esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In both SLS and SLM, post-processing treatments, such as polishing and heat treatment, are needed to overcome the problems of poor surface quality and porosity [9]. However,compared to SLS, SLM is characterized by a lower occurrence of porosity and better surface quality [12]. EBM is very similar to SLM. It uses a focused electron beam in a vacuum environment to melt the metal powder by the transmission of kinetic energy [16]. Currently, SLM is the most widely used 3D printing technique for metal processing in dental applications [9]. Its capability to manufacture structures of high density and strength with complex shapes and geometries, the negligible waste of material, and the easy processing, with no need for distinct binders or melt phases, are just some of its advantages. The fact that the process is still expensive, compared to conventional manufacturing, and the need for post-processing are specific disadvantages [17]. Co-Cr alloys are the most commonly used materials for PBF in dental applications. Dental metallic substructures work under conditions of variable stress in a highly aggressive environment, the saliva; their durability is defined by their mechanical properties and corrosion resistance. SLM is often preferred, not only over the conventional casting method but even over CAD/CAM milling, as it enables the manufacturing of dental prosthetic restorations more quickly and less expensively. SLM substructures are characterized by good internal fitting, the necessity for nominal adaptation, and ceramic bond strength [18].
## 3 Experiments and Results
### Extraction Characteristics of Asymmetric Convolution and ACE Module for Cracks  
Some existing studies [51; 52; 53] have shown that decomposing the standard n \(\times\) n convolution into 1 \(\times\) n and n \(\times\) 1 convolutions for concatenation can reduce the number of parameters and the computation, but since in practice the kernels learned in deep networks have distributed feature values with an intrinsic rank higher than one, the direct application of 1 \(\times\) n and n \(\times\) 1 concatenation transformations can lead to significant information loss. Based on [48], we found that convolutional kernels with enhanced skeletons were more effective for feature representation. We used asymmetric convolution and convolution to extract the crack features, and the results are shown in Figure 11.  
From Figure 11, we can see that the 1 \(\times\) 3 convolution is more sensitive to horizontal cracks and the 3 \(\times\) 1 convolution is more concerned with vertical cracks, while the 3 \(\times\) 3 convolution plays more of a role in strengthening the backbone with better global feature extraction but a less good grasp of detail. Parallelization of the above three convolutions enhances the details of the cracks and the representation of the main features, but the feature representation is more saturated, and more noise is introduced compared to the 3 \(\times\) 3 convolution. Hence, after parallelization, we use a 5 \(\times\) 5 convolution kernel for expansion, which not only deepens the number of network layers but also performs inter-channel information interaction to reduce the noise and refine the crack features.  
Figure 10: Input image (**top left**), feature maps extracted by involution in the second stage in Rednet-50 [41], and fused image of feature maps (**bottom left**). The pictures are represented using 32 bits.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 6 Seq2 Seq Model for Detection of Malicious MQTT Traffic  
To detect malicious MQTT traffic, we used the Seq2 Seq model [8], as shown in Figure 12. The input data length was set to 50, the embedding size was set to 128, and the number of LSTM nodes was set to 128. The dropout rate was 0.2, and the Softmax function was used. The batch size was 32, and the maximum epoch was 10. Using the Seq2 Seq model, we determined whether the input data were normal or indicative of DoS, flood, SlowTe, malformed, or brute-force attacks.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.4 Improved Decision-Making  
IoT and Distributed Computing Systems transform business decision-making processes from reactive to proactive data-driven approaches. IoT devices continuously generate a huge amount of information from varying fonts (e.g., machinery) and operator interactions [60]. This data provides an intertwined perspective of business operations, client behavior, and market trends, forming the basis for informed and timely decisions. Distributed computing systems facilitate the organization of the massive volumes of information created by IoT machines [61]. These systems distribute the computational load across multiple nodes, allowing efficient and scalable data processing. This decentralized approach ensures data can be analyzed close to its source, reducing latency and enabling real-time analytics [62]. For instance, IoT sensors can monitor traffic patterns, air quality, and energy usage in a smart city. Distributed systems can process this data on time to ease its stream, reduce pollution, and manage energy distribution, supporting quick and effective decision-making by city planners and administrators.  
