esa-sceva/satcom-chunk-collection huggingface dataset:
# Life Cycle Assessment of Medical Waste Management: Case Study for Istanbul
## 2 Materials and Methods  
### Case Study  
Medical waste generated in healthcare facilities located on both the European and Asian sides of Istanbul was considered in this study. Istanbul is located in the northwest of Turkey and covers a total area of 5343 km\({}^{2}\), comprising 39 districts, 25 of which are located on the European side, while the remaining are on the Asian side.  
Medical waste is collected by the Istanbul Environmental Management Industry and Trade Inc. (ISTAC), a subsidiary of the Istanbul Metropolitan Municipality (IBB). ISTAC disposes of medical waste at 2 landfill plants (Komurctioda landfill site on Asian side, Odayeri landfill site on European side), an incineration plant, and a sterilization plant. The first Medical Waste Incineration Plant in Turkiye was established in 1995, whereas the Medical Waste Steam Sterilization Plant has been in operation for 12 years [27].  
There are 16,248 healthcare facilities generating medical waste in Istanbul, 256 of which are hospitals, while the remaining provide medical services other than those of hospitals. Pathological waste is disposed of by being collected separately from other infectious wastes. Medical waste is collected by 64 licensed trucks and transported to Medical Waste Disposal Plants [27].  
Pathological and medical wastes collected by licensed medical waste collection trucks are incinerated at a temperature between 850 and 1200 \({}^{\circ}\)C in the rotary furnace at the Medical Waste Incineration Plant. The waste volume is decreased by 95%, whereas the mass of waste is reduced by 75%, through incineration. The capacity of the plant is designed to be 24 tonnes/day. Heat recovery is achieved by the thermal energy released during the incineration process, and around 540 kW of electricity is produced. Flue gases are controlled in the treatment units and continuously monitored by the emission measurement system installed on the chimney [27].  
Medical wastes transported to the Medical Waste Sterilization Plant are sterilized with four autoclaves whose capacities are approximately 1.5 tonnes per hour. The sterilization process takes place at 145 \({}^{\circ}\)C under 3 bar pressure for 45 min, which is continuously controlled to ensure the elimination of any infection risks. The sterilized medical wastes are sent to the 2 nd class landfill for disposal by specially designed waste transport vehicles [27].
###### Abstract  
A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.  
displacement reconstruction; frequency domain integration; low-frequency attenuation; modal analysis; modal parameter identification; stochastic subspace identification Article
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 2 Related Works
### Semantic Segmentation
In addition, a criss-cross attention network was introduced in [48] for semantic segmentation, which can effectively extract image contextual information with a low computational cost. Very recently, transformer-based semantic segmentation models have shown great potential and have outperformed most CNN-based segmentation models. To our best knowledge, ViT [26] is the first work to verify that a pure transformer framework can also obtain state-of-the-art performance in the task of computer vision. The core idea of ViT is to split an input image into patches and consider them a sequence of tokens. These tokens are fed into multiple transformer layers to generate the features of an image, which are used for the task of image classification. Furthermore, PVT [49] equips the standard transformer with a pyramid structure to handle dense prediction tasks. Some experimental results demonstrate that this pyramid transformer has better representation performance than CNN-based network models. [PERSON] [50] introduces the multi-scale feature hierarchical strategy into the transformer, which computes the feature representation with shifted windows. This hierarchical transformer has the flexibility to model at different scales due to the efficiency of the shifted windowing scheme. In fact, the self-attention used in the transformer is a very effective technique for encoding spatial information. Thus, the current best position of semantic segmentation is dominated by transformer-based models. However, self-attention is computationally expensive. To handle this issue, [PERSON] et al. [51] developed a convolutional attention-based segmentation model called SegNeXt. For encoding contextual information, convolutional attention is more efficient and effective than the standard self-attention mechanism. Due to the simplicity and efficiency of cheap convolution operations, this model can significantly improve the segmentation performance with low computational complexity. Despite this, compared to CNN-based models, the inference speed of transformer-based segmentation models is slow, which is undesirable for real-time applications.
