esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Supervised Approach for Optimizing Segmentation  
The supervised approach was employed to optimize the parameters (i.e., scale, shape, and compactness) of the multiresolution segmentation algorithm for landslide identification and for differentiation from non-landslides (bare soil, cut slope, and vegetation). The optimized parameters rapidly increased the accuracy of classification to the optimum level by delineating the segmentation boundaries of the landslide. The application of optimized segmentation parameters allowed for the spatial and textural identification of features (landslide and non-slides). In our proposed method, accurate segmentation results should be first obtained prior to performing subsequent steps.  
The optimal parameters of the multiresolution segmentation algorithm were obtained. The selected values for the three parameters are shown in Table 3. The initial segmentation parameters set in the supervised approach were 50, 0.1, and 0.1 for scale, shape, and compactness, respectively. After 100 iterations with these initial values, the optimal values obtained for scale, shape, and compactness  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimized Parameter** & **Suitable Value** & **Description** \\ \hline Minibatch size & 126 (RNN) & Number of training cases over which the \\  & 64 (MLP-NN) & Adam update is computed. \\ Loss function & categorical cross-entropy & The objective function or optimization score \\  & & function is also called as multiclass legless, \\  & & which is appropriate for categorical targets. \\ Optimizer & Adam & Adaptive moment estimation \\ dropout rates & 0.6 & Dropping out units (hidden and visible) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimized model hyper-parameters; RNN: recurrent neural networks, MLP-NN: multi-layer perceptron neural networks.  
Figure 7: Architecture of the RNN model.  
were 75.52, 0.4, and 0.5, respectively in the analysis area. Meanwhile, the test area values were 100, 0.45 and 0.74, respectively. Figure 8a,b show the initial and optimal segmentation processes. The results of optimized segmentation accurately delineated landslide objects in the analysis and test areas.
# Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
## 2 Related Work
### Reinforcement Learning-Based Algorithms  
Reinforcement learning (RL) has emerged as a powerful algorithm for multi-agent task allocation, allowing agents to adapt and optimize task distribution strategies through real-time interactions with dynamic environments. Its ability to manage complex, evolving scenarios and enhance decision-making processes makes it especially suitable for multi-agent systems [34; 35].  
For instance, [PERSON] et al. proposed a multi-agent deep reinforcement learning algorithm for formation control, which integrates an attention mechanism and adaptive accuracy to enhance agent coordination and precision [5]. Similarly, [PERSON] et al. developed a joint optimization algorithm for data acquisition and trajectory planning in UAV-assisted IoT networks. Their algorithm maximizes energy efficiency while adhering to mobility, safety, and task constraints, framing the problem as a constrained Markov decision process, and using multi-agent deep RL to optimize agent movement policies [36]. These algorithms demonstrate the potential of RL in optimizing agent behaviors for specific applications with controlled constraints.  
[PERSON] et al. introduced a federated deep RL algorithm for agent deployment and resource allocation in 6G networks, enabling agents to make real-time decisions based on local observations while pursuing a global optimal solution [37]. Their algorithm significantly improves network throughput and convergence, as demonstrated in simulations. Moreover, [PERSON] et al. developed a multi-agent collaborative RL algorithm for agent resource allocation in wireless networks, focusing on optimizing interference management and network capacity. This framework integrates federated learning to facilitate data sharing and cooperation among agents, outperforming traditional RL algorithms in simulation [23].  
**Comparison with GA-PPO:** Unlike the aforementioned RL-based algorithms that rely on centralized or federated frameworks, GA-PPO employs a decentralized algorithm, offering improved scalability and robustness. While [PERSON] et al.'s [5] algorithm enhances coordination through an attention mechanism, GA-PPO's use of genetic algorithms for exploration and proximal policy optimization (PPO) for dynamic policy refinement provides greater flexibility and adaptability. This allows GA-PPO to adjust in real time without the need for centralized control. In comparison to [PERSON] et al.'s [36] constrained Markov decision process, which is limited to specific task constraints, GA-PPO does not require predefined constraints, enabling it to operate across a wider range of environments. Furthermore, while [PERSON] et al.'s [37] federated RL algorithm relies on local coordination between agents, GA-PPO eliminates the need for central coordination, making it more robust to network failures. This decentralized nature makes GA-PPO particularly well suited for large-scale applications, where adaptability and resilience are critical.
