esa-sceva/satcom-chunk-collection huggingface dataset:
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 2 Related Work
### Routing Protocol Based on RL  
To cope with the dynamic environment of VANETs, RL [35] has been widely explored in route discovery and attack detection. A decentralized routing protocol based on a multi-agent RL is put forward in [36]. In the protocol, the problem of how vehicles learn from the environment is modeled as a multi-agent problem. A vehicle is considered as an agent, and it autonomously establishes the transmission packet path with a neighbors. In addition, a geographic routing based on Q-learning algorithm is put forward in [37]. Each vehicle holds a Q-table, and an optimal relay node is selected by querying the Q-table. [PERSON] et al. [38] suggested a V2X routing protocol based on intersection. They employed a multidimensional Q-table, and the Q-table is used for selection of the most appropriate road segment to transmit the packet.  
Unfortunately, Q-learning needs to maintain a Q-table. As the number of nodes increases, the Q-table is larger. To overcome this issue, a DRL-based collaborative routing protocol is put forward in [39]. The problem of minimizing the delay is formulated as an MDP, and the solution of the MDP is solved by Deep Q-network (DQN) [40]. Similarly, in [41], a deep reinforcement learning is used to tackle with the network dynamics in VANETs.  
In addition, RL has been widely used to detect attacks to reduce the packet loss ratio in VANETs. In [42], an adaptive multi-agent RL algorithm is designed to detect packet drop attacks. In [43], the malicious vehicles activity are detected by Q-learning to prevent malicious vehicles from forwarding packets. Besides, in [44], an intelligent detection algorithm is put forward. For sake of realizing the detection of black hole attack, four network parameters are taken into consideration. In [45], the IoT security issue is discussed and put forward a protection scheme using effective decision-making strategy of appropriate features.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
###### Abstract  
The fourth industrial revolution is accelerating industrial automation. In industrial networks, manufacturing processes require hard real-time communication where the latency is less than 1 ms. Time-sensitive networking (TSN) technology over Ethernet already supports deterministic delivery for real-time communication. However, TSN technologies over wireless networks are currently in their initial development stage. Therefore, this study presents an overview of TSN research trends in wireless communications. This paper focuses on 5G networks and IEEE 802.11. We summarize standardization trends for TSN in 5G networks and introduce the TSN technologies for 802.11-based WLAN. Then, we introduce the integration scenario of 5 GS with WLAN. This study provides insights into wireless communication technologies for wireless TSN.  
time-sensitive networking (TSN); 5G; 802.11; low latency; reliability; industrial automation
## 1 Introduction  
It has been a long time since data was referred to as \"the oil of the 21 st century\". Today, many of the world's most valuable companies thrive by collecting and monetizing vast amounts of data. However, such data often include sensitive personal information, making it inaccessible for public or collaborative use--not only due to commercial interests but also to mitigate privacy risks. Despite the significant potential of data sharing in addressing pressing societal challenges such as crime prevention, public health, and elder care, privacy concerns continue to severely limit the practical use of sensitive datasets across institutional boundaries. To address this challenge, _privacy-preserving machine learning (PPML)_ has emerged as a key paradigm for enabling data-driven insights without compromising confidentiality.  
One of the most prominent PPML frameworks is federated learning (FL) [1], which allows multiple parties to collaboratively train machine learning models without sharing their raw data. Each participant trains a local model and shares only model updates with a central server, preserving data privacy. However, FL assumes that participants have sufficient computational capacity for local training. In many real-world settings,particularly for devices or institutions with limited resources, local computation must be outsourced, raising new privacy concerns-even under semi-honest threat models.  
This paper focuses on another type of PPML scenario in which a local entity securely outsources prediction computations. In our setting, both the data and the model remain encrypted, ensuring the confidentiality of sensitive user input and the intellectual property of the model provider. This setup is especially effective in protecting against model inversion attacks [2], which constitute a well-known vulnerability in machine learning-as-a-service (MLaaS) platforms. To implement secure outsourced prediction, we employ _homomorphic encryption (HE)_[3], particularly in its somewhat homomorphic variant, named _somewhat homomorphic encryption (SHE)_[4], which supports a limited number of encrypted additions and multiplications with significantly improved efficiency over fully homomorphic schemes. This enables practical encrypted inference in real-world scenarios. Among the various machine learning models, decision trees are particularly attractive for privacy-preserving inference due to their interpretability and low computational cost. As a result, a growing body of research has focused on designing cryptographic protocols for _privacy-preserving decision tree (PPDT)_ inference that minimize both communication and computational overhead while preserving privacy.
