esa-sceva/satcom-chunk-collection huggingface dataset:
# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 2 Proposed Methodology
### Data Acquisition  
The proposed work mainly emphasizes on the concept of inferring emotion tags from object categories. Thus, we required a dataset that contains real-life object categories that we encounter in our daily life. For this purpose, we utilized a state-of-the-art object-detection dataset named Caltech-256 [40]. Generally, this dataset comprises of 256 object categories and a total of 30,608 images. Moreover, each category has a maximum of 827 and a minimum of 80 images. Specifically, this dataset is chosen on account of two reasons: 1) the dataset contains most of the real-life living and non-living object categories, with which a person might have interactions in their routine life and, 2) the dataset of includes those types of images that are excessively uploaded and shared on different social media platforms and may induce certain emotions on the viewers. Hence, this dataset is appropriate to validate the classification performance for inferring emotion tags based on object categories. Besides this, Caltech-256 dataset consists of a hierarchy of object categories, i.e., top-level (node-level) object categories that are twenty (20) in number, including animal, transport, electronics, nature, music and food categories, etc. Likewise, there 256 bottom-level (leaf-level) object categories, which are derived from twenty top-level object categories and entail no more sub-categories. As an example, the top-level animal object category further contains bottom-level categories like frog, snake, ostrich, and crab, etc. Figure 3, presents some of the example images of bottom-level object categories (represented by black color blocks), which are derived from the top-level object categories of Caltech-256 dataset (as a specimen represented by red color blocks).
## 2 Experimental Section
### Gel Electrophoresis and Differential Pulse Voltammetry (DPV)  
Different mixtures of SH-CP, target DNA, AP and Nt.BbvCI endonuclease were incubated in reaction buffer (1\(\times\) NEB Buffer 4) for 1 h at 37 \({}^{\circ}\)C. A 15% polyacrylamide gel was prepared by mixing 10.0 mL 30% bis-acrylamide, 140.0 \(\mu\)L 10% ammonium persulfate and 10.0 \(\mu\)L TEMED in 20.0 mL 1\(\times\) TBE. Then, 15.0 mL of freshly prepared polyacrylamide gel was transferred to sandwich clamps allowed to solidify for 60 min, and 8.0 \(\mu\)L of the reaction product was then loaded into the gel. Gel electrophoresis was performed with a DYY-8C electrophoretic apparatus (Beijing WoDeLife Sciences Instrument Co., Ltd., Beijing, China). The gel was run at 100 V for 1.5 h, stained with ethidium bromide (EB), visualized under UV light and finally photographed with a digital camera. Different modified gold electrodes (AuEs) (a) HT/SH-CP/AuE; (b) Nt.BbvCI/target DNA/AP/HT/SH-CP/AuE; (c) target  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Note** & **Sequence(5’-3’)** \\ \hline Target & AATATACGCTAAGCCACGTCCA \\ \hline Capture probe (SH-CP) & SH-TTTTTTTTTTTTATGGACGTGCTCCTCAGCTT \\ \hline Assistant probe (AP) & GCTGAGGAAATTAGGGTATATT \\ \hline \multirow{2}{*}{Padlock probe (PP)} & pCGTCCATGTAGTAGAGACGCAGTTATGGCACAAAA \\  & CCACACAGATCCAAAACCCAACCCGCCCCTACCGGAGGCCA * \\ \hline Single base mismatchsequence (sDNA) & AATATACGCCAAGCCACGTCCA \\ \hline non-complementary sequence (nDNA) & CGGTTCCGATCCATCCAGG \\ \hline \multicolumn{2}{l}{* The underlined letters is completely complementary with the G-quadruplex sequence, and p in the padlock} \\ \multicolumn{2}{l}{probe represents phosphate at the 5’ end.} \\ \end{tabular}
\end{table}
Table 1: The oligonucleotide sequences used in our strategy.  
