esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
## 3 Methodology
### Self-Attention Layer  
The limitation of an encoder-decoder model is their fixed-length connection [29] of the intermediate semantic vector because the encoder compresses the whole sequence of information into a fixed-length vector. This approach has two shortcomings: (1) The semantic vector cannot fully represent the sequential information, which results in the information in the input sequence not being fully utilized. (2) An input sequence's length grows when the later-entered information may overwrite the first-entered information. To tackle the limitations of the encoder-decoder, self-attention [11, 47, 48, 49] is implemented to extract as much feature information as possible from the input sequences. Thus, to enhance the ability of the proposed model, and we incorporate token-level multi-head attention to the output \(H=[h_{1},h_{2},h_{3}\ldots h_{n}]\) of concatenated BiLSTM states to learn the internal structure of sentences and capture token-level dependency. Self-attention [31] before the CRF [11] automatically focuses on the specific Sindhi tokens that play a crucial role in NER and captures the essential semantic information while reducing the focus on less important information in the input sequence. We use the scaled dot product to calculate the similarity between query \(Q\) and key \(K\) matrices to obtain the weights of each word. Then, we normalize the obtained score and calculate the weights using softmax. Afterwards, we obtain the final attention by a weighted sum of the value matrix \(V\) and the weights. The intuition of the self-attention is described as follows:  
\[\text{attn}(Q,K,V)=\text{softmax}\left(\frac{QK^{T}}{\sqrt{d}}\right)V \tag{4}\]  
where \(Q\in\mathbb{R}^{n\times 2d_{h}},K\in\mathbb{R}^{n\times 2d_{h}}\), and \(V\in\mathbb{R}^{n\times 2d_{h}}\) are the query, key, and value matrices, respectively. \(d_{h}\) is the dimension of both \(\overrightarrow{h}\) and \(\overleftarrow{h}\) hidden units of BiLSTM, which equals \(2d_{h}\). Firstly, the attention mechanism linearly maps \(\mathbf{Q}\), \(\mathbf{K}\), and \(\mathbf{V}\) in \(h\) times with different weights matrices. Then \(h\) projections perform parallel scaled dot-product attention. Finally, these results of an attention layer are concatenated and once again mapped to get the new representations. In our setup, we use self-attention as a set \(Q=K=V=H\), which aims to capture the inter-word dependencies in the input sequence, where \(H\) denotes the output of the BiLSTM network. Formally, the function can be stated as follows:  
\[\mathbf{h}_{i}=\text{attn}\left(Qw_{i}^{Q},Kw_{i}^{K},Vw_{i}^{V}\right) \tag{5}\]  
\[\mathbf{H}^{\prime}=(\mathbf{h}_{i}\parallel\ldots\parallel\text{head}_{h}) \mathbf{w}_{o} \tag{6}\]  
where \(w_{i}^{Q}\in\mathbb{R}^{2d_{h}\times d_{k}}\), \(w_{i}^{K}\in\mathbb{R}^{2d_{h}\times d_{k}}\), and \(w_{i}^{V}\in\mathbb{R}^{2d_{h}\times d_{k}}\) are the trainable projection parameters, and \(d_{k}=2d_{h}/h\). \(w_{o}\in\mathbb{R}^{2d_{h}\times 2d_{h}}\) are the trainable parameters.
## 4 Semantic Interoperability Provision in Heterogeneous Healthcare Service Environments
### Semantic Interoperability in Healthcare Data Models
#### 4.2.2 Semantic Annotation of Data Generated from Heterogeneous Sources  
The semantic annotation process enables the annotation of the data based on the standard semantic ontology data model. It provides annotation using the selected annotation description language. Semantic annotation provides enhancement of content by interlinking machine processed data to the mined concepts of a semantic ontology graph. Through semantic annotation process, information is attached to existing contents based on the concepts of the ontology model. Semantic annotation allows heterogeneous data to be transformed into meaningful information using an ontology model that can be inferred, exchanged, and reused. Features of semantic annotation capability include describing the associations among concepts and ontology models, associating information to the ontology graphs, and assigning semantic concepts and properties to the target data.  