## 4 State-of-the-Art Baseline  
The current state-of-the-art models were obtained from the paperswithcode website [29], based on the best performance on the text classification subtask on diverse datasets.  
Extensive use of recurrent networks to process sequential data was common before the Transformer era. An example of such a model is the bidirectional LSTM [30] network [31], which processes pre-trained word representations. A hierarchical variant of this model is the HDLTex network [32].  
Most models use the Transformer architecture, which has been modified specifically for the task of document classification on shorter texts. Due to their growing popularity, the extension of these models, along with additional tricks, were used to process long texts more efficiently. A simple approach was to fine-tune the BERT model for document classification by including additional fully connected layers and a \(softmax\) cost function [33]. A similar approach was used in the paper \"How to Fine-Tune BERT for Text Classification?\" [34], where pre-training was additionally used on a training dataset, followed by fine tuning. Another architecture based on this architecture is the XLNet [35] autoregressive model, which uses two-stream self-attention for target-aware representation and, inspired by the TransformerXL [36], uses relative position encoding.  
A different, yet very promising, graph approach is used in many current SOTA models, such as Massage Passing Attention Networks [37], which is the best model on the Reuters dataset. In this model, documents are represented as graphs built from a word co-occurrence matrix and a special node representing the document. In this model, a sentence representation is obtained by iteratively passing messages between nodes, and classification is performed on the glued representations of all sentences. A similar approach was presented in graph-based attention networks [38], which treat documents as nodes whose representations are vectors obtained by the bag-of-words method on which a self-attention operation is performed, together with neighbouring nodes. Similar assumptions were used in the Learnable Graph Convolution Network [39] model, in which an additional pre-learning step is the selection of a subgraph that allows the graph to be transformed into grid-like one dimensional data before training and the use of standard convolution layers. The best models were selected for comparison in the following section of the paper.
## 2 System Description
### Signal Model  
Consider an OFDM system employing \(N\)-point DFT and CP with a length of \(N_{g}\). After taking an \(N\)-point inverse DFT (IDFT) on complex symbols, the output of IDFT is the baseband OFDM symbol block \(x_{q}(u),u=0,1,\cdots,N-1\). In the beginning of OFDM symbol block, a CP is added which is a copy of the last \(N_{g}\) samples, generating one effective OFDM symbol \(x_{q}(u)\), \(u=-N_{g},-N_{g}+1,\cdots,N-1\). Thus, the \(u\)-th time-domain sample during the \(q\)-th period \(x_{q}(u)\) can be represented by  
\[x_{q}(u)=\sum_{k\in\mathcal{S}}X_{q}(k)e^{j2\pi ku/N}, \tag{2}\]  
where \(X_{q}(k)\) denotes the signal at the \(k\)-th subcarrier over the \(q\)-th duration and \(\mathcal{S}=\{k|-N/2\leq k<N/2\}\). Thus, the total duration of one OFDM symbol is \((N+N_{g})T_{s}\) with \(T_{s}\) being the sampling time of any OFDM signal block. Since the PSS is present twice per frame, the observation window for PSS detection includes \(N_{o}\) adjacent OFDM symbols that compose a half frame, where \(N_{o}\) is either 60 or 70  
Figure 2: Initial cell search procedure in an long term evolution downlink.  
depending on the extended or normal CP mode, respectively. For notational simplicity, we consider the situation where the PSS is transmitted on the \(l\)-th OFDM symbol within the observation window \(q=1,2,\cdots,N_{o}\), namely, \(X_{l}(k)=P_{i}(k)\). Since the location of the SSS sequence differs from duplex mode, it follows that \(X_{l-1}(k)=S(k)\) for FDD mode, while \(X_{l-3}(k)=S(k)\) for TDD mode.  
