esa-sceva/satcom-chunk-collection huggingface dataset:
# Single Trace Side Channel Analysis on NTRU Implementation
## 3 Proposed Single Trace Side Channel Analysis on NTRU Implementation
### NTRUEncrypt
#### 3.2.2 Experiment  
Figure 3(a) is a full trace of the NTRUEncrypt porting on the KLA-SCARF AVR and is captured with a Lecroy HDO6104A oscilloscope at a 250 M sampling rate [12,31]. The parameters for the experiment are \(N=49\), \(p=3\), \(q=2048\), and a private key is as follows.  
\[f =\big{\{}1,1,1,1,1,0,1,0,-1,-1,1,0,0,1,1,-1,-1,1,0,0,0,-1,0,-1,- 1,1,-1,1,0,0,0,-1,0,0,-1,\] \[\quad 0,1,0,1,-1,0,0,-1,-1,1,1\big{\}}\]  
The \(p\) and \(q\) follow the proposed parameter but \(N\) is smaller than the standard because of the experimental environment.  
Figure 3(c) depicts the power consumption of steps 1 to 3 in Algorithm 3. As mentioned above, the high peaks represent the moment when \(p\) is multiplied by \(-1\). Also, in the Figure 3(c), there are the low peaks related to the coefficient 0 and 1. Thus the relative orders of \(-1\) and others can be recovered by analyzing Figure 3(c).  
The following process is to recover the coefficients 0. For each coefficient of the cipher-text, there are \(N\) multiplications with the private key. During the \(N\) operations, the operation of the private key 0 appears in the same order, so the low peaks appear regularly on the whole power trace (Figure 3(a)). To recover the degree, we should classify a set of multiplications by SPA among the trace. The multiplication between cipher-text and private key occurs after computing \(pF\), and the total recovered number of multiplications is \(N^{2}\). To reduce the noise, one can average multiple power consumption trace. Figure 5 illustrates the average of 10 traces. Figure 3(b) is an enlarged plot of four low peaks to deduce that peaks are identified. Lastly, with the three coefficients recovered from the analysis, the private key \(f\) is obtained.  
Figure 4: **(a)** Full trace of NTRUEncrypt: **(b)** Enlarged Low Peaks: **(c)** Result of SPA against \(f=pF+1\).
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
###### Abstract  
Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.  
Federated Learning; Artificial Intelligence; privacy preserving; communication efficiency 12 March 2020; Accepted: 14 April 2020; Published: 21 April 2020
## 3 System Design with Simulation and Experimental Results
### System Simulation and Experimentation
#### 3.3.2 Experimental Test
In this study, both the 4 FSK and 4 FSK-LDPC systems achieve a maximum LOS communication range of 900 m. Notably, the 4 FSK-LDPC system achieves a BER of approximately \(10^{-5}\) at 500 m.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 3 Experiment Results  
### Accuracy Verification of Short-Term Clock Prediction  
In this section, we use the IGS final product to analyze high-precision real-time clock prediction to demonstrate the improved method, and then compare the accuracy with the IGU-P, showing the advantages of the improved model. At last, the practical effectiveness of this model is verified by the real-time clock product.
# Dynamics Analysis of Space Netted Pocket System Capturing Non-Cooperative Target
## 2 Structure of the Space Netted Pocket System  
The structure of the netted pocket system is presented in Figure 1. There are eight flexible support inflatable rods for the netted pocket system, which are connected by a rope net [17]. The inflatable rods are evenly distributed in a regular octagon, and the diameter of each rod is 0.1 m. The upper square rope net and the lower triangular rope net maintain their shape by relying on the adjacent inflatable rods. The maximum diameter of the net pocket is 15.12 m, and the maximum depth is 12 m.  
The diameter of the opened net is adjusted by controlling the length of the closing rope, which is pulled by the rope retractor installed at the end of each inflatable rod.
## 6 Current Challenges and Future Prospects
### Present Obstacles Faced by AM Technologies
#### 6.2.4 Wire Arc Additive Manufacturing  
Using electric arc welding, wire arc additive manufacturing (WAAM) is a high-tech process that builds 3D objects one layer at a time [164]. It provides benefits such as fast build-up rates [165], affordability [166], and the capacity to create large components [167]. The accurate placement of metal wire in WAAM allows for the making of intricate parts that have strong mechanical features. It is used in fields like aerospace, car making, shipping, and energy, changing how things are made by offering more design options, faster production times, and greener processes.  
In order to maintain uniform layer creation, WAAM must overcome challenges with arc voltage, wire feed rate, and movement speed control [168]. Achieving consistent bead shapes on complex forms and angles is difficult. Managing residual stresses and distortion from thermal cycling and rapid solidification is crucial to prevent dimensional inaccuracies and structural issues. Ensuring material quality by addressing issues like holes, unwanted particles, and impurities is necessary to meet performance standards. Additional steps like cutting can complicate and increase costs. For consistent manufacturing across industries, effective control and monitoring systems, standardization, and certification are necessary [169].  