To provide semantic annotation of heterogeneous healthcare data, a generic semantic annotation algorithm has been developed. The algorithm takes a collection of ontology graphs [\(\bar{\mathcal{L}}\Phi\)] and a pointer to the registry entries (\(\mathbb{R}\)e). The process involves loading concepts that need to be annotated with selected semantic ontology. Queries are performed on reference ontology graphs to extract semantic annotations. The algorithm contains several nested operations, in which the first series of iterations compute similarities on each extracted concept and generate a semantic graph. In the second phase, the entries are checked, and a validation is performed to evaluate the annotations. Moreover, validated entries are stored in the registry, and a base ontology instance is created to maintain annotated concepts. Also, verified semantically annotated graphs are either added to the annotation queue for quick reference or stored in the graph dataset of the semantic repository based on the computed weights. To visualize the process, a flow chart of the algorithm is presented in Figure 7.
## 4 c-di-AMP Binding Molecules
### Rck_C Domain Protein KtrA  
The first c-di-AMP binding protein isolated and characterized from bacterial cytoplasmic extract through c-di-AMP affinity chromatography was potassium transport protein, KtrA, an RCK (regulator of conductance of K\({}^{+}\)) domain-containing protein from _S. aureus_[17]. RCK domains are commonly found in potassium ion transport systems and are involved in the regulation of ion channel gating [58]. KtrA possesses two RCK domains, an N-terminal RCK (RCK_N) domain that is able to bind to a variety of molecules such as ATP, ADP, NAD, and NADH, and a C-terminal RCK (RCK_C) domain that binds to c-di-AMP [58]. KtrA associates with KtrB, a membrane-integrated protein that forms the potassium ion channel [65]. c-di-AMP binding to KtrA inhibits potassium transport [17; 20; 25]. Furthermore, _ktrA_ mutated _S. aureus_ exhibits lower survival rates than the wild type strains when  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**c-di-AMP** & & & & & & \\
**Rreceptor Proteins** & **Location in the Cell** & **Protein Structure** & & & **Phenotypes by Deletion or Overexpression of the Genes** & **References** \\
bacteria are grown in medium containing low potassium concentrations [17; 32]. The link between c-di-AMP and potassium ion channel proteins is found not only in _Staphylococcus_ strains but also in _Bacillus_ and _Streptococcus_ species [17; 20; 25].
# HORSIC+: An Efficient Post-Quantum Few-Time Signature Scheme
## 4 Analysis
### Comparison with HORS and Horst
#### 4.3.3 Comparison  
Table 2 compares HORSIC+ with HORS and HORST for the same security levels (96 bit, 352 bit). For simplicity, we assume that HORST does not apply any optimizations in [13]. The table shows that a HORSIC+ signature size is smaller than a HORS and HORST signature size with a comparable security level. With parameters \(n=128\), \(t=2^{10}\), \(k=10,z=22,w=13\), HORSIC+ signatures are 37.5% shorter than HORS signatures and 61.5% shorter than HORST signatures to offer a 96-bit security level. With parameters \(n=256,t=2^{16},k=26,z=35,w=10\), HORSIC+ signatures are 18.75% shorter than HORS signatures and 45.8% shorter than HORST signatures to offer a 352-bit security level. HORSIC+ reduces the signature size at the cost of increased overhead in key generation, signing, and verification. The key generation overhead and the signing overhead of HORSIC+ are larger than those of HORS and HORST. However, it does not affect the usability of HORSIC+, since the key generation has to be performed only once and the signing overhead is still tolerable. Since asymmetric key algorithms are typically hundreds to thousands of times slower than symmetric key algorithms and hash algorithms [34], the costs of signing HORSIC+ (130 with 96-bit security level and 260 with 352-bit security level) are relatively low.