## 3 Results and Discussion
### The Selection and Fusion of Data
#### 3.3.3 Fusion of Spectra and Texture Data  
Data fusion is a function that combines multiple features into one, which aims to enhance the information extracted from the images, as well as increase the reliability of the interpretation [44]. As shown in Figure 6, two typical samples with different water-holding capacity had significant differences in both spectral curve and texture features. Sample A had firm and complete muscular structure and clear texture features, as well as lower reflectance of spectral curve. While sample B was soft with blurred texture features, and the reflectance of the spectral curve was higher. The key factors determining the WHC of the chicken meat, such as external physical structure and internal chemical composition, can be interpreted simultaneously by spectral and spatial information of hyperspectral images. Therefore, the integration of the spectral and textural features of the HSI image, as an effective information enhancement method, has been adopted to further improve the performance of HSI in predicting the WHC traits.  
Figure 5: Key wavelength band images of chicken breast fleet.  
Figure 6: Typical samples with different water-holding capacity. (Sample A: DL = 0.48, EF = 70.28, WG = 129.90; Sample B: DL = 2.27, EF = 73.45, WG = 98.03). DL: drip loss, EF: expressible fluid, and WG: salt-induced water gain.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 2 Related Works
### Traditional Machine Learning-Based Rumor Detection  
The objective of automated rumor detection is to distinguish between rumors and normal posts, which is typically formulated as a binary classification problem. Consequently, early automated rumor detection methods often relied on traditional classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), Naive Bayes (NB), Random Forest Classifier (RFC), and K-Nearest Neighbours (KNN), among others. These models are heavily dependent on handcrafted features.  
Much of the initial research into rumor detection focused on the selection and extraction of features. Commonly adopted features in state-of-the-art (SOTA) rumor detection methods include those derived from text content, user profiles, and propagation patterns. These features encompass counting the number of sentiment words, URLs, retweets, hashtags, etc. Supervised machine learning methods for rumor detection predominantly concentrate on characterizing one or a combination of these features.  
For instance, [PERSON] et al. [9] analyzed features including text content, user information, propagation patterns, and Twitter memes (hashtags, URLs, and user mentions). [PERSON] et al. [28] built Bayes classifiers using Twitter meme-based features to detect rumors on Twitter, reporting that these features differ between rumor-related tweets and normal tweets. [PERSON] et al. [18] introduced temporal, structural, and linguistic features, then trained DTC, RFC, and SVM classifiers to identify rumors. [PERSON] et al. [3] proposed a user behavior-based rumor identification scheme, treating user behaviors as hidden clues to identify potential rumormongers or rumor posts on microblogs. Their study incorporated user behavior features, content-based features, propagation features, and multimedia feature, and employed classifiers such as LR, SVM, DTC, NB, and KNN. Notably, the KNN model achieved higher precision when using user behavior features.  
While these models have shown acceptable performance, they rely heavily on the quality of feature engineering. Certain distinguishing features, such as hashtags, may not be available in all datasets, and the characteristics of rumors can change over time, limiting the generalizability of these models. However, when conducting research on applying active learning in rumor detection, these models may offer sufficiently convincing evidence.
## 3 Results and Discussion  
### Seasonal Biomass Production  
Microalgae growth is not only affected by solar irradiance, temperature, phosphorus, and nitrogen but also depends on other parameters such as pH, dissolved carbon dioxide, bacteria, etc. These parameters not mentioned in this work are each maintained at their optimum value, which corresponds to a specific growth rate equal to unity. In summary, the focus was exclusively on the influence of these physical parameters on the variation in microalgal growth. Furthermore, this disregards the interactions between microorganisms and the various components of the culture system, which are essential for model fidelity.  
Figure 3 illustrates the typical meteorological sequences (TMSs) for each season, detailing the PAR and temperature estimates for each day of the sequence.  
Figure 3: Typical meteorological sequences (TMSs) data from a northern locality in the Madrid region, Riosequillo, to produce the biomass concentration curves shown in Figure 4. \(T_{mm}\) and \(T_{mx}\) represent the average daily minimum and maximum temperature.  