As WAAM technology progresses, its use in sectors like aerospace, the automotive industry, and energy is expected to grow. WAAM is appropriate for structural components, tools, and repairs due to its capacity to create massive, robust, and reasonably priced parts. Future improvements will focus on better process control and automation for increased accuracy, addressing residual stress and warping, and expanding material options. Developments in multi-material deposition may result in components with different characteristics. Rapid prototyping and on-demand manufacturing find WAAM appealing because of its scalability and cost benefits, which minimize production delays and material waste [145; 170; 171].
# FACTMS: Enhancing Multimodal Factual Consistency in Multimodal Summarization
## 7 Conclusions  
In this paper, we define and analyze the multimodal factual inconsistency problem in the multimodal summarization task. To address this problem, we propose a fact-aware multimodal summarization model via a vision-based text content filter and a dual heterogeneous knowledge graph, which fuses factual knowledge in both textual and visual modalities to generate summaries. Experiment results show that our model is effective in producing summaries that are consistent with the multimodal content. Our model differs from the text-only summarization models, which aim to make summaries consistent with the text content only. It is also different from the existing MS works, which only focus on the informativeness and fluency of summaries. By extracting textual and visual knowledge and exploiting the correlation between multimodal content, we can enhance multimodal factual consistency in multimodal summarization.  
Conceptualization, C.L. and M.Z.; methodology, C.L. and M.Z.; software, H.Y. and M.Z.; validation, C.L., H.Y. and M.Z.; formal analysis, M.Z.; investigation, M.Z.; resources, C.L.; data curation, C.L.; writing--original draft preparation, M.Z.; writing--review and editing, C.L. and M.Z.; visualization, H.Y. and M.Z.; supervision, C.L.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Figure 4: Examples of the multimodal input and the summaries generated by FactMS and MS baselines.
## 4 Results and Discussion  
### Data Description  
The data used in this study are from a wind farm located in southern China. There are 24 wind turbines on the wind farm, installed with direct-drive, variable-speed, variable-pitch generators. One year of alarm data and maintenance records are available. The SCADA system in wind turbines is configured with 102 warning alarms and 266 fault alarms. There are a total of 240 maintenance records. After matching alarm lists and their root faults, we obtain 240 pairs of data. Each pair of data consists of an alarm list and its root faults.  
For the sake of verification, we select the faults that have more than five records in order to extract the feature vectors. Ultimately, six faults are selected. They are the pitch-motor driver fault, pitch-system communication fault, hub speed encoder fault, high temperature of generator stator, wind vane fault, and vibration sensor fault. The number of alarm lists belonging to each fault is shown in Table 3. Forty-six alarm lists are used to extract the feature vectors of faults. Forty-four alarm lists are used to test the proposed method. These alarm lists are named as the test set one. The other 150 alarm lists, the root faults of which are not among the selected six faults, are also used in the test phase. These alarm lists are named as the test set two.
# Low Cost Circularly Polarized Antenna for IoT Space Applications
## 2 Antenna Design and Simulations
### Radiating Elements
Considering an amplitude imbalance of 1 dB, a 35 MHz bandwidth is observed. Considering a phase imbalance of \(5^{\circ}\), an 87 MHz (10 %) frequency bandwidth is obtained. Figure 3: Feeding circuit schematic.
## 6 Results  
During the period between February and November 2022, the system underwent rigorous testing and fine-tuning across several crop cycles. The measurable results of this testing phase encompass two main elements:  
* The number of discrepancies detected and fixed by the system: On average, the system detected and addressed approximately 10 discrepancies per day. Most of those discrepancies were ignored because the overall capabilities of the network were preserved. Some discrepancies resulted in automated actions, leading to adjustments in the configuration of the system. The corrective action described in Section 3 is an example of such an action, resulting in the creation of a virtual sensor to replace the failed one. It is worth mentioning that after the failed sensor was replaced with the new one, another corrective action was necessary to replace the virtual sensor again with a new one. In total, we counted approximately 500 corrective actions automatically performed by the system. The average execution time of the automated action took 5 min with full deployment, followed by an hourly deployment cycle, which took less than an hour. The same action, when executed by the human operator, requires 2-24 h, provided that operators work in shifts of 12 h per day 6 days per week. The benefit of automation related to validation of the capabilities and performance of automated actions reduces the efforts by a factor of 100 compared with manual actions.
