esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Comparative Test Results and Analyses  
To further verify the effectiveness of this method in the kidney segmentation task, methods in the field of kidney organ segmentation, as well as methods in the field of medical image segmentation, are selected for comparison in this experiment, which contain classical segmentation algorithms including FCN, U-Net, etc., the improved U-Net algorithm in the field of kidney segmentation, and the NaAUNet algorithm that adopts a similar method of multiscale feature fusion as in this paper, as well as the 3D U-Net algorithm and Hybrid V-Net algorithm which are better known in the field of 3D kidney segmentation. The comparative experimental results are presented in the Table 4. The kidney segmentation results obtained using the method proposed in this paper are significantly higher than those of two classical segmentation algorithms. Compared to FR2 PattU-Net and MsAUNet, which also employ multi-scale fusion, our method demonstrates a slight advantage in terms of the Dice coefficient. Both MsAUNet and FR2 PattU-Net utilize feature pyramid networks and attention mechanisms. However, there may be issues of attention deficiency in these methods. In such cases, the main information of the model lies in the spatial positional information of the feature maps rather than the semantic features carried by the channel dimensions. Since channel attention coefficients are typically less than 1, applying channel attention can further weaken the expression of feature map information. Therefore, our segmentation results outperform those of existing methods in the kidney segmentation domain. Given that our 2D model achieves results similar to 3D methods in the context of kidney segmentation, it indicates that our method exhibits strong competitiveness in liver segmentation tasks.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 3 Overview of Fault Detection and Diagnosis (FDD) in DHC Systems
### Diagnostics of Sensors and Actuators  
The digitalization of the DHC sector is becoming crucial. Moreover, in order to control efficiently the systems and to be able to detect malfunctions, the introduction of more and new sensors and actuators is necessary. However, these components may also fail, and it is important to be able to detect it. There are several simple complementary methods, which should be implemented in all sensors and actuators of the whole facility:  
* Monitoring of raw voltage/current sensor signals to detect short circuits to detect out-of-range values.
* Monitoring of incoherent values of the measures such as instabilities or impossible values to reach (e.g., ambient temperature above 70 \({}^{\circ}\)C).
* Considering the size of deviation, duration of the fault and average frequency of appearance.
* Creation of strategies identifying when the actuators and sensor will be tested, taking advantage of specific operation points such as stationary behavior, opening/closing of valves, etc. The continuous diagnosis of some variables may lead to false fault detection, which may suppose an extra cost for maintenance companies.  
In the literature, some specific methods applied to heat pump (HP) sensors are investigated. [PERSON] et al. (2019) [PERSON] et al. (2019) propose a data-driven statistical model optimized and applied for sensor fault detection and diagnosis (FDD) using subtraction clustering and k-means clustering combined to identify and classify modelling measurements of unsteady operating conditions. Moreover, in order to calibrate the HP sensors, a method called virtual in situ calibration (VIC), based on the Bayesian inference and Markov Chain Monte Carlo (MCMC), was very effective in detecting and correcting the systematic and random errors of various sensors installed in a PVT/heat pump system [PERSON] et al. (2019). As the VIC method can effectively improve the measurement certainty, the sensors with relatively low accuracy can be used to achieve a higher precision, which is able to significantly reduce the cost of equipment. VIC uses a certain grade of modelling, and it seems not so hard to implement.
## 3 Numerical and Experimental Results
Stable mode-locking regimes were achievable with the same tuning methods as in the previous stage and Figure 5 shows the typical output pulse characteristics of such regimes. An average optical power of 204 mW was measured from the laser output, corresponding to 38 nJ of pulse energy. The pulse duration was 14.5 ps, and could be compressed to 1 ps with the same de-chirping mechanism. The de-chirped pulse's AC is shown in Figure 5b, together with the Fourier limit calculated from the measured spectrum (shown in Figure 5a), from which it can be concluded that the pulse was  
Figure 3: Simulated evolution of pulse parameters with respect to pump power. Shaded areas represent energy levels for which single and multiple pulse regimes coexist. de-chirped to 1.2 times its Fourier limit at best. This configuration brought a higher pulse energy and a shorter pulse duration, but with decreased pulse quality, as the pulse could not be de-chirped completely. This was attributed to excessive nonlinear phase in the pulse indicating that the linear stretching effect of dispersion was not enough to compensate for the nonlinearity introduced by high peak powers. The results of configurations with longer DCF lengths in the cavity confirming this method could not be extended to achieve a much better energy scaling. We eventually implemented configurations with higher GDD, for instance using 30 m of DCF in the cavity, which corresponded to a GDD of +3.57 ps\({}^{2}\). In this case, the stability of the mode-locking regimes was more and more compromised in this process as Q-switching mode-locking regimes started to dominate the laser operation.
