esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Improved SSD Networks
### Loss Function  
Multiple objects often cluster together in an image, which can easily cause mutual occlusion, and greatly increases the difficulty of object positioning. The detector is easily confused since these objects have similar appearance features. As the detection results are required to be further processed by NMS, the blocked object is easily suppressed by other objects, resulting in missed detection.  
Inspired by the literature [33,34], based on SSD's multi-task loss function, we add Repulsion loss to improve the loss function, our overall loss function can be defined as follows,  
\[L=L_{SSD}+L_{Rep} \tag{2}\]  
where \(L_{SSD}=\frac{1}{N}\left(L_{conf}(x,c)+\alpha L_{loc}(x,l,g)\right)\), \(L_{conf}\) is the classification loss function, and \(L_{loc}\) is the positioning loss function. \(N\) is the number of prediction boxes (positive examples) whose prediction box and real box cross ratio is greater than the threshold, and \(\alpha\) is the ratio used to adjust the classification and positioning loss functions.  
\(L_{Rep}\) represents the repulsion loss and repulses other prediction boxes close to this target. \(L_{Rep}\) can be defined as follows,  
\[L_{Rep}=\sum\
olimits_{p\in P+,g\in G/[g_{iou\_max}]}log(p,g) \tag{3}\]  
where \(P+\) is the set of all positive proposals, and \(G\) is the set of all ground-truth boxes in an image. \(g\) represents other ground-truth boxes except for the maximum IOU (Intersection over Union), which is the repulsion box corresponding to each prediction box. \(LOG(p,g)=\frac{b_{p}\cap b_{g}}{b_{g}}\) represents the ground-truth box with the largest IOU of the prediction box and all ground-truth boxes, then \(g_{iou\_max}\) can be calculated as follows,  
\[g_{iou\_max}=argmax_{g\in G,p\in P}\frac{b_{g}\cap b_{p}}{b_{g}\cup b_{p}} \tag{4}\]  
It can be seen from Equation (3) that the larger the IOG of the predicted box \(p\) and the surrounding ground-truth box \(g\), the greater the loss. In the process of backpropagation, \(L_{Rep}\) tends to the minimum value, so it can effectively prevent the predicted box is offset the surrounding ground-truth objects and other predicted boxes with different designated objects, respectively.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies
#### 2.2.1 Sigfox  
Sigfox [18] is a proprietary end-to-end solution for IoT connectivity. Sigfox positions itself as an alternative network operator, and deploys base stations around the world, as we can see in Figure 4. This technology uses Binary Phase Shift Keying (BPSK) modulation over an Ultra-Narrow-Band (UNB) carrier of the sub-GHz ISM bands. UNB greatly reduces noise levels, which extends the communication range. The counterpart is a very slow data rate of 100 bps. To respect the duty cycle regulation imposed on the sub-GHz bands, Sigfox limits up-link communications to 140 transmissions of 12 bytes payload, and down-link to 4 transmissions of 8 bytes payload, per day and per device. The Sigfox network is available in several countries of the world, as we can see in Figure 4. Because it is the same network, which is managed by a single operator, there is no roaming involved when using Sigfox in different countries.
# Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression
## 4 Results and Discussion
### Temporal Cross-Correlation Analysis
The TLCC chart of Figure 4b shows the highest cross-correlation with zero time offset, and the correlation value is \(-0.10\). It indicates that the maximum temperature might affect the distress initiation without time lag in daily time scales during the study time period. In Figure 4d,f, the highest cross-correlation coefficients were observed in the positive offset equaling nine. The positive offset of 9 days had correlation values for the minimum temperature and daily average temperature of \(-0.14\) and \(-0.12\), respectively. It indicated that both weather variables above might affect the probability of distress occurrence after a 9-day interval. All three temperature-dependent variables have negative correlations between distress occurrence, with the time lag or without. The time lag of precipitation and relative humidity are two and zero, as shown in Figure 4h,j. The highest correlation values of precipitation and relative humidity are 0.24 and 0.23. It can be inferred that the precipitation value might be highly relevant to the possibility of distress initiation two days later. Furthermore, the time lag between relative humidity and distress occurrence is a zero time interval. This indicates that distress initiation is more likely influenced by present relative humidity. At the same time, the correlation values of both precipitation and relative humidity are positive, which means a higher value would increase the probability of distress occurrence. The WTLCC of relevant data was calculated to extract the change of temporal correlations between No_DIS_INI and each meteorological variable, as shown in Figure 5. Each row value of the WTLCC charts represents the signals' time series being divided equally into different parts of time epochs, and the cross-correlations were calculated in each window. The time-lag (offset) values of WTLCC were related to the results of TLCC values between the accumulation of distress initiations and meteorological variables shown in Figure 5. The WTLCC of maximum temperature (Figure 5a) showed that the correlation values with zero time offset are negative for most of the epochs from the beginning to the end time epochs. Moreover, the minimum temperature and the daily average of temperature in WTLCC charts (Figure 5b,c) showed that the correlation values with the time offset of nine days were dominantly negative in all time epochs. Furthermore, the correlation value of relative humidity (Figure 5e) showed positive values with no time lag from epoch zero to the end, which was similar to the result of TLCC (Figure 4j). Finally, the correlation coefficient of the daily average of precipitation with a two-day time lag showed slightly negative values for most of the time epochs, despite two highly correlated values being observed. One reason for the result is that the precipitation signal contains too many zero values, and too many zero values might be dominant in the WTLCC value if time windows are in small intervals.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Coating Methods
#### 5.2.2 Layer-by-Layer Deposition  
Layer-by-layer assembly is a simple, cost-effective method that can be used for large-scale deposition of active materials in films [161]. The micrometre scale film thickness can be easily controlled in this method, whereby layers with opposite charge are alternatively deposited.  
