esa-sceva/satcom-chunk-collection huggingface dataset:
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 1 Introduction  
Since the 1960s, the integration of information and communication technologies (ICT) into healthcare--referred to as e-health--has steadily grown in both clinical settings and educational contexts [1, 2]. E-health plays a crucial role in delivering healthcare services remotely [3], helping to overcome barriers related to geographic distance or the specific needs of families and individuals [4, 5]. The use of tele-health solutions has notably expanded for both therapeutic purposes (e.g., tele-rehabilitation and tele-intervention) and evaluations (e.g., tele-assessment, online assessments, and videoconferencing assessments) of cognitive functions.  
Cognitive functioning encompasses the mental activities involved in acquiring, processing, storing, and retrieving information from the environment. These include a broadrange of abilities, such as intelligence, language, learning, memory, attention, executive functioning, perception, visual-spatial skills, motor coordination, and academic abilities.  
E-health tools are regarded as practical and dependable alternatives for cognitive assessments. Compared to traditional paper-and-pencil tests, these digital formats can offer more engaging, multi-sensory environments that boost motivation and cooperation, especially among children and young adults [6].  
Additionally, as digital technologies have become an integral part of daily life, using them for cognitive evaluation adds ecological validity by reflecting real-world conditions. Lastly, these tools enable the collection of digital data on individuals with special needs, which can support clinical and diagnostic decision making [7].
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 2 Coupling through Gratings: Theory and Fundamentals  
Photonic single mode waveguides are usually high index regions structured to support orthogonally polarized guided fundamental modes. There is consequently a considerable mismatch between the propagating vectors of light arriving from a low index cladding and that of a guided mode in a planar waveguide. This mismatch can be compensated by wave of a grating.  
Basically a grating is a periodic corrugation of high and low refractive index regions. The corrugation can either be by material or structural means. On a photonic chip, grating lines are implemented through etching of high index region. Figure 2a illustrates a typical fiber-chip surface grating coupler on SOI. The situation is better depicted by the k-space diagram in Figure 2b. The phase matching criteria for coupling from free space to guided modes is expressed as [9]:  
\[\beta=k_{fr}n_{sup/sub}sin\theta+mG \tag{1}\]  
Here \(\beta\) is the waveguide propagating vector corresponding to an effective grating index \(n_{g}\), \(G\) is the reciprocal lattice corresponding to grating period \(\Lambda\), \(k_{fr}\) is the free space propagating vector in the either the upper cladding superstrate of refractive index \(n_{sup}\) or the lower cladding substrate of index \(n_{sub}\), \(\theta\) is the off axis fiber tilt angle and \(m\) is the diffraction order. Although phase matching allows  
Figure 1: Schematic of edge coupler using inverse-tapers. The fundamental Gaussian mode in lensed fiber with a mode field diameter (MFD) of 3 μm roughly matches the size of the fundamental waveguide mode at the cleaved end of the overlay region.  