The combination of IoT and distributed systems facilitates the use of advanced analytics and machine learning algorithms. These systems could unveil regularities, forecast tendencies, and advance in knowledge [63]. In the financial sector, IoT devices can monitor market conditions, track asset performance, and gather economic indicators. Distributed systems can analyze this data using prospective heuristics to predict business tendencies, assess risks, and develop investment strategies [64; 65]. This level of predictive capability enables financial institutions to make proactive decisions, minimize threats, and capitalize on rising prospects. Furthermore, IoT and Distributed Computing Systems provide an integrated perspective of the organization's operations and market environment [66]. These systems allow executives to make more informed decisions aligned with business objectives. For example, real-time data on production efficiency, customer feedback, and market trends can inform key, long-term decisions (e.g., market expansion) [67; 68]. Distributed systems ensure that decision-makers have access to up-to-date and accurate information, enabling them to respond swiftly to changes in the business landscape.
## 4 Conclusions  
NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) with different Fe:Mn ratios were successfully prepared using a hydrothermal method followed by freeze-drying. The combination of the high conductivity of NDG with richer redox reaction from Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) resulted in the enhanced electrochemical performance of the composite with bimetallic oxide compared to the composite with a single metal oxide, which is due to Mn\({}_{3}\)O\({}_{4}\) and Fe\({}_{3}\)O\({}_{4}\) particles being attached on the wrinkled surface of NDG that helped to prevent agglomeration of the NDG sheets. Different Fe:Mn ratios in NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) were studied and the results showed that the ratio between the metal oxides also played an important role in enhancing the supercapac performance of the composite. The Fe:Mn ratio of 1:3 exhibited the highest specific capacitance and had excellent cyclic stability with the lowest \(R_{\rm ct}\) and ESR value, which indicates good energy storing ability, a superior life cycle, and fast charge transfer at the electrode-electrolyte interface. Therefore, NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) is a potential material for electrodes to be used in supercapacitors.  
Figure 8: (**a**) Capacitance retention of NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) in 1.0 M Na\({}_{2}\)SO\({}_{4}\) at a scan rate of 100 mV/s for 5000 cycles and (**b**) Nyquist plot of NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) after cyclic stability test in 1.0 M Na\({}_{2}\)SO\({}_{4}\) (the inset is the magnified Nyquist plots of NDG/Mn\({}_{3}\)O\({}_{4}\)/Fe\({}_{3}\)O\({}_{4}\) at the high-frequency range).  
**Author Contributions:** [PERSON] performed the experiments and analyzed the data. [PERSON], [PERSON], and [PERSON] wrote the paper. [PERSON] provided helpful discussion in order to improve the manuscript. [PERSON] supervised and gave guidance on the experimental design, analyzing data, and writing the manuscript.  
**Funding:** This research was funded by the Ministry of Education, Malaysia through the Fundamental Research Grant Scheme (01-02-13-1388 FR).  
**Acknowledgments:** The authors would like to thank Universiti Putra Malaysia for the laboratory facilities.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Communication Overhead Analysis
The total communication overhead for the message of Equation (16) is \(\big{|}\mathrm{PS_{j}}\big{|}+\big{|}\mathrm{PS_{ij}}\big{|}+\big{|}+\big{|}+ \big{|}t_{ijk}\big{|}=64+64+4+64+64=324\) bytes. (4) [PERSON] et al. (22):  
The structure of the transmitted message is defined in Equation (17), where group ID is used to identify the group where the vehicle belongs. The message payload includes information about the vehicle's position, time of transmission, the direction of travel, and traffic events. A timestamp of 4 bytes is used to prevent the message replay attack. The signature over the message consists of 3 elements of \(G_{2}\). The Time-To-Live (\(TTL\)) controls how long the message is allowed to remain in the network. \[m=\Big{\{}L_{payload}\,L_{groupID}\,L_{msgID},L_{timestamp}\,L_{ signature}\,L_{TTL}\Big{\}} \tag{17}\]  
The total communication overhead for the message of Equation (18) is  
\[\big{|}L_{groupID}\big{|}+\big{|}L_{msgID}\big{|}+\big{|}L_{timestamp}\ |+|L_{ signature}\ |+|L_{TTL}|=2\ +\ 2\ +\ 4\ +\ 3\times 64\ +\ 1\ =\ 201\ \mathrm{bytes}. \tag{18}\]  
(5) [PERSON] et al. (23):  
The parameters for transmitted messages are represented by Equation (19):  
\[m=\{M_{i},ID_{i},\sigma\ _{i},\ T_{i}\} \tag{19}\]  
where a message payload \(M_{i}\), a pseudo-identity \(IDi\in G_{2}\) consists of two parts \((ID1,\ ID2)\), a signature \(\sigma i\in G_{2}\), and a timestamp \(Ti\). The communication overhead of one message is \(|ID_{i}|+|\sigma\ _{i}|+|T_{i}|=2\times 64+64+4=196\) bytes. (6) [PERSON] et al. (24):  
The parameters for the transmitted message are calculated using Equation (20):  
\[m=\{M,gpk,\ \sigma,\ T\} \tag{20}\]  
where a message payload \(M\), \(gpk=\ (g_{1},\ g_{2},\ h,\ u,\ v,\ w)\) represents the group public key that consists of 5 elements: \(g_{1}\in G_{1},\ g_{2}\in G_{2}\), \(\{h,\ u,\ v\}\in G_{1}\), and \(w\ \in G_{2}\). Additionally, a group signature \(\sigma\) consists of three elements of \(G_{1}\) and six elements of \(F_{p}\), and a timestamp \(T\). The total communication overhead for the message of Equation (20) is \(|gpk|+|\sigma|+|T|=32+64+(3\times 32)+64+(3\times 32)+6\times 32+4=548\) bytes.
