esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Statistical Comparison against ISRs Data  
We implemented a statistical comparison among Swarm (original and corrected values), IRI, and ISRs \(T_{\mathrm{e}}\) data for the Jicamarca, Arecibo, and Millstone Hill locations to verify the ability of Swarm and IRI to describe the diurnal and seasonal trends of \(T_{\mathrm{e}}\) shown by ISRs data, and their accuracy. Unlike [PERSON] et al. (2014), we did not look for correspondences (in both time and space) between ISRs and Swarm data because of the limited number of correspondences achievable (of the order of some hundreds from 2014 to 2020, for each satellite). Instead, we applied a statistical approach by comparing the diurnal and seasonal trends exhibited by both \(T_{\mathrm{e}}\) values collected by ISRs and by Swarm B for the ISRs locations. In this way,  
Figure 3: Statistical distributions of the residuals between \(T_{\mathrm{e}}\) values measured by Swarm and modeled by IRI (TBT-2012+SA default option): (left panels, in blue) original Swarm-recorded values, (right panels, in green) Swarm values corrected with [PERSON] et al. (2014). From top to bottom, the analysis refers to Swarm A, B, and C. In each panel, some statistical metrics are reported in the upper left corner. we could investigate how much Swarm, and also the IRI model, can describe the diurnal and seasonal \(T_{\rm e}\) trends at equatorial, low, and mid latitudes by making a comparison with Jicamarca, Arecibo, and Millstone Hill data, respectively. This comparison and those of the following section were limited to the Swarm B satellite because it is the nearest to the IRI vertical profile anchor point at 550 km of altitude. Moreover, as follows from Figures 2 and 3, the statistical analysis for all three satellites was very similar, with slightly larger differences for Swarm B. Specifically, \(T_{\rm e}\) values collected at Jicamarca, Arecibo, and Millstone Hill in the range (\(510\pm 20\)) km were binned as a function of the magnetic local time (MLT, in QD coordinates) and season, and corresponding boxplots were generated. The binning in 15-minutes wide (in MLT) bins allowed describing the diurnal trend of \(T_{\rm e}\) at ISRs location. The seasonal trend was studied by binning data in four periods (expressed in day of the year, day) around solstices and equinoxes and covering the entire year:  
* March Equinox: \(35\leq\) day \(\leq 125\);
* June Solstice: \(126\leq\) day \(\leq 217\);
* September Equinox: \(218\leq\) day \(\leq 309\);
* December Solstice: day \(\leq 34\) OR \(\log\geq 310\). No solar activity data selection was performed because the quantity of both ISRs and Swarm data does not allow further split, and because the investigation of \(T_{\rm e}\) dependence on solar activity is outside of the scope of this study. As a consequence, the results in Figures 4-6 are representative of average solar activity conditions of the years covered by ISRs datasets (see Section 2.2), and of the years 2014-2020 for Swarm and IRI.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 3 Positioning Algorithm
### TOA and Carrier Phase Measurements
It is worth noting that the reference device can be either a UE with a known exact location or a BS. For some positioning scenarios, the deployment of additional hardware can cause a significant overhead; therefore, 3 GPP has agreed on selecting the reference device, i.e., the device with the known location can be a UE and/or a BS (also known as evolved gNB) [30].
