esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rare Case: Adenomatoid Odontogenic Tumor Mimicking Follicular Cyst in a Young Patient
## 1 Introduction
Follicular cysts typically appear as radiolucent lesions surrounding the crown of an unerupted tooth, originating from the cervical area of the tooth itself. From an etiological perspective, they are classified as developmental odontogenic cysts. Histologically, they consist of an epithelial lining surrounded by a wall of loose fibrous tissue. Embryonically, they originate from remnants of reduced enamel epithelium that undergo cystic degeneration, resulting in fluid accumulation in the central area of the lesion, which remains attached at the cementoanemel junction [13]. The most common odontogenic cyst is the radicular cyst, with the follicular cyst ranking second, representing 20% of all epithelium-lined cysts found in the jawbones. The peak occurrence of follicular cyst is observed during the second and third decades of life, showing a slight preference for males [14]. Typically, a follicular cyst encases the crown of an impacted tooth, with the mandibular posterior region being the most prevalent site. Apart from mandibular third molars, other relatively frequent locations, in decreasing order, include the maxillary canine, maxillary third molar, and mandibular second premolar [15]. This lesion is often incidentally detected during routine radiographic examinations or clinically as an asymptomatic swelling. Typically, its radiographic appearance is characterized by a well-defined, single-chambered, symmetric radiolucent area around the crown of an impacted tooth. However, the radiographic depiction of the cyst's relationship with the crown may vary. The central variant, most commonly observed, involves the cyst enveloping the tooth's crown, with the crown protruding into the cystic cavity, while the root(s) remain exterior to the cyst [16]. Large follicular cysts may have a radiographic appearance that may be comparable to other pathological conditions [17]. The correct diagnosis of follicular cysts or adenomatoid odontogenic tumor is essential as the surgical treatment and follow-up are different. Due to the general lack of data in the literature, the aim of this study was to describe a rare case of adenomatoid odontogenic tumor that mimicked a follicular cyst with an impacted maxillary canine. The study delved into diagnostic considerations and treatment modalities regarding these lesions.
# Spot Weld Inspections Using Active Thermography
## 3 Results  
Due to welding, the metallic parts are connected together, resulting in a place where the heat can be distributed through more material. In addition the internal structure of the metal is changed in welds that are properly performed. Using infrared thermography, a difference in heat transfer can be observed between the welded area and the rest of the sample. The welded regions will show different cooling down in comparison to the non welded regions. This difference can be emphasized using post-processing. The difference in internal structure of a decent weld will also result in a difference in cooling down and can again be emphasized using post-processing. A more thorough explanation on why thermography can be used to inspect spot welds can be found in [14].  
The results of the different heating techniques and post-processing are shown and discussed below. The results are divided into different sections depending on the used excitation method.
## 4 State-of-the-Art Review
### Biomass  
[PERSON] and his co-authors took a different route and chose to review the various processes for conversion of biomass into hydrogen, first dividing them into two main groups: thermo-chemical processes and biological conversions [119]. They went on and discussed the various systems in terms of their energetic and energetic aspects and also summarized potential methods for comparison purposes. Carrying out a simulation with a wide range of pressure and temperature conditions brought as a conclusion that the maximum energy efficiency values for the gasification reaction is around 46.54%.  
[PERSON] et al. chose to address the impact of temperature on biomass combustion and gasification, in terms of SO\({}_{2}\)/NO\({}_{x}\) emissions [120]. They found that, for three different algae biomass species, both emissions increase with an increase in combustion temperature; particularly, NO\({}_{x}\) peak formation was further accelerated with this increase in temperature. On the other hand, SO\({}_{2}\) emissions were significantly higher at 900 \({}^{\circ}\)C when compared with 700 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, but no second-peak formation was particularly relevant.  
[PERSON] explored hydrogen and syngas production by superadiabatic combustion (SAC), stating at the outset that, at present, the most effective method of hydrogen production is the conversion of the hydrocarbon sources [121]. The author deduces that even though there are diverse kinds of techniques being explored for hydrogen production, unfortunately thermal reforming of methane and other fossil fuels (seen before) will still continue, until alternative clean technologies are popularized. Superadiabatic combustion of biomass may just be one of those alternatives, as decomposition and biomass gasification has demonstrated excellent performance. However, [PERSON] concludes, research has yet a long way to go before materializing SAC systems for practical applications--particularlyby considering the risks associated with storage and transportation of hydrogen (the reason why onsite production is receiving more attention).  
