esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Analog to Photon-Counting Signal Gluing
#### 2.3.3 Range-Corrected Signal  
The logarithm of the Range-Corrected-Signal (RCS) is constructed from the glued PC rates \(\mathcal{R}_{i,\text{meth}}\), where meth can denote the \(\chi^{2}\)-gluing method, or the likelihood-based one with or without ringing correction:  
\[\mathcal{S}_{i,\text{meth}} =\ln\Bigl{(}R_{i}^{2}\cdot\mathcal{R}_{i,\text{meth}}\Bigr{)}\,, \tag{30}\] \[s_{l,L}^{2}(\mathcal{S}_{i,\text{meth}}) =\frac{s_{l,L}^{2}(\mathcal{R}_{i,\text{meth}})}{\mathcal{R}_{i, \text{meth}}^{2}}+\frac{4}{12}\biggl{(}\frac{w}{R_{i}}\biggr{)}^{2} \tag{31}\]  
and \(w=c/\left(2f_{s}\right)\) is the sampling bin width.  
Figure 14 compares \(\mathcal{S}_{i}\) from the three gluing algorithms for different scenarios: a rather background-free elastic line (Figure 14 left), a Raman line (Figure 14 centre) and an elastic line dominated by strong photon background (Figure 14 right). We can see that the three methods produce comparable results, with deviations \(\lesssim 25\%\) for all cases and ranges. Note that the different transition ranges from amplitude to photon-counting signals between the two gluing methods generate a region of apparently stronger fluctuations in the residuals. The strongest deviations are found when comparing the ringing-corrected signals with those without such a correction, on the overdriven LOTRs. As expected, the closer the photon-counting signal range approaches ground, the stronger such differences become visible. We believe, however, that the ringing-corrected glued signal describes the true photon rates better, and that it is actually the (traditional) reference \(\chi^{2}\) method that deviates. Nevertheless, such systematic deviations may mask aerosol transmission retrievals of the same magnitude, and a future upgrade of the system shall solve the issue by hardware  
Figure 13: Visualization of the likelihood-based gluing process applied to an 387 nm elastic channel with amplitude saturation registered by an LOTR with 20 MSamples/s. See Figure 12 for details.  
rather than software. Apart from the ringing correction, deviations of \(\lesssim\)10% are found, particularly at low ranges. The 355 nm elastic line currently fulfils the \(<\)3% requirement (see Section 1), whereas the Raman line seems to be close to reaching that level, if the ringing correction can be trusted. Only the case of a 532 nm elastic line with unusually strong background light contamination clearly fails this test, with systematic deviations \(\lesssim\) 10% between the different gluing methods, even if the ringing correction can be trusted.
## 4 Experiments
### Structural Parameter Identification  
Based on the EKF algorithm, the identification results for the stiffness under different damage cases are shown in Figure 17. Identified stiffness of each story in case 1 is shown in Figure 17a, the EKF method exhibits excellent convergence, and all identified parameters can rapidly converge to their exact values even if the identified curve of the 5 th story has some deviation. The damaged stiffness identification results for cases 2 and 3 are shown in Figure 17b,c, respectively. The stiffness can be also accurately identified. These results indicate the effective identification of the EKF for structural stiffness. Furthermore, the detection results for damage degree under different damage cases are shown in Figure 18. The mean degree of the four tests is used to determine the degree of damage. As shown in Figure 18a, the estimation results of each story are close to 0% when the five-story steel frame has no damage occur. In case 2, the 4 th story has the damage with the degree of 10.5%, while others have no damage, as shown in Figure 18b. The identified damage degree is 14.3% which shows the sufficient recognition accuracy. Meanwhile, the identified damage degree of the 4 th story in case 3 is 21.3%. Considering the actual damage degree of case 3 is 22.7%, damage at case 3 actually is also well identified. Such experimental results show that the EKF is applicable for detecting damage in multi-DOF systems.  
Figure 16: Horizontal acceleration responses of each story in damage cases: (**a**) case 1; (**b**) case 2; (**c**) case 3.  
Figure 17: Stiffness identification of each damage case: (**a**) case 1; (**b**) the 5 th story in case 2; (**c**) the 4 th story in case 3.  