## 3 Factors Influencing U-WSN Design
### Power Consumption and Network Lifetime
#### 3.4.1 Power Consumption during Air-Ground Communication  
The Signal-to-Interference-and-Noise Ratio (SINR) is used to measure the achievable throughput and link quality. Suppose that, the SINR is denoted by \(S_{intr}\), then,  
\[S_{intr}=\frac{P_{r}}{P_{s,r}+P_{n,r}}, \tag{3}\]  
where \(P_{s,r}\) is the interference power summing at the receiver, \(P_{n,r}\) is the receiver noise power, \(P_{r}\) is the received power from the serving transmitter. For a particular application, in order to achieve the required throughput, the SINR is set at a given throughput (denoted by \(S_{intr,0}\)) through adjusting the transmitter power. The energy consumption involved in the process of delivering a message (from a ground node) towards an aerial collector over a session interval \(\Delta t\), can be given as the following [48],  
\[E_{S_{i}}=S_{intr,0}\cdot(\overline{P_{1,a}}+P_{n,p})L_{S_{k}^{i}}\cdot\Delta t, \tag{4}\]  
where \(\overline{P_{1,a}}\) is the average aerial interference power, and \(P_{n,p}\) is the platforms noise power. Generally, the main focus is in the energy consumption of the sensor nodes, and it assumes that the network is not constrained by the possible limitation in the aerial platforms energy. Accordingly, its energy consumption is not account. Then, the total energy consumption is obtained by  
\[E_{all}=\Sigma_{i=1}^{N}E_{S_{i}}. \tag{5}\]
# Organic Crystals for THz Photonics
## 5 Applications of Organic THz Sources
### Defense and Security  
Terahertz waves can, at least partially and at certain THz frequencies, penetrate various non-metallic materials such as fabrics, wood, paper, cardboard and plastics. Therefore, they can be used in defense and security screening to uncover the concealed illicit drugs or explosives within packages. Since many dangerous materials have unique spectral fingerprints in the THz range, it is possible to combine both, detection by THz imaging and subsequent THz spectral identification with THz-TDS. Organic crystalline THz sources can be used in the same way as inorganic sources for close-by-inspection of envelopes or suspicious powders hidden under clothes. THz time-domain spectroscopy and imaging system based on DSTMS was used to study the effects of various hiding techniques with different paper and textile barriers based on analyzing the spectral features for drugs and explosive simulants [214]. Besides deposited and impregnated powder samples, mixtures with polyethylene in a form of a pellet were also analyzed hidden behind or within textile or paper barriers simulant. The study confirmed that rapid detection and identification of concealed simulants is possible in the frequency range from 1.5 THz to 4.0 THz by using the spectral peak analysis method and a priori known characteristic spectral features from the database [214]. The DSTMS-based THz system was also used in a comprehensive study where different THz technologies were compared for detection and identification of 18 explosives in a pellet and powder forms [215]. The selected DSTMS crystals allowed access to higher frequencies compared to the inorganic-based systems (see Figure 16). In some cases, the results showed that minor absorption peaks, bands and shoulders that are visible in the respective frequency region in the Fourier-transform infrared spectroscopy data can appear more prominent in THz-TDS measurements [215].  
Figure 15: (**a**) THz spectra of various concentrations of CaCO\({}_{3}\) in a polyethylene (PE) matrix; (**b**) peak frequency position of pure calcite (C-SA) in comparison with natural calcite of laboratory grade, calcite treated with additives and surface-modified calcite powders. The different size of particles is presented by circles of varying diameter and the error in the peak position is presented by a value of standard deviation (SD) next to each circle [174]. Reprinted from Ref. [174] with permission from Elsevier.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 4 G.727 DPCM Encoder
### Adaptive Quantizers  
The quantizers in G.726/G.727 adapt the step size to track the dynamic range of the prediction error signal. The step size is adapted according to two scale factors every sampling instant [24; 25]. One scale factor, called the unlocked step size, \(\Delta_{u}\), adapts as  
\[\Delta_{u}(n)=\Delta_{u}{}^{\beta}(n-1)M(|I(n-1)|) \tag{6}\]  
where \(\beta=(1-2^{-5})\) and the \(M\) parameters are multipliers that are greater than one for outer quantizer levels and less than one for the inner levels.  
In addition to the unlocked step size, there is another step size in G.726/727, denoted as \(\Delta_{l}\), that adapts more slowly. This step size adapts according to the logarithm of its value as well as the logarithm of \(\Delta_{u}\). Defining \(\
abla_{l}(n)=log\Delta_{l}(n)\), the adaptation rule is  
\[\
abla_{l}(n)=\frac{63}{64}\
abla_{l}(n-1)+\frac{1}{64}\
abla_{u}(n) \tag{7}\]  
The final step size is a weighted combination of the unlocked and locked step sizes as  
\[\
abla(n)=w\
abla_{u}(n)+(1-w)\
abla_{l}(n) \tag{8}\]  
where \(w\) is chosen based on several different rules [25; 26].
# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 3 Materials and Methods
### Service Module  
#### 3.2.1 Patient Physiological Monitoring Information  
Patient physiological data measured by medical sensors can be transmitted through the MQTT protocol to the healthcare provider in charge of the patient so that the healthcare provider can monitor and manage the patient's condition in real time at any time.  
In this study, an Arduino microcontroller and a DHT11 temperature/humidity sensor were used to simulate a medical sensor device to send the patient's body temperature/humidity information. The actual temperature/humidity data of the patients are measured by the smart T-shirt and smart adult diaper. Through the Publish/Subscribe transmission mode of the MQTT protocol, a dedicated physiological monitoring information topic (Topic) is created, and the sensed physiological information (e.g., body temperature or diaper humidity) is published to this Topic. At the same time, the healthcare personnel responsible for the patient's care can register for this Topic so that the healthcare personnel who have registered to this Topic can continuously receive the latest physiological conditions of the patient to confirm the effectiveness and correctness of the MQTT protocol.  
