esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Results and Discussion
### Analysis of the Calculated Settings for Individual Users
#### 5.3.3 Scaling Interaction Mechanisms  
All participants with auditory impairments were assigned indirect controls for the _Scaling_ task. Participants with physical disabilities were mostly assigned indirect controls (80%), as were participants with cognitive and neurological disorders and/or specific learning disabilities (75%) and multiple disabilities (91.7%).  
Similar to results for the _Rotation_ task, all participants from age groups Under 18, 45-54, and 65 or older were assigned indirect controls for the _Scaling_ task. Most of the participants from age groups 25-34 years were also assigned indirect controls, i.e., 91.7% of them. Participants in the age group 18-24 were mostly assigned indirect controls (81.6% of them), as well as participants in the age group 55-64 (60% of them). This is the same proportion of assigned interaction mechanism as these groups have for the rotation task. Both interaction mechanisms were assigned to an equal share of participants in the 35-44 age group (50% touch gesture, 50% indirect controls).  
In general, the results show that the _Translation_ task stands out among the tasks concerning the assignment of interaction mechanisms. Looking at the results for all participants, both interaction mechanisms (touch gestures and indirect controls) were assigned in equal proportion to the _Translation_ task. Observing the assigned interaction mechanisms for this task for different disability groups, most participants from each group were assigned indirect controls as an interaction mechanism (besides one participant from the visual impairment group). As far as different age groups are concerned, the 35-44 years group stands out in which most participants were assigned touch gestures. In general, for the _Rotation_ and _Scaling_ tasks, a significantly higher proportion of participants were assigned the interaction mechanism of indirect controls. In the context of age groups, the group 35-44 years stands out again, in which indirect controls and touch gestures were assigned to the participants in equal proportions for the _Scaling_ task.  
These results suggest that the tasks of _Rotation_ and _Scaling_ using the touch gesture interaction mechanisms were more difficult than those using indirect controls for most of the participants, while the _Translation_ task had a similar proportion of participants finding each interaction mechanism easy/difficult.
## 6 Space-Segment Optimization Aspects
### Constellation Maintenance
#### 6.6.4 Satellite (Sat) Replacement  
A failure of one or more satellites can result in the service deterioration for a constellation providing global coverage. In such a case, having spare satellites and a good replacement strategy are parts of the constellation design in order to make sure that, in the case of a failed or terminated satellite, a replacement is deployed without much delay [89].  
[PERSON] et al. discuss replacement-and-spare strategies in [127; 137], dividing them into categories with no-replacement planned, launch-on-demand by ground spares, or on-demand manufacturing, Sat spares in parking orbit and in-orbit spares. In the case of navigation-and-communication satellite constellations, continuous service and reliability are achieved by overpopulating the constellation with one or two extra satellites per orbital plane. In [138], an inventory-management approach for mega-constellation satellite replacement was proposed since traditional spare strategies cannot be applied due to limited scalability. The strategy implements spares as a supply chain with ground facilities as suppliers, satellite parking orbits as warehouses, and in-plane orbital spares as retailers in order to minimize the spare-strategy cost. The service ability and the satellite reliability drive the selection of the strategy for replacement.
Inversion Method of Tidal Level Based on GNSS Triple-Frequency, Geometry-Free, Non-Ionospheric Phase Combination  
[PERSON]  
1  
[PERSON]  
1  
[PERSON]  
1  
[PERSON]  
2  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci)]([https://www.mdpi.com/journals/applsci](https://www.mdpi.com/journals/applsci))  
Footnote 3: [[https://www.mdpi.com/journals/](https://www.mdpi.com/journals/)]([https://www.mdpi.com/journals/](https://www.mdpi.com/journals/))  
receiver; a physical model for height measurement using SNR, that is, classical GNSS-MR technology, has been proposed; and this method has been used to study snow-depth monitoring. Subsequently, the authors of [8; 9; 10] carried out inversion research on soil humidity, snow-depth change, and ocean tide level and developed corresponding GNSS-MR software. In view of the fact that GNSS-MR technology only uses SNR data, when using GNSS for snow-depth monitoring, the authors of [11] proposed a method of snow-depth inversion with an L4 signal without geometric distance. Due to the interference of the ionosphere, the inversion accuracy of this method is low. In reference [12], to tackle the poor accuracy of snow-depth inversion caused by ionospheric interference in the L4 method, a triple-frequency snow-depth inversion method without geometric distance and ionospheric phase combination is proposed and compared with the classical GNSS-MR method, and the same classical experimental results are obtained.  