Figure 18: Identified damages at different damage cases: (**a**) case 1; (**b**) case 2; (**c**) case 3.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 8 Challenges and Future Trends
### Arabic-OCR-Dataset Challenges  
* Most Arabic OCR datasets are simple, made for simple use cases, unaware of context, and domain-agnostic. As problems become more complex, the need arises for more sophisticated, high-quality, and challenging datasets that are domain-specific; for example, datasets of common handwriting are not sufficient when there is a need to recognize ancient writings;
* Annotation is a problem faced by any user working on any Arabic OCR due to the lack of Arabic datasets. This can be overcome either by producing more advanced Arabic datasets or by developing solutions to accelerate the Arabic-dataset-annotation process;
* There is a need for more challenging multilingual datasets, including the Arabic language, that can be used in challenging cross-lingual and cross-cultural scenarios as a step towards general artificial intelligence;
* Multi-font is a property that current Arabic OCR datasets lack. Considerable efforts should be centralized around the gathering or creation of datasets with multiple authentic types of handwritten font, preferably through collaboration with calligraphists, to generalize the recognition ability beyond the commonly used fonts. This step will help to cover a large spectrum of fonts, enabling the era-wise geospatial-temporal typographical analysis of Arabic fonts and scripts.
## 2 Antenna Design  
### Configuration of the CP Element  
Figure 1 displays the configuration of the CP element. Figure 1a observes that two pieces of substrate are tightly stacked. The thickness of substratel is 1.27 mm, and the material is TMM3 (\(\varepsilon\)r = 3.45, tan \(\delta\) = 0.002). The thickness of substratel2 is 0.203 mm, and the material is RO4003C (\(\varepsilon\)r = 3.55, tan \(\delta\) = 0.003). Figure 1b depicts the top metallic layer of the element. It is composed of a ME dipole and eight vertical metallic plates. The plates are fabricated by using vias and metallic strips. As shown in Figure 1c, the bottom of substrate2 has a microstrip line printed on it. Electromagnetic energy is coupled from the microstrip line to the ME dipole through the slot carved on the ground plane of the center metallic layer.  
The choice of dimensions is discussed. Firstly, the dimensions of the crossed ME dipole are determined. As we know, each arm of the ME dipole is about a quarter wavelength in substrate. It means that L1 and L2 should be around 2 mm. Then, the dimensions of the coupling slot on the ground are selected. The slot is used to couple energy to the ME dipole, because by itself, it does not radiate energy. Therefore, the length of the slot should be less than half-wavelength in substrate, which is chosen as 3 mm. Then, the dimensions of the vertical metal plates are determined. The metal plates work as parasitic directors. Therefore, the length for the metal walls also needs to be shorter than half-wavelength in substrate, which is chosen as 2.9 mm. Based on these initial values, the detailed dimensions are carefully optimized by full-wave simulation software Ansoft HFSS ver. 2018. Table 1 displays the optimal dimensions for the element.
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 2 Methodology
### Loss Function  
Since the velocity \(x\)-component \(u\), velocity \(y\)-component \(v\), and pressure \(P\) consist of continuous values, the prediction of each field is similar to a regression task. Hence, the proposed model is trained by minimizing the difference between the predicted and target field at each pixel and the loss function is formulated as  
\[Loss=\frac{1}{HW}\sum_{i}^{H}\sum_{j}^{W}\Big{(}\|u_{pred_{i,j}}-u_{target_{i, j}}\|_{1}^{1}+\|v_{pred_{i,j}}-v_{target_{i,j}}\|_{1}^{1}+\|P_{pred_{i,j}}-P_{ target_{i,j}}\|_{1}^{1}\Big{)}, \tag{4}\]  
where \(\|\cdot\|_{1}^{1}\) represents the mean absolute error (MAE) and subscripts \(i\) and \(j\) denote the positions of the row and column, respectively.
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 2 Strategies
### Code Changes to the RTKLIB Demo5
Continuing with the topic of the double differencing, an adjustment was made that allowed the code to avoid using a satellite with a cycle slip as a reference satellite, as previously, the code just utilized the satellite with the highest elevation. Additionally, a bug was identified and fixed in the geometry-free cycle slip detection, where the code was not checking the L1 frequency for cycle slips and thus, was not progressing as it was supposed to, given a case where only L1 measurements encountered cycle slips.