Figure 4 shows the growth curves for the four meteorological seasons of the year using selected TMS from a site in the north of the Madrid region (Riosequillo). The result in Figure 4 reveals the difference in the production of algal biomass between the four seasons of the year, with a higher concentration in summer. This may be explained by the fact that the growth of these microorganisms is intrinsically dependent on the intensity of these two physical parameters. Figure 4 also shows that biomass production is higher in the autumn than in the spring. This difference in production between these two periods of the year appears to be mainly due to the temperature, which is higher in the autumn (Figure 3). A comparison of the PAR over these two periods shows that it is higher in spring. In spring and winter, the microalgae concentration varies very little, even decreasing to 98.5 mg/L on the seventh day in the lowest temperature period (winter). However, in Figure 4, it appears stationary for these two seasons due to the relatively large variation obtained in summer. Furthermore, a general analysis of the curves in all seasonal figures (Figures 5 and A1-A3) also shows the biomass concentrations that are the most likely to be obtained throughout the day of cultivation.  
The approach of this case study produced statistically acceptable results. By comparing the results with those obtained by [27], although the latter worked with a different species of algae, the order of magnitude of the final concentration of microalgae produced for the summer season was 400 mg/L. Furthermore, the study carried out by [39] on _Chlorella vulgaris_ growth under four culture conditions with different physicochemical properties gave approximate results in the same range. However, these results were obtained over a period ranging from 8 to 14 days.
# Nighttime Image Dehazing Based on Point Light Sources
## 3 Proposed Method
### Night-Time Dehaze  
#### 3.4.1 Estimating Atmospheric Light  
According to the Retinex theory, the modified  
\[I_{i}^{\lambda}=L_{i}^{\lambda}R_{i}^{\lambda}t_{i}^{\lambda}+A_{i}^{\lambda}(1 -t_{i}) \tag{13}\]  
where \(R_{i}^{\lambda}\) is a high-frequency component, the rest of items have the characteristics of smooth change, which belong to low-frequency components. When the pixel position i is far away from the light source, it is less affected by the artificial light source. After processing the image with a low-pass filter, the low-frequency components can be obtained to estimate the ambient light:  
\[\Phi\Big{(}I_{i}^{\lambda}\Big{)}=\Big{(}I_{i}^{\lambda}\Big{)}_{L}\approx L_{ i}^{\lambda}t_{i}^{\lambda}+A_{i}^{\lambda}(1-t_{i}) \tag{14}\]  
where \(\Phi(\cdot)\) represents low pass filter, \(\big{(}I_{i}^{\lambda}\big{)}_{L}\) represents low frequency components of the image. At the near light source, considering the large deviation of the atmospheric light from the near light source, the influence of the artificial light source far exceeds the surrounding environment.  
Since the ambient light will be low after superimposing the influence value of the light source, which will affect the subsequent dehazing effect, the atmospheric light deviation coefficient \(A_{0}\) is introduced to modify the atmospheric light value \(\tilde{A}_{i}^{\lambda}\) near the light source:  
\[A_{i}^{\lambda}=\left\{\begin{array}{l}\tilde{A}_{i}^{\lambda}+A_{0},i\in \Omega\\ A_{i}^{\lambda},i\
otin\Omega\end{array}\right. \tag{15}\]  
It has been verified by a large number of experiments that the dehazing effect of \(A_{0}\) in the range of 0.1-0.16 is better.
## 5 Results and Discussion
### System Implementation Details  
The Hyperledger Fabric framework provides fine-grained access control. At the beginning of a consortium network, there could be two or more healthcare center and network members, who are patients, physicians, researchers, etc. Each member has specific reading, creating, updating, and deleting permissions. All stakeholders (healthcare centers, patients, physicians, insurance, researchers, etc.) need to be identified by the network administrator or the so-called authority management through a membership service provider (MSP). The MSP governs the identities of all nodes in the system (clients, peers, and OSNs) and is responsible for issuing node credentials for authentication and authorization with the help of a Fabric certificate authority (Fabric CA). The MSP manages identities and permissioned access for network participants and allows an identity to be trusted and recognized by the rest of the network. Once all the members are authorized, they can access the system securely and privately. Authorized users can access the system through the Hyperledger Fabric software version 2.2 development kit (SDK), which allows applications to interact with a Fabric blockchain network. The SDK provides a simple application programming interface (API) that encapsulates all access to the ledger by enabling an application to interact with Fabric, verifying identities, communicating with chaincode queries, or receiving ledger updates. Once the authorized users log in, they submit a proposal containing the identity to the endorser nodes; the endorser cryptographically signs a message, called an endorsement, and sends it back to the client in a proposal response. The client collects endorsements based on the endorsement policy of the chaincode and passes it to the OSN to order them into groups of blocks with cryptographic signatures of the ordering peers and broadcasts these blocks to the committing peers as a final validation step before changes are made to the ledger. Once this is finished, the block will be written into the ledger. Figure 5 illustrates the system's implementation.
