esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compression-Based Toolkit for Modelling and Processing Natural Language Text
## 4 Sample Applications Provided by the Toolkit
### Text Classification  
The method ClassifyFile in Algorithm 6 performs text classification on a text file. It classifies the file \(SourceFile\) by finding which model compresses the text in the file best. This symbol-based method of text classification has been found to be competitive in many classification tasks compared to standard feature-based (word) classification schemes often with state-of-the-art results [31]. It has been used to accurately identify the natural language in which the source text is written [32], and to identify the most likely author of the text [17], for example. Recent results have shown that it can classify gender [33], recognise emotions [34] and even identify whether the text has been written by a person who is dyslexic or not [35].  
```
1 method ClassifyFile (\(SourceFile\))
2 Best\(Codelength\gets 0.0\);
3 Best\(ModelTitle\leftarrow\) NULL;
4 Source\(Text\leftarrow\) TXT_load_text (\(SourceFile\));
5 TLM_reset_model ();
6 whileModel\(\leftarrow\)TLM_next_model ()do
7\(Codelength\leftarrow\) CodelengthText (\(SourceText\),\(Model\));
8 if(Best\(Codelength\) = 0.0)or(\(Codelength\) \(<\)Best\(Codelength\))then
9 Best\(Codelength\gets Codelength\);
10 Best\(ModelTitle\leftarrow\) TLM_get_title (\(Model\));
11
12 end if
13 TXT_release_text (\(SourceText\));
14 returnBestModelTitle;
15
16 end for
```  
**Algorithm 6**A compression-based method for text classification  
The method calls TLM_reset_model and TLM_next_model to cycle through all the models that have previously been loaded into memory (lines 5 and 6). The compression codelength is calculated on line 7 by calling the CodelengthText method that was defined in Algorithm 4. TXT_load_text is used to load the source file once into the text object \(SourceText\) (line 4). Note that the routine TXT_load_text appends the sentinel symbol onto the end of the text.  
The method works by cycling through all the models to compute their compression codelengths on the source text, and returns the title (obtained using the call to TLM_get_title on line 10) of the best performing model.
## 4 Results and Discussion
### Findings Based on Research Questions  
In response to the research question raised in the introductory section, the following conclusions were drawn based on the experiments conducted.  
RQ1: How effective is the FPA algorithm in comparison to baseline classifiers in Android malware detection?  
It was observed from the experimental results that FPA produced significantly improved detection performance when compared with the baseline classifiers. This superior detection performance was observed on both the Malgenome and Drebin datasets.  
RQ2: How effective are the enhanced variants of FPA (Cas_FPA and RoF_FPA) in Android malware detection?  
According to the experimental results and analyses, Cas_FPA and RoF_FPA performed better than FPA alone on both the original and SMOTE-balanced Malgenome and Drebin datasets. Additionally, it was observed that the deployed data sampling method resolved the latent class imbalance problem and subsequently improved the detection performances of the models evaluated, especially FPA, Cas_FPA, and RoF_FPA.  
RQ3: How well do the proposed FPA and its variants perform as compared to current state-of-the-art methods in Android malware detection?  
It was gathered from the experimental results that the proposed FPA, Cas_FPA, and RoF_F_FPA algorithms, in most cases, had superior detection performance compared to existing state-of-the-art Android malware detection models.
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 2 Radar Signal Modeling
### Modeling Settings  
First, the semi-empirical frozen soil-water dielectric mixing model was used to estimate the dielectric constant for the three same soil types defined in [PERSON] et al. [14] (Silty-clay, Silt-loam and Sandy-loam, Table 1) with a soil temperature \(T_{s}\) between 253 \({}^{\circ}\)K and 283 \({}^{\circ}\)K. The simulated dielectric constants were then used in the IEM_B to estimate the backscattering coefficients in VV polarization, with a radar frequency of 5.405 GHz, an incidence angle of 40\({}^{\circ}\), soil moisture \"_m\({}_{v}\)_\" of 30 vol%, and soil roughness \"_Hrms_\" of 1 cm.
