esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 3 Lab-in-a-Box (LiB) Architecture
### Modular System Architecture
#### 3.1.3 Microcontroller  
Microcontrollers or data aggregators are essential parts that acquire the data from various sensors. They are essentially small computers with CPU, RAM, Read Only Memory (ROM), clock generation circuitry, and other functionality. Microcontrollers have peripheral features that enable them to interact and communicate with other hardware. They have output ports that can be driven high or low tocontrol other circuitry or to initiate action in other hardware in the system. They also have pulse width modulation (PWM) enabled pins that can programmatically generate pulses to control motors, servos or in some cases provide control signals. Most microcontrollers have analog to digital converters that convert the analog values from an analog sensor into digital values to be processed and/or transmitted to the wireless transceiver. Some microcontrollers have a network stack that provides wireless connectivity to send the data to the central gateway device wirelessly. Typically, most modern microcontrollers have several power-saving modes, ideal for intermittent actions to save power.  
Microcontrollers have different interrupt capabilities, including both software and hardware interrupts, so that the signal can be monitored in parallel with the main firmware, especially useful in scenarios where the reception of a control signal or span of an event is very brief. To communicate with digital sensors and other active components or transceivers, microcontrollers have an array of communication peripherals built into them such as SPI, I2C, UART, Modbus, and CAN-bus. Newer microcontrollers also have crypto-engines to make encryption faster and more power efficient in smaller devices.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 2 Overview of Signal Modelling and Compressed Sensing
### Overview of Compressed Sensing  
Compressed sensing was initially proposed by [PERSON] and [PERSON] [16; 17]. The fundamental idea of this theory is: if the signal can be represented sparsely or approximately sparsely in a particular transformation domain, a low-dimensional measurement matrix can be designed to project the signal from the high-dimensional space to the low-dimensional space. A sparse reconstruction algorithm can then be applied to recover the original signal with high probability from a small number of projected values. Compressed sensing can be simply summarized into the following three steps:  
Step 1. Signal sparse representation. Signal sparsity or near sparsity is the first prerequisite for compressed sensing. The signal \(x\) can be sparsely represented in a certain transformation domain. There is a sparse vector \(a\) and a sparse base matrix \(\psi\), which satisfies \(x=\psi a\). \"Sparse\" refers to the fact that the number of non-zero elements in vector \(a\) is far less than the length of the vector.  
Step 2. Sensing matrix designing. A measurement matrix \(\phi_{M\times N}\) is used to measure the signal to obtain the observation set \(y=\phi x=\phi\psi a\), and \(\Gamma=\phi\psi\) is the sensing matrix. The measurement matrix \(\phi\) must meet the restricted isometry property (RIP) [18]:  
\[(1-\delta_{K})\|x\|_{2}^{2}\leq\|\psi x\|_{2}^{2}\leq(1+\delta_{K})\|x\|_{2}^{2} \tag{5}\]  
where \(K\) is the sparsity of signal \(x\), \(\delta_{K}\) is the restricted isometric constant (RIC), and \(\delta_{K}\in(0,1)\)[19].  
The RIC is usually difficult to calculate. [PERSON] provides an equivalent condition for the RIP condition, which states that the measurement matrix is not related to the sparse basis matrix [20].  
Step 3. Sparse signal reconstruction. The exact or approximate solution \(\hat{x}\) of signal \(x\) can be solved by using the optimization problem in the sense of \(l_{0}\) or \(l_{1}\) norm:  
\[\hat{a}=\text{argmin}\|a\|_{0 or1},\;\;s.t.\;\;\;y=\Gamma a \tag{6}\]  
Thus, the reconstructed signal can be obtained:  
\[\hat{x}=\psi\hat{a} \tag{7}\]  
Apparently, the sparsity of the signal is a prerequisite for compressed sensing. In the case of bistatic radar sea target detection echo, the bistatic first-order strong sea clutter occupies only a small number of frequency units, and its frequency distribution is sparse. This echo model meets the reconstruction requirements of compressed sensing. In order to ensure high reconstruction accuracy, the signal is required to have a high signal-to-noise ratio for sparse signal reconstruction under noise background. The clutter-to-noise ratio of strong sea clutter is typically more than tens of dB, which satisfies the reconstruction requirements. Therefore, the proposed compressed sensing optimization for sea clutter reconstruction in this paper is reasonable.