for perfectly vertical coupling, this configuration can lead to a strong second order diffraction while exciting propagating mode from waveguide. Hence, fiber is always positioned with a slight tilt to the normal. Depending on fulfilling the above \(Bragg\) condition, light is diffracted to one of the guided waveguide modes. The phase matching equation for first order diffraction to either top or bottom cladding is simplified to obtain the grating period:  
\[\Lambda=\frac{\lambda_{fr}}{n_{g}-n_{sup/sub}sin\theta} \tag{2}\]  
where \(\lambda_{fr}\) is the free space wavelength. The average effective grating index \(n_{g}^{eff}\) of the fundamental propagating mode can be expressed as:  
\[n_{g}^{eff}=fn_{etch}^{eff}+(1-f)n_{slab}^{eff} \tag{3}\]  
where \(n_{slab}^{eff}\) and \(n_{etch}^{eff}\) are the effective indices of the fundamental orthogonally polarized TE or TM mode in slab and etched regions respectively. \(f\) is the fill factor which defines the proportion of grating period that is etched.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & **Membrane** & **Membrane** & **NaCl Feed** & \multirow{2}{*}{**Hot Side Temp** (\({}^{\circ}\)**C)} & **MD Permeate** & **Ref.** \\  & **Material** & **Morphology** & **Solution** & & **Flux (Lm\({}^{-2}\)**h\({}^{-1}\)**)** & **Ref.** \\ \hline SGMD & Al\({}_{2}\)O\({}_{3}\) & Disk & 4.0 w\(\%\) & 70 \({}^{\circ}\)C (80 \({}^{\circ}\)C) [90 \({}^{\circ}\)C] & 9.9 (13.1) [19.8] & [8] \\ AGMD & ZrO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) & Tubular & 1.0 [M] & 65 \({}^{\circ}\)C (80 \({}^{\circ}\)C) [95 \({}^{\circ}\)C] & 1.0 (2.9) [6.9] & [23] \\ AGMD & Al\({}_{2}\)O\({}_{3}\)/ZrO\({}_{2}\) & Tubular & 1.0 [M] & 75 \({}^{\circ}\)C (85 \({}^{\circ}\)C) [95 \({}^{\circ}\)C] & 1.7 (2.9) [5.0] & [24] \\ AGMD & ZrO\({}_{2}\) & Tubular & 0.9 w\(\%\) & 50 \({}^{\circ}\)C (60 \({}^{\circ}\)C) [70 \({}^{\circ}\)C] & 2.3 (0.9) [7.0] & [25] \\ AGMD & Titania & Tubular & 0.8 [M] & 70 \({}^{\circ}\)C (80 \({}^{\circ}\)C) [90 \({}^{\circ}\)C] & 0.8 (1.5) [2.8] & [26] \\ DCMD & Titania & Tubular & 0.8 [M] & 70 \({}^{\circ}\)C (80 \({}^{\circ}\)C) [90 \({}^{\circ}\)C] & 0.6 (1.0) [2.5] & [26] \\ DCMD & Al\({}_{2}\)O\({}_{3}\) & Planar & 0.5 [M] & 53 \({}^{\circ}\)C & 9.0 & [27] \\ VMD & ZrO\({}_{2}\)/Ti & Tubular & 0.1 [M] & 40 \({}^{\circ}\)C & 10.8 & [27] \\ AGMD & ZrO\({}_{2}\)/Ti & Tubular & 0.5 [M] & 75 \({}^{\circ}\)C (85 \({}^{\circ}\)C) [95 \({}^{\circ}\)C] & 2.7 (3.3) [4.7] & [27] \\ DCMD & Al\({}_{2}\)O\({}_{3}\) & Planar & 4.0 w\(\%\) & 60 \({}^{\circ}\)C (70 \({}^{\circ}\)C) [80 \({}^{\circ}\)C] & 7.1 (1.5) [17.0] & [28] \\ DCMD & Al\({}_{2}\)O\({}_{3}\)/ZrO\({}_{2}\) & Tubular & 1.0 [M] & 60 \({}^{\circ}\)C (95 \({}^{\circ}\)C) & 1.1 (6.9) & [29] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A list of the MD permeate flux values for ceramic membranes published recently. Figure 6: The performance of as-prepared hydrophobic hollow fiber sintered at 1400 \({}^{\circ}\)C with different feeding temperature (70, 60 and 50 \({}^{\circ}\)C) in VMD.