# Decentralized Inner-Product Encryption with Constant-Size Ciphertext
## 2 Preliminaries
### Definition of Decentralized Inner Product Encryption
#### 2.3.3 Security Model  
The security definition used in our manuscript is the security against indistinguishability under selective chosen-plaintext attacks (sIND-CPA). \"Indistinguishability\" means that given a ciphertext, which is the encryption of one of two messages chosen by an adversary, the adversary tries to tell which of the two messages is encrypted. In addition, \"chosen-plaintext attacks\" means that an adversary is allowed to obtain the ciphertext for the plaintext of its choice. Finally, \"selective\" means that an adversary chooses a target vector and submits to the challenger before Setup phase.  
**Definition 3** (The sIND-CPA Security).: _Let \(\mathcal{A}\) be a probabilistic polynomial-time adversary. We define our security via the following interactive game between \(\mathcal{A}\) and a challenger \(\mathcal{C}\):_  
* _Initialization_. \(\mathcal{A}\) chooses an attribute vector \(\vec{Y}^{*}=(y_{1}^{*},y_{2}^{*},\ldots,y_{\ell}^{*})\) and sends \(\vec{Y}^{*}\) to \(\mathcal{C}\).
* _Setup_. \(\mathcal{C}\) runs the Setup algorithm to generate \(PK_{i}\) and \(MSK_{i}\), where \(1\leq i\leq n\), is the index of authority. \(\mathcal{C}\) sends \(PK_{1},\ldots,PK_{n}\) and \(MSK_{1},\ldots,MSK_{n-1}\) to \(\mathcal{A}\).
* KeyExtract oracle: \(\mathcal{A}\) sends a predicate vector \(\vec{X}\) and a global identity \(GID\) to \(\mathcal{C}\), and \(\mathcal{C}\) returns the private key of \(\vec{X}\). There is a restriction, that is, \(\langle\vec{X},\vec{Y}^{*}\rangle\
eq 0\).
* _Challenge_. \(\mathcal{A}\) submits two distinct messages \(M_{0},M_{1}\) of the same length to \(\mathcal{C}\). \(\mathcal{C}\) then randomly chooses \(\beta\in\{0,1\}\) and generates ciphertexts \(C^{*}=Encrypt(pp,\{PK_{i}\}_{i=1,\ldots,n},M_{\beta},\vec{Y}^{*})\). Then, \(\mathcal{C}\) sends \(C^{*}\) to \(\mathcal{A}\).
* _Phase2_. Same as Phase1.
* _Guess_. \(\mathcal{A}\) will output a bit \(\beta^{\prime}\in\{0,1\}\) and win the game if \(\beta^{\prime}=\beta\). The advantage of \(\mathcal{A}\) winning the game is defined as: \[\mathbf{Adv}_{\mathcal{A}}^{\text{sIND-CPA}}=\bigg{|}Pr[\beta^{\prime}=\beta] -\frac{1}{2}\bigg{|}.\] A DIPE scheme is sIND-CPA secure if for all PPT adversaries \(\mathcal{A}\), \(\mathbf{Adv}_{\mathcal{A}}^{\text{sIND-CPA}}\) is negligible.
