esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Modified Electrode Types
#### 1.1.4 Nanoparticle-Modified Electrodes [27; 44]
Some of the most promising nanoparticle materials that have been used for electrode modifications include graphene, carbon nanotubes, metal nanoparticles, and various types of metal oxides. Nanoparticles can provide additional active sites for the adsorption of antibiotics, increase the electrochemical surface area, and improve the electron transfer between the analyte and the electrode; thus, they can provide enhanced sensitivity, selectivity, and stability compared to unmodified electrodes. Despite the many advantages of nanoparticle-modified electrodes, there are also some challenges and limitations to their use. For instance, the synthesis and functionalization  
Figure 2: Methods that are employed for electrode modification with nanomaterials. of nanoparticles can be difficult and time-consuming, and the stability of these materials can vary depending on the conditions of the electroanalytical measurement. Additionally, electrochemical signal interferences from other compounds with similar chemical structures can limit the selectivity of these sensors. Conclusively, Figure 3 summarizes some of the most common materials and nanomaterials used in electrode modification [47]. In the case of antibiotics, Kanamycin was sensed using a label-free electrochemical immunosensor that was fabricated as a nanocomposite of thionine (TH)-functionalized graphene sheets (GS), Ag NPs, and magnetic NPs [48]. In this study, graphite powder was used to synthesize graphene oxide through modified Hummer's method followed by the reduction of graphene oxide to produce graphene sheets. Then, the thionine as a mediator was adsorbed on the graphene sheets by \(\pi\)-\(\pi\) stacking to produce TH-GS, which was used to modify the GCE surface. Afterward, the synthesized Ag@Fe\({}_{3}\)O\({}_{4}\) NPs were mixed with kanamycin antibodies for one day to produce Ag@Fe\({}_{3}\)O\({}_{4}\)-Ab. The glutaraldehyde (GA) solution as a bifunctional linker was added to the electrode surface to save aldehyde groups for the combination between TH-GS and Ag@Fe\({}_{3}\)O\({}_{4}\)-Ab. Finally, an electrode modified by TH-GS/GA/Ag@Fe\({}_{3}\)O\({}_{4}\)-Ab as a label-free immunosensor was used for kanamycin detection. Each component of this TH-GS/GA/Ag@Fe\({}_{3}\)O\({}_{4}\)-Ab label-free immunoassay has a role in the sensitive detection of kanamycin. Thus, the sensitivity toward the antibiotic has been enhanced in two ways, enlarging responding electrochemical signals and increasing the loading capacities of antibodies. In this study, kanamycin was accurately detected over the concentration range of 0.1 to 34 nM and a detection limit of 0.04 nM using the SWV technique.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 4 Joint Problem of Train Rescheduling and Track Closure Planning
### Mathematical Model
#### 4.5.1 Track Closure Planning  
The problem of track closure planning deals with finding of the possibly largest set of track closures (containing all obligatory closures) with fixed start times of closures that can be performed without interfering with a train run timetable. To this end, a binary decision vector \(\mathbf{y}=[y_{n}]_{n\in\mathbf{C}}\) responsible for the planning of closures is introduced, where \(y_{n}=1(0)\) if the \(n\)th closure is selected for the execution (otherwise). A positive-valued time shift (shift) of the closure earliest starting time \(\mathbf{\Delta t}^{\text{C}}=[\Delta t^{\text{C}}_{n}]_{n\in\mathbf{C}}\) is the second decision variable for planning the track closures. When the time shift is determined, the closure must start at the earliest starting time plus shift. Decision variables \(\mathbf{\Delta t}^{\text{C}}\) and \(\mathbf{y}\) define the resulted track closure plan \(\mathbf{S}=[S_{n}]_{n\in\mathbf{C}}\), where \(S_{n}=<R^{\text{C}}_{n,j,k}\cdot y_{n},\max\{t^{\text{C}ES}_{n},t^{\text{CES}}_{ n}+\Delta t^{\text{C}}_{n}\},\min\{t^{\text{CLS}}_{n},t^{\text{CLS}}_{n}+ \Delta t^{\text{C}}_{n}\},T^{\text{C}}_{n}>\), and have to satisfy the constraints concerning obligatoriness, disjointness, and starting times of closures (see Formulas (2)-(4)). The track closure planning has to take into account the existing train timetable, which can affect likely conflicts in the track possessions by trains and closures.