# Carbon Fiber Traces in Cracked Surfaces of Mortar Prisms
Hence, the cracked surface, which represents the lowest resistance place in the force propagation direction, will be mostly the plane mortar parts. Since the fiber clusters are randomly aligned in a prism and the cracks on the surface will try to avoid the places where fiber strands/clusters are located, bumps appear on the surface because of the cracks bypassing these randomized localizations to find the lowest resistance places. As the fiber percentage increases, more randomized localizations of fiber clusters will be achieved due to increased overlapping and aggregating among fibers. Since the cracks are bypassing these randomized localizations to find the lowest resistance places, more and bigger bumps appear on the surface, i.e., the surface will be bumpier as the fiber amount increases. Hence, the traces of fiber strands/clusters along the cracked surface will explain why the prisms with a higher fiber content will have lower strength than those with a certain fiber amount. The fiber traces along the surface are classified into five groups: uprootedness, avul-sion, separation, overlay and dividedness. Since the overlay can be grouped with the separation, the groups are not too different from those in the reference [11]. The uprootedness represents the trace of pulling one end of fiber clusters/strands out completely from the mortar. The avulsion is the trace of detaching the fiber cluster held by the cement from the mortar. Each of these two have two different types based on whether fibers remain in the mortar part or not for the uprootedness, and whether there are fiber vestiges in the mortar parts or not for the avulsion. Separation is the trace of separating two overlapped and aggregated fiber clusters into two parts. The overlay represents the trace of separating two overlapped fiber clusters of different directions into two parts. For the separation and overlay, one or both tip parts of the clusters are in the mortar. The dividedness represents the trace of breaking the mortar locally or fiber strand mixed mortar into two parts. This breaking is different from those in plane prisms because it appears as a set of valleys and plateaus/hills, and one or both of them contains fiber strand mixed mortar and/or s cemented fiber chunk. The resistance of these four groups to the applied force may not be too different from that of the uprootedness because they all appear on the cracked surfaces. In this paper, to explain why the strength of the prisms with higher carbon fiber contents is reduced, five fiber traces appearing along the cracked surface are identified, and their behaviors are analyzed. For this purpose, mortar prisms with five different carbon fiber contents, namely, 0%, 0.5%, 1%, 1.5% and 2%, are prepared. For each percentage, two prism samples are used.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 5 An Overview of Attack-Resilient Control and Communication Techniques  
In this section, we provide an overview on some of the recently developed control and communication techniques that aim to achieve resiliency against DoS attacks. These techniques rely on the modeling and analysis approaches discussed in previous sections.  
Figure 7: A multi-hop network between the plant (\(v_{\mathrm{P}}\)) and the controller (\(v_{\mathrm{C}}\)). State measurement packets on this network are transmitted over two paths \(\mathcal{P}_{1}\) and \(\mathcal{P}_{2}\), which are both subject to malicious packet-dropping attacks.
# Cooperation Based Proactive Caching in Multi-Tier Cellular Networks
## 2 System Model  
Suppose a modern cache enabled multitier cellular network consisting of T tiers designated as \(\mathcal{T}=\{1,\ldots,T\}\) of base stations of M number. The set of base stations could be denoted by set \(\mathcal{M}\) which is equal to \(\{1,\)...,M\(\}\), and it could easily serve N number user terminals (UTs) from the set \(\mathcal{N}=\{1,\ldots,N\}\) which are installed in a certain geographical area that consists of residential area, factories, colleges, schools, etc. As the network is multi-tier so it comprises of pico BSs, micro BSs, and a single macro BS to serve greater number of users. Each BS m of corresponding tier T is equipped with its limited physical storages \(\mathsf{S_{mt}}\). Then total the storage capacity of the network is denoted as \(\mathsf{S=\{S_{11},S_{21},\ldots,Sm_{1},S_{12},S_{22},\ldots,\)\  
to micro BS and macro BS. On the other hand, a macro base station will cache more files as compared to micros and picos. Similarly, the number of users for pico is lesser as compared to micro and macro. In the multi-tier system, there will be more pico then micro and macro. As the capacity is limited, only popular content can be stored. Thus, the base station will offload the traffic, thereby, reducing traffic on the back haul and improve the quality of services.  
Let us consider a base station \(\text{BS}_{\text{m}}\) receives a set of requests \(R(m)\) for certain data during time interval \(\text{T}\)' seconds. These requests are Zipf-like distribution in nature. Let \(\text{X}_{\text{t}}\) be the cache decision matrix that depends on the popularity matrix \(\text{P}\) of the contents and \(C(m)\) is the set of cached contents. Then the cache hit ratio is given in Equation (1).  