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
## 3 System Performance Analysis  
### SE Analysis  
Generally, SE is defined as the ratio of the system transmission bit rate to the total bandwidth occupied by the transmission symbol. The duration of a single MM-OTFS-IM signal frame is \(NT\), the spectrum bandwidth occupied is \(M\Delta f\), and the number of transmitted bits is \((p_{1}+p_{2})g=[\lfloor\log_{2}((mn)!)\rfloor+mn(\log_{2}(M_{mod}))]g\). Therefore, the SE of the MM-OTFS-IM system is  
\[SE_{MM-OTFS-IM} =\frac{[\lfloor\log_{2}((mn)!)\rfloor+mn(\log_{2}(M_{mod}))]g}{NT \times M\bigtriangleup f} \tag{11}\] \[=\frac{\lfloor\log_{2}((mn)!)\rfloor+mn(\log_{2}(M_{mod}))}{mn}\] \[=\frac{\lfloor\log_{2}(s!)\rfloor+s(\log_{2}(M_{mod}))}{s}\]With the same parameters, the SE of the OTFS, OTFS-IM, and OTFS-DM-IM systems are expressed as  
\[SE_{OTFS}=\log_{2}(M_{mod}) \tag{12}\]  
\[SE_{OTFS-IM}=\frac{\lfloor\log_{2}(C(s,k))\rfloor+k(\log_{2}(M_{mod}))}{s} \tag{13}\]  
\[SE_{OTFS-DM-IM}=\frac{\lfloor\log_{2}(C(s,k))\rfloor+k(\log_{2}(M_{modA}))+(s- k)(\log_{2}(M_{modB}))}{s} \tag{14}\]  
where \(k\) represents the number of selected grids in the subblock, \(M_{modA}\) is the modulation order of the signals in the selected grids of the OTFS-DM-IM system, and \(M_{modB}\) is the modulation order of signals in the unselected grids.  
In the OTFS system, all grids are activated to transmit information with the same modulation mode, so the SE of the OTFS system is only related to the modulation order. The SEs of the OTFS-IM, OTFS-DM-IM, and MM-OTFS-IM systems are related to the subblock structure due to the index modulation. In the OTFS-IM system, only some of the grids are activated, so the SE is low. The OTFS-DM-IM and MM-OTFS-IM systems transmit some additional index bits by activating all grids, so these two systems achieve higher SE. The OTFS-DM-IM system can carry \(\lfloor\log_{2}(C(s,k))\rfloor\) extra bits per subblock. The number of additional bits of MM-OTFS-IM each subblock is \(\lfloor\log_{2}(s!)\rfloor\). By comparison, the MM-OTFS-IM system can carry more extra bits.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 2 Materials and Methods
### Acoustic Feature Extraction
#### 2.2.2 Spectrogram  
A spectrogram is a time-frequency representation of the audio signal, and it has been widely used for swarming and queen absence detection [31; 34]. The spectrogram is commonly measured with a short-time Fourier transform (STFT), where the signal is divided into equal-length segments by a moving window, and then each segment is transformed using the fast Fourier transform (FFT). In this study, a Hann window with a length of 125 ms with a 50% overlap was used. Figure 4 shows the spectrogram of a 24 h recording of the beehive previously shown in Figures 2 and 3a. In the top figure, the \(\times\) symbols depict the instances in which environmental noises were detected. From the bottom spectrogram plot, their influence on the spectrogram can be seen as sudden bursts of energy below 64 Hz, thus coinciding with important hive sounds that were present throughout the day.