At present, the research on tide level inversion using gnss-mr technology is based on navigation signal signal-to-noise ratio data, and there is a lack of tide level inversion research using more abundant navigation signal carrier-phase observations. Moreover, for early CORS stations, some GNSS receivers do not provide navigation signal-to-noise ratio data. Therefore, carrying out tide level inversion using carrier-phase observations can better enhance the universality of GNSS tide level monitoring. Based on the above problems, this paper carries out the research work of tide level inversion based on triple-frequency combined-phase observations, so as to provide a new method for GNSS tide level inversion.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Optical Backbone and Submarine Cable  
With the reducing price of optical fiber cable and the requirement of high-speed broadband networks, fiber cable becomes the default choice to establish a nation-wide broadband network. RTDF funded project as initiated by the NTA will establish a fiber backbone connecting every district head quarter by 96-core fiber and every rural municipality wherever possible by a 48-core fiber backbone. Due to powerful wind at high altitudes and frequent snow fall with zig-zag locations in rural areas of Nepal, in most places, underground backbone fiber installation is not viable. This issue can be avoided if we deploy optical submarine cables recognized as fiber packed armored cable (F-PAC) in Nepalese rural areas. As per the proposal of [PERSON] et al. [28], their practical implementations indicate that F-PAC cabling can have aerial laying and under-ground laying with sufficient counter measures against birds and animal attacks. Hence, it is more cost effective with better safety, security, and reliability than deploying wireless and satellite links for broadband communities [29].
## 3 LoRaWAN Background
### LoRaWAN ED Classes  
LoRaWAN defines three different classes of EDs, namely Class A (All), B (Beaconing) and C (Continuously listening). The classes differ for the performance target they aim to achieve, i.e., the reduction of the power consumption or the reduction of the latency for downlink communications. In particular, Class A enables bi-directional communications with the lowest power consumption. Class B supports predefined receive windows for downlink communications, thus achieving a trade-off between power consumption and downlink latency. Class C always allows downlink communications except during the transmissions, thus achieving the lowest downlink latency. Since the Class A features shall be supported by all the LoRaWAN commercial-off-the-shelf (COTS) devices, in this paper we focus on Class A EDs.  
Class A communications are always started by an ED. An ED can send an uplink message at any time. Once the uplink transmission is finished, after a delay whose duration is defined in the specifications, the ED opens two short receive (downlink) windows following the scheme in Figure 3.  
The minimum duration of a receive window corresponds to the time taken by the ED radio transceiver to sense the downlink message preamble. Once a preamble is detected, the receiver remains active until the downlink message is received. The NS can respond during either the first (RX1) or the second receive window (RX2), but does not use both.
## 4 Results and Discussion
### Characterization Results of Diplexers
#### 4.1.3 Edge-Coupled Diplexer  
A comparison of the simulated and measured results of the edge-coupled diplexer for combined bands n257 and n258, and n260, is shown in Figure 14b. The maximum passband insertion loss of this diplexer is 3 dB, the return loss is better than 14.3 dB and the VSWR is less than 1.48. The isolation between the two filters is better than 40 dB. The cross-over point of both filters is at 32.73 GHz, with insertion loss of 27 dB.
## 4 Evaluation
### Security  
Healthcare applications deal with sensitive data such as medical records, health conditions, and quality of life. The illegal usage of these data may lead to several attacks. For this purpose, we launched a masquerading attack based on the scope of this study. The prevention of masquerading attack also minimizes the risk of ransomware.  
