esa-sceva/satcom-chunk-collection huggingface dataset:
# EGMStream Webapp: EGMS Data Downstream Solution
## 1 Introduction
Specifically, the EGMS provides InSAR data at three increasing processing levels [43]: basic (L2a), GNSS-calibrated (L2b), and Ortho (L3). The L2a data deliver relative InSAR velocity and displacement information along the Line of Sight (LoS) for each Measurement Point (MP), tied to a stable reference point. The L2b data are an advanced product, offering absolute deformation maps thanks to the combination [44] with the velocities recorded by the GNSS network across Europe by the EUREF Permanent Network Densification Product Portal [45]. The L3 Ortho products provide vertical and horizontal (east-west) velocity components derived from the L2b data sampled on a regular grid with a resolution of 100 m.  
On the EGMS Explorer [46], only data from the last release are available for visualization, while data from both of the last two releases are downloadable in ZIP format, which includes a CSV (Comma-Separated Values) file (_.csv_), after logging into the EU portal [47]. Nevertheless, the huge volume of data is hard and impractical to handle in Geographic Information System (GIS) environments, highlighting the need for a semi-automated and user-friendly approach to download and convert the data. To address this issue, the standalone software EGMSstream [48] was developed in 2022, which features a visual interface to automatically download, convert, and crop data into smaller and more manageable subsets. This first version of EGMSream has been developed as a desktop application based on R and by using the Shiny package [49, 50]. The increasing volume of available data, the growing number of users due to the diffusion of the EGMS, and the expanding range of potential applications have highlighted some limitations of the desktop version. Specifically, the need for local software installations and the consequent reliance on personal computer performances for downloading and processing large datasets posed significant challenges. To address these issues, a new version has been developed featuring a server-based architecture accessible through a flexible and user-friendly webapp platform interface by the link [[https://egmstream.unifi.it](https://egmstream.unifi.it)]([https://egmstream.unifi.it](https://egmstream.unifi.it)). This new approach requires only an internet connection, eliminating the need for high-performance local hardware. The EGMSstream webapp has been built in Python (version 3.13.2) and JavaScript (ECMAScript 2023) and developed using Docker (version 28.0.1) [51]. This new version introduces a scalable architecture accessible via a web browser, not requiring any user-side software installations and making EGMS data and ground deformation analysis capabilities widely accessible worldwide. Moreover, the EGMSstream webapp significantly enhances data processing efficiency by enabling the parallel processing of large datasets and introducing new features.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 4 Field Test  
### Study Area  
To evaluate the effectiveness of grid-type and contour-type route planning methods, we chose a study site in Liugui District, Kaohsiung City, Taiwan (Figure 8). The Liugui area is characterized by undulating terrain with elevations ranging from 330 to 924 m above sea level. The upper slopes of the site exhibit a collapsed topography with well-defined erosion gullies, while the lower slopes are relatively flatter. The average slope is 38 degrees, the minimum is 14 degrees, and the maximum is 86 degrees. This terrain presents a challenging environment for UAV-based photogrammetry, as it requires careful route planning to ensure that images are captured with sufficient overlap to generate accurate terrain models. Our study assessed whether the contour-type route planning method could outperform the commonly used grid-type method regarding terrain model accuracy and completeness. By comparing the results of both methods, we aimed to identify the most suitable approach for acquiring reliable terrain information in mountainous areas.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 3 Case Study
### SHAP Analysis
The effect of \(L\) on the residual strength is significant, as the size of the crack or defect increases, the residual strength decreases. In engineering design, it is essential to consider the size of defects and conduct inspections to ensure that the material does not fail due to defects during service. There is a positive correlation between \(\sigma_{u}\) and residual strength. \(\sigma_{u}\) represents the maximum load-bearing capacity under ideal conditions, while residual strength considers the material's load-bearing capacity after damage. As the damage increases, the residual strength falls below the \(\sigma_{u}\). Finally, the relationship between \(w\) and the residual strength is negative. As the defect width increases, the residual strength typically decreases. However, the exact extent of this effect needs to be analyzed, considering various factors such as material properties, loading conditions, and defect morphology.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) Data Mining  
The methodological workflow adopted, and main processing activities carried out can be divided into three components:  
* Processing of SAR satellite data and change detection products for image pairs.