The signals transmitted from the eNodeB are passed through the multipath fading channel with additive white Gaussian noise (AWGN). It is assumed that the maximum delay spread of the multipath channel does not exceed the CP interval. At the MTC UE terminal, the received signal is down-converted to baseband and sampled with duration of \(T_{s}\). Because the FFO and STO marginally affect the accuracy of existing IFO detection schemes proposed in [16; 17], it is assumed that the FFO and STO are perfectly known at MTC UE. Accordingly, the time-domain sample during the \(q\)-th period \(y_{q}(u)\) can be given as [14]  
\[y_{q}(u)=e^{\beta qvN_{g}\rho}e^{\
u\
u\rho}\sum_{p=1}^{L}h_{q}(p)x_{q}(u-p)+w_ {q}(u),\ |v|\leq M, \tag{3}\]  
where \(\rho=2\pi/N\), \(v\) represents the IFO normalized by subcarrier spacing \(\Delta_{f}=1/NT_{s}\), \(h_{q}(p)\) is the channel impulse response with \(L\) resolvable paths, \(M\) is the finite number of possibilities of \(v\), and \(w_{q}(u)\) denotes a zero-mean AWGN. After FFO recovery and CP removal, the DFT unit is used to produce the frequency-domain version of \(y_{q}(u)\), which is written by [17]  
\[Y_{q}(k)=H_{q}(k-v)X_{q}(k-v)e^{\beta qvN_{g}\rho}+W_{q}(k),\ k\in\mathcal{S}, \tag{4}\]  
where \(H_{q}(k)\) is the DFT of \(h_{q}(p)\) and \(W_{q}(k)\) is the DFT of \(w_{q}(u)\) with variance \(\sigma_{W}^{2}\).
## 3 Results
### Field Experiment--The Influence of Wetting Agent and Nozzle Type on Copper Hydroxide Deposit  
The influence of the nozzle type and wetting agent on the quantity of copper hydroxide deposit in field conditions is presented in Table 2.  
The deposit of copper hydroxide on the sugar beet leaves was statistically significantly lower when using the standard SF nozzles compared to the deposit when using the TD ADF nozzles. During the application of fungicide based on copper hydroxide without the wetting agent, the deposit was 31.5% when applied with SF nozzles. Using TD ADF nozzles with two jets the recorded deposit was 40.82%, indicating that TD ADF nozzles made a 10.1% higher deposit (Table 2).  
The copper hydroxide treatment with the addition of a pinole-based wetting agent at a rate of 0.3 L ha\({}^{-1}\) did not significantly affect the deposit quantity. With further increases of the wetting agent (0.6 and 1 L ha\({}^{-1}\)) statistically significant increase occurred. The deposits reached 43.3% with SF nozzles and 50.73% with TD ADF nozzles when the wetting agent was added into the solution (Table 2).
While the CE approach sounds promising, it is far from obvious if it can make a significant contribution to the design of an environmentally sustainable economy. Several limitations and uncertainties exist that can prevent the environmental superiority of a CE approach over the linear way of today's production [12; 13]. For example, a high percentage of recycled materials in a product could reduce the lifetime of that product. Thus, increasing the share of recycled materials does not necessarily lead to a better environmental performance [14]. Recycling practices could also result in more GHG emissions of production due to additional transport needed for collecting products from the costumer and bringing them back to the producer for recycling/reusing [15].  
In order to cope with the uncertainty that comes along with applied and new CE strategies, a robust and science-based method is required in order to assess the environmental impacts of circular product systems compared to a linear product system. The Life Cycle Assessment (LCA) method can fulfil those needs by: (1) quantifying various environmental impacts; (2) taking into account the entire life cycle; (3) relying on high scientific standards; while (4) being transparent and accepted by the scientific community [16; 17]. Some LCA studies on textile recycling have already been published [18; 19; 20; 21]. However, most of the studies focus only on one or two indicators, foremost on \"climate change\" and \"energy use\". Only a few of these studies address a broader set of environmental impacts. Thus, there is currently the risk that relevant impacts so far have been missed and problems have simply been shifted. In addition, most studies consider that textile waste sent to recycling is free of environmental burdens; however, textiles can still retain strong toxic substances that need further treatment [22; 23; 24]. In addition, these studies are focusing on end-of-life (EoL) strategies, i.e., recycling/reuse versus incineration or landfilling. To the best of the authors' knowledge, no study has compared the complete life cycle of a linear and circular textile product system so far.  
