esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 2 Related Work  
As the deployment of a single SDN controller to manage large scale scenarios is not an effective approach [35], distributed SDN controller architectures are unavoidable to tackle the huge volume of signaling and traffic of future dense vehicular networks. However, a challenge from the adoption of the distributed approach is how network devices should be assigned to the instances of SDN controllers. Such a NP-Hard optimization problem was initially defined as the Dynamic Controller Provisioning Problem in [36]. There exist various goals when approaching the analysis of such problem: minimizing the statistics collection cost, synchronization, assignment of switches to the controllers [36], minimizing the control flows, response time, and optimize the number of SDN controllers [37; 38; 39], maximizing the allocated resources and security [40; 41]. Such goals can be seen in Table 2 and the pros/con in Table 3 also were analyzed in different contexts as discussed in the next subsections.
## 2 State of the Art
### Negation in Sentiment Analysis
In the context of sentiment analysis, the focus has shifted towards incorporating modifier detection. Specifically, when considering negations, it is widely recognized that they can have a significant impact on the polarity of sentiment, potentially reversing it from positive to negative or vice versa. The extent of negations' influence on sentiments varies depending on the granularity of the classification task at hand. For instance, when classifying entire documents, the study conducted by [PERSON] et al. [26] demonstrated that the inclusion of negations has minimal impact on improving the classification performance. [PERSON] and [PERSON] [65] propose a novel approach to address this challenge within the context of sentiment classification. The number of positive and negative terms occurring in a document is counted to classify documents into positive or negative categories. If the document consists of more positive terms than negative terms, it is classified as positive and vice versa. Weightings for each word are summed up to consider modifiers, where positive words are assigned a weight of \(+2\), while negative words are assigned a weight of \(-2\). When a word is negated, its sign is reversed. If a word is intensified, its weight is adjusted to \(+3\) or \(-3\); if it is diminished, its weight is adjusted to \(+1\) or \(-1\), respectively. If the cumulative weight is greater than zero, the sentence is classified as having a positive sentiment; otherwise, it is classified as having a negative sentiment. The approach proposed by [PERSON] and [PERSON] in [66] has been experimentally validated, showing that incorporating diminishes, intensifiers, and negations in this manner can significantly improve the performance of a sentiment classification system. The authors also pointed out that negation reverses the semantic polarity of a particular term while [PERSON] et al. in [67] state that the sentiment of the words in the negation scope will be changed by negation with different intensities. [PERSON] et al. state in their work [28] that the challenges associated with polarity shift can potentially be addressed by refining sentiment orientation values and modifiers. Furthermore, the concept of polarity shifts aligns with the pragmatic reality of negation, as proposed by [PERSON] [68]. [PERSON] suggests that affirmative and negative sentences exhibit an asymmetrical nature, further supporting the notion of polarity shifts. The intensity of the negotiated word was also included in work by [PERSON] in [54], where the author points out that the best result is met by using nominal logistic regression together with sentiment lexicon, signal negation, negation quantifiers, intensifiers, and neutralizers. Furthermore, [PERSON] et al. [69] present an unsupervised model that utilizes a fixed window of four words to determine the scope of negation.
# Optimization of Wiring Harness Logistics Flow
## 1 Introduction
### Our Previous Research
However, this manufacturing process inevitably generates scrap material, including metals such as copper, aluminum, steel, plastic, and other materials [20]. Recycling this scrap is essential to minimize waste, conserve resources, and reduce environmental impact. This paper will also highlight the importance of recycling wire harness manufacturing scrap and its implications for sustainable production. Recycling scrap material from wire harness manufacturing significantly reduces the environmental impact associated with waste disposal. Metals such as copper and aluminum are valuable resources that require substantial energy for extraction and processing. Recycling these materials minimizes energy consumption and greenhouse gas emissions associated with mining and refining [21]. The recycling of wire harness manufacturing scrap contributes to the conservation of valuable resources. Copper, in particular, is a critical component in wire harnesses due to its excellent electrical conductivity. Recycling copper scrap reduces the demand for virgin copper, leading to the conservation of natural resources and decreased reliance on mining activities [21,22]. Similarly, recycling plastic components from wire harness manufacturing reduces the consumption of fossil fuels required for plastic production, thereby promoting resource efficiency and sustainability [23,24]. The effective recycling of wire harness manufacturing scrap presents economic advantages. Recycled materials can be reintroduced into the manufacturing process, reducing the need for virgin materials and lowering production costs [25]. Furthermore, the recycling industry generates employment opportunities and contributes to economic growth. Additionally, as the demand for recycled materials increases, there is potential for developing new markets and industries centered around sustainable resource utilization. While the benefits of recycling wire harness manufacturing scrap are obvious, challenges such as material separation, contamination, and logistical considerations must be addressed. Implementing efficient sorting and processing techniques and collaborating with specialized recycling facilities can enhance the effectiveness of recycling initiatives. Additionally, raising awareness and training employees on the importance of proper waste segregation can optimize recycling efforts within manufacturing facilities. Prior research mainly focused on improving different aspects of the production process. This study offers a new perspective on wiring harness optimization, aiming more at optimizing existing production processes by taking on the challenge of a new approach and redefining the structure of the assembly line. The paper is organized as follows: Section 2 includes a description of the proposed methodology and the initial structure of the assembly line's simulation and analysis. In Section 3, the results are presented and analyzed, while Section 4 discusses the conclusion of the research and future work.