[PERSON] and [PERSON] have conducted a detailed review in their study [122], discussing mainly sawdust wood biomass-based hydrogen production systems and their applications. They performed a comprehensive sensitivity analysis on the hydrogen yield from steam biomass gasification, concluding in general that there are various key parameters affecting the hydrogen production process and system performance: pressure, temperature, current density, and the fuel utilization factor. At a particular set of values for these parameters, the authors found a strong potential to increase energy efficiency from 45% to 55%.
# An Investigation of Particle Swarm Optimization Topologies in Structural Damage Detection
## 2 Structural Damage Detection
### Fitness Function  
The FEM-based SDD is an optimization problem. Defining a fitness function that can minimize the discrepancy of the response between the measurement and model is one of the essential steps in optimization.  
Because the natural frequencies of the structure are easy to obtain with high accuracy, a fitness function that is composed of pure natural frequencies is considered. An efficient correlation-based index (ECBI) was introduced in [23] to formulate the fitness function, which is expressed as follows:  
\[S(\alpha)=-\frac{1}{2}\Bigg{[}\frac{\left|\Delta F^{T}\cdot\delta F(\alpha) \right|^{2}}{(\Delta F^{T}\cdot\Delta F)(\delta F^{T}(\alpha)\cdot\delta F( \alpha))}+\frac{1}{m}\sum_{s=1}^{m}\frac{\min\!\left(f_{s}^{d},f_{s}(\alpha) \right)}{\max\!\left(f_{s}^{d},f_{s}(\alpha)\right)}\Bigg{]} \tag{7}\]  
where \(\Delta F\) is the change of natural frequency vector before and after damage, which is defined as:  
\[\Delta F=\Bigg{\{}\Delta f_{s}=\frac{f_{s}^{h}-f_{s}^{d}}{f_{s}^{h}}\Bigg{\}} \quad(s=1,2,\ldots,m) \tag{8}\]where \(f_{s}^{h}\) and \(f_{s}^{d}\) are the \(s\)th component of the healthy (undamaged) natural frequency vector \(F_{h}\) and damaged natural frequency vector \(F_{d}\), respectively.  
\(\delta F(\alpha)\) represents the change of natural frequency vector that is predicted by FEM with regard to the healthy natural frequency vector, which is denoted as:  
\[\delta F(\alpha)=\left\{\delta f_{s}(\alpha)=\frac{f_{s}^{h}-f_{s}(\alpha)}{f_ {s}^{h}}\right\}\quad(s=1,2,\ldots,m) \tag{9}\]  
where \(\alpha=(\alpha_{1},\alpha_{2},\ldots,\alpha_{D})\) is the damage vector and \(f_{s}(\alpha)\) is the \(s\)th component of the natural frequency vector that is predicted by the FEM. In this article, \(m=9\) is used uniformly. Besides, when compared with other forms of the fitness function composed of pure natural frequencies, the superiority of fitness function that is shown in Equation (7) has been demonstrated in [24].  
The objective of SDD is to search for a specific damage vector \(\alpha^{*}\) for which its predicted natural frequency vector exactly matches with the modeled damage vector \(\alpha\). When \(\alpha^{*}=\alpha\), this fitness function reaches its minimum value of \(-1\). Therefore, the SDD problem is transformed into an inverse optimization problem:  
\[\begin{split}& arg\;min\;S(\alpha^{*})\\ &\text{where}\quad\alpha^{*}=(\alpha_{1}^{*},\alpha_{2}^{*}, \ldots,\alpha_{D}^{*})\\ &\text{subject to}\quad\alpha_{j}^{*}\in[0,1](1\leq j\leq D) \end{split} \tag{10}\]  
Subsequently, the optimization algorithms, such as the popular PSO algorithm, can be employed to find a damage vector \(\alpha^{*}\) to minimize Equation (7).
# A Review of Advanced CMOS RF Power Amplifier Architecture Trends for Low Power 5G Wireless Networks
## 1 Introduction
### The Modern Wireless Network  
Modern wireless networks comprise different output power and number of user supporting radio access nodes called cells [10]. Due to recently increased capacity, a shift in cellular network infrastructure deployment is occurring away from traditional (expensive) high-power tower-mounted base stations and towards heterogeneous elements. Examples of heterogeneous elements include microcells, picocells, femtocells, and distributed antenna systems (remote radio heads), which are distinguished by their transmit powers/coverage areas, physical size, backhaul, and propagation characteristics. This shift presents many opportunities for capacity improvement, and many new challenges to co-existence and network management [11]. To accommodate high mobility users in a heterogeneous network, such as users in vehicles and high-speed trains, a paper [2] proposed the mobile femtocell (MFemtocell) concept. All latter cell types essentially define the radiated RF power which directly affects the PA requirements.  