The masquerading attack uses a fake identity to gain unauthorized access [58]. To launch this attack on MIRA, we asked the members of each group to shift their positions. Suppose the participants on positions C, B, and A will shift to A, C, and B respectively. Then, the adjacent member gets access to the authenticated user account of MIRA and starts an interaction. During the analysis of natural language input, MIRA verifies the device identifier, but is unable to validate the MFCC value. Therefore, MIRA holds the ongoing session and asks the participant for identity verification as 'Sorry for the interruption, malicious activity was detected. To proceed with the ongoing session, please enter your seven digit identity verification key'. At this stage, the user has to enter the identity verification key to interact with MIRA. Moreover, if an unauthorized user wants to interact after session time out (60 minutes), then MIRA will respond as 'I am sorry, but I am not able to verify your identity. Do you want me to assist you through the registration process?'. Furthermore, one smart device identifier can bind with multiple user identities, which means that more than one user can use the same device, but registration is mandatory for each user. The results show that MIRA prevents against masquerading attacks because none of the participants were able to interact with other user applications due to voice-based authentication.
## 3 Design of FairMOT Mixed Model
The appearance features are represented by the vector \(f\) extracted through a CNN, and the motion features are represented by the state vector \(x\) obtained through the Kalman filter. DeepSORT is an algorithm for multi-object tracking that utilizes both the appearance and position information of objects to track them. After extracting the appearance feature vector fi of each tracked object through a neural network, the minimum cosine similarity cos \(\theta\) between the appearance feature vectors \(f_{i}\) of the current frame and \(f_{j}\) of the previous frame is calculated to determine the similarity as shown in Equation (3). The cosine similarity indicates the similarity between two vectors \(f_{i}\) and \(f_{j}\) based on the angle between them, with a value closer to 1 indicating higher similarity. \[cos\ \theta=\frac{(fi\cdot fj)}{(|fi||f|)} \tag{3}\]  
The appearance information through cosine distance is effectively used to retrieve the ID of objects that have been occluded for a long time [19]. DeepSORT calculates the similarity of motion information between the detection bounding box \(d\) and the predicted bounding box \(e\) using the Mahalanobis distance, as shown in Equation (4). \[d_{M}(d,e)=(d-e)^{T}S^{-1}(d-e) \tag{4}\]  
The Mahalanobis distance represents the distance between two vectors \(d\) and \(e\), considering the data distribution using the covariance matrix \(S\). A smaller value indicates higher similarity between the two vectors [16]. The final score is a combination of the cosine similarity and the Mahalanobis distance, using weights \(\alpha\) and \(\beta\) to comprehensively evaluate the similarity between objects, as shown in Equation (5). \[Final\ Score-\alpha\cdot cos(\theta)+\beta\cdot d_{M} \tag{5}\]  
The final results are obtained by assigning IDs to each object through the associated cost matrix using the Hungarian algorithm. The Hungarian algorithm finds the optimal matching between objects and tracks based on the cost matrix, with a time complexity of \(O(n^{3})\). The steps of this algorithm are as follows:  
* Subtract the minimum value in each row to perform row minimization. * Subtract the minimum value in each column to perform column minimization. * Find the minimum number of lines that cover all zeros in the minimized matrix. * Find the smallest uncovered element and subtract this value from all uncovered elements, adding it to all covered elements. * Repeat this process to find the optimal matching. The Hungarian algorithm plays an important role in multi-object tracking by finding the optimal association between objects and tracks. This helps maintain consistent IDs for objects and improves the accuracy of tracking through optimal matching [16]. Through this process, the DeepSORT algorithm can effectively track objects. The Mahalanobis distance and the Hungarian algorithm play critical roles in maximizing the efficiency and accuracy of underwater fish detection and tracking by considering data distribution and optimal matching.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 3 Interpolation Methods
### Extended Sinc Interpolation  
Sinc interpolation is the method that is used in the reconstruction of bandlimited signals. Assume that the signal bandwidth is known and the signal is sampled at the rate \(f_{\rm s}\), which is two times or more higher than the maximal frequency in the considered frequency band \(f_{\rm max}\), i.e., \(f_{\rm s}\geq 2f_{\rm max}\). Then, by employing the sampling theorem [28], the signal can be perfectly reconstructed from its samples by employing the following relation  
\[y(\tau)=\sum_{i=-\infty}^{\infty}y_{i}\,\text{sinc}\bigg{[}\frac{\pi(\tau- \tau_{i})}{T_{\rm s}}\bigg{]}, \tag{24}\]  
where \(y_{i}=y(iT_{\rm s})\) and \(\tau_{i}=iT_{\rm s}\). Here, the interpolation kernel is represented by normalized sinc functions and \(T_{\rm s}\) is the sampling time. However, since the sinc function is infinite, it is difficult to implement an ideal reconstruction of a sampled signal via (24). Furthermore, the problem becomes even more complicated, when signal is complex-valued and phase control under reconstruction procedure is required.  