Hence, in order to fill this gap, this study compares the environmental consequences of the change from a linear to a circular product system using the example of the wear2 wearTM functional workwear jacket made of polyester (PES). Eleven different impact categories are considered and the allocation of waste is not environmental burden free. Section 2 of the manuscript describes the methodology applied here, the wear2 wearTM product system, its stakeholders and production steps, the circular design and the cornerstones of the investigated PES jacket and its translation into LCA. Subsequently in Sections 3 and 4, the results are shown and discussed, before we outline in the final section the conclusions of this case study for the wear2 wearTM product system, as well as general conclusions for CE for the textile industry as a whole.
## 3 Benchmark Results
### Basic Costs  
The cost of the basic operating system functions was measured with the default operation system applications on, the serial port connectivity enabled, all network interfaces disabled, and without running any end-user application. Then, we evaluated the screen consumption by switching on the display and computing the introduced overhead. Eventually, we analyzed the network interface costs (we only evaluated these costs for the Hx2790 device, since our iPAQ3970 is not equipped with this interface).  
Figure 1 shows the results.  
The most contributing factor in the basic power costs is the wireless network interface. The network card consumption in idle state and configured with a transmission power of 15 dBm (\(\sim\)32 mW) and a sensitivity of \(-80\) dBm is 792.68 mW. When using the communication channel, data transmission costs 10.40 \(\mu J\) per byte, while the reception is 3.48 \(\mu J\) per byte. Transmitting requires more energy than receiving, but the difference is not so significant bearing in mind that both processes have associated an intrinsic idle state cost. Network idle costs are critical and a main concern for ubiquitous networks.  
The next contributor to basic power costs is the screen, which is usually switched on if the user is working with the PDA. In iPAQ3970, the use of the screen at maximum luminescence costs 406.58 mW, while in Hx2790 this power is 501.06 mW. This cost can be reduced if the screen brightness is set at low levels. The consumption rate is linearly proportional to the glow.  
Finally, the basic operating system functions use up 375.64 mW in iPAQ3970, and 213.28 mW in Hx2790. The differences between the two devices are due to the processor, operating system, and battery.  
The exposed results show that the power overhead for executing cryptographic algorithms is higher in the newest and fastest PDA: Hx2790. Nevertheless, this does not mean that this machine is less efficient for cryptography, as we will see in next sections. Hx2790 can better exploit its resources when necessary so that operations are fast.  
Figure 1: Average energy consumption (mW) of a PDA.  
Moreover, we studied whether the battery charge affects the performance of a device, and we confirmed that it does. The process time to execute an algorithm when the battery drain is at 25% is the same that in full charge. Still, the instant current increases and so also the total spent energy. In average, the costs induced when the battery is at 25% are around 16% higher.
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 3 Plant Extracts and Phytochemicals against Bacteria Causing Foodborne Diseases
### Listeria monocytogenes  
_Listeria monocytogenes_ (_L. monocytogenes_), a Gram-positive, non-spore-forming, facultative anaer whole widely affecting food, meat, poultry and seafood [152]. This pathogen grows between 0.4 and 50 \({}^{\circ}\)C [104]. The virulence factors of _L. monocytogenes_ include the production of beta hemolysis, catalase and superoxide dismutase. It has been implicated in foodborne outbreaks [153]. This microbe can survive refrigeration, high salt content and low pH [154]. These properties enable _L. monocytogenes_ to contaminate food even after food postprocessing [155]; _L. monocytogenes_ being a foodborne pathogen is challenged with extracts and compounds derived from medicinal plants. In a study by [PERSON] et al. in South Korea, 69 herbal extracts were tested for inhibition of _L. monocytogenes_. Psoralaee Semen and Sophorae Radix extracts were found potent in their study [156]. Attachment of _L. monocytogenes_ to the human intestinal epithelium and its subsequent invasion results in Listeriosis. Motility, lecithinase and hemolysis production are the major virulence factors of this pathogen. The virulence factors, i.e., motility, lecithinase and hemolysis production were also decreased. Furthermore, the expression of the virulence genes was downregulated by cinnamaldehyde, carvacrol and thymol more than 3-folds [157; 158]. The essential oils are reported to possess antiliserdi activity. Essential oils of _Zataria multilora_ [PERSON] from Turkey [159], _Carum copticum_ [PERSON]. & Hook.f. ex C.B.Clarke from Iran [160], _Thymus capitatus_ [PERSON][PERSON] from Tunisia [161], _Cymbopogon citratus_ [PERSON] from Brazil [162], _Eryngium foetidum_ _[PERSON]_ from Italy [163] have been reported to have the potential of inhibiting _L. monocytogenes_.