# Thickness-Stretch Vibration of an Infinite Piezoelectric Plate with Flexoelectricity
## 3 Results  
To investigate the effect of flexoelectricity on the thickness-stretch vibration of an infinite piezoelectric plate, the material properties of BaTiO\({}_{3}\) crystals were used as follows [25]: \(c_{33}\) = 162 GPa, \(\varepsilon_{33}\) = 12.6 \(\times\) 10\({}^{-9}\) CV\({}^{-1}\)m\({}^{-1}\), \(\varepsilon_{33}\) = 18.6 Cm\({}^{-2}\), \(\rho\) = 5800 kgm\({}^{-3}\). The flexoelectric coefficient \(\mu_{3333}\) used in their work is 10 \(\mu\)Cm\({}^{-1}\). However, other papers have reported that the flexoelectric coefficients of BaTiO\({}_{3}\) can be as large as 100 \(\mu\)Cm\({}^{-1}\) at a flexure frequency of 1 Hz [26]. Thus, we chose to vary the flexoelectric coefficient \(\mu_{3333}\) from 10 \(\mu\)Cm\({}^{-1}\) to 30 \(\mu\)Cm\({}^{-1}\) in this work, to investigate the flexoelectric effect on the thickness-stretch vibration frequencies of one-dimensional micro plates.  
The frequency shift of the thickness-stretch vibration with flexoelectricity was calculated in this work. The frequency shift was defined by \(|\omega-\omega_{0}|/\omega_{0}\times 10^{6}\) and the unit was ppm (parts per million). \(\omega_{0}\) was the fundamental thickness-stretch mode without flexoelectricity, as given in Equations (30) and (31). Figure 2 demonstrates the results for both the open and short circuit boundary conditions. Firstly, for both conditions, the frequency was smaller than the case without flexoelectricity for the thickness-stretch vibration. This means that the flexoelectricity decreases the vibration frequency of the thickness-stretch mode. By increasing the thickness of the plate, it was found that the frequency shift decreases, implying that the flexoelectricity is size dependent and becomes much weaker as the thickness increases. For both electric boundary conditions, we observed the same tendency of linear frequency shift. The effect of the flexoelectric coefficient was also investigated by choosing different values. By increasing the flexoelectric coefficient, the frequency shift becomes large, which implies a more significant flexoelectric effect. Finally, it was observed that the frequency shift for the open circuit boundary condition was slightly more significant than for the short circuit boundary condition. Although the relative value of the frequency shift is only about 14 ppm, it is important for miniaturized high-frequency and high-precision piezoelectric devices, for the applications of next-generation transducers and resonators.  
Figure 2: Frequency shift of fundamental thickness-stretch vibration frequency with flexoelectricity for open circuit (OP) and short circuit (SC) boundary conditions.