## 3 Results
### Block Diagram of the Simulator
#### 3.1.5 Aerodynamic Disturbance Force  
Wind exerted forces are coded in a Simulink block using (30)-(32). The antenna area \(A=10.78\) m\({}^{2}\), corresponds to 1.8 m of the diameter disc; thus, the system is exposed from a minimal to a maximum disturbance, corresponding to the minimal when the antenna exposed lateral area is \(A_{0}=0.40\) m\({}^{2}\). We considered a temperature of 15 degrees Celsius, an atmospheric pressure of 700 hPa, a relative humidity of 20% and a nominal air density  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Latitude** & **Longitude** & **Altitude (masl)** & **Wind Ve (m/s)** \\ \hline LMT & \(18^{\circ}59^{\prime}9^{\prime\prime}\) N & \(97^{\circ}18^{\prime}53^{\prime\prime}\) W & \(4580\) & \(14\) \\ ALMA & \(23^{\circ}17^{\prime}9.41^{\prime\prime}\) S & \(67^{\circ}45^{\prime}11.45^{\prime\prime}\) W & \(5059\) & \(16\) \\ IRO & \(23^{\circ}11^{\prime}6.72^{\prime\prime}\) S & \(46^{\circ}33^{\prime}29.88^{\prime\prime}\) W & \(819\) & \(7\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground station locations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground Station** & **Date and Local Time of Data** & & \\  & **Samples** & **Azimuth \& [deg]** & **Elevation \& [deg]** \\ \hline LMT & 13 January 2021 & 13:07:39 p.m. & 9.90 & 10.10 \\  & & 13:11:15 p.m. & 84.50 & 45.60 \\  & & 13:14:49 p.m. & 158.50 & 10.00 \\ ALMA & 15 January 2021 & 11:08:44 a.m. & 16.10 & 5.00 \\  & & 11:13:09 a.m. & 88.20 & 35.00 \\  & & 11:17:36 a.m. & 159.40 & 5.00 \\ IRO & 15 January 2021 & 11:11:50 a.m. & \(-72.40\) & 8.00 \\  & & 11:13:48 a.m. & \(-101.40\) & 10.90 \\  & & 11:15:46 a.m. & \(-130.10\) & 8.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: COSMOS 1500 data from Orbitron.  
\(\rho=1.0236\) kg/m\({}^{3}\) corresponding to dry and low moisture conditions at each site altitude; see Table 1.  
Therefore, for the whole travel, the system is subject approximately to magnitudes of \([80,50]\), \([90,560]\), \([20,85]\)\(N\) of wind disturbance force corresponding to laminar wind velocity of \((14,16,7)\) m/s for LMT, ALMA and IRO stations, respectively. Notice that the first two corresponds already to rough wind conditions, also called near gale in nautical terms.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
###### Abstract  
LoRa is a low-power and long range radio communication technology designed for low-power Internet of Things devices. These devices are often deployed in remote areas where the end-to-end connectivity provided through one or more gateways may be limited. In this paper, we examine the case where the gateway is not available at all times. As a consequence, the sensing data need to be buffered locally and transmitted as soon as a gateway becomes available. However, due to the Aloha-style transmission policy of current LoRa-based standards, such as the LoRaWAN, delivering a large number of packets in a short period of time by a large number of nodes becomes impossible. To avoid bursts of collisions and expedite data collection, we propose a time-slotted transmission scheduling mechanism. We formulate the data scheduling optimisation problem, taking into account LoRa characteristics, and compare its performance to low complexity heuristics. Moreover, we conduct a set of simulations to show the benefits of synchronous communications on the data collection time and the network performance. The results show that the data collection can reliably be achieved at least 10 times faster compared to an Aloha-based approach for networks with 100 or more nodes. We also develop a proof-of-concept to assess the overhead cost of communicating the schedule to the nodes and we present experimental results.  
LoRa; scheduling; resource allocation +
Footnote †: journal: Sensors
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
###### Abstract  
Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueezeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueezeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm's performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy.  
SqueezeSAR; parallel computing Spark; Hadoop; spatial adaptive filtering Article  
A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment.
## 4 Implementation  
### Hardware and Software  
In order to implement the proposed system, it was necessary to use different software and hardware, both for the development of the application and for its execution and visualization. Specifically, the proposed IAR application was developed using two Microsoft HoloLens first-generation smart glasses. For most of the development of the application, Unity 2019.3.3 [58] and Microsoft HoloToolkit were used to implement 3D model interaction and visualization. HoloToolkit provides certain features like gesture recognition and built-in buttons that simplify the creation of interaction events with the 3D model.  
The application runs autonomously on the smart glasses. A local network connection is only necessary if several users want to share the AR experience. In such a case, the devices share the information about the state of the environment among themselves so that all the participants in the experience see the 3D elements at the same position at the same time.
## 3 Results and Analysis of Influencing Factors
### The Impact of Casing Properties on Signal Attenuation  
Electromagnetic wave transmission in wells with casing often results in shorter distances and relatively weaker signal strengths. To gain a deeper understanding of the mechanism by which metal casing affects electromagnetic wave transmission, it is nec  
Figure 8: The impact of drill string magnetic permeability on signal attenuation.  
essary to conduct a detailed exploration of factors such as casing conductivity, relative magnetic permeability, and inner diameter.