The temperature measurement smart T-shirt and the smart adult diaper [36] are then applied to the system shown in Figure 7 to transmit the actual physiological information. The Bluetooth Hub at the entrance of the ward or the APP-Hub (BT/WiFi) on the smartphone will run a temperature and diaper humidity information collection program to collect the temperature and diaper humidity information of the care recipient using the multi-channel Bluetooth transmission protocol. The smartphone or Bluetooth Hub will then transmit the collected physiological information to the back-end database for storage and monitoring (Figure 10) using the MQTT protocol.  
When the information monitoring server finds that the monitored information is abnormal or in an emergency situation, the monitoring server will then push the abnormal value and the emergency situation to the relevant healthcare and management personnel through the push server for emergency disposal. The abnormal values are physician-defined measurement criteria. The data transmission process is shown in Figure 15.  
In addition to collecting data from devices through cell phones in Bluetooth monitoring mode, the data collector (Hub) will use Bluetooth to collect data from all devices in the range via broadcasting and upload them to the back-end System Server/Data Server for storage. When there is an abnormality in the data, the information monitoring server sends a notification to the Push Server and then pushes the notification to the caregiver's cell phone to remind the caregiver of the abnormal condition, such as the patient's body temperature is over 38 or below 32 degrees, the diaper humidity is higher than 85%, or even the sensor tag power is below 20%, etc.  
Figure 14: (**a**) Sensor data format, (**b**) positioning data format of medical equipment, and (**c**) data format of message exchange.
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 3 Case Study
### Data Collection  
#### 3.1.1 Energy Demand  
The annual energy consumption profile of the small energy community of Marruge, in Tondela, Portugal, is presented in Figure 8, wherein a markedly elevated consumption pattern distinguishes the months of January, February, and December. The maximum load recorded was 43.68 kW, in February.
# Frame-Based Phone Classification Using EMG Signals +
## 3 Results
### Speaker-Dependent, Session-Independent Classification  
To evaluate session-independent classification, we first used the models from the previous section (session-dependent experiments) and tested them using data from another session. We then trained new models using a variable number of sessions from the same speaker to analyze the impact of additional data on the classification accuracy. The results (see Table 4) show that the testing accuracy decreases in a session-independent scenario. This decrease in testing accuracy is not the same for every speaker. However, when additional sessions are included in the set of training data, the testing accuracy increases. Nevertheless, it is always lower than the testing accuracy obtained with session-dependent classification.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Speaker** & **Session** & **Validation Accuracy** & **Testing Accuracy** \\ \hline \multirow{4}{*}{001} & 101 & 50.48 \(\pm\) 1.01 & 46.42 \\  & 102 & 49.12 \(\pm\) 0.86 & 47.15 \\  & 103 & 45.80 \(\pm\) 0.66 & 45.53 \\  & 104 & 50.41 \(\pm\) 1.05 & 50.54 \\ \hline \multirow{4}{*}{002} & 101 & 43.71 \(\pm\) 0.48 & 42.61 \\  & 102 & 42.80 \(\pm\) 0.96 & 42.52 \\  & 103 & 38.76 \(\pm\) 1.35 & 38.05 \\  & 104 & 39.39 \(\pm\) 0.77 & 39.64 \\ \hline \multirow{4}{*}{003} & 101 & 46.73 \(\pm\) 1.12 & 45.27 \\  & 102 & 42.41 \(\pm\) 1.07 & 39.45 \\ \hline \multirow{4}{*}{004} & 101 & 43.22 \(\pm\) 1.50 & 38.44 \\  & 102 & 41.29 \(\pm\) 1.37 & 39.62 \\ \hline \multirow{4}{*}{005} & 101 & 43.61 \(\pm\) 1.56 & 41.19 \\  & 102 & 51.45 \(\pm\) 0.54 & 50.40 \\ \hline \multirow{2}{*}{006} & 101 & 35.92 \(\pm\) 1.17 & 35.27 \\  & 102 & 28.39 \(\pm\) 1.31 & 24.72 \\ \hline \multicolumn{2}{c}{Average} & 43.34 \(\pm\) 5.80 & 41.68 \(\pm\) 6.14 \\ \hline \end{tabular}
\end{table}
Table 3: Speaker-dependent, session-dependent classification results.  
On the other hand, contrary to the testing accuracy, the validation accuracy decreases as more sessions are added. This is an indication of less over-fitting, as it shows better generalization capability.  
