esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cerebral palsy (CP) is the leading cause of lifelong physical disability, affecting approximately 1 in 500 live births and an estimated 17 million people globally [1, 2]. This condition encompasses a group of non-progressive motor disorders resulting from brain damage occurring before, during, or shortly after birth, which leads to impairments in movement, posture, and coordination.  
Children with CP frequently experience challenges such as neuromuscular control deficits, spasticity, balance issues, and diminished strength, all of which contribute to difficulties in daily activities and social participation [3; 4]. Among these challenges, upper extremity dysfunction is particularly impactful, significantly limiting independence and quality of life [5; 6]. Reduced hand function, often characterized by weakness, sensory impairment, and spasticity, is a key predictor of restricted participation in everyday tasks, including grasping, writing, and self-care [5; 7; 8] and possibly increased burden on parents.  
Functional limitations in upper extremities are a major barrier to independence in activities of daily living (ADLs), such as feeding, dressing, and grooming. Consequently, targeted interventions to improve upper extremity function are critical for enhancing independence and social participation in children with CP [6; 9]. Early therapeutic approaches are essential to leverage the neuroplasticity of the developing brain and address motor impairments at an early stage [10].  
Whole-Body Vibration (WBV) therapy has recently emerged as a promising intervention to improve neuromuscular function, reduce spasticity, and enhance coordination in various populations, including individuals with neurological disorders [11; 12; 13]. By using mechanical oscillations, WBV stimulates neuromuscular adaptations akin to strength training, promoting improvements in muscle tone and upper extremity function [14; 15]. While WBV has been extensively utilized among athletes for muscle strengthening, its clinical application has expanded to address spasticity and functional impairments in neurological disorders [12; 16]. However, its use in managing CP remains relatively underexplored.  
Existing research, including a systematic review by [PERSON] et al. (2018) [12], suggests potential benefits of WBV in reducing spasticity and enhancing coordination in CP. However, the evidence is limited, particularly in pediatric populations, underscoring the need for further investigation. This pilot study aims to evaluate the feasibility and preliminary effects of Upper Extremity Vibration Therapy using a Whole-Body Vibration Platform (UEVT-WBV) (Novotec Medical GmbH, Pforzheimer, Germany) in children with CP. By exploring its potential as an adjunct to conventional physiotherapy, this study seeks to provide foundational data to guide future research, emphasizing feasibility over definitive therapeutic claims.
## 5 Perceptual Audio Features Extraction Techniques
### Time Domain Perceptual Features
#### 5.1.2 Perceptual Autocorrelation-Based Features  
Autocorrelation is a measure of the self-similarity of the signal in the time domain with diverse applications to audio feature extraction. In this section, we revise those features derived from autocorrelation providing a measure of perceptual-based parameters related to acoustic phenomena.  
* **Autocorrelation function features:** or ACF for short, this feature introduced by [PERSON] in [159], has been subsequently applied by the same author to environmental sound analysis [29] and recently adapted to speech representation [160]. To compute ACF, the autocorrelation function is firstly computed from the audio signal, and then this function is parameterized by means of a set of perceptual-based parameters related to acoustic phenomena (signal loudness, perceived pitch, strength of perceived pitch and signal periodicity).
* **Narrow-band autocorrelation function features:** also known as NB-ACF, this feature was introduced by [PERSON] and [PERSON] [15], where the ACF concept is reused in the context of a filter bank analysis. Specifically, the features are obtained from the autocorrelation function of audio signals computed after applying a Mel filter bank (which are based on the Mel scale, a perceptual scale of pitches judged by listeners to be equal in distance from one another). These features have been shown to provide good performance for indoor and outdoor environmental sound classification. In [161], the same authors improved this technique by substituting the Mel filter bank employed to obtain the narrow-band signals by a Gammatone filter bank with Equivalent Rectangular Bandwidth bands. In addition, the Autocorrelation Zero Crossing Rate (AZCR) was added, following previous works like the one by [PERSON] _et al._[43].