## 2 Methods
### Feature Fusion  
Piezoelectric acceleration sensors and tomography pictures are combined after features are extracted. To this end, we calculated the feature vectors of the data collected by different sensors (i.e., vibration signals and ECT images). We spliced the single feature vectors in the data, which involved the same type in obtaining a new feature vector.  
The feature vector is rich in feature information and can better identify flow patterns than the feature vector from information from a single sensor. In feature layer fusion, the key is to balance the feature vectors extracted from multiple sensor data. Multiple feature vectors have to balance in order to allow fused features to have the same numerical scale and length. Therefore, applying the min-max normalization technology [25] to the obtained feature vectors and then joining them to synthesize a single vector is used. This approach aims to modify the individual feature vectors' value ranges while converting those values into a fresh set of features with the same numerical scale. According to the formula in (7), the min-max normalization strategy preserves the original fractional distribution and converts the values to a standard range [0, 1].  
\[a^{\prime}=\frac{a-\min(F_{a})}{\max(F_{a})-\min(F_{a})} \tag{7}\]  
where \(a\) is the value to be normalized, \(a^{\prime}\) is the normalized value, \(F_{a}\) denotes the function that generates \(a\), \(\min(F_{a})\) and \(\max(F_{a})\) denote the minimum and maximum values of \(F_{a}\) for all possible values of \(a\), respectively.  
The size of the data feature vector acquired by the piezoelectric accelerometer for each vibration signal data is fixed; conversely, the feature vector of the ETC laminar imaging image is extracted. Firstly, it is necessary to ensure that the two types of signal data set input to the network model are consistent in number and keep the correspondence in the signal. Secondly, the two types of signal data feature sets are convolved, pooled, normalized, and processed through other series of operations to ensure that the feature vectors of the two signal data types have the same length. Finally, the feature vectors are connected for fusion.
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
###### Abstract  
With the surge of ubiquitous demand for high-complexity and quality mobile Internet-of-things (IoT) services, new cooperative relaying paradigms have emerged. Motivated by the long and unpredictable end-to-end communication in relay-aided IoT networks, there is a need to introduce novel modulation schemes for very low bit error rate (BER) communications. In this paper, a practical modulation mapping scheme has been proposed to reduce decoding errors. Specifically, a hybrid automatic repeat request (HARQ) system has been used with an intermediate relay to transfer a message from a source to a destination. The design of modulation mapping has been optimized by first formulating the objective as the quadratic assignment problem. Later, the solution to the mapping problem is provided using an iterative search method. To validate the proposed solution, extensive simulations have been performed in MATLAB. The results show that the proposed solution outperforms the conventional relay retransmission and the heuristic design approaches.  
relay-aided; HARQ; IoT network; quadratic assignment +
Footnote †: journal: Applied Sciences
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 12 Security Issues concerning the Data Link Layer
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Network Performance** \\ \hline Network availability & The availability of a network will influence QoS and network performance. The user or software may receive unexpected or unwanted results if the network is down. The availability of several things used to build a network, such as redundant network devices or interfaces; power supplies in routers and switches; processor cards; resilient networking protocols; various physical links; etc., can render the network unavailable for users, causing a decline in network performance. \\ \hline Bandwidth & Network carriers have a limited amount of bandwidth; when oversubscribing to bandwidth, a customer must always have it available. This encourages consumers to bid for the limited amount of B.W. available. They receive B.W. depending on the traffic generated by other network users at any given time. When subscribers use the same network infrastructure, guaranteed B.W. subscribers must have preference over available B.W. subscribers’ traffic to ensure B.W. subscribers’ SLAs are reached even when the network is congested. \\ \hline Throughput & Throughput is the number of packets that successfully reach their destinations. Bits per second or data per second may describe the throughput power. The arrival of packets is critical to the high-performance operation of a network. When using services or apps, people expect their requests to be heard and responded to quickly. Low throughput implies packet loss, which contributes to bad or slow network efficiency. Throughput is a metric that calculates network speed, but a low value may impact network efficiency, causing packet loss, latency, and jitter. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: QoS parameters and their effects on network performance at the data link layer.