Analyzing macrocells, such as mobile base stations, the power requirements are very different and can go up to tens and even hundreds of watts. This requires amplification devices that have a high breakdown voltage and with enough gain at high frequencies. As a result, medium- and high-power PAs are usually implemented in _III-V_ semiconductors [12]. The highest powers from hundreds of watts up to kilowatts at frequencies above 1 GHz are obtainable using _GaN_, _Si_ bipolar junction transistor (BJT) and _GaAs_ process devices [13]. The downside to the latter processes is that it is not possible to include performance enhancing functionality, including complex bias circuitry, self-testing or calibration capabilities as well as high density digital processors. This can be further seen, that there are only a handful of papers on _GaAs/GaN_ and other _III-V_ semiconductor-based transceivers published [14; 15; 16].  
The CMOS process is not very suitable for the medium-high power range due to the inability meet the power added efficiency (_PAE_) at a given output power 1 dB compression point (_P1 dB_). On the other hand, pushing mobile devices to lower powers is useful from a design perspective as non-PA components (digital controllers, RF transceiver blocks, switches, etc.) can readily be integrated with  
Figure 2: Updated radio frequency (RF) power amplifier (PA) research trends [9].  
the PA in a single chip [17]. As a result, agile CMOS RF transceiver ICs are dominating low power (micro-, pico-, femtocells) device market. It is to be noted, that the main scope of reviewed papers are PAs implemented in scalable processes CMOS and _SiGe_ BiCMOS with output powers suitable for portable wireless devices under 32 dBm (1.5 W) in the low- and high- 5G network frequency ranges.
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
## 4 Email XSS Detection Framework
### Deep Learning Algorithm
#### 4.4.2 Attention  
Attention is an effect-enhancing mechanism based on the RNN model, which named Attention Mechanical [29]. At present, the Attention mechanism is very popular because it gives the model discriminating power. When people observe something, they usually do not look at it as a whole. They tend to observe the important parts of things first, then connect the information of different parts in the series, and finally form the overall impression of the thing. The Attention mechanism refers to the characteristics of human observation. The Attention mechanism can also help the model assign different weights to each part of the input X, extract more important information, which makes the model have more accurate predictions without incurring more computational and storage overhead. In this paper, the global Attention is used to optimize the Bidirectional-RNN model. The basic principle is shown in Figure 4.  
In the Attention mechanism, all hidden states are used to calculate the weight of the Context vector. For a variable length alignment vector \(a_{t}\), the actual length is equal to the length of the input statement in the encoder. At time \(t\), the value of \(a_{t}\) is first calculated according to two hidden states (\(h_{t}\) and \(h_{s}\)). The calculation formula is following:  
Figure 3: The word vector of “img” training by Word2 Vec.  
\[a_{t}(s)=align(h_{t},h^{\prime}_{s})=\frac{exp(score(h_{t},h^{\prime}_{s}))}{\sum_ {s^{\prime}}exp(score(h_{t},h^{\prime}_{s^{\prime}}))}. \tag{1}\]  
The score is used to evaluate the correlation function between \(h_{t}\) and \(h_{s}\), and also used as an alignment function. Its calculation formula is as follows:  
\[score(h_{t},h^{\prime}_{s})=\left\{\begin{array}{c}h^{\top}_{t}h^{\prime}_{s }\,\ \ \ \ \ \ dot\\ h^{\top}_{t}W_{a}h^{\prime}_{s},\ \ \ \ \ \ general\\ v^{\top}_{a}tanh(W_{a}[h_{t};h^{\prime}_{s}]),\ \ \ \ \ \ \ concat\end{array}\right. \tag{2}\]  
After the alignment vector \(a_{t}\) is calculated, the context vector \(c_{t}\) can be calculated averaging. An email often contains too much information, and in fact, the effective XSS payload is only a small part. When training email samples, it is only a small part of the information that contains the XSS payload has training value, and the rest are low-value text. Therefore, using the Attention mechanism can make the model more focused on the effective XSS payload in the email, rather than treating all texts equally.