DNA/AP/HT/SH-CP/AuE (1.0 \(\upmu\)M AP, 10.0 fM target DNA and 5.0 U Nt.BbvCI) incubated in 10.0 mM Tris-HCl containing 10.0 mM RuHex (pH 7.4) for 1 h followed by DPV detection with a pulse amplitude of 50 mV and a pulse width of 0.05 s, respectively. DPV is used for electrochemical measurements widely, which can be considered as a derivative of linear sweep voltammetry or staircase voltammetry, with a series of regular voltage pulses superimposed on the potential linear sweep or stairsteps. The current is measured immediately before each potential change, and the current difference is plotted as a function of potential. Therefore, DPV can decrease the interference of impurity, improve sensitivity and detection limit.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 4 Overview of AMQP in IoT Systems
### Analysis of AMQP Normal Traffic  
Figure 1 illustrates the communication process between an AMQP server and an IoT device, as captured using the Wireshark tool. The process begins with a TCP three-way handshake (SYN, SYN/ACK, and ACK), establishing the initial connection. Following this, the AMQP protocol is employed to connect to the RabbitMQ server using commands such  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Feature** & **AMQP** & **MQTT** & **CoAP** \\ \hline Primary Use Case & Enterprise-grade messaging & Lightweight communication & RESTful IoT interactions \\ \hline Transport & TCP & TCP & UDP \\ \hline Authentication & Strong (e.g., TLS, RBAC) & Limited & Limited \\ \hline Encryption & Mandatory (TLS recommended) & Optional (TLS/SSL) & Optional (DTLS) \\ \hline Key Vulnerabilities & Broker exhaustion, replay, MitM & Brute force, low-rate DoS & Amplification, eavesdropping \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of AMQP, MQTT, and CoAP protocols.  
as connection.start, connection.tune, and channel.open. These actions are confirmed by acknowledgment (ACK) flags to ensure reliable communication. IoT devices then declare a queue and use the basic.consume command to subscribe and publish messages, with ACK flags verifying the successful exchange.  
Figure 2 provides an in-depth view of normal network traffic data exchanged between an AMQP server and an IoT device. The data exchange begins at line 6 in the capture. Key elements of the TCP connection are displayed, including the following:  
TCP Length: The column shows a value of 508. Sequence Number: The initial sequence number is 1, and the next sequence number increments to 509, reflecting the addition of the TCP length. ACK Number: The ACK value is 9, corresponding to prior traces in the network traffic. The window size for the IoT device consistently starts at 64,240, while the AMQP server's window size begins at 65,160. These window sizes adjust dynamically based on the data traces as the systems continue the data exchange process. The figures demonstrate how reliable communication is maintained during normal traffic between the AMQP server and the IoT device.
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
## 2 Description of the Protocol Protecting Private Keys with MFA
### Role of the Keystore  
One of the problems that has to be comprehended is the potential loss or failure of the token. One possible mitigation can come from a Keystore corporation that can store the reference table CT-T generated during an enrollment cycle from the token. When the token is lost, the user can ask the Keystore to transmit the table CT-T, then use it as part of the full MFA process. Such a recovery operation is still relatively secure, as malicious parties can do very little from the table CT. To intercept the ephemeral key K, the malicious parties still need RN\({}_{1}\), RN\({}_{3}\), the password, the biometric information, and the virtual token. A new token and a new enrollment cycle will be needed to re-establish the full protection. The Keystore corporation can also provide valuable services to reduce the rates of errors in the key recovery process, and the use of ternary cryptographic schemes. Rather than generating the tables CT-T from the physical token and CT-bio from the biometric scheme by a single operation, multiple cycles allow a better identification of the flaky positions' \"x\". For example, 100 repetitive cycles have been successfully demonstrated to catch 99.99% of the flaky positions. The Keystore corporation can generate a masking table CT\({}_{\text{Mask}}\) from CT to capture the flaky positions. For example, the position (_i, j_) of the masking table CT\({}_{\text{Mask}}\) can be a \"0\" when a solid state of \"0\" or \"1\" is detected, and a state of \"1\" when the position is flaky. The Keystore will transmit the masking table CT\({}_{\text{Mask}}\) to the user, who can skip the flaky positions when generating the ephemeral key K. The resulting key K will have a lower rate of bad bits during subsequent recovery cycles and can be corrected by ultra-light schemes.  