Assume that the range time \(\tau_{\rm p}\) defined in (4) satisfies the condition \(\tau_{0}<\tau_{\rm p}<\tau_{1}\), which is similar to the case for linear and cubic interpolations. To implement the reconstruction procedure, the normalized sinc kernel presented in (24) can be truncated up to a finite number of sinc functions \(2L+1\), where \(L\) is a nonnegative integer, i.e., \(L\in\mathbb{Z}\) and \(L\geq 0\). The functions are equally translated in both directions from the interpolation point and normalized with the sampling time \(T_{\rm s}\) to reach zero value at known range-time samples \(\tau_{i}\). Then, the resulting filter for data estimation at range time \(\tau_{\rm p}\) based on sinc interpolation gets the form  
\[y(\tau_{\rm p})=\sum_{i=-L}^{L}\tilde{y}_{i}w_{i}\,\text{sinc}\bigg{[}\frac{ \pi(\tau_{\rm p}-\tau_{i})}{T_{\rm s}}\bigg{]}, \tag{25}\]  
where \(\tilde{y}_{i}\) are the reference data samples, the phase of which is controlled through the procedure (6) and may vary with a factor of \(\pi\) subject to their corresponding location in the complex plane, i.e., in the right \(\mathbb{C}_{+}\) or in the left complex half-plane \(\mathbb{C}_{-}\). Furthermore here, \(w_{i}\) denotes the Hanning window, which is given by  
\[w_{i}=0.5+0.5\cos\bigg{(}\frac{\pi i}{L}\bigg{)},\qquad-L\leq i\leq L, \tag{26}\]  
for \(i\in\mathbb{Z}\) and used to suppress the Gibbs phenomenon in the truncated normalized sinc kernel [22].
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 3 Description of the Studied Area  
The considered area for this study is Alrashda village, which is located 10 km northwest of Mut town, the administrative center of the Dakhla Oasis in the New Valley Governorate in Egypt, at 28.938\({}^{\circ}\) east longitude, 25.576\({}^{\circ}\) north latitude, and an altitude of 243 m. The reason of choosing this village because of its comparatively high solar, wind, and biomass energy potential. The proposed mathematical model is used for designing a small scale stand-alone hybrid system to feed a range of loads which are represented in residential loads, where the peak loads are occurred during the summer and in the evening period from 19:00 to 23:00 p.m. In Figure 2, the profile of the proposed loads during a year is depicted, which shows that the average residential load of the village has reached about 260 kW, with a maximum load of 410 kW. Figures 3-5 illustrate the plot of hourly data of the solar radiation, temperature, and wind speed which are obtained from the NASA Surface Meteorology and Solar Energy website for 20 years for the selected area. Figure 3 presents the short-wave solar irradiance of the studied area during a year, where the yearly radiation rate is between 2.45 kWh/m\({}^{2}\)/day to 10.94 kWh/m\({}^{2}\)/day, with the average yearly radiation on this site's horizontal surface is around 6.89 kWh/m\({}^{2}\)/day, while the yearly ambient temperature of the selected site is indicated in Figure 4, which showed that the maximum ambient temperature can be reached, is 40\({}^{\circ}\). Figure 5 illustrates the annual wind speed for the selected location with a maximum wind speed of about 13.9 m/s and an average in the range from 8.71 m/s to 9.89 m/s. As previously mentioned, the biomass feedstock used in this study was the sugarcane bagasse. The sugar can crop is considered one of the strategic crops in Egypt, where the harvest period begins during January of each year and extends until May. The amount of biomass feedstock available at the selected site was assumed to have a variable values over the year, the monthly biomass consumption rate is presented in Figure 6, with an average of one ton/day.  