* Creation of a project catalogue (database).
* Creation of the spatial analysis project on a GIS platform.
###### Abstract
The test specimens were provided with an initial notch before the test, and subsequently, static and cyclic/fatigue fracture tests were performed in a three-point bending configuration; see Figure 2b. The outcome of each static fracture test is a vertical force vs. midspan deflection and vertical force vs. crack mouth opening displacement diagram. The mechanical fracture parameters are primarily obtained through the direct evaluation of fracture test data via the effective crack model [20], the work-of-fracture method [21], and the double-\(K\) fracture model [22]. The vertical force vs. midspan deflection diagrams are also used to obtain mechanical fracture parameters indirectly--based on a combination of fracture testing and inverse analysis [23]. The inverse analysis has several advantages, such as its ability to identify parameters whose direct testing and evaluation are difficult or even impossible. The representative of such parameters is the tensile strength. The outcome of cyclic/fatigue fracture tests is represented by a Wohler curve [4]. For the present study, sodium hydroxide was used as an alkaline activator to produce appropriate test specimens with low potential of cracking. It was experimentally verified by authors (see Figure 1) and confirmed by other researchers that such AAS material reaches sufficiently high strengths together with noticeably lower shrinkage [24; 25], and thus, the risk of cracking before the test is reduced. The complex information about the material included the mechanical, fracture, and fatigue characteristics of AAS fine-grained composite used as the input values for a numerical simulation are presented herein. The standard test specimens with nominal dimensions of 40 \(\times\) 40 \(\times\) 160 mm\({}^{3}\) were manufactured and tested under typical configurations. Non-traditional curing conditions of ambient temperature and high relative humidity (RH) \(\geq\) 95% for a long period was chosen to prevent the cracking of the material before fracture and fatigue tests. In practical applications of investigated material, these non-traditional curing conditions can be found in the design of precast elements rather than in in situ applications.
# TCNformer Model for Photovoltaic Power Prediction
## 2 Preliminary
### Problem Definition  
The present study abstracts the photovoltaic power prediction problem as a multistep time series prediction problem, which can be defined as a data series with an input of \(I\times n\) and an output of \(O\times 1\), where \(I\) is the length of the input data, and \(O\) is the length of the output data. For example, under a 15-min sampling frequency, if the historical data of photovoltaic power in the past 30 days are used to predict the photovoltaic power data in the future 24 h, the \(I\) length is 2880, and the \(O\) length is 96.
## 2 Preliminaries and Notations
### Definition (Curve25519)  
Curve25519 employs the prime number \(2^{255}-19\) for modulo arithmetic and the Montgomery curve equation \(y^{2}=x^{3}+ax^{2}+x\). For an efficient and fast implementation, the Montgomery ladder is utilized over the projective coordinates of the elliptic curve, ensuring speed and security against side-channel analysis; further detail is give in [32].  
In the paper, the output of the 32-byte HKDF algorithm is denoted by KDF. The details of the HKDF algorithm are given in [32]. The input of the HKDF is the concatenation of F and KM, where \(F\) represents a 256-bit sequence of ones, and KM is the secret key material. In addition the notation XDH is used for the extended Diffie-Hellman given in [33], and \(\lambda\) is used for the security perimeters.
