esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Post-Occlusive Reactive Hyperemia  
Distributions of individual differences in the RP for PORH are shown in Figure 3B. Contrary to the baseline phase, significant individual differences were found for each frequency interval using a Wilcoxon signed-rank test. The median and IQR of each distribution was as follows: endothelial NO-independent (6.46 [3.29-8.96], \(p\) = 0.0005), endothelial NO-dependent (2.39 [0.80-3.97], \(p\) = 0.0005), neurogenic (0.65 [0.14-1.39], \(p\) = 0.0038), myogenic (\(-0.30\) [\(-0.57\)-(\(-0.09\))], \(p\) = 0.001), respiratory (\(-0.26\) [\(-0.34\)-(\(-0.13\))], \(p\) = 0.0002), and cardiac (\(-0.25\) [\(-0.42\)-(\(-0.10\))], \(p\) = 0.0002).  
Figure 2: Flowchart of implemented algorithm. COI: cone of influence; NaN: not a number.  
Group-level comparisons for each frequency interval without and with the COI are shown in Figure 4B. The median and IQR for each frequency interval without and with the COI-correction were as follows: endothelial NO-independent (without and with correction: 9.72 [6.76-11.76] vs. 2.47 [1.27-4.44], endothelial NO-dependent (without and with correction: 4.33 [2.59-7.09] vs. 1.15 [0.76-2.47]), neurogenic (without and with correction: 1.89 [1.25-2.62] vs. 1.12 [0.81-1.91]), myogenic (without and with correction: 0.84 [0.51-0.93] vs. 1.06 [0.97-1.41], respiratory (without and with correction: 0.33 [0.22-0.42] vs. 0.51 [0.44-0.77]), and cardiac (without and with correction: 0.25 [0.16-0.59] vs. 0.54 [0.32-0.95]) physiological influences.  
Figure 3: The boxplots of individual differences between the results obtained without and with the COI correction in relative power (the ratio between the median power within a given frequency interval and the median power of the entire power spectrum) in the frequency spectrum of the LDF signal acquired at the volar forearm of eighteen healthy participants during baseline (**A**) and PORH (**B**). The horizontal lines in the box indicate the first quartile, the median, and the third quartile. Plus signs represent outliers. * \(p\) < 0.05, statistically significant differences (non-parametric Wilcoxon signed-rank test; N = 18). In the baseline, no statistically significant differences were observed. In PORH, individual differences associated with endothelial NO-independent (endo NOi), endothelial NO-dependent (endo NO), neurogenic (neuro), myogenic (myo), respiratory (resp), and cardiac (card) physiological influence were significantly altered. Mind that the scales presented in Figures (**A**) and (**B**) differ.
# Wearable 256-Element MUX-Based Linear Array Transducer for Monitoring of Deep Abdominal Muscles
## 5 Summary  
We designed and developed a wearable ultrasound probe with 256 elements based on an integrated custom-made 1:4 multiplexing ASIC. The probe was designed to image deep abdominal muscles so that it can be used as biofeedback during exercise. The challenge of integrating a large footprint fully populated B-mode array in a wearable form factor was tackled by combining the 1:4 ASIC-MUX with portable 32-ch electronics and an adapted beamforming scheme. The MUX-based system was characterized on phantoms and showed an imaging performance comparable to a fully populated array. In a first proband experiment, data from deep abdominal muscles (TrA) could be acquired with the worn probe in different postures and during exercise with reliable acoustic contact. Using a semi-automated segmentation algorithm, the thickness variation of the TrA, which is an established metric for the effectiveness of deep abdominal muscle training, could be retrieved from the ultrasound data. Thereby, we demonstrated the potential of our system and approach as ultrasound-based biofeedback in physiotherapy. Future work will aim to overcome the current limitations in terms of wireless data transfer (by onboard processing and data reduction prior to transfer and more performant transfer protocols) and validate the approach with a larger target population.  