## 4 Numerical Experiment Results and Discussions
### Performance of the Overdetermined Optimal Filter for Single Noise Incidence  
In the previous subsection, we evaluate the performance of the full-rank optimal filters and found that in the range of incidence angles from \(-90^{\circ}\) to \(90^{\circ}\), no single full-rank filters could achieve identical attenuation performance as the exact optimal filters obtained for each incidence. Therefore, we shall consider the overdetermined optimal filter method, as expressed in (18). In Figure 11, we compare the full-rank optimal filter case 1 with the overdetermined optimal filter that selects all 181 incidences from \(-90^{\circ}\) to \(90^{\circ}\) at \(1^{\circ}\) interval (termed as \"case 1\"). Although the overdetermined optimal method does not outperform the full-rank filter at all incidences, its attenuation performance is, on average, superior across the entire range of incidences, especially at higher frequencies. Furthermore, we investigate the influence on the attenuation performance with respect to the number of incidences used in the overdetermined factorization. On this note, we consider three overdetermined formulations, which employs (i) 181 incidences at \(1^{\circ}\) interval, (ii) 19 incidences at \(10^{\circ}\) interval, and (iii) 10 incidences at \(20^{\circ}\) interval. Based on the results in Figure 12, we can observe that the performance of the overdetermined optimal filter case 2 with 19 incidences resembles the case 1 with 181 incidences, whereas the case 3 with 10 incidences yielded inferior overall performance. Thus, we can compute the overdetermined optimal filter with only 19 incidences at \(10^{\circ}\) interval.  
Figure 10: Noise reduction performance of two cases of the full-rank optimal filters that only consider the negative and positive directions, respectively, at frequency (**a**) 400 Hz, (**b**) 1000 Hz.  
Figure 9: Noise reduction performance of two cases of the full-rank optimal filters that only consider the negative directions, at frequency (**a**) 400 Hz, (**b**) 1000 Hz.
# Investigation into the Effect of Atmospheric
## 1 Introduction
(Source: [[http://visibelearth.nasa.gov/view.php?id=46823](http://visibelearth.nasa.gov/view.php?id=46823)]([http://visibelearth.nasa.gov/view.php?id=46823](http://visibelearth.nasa.gov/view.php?id=46823)); Credit: NASA map by [PERSON], based on data from the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and on the GEOS-Chem model. Caption by [PERSON] and [PERSON]). signals are refracted by the atmospheric particulate matters or not. There is no standard model of the atmospheric PM\({}_{2.5}\)/PM\({}_{10}\) particles on GNSS signals in GNSS data processing, and the authors could only find an approximate generic model of non-gaseous atmospheric constituents (<1 mm) [10] in the literature. This paper investigates the effect of the concentration of PM\({}_{2.5}\)/PM\({}_{10}\) particles on GPS signals and validates the aforementioned approximate generic model with a carrier-to-noise ratio (CNR) based empirical method. Note that GPS is used in this investigation because it is the only GNSS providing global coverage with code division multiple access (CDMA) signals and that is in its full operational capacity (FOC) currently. The frequency division multiple access (FDMA) based GLONASS is not used in this investigation because physical interaction is usually frequency dependent. Moreover, the long constellation geometry repeatability (i.e., 8 days) of GLONASS would cause greater meteorological uncertainty in the proposed empirical method of this paper. The objective of this paper is to investigate any effect of the concentration of atmospheric PM\({}_{2.5}\) and PM\({}_{10}\) on GPS signals based on single-station GPS dual-frequency data collected on the roof of the Science and Engineering Building (SEB) at the University of Nottingham Ningbo China (UNNC). This paper is divided into five sections: Section 1 presents the introduction and background of this research. Section 2 describes briefly the physics relevant to GNSS atmospheric refraction, and the approximate generic model of non-gaseous atmospheric constituents. The methodology of this investigation and the experimental data description are given in Section 3. Processing results and analysis are presented in Section 4. Finally, concluding remarks are given in Section 5. Figure 2: Satellite map of PM\({}_{2.5}\) over China in 2008–2010 based on data from the MISR instrument on the Terra satellite, the MODIS instrument on the Terra and Aqua satellites, and a chemical transport model called GEOS-Chem. (Source: [[http://earthobservatory.nasa.gov/IOTD/view.php?id=77495](http://earthobservatory.nasa.gov/IOTD/view.php?id=77495)]([http://earthobservatory.nasa.gov/IOTD/view.php?id=77495](http://earthobservatory.nasa.gov/IOTD/view.php?id=77495)); Credit: NASA Earth Observatory image by [PERSON], using data provided by [PERSON], [PERSON] and [PERSON], Yale Center for Environmental Law & Policy. Caption by [PERSON]).