## 4 Experimental Results
### Classification Results  
The generator networks generate adversarial synthetic samples similar to malicious samples when the conditional synthetic \(\chi\) and the real \(\times\) match. This enhances the training data for the machine learning procedure used by the proposed MDGAN model. The confusion matrix shown in Figure 6 displays the effectiveness of the adversarial training in the proposed MDGAN method combined with the multi-face data input mechanism that has learned and categorized the malware family. The malware family samples count shown in Figure 5 displays the total number of samples present in each malware type of the family. The malware family classification result shown in the confusion matrix witnesses the superiority of the proposed framework over other recently reported mechanisms. The DroidDream and Iconosys have not learned the feature set more effectively and obtained 85.7% and 88.5% precision values with their 81 and 152 samples in the family as given in Table 3. The Adrd and Geinimi both have the highest inter-class variation due to which they have achieved the highest precision of 100%, while the FakeInstaller achieved the second highest precision of 98.9% due to its highest samples strength of 925 samples in the family. The mean precision value obtained by the proposed MDGAN is 95.1%, which is higher than DNN-RNN, CNN-raw opcodes, DroidDetective, API calls-yerima, and CNN-BiLSTM-NB with precision of 90%, 87.2%, 89.5%, 94.3%, and 90.0%, respectively. The mean recall value of MDGAN is 94.6%, which is slightly lower when compared to the 96% value attained by DroidDetective given in Table 4. The mean F1-score of MDGAN is 94.7%, which is superior to DNN-RNN, CNN-raw opcodes, DroidDetective, API calls-yerima, and CNN-BiLSTM-NB with F1-score values of 87.1%, 86.2%, 92.1%, 92.3%, and 86.3% respectively. The mean accuracy achieved by MDGAN is 96.2%, which is superior to DNN-RNN, CNN-raw opcodes, DroidDetective, API calls-yerima, and CNN-BiLSTM-NB with a mean accuracy of 90%, 87.4%, 86.0%, 91.8%, and 88.1%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Expression** \\ \hline Mean Precision & (5) \\ Mean Recall & (6) \\ Mean Fscore & (7) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation Parameters.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 5 Performance Results  
This section discusses the localization improvements of GNSS based on the use of cmWave or mmWave cellular networks. The simulation methodology is based on the definition of the outdoor urban, suburban and rural scenarios in Section 2, the characterization of GNSS, cmWave and mmWave observables in Section 3, where differential or precise corrections are applied to the GNSS code observables, and the positioning algorithms described in Section 4. The performance results are mainly obtained in terms of position accuracy and availability.  
The representative network deployment shown in Figure 1 is used to determine the satellite visibility. The mobile location is defined with a rectangular grid of points with a resolution of 100 m over the coverage area of 4.35 km by 3.6 km, which results in 1584 different mobile positions. The urban areas are represented by 14.8% of the positions, i.e., 13.4%, 1.3% and 0.1% with 30\({}^{\circ}\), 50\({}^{\circ}\) and 70\({}^{\circ}\) of elevation mask, respectively. The suburban scenarios are widely characterized with a 79.2% of the positions, while the rural scenarios is only shown in the 6% of the positions. The GPS constellation is only considered in order to assess nominal GNSS conditions. Thus, the use of multiple constellations is left for future work. For each mobile position, the positioning performance is evaluated over 200 iterations for the case of GPS and cmWave observables. Considering the same configuration as in the example of Figure 6, the cmWave observables are obtained with a threshold-based ToA estimator by using a 90-MHz signal bandwidth and a time-delay estimation range defined as \(\tau\in[-20,100]\)\(/c\). The LoS propagation conditions of the cmWave signals depend on the distance to the BS, by following the UMi, UMa and RMa models in [29]. Depending on the LoS conditions, the cmWave observables are obtained from the pre-computed distribution of the LoS or NLoS ranging bias. This realistic scenario helps to identify the benefits achieved with the hybridisation between GNSS and cmWave signals. The use of the mmWave technology is only studied for urban scenarios with LoS-dominant connectivity. Also, the case with multiple (up-to three) BSs for a dense deployment scenario of mmWave access points is considered. Finally, BS and UE are considered at same height, thus elevation of AoD and AoA are not considered in the simulation model.
# Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction
## 5 Our Latest Result for a Particular Tokyo EQ on 7 October 2021
### Isumi Station  
Figure 2b illustrates the milk yield of 60 Holstein dairy cows at the Isumi station, with the same notations as in Figure 2a. The epicentral distance of this station is D = 48 km.  
Figure 2: (**a**) Temporal (daily) evolution of cows’ milk yields (denoted by red curve) around the mean (zero level) at the station in Chiba-shi with the shortest distance from the EQ epicenter (D = 15 km). The vertical bar for each day is the error bar. The blue area indicates the normal condition within our criterion of plus/minus 1.5\(\sigma\) (\(\sigma\) is the standard deviation), and we define an anomaly outside this blue region, but mainly exceeding \(-\)1.5\(\sigma\). The EQ day is indicated by a vertical red broken line, and the analysis period is one month before and one week after the EQ. (**b**) Same as (**a**), but for Isumi (D = 48 km), (**c**) Asahi (D = 57 km), and (**d**) Minami-boso (D = 63 km).  
It is seen from this figure that we can detect abnormal depletions in cows' milk yield on 10 September (or 28 days before the EQ), 23 September (or 14 days before the EQ), and 7 October (the day of EQ). All of these depletions are just below \(-1.5\sigma\), so these are not so conspicuous when compared with the anomaly in Chiba-shi in Figure 2a. However, we can recognize a prolonged period of depletion over three successive days, 24, 25, and 26, after the anomalous day of 23 September.
## 3 Problem Formulation  
In a typical recommendation scenario, the user-item interaction data can be modeled as a bipartite graph:  
\[\mathcal{G}_{I}=(\mathcal{U},\mathcal{I},\mathcal{E}_{I}) \tag{1}\]  
where \(\mathcal{U}\) and \(\mathcal{I}\) denote the sets of users and items, respectively, and \(\mathcal{E}_{I}\in\mathcal{U}\times\mathcal{I}\) represents observed interactions (e.g., clicks, ratings). The interactions are stored in a binary matrix \(\mathbf{R}\in\{0,1\}^{|\mathcal{U}|\times|\mathcal{I}|}\), where  
\[\mathbf{R}_{ui}=\begin{cases}1,&\text{if user u interacted with item i}\\ 0,&\text{otherwise}\end{cases} \tag{2}\]  
To incorporate external semantic information, a knowledge graph (KG) is defined as  
\[\mathcal{G}_{K}=(\mathcal{E}_{K},\mathcal{R}_{K},\mathcal{T}_{K}) \tag{3}\]  
where \(\mathcal{E}_{K}\) is the set of entities (e.g., items, actors, and genres), \(\mathcal{R}_{K}\) is the set of relation types, (e.g., \"belongs to a category\", \"has a tag\", etc.), and \(\mathcal{T}_{K}=\{(h,r,t)\}\) is the set of relational triples, with head entity \(h\), tail entity \(t\), and relation \(r\in\mathcal{R}_{K}\).  
Given both the interaction graph \(\mathcal{G}_{I}\) and the knowledge graph \(\mathcal{G}_{K}\), the objective is to learn low-dimensional embeddings \(\mathbf{e}_{u}\in\mathbb{R}^{d}\) and \(\mathbf{e}_{i}\in\mathbb{R}^{d}\) for each user \(u\in\mathcal{U}\) and item \(i\in\mathcal{I}\), such that a scoring function \(y_{ui}\) estimates the likelihood of user \(u\) interacting with item \(i\):  
\[\hat{y}_{ui}=f(\mathbf{e}_{u},\mathbf{e}_{i})=\mathbf{e}_{u}^{\top}\mathbf{e }_{i} \tag{4}\]  
The final list of personalized recommendations is generated based on \(\hat{y}_{ui}\).  
The task of the recommendation model is to rank unobserved items for each user by predicting their relevance scores and recommending the top-K items.  
Despite recent progress, two major challenges remain in this task:  
* Long-tail sparsity: Many relations in real-world KGs follow a long-tail distribution. Rare relations (e.g., \"coproducer\") occur infrequently, making it difficult to learn meaningful embeddings and resulting in poor generalization for cold-start or niche items.
* Dynamic preference modeling: User preferences are not static but evolve over time. Most models use fixed embeddings that fail to reflect recent behavioral shifts or short-term interests.