* Overall performance of the crop processes: During the testing period, the system's configuration was frequently adjusted, resulting in the changes described in Section 4.1.2. The efficiency of the cultivation process was recorded after each change by gathering feedback from the farm's operator. As a result of this process, the average duration of the crop process was reduced, indicating improved productivity and optimized growth conditions for the plants.  
These measurable results highlight the effectiveness and positive impact of the system in detecting and rectifying discrepancies, as well as its contribution to enhancing the overall performance of plant cultivation.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 4 A 2-Port MIMO System Using Two Opposite 1 \(\times\) 4 Antenna Arrays
### Measurement Results Discussion of Two 1 \(\times\) 4 Arrays MIMO  
#### 4.3.1 Reflection, Transmission Coefficients, Radiation Pattern, and Gain Results  
Figure 23a illustrates the simulated and measured reflection coefficients (S11 and S22) of the proposed MIMO of a two 1 \(\times\) 4 arrays design. The measurement shows that S11 remains below \(-\)10 dB over the frequency ranges of 2.33:4.02 GHz, 4.25:4.4 GHz, and 4.6:6.62 GHz, where the minimum measured S11 is \(-\)25 dB, and the measured S11 appears quite equal to the measured S22. In contrast, it shows that the simulated S11 for the antenna is below \(-\)10 dB at 2.57:2.79 GHz and 3.03:6.86 GHz, but achieves a lower minimum simulated S11 of \(-\)22 dB. The simulated S11 appears quite equal to the simulated S22.  
This indicates that the fabricated antenna effectively covers all required frequency bands while maintaining good matching and high-performance antenna. Figure 23b illustrates the simulated and measured reflection coefficient (S12 and S21), showing that the  
Figure 22: The MIMO of two 1 \(\times\) 4 arrays: (**a**) the simulated top side, (**b**) the back side, (**c**) the fabricated prototype top side, (**d**) the back side, and (**e**) radiation efficiency.  
reflection coefficient S12 and S21 remain below \(-20\) dB over the frequency range, especially at resonance frequencies, indicating that the fabricated antenna has a high performance.  
Figure 24a,b present a radiation pattern of both simulated and measured electric fields at frequencies of 3.5 and 5.8 GHz, respectively. At 3.5 GHz, the peak gain increases to 9.18 dBi, showing strong directivity. The beam is directed at 191\({}^{\circ}\) with a beamwidth of 63.5\({}^{\circ}\). At 5.8 GHz, the peak gain reaches 10.8 dBi at 22\({}^{\circ}\), reflecting strong directional radiation. There is a narrower beamwidth of 52.7\({}^{\circ}\).  
Figure 23: A comparison of: (**a**) the measured and simulated S11, S22, and (**b**) the S12 and S21.  
Figure 24: E-plane radiation pattern: (**a**) measured and simulated at 3.5 GHz, (**b**) at 5.8 GHz, and (**c**) radiation pattern and gain setup.  
Figure 25 illustrates the changes in gain across the frequency band. The simulated gain starts from 4.6 dBi at 2 GHz and increases gradually, reaching 11 dBi at 6 GHz as a peak gain. At frequencies of 3.5 GHz and 5.8 GHz, the design exhibits gain of 9.1, 10.9 dBi, respectively. The measured gain starts from 4.5 dBi at 2 GHz and increases gradually, reaching 10.9 dBi at 6 GHz as a peak gain. At frequencies of 3.5 GHz and 5.8 GHz, the design exhibits gains of 8 and 10.9 dBi, respectively.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### HPC Infrastructure and Environment  
The computational environment as illustrated in Figure 2 was set up on a high-performance workstation running Ubuntu 24.04.1 LTS (via Windows Subsystem for Linux 2 on a Windows host). The hardware comprised an 8-core Intel Core i7-9800X CPU (16 threads), 15 GB of RAM, and two NVIDIA GeForce GTX 1650 GPUs (each with 4 GB VRAM). This modest-sized HPC setup was chosen to demonstrate that significant gains can be achieved even without a large cluster, and the pipeline is designed to scale out to multi-node clusters in future deployments. We leveraged Apache Hadoop (v3.3) for storage (HDFS, as noted) and Apache Spark (v3.3.2) for distributed data processing. Apache Spark was configured to use the local multi-core CPU resources as a cluster of executors, coordinating via a built-in Spark master. We also deployed OpenMPI (v4.1.1 via thepirun interface) to launch parallel processes on the single node, effectively simulating an MPI cluster of ranks on the multi-core CPU. Deep learning tasks were implemented in Python (v3.9) using TensorFlow (v2.12 with Keras API), compiled with CUDA 12.6 to utilize the dual GPUs. The system's software stack thus combined big-data processing tools (Spark, Hadoop), parallel computing libraries (MPI), and machine learning frameworks (TensorFlow, plus scikit-learn for some classical models). This integration was facilitated by the OS and driver configuration: running Spark and MPI inside WSL2 allowed direct access to Windows-installed GPU drivers for TensorFlow, ensuring that GPU acceleration functioned smoothly alongside the CPU-based distributed frameworks. Notably, TensorFlow was set to incrementally allocate GPU memory to avoid exhausting the 4 GB VRAM per card, and computations used mixed-precision where appropriate.  