# Approximation of the Riesz-Caputo Derivative by Cubic Splines
## 2 Materials and Methods  
### Fractional Boundary Value Problems  
We consider the one-dimensional boundary value problem  
\[\left\{\begin{array}{l}\frac{d^{\gamma}}{d|x|^{\gamma}}y(x)+f(x)y(x)=g(x), \ \ \ x\in(0,L),\\ \\ \rho_{r0}y(0)+\zeta_{r0}y(L)=c_{r},\ \ 1\leq r\leq m,\end{array}\right. \tag{1}\]  
where \(0<\gamma<2\), \(m=\lceil\gamma\rceil\), \(f\) and \(g\) are known functions and \(\rho_{r0},\zeta_{r0},c_{r}\) are known parameters.  
Here, \(\frac{d^{\gamma}}{d|x|^{\gamma}}y(x)\) denotes the Riesz-Caputo fractional derivative in space defined as  
\[\frac{d^{\gamma}}{d|x|^{\gamma}}y(x)\coloneqq\frac{c(\gamma)}{2}(D_{0,x}^{ \gamma}+(-1)^{m}D_{x,L}^{\gamma})y(x), \tag{2}\]  
where the left and right Caputo derivatives are defined by  
\[D_{0,x}^{\gamma}y(x) \coloneqq\frac{1}{\Gamma(m-\gamma)}\int_{0}^{x}\frac{y^{(m)}( \tau)}{(x-\tau)^{-m+\gamma+1}}d\tau, \tag{3}\] \[D_{x,L}^{\gamma}y(x) \coloneqq\frac{(-1)^{m}}{\Gamma(m-\gamma)}\int_{x}^{L}\frac{y^{( m)}(\tau)}{(\tau-x)^{-m+\gamma+1}}d\tau, \tag{4}\]  
where \(\Gamma\) is the Euler gamma function.  
In definition (2), \(c(\gamma)\) is a given function. Common choices are  
\[c(\gamma)=\frac{\pm 1}{\cos(\frac{\pi}{2}\gamma)},\qquad\qquad c(\gamma)=1.\]  
Here, we prefer the latter definition since in this case, the Riesz-Caputo derivative agrees with the ordinary derivative when \(\gamma\) is an integer. Existence and unicity of the solution to (1) were discussed in [29; 30]. For further details and properties of the Caputo and Riesz-Caputo derivatives, see [29; 31; 32; 33].
## 2 TU Delft Campus Geothermal Project  
The low-enthalpy reservoir of the TU Delft campus geothermal project lies in the West Netherlands Basin, an inverted rift basin. The stratigraphical setting of the West Netherlands Basin down to 3 km depth and the resistivity of the layers (obtained from logs) are shown in Figure 1[35]. The target low-enthalpy reservoir is the Delft Sandstone Member, located at a depth of 2.3 km. The porosity and permeability of the Delft Sandstone member reach 30% and 1130 mD, respectively [36]. Temperature logs from exploration wells in the region indicate a gradient of 30 \({}^{\circ}\)C/km. Therefore, the water temperature of the Delft Sandstone Member is expected to be around 75 \({}^{\circ}\)C. Overall, the Delft Sandstone Member has aquifer qualities suitable for geothermal energy production.  
Figure 1: Sub-surface layers under the TU Delft campus and their average depth and resistivity.  
A geothermal doublet is planned to be installed on the campus of TU Delft, in order to supply heating to the campus buildings. The doublet design, as shown in Figure 2, involves a production well and an injection well that will be drilled vertically next to each other in the first 0.8 km. Then, the boreholes deviate from vertical with different dips and azimuths, until they reach the reservoir with 1.5 km separation. After heating the campus buildings, the discharge water will be injected into the reservoir at a temperature between 30-50 \({}^{\circ}\)C, depending on the heat demand [37].  
The change in electrical resistivity of a pore-filling fluid with temperature can be determined from the following equation [38]:  
\[R_{(T_{2})}=R_{(T_{1})}[1+\alpha(T_{2}-T_{1})], \tag{1}\]  
where \(R_{(T_{1})}\) and \(R_{(T_{2})}\) are the electrical resistivities at a base temperature, \(T_{1}\), and a monitoring temperature, \(T_{2}\), respectively; and \(\alpha\) refers to the temperature coefficient of resistivity, which has a value of 0.025 \({}^{\circ}\)C\({}^{-1}\) for most brines [38]. According to Equation (1), the electrical resistivity of the TU Delft campus reservoir will change from 7 \(\Omega\)\(\cdot\)m to 11 \(\Omega\)\(\cdot\)m with a temperature drop from 75 \({}^{\circ}\)C to 50 \({}^{\circ}\)C.