\[\text{CHR}=\left|\frac{\sum_{m}C(m)\cap\ R(m)}{\sum_{m}R(m)}\right| \tag{1}\]  
In a t-tier wireless communication system, each tier will require its cache to be filled by the requested contents. The overall or the average cache hit ratio of the multi-tier system can be modelled, as:  
\[\alpha\ =\ \frac{\sum_{t\in\mathcal{T}}\sum_{m\in\mathcal{M}}\left|C(mt)\cap \ R(mt)\right|}{\sum_{t\in\mathcal{T}}\sum_{m\in\mathcal{M}}\left|R(mt)\right| }. \tag{2}\]  
Each tier has its own users and cache decision matrix \(\text{X}_{\text{t}}\) which depends on the popularity matrix \(\text{P}_{\text{t}}\left(\text{t}^{\prime}\right)\in\mathbb{R}^{\text{MxF}}\):  
\[\text{X}_{\text{mt}}\ =\ \left\{\begin{array}{c}1\text{ if content f is cached at }\text{Bsm}_{\text{t}}\text{ of tier t},\forall\text{m }\in\mathcal{M}\\ 0\text{ otherwise}\end{array}\right..\]
## 5 Conclusions  
This paper proposed a novel network intrusion detection model. It used the PCA-ADASYN algorithm for feature extraction from the intrusion dataset and employed the ResIncepNet-SA convolutional neural network to detect abnormal network traffic. The main conclusions are as follows:  
Ablation experiments showed that the proposed model delivered the best performance. By incorporating residual and attention modules into the InceptionNet network, the model reduced the MSE to 0.04749 and increased the accuracy to 0.94195.  
The combined utilization of PCA and ADASYN for dataset processing enhanced the model's prediction accuracy. The PCA-ADASYN algorithm improved accuracy by 6.35% and 2.34% compared with the PCA and ADASYN algorithms alone. Additionally, precision increased by 9.49% and 1.81%, recall by 6.32% and 1.95%, and F1-Score by 8.51% and 1.83%.  
The proposed model exhibited fast convergence and high prediction accuracy. Compared with classic deep learning models and those presented in other studies, the model achieved 0.96 accuracy after just three epochs. On the NSL-KDD dataset, the model's prediction accuracy reached 0.99339.  
The proposed model offered a new solution for network traffic intrusion detection, particularly for imbalanced datasets.  
In the future, we plan to expand our research by integrating more datasets. In addition, we will introduce a graph neural network model to discover hidden relationships between network traffic nodes. This will help identify potential attack paths and abnormal behaviors, allowing us to keep up with the evolving network environment and strengthen overall network security.  
Conceptualization, G.L. and H.D.; methodology, G.L.; software, T.Z.; formal analysis, G.L.; investigation, X.C.; data curation, T.Z.; writing--original draft, T.Z.; visualization, D.Y.; supervision, X.C. and D.Y.; project administration, H.D.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tianjin Science and Technology Commission, grant numbers 18 JCYBJC85600 and 2019 KJ094.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
We sincerely thank [PERSON], President of Hangzhou Zhijiang Switch Co., Ltd., for his generous financial support, which played a vital role in the success of this study. His contribution ensured the smooth progress of key aspects of the study, including data collection, model training, and the construction of the experimental platform. We also appreciate the trust and cooperation he extended to us throughout the process.  
Author [PERSON] was employed by the company Hangzhou Zhijiang Switchgear Stock Co., Ltd. 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.
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
###### Abstract
Since the proposed quantum communication uses the multiple-beam technique, which includes multiple parallels of transmitters and receivers to transmit the signal, it enables the reception of a large number of quantum signals and reduces the cost of its implementation [17]. This paper will focus on determining the important parameters to achieve the best performance of the multiple beam for the multi-photon protocol. Hence, a simulation of their behavior will be conducted using accurate mathematical analysis that has been derived and improved by previous researchers. The new contribution of this paper is the transmission of multiphoton using the multiple beam. Thus, the single-photon evaluation is beyond the scope of this paper. All the results in the performance analysis section are the comparison between the proposed multiple beam with the standard single beam in order to transmit the multiphoton. The remainder of this paper is organized as follows. Section 2 discusses related works. Section 3 explains the formulation of the atmospheric channel model. The formulation of quantum cryptography in FSO is demonstrated in Section 4 while Section 5 presents the implementation of MQC-MB in FSO. The parameter setting for the simulation is presented in Section 6 while numerical results are discussed in Section 7. Lastly, the conclusion and future works are discussed in Section 8.