## 2 Numerical Modeling and Validation  
### Literature Tests for GSPCW Walls  
As mentioned earlier, very limited experimental research related to GSPCW wall system has been performed to date. Therefore, the two GSPCW wall specimens tested by [PERSON] et al. [9] were selected in the study and used for numerical modeling verification. Figure 2 shows the schematic for and photo of the test walls, and the design details of the specimens are listed in Table 1. As shown, the two selected wall specimens have almost the same design parameters, except for the axial load ratio, which is the main variable of concern in the tests. The concrete grade is C40, and the steel grade is Q235B, based on the Chinese design code. Since the loading beam and the foundation beam are made of reinforced concrete, the steel beam of the H section is embedded in order to improve the stiffness of the foundation beam. More details relating to the two wall specimens can be found in Ye. [9].  
Figure 2: _Cont._  
The test setup of the wall specimens consisted of a bottom fixing device, out-of-plane supports, and vertical and lateral loading devices. The lateral loading protocol adopted both force control and displacement control loading protocols. The vertical load was applied using a hydraulic jack, and a steel beam was placed on the top of the loading beam in order to distribute the vertical loads in an averaged fashion. Before the application of formal lateral loading, lateral preloading was carried out to make sure that all the loading equipment, connectors, data acquisition system were in good condition. After that, a single-stage lateral load using load control protocol was applied until the specimen yielded. Once the specimen had yielded, displacement control multi-stage loading were applied until the lateral capacity of the specimen reached its peak, and then decreased to 85% of the load capacity, indicating failure.
## 2 Results and Discussion
### Volatile Components in HAB
#### 2.3.1 Skeleton Components  
Esters, alcohols, acids, aldehydes, and ketones are the skeletal components of Chinese Baijiu. A total of 179 esters were detected in this study. Among them, ethyl esters were the most representative, and the ethyl esters homologous C2-C12 and C14-C18 were all detected. Ethyl acetate, ethyl butanoate, and ethyl hexanoate are the key aroma compounds in Baijiu, which mainly contribute to its fruity and sweet aroma. In addition, some compounds with very low odor thresholds that cannot be found in the previous literature also contribute to the overall aroma, for example (Figure 5), ethyl 3-methylvalerate has an odor threshold of 8 ng/L and contributes to a strawberry flavor [31]; ethyl cyclohexanoate has an odor threshold of 1 ng/L and reveals strawberry and anise aroma [32]; ethyl cinnamate is known for its honey and cinnamon flavor with an odor threshold of 1 \(\upmu\)g/L [33].  
Aldehydes and ketones were the second major categories of identified volatiles, amounting to a total of 111 compounds, and the homologous series of straight-chain aldehydes from C2-C12 were all detected. (_E_, _Z_)-2,6-nonadienal is the strongest aroma aldehyde compound in HAB and is described as having a strong cucumber aroma [27]; 3-methyl-butanal presents cocoa and almond aroma with an odor threshold of 0.5 \(\upmu\)g/L [27]; 2,3-butanedione has been described as contributing a butter aroma with an odor threshold of 100 \(\upmu\)g/L [33]; (_E_)-2-octenal is a key aroma odorant of Chinese chixiang aroma-type Baijiu, which has a fatty flavor with an odor threshold of 15.1 \(\upmu\)g/L [27]. 3-Methyl-butanal, 2,3-butanedione and (_E_)-2-octenal were identified in this study for the first time.  
Alcohols are highly volatile constituents of alcoholic beverages transformed from sugar during fermentation. Among them, 39 out of the 81 alcohols were confirmed using authentic standards, and the homologous series of saturated straight-chain primary alcohols from C3-C12 were all detected. 2-Butanol, 1-butanol, and 3-methyl-1-butanol have odor thresholds of 50, 2.73, and 179 mg/L, respectively, in Baijiu. They are also important aroma compounds in HAB, contributing to the fruit or mellow flavor [27].  
A total of 23 acid compounds were identified in this study, most of which are saturated monocarboxylic fatty acids. The homologous series of straight-chain monocarboxylic fatty acids from C1-C10, C14, and C16 were all detected, among which butanoic acid, pentanoic acid, and hexanoic acid play an important role in the flavor of Baijiu, which contributes to rancid and cheesy odors [27]; 2-methyl butyric acid and phenylacetic acid have relatively low odor thresholds of 5.9 and 1.4 mg/L, respectively, and are the key food odorants (KFO) [34].