The effect of some lower-quality signals in a few sessions (mentioned in Section 3.1) is challenging to assess in these experiments because both training and testing sessions include some of these defective signals. For instance, both experiments for Speaker 004 show very low results because Session 004-102 is used either for training or testing.
## 4 Experiment and Results
### Experimental Results and Analysis
#### 4.4.2 Results and Analysis
By analyzing the Kappa coefficient in Table 2, compared with the 0.8579 of U-Net and 0.8810 of SegNet network, the Kappa coefficient of the proposed network reached 0.9113, which shows that the prediction results of the proposed method are more consistent with the true values. The trained model of the proposed method is more reliable. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Vegetation** & **Building** & **Water Body** & **Road** & **Recall(\%)** \\ \hline Vegetation & 12,595,908 & 444,983 & 117,472 & 39,885 & 95.44 \\ Building & 109,883 & 8,962,465 & 6106 & 38,433 & 98.31 \\ Water body & 404,832 & 6041 & 2,148,404 & 57 & 83.94 \\ Road & 197,785 & 113,828 & 2406 & 1,551,788 & 83.17 \\ Precision(\%) & 94.65 & 94.17 & 94.46 & 95.22 & 94.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix of MFCSNet.
# Simplified Routing Mechanism for Capsule Networks
## 3 Improved Routing Algorithm  
Our experiments on capsule network theory have shown that the \(\hat{u}_{\{j|i\}}\) input tensor in the dynamic routing algorithm has too large an impact on the output tensor and greatly increases the processing time. When calculating the output vector \(v_{j}\), the formula includes the input \(\hat{u}_{\{j|i\}}\) twice:  
\[v_{j}=\text{squash}\Big{(}\sum_{i}\mathit{softmax}\Big{(}b_{ij}+\hat{u}_{j|i}v _{j}\Big{)}\hat{u}_{j|i}\Big{)} \tag{6}\]  
To improve the routing mechanism between lower-level and higher-level capsules, the following modifications to the routing algorithm are proposed:  
\[v_{j}=\text{squash}\Bigg{(}\sum_{i}\mathit{softmax}\Bigg{(}b_{ij}+\sum_{j}\|v _{j}\|\Bigg{)}\hat{u}_{j|i}\Bigg{)} \tag{7}\]  
Let  
\[v_{j}=\begin{bmatrix}c_{11}&\cdots&c_{1m}\\ \vdots&\ddots&\vdots\\ c_{n1}&\cdots&c_{nm}\end{bmatrix} \tag{8}\]  
where \(c_{kl}\) is the value of the \(l\)-th neuron of the \(k\)-th capsule. If \(v_{j}\) is an intermediate capsule layer, then \(n\) is the number of output capsules. If \(v_{j}\) is an output capsule layer, then \(n\) is the number of possible object categories.  
Let  
\[v_{j}=\begin{bmatrix}v_{1}\\ v_{2}\\ \vdots\\ v_{n}\end{bmatrix} \tag{9}\]  
where \(\forall\ x\in\{1,\ 2,\ \ldots,\ n\}\), let  
\[v_{x}=\sqrt{\sum_{y=1}^{n}c_{xy}} \tag{10}\]  
This minimal modification makes the routing algorithm simpler and faster to compute. Our other proposed change concerns the squashing function. In the last capsule layer, we use a modified squashing function, as follows:  
\[\mathit{squash}_{\mathit{our}}(s)=\frac{s-e^{-|s|}s}{\|s\|+\varepsilon} \tag{11}\]  
where \(\varepsilon\) is a fine-tuning parameter. Based on our experience, we used \(\varepsilon=1\times 10^{-7}\) in this work. Figure 3 shows a simple example of our squash function in a one-dimensional case for different values of \(\varepsilon\).  
Figures 4 and 5 show a block diagram of the dynamic routing algorithm and our improved routing solution, where the main differences between the two methods are clearly visible.  
Figure 4: Block diagram of the dynamic routing algorithm by [PERSON] et al. (2011). (green: inputs, yellow: operations, blue: activations, purple: internal tensors).  
Figure 5: Block diagram of our proposed routing algorithm. (green: inputs, yellow: operations, blue: activations, purple: internal tensors).  
Figure 3: Our squash activation function with different \(\varepsilon\) values.