## 4 Discussion
### Thermoregulatory Difference between Classes  
Thermoregulation is a complex mechanism that is mostly regulated by the autonomic nervous system. Specifically, sympathetic cholinergic nerves mediate the cutaneous vasodilation in response to whole-body heating, whereas noradrenergic nerves are involved in the cutaneous vasoconstriction during whole-body cooling [50]. Concerning localized stimuli, skin warming induces cutaneous vascular responses due to temperature-sensitive afferent neurons and nitric oxide, whereas local cutaneous vasoconstriction in response to direct cooling of the skin is due to sensory and sympathetic noradrenergic nerves and non-neural mechanisms [50]. The thermal responses of an HC, PRP, and SSc participant's hands to a cold challenge are shown in Figure 6.  
PRP is associated with abnormal responses to the environmental temperature and activity (i.e., vasospasm) related to vessels' diseases [51]. Typically, PRP produces symmetrical ischemia lasting 15-20 min, involving both hands with a more sensitive finger. Patients with PRP usually exhibit mild episodes that do not hamper daily activities and generally improve with aging [52; 53], whereas SSc patients are affected by intense and frequent ischemic events, associated with ulcers in 25-39% of cases [54; 55].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**CCP**} \\ \hline
**Metrics** & **DCNN** & **SVC** & **3D-DCNN** & **SVC** \\ \hline Accuracy & 0.84 & 0.77 & 0.88 & 0.83 \\ Sensitivity & 0.83 + 0.08 & 0.77 + 0.09 & 0.88 + 0.04 & 0.80 + 0.20 \\ Specificity & 0.91 + 0.04 & 0.88 + 0.05 & 0.94 + 0.05 & 0.90 + 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall accuracy, sensitivity, and specificity of DCNN and SVC models on baseline images classification, and of 3D-DCNN and SVC model in classification of the CCP images. Best performances are shown in bold.  
SSc's vascular reactivity and occlusive disease are related to a complex interaction between endothelial cells, smooth muscle cells, the extracellular matrix, and intravascular circulating factors [51]. The impairment of the endothelium induces overproduction of the vasoconstritor endothelin-1 and underproduction of the vasodilitor nitric oxide and prostacyclin. The diminished production of vasodilatory neuropeptides together with an over-regulation of the vascular smooth muscle adrenoreceptor (\(\alpha\)2c-AR), leads to an abnormal vasoconstrtive response to stress or cold stimuli. Moreover, severe vasospasms induce repeated episodes of vasoconstriction, provoking occlusion of the microcirculation and injuries [56].  
The findings of the present study further confirm the capability of thermal IR imaging to provide information regarding the physiology of the superficial blood circulation [57]. Moreover, the good classification performance obtained using images acquired at rest demonstrated that the vascular impairment associated to RP and SSc generated peculiar superficial temperature distribution of the hands even in the absence of an ongoing bout of the disease.
# Ant-Balanced Multiple Traveling Salesmen: ACO-BmTSP
## 5 Conclusions and Future Work  
The multiple traveling salesman problem (mTSP) extends the widely used traveling salesman problem (TSP). The mTSP is more suitable for real-world modeling problems. This new approach aims to balance the tours traveled by all salesmen and to minimize the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Name_ & \(m\) & **TCX** & **ACO** & **GVNSSeq-VND** & **MOAT-ACO** & **ACO-BmTSP** \\  & & [46] & [28,47] & [29,38] & [48] & \\ \hline mTSP51 & 3 & 203 & **160** & **160** & **160** & 161 \\  & 5 & 154 & 118 & 118 & 118 & **117** \\  & 10 & 113 & 108 & 112 & 112 & **93** \\ \hline mTSP100 & 3 & 12,726 & 8817 & **8509** & 8511 & 8580 \\  & 5 & 10,086 & 6964 & 6767 & 6845 & **5773** \\  & 10 & 6402 & 6370 & 6358 & 6358 & **5265** \\ \hline mTSP150 & 3 & 18,019 & 13,885 & 13,376 & **13,169** & 13,211 \\  & 5 & 12,619 & 9270 & 8647 & **8467** & 9083 \\  & 10 & 8054 & 6132 & 5674 & 5565 & 6221 \\ \hline
11a & 3 & **77** & – & **77** & – & 78 \\ \hline
11b & 3 & **73** & – & **73** & – & 96 \\ \hline
12a & 3 & **77** & – & **77** & – & 78 \\ \hline
12b & 3 & **983** & – & 1101 & – & 1101 \\ \hline
16 & 3 & **94** & – & **94** & – & **94** \\ \hline mTSP128 & 10 & 5912 & – & **2980** & – & 6304 \\  & 15 & 5295 & – & **2305** & – & 6190 \\ \hline kroD100 & 3 & – & **8511** & – & – & 9828 \\  & 5 & – & 8467 & – & – & **7929** \\  & 10 & – & 6358 & – & – & **6304** \\ \hline rat783 & 20 & – & 1509 & 1579 & – & **1413** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other algorithms.  