Figure 4: The yearly ambient temperature of the studied area.  
Figure 5: The yearly wind speed of the studied area.  
Figure 3: The annual short-wave solar irradiance of the studied area.
# Spatiotemporal Image Fusion in Remote Sensing
## 2 Fusion Methods to Increase Spatiotemporal Resolution of Satellite Images
### Reconstruction-Based Spatiotemporal Image Fusion Methods
First, it searches for similar neighboring pixels in the searching moving window as:  
\[w(c,s) =\frac{1}{Z(c)}e^{-||P_{(c,0)}-P_{(s,0)}||2a^{2}} \tag{4}\]  
where \(c\) represents the central pixel, \(s\) represents the similar pixels, \(||P_{(c,t0)}-P_{(s,t0)}||2a^{2}\) is a Gaussian kernel and \(Z\)(c) is a normalizing constant:  
\[Z_{c}=\sum_{i}e^{-||P_{(c,t0)}-P_{(s,t0)}||2a^{2}} \tag{5}\]  
Second, it calculates the similar neighbor's weights based on the spectral differences between the pixels in the fine resolution and coarse resolution images, the temporal differences between the input and prediction dates of the fine resolution images and the spectral difference between the central pixels and neighboring pixels. The third step consists of predicting the spectral reflectance of the fine resolution pixels (\(FR\)) as:  
\[FR_{(c,ptr)}=\sum_{s=1}^{n}W_{s}*\left(p_{(s,t_{pr})}-p_{(s,t_{0})}+P_{(s,t_{0} )}\right) \tag{6}\]  
where \(p_{(s,t_{k})}\) and \(p_{(s,t_{0})}\) represent the coarse resolution images pixels that spatially correspond to high resolution pixels \(FR_{(s,t_{0})}\). As presented above, these methods are very efficient for spatiotemporal image fusion in relatively homogeneous landscape and when the input images have the same input spectral values. Nevertheless, there is a need for spatiotemporal fusion methods capable to use different images as input, i.e., images with different spectral values (such as learning-based methods) and methods capable to be applied in a heterogeneous landscape where pixels from the coarser satellite images are mixed (such as unmixing-based methods).
## 3 Test Result Analysis
### Unconfined Compression Strength (UCS)
#### 3.1.1 Effect of Sand on the UCS of Cemented Soil  
It is apparent from Figure 2a that the UCS of the CS-SS samples increased with the increase of sand particle content but showed different trends at different curing ages. For the curing age of 28 d, as the sand particle content increased from 0% to 30%, the UCS of the CS-SS samples with different sand particle sizes increased by 0.45 MPa (fine sand), 0.36 MPa (medium sand), and 0.27 MPa (coarse sand). For the curing age of 7 d, the UCS of the CS-SS samples increased by 0.15 MPa (fine sand), 0.18 MPa (medium sand), and 0.17 MPa (coarse sand) when sand particle content increased from 0% to 30%, and the sand particle size was more effective in enhancing the long-term strength of the cemented soil than the early strength.  
It is apparent from Figure 2b,c that the sand particle size had a certain influence on the UCS of the CS-SS samples. For the 7 d and 14 d strengths, the effect of sand particle size on the UCS showed a certain degree of dispersion, and no regularity was found. For the long-term (28 d) strength, the UCS of the CS-SS samples increased obviously with the decrease in sand particle size, and all test groups of the CS-SS samples showed the same variation law.