## 3 Materials and Methods
### Experimental Design
#### 3.5.4 Baselines  
To comprehensively evaluate the performance of the proposed method, federated learning (FL) [42], secure multi-party computation (MPC) [43], homomorphic encryption (HE) [44], and trusted execution environment (TEE)-based methods [45] were selected as comparative baselines. These approaches offer different privacy-preserving computation solutions, each with distinct technical characteristics and applications. For federated learning (FL), the FedAvg algorithm was implemented, with local training rounds set to 5 and global aggregation occurring every 10 rounds. Each client trains a model locally using stochastic gradient descent (SGD) with a learning rate of 0.01, and the aggregated model is updated at the central server. To ensure privacy, differential privacy (DP) noise was added during model updates, and communication overhead was optimized through model compression techniques. For secure multi-party computation (MPC), a secret-sharing-based protocol was employed, where data from each participant were split into multiple shares distributed among computing nodes. Secure summation and multiplication operationswere implemented using additive secret sharing, and computations were performed over a modular arithmetic field. The MP-SPDZ framework was used to simulate real-world privacy-preserving computations. For homomorphic encryption (HE), the BFV encryption scheme was adopted, enabling arithmetic computations on encrypted data. Additive and multiplicative homomorphism operations were supported, with polynomial modulus and ciphertext modulus set to balance security and computational efficiency. To reduce decryption overhead, batching techniques and ciphertext packing were applied. The Microsoft SEAL library was used for implementation. For TEE-based methods, Intel SGX was utilized to create a secure enclave, allowing encrypted data to be loaded, processed, and decrypted within a hardware-isolated environment. The model inference was conducted inside the enclave, ensuring confidentiality even against privileged system attacks. The Gramine framework was used for secure execution, and performance was optimized by minimizing enclave transitions and utilizing efficient memory access patterns. All baseline methods were evaluated on the same dataset and in the same environment to ensure fair comparisons.
## 2 Materials and Methods
### Determination of Total Phenolic Content  
Total phenolic content was analyzed by using the Folin-Ciocalteu method [16], with slight modification. The coffee extract was centrifuged at 842\(\times\)\(g\) for 10 min. The supernatant was diluted 20 times for espresso or five times for drip so that the measured values for each sample were within the calibration curve range. A diluted sample of 100 \(\mu\)L was added to a mixture of 200 \(\mu\)L of 0.9 N Folin-Ciocalteu's reagent and 800 \(\mu\)L of 20% Na\({}_{2}\)CO\({}_{3}\) and then incubated for 30 min at room temperature (RT). The absorbance was determined at 765 nm using a microplate reader (Spectramax M2E, Thermo Fisher Scientific, Waltham, MA, USA). A standard curve (50-500 \(\mu\)g/mL, R\({}^{2}\) = 0.9972) was constructed using gallic acid (Sigma Aldrich, St. Louis, MO, USA), and results were expressed as \(\mu\)g gallic acid equivalent (GAE)/mL.
# Structural Design, Simulation and Experiment of Quadruped Robot
## 3 Forward and Inverse Kinematic Solving
### Common Gait Patterns for Quadruped Robots  
Currently, the five most commonly used gaits are the walk, trot, pace, bound and gallop gait. The trot gait is the most common. It is a typical \"two-beat\" gait, where the two diagonal legs have identical movement characteristics, with the left front leg and right rear leg in unison and the right front leg and left rear leg in unison, as shown in Figure 11. Theoretically, the duty cycle coefficient in this gait is not subject to any restriction, and \(0<\beta<1\). Thus, the robot has a wide range of speeds and can choose different speeds for different terrains. In addition, some scholars have conducted experiments on the amount of oxygen consumed by horses in different gaits (walk, trot and gallop) using different movement speeds [37], and studies show that the trot gait is relatively low in oxygen consumption over a relatively large range of speeds, which also suggests that this gait has theoretically high energy efficiency [38; 39].  
Depending on the value of the duty cycle factor, the diagonal trot gait can be divided into three types. When \(\beta<0.5\), the robot has a brief vacant phase (the moment when all four legs are in the swing phase) and the speed of movement increases significantly, but the presence of the vacant phase also greatly increases the difficulty of control. When \(\beta>0.5\), the robot has four sets of supports when the swing leg switches, which ensures stability but reduces the speed of movement. When \(\beta=0.5\), two sets of diagonal legs switch seamlessly between the oscillating and supporting phases, which is somewhere in between; it also provides a balance between speed and robot stability, making it the dominant gait choice.