Figure 3: Block diagram showing how the terminal device can recover the reference table “CT” from RN\({}_{1}\) and three factors of authentication, then the ephemeral key K from RN\({}_{3}\) and CT. In normal operations, the key K encrypts or decrypts the private key Sk.
## 2 Acoustic Effects in Built Spaces  
### Reverberation  
Reverberation, \"the decay of sound after a sound source has stopped\" [17], consists of reflected, attenuated versions of a direct signal in a particular space. [PERSON] [18] models the response of the room as a sum of \"sinusoidal oscillations with different frequencies, each dying out with its own particular damping constant\". In an enclosed room, a large number of reflections from surrounding surfaces build up in a diffuse manner. The decay time of the reverberant signal is commonly defined by \(RT_{60}\), describing the time it takes for the reverberation level to decrease by 60 dB after suitable excitation of the space with an impulse signal. Depending on the shape and surface materials, the reflected echoes are not exact copies of the direct signal, which adds specific colouration (frequency enhancement or decrease) to a given audio signal. The complete impulse response of a room is commonly divided in three parts: direct sound, early reflections, and late reverberation (Figure 2).  
The direct sound is the origin sound and the first to be experienced by the receiver (depending on the distance from the source and on the medium conditions). The early reflections are the first set of reflections that arrive at the listener's ear and have a large impact on the perception of the size of a room and on colouration of the audio signal. The late reverberation is composed of the build up and decay of a large number of diffuse reflections. The late reflections are often considered to be uncorrelated aspects from the original signal. The time differences of the early reflections and late reverberation are not discernible by the listener due to the integration time of the human auditory system [8]. The transition between early reflections and late reverberation (mixing time) can be found with statistical methods [19; 20; 21]. When the direct sound is received within a certain time threshold from its first reflection, the listener is able to discriminate different sound phenomena or otherwise perceived as fused together. This effect is called precedence effect, and this time gap, i.e., the listener's _echo threshold_, was found to depend on the kind of sonic material [22] and, in more complex way, on some periodical similarities of the audio signal [23; 24].
## 3 Results
### Characterization
#### 3.1.2 FTIR Analysis  
In Figure 4 are reported the FTIR spectra of SBA-15, SBA-15-NH\({}_{2}\), SBA-15-Br, and SBA-15-PSSA. The spectrum of SBA-15 is similar to that reported in the literature [10; 23; 24]. Typically, the stretching bands of silanol groups appear at 3600-3200 cm\({}^{-1}\). Several types of silanols on the surface of silica, including hydrogen-bonded, isolated, and geminal, were reported to be observable at 3738 cm\({}^{-1}\), in the case of the first two silanol types, and at 3630-3180 cm\({}^{-1}\) in the case of germinal silanols [25]. Thus, the silanols in the as-prepared SBA-15 are mainly of the hydrogen-bonded type (the highest peak was at 3440 cm\({}^{-1}\)), combined with water hydroxyls [26]. The asymmetric and symmetric stretching bands of the siloxane framework, Si-O-Si, were observed at about 1086 and 803 cm\({}^{-1}\), respectively. The weak band at 970 cm\({}^{-1}\) is ascribed to the asymmetric stretching of the Si-OH bond [25; 27]. After functionalization, the intensity of this peak decreased simultaneously with those of the peaks at 1632 and 3440 cm\({}^{-1}\), as a result of the consumption of OH groups, indicating the success of the reaction [28]. The peak around 1632 cm\({}^{-1}\) is due to the bending mode of OH, including water H-O-H [29]; however, some authors assigned this band to an overtone of the Si-O stretching [30]. The spectrum of amino-functionalized SBA-15 (SBA-15-NH\({}_{2}\)) was characterized by an additional band at 1541 cm\({}^{-1}\), corresponding to N-H