# Analysis of Passive RFID Applicability in a Retail Store: What Can We Expect?
## 3 Influence of Retail Store Products and Propagation Phenomena on a Tag Sensitivity  
The influence of the environment has been analyzed by the variation of the signal impinging on the tag antenna. Even for a standalone tag (not attached to any product) at the range lower than \(r_{max}\) there is a possibility of low or no readings due to the radio channel, i.e., obstacles (depending on their size, material and position) in the vicinity of reader antennas and tags.  
In radio channel modeling, the wide-band channel is represented as a sum of multiple copies of an original transmitted signal, i.e., multipath components. This is because different replicas of the transmitted signal travel over electrically different path lengths. In indoor environments, each signal path suffers from various phenomena: reflections, diffractions, transmissions, and scattering. Thus, at the receiver, we get the sum of multipath components, with amplitude attenuated mostly due to lossy materials interacting with the signal or radio shadow, and different propagation delays due to different path lengths. In an idealized channel, when a Dirac impulse is applied at the input \(x(t)=\delta(t)\) the complex channel impulse response \(h(t)\) (in equation where asterisk denotes the convolution operator), identical to the output signal \(y(t)\), is given by:  
\[y(t)=\delta(t)*h(t)=h(t)\to h(t)=\sum A_{n}e^{i\phi_{n}}\delta(t-\tau_{n}) \tag{5}\]  
where \(A_{n}\) is the path gain, \(\phi_{n}\) the random phase (uniformly distributed), and \(\tau_{n}\) the propagation time delay of each multipath component. Together they represent the specific pair (\(A_{n}\),\(\tau_{n}\)) which describes and indicates each copy of the transmitted signal in the wide-band propagation channel [23].
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 3 System Model and System Security
### System Security
#### 3.2.2 Desired Requirements  
* **Anonymous Vehicle Authentication.** The purpose of anonymous vehicle authentication is to verify a vehicle's authentic and legitimate while without revealing the real ID of the vehicle.
* **Short-term Linkability.** In some cases, like broadcasting road condition, applications require that a recipient can link two messages sent out by the same OBU in the short-term.
* **Long-term Unlinkability.** In the long-term, messages from the same vehicle should not be able to be linked by attackers or eavesdroppers.
* **Traceability and Revocation.** There must be a TA in VANETs which can trace the OBU that abuses the VANET. In addition, once the compromised OBU has been revealed, TA must revoke it immediately to prevent any further damage.  
Figure 1: The VANET System Model. Three roles included: vehicle equipped with on-board unit (OBU), road side unit (RSU) and the trusted authority (TA).  
* **Non-repudiation.** Both OBUs and RSUs should not deny their behaviors and must be responsible for the decision.
* **Efficiency.** On the one hand, OBUs have resource-limited computing power to make VANETs economically viable. On the other hand, OBUs may move with the high speed. Suppose the application incorporates emergency information to be transferring to another vehicle, which has more probabilities to meet accident. This needs a quick response from the network to pass the information. A delay less than a second may cause severe damage and result in meaningless message. Therefore, the computation overhead and communication overhead at each vehicle must be as small as possible.
## 6 Case Study (2): Using Aerial Photographs to Calculate Area and Volume
### Calculating Area  
Function: Once the 3D model or map is generated, PIX4D facilitates the calculation of the area within the defined boundaries [31].  
Process: The software employs algorithms to interpret the spatial information from the images, determining the boundaries of the area of interest. The calculated area is then presented as an output, providing an accurate measurement based on the images captured by the drone and the precise spatial reference established by GPS. In summary, the area calculation process involves capturing aerial images with drones, precisely determining the spatial coordinates with GPS, and utilizing PIX4D software for image processing and area calculation. This integrated approach ensures a high level of accuracy in measuring and mapping the targeted area.  
The example in Figure 7 shows how the surface area of the structure was measured.  
Figure 7: Calucating the area by using the aerial photos.