# A Back-to-Back Gap Waveguide-Based Packaging Structure for E-Band Radio Frequency Front-End
###### Abstract  
This paper presents our research on an E-band Radio Frequency (RF) front-end packaging structure based on back-to-back gap waveguide (GW). This design effectively mitigates the impact of air gaps on performance and offers the advantage of large assembly tolerances. Additionally, its back-to-back structure enables structural stacking, which can reduce the overall packaging size. In terms of functionality, the structure integrates hybrid couplers, bandpass filters, and amplifier packaging structures. Notably, the hybrid couplers provide high port isolation, facilitating a higher isolation duplex function by simply connecting high-order bandpass filters at the output ports without the need for additional optimization. Furthermore, these couplers also serve as power dividers/combiners. When combined with the H-plane amplifier packaging structures, the output power of the module is theoretically increased by 3 dB. Based on the measurements, the results indicate that this structure operates within the frequency ranges of 71-76 GHz and 81-86 GHz. The common port return loss is below 12 dB, while the in-band insertion loss is less than 2.26 dB and 2.42 dB, respectively. These findings demonstrate excellent electrical performance and suitability for E-band communication systems.  
Keywords:bandpass filter; E-band; gap waveguide; power combiner _micromachines_
# Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions
## 4 Experiments
### Measurement Test-Bed
In order to eliminate the effects of the millimeter wave band converter's I/Q imbalances used in our test-bed, the frequency shifting is performed as follows, with the overall frequency plan illustrated in Figure 7. The broadband complex baseband OTFS signal with a sampling frequency of 512 MHz is first digitally interpolated inside the ZCU111 board to match the DACs' sampling frequency of 4.096 GHz. Then, it is  
Figure 6: Photo of assembled test-bed with the power amplifier and low noise amplifier subsystems mounted on the tripods. digitally shifted by 600 MHz, the I/Q corrections are applied, and then the resulting signal is upconverted to the millimeter wave band with a center frequency of the mixer equal to 59.6 GHz. This results in a useful signal centered around 60.2 GHz (59.6 GHz + 0.6 GHz) and its (partially compensated) image centered at 59.0 GHz (green color in Figure 7). The center frequency of the receiver mixer is set to 59.0 GHz. The downconverted signal is sampled by the ZCU111 board with a sampling frequency of 4.096 GHz, digitally shifted by \(-1200\) MHz, filtered, and decimated to the resulting baseband sampling frequency of 512 MHz. The receiver's I/Q imbalances could also be compensated, but as the receiving image band is centered around 57.8 GHz (59 GHz \(-\) 1.2 GHz, see the red-dotted rectangle in Figure 7), where there is no transmitted signal, this is of no concern.
## 2 Proposed STPA Configuration for JCAS, Its Design Pipeline, and Performance
### Proposed STPA and Its Design Pipeline
#### 2.2.2 Design Pipeline  
Note that the inherent problems of array tiling for the JCAS aperture in order to obtain the smallest SLL and a sparse irregular geometry for the accurate angle estimations are  
Figure 1: The configuration for JCAS in BS.  
Figure 2: The configuration of antennas at the BS.  
problems of non-convex optimizations and are difficult to solve. Instead, we employ local optimizations based on the foreknowledge in two steps, as it is shown in the flowchart of Figure 3. The main idea of this proposed local optimization based design pipeline is based on two scales of irregularity, namely the phase centers of tiles in the whole aperture (global scale), and the geometry of subarrays (local scale).  
* **Step 1: Global scale tiling**. Initially, a large CUPA is tiled for the minimum SLLs in the radiation pattern. The tiling problem is formulated based on maximum entropy in the distribution of the phase centers of the tiles in the array. In the schematic aperture shown in Figure 2, assume that \(N_{\text{sub}}\) antennas of JCAS aperture are grouped in tiles and fed by power dividers. The center of mass of such a tile could be considered as the center of phase for radiation from the whole tile. The irregularity in the distributions of these phase centers is measured with an information-theoretic entropy-based objective function, i.e., if the entropy of the phase centers at each row and column of the aperture is maximized, then the resulting phased array aperture has a desired SLL while scanning. To obtain more mathematical insight into tiling based on the maximum entropy, refer to Ref. [28].