## 5 III-V-on-Silicon Devices for Optical Sensing Applications
### III-V-on-Silicon Broadband Light Sources for Optical Sensing Applications  
#### 5.1.1 Superluminescent LEDs  
An important building block for the realization of a full on-a-chip spectroscopic system is an efficient broadband light source that can be integrated onto PICs. In many of such sensor applications high power and wide bandwidth sources are of critical importance, making superluminescent LEDs the preferred option. To achieve the wide bandwidth, we opted, in a collaboration with the University of California--Santa-Barbara, for a combination of quantum well intermixing and multiple die bonding. Other techniques use a dual QW design [57], multistate QWs [58], quantum dots [59] or use supercontinuum generation. The latter is discussed in paragraph 5.2.3, the others are difficult to design and optimize given their complex structure and only work well at specific drive currents. Quantum well intermixing is a technique where the composition of the quantum well and barrier is changed by atom disordering. Before bonding, the InP epitaxial layer stack is implanted with phosphorous atoms, which are annealed into the active region. Depending on the mask applied before the implant and the annealing duration and temperature, the band gap is locally blue shifted. A typical maximum shift is 100 nm at telecom wavelengths [60]. To shift the band gap further, a second die was bonded. Since each die was intermixed, four band gaps were transferred to SOI. The emission from these different active regions was combined in series, as depicted in Figure 20. This implies that the longest wavelengths propagate through all the other sections.  
After combining multiple die bonding and quantum well intermixing, a broadband superluminescent LED was fabricated with the process described in [60]. Each band gap section can be pumped separately, as shown in Figure 21a. In order to maximize the 3 dB bandwidth, the pumping currents are individually tuned, resulting in the spectrum shown in Figure 21b. The on-chip power is \(-8\) dBm and the 3 dB bandwidth is 292 nm, ranging from 1258 nm to 1550 nm.  
These devices were heterogeneously integrated using molecular bonding. A COMSOL heat simulation of the superluminescent LED shows a thermal resistance (normalized to surface area) of \(12.8\dfrac{Kcm^{2}}{kW}\) for a 50 nm thick BCB bonding layer and \(10.5\dfrac{Kcm^{2}}{kW}\) for molecular bonding, indicating that the largest source of thermal impedance is the buried oxide layer underneath the silicon waveguides, and not the thin DVS-BCB bonding layer.
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 5 Applications to the Internet of Things
### Applications in Mobile Ad Hoc Networks  
Conducting business transactions in MANETs is an interesting use case of the proposal, because in that scenario, a legitimate network node might want to share its own resources with other legitimate nodes to carry out such transactions. Due to their mobile nature, often, the nodes of a MANET do not have Internet access in many places. Thus, those legitimate nodes that have an Internet connection may want to rent their connection to other legitimate nodes. For this task, both nodes can establish a shared secret session key, which can be done with the scheme proposed in this paper.  
In general, two different scenarios for the use of variants of the proposed scheme are described below: To report authenticated information unidirectionally either without using secret keys or using a public key. On the one hand, a node may only want to transmit information from an authenticated way (see Figure 3) so that other legitimate nodes hearing the messages rely on the information that the sending node transmits, thanks to its knowledge of the secret network key used to generate the isomorphic graphs and solutions involved in the described protocol. Two specific examples of use cases within a MANET in this new scenario are event notification or dissemination of advertising shops. The transmission of advertising using the scheme proposed in this paper implies that only legitimate nodes can send advertising, which avoids massive spam from nodes that do not belong to the network. On the other hand, a legitimate node may want to spread its public key in an authenticated way and only to other nodes that also belong to the network (see Figure 3). This use case requires the application of another variant of the proposed scheme where sent beacons hide in their last segment the public key of the sender node. This implies that only legitimate network users can access the public key of the sender hidden in the last segment of the beacon, because the challenges and responses are based on a secret network key. This approach can be used whenever the spread of an event through a MANET requires the use of a digital signature scheme by legitimate nodes, which send their public keys to other legitimate nodes of the network in an authenticated way.