## 2 Description of the algorithm
### Tracking criterion and basic steps  
When the image pattern tracking is completed, it is expected that the reference image pattern and the tracked image pattern will match to each other at the interrogation window center. Since the high-peak position (_m*, n*_) of correlation function \(\Phi^{k}\)_(m,n)_ reflects the position deviation of the tracked image pattern from the reference image pattern in the _pq_-plane, the criterion for the tracked image pattern to match the reference pattern is set as \((m^{*})^{2}+(n^{*})^{2}\leq\varepsilon^{2}\), where \(\varepsilon\) is the position error tolerance limit. The basic procedure of the image pattern tracking at each frame (except for _k_=1) is given below:  
1. Estimate initial position of the tracked image pattern (_x, y_). Usually the tracked image pattern position in the previous frame can be used as the initial image pattern position.
2. Compute the correlation function \(\Phi^{k}\)_(m,n)_ with FFT acceleration.
3. Determine the high peak position of the correlation function, i.e. \((m^{*},n^{*})\), with a three-point Guassian curve fit to achieve a sub-pixel accuracy.
4. Check the tracking criterion \((m^{*})^{2}+(n^{*})^{2}\leq\varepsilon^{2}\), to see whether or not it is fulfilled. If the criterion is fulfilled, stop the tracking and accept (_x, y_) as the position of the tracked image pattern in the current frame; If not, continue to the next step.
5. Add the correlation high peak position (_m*, n*_) to the image pattern position (_x, y_), and then iterate step 2 to 4.  
The position error tolerance limit \(\varepsilon\) is given according to  
described in section 2.5, the position of the tracked image pattern is determined as (\(x_{r}\),\(y_{r}\)). The accumulated bias of the image pattern tracking is defined as  
\[\beta_{c}=\sqrt{\left(x_{r}-x_{o}\right)^{2}+\left(y_{r}-y_{o}\right)^{2}} \tag{4}\]
## 4 Results and Analysis
### Node-Based Analysis
#### 4.1.4 The Reliability of Transmission  
We implement a daemon for activities in Linux, which is intended for recovery activities (measurement, recording, transmission, etc. in this work) in parallel with the transaction execution when something goes wrong in the system. In addition, in order to prevent the overload of memory, we employ the memory recovery mechanism to a termly clean buffer stock. To test the reliability of transmission, we chose 3000 car horns which are different in length (1-3 s) for the test. A phone was used to randomly play a car horn every 1 min and four sensor nodes were used to record and transmit audio data. The experiment lasted about two days and we checked the number of data received on the server after the test. The results are as follows:  
The Transmission Control Protocol (TCP) that contains the retransmission mechanism was adopted in the proposed solution, resulting in no packet loss. However, the TCP may be invalid if the network is fluctuant or none, which means that the data cannot be transferred to the server. As can been seen in Table 3, several data were missed in the transmission of each node, but the missing rate is less than 0.1%, which is acceptable and reasonable for the proposed system.
## 2 Materials and Methods  
### Forward Model  
The post-processing algorithms considered in this publication combine data from multiple observations for imaging. The bowed linear array transducer that is used for the measurements is assumed to be stationary during each cycle of transmit and receive events. The data acquisition is performed such that one transducer element shoots at a time and multiple transducer elements record the echoes [1]. The data from all transmit and receive events at one location is then fused in post-processing.  
We consider each observation to be a superposition of multiple time shifted echoes as  
\[g_{mx,m_{xx}}(t) = \Re\left\{\sum_{d}\sum_{p}\alpha_{mx,m_{xx},p,d}h^{(a)}\Big{(}t -\tau_{m_{xx},m_{xx},p,d}\Big{)}\right\}+n(t), \tag{1}\]  
where \(g(t)\) is an A-scan and \(m_{tx}\) and \(m_{rx}\) index a transmitter and receiver, respectively. The analytic signal of the transmit pulse is given by \(h^{(a)}(t)\), \(\Re\{.\}\) takes the real part. Each A-scan consists of a superposition of echoes from multiple defects \(d\). The ultrasonic wave can have taken different propagation paths and modes from transmitter to defect to receiver,which are indexed by \(p\) (see also [11; 12]). The complex factor \(\alpha\) models the amplitude and phase change on the path, constructed from the transducer directivity, the interfaces, and the interaction with the defect itself. The time-shift \(\tau\) indicates the propagation time on the path. The variable \(n(t)\) indicates zero-mean additive white Gaussian noise.
# Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion
## 3 Background
### Algorithm Complexity Vulnerability  
* Algorithm complexity. Algorithmic complexity (AC) [25; 26] is a measure of how an algorithm performs as the size of its input, denoted as 'n', increases. It is typically divided into time complexity, which refers to the growth rate of the algorithm's execution time with respect to the input size, and space complexity, which relates to the memory requirements of the algorithm. The analysis of algorithmic complexity mainly focuses on the worst-case scenario as '\(n\)' approaches infinity. This theoretical evaluation helps in understanding the efficiency of algorithms, though actual execution time may vary due to factors like processor speed and compiler optimizations.
* Algorithmic Complexity Attack. An Algorithmic Complexity Attack (ACA) [27; 28; 29] is a form of cyber attack that exploits the worst-case performance of an algorithm to exhaust system resources, leading to Denial of Service (DoS). Attackers can craft inputs that trigger the most complex behavior of the backend algorithm, causing the exhaustion of CPU, memory, or disk space. Examples of such attacks include Zip bombs or specially crafted regular expressions causing ReDoS. These attacks are efficient as they do not require substantial server resources, relying instead on technical strategies to effectively disrupt services.
* Algorithmic complexity vulnerability. Algorithmic complexity vulnerabilities caused by design flaws in program construction stage are exploited for complexity attacks, triggering abnormal consumption of time or space resources. These vulnerabilities often occur because developers focus on average-case performance during algorithm selection and testing, overlooking potential resource exhaustion in worst-case scenarios. Temporal complexity vulnerabilities can lead to Central Processing Unit (CPU) exhaustion, while spatial complexity issues may consume all available Random Access Memory (RAM) or disk space. Such vulnerabilities are relatively common in software development due to a lack of consideration for extreme performance cases under certain inputs. A simple example of ACV is shown in Listing 1, in which the regex \"(a+) +5\" can perform poorly on certain inputs.
## 2 Composition of LEO Satellite Network and APINT Model
### APINT Model
#### 2.3.2 Active Telemetry  
Each node in the network also has the capability to actively send telemetry packets to other nodes along the path for active telemetry. Additionally, the nodes along the path can also perform metadata writing operations on the telemetry data. The propagation delay of active telemetry packet for each node is consistent with Equation (1). The active telemetry packet contains only a portion of the service packet header information, telemetry packet header, and telemetry data. The data length of active telemetry packet is relatively small, which means the transmission delay of the node can be set as a constant value \(t_{\text{at}}^{i}\). Therefore, the time consumed by the node to complete active awareness is as follows  
\[t_{\text{ap}}^{i}=t_{\text{pro}}^{i}+t_{\text{at}}^{i}+t_{\text{meta}}^{i}, \forall i\in P_{z},\forall z\in Z, \tag{4}\]
## Materials and Methods
### Membrane Preparation  
#### 2.4.1 Self-Standing Dense Membranes  
The neat PIL-based membranes were prepared using a solution casting method reported previously [55]. PIL or PIL-based composite material was dissolved as an 8 wt % solution in acetone. After homogenization and de-gassing, the polymer solution (2 mL) was cast onto a polyester film (76.2 um packaging grade), firmly fixed to the stainless steel frame (\(\oslash\) 50 mm), in a controlled environment at 25 \(\pm\) 1 \({}^{\circ}\)C and 20 \(\pm\) 1% relative humidity (RH). The polymeric film was left to dry for ca. 72 h. The dry membranes were removed from the frame and peeled from the support. Their smooth shiny surface and transparent bulk suggested good compatibility of the different components and the formation of a homogeneous mixture at the molecular level or a physical blend with a maximum domain size well below the wavelength of visible light. Thicknesses of the self-standing dense membranes were measured by electronic micrometre on ten positions for each membrane prepared and was equal to 48 \(\pm\) 2.82 um over all samples prepared. The membranes were additionally dried in the vacuum chamber for 3 h prior to time-lag measurements.