## 5 Experimental Results and Evaluation
### Security and Privacy Analysis  
In this section, we first discuss the security and robustness of SAUSA and evaluate the impact of several common attacks on the proposed scheme. Then, we briefly describe the privacy preservation of SAUSA. Regarding the adversary model, we assumed that the capability of attackers is bounded by probabilistic polynomial time (PPT) such that they cannot compromise the basic cryptographic primitives, such as finding hash function collisions or breaking the cipher-text without knowing the secret keys. Moreover, we assumed that an adversary cannot control the majority of miners within the Ethereum network.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 3 System Model  
As shown in Figure 1, this paper designs a comprehensive agricultural image storage scheme that combines blockchain and satellite networks. The system comprises the following five modules:  
1. _Image Acquisition Module._ This module includes ground equipment imaging, drone imaging, and satellite imaging. Ground equipment imaging involves installing sensors and cameras in the fields to collect high-resolution close-up images, providing detailed field monitoring data. Drone imaging involves drones cruising over the fields to capture images from different angles and heights. Satellite imaging is used when the other two methods are not applicable, offering wide-area coverage and multispectral images suitable for field monitoring in remote areas or extreme environments.
2. _Image Preprocessing Module._ The module is deployed on cloud or local servers and is responsible for denoising and enhancing the image data. The server uses a median filter algorithm to denoise the images, reducing noise interference and improving image quality. It also uses histogram equalization to enhance the images, improving contrast and detail to ensure high-quality data input.
3. _Image Encryption Module._ This module is also deployed on cloud or local servers and handles encryption and verification processes. The preprocessed image data is encrypted using homomorphic encryption, allowing denoising and enhancement to be performed on the encrypted data, thereby ensuring security during transmission and storage. Zero-knowledge proof technology is also applied to verify the consistency between the encrypted image data and the original data, ensuring data integrity and authenticity during processing.
4. _Image Storage and Blockchain Module._ The storage module utilizes IPFS for decentralized storage and records the hash value of the image on the blockchain. Storing the encrypted image data on IPFS achieves efficient and reliable file storage. Additionally, blockchain storage ensures data immutability and transparency, thus providing long-term data security.
5. _Image Verification Module._ The verification module is deployed on user terminals or servers to handle data verification and access control. The module verifies the integrity and authenticity of the encrypted image using zero-knowledge proof. It combines this with the hash value on the blockchain for further verification. Meanwhile, the module ensures that only authorized users can access and verify relevant data, thus protecting sensitive information.  
Figure 1: Agricultural image processing and storage model.
# An Architecture for Reliable Transportation of Delicate Goods
###### Abstract  
Adequate conditions are critical to avoiding damage or degradation of products during transportation, especially in the case of delicate goods like food products, live animals, precision machinery or art items, among others. The damages are not always readily identified: sometimes they are only detected several days or weeks after the merchandise has been delivered. Moreover, it may be hard to assess if the problems resulted from the transport conditions, and it may be even harder to prove it, making it difficult to determine and assign responsibilities. Also, transport is a global business, typically involving different companies and means (truck, train, plane, ship,... ). Usually, customers hire the service to a single commercial entity, but the service is performed by several companies, like transporters, stockists and dispatchers. To know whether the transport requirements are fulfilled or not is thus essential to assessing responsibilities and encouraging compliance by all the players in the process. In this paper, the authors propose an architecture that allows certifying, in an exempt manner, the conditions under which the transport of sensitive goods are carried out. In case of compliance, it protects the entities of the transport chain and ensures the customer that the merchandise has not been subject to conditions that may have affected its integrity or quality. If problems are detected, it allows to identify the non-compliant players and to assign responsibilities. The solution is based on ultra-low-power, low-cost devices (equipped with several sensors, a real-time clock, and Bluetooth Low Energy services), a mobile application and several cloud services (including a Coordinated Universal Time service).  
Keywords:transportation; delicate goods; IoT; ultra-low power; BLE; blockchain +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Evaportranspiration  
Given the spatial and temporal scale of our work and the analysis of uncertainties associated with the different components of the hydrological cycle, our analysis concludes that the trend in (P-Ru) is statistically significant at the 95% confidence level. Furthermore, even when extremely overestimated, changes in all three storage components (dS/dt(sw), dS/dt(uz) and dS/dt(gw)) do not account for the entirety of (P-Ru) changes, implying ET must have increased to maintain the region-scale water budget. Based on the more realistic trend analysis, storage changes are negligible, a conclusion in agreement with previous studies [49; 50; 51]. Despite uncertainties in estimates, both the overestimated storage analysis and the more rigorous trend analysis show ET has historically been increasing over the eastern United States.