* **Step 2: Local scale subarray irregularity**. The resultant tiled array can still be thinned by grouping some tiles in subarrays and removing others. A subarray is determined by a circle circumventing its tiles. The centers of these circles (subarrays) are obtained by the geometry of the sunflower array since the positions of elements are controllable, although it is an aperiodic sparse array (see Appendix A). Essentially, other deterministic sparse arrays, e.g., the circular array, could be utilized since this only performs as an initialization for the next convex optimization [29]. The radius of a circle is a thinning parameter for the aperture, which can be tuned to reach the desired SLL with a minimum number of tiles. It is assumed that the phase centers of tiles are still distributed with a maximum entropy after thinning of the aperture, but the inter-distances among subarrays increases the SLL, however, the resultant empty space between subarrays can be exploited to optimize the location of subarrays efficiently to decrease the SLL (see Appendix B).  
When the JCAS aperture with the desired SLL and beamwidth is achieved, we compare its performance with conventional CUPA and SPA.
# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
## 2 Materials and Methods  
### Study Area  
The UAV RGB imagery of the maize pest _Spodoptera frugiperda_ was captured at the Experimental Station (117.552616, 34.309942) of the China University of Mining and Technology, Xuzhou city, Jiangsu Province, China. Image are shown in Figure 1. The experimental site was invaded by the grass moth because of a later maize planting cycle than the surrounding fields. When we started the data collection, the larvae in the field were in a transition phase from low to medium age.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 2 Problem Formulation and Proposed Algorithm Description
### Improved Square Sine Wave Interpolation Algorithm  
The SSWI algorithm extracts the periodic components of the mixed pulse stream through the FFT algorithm [22]. The algorithm can suppress the influence of harmonics, interfering pulses, and missing pulses. In addition, the number of FFT points is only related to the sampling time and interpolation sampling rate. It is not affected by the pulse number and the PRI range of the input pulse stream, so the SSWI algorithm can still be computed efficiently even under dense pulses and a wide PRI range. However, the SSWI algorithm is unsuitable for staggered signals because its sub-PRIs will seriously affect the frame period estimation. The SSWI algorithm is also sensitive to PRI jitter.  
An improved SSWI algorithm is proposed to solve the above problems. A clustering-based SSWI method is proposed to solve the jitter error. In addition, a threshold criterion is designed to suppress the interference of sub-PRIs on the frame period estimation. The proposed algorithm is described in detail below.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 2 Materials and Methods
### Modeling  
#### 2.4.1 Focal Buffers  
There are both direct and indirect ways the LULCs affect UHI in an area, and the configuration of LULCs within varying distances and clusters is important to understand--previous analyses have described a distance-decay effect in relation to LULCs and their impacts on temperatures at a given location [28; 29]. This is, in effect. a realization of [PERSON]'s First Law of Geography, which states that \"everything is related to everything else, but near things are more related than distant things\" [30]. In order to reflect this principle in our analysis, we transformed each band in the analysis with a moving window average of varying spatial distances. This technique is not novel to our study and has played a central role in Land Use Regression literature [31; 32; 33; 34]. The product of this analysis (henceforth referred to as a 'focal buffer') was a new raster dataset in which the pixel values represent the average value within a stated distance. For example, if the specified distance was 50 m and the specified band was \"Band 2\", then the value of the output pixels from this analysis would reflect the average values in band 2 within 50 m. This analysis created focal buffers for each of the 10 analyzed bands (bands 2, 3, 4, 5, 6, 7, 8, 8A, 11, 12) at each of the distances defined in [PERSON] and [PERSON] (2017) (e.g., 50 m, 100 m, 150 m, 200 m, 250 m, 300 m, 350 m, 400 m, 450 m, 500 m, 600 m, 700 m, 800 m, 900 m, 1000 m). The results of these analyses were 150 focal buffer rasters for each study location, which when combined provided us with a multi-distance, multi-spectral abstraction of LULC characteristics. The transformation of Sentinel-2 bands into these focal buffers allowed for a reduction in error associated with spatial autocorrelation as well as increasing model performance [34].
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 1 Introduction
Next, Section 4 describes a simplified detection scheme and two transceiver integration schemes under direct and coherent detections. In Section 5, the technological and component options for different transmission distances are discussed. Finally, our conclusions and outlook are summarized in Section 6.