## 1 Introduction
The relay receives the data from the transmitter and passes the data to other devices in the group to enable reliable data transmission. Using a relay can also extend the transmission range or reduce the transmit power, which results in low interference to cellular devices or other D2D devices [35; 36; 37]. In OFDMA-based D2D systems, transmission through a relay should consider the interference from other sub-channels, but most studies do not take care of the interference. In this paper, we consider relay positions in a D2D group to minimize the outage probability from the interference from other sub-channels while ignoring other performance-degradation effects such as collisions, fading, or co-channel interference. When a transmitter sends data to multiple receivers in a group, the outage probabilities may not be sufficiently small, since the signal-to-interference ratio (SIR) can be low for receivers far away from the transmitter or close to interferers in other sub-channels. In order to reduce the outage probabilities, we consider repetitive transmission through a relay in addition to the direct transmission between the transmitter and receivers. In this paper, we construct three different relay scenarios considering the interference from other-channel interferers in the D2D group-casting system. We calculate outage probabilities resulting from the interference from other sub-channels, and we find the relay position achieving the lowest outage probability. The rest of the paper is organized as follows. Section 2 presents the system models and three scenarios for D2D group-casting with a relay. Section 3 addresses the outage probabilities for the three scenarios considering the interference from other sub-channels. The numerical results with various transmitter and relay positions in a D2D group are given in Section 4, and finally, Section 5 draws the conclusions.
## 4 Experimental Results
### Object Tracking and Abandoned Object Detection Result
Since algorithms such as YOLOv8 and DeepSORT perform an image-based analysis, they can increase computational power and cause an inefficient use of hardware. Instead of processing all the frames, processing only frames that will contribute to the learning of the model reduces the load on the CPU and GPU. It also reduces RAM consumption since unnecessary data are not stored. In this study, 50% processing power/hardware efficiency was achieved by performing keyframe detection before object detection.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 5 Numerical Experiments
### DCEP on Real Datasets  
The technique of FDA [31], as an instance of a GEP, is used in the binary classification of the GSE2187 dataset ([[https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2187](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2187)]([https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2187](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2187)) (accessed on 1 May 2005)), which is a large cRNA microarray dataset reflecting the drugs and toxicants response of rats. Only two categories (toxicants and fibrates, named as \(C_{1}\) and \(C_{2}\), respectively, for short) are used in classification, and the missing values are filled with mean values. The data are randomly divided into training data and testing data with a ratio of 1:1. The detailed information is displayed in Table 1.  
The number of local servers is set to be 5, and the maximum iteration steps of PM is set to be 10. The binary classification accuracy from distributed data depends on the classification threshold of centered data, which is:  
\[\textit{thres}=\frac{W_{c}\times(num_{C_{1}}\times m_{C_{1}}-num_{C_{2}}\times m _{C_{2}})}{num_{C_{1}}+num_{C_{2}}},\]  
with the mean of \(C_{1}\) and \(C_{2}\) (\(m_{C_{1}}\) and \(m_{C_{2}}\)). The classification accuracy is calculated with \(data\in\mathbf{R}^{num\times d}\) as:  
\[acc_{C_{1}}=\frac{\sum_{i=1}^{num_{C_{1}}}\delta_{1}(\hat{W}\times data^{ \prime},\textit{thres})}{num_{C_{1}}},\quad acc_{C_{2}}=\frac{\sum_{i=1}^{num _{C_{2}}}\delta_{2}(\hat{W}\times data^{\prime},\textit{thres})}{num_{C_{2}}},\]  
where \(\delta_{1}\) and \(\delta_{2}\) are the indicator functions. When the projected value is less than the mean threshold value, \(\delta_{1}\) equals 1 and 0 otherwise, and \(\delta_{2}\) is exactly the opposite. The repeated experiments are carried out 10 times and the classification accuracy in GSE2187 data is reported in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Toxicants (\(C_{1}\))** & **Fibrates (\(C_{2}\))** \\ \hline \(num\) & 181 & 107 \\ \(d\) & 8656 & 8656 \\ \hline \hline \end{tabular}
\end{table}
Table 1: GSE2187 data structures: number of data (\(num\)), data dimension (\(d\)).  
Figure 4: Performance of DCCA-PM and DGEP-PM in running time (\(T/s\)) and error (\(E\)) with respect to the number of local severs (\(N\)).  
The centered accuracy is regarded as the benchmark. Clearly, no algorithm can beat the performance of the best centered estimator. However, the classification accuracy of the proposed algorithm does not decrease much, even though there is the covariance divergence in the one-shot communication. Generally, DGEP including DGEP-SVD and DGEP-PM in the one-shot communication performs well in the distributed FDA application.