## 3 Problem Definition and Methods
### The Proposed SSA-GAN Model  
As shown in Figure 2, our model is an LSTM-based generative adversarial network consisting of a generator and a discriminator. The generator employs an encoder-decoder architecture. Based on the social force concept, the decoder uses several inputs to estimate pedestrian positions at each time step, including the features generated from the social and spatial interaction modules, the hidden states from the LSTM cells representing pedestrian intent, and random noise samples to emulate uncertain fluctuations and enhance the diversity of future trajectory generation. The discriminator, as in a typical GAN, evaluates the realism of the generated trajectory, motivating the generator to generate plausible future trajectories.
## 3 Theoretical Background
### Baseline Models
#### 3.1.1 Stacked Single-Target  
An SST regressor consists of training a Single-Target regressor for each element of the output curve [23], which means that each Single-Target regressor takes as input \(x_{i}\) and estimates \(y_{i}^{j}\). Thus, we need \(M\) regressors that share the same input \(x\) and have as targets a different time step of the output curve \(y\). Under this approach, the multi-output regression problem is broken down into single-output regression problems, and thus, any regression methods could be applied. In mathematical terms, we have that each of the \(M\) Single-Target regressors solves the following problem:  
\[\hat{y}_{i}^{j}=f(x_{i}),\quad j\in\{1,2,...,M\} \tag{1}\]  
where \(f\) represents the chosen regressor. The total output curve is estimated by a global model created by stacking each of the individual predictions: \(\hat{y}_{i}=\left[y_{i}^{1},\hat{y}_{i}^{2},...,y_{i}^{M}\right]\). Due to the significant computational effort that is required to train \(M=1150\) regressors, we  
Figure 2: Representation of output crash responses for the neck y-moment (**left**) and the head acceleration (**right**) for each of the \(N=140\) curves present in our data. The horizontal axis represents the time, where we note that each curve is composed of \(M=1150\) time steps. Note that the first curves may or may not have a second peak starting at \(t\approx 0.9\), while the second may present a second peak of varying amplitude at \(t\approx 1\), which indicates a complex behavior.  
choose to implement each Single-Target regressor as an independent Linear Regressor to predict each of the time steps of the output curve.
## 1 Introduction  
For the management of water resources, monitoring ecologically important fish species is crucial. Pelagic salmonids and cyprinids predate zooplankton in temperate lakes, and affect the species composition and size structure of the zooplankton community, which in turn affects the abundance of planktonic algae [1, 2], and further, influencing water quality and its suitability for public use [3, 4, 5]. The hydroacoustic assessment is an efficient method to estimate the density and size distribution of pelagic fish, and has been used in lakes for more than three decades, and in marine environments for much longer [6, 7]. The available equipment has been developing, and single beam transducers from the early stage are replaced by split beam echosounders. Nevertheless, a transceiver, first presented in 2012 [8], Simrad EK15, has kept the single beam character.  
Historical hydroacoustic data, recorded by means of older equipment are important for comparison with current and future surveys in a climate change perspective, and there is a need for calibration of old to new equipment. A few comparative studies are published, like a comparison of the single beam Simrad EY-M with the split beam Simrad EY200 [9] and with the Simrad EY500 with both single and split beam transducers [10]. The Simrad EY-M could serve as a common reference.  
This study was conducted in Lake Storsjeon in southeast Norway, and the aim was to compare density estimates and target strength distribution of echoes of the pelagic fish community based on two different single beam Simrad echosounders, an EY-M from 1982 and an EK15 from 2015. The effects of different ping repetition frequency and pulse duration on EK15-based density estimates were explored, and density estimates from five former years are also presented as a contribution to monitoring fish density.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 5 Simulation Results
### Optimal Loop Design  
As analyzed above, the ML-KF loop seems to have no advantage over the FPLL when the \(C/N_{0}\) is above 30 dB-Hz. To reduce the computation burden and achieve the optimal performance, a switchable loop is designed to switch between the ML-KF loop and FPLL. When the \(C/N_{0}\) is above 30 dB-Hz, the loop works in FPLL. When the \(C/N_{0}\) is below 30 dB-Hz, the loop switches to the ML-KF loop. The \(C/N_{0}\) of FPLL is estimated by a conventional power ratio method (PRM) [20].  