## 4 SLR Methodology
### Planning the SLR
#### 4.1.3 SLR Protocol  
The SLR protocol was designed to achieve the objective of this review by addressing the RQs outlined in Section 4.1.2. The protocol mainly contained the specification of each aspect of conducting the SLR. First, the identification of the potential bibliographic databases, the definition of the inclusion/exclusion criteria and quality assessment questions, and the selection of research articles are discussed elaborately in Section 4.2.1. In the second step, thorough scanning of each of the articles was performed, and relevant data were extracted and tabulated in a feature matrix. The feature set was defined from the knowledge of previous review studies mentioned in Section 3, motivated by the RQs outlined in Section 4.1.2. To support the feature extraction process, a survey was conducted in parallel which involved the corresponding/first authors of the selected articles. The survey responses were further used to obtain missing data, clarify any unclear data, and assess the extracted data quality. Upon completing feature extraction and the survey, an extensive analysis was performed to complement the defined RQs. Finally, to portray this SLR outcome, all the authors were involved in analysing the extracted features, and a detailed report was generated.
# Classification of Logging Data Using Machine Learning Algorithms
## 4 Results  
### ROZ Identification  
To evaluate the quality of ROZ classification, preliminary experiments were conducted with the mentioned range of machine learning models on the basis of a single dataset (Table 4). The division into training and test parts was identical in all computational experiments.  
SVM classifiers took tens or even hundreds of times longer to train than others (Appendix B). The best value of f1_score_macro was demonstrated by LightGBM, and it was 30 times faster than the second-best classifier (XGB). Therefore, the LightGBM classifier was further used to evaluate the effect of normalization and floating window size.  
The results of computational experiments with datasets in which the upper part of the data window is fixed (up_w = 5) and the lower part (dw_n) varies between 5 and 200 are shown in Tables 5 and 6. Table 5 shows the results for partially non-normalized values. Table 6 shows the results for normalized log data.  
These results show that using large-size floating data windows improves the classification results by about 7-9%. Using non-normalized values gives slightly better results, especially when the floating data window size is small.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
0 & 0.779 & 0.803 & 0.498 & 0.647 & 0.649 & 0.779 & 0.902 \\ \hline
5 & 0.807 & 0.805 & 0.633 & 0.659 & 0.699 & 0.807 & 2.503 \\ \hline
25 & 0.808 & 0.805 & 0.661 & 0.673 & 0.713 & 0.808 & 5.833 \\ \hline
50 & 0.814 & 0.815 & 0.668 & 0.681 & 0.721 & 0.814 & 10.47 \\ \hline
100 & 0.818 & 0.815 & 0.668 & 0.697 & 0.727 & 0.818 & 18.99 \\ \hline
200 & 0.826 & 0.815 & 0.679 & 0.739 & 0.744 & 0.826 & 31.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of k-fold cross-validation (k = 10) for normalized log data in the ROZ classification task.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
0 & 0.79 & 0.828 & 0.505 & 0.685 & 0.672 & 0.79 & 1.651 \\ \hline
5 & 0.825 & 0.843 & 0.636 & 0.698 & 0.726 & 0.825 & 6.386 \\ \hline
25 & 0.82 & 0.839 & 0.656 & 0.69 & 0.729 & 0.82 & 15.56 \\ \hline
50 & 0.827 & 0.842 & 0.664 & 0.696 & 0.734 & 0.827 & 26.56 \\ \hline
100 & 0.836 & 0.847 & 0.67 & 0.723 & 0.747 & 0.836 & 48.94 \\ \hline
200 & 0.837 & 0.815 & 0.68 & 0.73 & 0.741 & 0.837 & 73.61 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of k-fold cross-validation (k = 10) for non-normalized log data in the ROZ classification problem.