###### Abstract  
In this paper, the formation tracking problem for heterogeneous multi-agent systems with loss of multiple communication packets is considered using the iterative learning control (ILC) method. A dropout compensation ILC method is presented to construct effective distributed iterative learning protocols. The convergence conditions are given based on the frequency-domain analysis by using the general Nyquist stability criterion and Greshgorin's disk theorem. The results show that the multi-agent system with different packet loss rate can achieve formation tracking without reducing the convergence speed. Numerical simulation results show the effectiveness of the proposed dropout compensation ILC method.  
iterative learning control; multi-agent systems; formation tracking; packet loss; general Nyquist stability criterion; Greshgorin's disk theorem Article
## 8 Discussion
### Challenges
#### 8.1.6 Quality of Service  
In contrast to 5G, 6G's QoS will include additional metrics. Latency, throughput, and packet loss rate are some of the most used QoS KPIs. Because intelligence will become the primary necessity in the 6G era, compute capacity and storage will also need to be considered. Furthermore, rather than needing outstanding performance in a single area, future 6G systems will focus on the optimization of numerous measures, for which the complexity of determining the relationship exceeds the capabilities of current mathematical models. To overcome this issue, a variety of purpose-based learning models should be widely used.
# IoT Privacy and Security Challenges for Smart Home Environments
## 5 Some Existing Security Support for IoT
### RPL and Security  
Routing protocols are a core component of conventional networks, and this also applies to 6 LoWPAN networks. RPL [21] is an optimized IPv6 routing protocol designed by IETF especially for Low power and Lossy Networks (LLNs) and is primarily used by 6 LoWPAN networks. RPL is a distance-vector routing protocol, and its mapping topology is based on a Destination-Oriented Directed Acyclic Graph (DODAG) structure. A generic topology authentication scheme called Trust Anchor Interconnection Loop (TRAIL) for RPL has been presented in [26]. TRAIL can prevent the topological inconsistency attacks from spurious nodes by discovering and isolating the forged nodes. A round-trip message has been used by TRAIL to validate upward path integrity to the root node and help the nodes in the tree get genuine rank information. The innovation of TRAIL is that each node in the tree can validate its upward path to the root and detect any fake rank attacks.  
In the DODAG tree, it is essential for nodes to select their correct parent nodes, since every node except the root must have a parent node. The RPL rank is used to describe a node's position in the tree topology. In [27], the authors present a secure selection scheme to help a child node to choose an authentic parent node. In its selection algorithm, a node's threshold value will be calculated based on average and maximum rank values from its neighbour nodes to exclude spoofing nodes from becoming its parent.  
So existing solutions can ensure secure routing table generation in Smart Home networks.
## 3 Results
### Morpho-Anatomical Characteristics of Regenerated Octaploids  
The obtained octaploids produced normal and viable pollen (96.8% vs. 96.0% in regenerated tetraploids and 96.5% in initial plants) of higher diameter (than tetraploids, both, initials and regenerants) and longer (than initial plants) seeds able to germinate. The frequency of seed germination in octaploids was 77.9%. The nucleotypic effect (cell size increases resulting from the increases in the 2C value) was also visible in the stomata length of octaploids. The latter ones produced the longest stomata among the analyzed groups. Similarly, flower diameter and area in octaploids were greater than in tetraploid regenerants and tetraploids obtained from seeds (initial plants) (Table 3, Figure 4).  