# Spatial Sound Rendering Using Intensity Impulse Response and Cardioid Masking Function
## 4 Algorithm Implementation for Surround System
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pos.** & **Corr.** & **L** & **C** & **R** & **LS** & **RS** \\ \hline \multirow{3}{*}{1} & INA vs. OMNI & 1.004 & 0.929 & 1.150 & 0.333 & 0.339 \\  & INA vs. V\_INA & 0.563 & 1.026 & 0.515 & 0.067 & 0.053 \\  & Change [\%] & –44.0 & 10.4 & –55.2 & –79.8 & –84.4 \\ \hline \multirow{3}{*}{2} & INA vs. OMNI & 0.465 & 0.434 & 1.946 & 0.364 & 0.486 \\  & INA vs. V\_INA & 0.173 & 0.467 & 1.466 & 0.047 & 0.129 \\  & Change [\%] & –62.6 & 7.6 & –24.7 & –87.0 & –73.4 \\ \hline \multirow{3}{*}{3} & INA vs. OMNI & 3.251 & 1.519 & 0.765 & 0.715 & 0.699 \\  & INA vs. V\_INA & 2.620 & 1.342 & 0.218 & 0.250 & 0.066 \\  & Change [\%] & –19.4 & –11.6 & –71.5 & –65.1 & –90.5 \\ \hline \multirow{3}{*}{4} & INA vs. OMNI & 1.705 & 0.794 & 1.986 & 3.724 & 3.987 \\  & INA vs. V\_INA & 0.825 & 0.118 & 1.029 & 3.40 & 3.709 \\  & Change [\%] & –51.6 & –85.1 & –48.2 & –8.7 & –7.0 \\ \hline \multirow{3}{*}{5} & INA vs. OMNI & 0.416 & 0.184 & 3.265 & 0.865 & 2.845 \\  & INA vs. V\_INA & 0.113 & 0.050 & 2.966 & 0.455 & 2.687 \\  & Change [\%] & –72.8 & –72.8 & –9.2 & –47.4 & –5.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlation change of channel signals after application of masking algorithm. Figure 10: INA real vs. INA virtual for sound source position #2.
## 2 Potential Application Areas of CR-WSNs
### Transportation and Vehicular Networks  
The IEEE 1609.4 standard proposes multi-channel operations in wireless access for vehicular environments (WAVE). The WAVE system operates on the 75 MHz spectrum in the 5.9 GHz band with one control channel and six service channels. All vehicular users will have to contend for channel access and use it to transmit the information in the 5.9 GHz band. However, it still suffers from spectrum insufficiency problems. This spectrum scarcity issue and the requirements of cognitive radio in WAVE have been studied [47; 48; 49].  
Some preliminary works in CR-enabled vehicular communications have already been done [48]. Vehicular wireless sensor networks are emerging as a new network paradigm for proactively gathering monitoring information in urban environments. CR-WSNs are likely to be more relevant in this field. Although this area still needs to be examined, some protocols for highway safety using CR-WSNs have been proposed [50; 51].
## 2 Methodology
### System Model
(7) The final output comprises two paths that satisfy the optimization objectives. ```
Input :heterogeneous network \(G(V,E)\), source node \(v_{s}\), target node \(v_{t}\), optimization parameters Output :Multiple optimal transmission paths
1 Initial: the current network topology data and optimization parameters. Obtain network topology information: the link status and connectivity status of each node. Path search:
2a. Run a variant algorithm based on Depth First Search (DFS) to recursively search for all feasible paths in the network. b. Ensure that each path in the path set does not contain duplicate vertices. c. When the path length exceeds the known maximum path length, stop further search. Path filtering:
3a. Using NSGA-II for multi-objective optimization, select the best path to maximize path reliability and minimize transmission delay. b. The global search capability of NSGA-II avoids getting stuck in local optima during the iteration process. Topology simplification: a. Delete the network topology based on the first optimal transmission path. b. Ensure that there is no common link between the new path and the original path. Repeat path search and filtering: a. Re input the deleted new topology map into the path search and multi-objective optimization module. b. Obtain the second path. Return multiple transmission paths that meet optimization objectives. ```  
**Algorithm 1**Secure and Reliable Multi-Objective Optimization Multipath Transmission Algorithm for Heterogeneous Networks
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the IoT-23 Dataset Using a Supervised Machine Learning Approach
#### 4.1.3 Research Analysis According to [24]  
Research [24] has introduced anomaly detection methods for IoT networks using the IoT-23 dataset.  
Step 1: Data Preparation and Processing  
The IoT-23 dataset was divided into three subsets (Dataset_1, Dataset_2, Dataset_3), each focusing on different malicious labels. To address imbalance issues, oversampling techniques were employed. Irrelevant columns were removed, categorical data were converted to numerical data, and missing values were filled. The dataset was then split into training and testing sets, and feature scaling was applied.  
Step 2: Model utilization  
A Random Forest classifier was selected for its ability to handle large datasets and complex relationships. Hyperparameter tuning might have been used to optimize the model's performance. The model was trained on the training data and used to predict labels on the testing data.  
Step 3: Model evaluation  
The model's performance was evaluated using metrics like Accuracy, Precision, Recall and F1-score. A confusion matrix was used to visualize prediction errors. The results were compared to other ML algorithms.  