## 1 Introduction and Related Works
### Statistical Considerations  
For real-time traffic monitoring to be considered reliable, there needs to be a certain guideline for required sample sizes. [PERSON] et al. [PERSON] et al. (2018) investigated the sample size requirement for GPS-based traffic monitoring and asserted that existing methods either overestimate [PERSON] et al. (2018) or underestimate the required sample sizes. The authors found that, for reliably estimating travel times, delay and work-zone conditions require minimum sample sizes of 50, 20, 10, and eight for road sections with lengths of 0.5, 1.5, 2.5, and 3.3 km, respectively. The findings imply that, from the perspectives of GPS-based real-time traffic monitoring, there needs to be at least those minimum number of GPS receivers simultaneously on a particular section of the road. It could also be interpreted that the traffic monitoring procedure should wait until the minimum sufficient number of samples are collected prior to attempting to estimate the current traffic conditions when there are not enough simultaneous GPS probes. [PERSON] et al. [PERSON] et al. (2019) developed and validated a statistical model for travel mode identifications using smartphones.
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 4 Experiments
### The Effectiveness of Late-Fusion Strategy for SP-MIL Framework  
Drawing inspiration from ensemble learning, we propose a straightforward yet effective score-level late-fusion strategy, where we first use I3D and CLIP as feature extractors, respectively. Next, we train the respective classifiers, enhanced by the SP-MIL framework, and test the video segments using these classifiers to obtain the corresponding anomaly scores. The anomaly scores of video segments are subsequently fused using Formula (15) to produce the final anomaly scores. The comparison results are presented in Table 3. Notably, we integrate these two classifiers only during the testing phase, as the entire pipeline has already been trained. From Table 3, we can find that the fusion results of the SP-MIL framework demonstrate significant improvements over both baseline models, which indicates that the widely used I3D and CLIP features have much complementary information for the WS-VAD task.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Method** & **Feature Extractor** & \begin{tabular}{c} **UCF-Crime** \\ **AUC (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **ShanghaiTech** \\ **AUC (\%)** \\ \end{tabular} &
\begin{tabular}{c} **XD-Violence** \\ **AP (\%)** \\ \end{tabular} \\ \hline DeepMIL * & I3D+CLIP & 80.96 & 94.97 & 75.07 \\ DeepMIL+SP-MIL & I3D+CLIP & 84.81 & 97.81 & 76.63 \\ RTFM * & I3D+CLIP & 84.65 & 97.76 & 80.80 \\ RTFM+SP-MIL & I3D+CLIP & 86.35 & 98.10 & 84.42 \\ UR-DMU * & I3D+CLIP & 86.89 & 97.78 & 82.97 \\ UR-DMU+SP-MIL & I3D+CLIP & 87.76 & 97.92 & 84.32 \\ \hline \hline \end{tabular}  
* means that we re-implemented this method.  
\end{table}
Table 3: The effectiveness of the late-fusion strategy for SP-MIL framework.  
Figure 4: Illustrations of the t-SNE feature distributions of the baseline models and their enhanced models by using the proposed SP-MIL framework. The top sections of (1), (2), and (3) respectively represent the feature distributions of the DeepMIL, RTFM, and UR-DMU models. The bottom sections of (1), (2), and (3) represent the feature distributions of their enhanced models DeepMIL+SP-MIL, RTFM+SP-MIL, and UR-DMU+SP-MIL, respectively.