## 2 Related Research
### SDN (Software-Defined Networking)  
Software-defined networking (SDN) is a concept of defining and controlling networks through software, a framework designed and built to manage traffic handling methods and various functions in a centrally controlled system by separating the data and control domains. This concept was devised to overcome the complexity and flexibility limits of traditional hardware-centric network structures and to implement user-centric networks. It started with a paper published in August 2007 by Stanford University, proposing a way to facilitate network management and enhance security [11]. SDN provides an open interface that enables the development of software capable of controlling connectivity provided by a set of network resources and network traffic flow, along with potential inspection and modification of traffic performed in the network. As illustrated in Figure 2 below, SDN is composed of three layers, emphasizing four features: the central separation of the Infrastructure Layer and Control Layer, a central controller, an open interface between the Control Layer devices and Infrastructure Layer devices, and network programming by external applications [12].  
The Application Layer is where various programs utilizing SDN exist. It enables the use of various functions across the network through the APIs provided by the Control  
Figure 2: Architecture of SDN (software-defined networking) [13].  
Layer. These APIs are used for interaction between the Application Layer and the Control Layer and can be used by the Application Layer to receive information related to the Control Layer. The Control Layer is the layer that centrally handles the flow of network data, responsible for determining the routing and forwarding methods for specific packets. It controls the equipment of the data layer through the Control Data Plane Interface and provides an interface abstracting the network's functions to the Application Layer through APIs. The Infrastructure Layer is a layer composed of network devices capable of packet forwarding and processing. It houses the flow table, which is responsible for the actual transmission of data. It operates by receiving routing information from the Control Layer through the Control Data Plane Interface. These layers are controlled through interfaces. Because traditional network devices are single-box, there has been difficulty in changing or manipulating the equipment. Therefore, SDN is designed so that control is possible as long as one knows the standard interface, enabling escape from hardware vendors.
###### Abstract  
This study explored the possibility of using reverse osmosis (RO) reject water as a mixing water for producing cementitious bricks using calcium sulfoaluminate (CSA) cement along with gypsum, and it investigated the changes in the properties of CSA cement pastes when RO reject water was used. The results were compared with those obtained using purified water and seawater. Overall, the use of RO reject water improved the cement paste's strength. Given that the use of RO reject water very slightly affected ettringe formation but more significantly influenced the Al2O3-Fe2O3-mono (AFm) phases (i.e., monosulfate, kuzelite, and Friedel's salt) and amorphous aluminum hydroxide (AH3), the strength improvement was likely mainly due to the formation of Friedel's salt rather than ettringe formation. This study also demonstrated that the use of RO reject water for brick production satisfied the Korean Standards (KS) F 4004 and toxicity characteristic leaching procedure (TCLP); thus, it is recommended to use RO reject water as a mixing water to produce CSA cement bricks for use in construction.  
calcium sulfoaluminate cement; reverse osmosis; reject brine; brick +
Footnote †: journal: Applied Sciences
## 3 Adaptive Ensemble Square Root Kalman Filter
### Ensemble Kalman Filter
The EnKF algorithm not only overcomes the problem that Kalman filter is limited to linear systems, but also avoids the problem of large amount of computing resources when calculating prediction error covariance. Tangent linear model and adjoint model are no longer needed. The method also gives the optimal value of the estimated result and the confidence interval of the estimated value at the same time. The EnKF is a sub-optimal estimation method which uses the ensemble to describe the traditional Kalman filter algorithm. A prediction set estimation is used to calculate the state prediction error covariance matrix of the Kalman gain matrix. If each element of the set is updated with the same observation value and the same gain, the set will systematically underestimate the state update error covariance matrix. It even can lead to subsequent analysis degradation and filtering divergence. This problem can be alleviated by adding random perturbations to the measurements with correct statistical data. However, the introduction of random perturbation will inevitably increase the additional error of sampling error related to measurement error covariance. This additional error will reduce the covariance accuracy of the observation state error as well as the estimation accuracy.