Conceptualization, H.H., T.G., D.S., S.R., S.K. and M.F.; methodology, H.H., S.H.T., S.K. and M.F.; software, H.H., S.R., S.W. and H.S.; validation, L.P., O.S. and M.F.; formal analysis, M.F.; investigation, H.H. and M.F.; resources, M.F. and S.H.T.; data curation, L.P., O.S. and M.F.; writing--original draft preparation, H.S., L.P., S.K. and M.F.; writing--review and editing, H.S., L.P., S.K. and M.F.; visualization, H.S., S.K. and M.F.; supervision, O.S., H.H. and M.F.; project administration, M.F. and O.S.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federal Ministry for Education and Research BMBF, grant number 01 EC1906, project name ULTRAWEAR.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors thank [PERSON], [PERSON] and [PERSON] from DITABIS GmbH for the design of the probe holder and the electronics housing. We also thank [PERSON] for his careful proofreading of the manuscript and our colleague [PERSON] from Fraunhofer IBMT for his consultation on data analysis.  
Conflicts of Interest: Authors [PERSON] and [PERSON] were employed by the company Prema Semiconductor GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 3 Proposed Methods  
The power of the spoofing interference is similar to that of the genuine satellite signal compared to the suppressed interference. Due to the fact that deceptive interference is submerged in noise, angle estimation methods based on antenna arrays are difficult to achieve efficient direction finding for deceptive interference. The article utilizes the spread-spectrum characteristics of deceptive interference and implements conditioning, acquisition, and tracking of all GNSS signals received by the antenna array using the system and signal processing model described in Section 2, thereby obtaining measurements of the carrier phase. The paper proposes a moving array antenna-based method for localizing GNSS spoofing interference sources. The method first uses double-differenced carrier phase observations to distinguish between genuine GNSS signals and spoofing signals, thereby confirming and extracting the spurious spoofing satellite signals. Subsequently, this approach combines the raw carrier phase single-difference measurements from multiple observation points of an array antenna to achieve direct localization of the spoofing source. A comprehensive description of this methodology will be provided in this section.
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
###### Abstract  
The overlay integration of low-power femtocells over macrocells in a heterogeneous network (HetNet) plays an important role in dealing with the increasing demand of spectral efficiency, coverage and higher data rates, at a nominal cost to network operators. However, the downlink (DL) transmission power of an inadequately deployed femtocell causes inter-cell interference (ICI), which leads to severe degradation and sometimes link failure for nearby macrocell users. In this paper, we propose an adaptive network sensing (ANS) technique for downlink power control to obviate the ICI. The simulation results have shown that the ANS power control technique successfully decreases the cell-edge macro user's interference and enhances the throughput performance of macro users, while also optimizing the coverage and capacity of the femtocell. When compared with the Femto User Equipment (FUE)-assisted and Macro User Equipment (MUE)-assisted power control technique, the proposed technique offers a good tradeoff in reducing interference to macro users, while maintaining the quality of service (QoS) requirement of the femtocell users.  
adaptive network sensing; femto user assisted; macro user assisted; power control inter-cell interference; path loss +
Footnote †: journal:  
_Future Internet_
## 2 Related Work
### Relevant Research on Chinese Address Parsing
#### 2.1.3 Statistical-Based Address Parsing  
The fundamental concept of statistical segmentation is primarily derived from the understanding of the Chinese language. It suggests that characters combine to form words, and the greater the frequency of adjacent characters appearing in the same order, the higher the likelihood of constituting a word [23]. The statistical-based approach to address parsing treats the address string as an observational sequence and address component type annotations as sequences of states. Through the training of an address-parsing model on annotated address datasets, this method automatically annotates untagged addresses, achieving the division and identification of address elements [24]. Presently, the prevailing statistical models for Chinese address segmentation are predominantly founded on traditional probability statistics, such as Hidden Markov Models (HMMs), Maximum Entropy Markov Models (MEMMs), and Conditional Random Fields (CRFs) [25; 26].  