## 2 Materials and Methods  
### Thin Film Deposition  
Thin films were deposited by the electrospray method using the vertical configuration schematically presented in Figure 1. The setup was equipped with a 5 mL syringe (2) connected to a syringe pump (1) that provides a constant feed rate of the ZnO precursor of 15 \(\upmu\)L/min. A voltage of 18 kV was applied via a DC power supply (5) (Applied Kilovolts Ltd., An Adaptas Company, West Sussex, UK) to the emitter (3), which is a stainless-steel needle with inner and outer diameters of 241 and 508 microns, respectively. The film substrate was placed onto a collector (4), which is a stainless-steel-duralumin plate grounded safely and heated to 300 \({}^{\circ}\)C by a thermocontrolled heater (6). The emitter-to-collector distance was fixed at 6 cm. In order to obtain homogenous films in the lateral direction, movement in the x-y plane was added during film deposition. The linear rate of the movement was controlled by two motors and a controller (6). The substrate temperature was kept at 300 \({}^{\circ}\)C, the deposition time was 60 min, and the approximate film thickness was 200 nm. Two types of substrates were used: silicon wafers 2 inches in diameter and double-polished optical glass with a rectangular shape and dimensions of 24 mm \(\times\) 36 mm.  
The zinc oxide precursor solution was prepared with a two-step process. Firstly, 0.400 g of zinc acetate dehydrate was dissolved in 1.8 mL deionized (DI) water by stirring at room temperature for 5 min. In the second step, the aqueous solution was further diluted by adding 12 mL ethanol and a few drops of acetic acid for clearing. The solution was stirred for 2 h at room temperature. Cobalt acetate and aluminum nitrate were used for doping. Batch solutions with a weight concentration of 10 wt.% were prepared by dissolving cobalt acetate in DI water and aluminum nitrate in ethanol. Appropriate amounts of the batch solutions were mixed with the ZnO precursor solution in order to obtain a 5 wt.% doping level of ZnO, yielding 5% Co-doped ZnO (CZO) and 5% Co/Al-co-doped ZnO (CAZO). The ratio of Co/Al in the latter is 1, i.e., the CAZO film was co-doped with 2.5% Co and 2.5% Al. ZnO films doped with 5% Al (AZO) were also deposited. As mentioned above, the substrate temperature during spraying was kept at 300 \({}^{\circ}\)C in order to decompose the initial precursor and oxidize it to ZnO.
## 3 Results and Analysis
### Influence of TIL and Airflow Parameters on Temperature and Thermal Stress Evolution  
#### 3.3.1 Influence of TIL Thickness  
TIL thickness plays a crucial role in the temperature evolution of the airflow and the tunnel rock. Figure 8a presents the temperature and thermal stress variations in the tunnel ventilated for \(100\) h. The rock temperature \(T_{r}\) of rock surface increases with the thickened TIL, which grows from \(83.70\)\({}^{\circ}\)C to \(143.93\)\({}^{\circ}\)C and \(169.90\)\({}^{\circ}\)C as the TIL thickens (\(th_{i}\)) from \(5\) cm to \(10\) cm and \(20\) cm, respectively. This means that increasing the TIL thickness enhances the TIL insulation capacity. However, the enhanced insulation capacity by per TIL thickness significantly weakens as the total TIL thickness increases. For example, the rock temperature \(T_{r}\) of rock surface increases \(39.77\)\({}^{\circ}\)C when the TIL thickens from \(5\) cm to \(10\) cm, decreasing to \(25.97\)\({}^{\circ}\)C as the TIL thickness \(th_{i}\) grows from \(10\) cm to \(20\) cm. This attenuation has also been observed by [PERSON] et al. [3] and [PERSON] et al. [11], in which they indicated that the insulation capacity caused by per TIL thickness decreases with the total thickened TIL.  
The decrease in the temperature variation reduces the thermal stress caused by cold shock on the tunnel rock. Figure 8b shows the thermal stress variation with varying TIL thicknesses at \(t=100\) h. The thermal stress \(\sigma_{T}\) on the rock surface decreases with thickening TIL, which weakens from \(-57.19\) MPa to \(-13.46\) MPa as the TIL thickness \(th_{i}\) increases from \(5\) cm to \(20\) cm. Note that the reduction in thermal stress caused by increasing per TIL thickness, similar to that of the temperature evolution, also decreases with the thickened TIL. The stress \(\sigma_{T}\) difference caused by the TIL increases from \(5\) cm to \(10\) cm is \(32.53\) MPa, decreasing to \(11.20\) MPa as the TIL thickness \(th_{i}\) ascends from \(10\) cm to \(20\) cm.  
In summary, laying a TIL increases the temperature of the rock surface and decreases associated thermal stress, but the insulation capacity by per thickness significantly decrease as the total TIL thickness increases. Thus, only increasing the TIL thickness is unable to enhance the insulation effect infinitely.