# A Comprehensive Survey on Knowledge-Defined Networking
## 3 Knowledge Plane
### Knowledge Distribution and Management Plane
Dynamic routing of flows is achievable due to the decoupling of control and forwarding functions in a KDN architecture. QOGMP is a KDN control architecture that has a distributed control architecture, where there is a broker on the top level for traffic scheduling regarding flow coordination over multiple domains based on QoS requirements, which can be used to disseminate knowledge to end devices [227]. Some have attempted to give a high preference to high-priority traffic by allocating more resources while at the same time considering the end-to-end latency requirements by using weighting parameters, which is a joint resource allocation scheme for multiple traffic classes [228]. QoS APIs have been utilized as an extension to OpenFlow to control configuration and management of QoS parameters where aggregated bandwidth usage is accomplished by a rate limiter and flows are mapped to priority queues using queue mapping to tackle bandwidth and delay allocation [229]. Such QoS APIs can be utilized to disseminate knowledge and rules considering QoS parameters. In a multi-operator KDN environment where KDN controllers belong to different service providers, the KDN controllers directly communicate with each other to advertise a set QoS of their network to others where network service providers buy the best path from the other network for a price, which is an open exchange framework,having collaborations between the KDN controllers where rules and knowledge can be effectively exchanged between the networks considering QoS requirements [230]. Packet marking such as collaborative borrowing, queue management such as weighted random early detection, queue scheduling such as weighted round robin, and priority queuing, can be used for knowledge and rule dissemination, adhering to QoS management in KDN environments [231]. Resource allocation using multi-objective optimization can be used to allocate resources for different network flows, such as rules and knowledge, in a heterogeneous network environment [232].
## 3 Model Comparisons
### Data Analysis and Model Comparison with Real Application
#### 3.2.3. Tandem Computer Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model No.** & **Model Name** & **Model Parameter Estimation Results** & **MSE** & **R\({}^{2}\)** & **Adjusted R\({}^{2}\)** \\ \hline
1 & G-O model & \(\hat{a}=130.2\), \(\hat{b}=0.08317\) & \(12.9056\) & \(0.9857\) & \(0.9849\) \\
2 & \(m_{2}(t)\) & \(\hat{a}=130.2\), \(\hat{b}=0.08317\), \(\hat{c}=4.646\times 10^{4}\) & \(13.6647\) & \(0.9857\) & \(0.9840\) \\
3 & \(m_{3}(t)\) & \(\hat{a}=104\), \(\hat{b}=0.2654\) & \(28.0611\) & \(0.9689\) & \(0.9672\) \\
4 & Inflection S-shaped & \(\hat{a}=110.8\), \(\hat{b}=0.1721\), \(\hat{b}=1.205\) & \(10.5647\) & \(0.9890\) & \(0.9877\) \\
5 & [PERSON] exponential & \(\hat{a}=999.5\), \(\hat{b}=0.07685\), \(\hat{\gamma}=0.279\), \(\hat{a}=0.51\) & \(14.8438\) & \(0.9854\) & \(0.9827\) \\
6 & Yamada Rayleigh & \(\hat{a}=115.8\), \(\hat{b}=0.01721\), \(\hat{\gamma}=30.3\), \(\hat{a}=0.6548\) & \(49.4188\) & \(0.9514\) & \(0.9422\) \\
7 & Yamada Weibull & \(\hat{a}=121.1\), \(\hat{r}=1.027\), \(\hat{a}=245.2\), \(\hat{b}=3.542\times 10^{-4}\) & \(15.1750\) & \(0.9851\) & \(0.9823\) \\
8 & Delayed S-shaped & \(\hat{a}=104\), \(\hat{b}=0.2654\) & \(28.0611\) & \(0.9689\) & \(0.9672\) \\
9 & HD/G-O & \(\hat{a}=130.2\), \(\hat{b}=0.08317\), \(\hat{c}=0.2769\) & \(13.6647\) & \(0.9857\) & \(0.9840\) \\