[PERSON] [10] employed the HMM to annotate address components and incorporated constraints between the address components using the Viterbi algorithm, proposing a Chinese address-matching method grounded in natural language comprehension. [PERSON] et al. [27] synthesized the composition and features of Chinese addresses by segmenting address components and assembling them into an output sequence with feature annotations. They trained a CRF-based model for Chinese address parsing. [PERSON] et al. [28] applied CRF to Chinese address parsing, establishing a fuzzy matching method for local address information and a standardization approach for placenames, thus bolstering parsing precision. [PERSON] et al. [29] devised composite geographical name features based on geographical name element attributes, part-of-speech attributes, and syntactic attributes, utilizing a CRF model for recognition, which enhanced the accuracy and recall for recognizing Chinese geographical names. [PERSON] [30] amalgamated statistical techniques with rules, devising and implementing a Chinese address segmentation system grounded in both statistics and rules. This system employs rule algorithms to remove ambiguities and validate the deduced results, thereby elevating accuracy considerably.  
This approach shares core principles with statistical-based Chinese word segmentation. Its central attribute includes its autonomy from dictionaries but reliance on corpora. Nevertheless, its efficacy is curbed by the configuration of attributes, often leading to overfitting when attributes are overly abundant. Furthermore, relying on probability conditions alone presents challenges in serving as the foundation for address parsing [31].
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 4 AIS Solutions' Implementation
### Problem Formulation  
In this implementation, we used the CSE-CIC-IDS2018 dataset (available in [17]). We used this particular dataset for mainly two reasons. First is the fact that it was used in the simulation experiment for both methods, NPS and MNSA. The other reason for using this dataset is that AWS computing platforms were used to collect the traffic data for this dataset. Consequently, to obtain more coherent and accurate results, we ran the implementation on AWS using the IDS2018 dataset. This dataset contains seven different malware attacks: brute-force attack, Heartbleed attack, botnet, denial-of-service attack, distributed denial-of-service attack, web attacks, and infiltration of the network from inside [3].  
Figure 4: IoT system architecture implementation using AWS-1-.  
Figure 5: IoT system architecture implementation using AWS-2-.  
We represented the total number of dataset records as \(D_{Total}\), and we represented each record in \(D_{Total}\) as \(D_{i}\), where: i = [1 to 1 \(D_{Total}\)! ].  
The size of \(D_{i}\) was represented as \(Z\), and each \(D_{i}\) included the same number of features represented as \(F_{Z}\), where:  
\[F=[f_{1},f_{2},f_{3},\ldots F_{Z}] \tag{5}\]  
Each record \(D_{i}\) was associated with the ground truth \(Y_{i}\) where \(Y_{i}=0,1\). \(Y_{i}=0\) indicates that the record is benign and \(Y_{i}=1\) indicates that it is malicious. We defined \(Y\) as the vector including all the labels of \(Y_{i}\) for: i = [1 to 1 \(D_{Total}\)! ].  
The dataset \(D_{Total}\) was split into two parts, \(D_{Train}\) and \(D_{Test}\), where:  
\[D_{Total}=D_{Train}\cup D_{Test} \tag{6}\]  
Different from [3, 9], this work examines the realistic implementation of AIS solutions in constrained IoT systems. The objective of malware detection mechanisms is surely to maximize the detection rate of unknown malware while reducing false alarms (when benign files are wrongly classified as malicious). Thus, we maximized the number of correct predictions (\(\hat{Y}\)) by the AIS solutions (see Equation (7)). To this end, we studied how the different parameters of the AIS algorithms could be tuned to accommodate the given constrained conditions of the IoT systems, while we still achieved a high detection performance of unknown malware. In particular, we found the suitable number of positive and negative detectors, \(C_{Pos}\) and \(C_{Neg}\), the size of each detector \(L\), and the possible size of \(D_{Train}\) (indicated as \(|D_{Train}|\)), that would allow the highest number of correct predictions. We defined the range of the minimum and the maximum number of detectors as \(ND\), and the range of the minimum and the maximum number of \(D_{Train}\) as \(NT\). Equation (7) presents the problem formulation and the optimization constraint defined by the total memory \(TM\).  
\[\max_{|D_{Train}|,C_{Pos},C_{Neg},L} \sum_{k=1}^{|D_{Test}|}\hat{Y_{k}}==Y_{k}\] (7) s.t. \[ND((C_{pos}+C_{neg})\times L)+NT(D_{Train}\times Z)<=TM \tag{8}\]
## 3 Malicious Document Classification
### Overview  
We propose a simple but effective two-step classification framework to detect malicious documents containing plaintext features. Figure 2 shows the overview of the proposed framework composed of two steps: (1) feature extraction and (2) classification.  