bending; however, this band is one characterize C-N of amide II [31] as well. In addition, the absorption pattern in the area of the spectrum where the CH, NH, and OH bands appear (2850-3400 cm\({}^{-1}\)) is obviously different from that of silica, due to peak overlapping. After initiator functionalization, and the resulting formation of SBA-15-Br, no substantial spectral differences with SBA-15-NH\({}_{2}\) were observed, due to the amount of anchored initiator being too small [32]. The vibrational peak centered at 1639 cm\({}^{-1}\) is assigned to the amide C = O moieties, which overlapped with the peak due to the OH bending mode of silanol. After polymerization, the spectrum of the thus produced SBA-15-PSSA included new bands that can be attributed to PSSA, such as those at 1408 cm\({}^{-1}\), due to C-N and aromatic rings, at 846 cm\({}^{-1}\), due to C-H out-of-plane bending vibrations of para-disubstituted benzene [33; 34], and at 684 cm\({}^{-1}\), due to skeletal alkyl groups in the polymeric  
Figure 3: Transmission electron microscopy micrographs of **(A)** SBA-15 and **(B)** SBA-15-supported polystyrene sulfonic acids. SBA-15: pore-expanded mesoporous silica. Scale bars, 100 nm.  
chain [10]. The presence of separated bands at 3440 and 3355 cm\({}^{-1}\) may indicate a trivial effect of hydrogen bonding, a conclusion supported by the low OH and NH mole percentage with respect to the entire molecule [31; 35].
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 5 Discussion  
The integration of autonomous vehicles (AVs) into urban traffic systems has been a focal point in recent transportation research, primarily due to the potential benefits AVs can offer in improving traffic efficiency, reducing congestion, and minimizing environmental impacts. This study evaluates the impact of different AV driving behaviors (cautious, normal, aggressive, and platooning) on traffic performance at a signalized intersection. The discussion below provides a detailed comparison of our findings with previous research, highlights the implications of the results.
## 3 Finite Element Analysis  
Experimental studies were modeled with using the finite element method (FEM), and the centrifuge test results were compared with the numerical results. The experiments were modeled with the PLAXIS 2D [27] program, which can reflect the plane strain problem. There are many soil models in the finite element programs, and it is aimed to fully reflect the elastoplastic behavior of the soil properties in the study. For this purpose, the Hardening Soil model (HSM), which is one of the advanced soil models, was used to reflect the elastoplastic behavior of the soil in numerical analysis. Soil properties obtained from laboratory tests are presented in Table 4.  
In the numerical analysis, a rigid beam element of the strip anchor plate is modeled. The modulus of elasticity of the steel material was used as the stiffness values, EA and EI. Soil/structure interface behavior in PLAXIS can be modeled using parameters generated using the Rint interaction coefficient. In this study, fully rough interface conditions, Rint = 1 are assumed.  
Numerical studies were carried out in three stages. In the first stage, the mesh effect was investigated and the mesh density to be used in the analysis was determined. In the second stage, the uplift capacity/displacement curves obtained from the experiments were  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & **Unit** & **Loose Sand** & **Dense Sand** \\ \hline Unit weight, \(\gamma^{\prime}\) & kN/m\({}^{3}\) & 14.5 & 16.4 \\ Secant stiffness, \(E_{50}\) & kPa & 5000 & 20,000 \\ Initial stiffness, \(E_{OED}\) & kPa & 5000 & 20,000 \\ Unloading/reloading stiffness, \(E_{\text{l}IR}\) & kPa & 15,000 & 60,000 \\ Cohesion, \(c^{\prime}\) & kPa & 0 & 0 \\ Friction angle, \(\phi^{\prime}\) & (\({}^{\circ}\)) & 35 & 51 \\ Dilatancy angle, \(\psi\) & (\({}^{\circ}\)) & 0 & 20 \\ [PERSON]’s ratio, \(\
u\) & - & 0.2 & 0.2 \\ Power for stiffness stress & - & 0.65 & 0.50 \\ dependency, \(m\) & At rest earth pressure coefficient, \(K_{0}\) & - & 0.43 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of soil parameters used in FEM analyses.  