# Demosaicing by Differentiable Deep Restoration
## 4 Experiments
### Ablation Studies
#### 4.2.3 Effect of Patch-Based Optimization  
In our method, the combination coefficients of different bases are solved at overlapping local image patches. Instead of solving these coefficients patch by patch, an alternative way  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Preprocessing** & **Optimization** & **Basis Number** & **PSNR** \\ \hline Rearranging & No & — & 36.68 \\ Interpolation & No & — & 36.79 \\ PSC & No & — & 36.90 \\ PSC & Global & 4 & 37.61 \\ PSC & Global & 8 & 37.64 \\ PSC & Global & 12 & 37.67 \\ PSC & Global & 16 & 37.43 \\ \hline PSC & Local & 4 & **37.71** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study for variations on the test set of the MIT dataset [9].  
Figure 5: The performance of our method with different number of bases. The left axis is the PSNR on the test set of MIT dataset [9]. The right axis is the extra FLOPs caused by the differentiable closed-form solver.  
is to solve the set of combination coefficients for the whole image as in the work LSM [4]. We refer this kind of optimization as 'Global'. As shown in Table 2, our method with coefficients solved at local windows (which is referred as 'Local') generates higher PSNR than 'Global'. We further vary the number of bases for the 'Global' method. While a larger number of bases sometimes improves the result quality and produces similar results to our local method, we also find it makes the training process less stable in experiments.
###### Abstract  
In Northeast China, due to long-term, high-intensity continuous cultivation of black soil, the practice of \"overuse with insufficient nurturing\" has led to severe degradation of the black soil. Straw return is a crucial strategy for enhancing soil organic matter (SOM). However, the mechanism of combing straw return with different tillage methods on black soil microbial community structure and soil organic carbon (SOC) fractions remains unclear. A field experiment was conducted in black soil using four tillage treatments: conventional tillage without straw return (CK), no tillage with straw incorporation (NTS), rotary tillage with straw incorporation (RTS), and deep tillage with straw incorporation (PTS). Corn yield and the contents and fractions of SOC were measured, whereas the microbial structure at different soil depths was assessed by high-throughput sequencing technology. Meanwhile, the correlations between microbial diversity, changes in SOC fractions, and corn yield were analyzed. As a result, the straw return treatments significantly increased the contents of SOC in the 0-20 cm soil layer (up to 19.82 g kg\({}^{-1}\) under RTS) and its labile fractions, enhanced soil microbial diversity (with a 7.03-25.14% increase in the Bacterial Chao1 index), and optimized the microbial community structure. Fungal diversity under PTS was the most prominent in the 20-40 cm depth. Correlation analysis indicated that the active SOC fractions and microbial diversity jointly explain the yield variation. The conclusions of this study will provide a theoretical foundation for developing scientifically sound straw return strategies in agricultural production.  
straw return; black soil; soil labile organic carbon fractions; microbial diversity; yield 2025  
1
## 3 Improve L1 Optimization to Correct 100% of Corrupted Propagated Errors in Network Coding
### \"All-or-Nothing\" Problem about Network Coding in WSN  
Full rank of received packets is required to invert the linear mapping so as to recover the transmitted data packets. This requirement unfortunately results in a key drawback of network coding: either all of the packets in a session are recovered simultaneously or none can be recovered, which leads to long delays and low throughputs. Long delays and low throughputs are especially unbearable in WSN which has limited battery energy. If the packets received in the sink are not full rank, the successful decoding probability of L1 optimization will decline though it also has a great chance for decoding successfully.
## 3 Empirical Results
### Forecasting Results  
#### 3.2.1 Variable Selection Using Lasso via Hierarchical Interactions  
Selection of variables was done for VEN data using Lasso via hierarchical interactions and a summary of the results is presented in Figure 11. Twelve variables were selected and used in all the models in this research.  
Figure 11 shows the Lasso results for VEN data, with the graph showing the 12 selected variables. The plot helps us visualise MSE which is on the \(y\)-axis with log(\(\lambda\)) which is given on the \(x\)-axis, it is the cross-validation curve (with red dots) and the variables which are included at these points are on the top. The lower and upper curve limits are the 2 dotted lines which are the \(\lambda\)s. The error is very high when we have a few variables but as the number of variables increases the error approaches zero.