In the feature extraction step, (1) we first preprocess the given set of documents to read their plaintext data properly. Then, (2) we perform tokenization to extract only plaintext features from the electronic document. After that, (3) we construct a vector representation for each document on the basis of extracted plaintext features by applying a text analysis technique. The detailed process is described in Section 3.3.  
In the classification step, (4) a classification model is trained with the obtained vector representations of the training samples (i.e., documents with class labels). (5) By using the trained classification model, our framework predicts whether a given (unknown) document is malicious or not. The detailed process is described in Section 3.4.  
Figure 1: Malicious documents used as attack vectors.
## 3 Experiments and Results
### Model Parameter Adjustment
Likewise, in adjusting the number of convolutional kernels and observing their impact on model accuracy, a similar hump-shaped pattern emerged. The model's accuracy increases at first and then decreases with more convolutional kernels. This result may be attributed to the number of convolutional kernels exceeding a threshold, after which the model complexity becomes too high, with the training data not expanding sufficiently in tandem. Without more training data, it is challenging to properly support the training and learning of a highly complex model, so overfitting becomes more likely, causing the model's accuracy to decline. Accordingly, based on the Figure 7 results, the best combination of parameters to select was 3 \(\times\) 3 and 64 for the size and number of convolutional kernels, respectively.
## Materials and Methods
### Procedures  
#### Electromyography (EMG)  
The activity of the neck and shoulder muscles was measured through a bipolar surface EMG configuration (Neuroline 720-01-K, Medicotest A/S, Olstykke, Denmark). The electrodes (Ag/AgCl, Ambu Blue Sensor, N-00-S/25, Denmark) were positioned bilaterally, in accordance with the SENIAMrecommendations and earlier studies [24; 25; 26; 27]) with an inter-electrode distance of 20 mm. The electrodes were placed (1) above the upper trapezius muscle (mUTR), 20% medially from the centerpoint between the lateral edge of the acromion and seventh cervical vertebra, (2) above the upper neck extensor (mUNE) muscle, on the most prominent part of the muscle at the level of the fourth cervical vertebra, and (3) above the sternocleidomastoideus muscle (mSCM), over the main muscle belly at the 1/3 distance from the sternal notch to the mastoid process (Figure 1). Before affixing the electrodes, skin was shaved and cleaned with scrubbing gel (Acqua gel, Meditec, Parma, Italy) and 70% alcohol, to effectively lower the impedance to less than 10 k\(\Omega\).  
The EMG signal was pre-amplified with a gain of 400 and then transferred through an EMG probe (Direct Transmission System (DTS), Noraxon Inc, Scottsdale, AZ, USA) to a belt-receiver (TELEMyo TM 2400T, DTS, Noraxon Inc, Scottsdale, AZ, USA) with a bandwidth of 10-500 Hz and a common mode rejection ratio better than 100 dB. The signal was sampled at 1000 Hz while using a 16-bit A/D converter (DAQCard Al 16 XE 50, National Instruments, Austin, TX, USA) and stored on a computer via the laboratory interface (CED 1401, Spike2 software, Cambridge Electronic Devices, Cambridge, UK).