Figure 9: Comparison of results with literature [7] for loose sand.  
compared, and in the last stage, the comparisons in terms of the breakout factor were presented collectively.
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 5 Application Layer
### Query Model  
Before talking about this model, the two most important parts of data communication must be stated: query processing and data aggregation [50]. In addition to energy conservation, this model only provides useful data sets. Query approaches are based on a query agent that provides APIs that map user queries to lower-level semantics corresponding to the underlying routing and aggregating techniques. Query-based approaches support unicast, multicast, and broadcast communication models. Query agents will support a wide variety of routing and aggregation protocols, selecting the best combination based on the query type.
## 3 Mechanical Modeling of the Permanent Thin Wall
### Mechanical Model of the Thin Wall When Using One Backfill Material  
For the case of the same backfill material being used in both the room stopes and pillar slopes, if \(l_{1}=l_{2}\) (see Figure 1), the width of the thin wall does not change in the \(z\)-axis direction, as shown in Figure 2. In this case, it can be assumed that the lateral load \(q\) applied by the backfill materials to the thin wall does not change perpendicular to the wall. Therefore, the three-dimensional spatial problem can be simplified to a two-dimensional plane problem.  
As can be seen from Figure 2, the thin wall is only bearing lateral loads from backfill materials, and it can be regarded as a beam structure undergoing pure bending deformation; therefore, its deflection and stresses satisfy Equations (1) and (2), respectively.  
\[E_{o}I_{z}w^{\prime}=-M(x) \tag{1}\]  
\[\sigma=-\frac{M(x)}{I_{z}}y \tag{2}\]  
\[I_{z}=\frac{ab^{3}}{12} \tag{3}\]  
where \(E_{o}\) is the elastic modulus of the thin wall; \(I_{z}\) is the inertia moment of the wall cross-section about the neutral axis; \(b\) is the width of the wall; \(w\) is the deflection of the wall; and \(M(x)\) is the bending moment of the wall under the lateral load \(q\).  
The two ends of the wall are parts of the surrounding rocks, so fixed constraints are applied. From structural mechanics, under uniform load, the bending moment and shear force at the two end points of the beam are as follows:  
\[\begin{cases}\quad\quad F_{x=0}=F_{x=h}=qh/2\\ M_{x=0}=M_{x=h}=-qh^{2}/12\end{cases} \tag{4}\]  
From Equation (4), the bending moment along the \(x\) direction can be derived as follows:  
\[M(x)=-\frac{qh^{2}}{12}+\frac{qhx}{2}-\frac{qx^{2}}{2} \tag{5}\]  
Figure 2: Mechanical model of the thin wall when using one backfill material.  
By combining Equations (1) and (5), the deflection equation of the wall can be solved by integration:  
\[E_{o}I_{z}w=-\frac{q}{24}x^{4}+\frac{qh}{12}x^{3}-\frac{qh^{2}}{24}x^{2}+C_{1}x+ C_{2} \tag{6}\]  
where \(C_{1}\) and \(C_{2}\) are undetermined coefficients.  
Using Equation (6) and the boundary conditions of  
\[\begin{cases}w\,|\,_{x=0}&=0\\ w\,|\,_{x=h}&=0\end{cases} \tag{7}\]  
the deflection of the wall can be derived as follows:  
\[w=-\frac{q}{2E_{o}ab^{3}}\Big{(}x^{4}-2 hx^{3}+h^{2}x^{2}\Big{)} \tag{8}\]  
As expected, the deflection of the wall is directly proportional to the roof load and inversely proportional to the length of the mine wall, the elastic modulus, and the cube of the width.