\(\mathrm{F_{v}/F_{m}}\), indicating the maximum potential quantum efficiency of Photosystem II when all capable reaction centers are open, showed no significant differences between the three groups of plants, indicating that Photosystem II was not affected by the ploidy level or in vitro culture. Furthermore, the average values of \(\mathrm{F_{v}/F_{m}}\) were in the range of the optimal values of most plant species (0.79-0.84).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Initial Plant (Tetraploid)** & **Tetraploid Regement** & **Octaploid Regement** \\ \hline \(\mathrm{F_{v}/F_{m}}\) value & 0.822 [\(\pm\)0.059] a & 0.806 [\(\pm\)0.101] a & 0.815 [\(\pm\)0.074] a \\ \hline Stomata length [\(\mathrm{\SIUnitSymbolMicro m}\)] & 21.4 [\(\pm\)1.74] b & 20.5 [\(\pm\)2.28] a & 27.2 [\(\pm\)3.85] c \\ \hline Pollen diameter [\(\mathrm{\SIUnitSymbolMicro m}\)] & 19.3 [\(\pm\)1.70] a & 20.0 [\(\pm\)1.47] a & 24.7 [\(\pm\)1.47] b \\ \hline Flower diameter [\(\mathrm{\SIUnitSymbolMicro m}\)] & 3.4 [\(\pm\)0.362] a & 3.39 [\(\pm\)0.329] a & 4.6 [\(\pm\)0.555] b \\ \hline Flower area [\(\mathrm{\SIUnitSymbolMicro m}^{2}\)] & 37.0 [\(\pm\)8.11] a & 35.6 [\(\pm\)4.50] a & 45.9 [\(\pm\)8.99] b \\ \hline Pollen viability [\%] & 96.5 [\(\pm\)3.67] a & 96.0 [\(\pm\)8.75] a & 96.8 [\(\pm\)5.58] a \\ \hline Seed length [\(\mathrm{\SIUnitSymbolMicro m}\)] & 0.892 [\(\pm\)0.086] a & n.m. & 1.170 [\(\pm\)0.109] b \\ \hline \hline \end{tabular}
\end{table}
Table 3: Morpho-anatomical characteristics of initial tetraploids, tetraploid and octaploid regenerants of _Arabidopsis arenasa_ grown in hydroponics. Different letters mean statistical differences (\(p\leq 0.05\)) with one-way ANOVA, followed by the Tukey HSD test between each dependent variable.  
Figure 3: Frequency of nuclei with different DNA contents in various plant materials of _Arabidopsis arenasa_. Leaf (blade with petiole) of initial plants (**A**), 10–15-week-old callus on \(\frac{1}{2}\) MS supplemented with 2 mg L\({}^{-1}\) 2,4-D + 2 mg L\({}^{-1}\) BAP (**B**), callus treated with 1 mg L\({}^{-1}\) TDZ for 3 weeks (**C**). The (\(\Sigma>2\)C)/2C ratio was 0.292 [\(\pm\)0.161], 2.559 [\(\pm\)3.012], and 1.334 [\(\pm\)1.452] for **A**, **B**, and **C**, respectively. 2,4-D—2,4-Dichloroophenoxyoxyacetic acid; BAP—6-Benzylaminopurine; MS—Murashige and Skoog medium; TDZ—Thidiazuron.
## Abstract  
(1) Background: The validation of hygiene procedures in food industries is paramount to ensure that food contact surfaces are properly decontaminated before production. Rapid, sensitive and reliable tools are needed for routine hygiene validation in order to increase food safety levels. Two novel tools for biofilm detection (TBF 300) and detection of low levels of microbial contamination (FreshCheck) have been assessed. (2) Methods: Biofilms of relevant food pathogens: _Listeria monocytogenes_ and _Salmonella_ spp. were grown for 3 and 10 days to assess the performance of the biofilm detection product. Surfaces were inoculated with different levels of _L. monocytogenes_ to determine the limit of detection of FreshCheck. (3) Results: TBF 300 visibly stained 3 days-old biofilms of both pathogens, containing 5.0-5.4 log CFU/cm\({}^{2}\). FreshCheck showed a positive reaction with contamination levels as low as 10 CFU/cm\({}^{2}\) for _L. monocytogenes_. (4) Conclusions: Assessment of the hygiic status of food contact surfaces before production can be greatly improved with the use of the two novel tools evaluated in this study. The detection of microorganisms' presence at very low levels of contamination as well as identification of biofilm growth spots is available in a rapid and easy way, with a big potential contribution to food safety.  
Keywords:food safety; hygiene; microbial contamination; biofilms; rapid detection +
Footnote †: journal: Applied Physics Letters
## 4 Performance Evaluation and Comparison
### Results and Comparison
#### 4.2.2 NSL-KDD Dataset  
The NSL-KDD dataset showcases remarkable performance for our LSTM-based IDS, with an accuracy of 99.75%. The precision and recall for the \"BENIGN\" class are 99.56% and 99.94%, respectively, leading to an F1-score of 99.75%. Similarly, the precision and recall for the \"attack\" class are both exceptional at 99.94% and 99.57%, respectively, resulting in an F1-score of 99.75% as showing in Table 3, Confusion matrix for NSL-KDD dataset Figure 5, and ROC curve for NSL-KDD dataset Figure 6.