## 4 Discussion
In combination with a matching of monochromatic or narrowband backlight, the filter can operate as a barrier filter switching between a transparent and a non-transparent state. Illuminated with linear polarized light, the filter enables tuning the central wavelength of the narrowband transmitted light. The results obtained in the simulation and in the practical tests showed that novel application fields can be developed with very fast switchable color filters. The possible application fields of this technology in 3D display constructions are not only the central perspective barrier designs presented here for two-view and multiview 3D displays. Rather, the design rules shown by us in [6] also permit the application of the presented switchable, wavelength-based selection barrier display arrangement to parallel and divergent radiating 3D displays. The good color separation of Fabry-Perot cells or the alternative of filter cells with bandpass characteristics can be used in multi-layered displays (staked displays) [36; 37; 38; 39]. This would cause a significant crosstalk reduction. In the case of the time-consistent spectacle-free 3D representation, further investigations are necessary in order to realize large-format, wavelength-selective filter arrays. This relates, on the one hand, to improved, rapidly-switchable filter cells with bandpass characteristics that can be viewed from a large angular range. On the other hand, further work is also necessary in the matrix display drive in order to enable a good image separation of spatially-multiplexed 3D displays. Today's displays impress with their high resolution. In the proposed design, an HD-resolution 3D display requires more than 6000 switchable color filter strips with nematic layers in the sub-micrometer range. The necessary technology for large area components is not yet available.
## 2 Materials and Methods
### Soil Village Detection
#### 2.3.1 Rules-Based Approach  
The rules-based approach was adapted from [PERSON] et al. [32], following these rules:  
1. The calculation of the temporal difference in the Sentinel-1 VH band between two consecutive dates (\(\Delta\)VH = VH\({}_{\text{t}}-\text{VH}_{\text{t-1}}\)): If the value of \(\Delta\)VH exceeded the VH threshold, a potential soil tillage operation was detected. This step aimed to identify abrupt changes or discontinuities in the soil roughness through changes in the Sentinel-1 VH time series.
2. The verification of bare soil after a potential soil tillage operation was detected in step 1: If there were available NDVI values within a 30-day time window after the potential detection, the following applied: 1. If at least one NDVI value was lower than the NDVI threshold, the soil tillage was verified. If not, no soil tillage was detected. If there were no NDVI values available within a 30-day time window after the potential detection, the following applied: 2. If at least one CR (VV/VH) value was lower than the CR threshold, the soil tillage was verified. If not, no soil tillage was detected.  
The goal of the second step was to ensure that when potential soil tillage was detected in step 1, the soil was indeed bare. To achieve this, the NDVI time series was utilized. However, due to cloud coverage, the NDVI time series may have missing values. In such cases, a proxy for the NDVI was used, namely the CR of the Sentinel-1 time series [33; 34; 35]. The CR allowed us to infer bare soil in the absence of optical data.  
To determine the optimal values for the defined thresholds, we employed an optimization approach instead of using fixed values like [PERSON] et al. [32] (VH threshold: 2.5 dB; NDVI threshold: 0.3; CR threshold: \(-\)8 dB). For each parameter, an interval of possible variation was defined based on the thresholds proposed by [PERSON] et al. [32]. The interval for the VH threshold was between 0.5 and 5 dB, the interval for the NDVI threshold was between 0.2 and 0.5, and the interval for the CR threshold was between \(-\)3 and \(-\)20 dB (all variables had an interval of 0.01).
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 2 Systems and Methods
### Doppler Estimation and Compensation Implementation  
In this section, an UWA OFDM payload is designed and investigated to enable agile Doppler compensation with low computational overhead. Before the Doppler compensation, Doppler estimation is achieved by adopting the modified signal frame structure shown in Figure 1; in particular, the Doppler factor can be estimated by calculating the time compression or extension of the received preamble signal after the synchronization is achieved [23].  
Figure 1: The structure of the signal frame.
## 2 System Model
### Computation of the LLRs for the 4-PAM and the QPSK Modulation Schemes
\begin{table}
\begin{tabular}{c c c} \hline \hline \(LLR_{b_{m}}\) & **4-PAM** & **QPSK** \\ \hline \(LLR_{b_{0}}\) & \(4\,a\log\left(\frac{cosh(a-b)}{cosh(a+b)}\right)\) & \(\frac{2\,\mathrm{Im}(x)}{\sigma_{n}^{2}}\) \\ \hline \(LLR_{b_{1}}\) & \(-2\,b+\log\left(\frac{cosh(c)}{cosh(a)}\right)\) & \(\frac{2\,\mathrm{Re}(x)}{\sigma_{n}^{2}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Closed-form expressions for the \(LLR_{b_{0}}\) and \(LLR_{b_{1}}\) of both 4-PAM and QPSK modulations. Figure 4: Gray mapping between 4-PAM and QPSK symbols performed in the satellite node.