10 & Yamada imperfect(1) & \(\hat{a}=130.2\), \(\hat{b}=0.08317\), \(\hat{a}=9.363\times 10^{-10}\) & \(13.6647\) & \(0.9857\) & \(0.9849\) \\
11 & Yamada imperfect(2) & \(\hat{a}=130.2\), \(\hat{b}=0.08317\), \(\hat{a}=1.04\times 10^{-9}\) & \(13.6647\) & \(0.9857\) & \(0.9849\) \\
12 & P-Z(1997) model & \(\hat{a}=1.589\times 10^{-8}\), \(\hat{b}=0.1721\), \(\hat{c}=110.8\), & \(11.9733\) & \(0.9890\) & \(0.9860\) \\  & & \(\hat{a}=103.6\), \(\hat{a}=57.49\), \(\hat{b}=0.08193\), & \(10.8786\) & \(0.9906\) & \(0.9873\) \\
13 & Fault removal model (2003) & \(\hat{p}=0.9993\), \(\hat{c}=4916\), \(\hat{b}=4.827\times 10^{-5}\) & \(10.8786\) & \(0.9906\) & \(0.9873\) \\
14 & P-Z coverage model & \(\hat{a}=73.19\), \(\hat{b}=0.3563\), \(\hat{a}=0.02588\) & \(27.1588\) & \(0.9716\) & \(0.9683\) \\
15 & SRGM-3 model (2011) & \(\hat{A}=83.46\), \(\hat{a}=0.3433\), \(\hat{b}=37.1\), \(\hat{p}=0.003678\) & \(14.9250\) & \(0.9853\) & \(0.9826\) \\
16 & proposed model & \(\hat{a}=160.4\), \(\hat{A}=0.5101\), \(\hat{a}=0.7292\), \(\hat{c}=87.26\) & \(\hat{b}=0.2813\), \(\hat{b}=6.468\), \(\hat{r}=7.007\) & \(2.3192\) & \(0.9981\) & \(0.9973\) \\ \hline \hline \end{tabular}  
Notes: The bold number means the result of the best SRGM in this column.  
\end{table} 
Table 8: Comparison of goodness-of-fit power of SRGMs for DS-3. Figure 8: (X, Y, Z) represents (MSE, \(R^{2}\), Adjusted \(R^{2}\) ) values for DS-3.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 2 Telecommunication Systems in an Isolated and Constrained Environment
### The Flying Systems  
The third solution, consisting of flying systems, can be separated into two items:  
* The balloons, and
* The drones.
## 3 Biosurfactants
### Application of Selected Biosurfactant Cosmetics
#### 3.3.2 Sophorolipids  
Sophorolipids are composed of sophorose (glucose dimer) and a long chain of hydroxyl fatty acids. Sophorolipids are mainly produced by the yeasts, including _Candida bombicola_, _Starmerella bombicola_, _Candida bogoriensis_, and _Candida apicola_[91; 107].  
The sophorolipids lactone form has activity typical of biocides, which have better foaming ability and better solubility in water than the lactone form. In cosmetic formulations, sophorolipids have antibacterial properties (against _Pseudomonas aeruginosa_, _Staphylococcus aureus_, and _Escherichia coli_) and antifungal activity (against _Candida albicans_ and _Aspergillus niger_). Sophorolipids were shown to be effective inhibitors of bacterial pathogens when combined with antibiotic or natural compounds such as sericin and calcium alginate, without any side effects on skin tissue. They can potentially be used in the local treatment of bacterial wounds and skin lesions, because of shortening the wound healing time [108; 109] and antiviral, antibacterial, antimicrobial, and antibioofilm properties [92; 110]. The long carbon chains of sophorolipids (22 carbon chains) had the strongest antibacterial effects on the tested bacteria _Cutibacterium acne_. They exhibit antibacterial properties against _Propi-onibacterium acnes_ in different plant-based composites pectin and alginate. Sophorolipids derived from hydrolyzed horse oil showed anti-wrinkle effects as well as to improve skin elasticity. They may be useful as emulsifiers for cosmetic and skincare pharmaceutical formulations [111; 112; 113]. Sophorolipids are known to be effective in skin delivery of molecules for application in cosmetic and pharmaceutical formulations. They increase the penetration of active components across the skin barrier and improve the skin absorption, for example: lactoferrin [114] or mogrosides V [115].