As shown in Figure 3, a data processing pipeline was developed consisting of six scripts designed to support water demand forecasting using advanced machine learning techniques. The pipeline integrates several high-performance computing (HPC) and parallel processing technologies, including Apache Spark combined with the Hadoop Distributed File System (HDFS), the Message Passing Interface (MPI), and CUDA-enabled GPUs for accelerating TensorFlow computations. Additionally, Optuna was employed for hyperparameter optimization. The study further details the execution phases, optimization procedures, and logging mechanisms implemented throughout the forecasting process.  
Figure 3: HPC Water Demand Forecasting Pipeline.  
Figure 2: Hardware Infrastructure and Software Stack.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### UWB IPS Topologies
#### 3.2.1 Two-Way Ranging (TWR)  
Two-way ranging is a basic and effective method for calculating indoor objects' position. The distance between an anchor and a tag is calculated by measuring the time of flight (ToF) of ultra-wideband waves between them, where the time of flight of the waves between the sender and the recipient is multiplied by the speed of light (the speed of propagation of radio waves). There are several methods to implement TWR, including single-sided TWR (SS-TWR), symmetrical double-sided TWR (SDS-TWR), alternative double-sided TWR (AltDS-TWR), and asymmetrical double-sided TWR (ADS-TWR). In a two-coordinate medium, at least three anchors are required to calculate the positioning of the tag, where each tag-anchor distance is calculated separately and represents the radius of a tag-anchor circuit. The tag position can be accurately calculated by intersecting the three tag-anchor circles, where the intersecting point represents the object's expected position [11; 12; 13]. Figure 6 explains the UWB signal propagation through the distance calculation phase in SDS-TWR. To measure the distance between an anchor and a tag, three messages need to be exchanged. The tag starts the TWR by sending the first poll message request to the selected anchor, which takes the time of flight t1 to reach the anchor. The anchor requires the time Td1 (delay time) to record and process the request and replay the response to the tag. The total time from the tag request until the tag receives the anchor response is the round time Tr1 = t1 + Td1 + t2 = 2 ToF + Td1. The tag records the time Tr1 and composes and sends the final set message, including the final data packet, which includes all of the required time data to calculate the distance by the anchor. The second-round time Tr2 starts from the anchor response until the anchor receives the final packet Tr2 = t2 + Td2 + t3 = 2 ToF + Td2.  
From Figure 6, we can calculate the UWB signal's ToF using the following equation [14]:  
\[\textit{ToF}=\frac{\left(\left(\textit{Tr1}+\textit{Tr2}\right)-\left(\textit {Td1}+\textit{Td2}\right)\right)}{4} \tag{10}\]
# A Traceable Spectral Radiation Model of Radiation Thermometry
### Black Body Radiation Thermometry (BBRT)  
A black body (BB) is an ideal radiation source that has a maximum (or ideal, perfect) emissivity value (of 1) at all wavelengths (\(\epsilon(\lambda)=1\)) and in all directions, and does not reflect incident radiation. Therefore, the result of a non-contact temperature measurement on a black body always corresponds only to the temperature of the object and is independent of other environmental influences (neglecting the influence of the atmosphere).  
While traceability is easily obtained with such measurements, the black body radiation thermometry (BBRT) method is only applicable to measurements on black bodies, which is an idealization of real properties and only suitable (with negligible effects) when laboratory black bodies are used. In practice, laboratory black bodies are the best approximation of the black body with the highest achievable emissivity, which ideally approaches the emissivity of the theoretical black body (\(\epsilon_{LBB}(\lambda)\approx\epsilon_{BB}(\lambda)=1\)). Despite the ease of measurement, black bodies are difficult to achieve in practice and are usually not feasible.  
Established protocols for calibration of non-contact temperature measuring instruments, and consequently most scientific publications in this field, mostly refer to calibrations of radiation thermometers using black bodies. Such calibration protocols are described in [12; 13; 14; 15; 16; 17; 18; 19]. BBRT is considered traceable and is therefore used by calibration laboratories to transfer radiation references. This means that black body calibrations are used to transfer reference measurements from the highest levels of traceability to lower levels.  
The BBRT model is used by the simplest measuring instruments that operate with a fixed instrumental emissivity value of 1 (e.g., transfer reference thermometers and many industrial sensors), and is also available in the majority of radiation thermometers, which have adjustable instrumental parameters that can be set to a value of 1.