrium where neither agent reveals in Stage 1, and, in Stage 2, switches if and only if \(\theta_{i}\leq\theta\). **Remark 3**: _The characterization of this equilibrium, in terms of \(\hat{\theta}\), is as follows: In Stage 1, there is no information revelation. In Stage 2: agent \(i\) adopts her own idea, \(\theta_{i}\), if and only if \(\theta_{i}\geq\hat{\theta}\), where \(\hat{\theta}=F^{-1}(\frac{\varsigma-\hat{\theta}}{2c})\) and \(\theta_{i}<\hat{\theta}<\theta_{h}\), and switches to the other agent's idea otherwise._
## 3 Results and Discussion  
### Material Characterization  
The transmission electron microscopy images of the GNPs at two magnifications are shown in Figure 1.  
TEM images show the predominance of GNP agglomerate with sizes ranging between 12 and 97 nm. Furthermore, these GNPs fit as graphene-based materials (GBMs), having a thickness between 2 and 10 layers (Figure 1) according to the European GRAPHENE Flagship Project for the classification of GBMs [21]. The zeta potential (\(\zeta\)) values of the GNPs are shown in Table 1 as a function of pH, with negative results between pH 5 and 12.  
The DLS values (Table 2) show that the rate of aggregation of particles in solution depends on the preparation of the nanofluid with an aqueous solution or DMEM as expected [22].  
The GNPs show different PdI values in DMEM than in water, which could explain the different DLS sizes determined in bod fluids. These high differences in particle sizes found  
\begin{table}
\begin{tabular}{c c} \hline \hline
**pH** & **GNPs** \\ \hline
12 & \(-\)38, 6 \\
10 & \(-\)33, 2 \\
7 & \(-\)20, 9 \\
5 & \(-\)2, 13 \\
3 & 2, 69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Zeta potential (\(\zeta\)) in mV versus pH of graphene nanoplatelets (GNPs) in water solution.  
Figure 1: Transmission electron micrographs of graphene nanoplatelets (GNPs). The enlarged image shows the 8 layers of these GNPs.  
in DMEM and water must be related to components present in DMEM that facilitate the aggregation rate, as well as the formation of corona in the particles [23].  
The Raman spectrum of the GNPs is shown in Figure 2.  
The Raman spectroscopy shown in Figure 2 can be used to define the defects of GNPs [24]. The D band, located at 1351 cm\({}^{-1}\), refers to defects located in graphene, and the G band, located at 1578 cm\({}^{-1}\), is related to sp\({}^{2}\) hybridized carbon bonds ordered in the graphene lattice [25]. Thus, the relation of the intensity of the D band with the G band (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)) is related to the degree of defects in this material. The I\({}_{\text{D}}\)/I\({}_{\text{G}}\) value of the GNPs was 0.11, indicating the structural quality of the GNPs [24]. The position of the 2D band, for smaller values (in this case 2676 cm\({}^{-1}\)), also indicates that the material has few layers of graphene sheets in its composition, as indicated in the HR-TEM images [26].
# A Multi-Constrained Optimization Method for THz Backward Wave Oscillators
## 1 Introduction
However, for the optimization of THz BWOs that operate in linear regions, non-randomized iteration optimization becomes suitable for at least three reasons [7]: (i) small changes in geometrical parameters will not result in sudden and big changes in the output performance [13]; (ii) unlike the random solutions, local solutions will satisfy the constraints on the working parameters; (iii) it is time-consuming to calculate the working parameters for a candidate solution, so randomized optimization can be inefficient in finding candidate solutions. Therefore, the non-randomized optimization method (the second type) is more suitable and is applied in this paper for the optimization design of THz BWOs. To improve the efficiency of local search optimization, the orthogonal design method is included in the proposed optimization method. Previous studies have shown that iteration optimization methods such as local search algorithms (LSAs) can be improved using orthogonal design [7; 16]. Orthogonal design is applied for generating local solutions. It can decrease the amount of calculation by several orders of magnitude. However, it still requires lots of calculations to produce local candidates at each iteration step using the gradient descent method [11]. To deal with this problem, a new greedy algorithm is proposed based on the investigation of the optimization problem that provides rapid iteration and can save lots of calculations. As for the multiple constrains, a specifically designed evaluation function is proposed for choosing the best candidate solution effectively. It will be proved that the proposed new optimization method using local search algorithms can converge to the same global optimum from different initial solutions and is more efficient than the previous method presented by us in Ref. [7]. The following sections of this paper are arranged as follows: the optimization problem is briefly described in Section 2; the optimization method is presented in Section 3; the validations of the method and the case studies are shown in Section 4, followed by the conclusion in Section 5.
## 3 Proposed Channel Estimation Schemes  
The non-orthogonality nature of DBT-MC-FTN signals in the two dimensions challenges the channel estimation. In order to adapt DBT-MC-FTN to the practical fading channel, two pilot-aided channel estimation schemes have been developed, i.e., the non-orthogonal channel estimation scheme based on the time-division-multiplexing DBT-MC-FTN symbol-level pilot, and the orthogonal channel estimation scheme via the frequency-division-multiplexing subcarrier-level pilot within the DBT-MC-FTN symbol. The two CE schemes are depicted in Figure 1a where Scheme I is based on the time domain pilot insertion and the pilot symbols can be compressed flexibly in both time domain and frequency domain by the same factor as the data symbols, and Scheme II presents the frequency domain pilot transmission scheme and the pilot symbols remain orthogonal regardless of the changes in the compression factor of the data symbols.