# Impact Analysis and Optimization of Key Material Parameters of Embedded Water-Stop in Tunnels
## 2 Optimization of Key Parameters for Water-Stop Materials  
### Failure Mechanism of Embedded Water-Stop  
Mountain tunnel circumferential joints are generally divided into two types: deformation joints and construction joints [19; 20], and the embedded water-stop is set in the middle of the lining thickness along the circumferential direction. Under normal working conditions, due to the deformation of the lining, the embedded water-stop at the deformation joint is usually in a state of uneven settlement or expansion. The service life of rubber is closely related to its stress and strain, composition, and environment [21; 22]. Its waterproof principle mainly includes two aspects: seepage resistance and seepage flow path.  
Seepage resistance is composed of contact pressure and adhesion force formed by extrusion sealing. When the other variable is not considered, the water pressure is greater than the contact pressure, or the water pressure is greater than the bonding force, then leakage occurs, as shown in Figure 1.  
When considering both factors simultaneously, as the water pressure increases, the contact pressure first fails, and then the bonding force begins to bear the pressure. As the failure occurs, the joint leaks water [8]. The seepage flow path can consume groundwater head pressure and enhance the waterproof reliability of the water-stop.  
When the deformation of the water-stop is large, the stress may exceed the critical stress value. Being in a state of high stress for a long time will cause the mechanical properties of the water-stop to decline rapidly, reducing its service life and leading to waterproof failure before reaching the expected service life. Deformation can also damage the bond, decreasing the water-stop waterproofing performance.  
When the stress level of synthetic rubber and other polymers does not exceed 20% of their tensile strength [23], their service life can reach more than 100 years. From the specifications, the tensile strength of rubber water-stops should be more than 10 MPa. In order to determine whether the rubber material fails during the service of tunnel joint water-stops, a critical stress value of 2 MPa can be used for the study of lining joint water-stops [23,24].
## 5 Case Study
### Experimental Settings  
We observe the distribution of the patients in the NUH Emergency Department waiting area for three days. The observation period is 15 h per day from 08:00 to 23:00. It is found that the observation periods of a day can be divided into three categories:  
* Peak time (8:00 to 10:30): 58 BSNs are in the waiting area on average, thus the average area per BSN is 2 square meters per person;
* Moderate time (10:30 to 21:00): 40 BSNs are in the waiting area on average, thus the average area per BSN is 3 square meters per person;
* Off-peak time (21:00 to 23:00): 16 BSNs are in the waiting area on average, thus the average area per BSN is 6 square meters per person.  
In the analysis, we consider all BSNs transmitting at the same power \(\Omega_{s}\in[-25\ \mathrm{dBm},\)0 \(\mathrm{dBm}]\). The signal transmitted in the same BSN attenuates according to the on-body path loss model [36,47], while the interference signal reaches the BSN of interest according to the inter-body path loss model [37], as listed in Table 2. For simplicity, the duty cycles of all BSNs are set the same as well. In addition, we assume all the BSNs operate on the same channel. The performance of multiple available channel scenario can be easily obtained from that of the single channel scenario with the same number of BSNs per channel. In this paper, we are concerned about the performance of BSNs instead of system performance, thus consider PER of BSNs in the simulation results. Moreover, BSN users may tend to congregate sometime in some specific areas when emergency event happens. For most of the time, the BSN users tend to be seated evenly in the waiting area of the emergency department of the hospital. In this paper, we study the average interference effects for the common cases.  
Figure 2: Floor map of waiting area in National University Hospital (NUH) Emergency Department.
# Recent Developments in Low-Power AI Accelerators: A Survey
## 3 Method
### Criterias for Acceptance  
The criterias for accepting an article were based on (i) the year it was published, (ii) if it introduced an AI accelerator, and (iii) if it could be counted as low-power. The year it was published is self explanatory; we only accepted papers published between the years 2019 and 2022.  
Regarding if an accelerator accelerated AI or not, we based our decisions on our own experiences and knowledge of current and emerging research in AI. In this survey, the AI applications/algorithms are referred to as the acceleration targets. The total number of targets found from the surveyed accelerators are presented in Table 1. Accelerators that accelerate General Matrix Multiplications (GEMM) have been included due to the prevalent use of matrix multiplication in a variety of AI algorithms, including DNNs. Consequentially, MAC and other types of matrix algorithms were included for similar reasons. We count Graph Mining and Personalized Recommendation (PR) systems as AI applications too. As such, the acceleration targets are as follows: ANN, Multilayer Perceptron (MLP), DNN, CNN, Generative Adversarial Network (GAN), GEMM (divided into sparse and dense), PR system, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Transformer, MAC, Sparse Matrix Transposition, Matrix Algorithms, Graph Mining, and SNN.  