Layer-by-layer coating of hybrid MnO\({}_{2}\)-carbon nanotubes and MnO\({}_{2}\)-reduced graphene oxide through alternate spray deposition was employed for the fabrication of a cost-effective, flexible layered micro-supercapacitor with excellent electrochemical performance [162]. To increase the surface area of electrodes, silver nanowires were wrapped around carbon fibres via an inverted layer-by-layer deposition technique with polydimethylsiloxane (PDMS) for flexible, thin film supercapacitors [163], shown in Figure 9.  
Figure 8: Schematic of the fabrication process of a flexible micro-supercapacitor with patterned solid-state electrolyte on a PET substrate. Figure reprinted with permission from [PERSON] et al. [159]. Copyright 2015 the American Chemical Society.  
Figure 9: Illustration (**a**) and photographs of produced flexible PDMS/silver nanowires/carbon fibre film (**b–d**), and the patterned electrodes (**e,f**). Figure reprinted with permission from [PERSON] et al. [163]. Copyright 2018 Elsevier.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Hyper-Parameters Analysis
#### 4.2.4 Hyper-Parameters in Piecewise Loss Function  
In the soft-margin triplet loss function shown in Equation (5), the hyper-parameter \(\alpha\) achieved a satisfied effect in the value of 20 in other experiments [14] and ours. The \(\alpha\) value remains in the piecewise triplet loss for better convergence in early training. In Equation (7), the threshold value \(M\) is set to 0.8 to ensure the distinction between positive and negative samples. The hyper-parameter \(\beta\) represents the amplitude of the positive sample constraint, which has great influence on model training results. Different values of \(\beta\) are tested in Figure 5. The results illustrate that \(\beta\) achieves the best effect at 0.01.
## 1 Introduction
These authors [22] pointed out that mortars with 5% and 10% of red mud would be safe, despite of releasing more heavy metals than mortar without red mud, and those mortars with red mud even showed lower concentrations of cadmium compared to the reference mortar. In view of that, these authors suggested that the hydration products of cement-based materials with red mud could contribute to a fixation of contaminants and they would be especially good for retention of cadmium contaminants. The durability and mechanical performance of cement-based materials is directly related to their microstructure [32, 33, 34], so the analysis of the changes in the pore network of the abovementioned materials is interesting. However, there have been few studies examining the microstructure of cementitious materials with red mud [21, 22]. Among the different techniques for analysing the changes in the pore network of those materials, nowadays non-destructive techniques are a relevant field of research. One of these techniques is the relatively novel impedance spectroscopy, which is being increasingly used. This technique has recently been applied for cement-based materials which incorporate different kinds of additions. Some of these additions are well-known and widely used, such as slag and fly ash [35], and others are relatively new, like waste brick powder [36] and glass powder [37]. Impedance spectroscopy has also been successfully used for studying how aggressive and non-optimum environments [14, 38] affect the evolution of the microstructure of cement pastes, mortars and concretes. Nevertheless, it has only been used for studying the development of the corrosion phenomenon of steel barsreinforced concrete with red mud [26] and there is no experience related to the use of impedance spectroscopy for following the changes in the microstructure of mortars with this addition. Thus, the main objective of this work is to analyse the short-term effects of the addition of red mud in the microstructure, mechanical properties and durability of mortars which incorporate several percentages of this addition (up to 20%) as a clinker replacement. In order to have a reference, mortars made with Portland cement with fly ash (a more conventional and widely used addition) and mortarsmade using ordinary Portland cement without additions were also studied. The well-known mercury intrusion porosimetry and the non-destructive impedance spectroscopy technique were used for characterising the pore network of the specimens. Moreover, differential scanning calorimetry was performed for checking the possible pozzolanic activity of the red mud. Regarding the durability, the non-steady state chloride migration coefficient was determined. Finally, in order to assess the effects of the red mud addition in the mechanical behaviour of the mortars, compressive and flexural strengths were also studied.