# L-Band Erbium Fiber Laser with Tunable and Narrow Sub-kHz Linewidth Output
## 1 Introduction
used the MZI-based filter, Sagnac ring resonator effect, and optical tunable filter in the ring cavity to achieve tunable wavelength output with 3 dB linewidth of 0.02 nm (\(\sim\)2.5 GHz) in C-band operation bandwidth [18]. Therefore, although all the above methods can achieve stable SLM operation, most of them can only obtain kHz-level linewidth output. In this paper, we designed a fiber ring laser configuration by utilizing an L-band EDFA as a gain medium for stable and tunable SLM wavelength output. To generate and maintain a constant SLM process and narrow linewidth output, a short length of 2 m EDF-based SA and a 100 m single-mode fiber (SMF) induced RB effect were combined in cascade in the fiber ring cavity. In the measurement, we exchanged the positions of EDF-based SA and SMF-induced feedback RB to result in two EDF ring laser structures. Here, we also performed the various output appearances of the two fiber laser architectures designed. To tune and select different wavelength outputs with SLM behavior, a wideband tunable bandpass filter (TBF) was added to the ring cavity for continuous tuning. In this experiment, the wavelength tunability of two presented EDF lasers started at a wavelength of 1558.0 nm and ended at 1618.0 nm (obtainable bandwidth = 60 nm). The measured OSNR and output power of the first and second EDF laser architectures were from 46.72, 68.44 dB, and 49.09 to 68.71 dB; and \(-\)14.73, 0.78 dBm, and \(-\)14.17 to 1.74 dBm over the achievable selection range, respectively. And all the achieved 3 dB Lorentzian linewidths for both presented EDF lasers were 313 Hz. This means that the cascaded EDF-SA and feedback RB injection could result in a narrow laser linewidth. Additionally, the output stabilities of power and center wavelength of the two lasers were less than 0.47 and 0.22 dB/40 min and 0.032 and 0.024 nm/40 min, respectively. Compared with previous studies [2], the presented EDF laser can reach better sub-kHz linewidth output and wide wavelength by adjusting the bandwidth. As a result, the proposed L-band EDF laser has a simple architecture and low cost and achieves a broad tuning bandwidth of 60 nm, stable SLM wavelength, and narrow linewidth output.
## 6 Results
### Evaluation Metrics  
In this subsection, we will discuss the relevant metrics used to evaluate our system for different tasks. Additionally, we discuss how these metrics should be interpreted in the context of privacy and the intuition behind the expected trends. In the case of object detection, we use precision and recall at specific intersection over union (IoU) thresholds. We can summarize these metrics and their relevance to privacy as follows:  
* Intersection over union (IoU): In object detection, each of the input images has target or ground truth bounding boxes, which are the true locations of the objects in the image. Ideally, the predicted bounding box should have maximum overlap with the ground truth and be of similar proportions. A high IoU score for a predicted bounding box suggests high accuracy. \[IoU=\frac{\text{Common area}}{\text{Total area spanned by both boxes}}\]
* Precision: It measures the reliability of the positive predictions made by the model. In object detection, a prediction is \"positive\" if the IoU score for the prediction is greater than a predefined threshold. For example, [EMAIL_ADDRESS] is interpreted as the average precision at the IoU = 0.5 threshold i.e., if the IoU score for a target-prediction bounding box pair exceeds 0.5, it is considered a positive prediction, and negative otherwise. \[Precision=\frac{\text{True Positives}}{\text{True Positives + False Positives}}\] If a bounding box prediction is made but the IoU score is below the chosen threshold, it is considered a false positive. Even though it is undesirable, a false positive will only lead to the protection of a nonsensitive area; that is, privacy is not affected. Thus, while we want precision to be as high as possible, we note that reduced precision is not much of a privacy concern.
* Recall: It measures how well the system can identify all relevant objects in the video frame. This is particularly important in the context of privacy because false negatives are objects that the model was not able to detect. Undetected objects will not be blurred and can potentially leak sensitive information. \[Recall=\frac{\text{True Positives}}{\text{True Positives + False Negatives}}\]
## 2 Analysis of Influence of SSO on DFIG System Operation
### The Establishment of the Mathematical Model for an RSC with SSO
\tag{2}\]  
In Equation (2), \(\phi\) represents the phase difference between the sub-synchronous current component generated by the RSC control and the sub-synchronous current component in the original stator and \(h\) is the amplitude amplification ratio between the two; \(I_{\text{n}}\) is the RMS value of the original stator sub-synchronous current component; \(\omega_{\text{n}}\) is the angle  
Figure 1: Schematic diagram of SSO propagation in RSC control system. frequency of the grid sub-synchronous oscillation; and \(\varphi_{\text{in}}\) is the initial phase angle of the stator sub-synchronous current in the d-q coordinate system. Superimposing the sub-synchronous current output from the DFIG results in its superimposition with the original sub-synchronous current in the stator. When they satisfy a certain phase relation, the amplitude of the sub-synchronous current with frequency \(\omega_{\text{n}}\) is increased. At this time, the line with higher series compensation in the power grid interacts with the RSC, and a positive feedback link is formed between them to generate the sub-synchronous current of the stator, resulting in the divergence of oscillation.