Step 4: Result verification  
The Random Forest model effectively detected malicious IoT traffic in the engineered IoT-23 dataset. It outperformed other models and demonstrated consistent results across different subsets. This research provided valuable insights for developing robust IoT security solutions.
## 5 Conclusions  
In this paper, four types of field-scale energy slabs were constructed in a test bed with various configurations. Then, a series of field experiments was conducted to evaluate the thermal performance of energy slabs according to pipe materials and configurations. Some findings are summarized as follows:  
Figure 15: Temperature variation according to depth at center of each high-density polyethylene (HDPE) energy slab (for location of A, B, A’ and B’ refer to Figure 3). (**a**) Wall-type energy slab (HDPE, without thermal insulation); (**b**) Floor-type energy slab (HDPE, with thermal insulation).  
1. The field experiments indicate that increasing the thermal conductivity of pipe materials can desirably enhance the thermal performance of energy slabs when the other construction conditions are predetermined. However, the extremely high thermal conductivity of the heat exchange pipe could lead to excessive heat transfer through the surrounding media, which may impair the long-term thermal performance of energy slabs.
2. There was no difference in heat exchange performance between the wall-type energy slab and the floor-type energy slab. Therefore, the energy slabs can be used in both the wall and floor slab in the underground structures to maximize the use of geothermal energy in a limited underground space.
3. It is hard to expect sufficient heat exchange performance in energy slabs installed without a thermal insulation layer, because the temperature in the concrete slab containing heat exchangers is naturally governed by the ambient air temperature if there is no appropriate thermal insulation. This result implies that the installation of a thermal insulation layer in energy slabs is essential, and a thermal insulation material with suitable thermal conductivity to minimize the influence of ambient air should be used.
4. The results of the TPTs showed that the average heat exchange amount of the energy slab was estimated to be about 400 W for 25 m\({}^{2}\) of the energy slab unit. This value is not comparable to the conventional GHEXs because the field test conditions were not favorable. If energy slabs are installed in an actual underground structure, a larger heat exchange amount would be expected with the aid of the higher thermal conductivity of ground formation and less influence of the ambient air temperature. Additionally, the successful application of energy slabs in large-scale underground structures, such as underground shopping centers and subway stations, would be suitable for replacing the conventional GHEXs.  
Funding acquisition, H.C.; Investigation, S.L.; Methodology, S.P.; Project administration, H.C.; Software, S.P.; Supervision, H.C.; Validation, M.K.; Visualization, M.K.; Writing-original draft, S.L.; Writing-review & editing, H.C.  
This research was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP), Ministry of Knowledge Economy (No. 20153030111110) and National Research Foundation of Korea (NRF-2017R1C1B5017580).  
The authors declare no conflict of interest.
## 2 Related Work
While there are many methods in the literature that can calculate the traffic matrix in a short time, most of them have requirements like partial measurement of flows, training a neural network, high processing power etc. For IoT networks that exhibit limited set of traffic matrices, identification of a traffic matrix may be simpler than trying to calculate it. In this work, we investigated the feasibility of applying a brain-inspired Bayesian attractor model (BAM) and showed that it can identify the traffic in a reasonable time without such requirements.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
## 2 System Description
### Encryption Algorithm  
In the process of handshake and secure data transmission, different encryption algorithms are used in different phases. For HTTPS, encryption algorithms are required for key exchange, authentication, data encryption, and signature, but the encryption algorithms applied to each part are different. The strength of the algorithm is closely related to the efficiency of encryption. The CAN-SEAT strategy differentiates clients by differentiating these encryption algorithms.  
Figure 1: A flow diagram of client-aware secure data transmission methods. The messages with * are for two-way authentication.  
Key exchange: In the SSL/TLS protocol, the key exchange is used to generate the key for data transmission encryption. Because of the higher security requirements, key exchange requires the use of asymmetric encryption algorithms. The algorithms actually used are RSA, Diffie-Hellman Ephemeral (DHE), and Elliptic Curve Diffie-Hellman Ephemeral (ECDHE).  
Authentication: At the start of the session, the SSL/TLS protocol may require both communicating ends to prove their identity, typically using digital signatures for authentication. Asymmetric encryption is also required for authentication. The actual algorithm includes RSA and ECDSA. In practice, to avoid the cryptographic operations being repeatedly performed, authentication and key exchange are generally used in combination before data transmission.  