Figure 11 shows the performance comparison between the optimal loop and FPLL. The \(C/N_{0}\) of ML-KF loop is calculated by (8) and (29) and outputs at a rate of 50 Hz. To improve the estimation accuracy, estimation values are averaged in groups of 20. Therefore, the actual output rate of \(C/N_{0}\) is 2.5 Hz. The receiver moves at the vehicle-level velocity. The initial \(C/N_{0}\) is 45 dB-Hz. It drops to 22.5 dB-Hz form 10 s to 20 s. Then, it rises to 45 dB-Hz again from 30 s to 40 s. The frequency errors of the optimal loop and single FPLL are compared. It can be seen from Figure 11 that the FPLL outputs big errors when \(C/N_{0}\) is low. However, the optimal loop switches to ML-KF loop at 16.2 s and switches to FPLL at 33.2 s. Therefore, it can maintain high accuracy during the whole process. It can be seen from the estimated \(C/N_{0}\) curve that the proposed \(C/N_{0}\) estimation method is also effective.  
Figure 10: Performance comparison in vehicle-level dynamic circumstances. (**a**) Tracking probability; (**b**) Frequency error; (**c**) Bit error rate.
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 4 Results and Evaluation
### HDR-to-xHDR Conversion
This adjustment ensures a more controlled and fitting increase in brightness during the conversion process. Therefore, we propose to compute the value of \(m_{\text{i}}\) for HDR-to-xHDR conversion through the following formula:  
\[m_{\text{i}}=0.214\times\frac{\text{max\_ldr\_lum}}{\text{max\_hdr\_lum}}, \tag{7}\]  
where max_ldr_lum denotes the maximum luminance in an LDR video sequence, and max_hdr_lum represents the peak luminance value used in mastering the HDR video sequence being processed. Figure 15: Flowchart of the proposed pipeline for HDR-to-xHDR conversion. A 1000 cd/m\({}^{2}\) HDR image is up-converted to extended high dynamic range (xHDR) with a peak luminance up to 6000 cd/m\({}^{2}\). Since the highlight regions of HDR content are primarily located near the highest brightness levels [58; 59], most of them are expanded to the highest luminance levels during the dynamic range expansion operation. Due to the limitations of peak brightness, some highlights have been compressed to the boundaries of the peak brightness, nearing saturation. Consequently, when converting HDR to xHDR, the highlight detection thresholds in the boosting step should differ from the original values used in LDR-to-HDR conversion. Specifically, they should be set closer to the maximum luminance value. This adjustment ensures the preservation of the original artistic intent regarding highlights, selectively enhancing the brightness only in those highlights presumed to be compressed due to peak brightness constraints in the original HDR content. Consequently, the highlight boosting step was modified in such a way that pixels are considered as highlights if their luminance value exceeds 0.96 \(\times\) max_lum or any of their RGB channels has a value higher than 0.98 \(\times\) max_hdr_lum, where max_hdr_lum represents the luminance level used in the HDR mastering obtained from the video metadata. Figure 16 shows examples of HDR frames alongside their corresponding expansion maps computed using this approach.
# Person Search via Deep Integrated Networks
## 3 Overview of the Deep Integrated Networks for Person Search
### Pedestrian Detection Network
#### 3.1.3 ROI Pooling and Classification Layer  
After the proposals were predicted, their corresponding features were extracted for further classification to obtain the final detection result. The size of the input for the classification network [16, 71] is generally pre-set and fixed, which limits the aspect ratio and the scale of the input [72]. However, the scales--i.e., the width and height--of proposals varied. Fitting the proposal's scale to the fixed size for the classification network by either cropping [16, 65] or warping [68] may result in a region drop or geometric distortion [71]. Hence, inspired by Spatial Pyramid Pooling [71], one ROI pooling layer was proposed in [35], which received two kinds of information: the coordinates of the proposals and the feature map from the sharing convolution layer. The ROI pooling layer would extract the features for each proposal before classification. Three parameters--namely pool_w, pool_h and spatial_scale--were set to correspond to the output size of the ROI pooling layer in terms of the width, height and scale mapped between the proposal and feature maps to obtain a fixed-length representation. Max pooling was applied for each pooled region after mapping.  
Two fully-connected (FC) layers and one softmax layer were set in the end of the pedestrian detection network. The softmax layer output the probability belonging to the pedestrian class. A proposal with a probability larger than 0.9 was kept, and linear regression for the bounding box as shown in Equations (7) and (8) was applied to obtain the refinement coordinate.