## 3 Results Analysis
### Experimental Results for Sleep Measurements
#### 3.2.1 Idle System Test Results  
As in the previous section, measurements were performed on an idle system to establish a baseline when comparing the different load scenarios. As shown in Figure 10, median values of miss times during _HPTSleep_ function on all platforms, except Virtual Box, are in the same range between 80 and 116 ns. The invocation of _uSleep()_ function shows miss times that are almost three orders of magnitude greater than _HPTSleep_ function: 62 \(\upmu\)s compared to 82 ns on Host OS (Figure 10a); 109 \(\upmu\)s compared with 94.5 ns on Xen (Figure 10b); 131 \(\upmu\)s compared with 5.12 \(\upmu\)s on Virtual Box (Figure 10c); 97 \(\upmu\)s compared with 116 ns on QEMU (Figure 10d); and 63.1 \(\upmu\)s compared with 95 ns on VMware (Figure 10e). Measurements with _HPTSleep_ operations are distributed near to log-normal distribution for Host OS, Xen, QEMU and ESXi, which is shown below in a CCDF plot. Figure 11 shows that the respective distributions of the data are grouped together by the chosen sleep method. _HPTSleep_ for Xen, Host OS, ESXi, and QEMU are situated in the left part of the plot near median values, which is an indication of log-normality.  
Figure 10: Measurements of miss time for the Idle testbed on: (**a**) Host OS; (**b**) Xen; (**c**) Virtual Box; (**d**) QEMU; and (**e**) VMware ESXi.
## 4 Temperature Finite Element Model and Test Comparison
### Temperature Finite Element Model
#### 4.1.1 Temperature Field Simulation Parameters  
The heat exchange between the surface of a structural surface and the surrounding environment is a complex process in the natural environment, involving radiative heat exchange and convective heat transfer. Radiative heat exchange is typically related to solar radiation, sky radiation, and the radiation from the ground and other objects, while convective heat transfer is mainly related to air in motion. In engineering practice, these two types of heat exchange are usually considered as a combined factor, namely, the comprehensive heat transfer coefficient. [PERSON] et al. [43] indicates that the comprehensive heat transfer coefficient on the surface of bridges mainly depends on the wind speed at the structural surface, demonstrating the impact of wind speed on heat exchange. As wind speed increases, it leads to an increase in convective heat transfer, enhancing the efficiency of the comprehensive heat transfer. The comprehensive heat transfer coefficient is as follows [43]:  
\[h=13.5+3.88v \tag{12}\]  
where \(v\) is the wind speed at the structural surface, and \(h\) is the comprehensive heat transfer coefficient.  
In the numerical simulation of heat exchange for U-shaped girder bridges, parameters such as the density, heat conductivity, and specific heat of concrete are relatively stable and can be determined based on the material of the test bridge and the climatic conditions of the region, with specific values found in Table 1. In addition, solar radiation is calculated using the improved ASHRAE clear sky model, and atmospheric temperature and wind speed are based on the standards of the national meteorological administration [44].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Simulation** \\ **Parameter** \\ \end{tabular} } & \multirow{2}{*}{**Density (kg/m\({}^{3}\))**} & \begin{tabular}{c} **Coefficient of** \\ **Heat** \\ **Conductivity** \\ **w/(m\(\cdot\)k)** \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} **Specific Heat** \\ **J/(kg\(\cdot\)k)** \\ **b**/(m\({}^{2}\cdot\)k) \\ \end{tabular} } &
\begin{tabular}{c} **Total Heat** \\ **Exchange** \\ **Coefficient** \\ **h/(m\({}^{2}\cdot\)k)** \\ \end{tabular} \\ \hline Parameter value & 2650.0 & 2.0 & 930.0 & 13.5 + 3.88\(v\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of temperature field.  
Figure 9: Finite element model of the U-shaped girder bridge: (a) three-dimensional model; (b) cross section.