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 6 Discussion  
In this article, we have introduced a new type of algorithm for constructing cryptographically strong S-boxes. The main idea of the proposed method is to determine S-box function values step by step and examine the partial Difference Distribution Table during the process. Setting and enforcing quality criteria during the S-box construction leads to a stochastic search algorithm with early rejection sampling. Our analysis of the algorithm, as confirmed by experimental results, shows that it outperforms purely random searches.  
There are many open questions and open research topics related to our proposal. In our proposal, we focus solely on the differential properties of the S-box. To generate a good S-box that satisfies multiple criteria, these criteria can be evaluated after the algorithm reaches an S-box that fulfills the prescribed differential properties. If the S-box does not meet other criteria, it is possible to resume the original search after backtracking.  
An open question is whether other criteria can be incorporated directly into the search based on the partial S-box. For certain criteria, this might be challenging. If we consider nonlinearity as an example, the nonlinearity is the minimum distance of any component of our function to any linear Boolean function. Adding additional value to a partial S-box can increase certain distances while decreasing others.  
Another open question is whether it is possible to improve our method using stochastic search heuristics such as hill climbing, simulated annealing, or evolutionary algorithms. One direction to consider is changing the definition of the algorithm step from permuting two function values to filling in a partial S-box. It is, however, unclear what the fitness landscape would look like, as well as whether the optimization heuristics would provide an additional increase in performance.  
Conceptualization, S.M. and P.Z.; methodology, S.M. and P.Z.; software, S.M.; validation, S.M. and P.Z.; formal analysis, P.Z.; investigation, S.M. and P.Z.; resources, P.Z.; data curation, S.M.; writing--original draft preparation, S.M. and P.Z.; writing--review and editing, S.M. and P.Z.; visualization, S.M. and P.Z.; supervision, P.Z.; project administration, P.Z.; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovak Research and Development Agency under Contract no. APVV-19-0220.  
**Institutional Review Board Statement:** Not applicable.  
Figure 2: Graphical representation of comparison of the results obtained using different generation methods and different prescribed maximal value for an SBox size of \(n=6\).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank the anonymous reviewers for their comments and suggestions that improved this paper's readability and content.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lifetimes of Used Nuclear Fuel Containers Affected by
## 1 Introduction  
Deep geological repositories (DGR) are considered the safest long-term nuclear waste disposal solution [1]. They involve the excavation of an underground site where used fuel containers (UFC) are placed for permanent storage. The basic idea of the design is to use several barriers of different materials to protect and contain the UFCs. A number of countries are currently developing DGRs, among them are Finland, Sweden, and Canada [2, 3, 4].  
The Canadian DGR design is planned to host approximately 4.6 million CANDU (Canada Deuterium Uranium) used fuel bundles contained within 96,000 UFCs in a suitable rock formation, either crystalline or sedimentary. The vaults containing the UFCs will be located at a depth of about 500 m underground [5]. The design is based on a multiple-barrier system which is designed to contain and isolate the nuclear waste. Two important layers of protection in this system are provided by the UFCs and the highly compacted bentonite (HCB). The UFCs are 2.5 \(\times\) 0.556 \(\times\) 0.556 [m] canisters made of steel with a 47-mm-thick cylindrical shell with 30-mm-thick hemispherical caps and an external 3-mm-thick copper coating [5]. The HCB is an absorbent aluminum phyllosilicate clay that has been compacted to achieve a designed dry density and is the material responsible for isolating the UFCs from the host rock of the vault and preventing any inflow or outflow of materials. Bentonite swells upon contact with water which makes it a natural water barrier and a sealer to the placement rooms. Buffer boxes made of HCB are created to enclose the UFCs, these boxes are then placed inside the placement rooms. The space between thebuffer boxes and the host rock is backfilled with bentonite pellets dubbed 'gapfill' in this work (Figure 1).
# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 3 IATN for Bearing Fault Diagnosis under Variable Working Conditions
### Dataset  
To evaluate the performance of the proposed method more comprehensively, this paper verifies its effectiveness on three datasets.  
The first dataset is private. As Figure 4 shows, it is a dual-rotor test bench to simulate dual-rotor engine operation. The low-pressure rotor (LR) and the high-pressure rotor (HR) are driven by different motors and rotate in the same direction at different speeds. The LR is supported by the No. 1 and No. 4 bearings, and the high-pressure rotor HR is supported by the No. 2 and 3 bearings. The inter-shaft bearing is No. 3, and the experimental bearing is between the two rotors. The outer ring of the inter-shaft bearing rotates with the HR at a higher speed, and the inner ring rotates with the LR at a lower speed.  
This paper collects vibration data under four working conditions (WDs), as shown in Table 3. Each working condition includes bearing data in nine different health states, including normal state, three degrees of inner-ring failure, three degrees of rolling-element failure and two different degrees of outer-ring failure. The sampling frequency is 20.48 kHz. In addition, this paper uses a sliding window of a length of 1024 to sample the raw signal without overlap and collect 100 samples for each health state under each operating condition.  
The second dataset is the public Case Western Reserve University dataset (CWRU). The CWRU dataset has four working conditions: 1797 rpm (R0), 1772 rpm (R1), 1750 rpm (R2) and 1730 rpm (R3). Under all working conditions, this paper selects normal data and three different degrees of fault data of the inner ring, outer ring and rolling elements collected at the driving end as experimental data. The sampling frequency is 12,000 Hz.  
The third dataset is the public Ottawa dataset. The Ottawa dataset has four working conditions: increasing speed (O0), decreasing speed (O1), increasing then decreasing speed (O2) and decreasing then increasing speed (O3). Under all working conditions, this paper selects normal data and fault data of the inner ring and outer ring. The sampling frequency is 200,000 Hz.  
The training epoch is uniformly set to 300 using the Adams optimizer, and the learning rate is 0.001. The code is written through the Pytorch 2.2 framework and runs on the GPU of GPTX1650.
# On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning
## 1 Introduction
### Computational Geometry  
Currently, computational geometry concerns the computational complexity of geometric problems within algorithm analysis. However, the phrase has also been used in at least two other contexts. [PERSON] refers to this field as \"computational geometry\" in his study [4] since [PERSON] [5] investigated geometric modeling using spline curves and surfaces. However, this subject is more closely related to numerical analysis than geometry.  
The term \"Computational Geometry\" has already been used in at least two other situations by [PERSON] et al. [6], and geometric algorithms have been created in various settings such as B-spline curves and surfaces [7]. Here, we try to examine these related activities from a proper perspective and compare them to the connotation that is currently in use:  
* [PERSON], [PERSON], and [PERSON] have successfully handled the problem of geometric modeling using spline curves and surfaces [7], which is more closely related to numerical analysis than geometry in terms of spirit;
* Many computer scientists believe that [PERSON] Ph.D. thesis at Yale University in 1978 [8] or perhaps his earlier paper on geometric complexity [9] are the two works that gave rise to the field of computational geometry, which is frequently referred to as a new one in the area of computing science;
* Others would argue that it started 10 years earlier with [PERSON]'s Ph.D. thesis at Cambridge University in 1968 [10], or possibly with his later papers on computational geometry [4];
* Finally, others would argue that the formal examination of Minsky and Papert [11] into which geometric properties of a figure can and cannot be identified (computed) using different neural network models of computation is where it all started.  