# Adaptive Piecewise Poly-Sinc Methods for Ordinary Differential Equations
## 2 Background
### Collocation Method
#### 2.4.1. Initial Value Problem  
The IVP is transformed into an integral equation. We briefly discuss the approximation of indefinite integrals using Poly-Sinc methods ([45] SS 9.3). Define  
\[(\mathcal{J}^{+}w\,y)(x)=\int_{a}^{x}y(t)w(t)\,\mathrm{d}t\,, \tag{8}\]  
where the weight function \(w(x)\) is positive on the interval \((a,b)\) and has the property that the moments \(\int_{a}^{b}x^{j}w(x)\,\mathrm{d}x\) do not vanish for \(j=0,1,2,\ldots\). Let \(A^{+}\) be an \(m\times m\) matrix whose entries are  
\[[A^{+}]_{k\,j}=\int_{a}^{x_{k}}u_{j}(x)w(x)\,\mathrm{d}x\,,\]  
where \(u_{j}(x),j=-N,\ldots,N\), are the Lagrange basis polynomials stacked in a vector \(L(x)=(u_{-N}(x),\ldots,u_{N}(x))^{\top}\), and \((\cdot)^{\top}\) is the transpose operator. The interpolation points \(\{x_{j}\}_{j=-N}^{N}\) are the Sinc points generated in the interval \([a,b]\) as discussed in Section 2.1.2.  
Then, the indefinite integral (8) can be approximated as  
\[(\mathcal{J}^{+}_{m}w\,y)(x) =\sum_{j=-N}^{N}(\mathcal{J}^{+}_{m}w\,y)(x_{j})u_{j}(x)\] \[\approx\sum_{j=-N}^{N}\biggl{[}\sum_{k=-N}^{N}y(x_{k})[A^{+}]_{jk }\biggr{]}u_{j}(x)\] \[=\sum_{j=-N}^{N}u_{j}(x)\sum_{k=-N}^{N}y(x_{k})[A^{+}]_{jk}\] \[=L(x)^{\top}A^{+}Vy,\]  
where \(Vy=(y(x_{-N}),\ldots,y(x_{N}))^{\top}\). We state the following theorem for IVPs [30].  
**Theorem 1** (Initial Value Problem ([30] SS1.5.8)).: _If \(y\in\mathbb{M}_{a_{N}\beta_{k}}(D)\), then, for all \(N>1\)_  
\[\|\mathcal{J}^{+}w\,y-\mathcal{J}^{+}_{m}w\,y\|=O(\epsilon_{N}),\]  
_where \(\epsilon_{N}=\sqrt{N}e^{-\beta\sqrt{N}}\)._
## 2 Literature Review
### Void Ratio (e)  
As presented in Table 1, the influence of the void ratio on the damping ratio is not straightforward based on the literature review. In [14], [PERSON] displayed a total lack of impact of \(e\) on the damping ratio \(D\). Moreover, [31; 32] confirmed that the void ratio had not affected both the minimal damping ratio and the damping ratio curve. However, [PERSON] and [PERSON] in [20] presented utterly different results. They investigated soils with void ratios from 0.4 to about 1.2. Their research indicated that the damping ratio decreases with the increase of the void ratio from 0.4 to about 0.65. That confirms the conclusion presented in Table 1[13]. Nevertheless, a further increase in the void ratio leads to the opposite behavior of the damping ratio. That phenomenon was confirmed in [22]. The rise of the damping ratio with a void ratio above 0.65 is connected with soil structure, which is much more sensitive for strain and frequency range of loading, which leads to increased intermolecular interaction, and thus increased energy dissipation, hence the increases of the damping ratio value [22]. Therefore, the influence of the void ratio on the damping properties of cohesive soil is still not clarified.