## 1 Background
### Introduction
Here, the authors compared closed questions with a question about the frequency with which people worried about a crime in the past year. The result was that asking a question about the frequency of a crime resulted in lower levels of worry being reported, when compared to responses to the closed question. This finding regarding the fear of crime might also extend to other areas of risk. Some respondents expressing high concerns on closed survey questions may also be concerned in their daily life. In contrast, for other subjects expressing high concerns, the risk might not be relevant in everyday life. The current study aims at extending the existing literature by transferring this latter idea to another risk area, namely the potential health risks of radio frequency electromagnetic fields (RF EMFs). The approach is similar to that of [PERSON] [15], however, in contrast to that study, we do not only compare the closed question with a frequency question. In line with the ideas of the Austrian sociologist [PERSON] on the allocation of attention in everyday life [16; 17] we suggest two theoretical concepts--thematic and discursive relevance--highlighting the issue of the _endurance_ of risk-related concerns. [PERSON] coined the concept \"thematic relevance\" in the 1930s when he developed his theory of relevance structures. It refers to the emergence of themes in the human consciousness. In other words, it defines the object of attention. [PERSON]' concept is of special importance for risk perception research. Here, the question is whether the risks that are asked in questionnaires are of relevance for the respondents. Previous risk perception research demonstrated remarkable differences between imposed and intrinsic risk relevance. For instance, [18] analyzed what risks people perceive and assess in their daily activities. It revealed that answers to simultaneous risk questions (i.e., made at the time the risk was experienced) differed from those that were retrospective or generic risk questions. In addition to thematic relevance, we introduce the concept of discursive relevance. It describes the communicative importance of a topic in everyday discussion. Risk research has only recently taken this perspective into account [19]. In the following study, thematic relevance describes how often someone usually thinks about a risk in a given period of time, for instance, during the last two weeks. Discursive relevance goes even further, capturing how often respondents discuss a risk with other people. Based on these theoretical considerations, one could differentiate between at least four groups concerning RF EMF risk potentials: (1) subjects with low levels of concern and low thematic and discursive relevance; (2) subjects with high levels of concern but low thematic and low discursive relevance; (3) subjects with high levels of concern and high thematic relevance and low discursive relevance; and (4) subjects with high levels of concern, high thematic and high discursive relevance. Theoretically, however, other combinations are also possible.
## 2 Study Area and Data
### Data  
In this paper, 99 images from the Sentinel-1 ascending orbit and 91 images from the descending orbit covering the Kunming area from February 2018 to May 2021 were selected as experimental datasets, as shown in Table 1. The spatial resolution of the Sentinel-1 images is approximately 5 m \(\times\) 20 m (range \(\times\) azimuth direction). During data processing, the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) with a resolution of 30 m provided by the National Aeronautics and Space Administration (NASA) was used to remove the terrain phase. Precision orbit data provided by the European Space Agency (ESA) were used for orbit correction to improve the registration and baseline estimation accuracy [20].
## 1 Introduction  
The Gram-negative bacterium _Legionella pneumophila_ is responsible for the majority of legionellosis cases and is a significant contributor of community acquired, and hospital acquired pneumonia with a case fatality rate ranging from 5% to 80% [1,2]. _L. pneumoniae_ is an aquatic pathogen that is ubiquitously found in nature, in both anthropogenic structures and in environmental waters [3, 4, 5, 6, 7]. _In vitro_, _L. pneumoniae_ is able to produce monospecies biofilms (Figure 1) that is believed to contain an extracellular matrix [8,9]. In the environment, _L. pneumoniae_ can be found in several different habitats, including multispecies biofilms. In naturally occurring multispecies biofilms, the colonization with _L. pneumoniae_ can be influenced by several other species of microorganisms [10,11]. Of these microorganisms, protozoa are arguably one of the most important in determining _L. pneumoniae_ persistence, as the pathogen uses protozoa to replicate intracellularly [12]. Co-evolution with multiple species of protozoa has resulted in the development of mechanisms that allow _L. pneumoniae_ to occupy a very broad host range, and to infect human cells [13, 14, 15].  