Fundamental theories and algorithms mentioned in the Handbook on Computational Geometry [2] are convex hull computations, Voronoi diagrams, Delaunay triangulation, arrangements, triangulation and mesh generation, polygons, shortest paths, proximity algorithms, visibility graphs, geometric reconstruction, curve and surface reconstruction, computational convexity, and computational and quantitative real algebraic geometry. Here, we demonstrate the application of computational geometry in robotics to solve specific path-planning problems for single- and multi-robotic systems.  
To solve a problem using computational geometry, the following components are required:  
* Size of input to the algorithm;
* Dimension of the problem;
* Properly defined constraints;
* Objective functions;
* Modality of the algorithm.  
Figure 1 depicts the overview of CG-based problem-solving specific to robotics and path-planning problems. The CG-based solutions can be extended to a path-planning problem for multiple robots by considering a distributed algorithm running on each robot for achieving common objectives.
## 4 Segment Level Bi-LSTM--CRF Model for Arabic Dialect POS Tagging  
A conditional random field (CRF) is an undirected graphical model that was successfully experimented in various sequence labeling tasks such as POS tagging, word segmentation and named entity recognition (NER). The CRF model can prevent the limited feature selection in Hidden Markov models and MEM by considering the correlation between labels in neighborhoods. Moreover, it can acquire a global optimum via a process of global feature normalization. For a given sequence \(s\ =\ \{s_{1},s_{2},\ldots,s_{n}\}\), Where \(s_{i}\) is the vector of the \(i\)th Arabic dialect segment and let \(L\ =\ \{l_{1},l_{2},\ldots,\ l_{n}\}\) be a sequence of labels for \(S\), where \(l_{i}\) is the label of the \(i\)th Arabic dialect segment. The following equation can express the linear-chain CRF model:  
\[p(L|S;W,b)\ =\ \frac{\prod_{i=1}^{n}\varphi_{i}(l_{i-1},l_{i},S)}{\sum_{Q\in \varphi(S)}\ \prod_{i=1}^{n}\varphi_{i}(Q_{i-1},Q_{i},S)} \tag{4}\]  
where \(\varphi_{i}(Q_{i-1},\ Q_{i},\ S)\ =\ \exp\left(W_{Ql}^{T}S_{i}+b_{Ql}\right)\) is the potential function corresponding to a label pair \(\mathrm{r}(Q,l)\), \(W_{T}\) is the weight vector, \(b\) is bias and \(\varphi(S)\) denotes the set of possible label sequence for \(S\). The current study had developed a segment-level Bi-LSTM-CRF model to do POS tagging task for Arabic dialect as shown in Figure 1. First, the Arabic Dialect segments with its labels (tags) were retrieved and a vocabulary dictionary for the segments and its labels were created. Then, a reverse vocabulary dictionary was also created to map the segments and its tags to a sequence of numbers and then, the sequences were padded. The study used an equal-length input, so the sentences were padded to a length of 25 for both dialects (Levantine and Maghrebi). The model consists of a forward LSTM, backward LSTM and CRF layer. The key point is to use forward LSTM to capture the previous input feature, backward LSTM to capture future input feature and CRF layer to obtain sentence level tag information. Let's assume \(X\ =\ \{x_{1},x_{2},\ldots x_{n}\}\) represents a general input sequence, \(y\ =\ \{y_{1},y_{2},\ldots y_{n}\}\) represents the tag sequence for \(X\) and \(P_{n*k}\) denotes to the probability matrix, where \(k\) is the number of tag types. The optimal tag sequence can be obtained by maximizing the target function.  
\[\mathrm{s}(X,y)\ =\ \sum_{i\ =\ 0}^{n}A_{y_{i};y_{i+1}}+\sum_{i\ =\ 0}^{n}P_{i,y_{i}} \tag{5}\]  
where \(P_{i,j}\) is the probability that the \(i\)th segment is tagged as the \(j\)th tag and \(A\) is the state-transition matrix, where \(A_{i,j}\) is the probability of transferring from the \(i\)th tag to \(j\)th tag.  
Figure 1: Arabic Dialect Segment-Level Bi-LSTM-CRF part-pf-speech tagging model.