We set the upper bound on power to be less than 10 W. The maximum power of a low-power device (\(\leq\)10 W) in this paper was derived from reviewing a variety of research papers on AI accelerators. A higher value of power was not chosen to take into consideration the power that other components of a device might need, i.e., camera, extra memory, and sensors.  
Low-power AI accelerators where the authors are affiliated with a company are also considered. The same criteria as mentioned before were used, except for the mentioned conferences and workshop, company affiliated accelerators from known AI accelerator vendors were also included in this survey. We searched for vendors that we know design accelerators specific for AI acceleration.  
As presented in Table 2, there are more accelerators presented in academic research papers than accelerators where the authors are affiliated with a company. This will be reflected in the result, mostly when analyzing the company accelerators.
## Security Analysis
### Non-Repudiation  
Non-repudiation is achieved in our proposed method. In every communication phase, the sender must sign their private key with the message; when the receiver receives the message, they must verify the hash value from the sender. If the equation is equal, the signatures are valid. So the message that is sent by the sender will have the ability of non-repudiation. Table 3 shows the non-repudiation of the proposed scheme.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 5 Performance Evaluation
### Experimental Results
#### 5.2.2 Space Overhead  
This experiment evaluates the space cost of \(pk\), \(sk\), index and trapdoor. The experiment results are illustrated in Figure 3.  
1. \(pk\) size. The \(pk\) size in these four schemes are all linear with \(n\). Our scheme is the best of these four schemes since it needs less elements in group \(G_{1}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **ZZ11 [7]** & **OT15 [8]** & **ZLW19 [10]** & **Our Scheme** \\ \hline pk & \((n+2)L_{1}+2L_{2}\) & \((12n+16)L_{1}+L_{2}\) & \((2n+3)L_{1}+L_{2}\) & \((n+2)L_{1}+L_{2}+3L\) \\ sk & \(|Z_{q}|\) & \((12n+16)L_{1}\) & \((2n+3)|Z_{q}|\) & \((n+4)|Z_{q}|\) \\ Index & \((n+1)L_{1}+(n+2)L_{2}+|Z_{q}|\) & \((5n+1)L_{1}+L_{2}\) & \((2n^{2}+4n)L_{1}+L_{2}\) & \((n+2)L_{1}+|Z_{q}|\) \\ Trapdoor & \((n+2)L_{1}+L_{2}\) & \(11L_{1}\) & \((2n+2)L_{1}+|Z_{q}|\) & \((n+2)L_{1}\) \\ \hline Denotation & \(L_{1},L_{1},L_{2}\) and \(|Z_{q}|\): the size of an element of \(G\), \(G_{1}\), \(G_{2}\) and \(Z_{q}\), respectively. & \\  & \(n\), \(m\): the number of keywords in an index keyword set and a query, respectively. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with previous SPE-CKS schemes on space complexity.  
_sk_ size. ZZ11 only needs one integer in \(Z_{q}\). Although \(sk\) size in OT15, ZLW19 and ours are all linear with \(n\), both ZLW19 and our scheme need less space cost since the space cost of \(Z_{q}\) is less than that of \(G_{1}\). In addition, the \(sk\) size in our scheme is only a half of that in ZLW19.
* Index size. Our scheme needs less space cost than OT15 and ZLW19, although the index size of OT15, ZLW19 and our scheme are all linear with \(n\). This is fit to the theoretical analysis. The index size of ZLW19 is linear with \(n^{2}\), so it is not as efficient as our scheme.
* Trapdoor size. The space complexity of trapdoor in OT15 and ZLW19 are \(O(1)\) and \(O(m)\), respectively. Thus, these two schemes need less storage cost. The space cost of trapdoor in ZZ11 and our scheme are both linear with \(n\). Our scheme needs less storage consumption than ZZ11 for trapdoor since our scheme needs less group element in trapdoor.  
Figure 2: Impact of \(n\) on the time cost of key generation (**a**); index building (**b**); trapdoor generation (**c**) and testing (**d**). (\(D=1000\), \(m=5\) and \(n=\{5;10;15;20;25\}\)).