# Improved Anomaly Detection by Using the Attention-Based Isolation Forest
## 4 Attention-Based Isolation Forest
### Keys, Values, and Queries in iForests
For these leaves, the keys \(\mathbf{A}_{1}(\mathbf{x}),\ldots,\mathbf{A}_{T}(\mathbf{x})\), values \(h_{1}(\mathbf{x}),\ldots,h_{T}(\mathbf{x})\), and attention weights \(\alpha(\mathbf{x},\mathbf{A}_{k}(\mathbf{x}),\mathbf{w})\), \(k=1,\ldots,T\), are computed. The products \(\alpha(\mathbf{x},\mathbf{A}_{k}(\mathbf{x}),\mathbf{w})\cdot h_{k}(\mathbf{x})\) are summed in accordance with Nadaraya-Watson regression to obtain the expected path length \(E[h(\mathbf{x})]\), which is compared with the threshold \(\gamma\) to make a decision on whether instance \(\mathbf{x}\) is normal or anomalous.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 3 Signal Model  
In this section, the OFDM signal model used for ITS-G5, LTE-V2X and NR-V2X is introduced. More specifically, we present the frame structure and the physical layer parameters of these three standards, as they have a direct impact on the time-frequency features and the periodic behavior of the signals, needed for identification purposes.  
Assuming that an OFDM symbol consists of \(N_{c}\) subcarriers at frequencies \(f_{0}\), \(f_{1}\),..., \(f_{N_{c}-1}\) separated by \(\Delta f\), the baseband equivalent transmitted signal \(x(t)\) is given by:  
\[x(t)=a\sum_{k}\sum_{n=0}^{N_{c}-1}s_{n,k}e^{\mathrm{i}2\pi f_{n}(t-T_{cp}-kT_{ s})}g(t-kT_{s}), \tag{13}\]  
where \(a=\sqrt{E_{s}/N_{c}}\) is the amplitude factor with \(E_{s}\) representing the signal power. \(s_{n,k}\) denotes the transmitted symbol within the \(n\)-th subcarrier and the \(k\)-th symbol period. These symbols are assumed to be independent and identically distributed (i.i.d) random variables with values drawn from an \(M\)-ary Quadrature Amplitude Modulation (QAM) constellation. \(T_{s}\) is the symbol period given by \(T_{s}=T_{u}+T_{cp}\), with \(T_{u}=1/\Delta f\) denoting the useful symbol duration and \(T_{cp}\) the length of the Cyclic Prefix (CP). The function \(t\mapsto g(t)\) is the pulse shaping filter.  
Therefore, the baseband-equivalent received signal affected by the AWGN channel is expressed as:  
\[y(t)=a\sum_{k}\sum_{n=0}^{N_{c}-1}s_{n,k}e^{\mathrm{i}2\pi f_{n}(t-T_{cp}-kT_{ s})}g(t-kT_{s})+n(t), \tag{14}\]  
where \(n(t)\) denotes the zero mean white Gaussian noise of variance \(\sigma_{n}^{2}\).
# MTRSRP: Joint Design of Multi-Triangular Ring and Self-Routing Protocol for BLE Networks
## 5 Network Performance  
### Simulation Setup  
This simulation involved the development of a discrete event simulator within a MATLAB R2020a environment to assess the performance of multi-ring networks under various conditions. The simulator was meticulously configured with specific parameters: a simulation scenario involving 45 randomly distributed nodes to evaluate the performance disparity between three multi-ring network design cases and the CORP [12] and BLEmesh [13] schemes. From the perspective of network architecture, the multi-ring network design adopts a hierarchical mesh topology that combines the elements of the mesh and ring topologies. In contrast, the CORP design primarily relies on a mesh topology  
Figure 5: An example of self-routing packet forwarding in MTRSRP.  
constructed in a decentralized manner, whereas BLEmesh constructs the mesh topology in a fully distributed manner. In this study, the CORP and BLEmesh methods were selected as benchmarks for a comparative analysis.  
During the simulation process, each node uses a fixed transmission range of 10 m and a time-division duplex scheduling mechanism in both the piconets and scatternet considering the frequency-hopping channels between different piconets. This mechanism was employed to avoid packet collisions and optimize the data packet transmission process. The packet generation at each node followed a Poisson process model, with the packet generation rate representing the number of new packets generated per second. Packet generation follows a Poisson distribution with rates ranging from 1 to 10 packets per second to emulate stochastic traffic conditions. To manage these packets, each node is equipped with a first-in-first-out (FIFO) queue with a capacity of 400 data packets. The system randomly selects source-destination pairs for each routing path for packet transmission. A taildrop mechanism was employed when the FIFO buffer capacity reached its limit. This mechanism involves dropping newly received packets at each node to ensure stable operation of the network.