Data encryption: After establishing the connection, it is necessary to encrypt the data transmission. Because the amount of data is usually large, the data are encrypted using an efficient symmetric encryption algorithm, including the Triple Data Encryption Algorithm (3 DES), the Advanced Encryption Standard (AES), CAMELLIA, ARIA, and ChaCha20, which remain relatively secure. There are three encryption methods: stream encryption, block encryption, and Authentication Encryption with Associated Data (AEAD). At present, ChaCha20 is the only secure stream encryption algorithm currently in use for SSL/TLS. Regarding block encryption, because of the low encryption efficiency of 3 DES, the actual algorithm used are AES, CAMELLIA, ARIA, etc. Among them, AES is still the most common algorithm. Compared with the same algorithm in the previous two methods, AEAD is more efficient and secure, but it is only supported in the new version (TLSv1.2 and subsequent versions). Currently, in TLSv1.3, the algorithms only include ChaCha20 and AES [21].  
Signature: In SSL/TLS, the common hash signature algorithms include the Message-Digest Algorithm (MD5), Secure Hash Algorithm 1 (SHA1), SHA, SHA256, and SHA384 with the Hash-based Message Authentication Code (HMAC). In previous versions of TLSv1.2, the signature algorithm was applied to the Message Authentication Code (MAC) and the Pseudorandom Function (PRF) algorithm. In later versions, the PRF algorithm must be used.
## 5 Results and Performance Analysis
### Mathematical Security Analysis  
A mathematically grounded security analysis of a Zigbee-based IoT network focuses on DoS and MITM attacks vectors and how cryptographic and threat detection mechanisms are working with the combination of blockchain and RBF to provide security guarantees. The following subsections describe the steps of mathematical modeling.  
1. System Model: * Let \(D=\{d_{1},d_{2},\ldots,d_{n}\}\) be the set of Zigbee devices. * Each device \(d_{i}\) is connected via the Zigbee protocol to a coordinator. * Communication between nodes is encrypted using AES-CCM and authenticated via ECC (Elliptic Curve Cryptography.)
2. AES-CCM Encryption: Data \(m\in\{0,1\}^{l}\) are encrypted as: \[c=\text{AES-CCM}_{k}(m,\text{nonce})\] (4) where: K is a 128-bit secret key and nonce is unique for each session.
3. ECC-Based Authentication: Each device has: * Public-private key pair \((P_{t},S_{i})\). * Digital signature using ECDSA: \[\text{sig}=\text{ECDSA}_{S_{i}}(H(m))\] (5) where H(m) is a secure hash of message m Receiver verifies: \[\text{Verify}_{P_{t}}(m,\text{sig})=\text{True}\] (6) ECDLP (Elliptic Curve Discrete Logarithm Problem) assumption: * Given P = kG, it is infeasible to find k. This provides resistance to MITM.
4. RBF-Based Intrusion Detection Let: \[\vec{x}=[x_{1},x_{2},\ldots,x_{d}]\in\mathbb{R}^{d}\] (7) be the feature vector for a communication session (e.g., timing, hop count, entropy, RSSI). The RBF output: \[f(\vec{x})=\sum_{i=1}^{N}w_{i}\cdot\phi(||\vec{x}-\vec{c}_{i}||)\quad\text{ where }\phi(r)=e^{-\gamma r^{2}}\] (8) If: \[f(\vec{x})<\theta\Rightarrow\text{Anomaly Detected}\] (9) trained on normal traffic, the RBF network can detect attacks such as MITM, DoS.  
* Blockchain-Based Security Logic
* Let \(T=\{t_{1},t_{2},\ldots,t_{m}\}\) be a sequence of transactions (registrations, data logs)
* Each transaction is: \[t_{i}=H(d_{i}\|\text{timestamp}\|\text{signature})\] (10)
* Block \(B_{j}=\{t_{i},\ldots,t_{k}\}\), linked by: \[\text{Hash}(B_{j-1})\Rightarrow\text{immutability}\] (11) Our framework provides the auditability, for which all Zigbee devices' actions are recorded, with decentralization, and for which there is no single point of failure and resistance to sniffing via encrypted logs.