###### Abstract  
Targeting the concern that nearby inflexible buildings may be at risk for safety issues due to the surface deformation caused by foundation pit excavation disruptions, this paper took the large-scale foundation pit in the Hongshaquan second mine stope in Xinjiang as the research backdrop. To examine the deformation mechanism, generic numerical simulation models were built with varying excavation depths. The unloading effect of foundation pit excavation was addressed using the Fourier integral approach, which is based on elastic theory. An elastic theoretical analytical approach for the surrounding deformation during disturbances due to the excavation of foundation pits was derived by superimposing the unloading impact of the surrounding soil and including pertinent boundary conditions. By contrasting the outcomes of the numerical simulation with the theoretical analysis and the real on-site monitoring data, the accuracy of this approach was confirmed. The findings indicated that the deformation of the surrounding ground surface rises as the excavation depth grows during the foundation pit excavation process in open-pit mines. The deformation decreases with increasing distance from the slope crest to the monitoring location. The deformation of the surrounding ground surface reduces as the rock and soil mass's elastic modulus and Poisson's ratio rise. However, the deformation of the surrounding ground surface increases as the excavation depth and slope angle rise. This study offers fresh ideas and approaches for examining how the surrounding ground surface deforms while a foundation hole is excavated.  
Keywords:foundation pit excavation; Fourier integral; open-pit mine stope; elastic theory; surface deformation _Article_
## 3 Results and Discussion
### Gastrointestinal Stability of Phenolic Compounds from Purslane
The presence of chlorogenic acid as dominant and the presence of ferulic acid in purslane was demonstrated by other authors [46; 47]. Protocatehuic acid was found to be the dominant phenolic compound in the methanolic extracts of purslane seeds [48]. The results from Table 2 show a high stability of phenolic acids from purslane powder extracts after the gastric digestion phase (average: 83.55%), and a lower stability after the intestinal digestion phase (average: 69.42%). Furthermore, the grinding techniques could influence the gastrointestinal stability of phenolic acids, but there are no similar studies to compare the results obtained. Other authors also report a high stability of chlorogenic acid under gastric conditions and a low stability under intestinal conditions [49]. Studies with rats showed that chlorogenic acid in its intact form can be absorbed in the stomach [49]. Figure 4: Total phenolic content of unground and ground purslane before and after in vitro digestion. The values represent the means \(\pm\) standard deviations of three biological replicates and three technical replicates. Different small letters indicate significant differences between the samples in the same digestion phase, while different capital letters indicate significant differences among one sample after different digestion phases (one-way ANOVA, Duncan test, \(p\leq 0.05\)). Control = freeze-dried, unground purslane; PPK = purslane powder after grinding with a knife mill; PPB = purslane powder after grinding with a ball mill; PPP = purslane powder after grinding with a planetary ball mill. Particle size had a significant effect on the gastrointestinal stability and antioxidant activity of the phenolic compounds and is consistent with the results of this study. Particles of smaller size have a larger specific surface area, \(S\left(S\propto 1/x_{\text{m}}\right)\). It is the larger specific surface area that ensures a higher rate of mass transfer of beneficial components from the plant material. Therefore, superfine powder provides higher phenolic stability, bioaccessibility, and antioxidant activity [50; 51].
## 4 Results and Discussion
### Verification of Downhole Water Exploration and Release Drill Holes  
To further validate the grouting transformation effect in the ground area covering the D1, D2, D3, D4, D5, and D6 hole groups, Banji Coal Mine conducted underground borehole water exploration and drainage operations to assess the transformed grouting zone. A total of 54 drill holes were created across the study area, with water influx rates generally ranging between 0 and 70 m\({}^{3}\)/h, though some areas exceeded 10 m\({}^{3}\)/h, with local peaks reaching 70 m\({}^{3}\)/h. These drill holes provided extensive coverage of the designated study area, and the contour map of water influx revealed through the drilling process is presented in Figure 7.  
A comprehensive analysis shows that the areas with significant water influx are concentrated in the central and western parts of the 110504 working face, with a maximum recorded influx of 70 m\({}^{3}\)/h, aligning with the relatively weak grouting zones identified in the evaluation. The water influx patterns exposed by underground drilling largely coincide with the zoning results from the evaluation model, demonstrating that the proposed comprehensive grouting effect evaluation system accurately reflects the ground conditions. The good correspondence between the model's results and the actual underground drilling validation proves the rationality and scientific soundness of this evaluation method, making it a reliable reference for assessing grouting effects in similar conditions.