total cost of traveled paths. This new approach obtains balanced tours for the mTSP and obtains better results in several problems than those of other works found in the literature.  
In addition to the proposed ACO algorithm naturally extending the single-TSP ACO, which has one salesman, to the mTSP with \(m\) salesmen, the optimization algorithm uses only one objective function to minimize the total cost and balance the tours.  
In future work, local search mechanisms will be implemented in order to increase the ACO-BmTSP algorithm's performance. On the other hand, the algorithm will be used in real engineering problems in which the solution of the mTSP solves the problem, e.g., in offshore wind farm layout.  
Conceptualization, S.d.C.P. and E.J.S.P.; software, S.d.C.P. and E.J.S.P.; validation, S.d.C.P., E.J.S.P., and P.B.d.M.O.; formal analysis, S.d.C.P.; investigation, S.d.C.P.; writing--original draft preparation, S.d.C.P.; writing--review and editing, S.d.C.P., E.J.S.P., and P.B.d.M.O.; supervision, E.J.S.P. and P.B.d.M.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
## 1 Introduction  
The aggressive downscaling of metal oxide semiconductor field-effect transistors (MOSFETs) has raised some problems regarding the off-current (\(I_{\text{off}}\)) and supply voltage [1]. A tunnel field-effect transistor (TFET) has been considered as one of the most promising candidates for extremely low-power applications thanks to its low \(I_{\text{off}}\) and abrupt on-off switching [2; 3]. However, TFETs suffer from low on-current (\(I_{\text{on}}\)) [4]. For higher \(I_{\text{on}}\), several strategies have been proposed: the introduction of low bandgap materials [5], and hetero-gate-dielectric (HG) [6] and hetero-material-gate (HMG) structures [7]. Additionally, gate-normal TFETs were proposed to boost \(I_{\text{on}}\) by increasing the tunneling cross-sectional area [8; 9; 10; 11]. The band-to-band tunneling (BTBT) generation of gate-normal TFETs occurs perpendicular to the channel in the gate-source overlap region. Thus, the gate-normal tunneling area can be enlarged by increasing the gate-source overlap length (\(I_{\text{ov}}\)) to increase \(I_{\text{on}}\).  
Recently, silicon nanowire (SiNW) TFETs have been developed for superior subthreshold swing (\(SS\)), \(I_{\text{off}}\), and short-channel-effect immunity [12; 13]. The fabrication of SiNW TFETs using a top-down process has great advantages in terms of being a reproducible process, having compatibility with CMOS and having good control over the dimensions, while a bottom-up process has been seriously limited due to a difficulty in the fabrication process [14; 15; 16]. In this manuscript, as shown in Figure 1, gate-normal NWTFETs are discussed, which combine the advantages of gate-normal TFETs and NWFETs. One of the most serious problems of gate-normal NWTFETs is the hump behavior stemming from the three-dimensional corner effect: the surface potentials are different between the corner and side of an NW. This means that different BTBT turn-on voltages exist in an NW, which induces the hump behavior of degrading \(SS\) and \(I_{\text{on}}\)[17]. Thus, it is necessary to suppress the corner effect of gate-normal NWTFETs. There are several studies on the hump behavior of TFETs and various methods of mitigating hump behavior [18; 19; 20; 21]. However, this study discusses the hump behavior originated from the geometrical effect of the three-dimensional NW structure. Even if many studies have been performed regarding the corner effect of NWMOSFETs [22; 23; 24; 25], that of gate-normal NWTFETs has rarely been discussed.  
In this manuscript, the hump behavior of gate-normal NWTFETs is investigated and its solution is proposed by three-dimensional (3D) technology computer-aided design (TCAD) simulation.