Recent reports suggest that the growth of _Legionella_ in biofilms may lead to enhanced virulence. _L. pneumoniae_ isolates from serogroups 1, 10, and 12 that were collected from biofilms were more cytotoxic towards amoeba than reference outbreak and worldwide epidemic strains [16]. Moreover, initial data suggest that biofilm-derived _Legionella pneumophila_ evades the innate immune response in macrophages [17]. As legionellosis is not transmitted from person to person, insights into the ecology of _L. pneumoniae_ may yield information that can be used to prevent the colonization of anthropogenic systems by _L. pneumoniae_. In light of recent discoveries, this review intends to provide an overview of the findings on biofilm production and colonization by _L. pneumoniae_.  
Figure 1: Monospecies biofilm of _L. pneumoniae_ labelled with the DNA stain Syto62. Scale bar represents 100 μm.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 4 Description of Products
### Packages of Interferogram Products  
There is one package per interferogram. It contains a set of files related to a given interferogram that are synthesized in Table 1.  
* **Wrapped/Unwrapped Interferogram** InW (InU, respectively) is the wrapped (unwrapped, respectively) merged interferogram. Wrapped interferograms in radar geometry are provided both in 2 and 8 (or 16) looks. The initial number of looks of the interferograms before they are geocoded is given in their names and metadata. The ground pixel spacing is chosen according to the initial pixel spacing in radar geometry. The InWF and InU products are spatially  
\begin{table}
\begin{tabular}{l l} \hline \hline
**File Type** & **Description** \\ \hline APS & Interferogram Atmospheric Phase Screen from Global Atmospheric Model \\ InW/InU & Wrapped/Unwrapped Differential Interferograms \\ InWF & Wrapped Differential Filtered Interferograms \\ Coh & Spatial coherence \\ \hline \hline \end{tabular}
\end{table}
Table 1: Products of the interferogram package.  
filtered. All interferograms have been corrected using spectral diversity and the ERA5 atmospheric model.
* **Spatial coherence** Coh is the spatial coherence of the wrapped interferogram. It can thus be considered as a proxy of the quality and reliability of the signal. Typically, the spatial coherence will be very low (close to zero) at points located on water--e.g., sea, lakes, rivers, snow--and close to one at locations with stable backscattering properties where there is no vegetation--e.g., deserts, rocky mountains without snow, roads, buildings, etc.
* **Atmospheric phase screen** APS is the phase delay induced by the atmosphere as predicted by the ERA-5 atmospheric model [50] and mapped on the DEM [48,49].  
Each zipped interferogram package is about 2.2 Gb for 25 processed consecutive bursts (450 km long segment). This large size is partly due to the interferogram product in 2 looks and in radar geometry (i.e., with a spatial resolution of about 30 m, 1.3 Gb), whereas 8-looks interferograms, APS or coherence maps in ground geometry (pixel spacing of 0.00111 degree, 124 Mb) and in radar geometry (resolution of 120 m, 82 Mb) are less heavy.
## 3 Network Models and Related Definitions
### Definition of Terms  
**Definition 1**.: _(Neighbor node): In hybrid WSNs, there exist two nodes \(s_{i}\) and \(s_{j}\) with Euclidean distance \(d\big{(}s_{i},s_{j}\big{)}<2R_{s}\), then nodes \(s_{i}\) and \(s_{j}\) are said to be one-hop neighbors of each other._  
**Definition 2**.: _(Redundant node): A sensor node \(s_{i}\) is said to be a redundant node of node \(s_{j}\), if its sensing area is overlappingly covered by the sensing area of its neighbor node \(s_{j}\), or if node \(s_{i}\) is jointly covered by the neighbors node \(s_{i+1,i-1,2,\ldots,N-1}\) of node \(s_{j}\)._  
**Definition 3**.: _(Maximum distance travelled by a node): In this paper, the deployed redundant mobile nodes in hybrid WSNs are taken as repair nodes, and a swarm intelligence optimization algorithm is used to determine the optimal repair location of the redundant mobile nodes, so as to repair the coverage holes in the area. The maximum distance that the redundant node \(s_{i}\) can move is limited by considering the node energy consumption and moving distance factors:_  
\[d_{max_{i}}=\sqrt[n]{\frac{E_{res_{i}}}{l_{i}}} \tag{6}\]  
_where \(d_{max_{i}}\) is the maximum distance moved by the redundantly moving node \(s_{i}\); \(E_{res_{i}}\) is the current residual energy of node \(s_{i}\); \(l_{i}\) indicates the number of times node \(s_{i}\) has currently moved; the default is \(1\); and \(n\) is usually taken as \(2\sim 4\)._  
**Definition 4**.: _(Mobile node utilization): In hybrid WSNs, nodes are randomly thrown in the monitoring region in a certain ratio. The main job of the mobile node is to take care of the repair of holes, but when the quantity of blind zones in the region is small, some of the mobile nodes will act as static nodes. The ratio of the quantity of mobile nodes to repair holes to the overall quantity of deployed mobile nodes is called mobile node utilization._