## 1 Introduction
These methods offer significant advantages, such as the ability to analyze diverse samples without requiring extensive preparation, enabling nondestructive and efficient measurements. Additionally, they are characterized by their rapid testing capabilities, cost-effectiveness, and environmentally sustainable nature. Such attributes make these techniques highly versatile, supporting applications across a wide spectrum of activities, from on-farm evaluations and real-time in-line monitoring to traceability during transportation and storage, as well as quality checks at the retail or consumer level [20; 21; 22; 23]. These methods are now considered viable candidates for addressing key limitations of conventional techniques in food fingerprinting applications. Motivated by these evolving needs and developments, this survey aims to critically examine the state-of-the-art applications of vibrational spectroscopy in food authenticity and traceability (the literature search methodology is detailed in the Supplementary File). Specifically, we seek to address the following key questions: What are the current analytical challenges in food authentication and how can vibrational spectroscopy address them? What types of food matrices and adulteration scenarios have been explored using MIR, NIR, and Raman spectroscopy? How are these techniques integrated with chemometric and machine learning models to enhance detection capabilities? What are the current limitations and future opportunities for implementing these techniques in real-time, industrial food environments? By answering these questions, we aim to provide a comprehensive overview of vibrational spectroscopy-based food fingerprinting approaches, highlight their practical relevance, and identify future research directions. As depicted in Figure 1, selecting an appropriate technique fundamentally depends on the primary goal of the analysis. Addressing this requires a well-defined understanding of food fingerprinting and its objectives. Furthermore, developing a robust food authenticity protocol involves the careful consideration of multiple factors. These include the selection of suitable methods or techniques, the design of experiments aligned with the specific research question, the formulation of an effective sampling strategy, the establishment of clear criteria to define \"authentic\" food, and the practical application of the resulting data (Figure 2). Therefore, in this review, we introduced the fundamental principles and instrumentation of vibrational spectroscopy techniques used in food analysis; reviewed recent applications of MIR, NIR, FTIR, and Raman spectroscopy in food authenticity and traceability; discussed the integration of chemometric and machine learning approaches to enhance analytical performance; and concluded current challenges, limitations, and future perspectives in applying these techniques to real-world food systems.
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Authentication Delegation
#### 6.2.1 Confidentiality  
Most of the protocols in this domain ensure confidentiality against \(\mathcal{A}_{weak}\), as they use strong security techniques (e.g., DTLS [22; 38], OAuth [39; 40], certificates [41], zero-knowledge proofs [19]). Since \(\mathcal{A}_{weak}\) can observe the communication but not corrupt it, using proper encryption and secure authentication mechanisms protects the transmitted data.  
Considering security against \(\mathcal{A}_{middle}\), confidentiality is compromised in several protocols due to \(\mathcal{A}_{middle}\)'s ability to exploit internal vulnerabilities or weak access controls [39; 40; 42; 43; 44]. Internal accomplices may potentially bypass encryption mechanisms or gain unauthorised access to sensitive data. However, most protocols that include, e.g., DTLS [22; 38] or certificates [41] are generally secure against \(\mathcal{A}_{middle}\) in terms of confidentiality. These protocols rely on strong authentication mechanisms, secure session management, and encryption to ensure that even if the adversary can observe the communication, they cannot easily access the sensitive data even in the presence of internal threats.  
For \(\mathcal{A}_{strong}\), confidentiality is compromised across almost all the protocols discussed, as this type of internal adversary has extended control over the system. They can manipulate access control, corrupt encryption keys, or modify communication channels. Because \(\mathcal{A}_{strong}\) has access to internal resources, they can bypass most security mechanisms that are effective against external threats. Nevertheless, protocols that employ a decentralised system architecture [45; 46] can maintain confidentiality even against such powerful adversaries.
# Can Anaerobically Digested Food Effluent Support _Arthrospira platensis_ Cultivation in Open Ponds?
## 2 Materials and Methods
### Statistical Analysis  
The differences in growth and nutrient removal rates among microalgae cultivated in raceway ponds with varying N-NH3 concentrations were statistically analysed using one-way ANOVA. Before performing the ANOVA, the assumptions of normality (Shapiro-Wilk test) and homogeneity of variance (Brown-Forsythe test) were assessed and met, and data were screened for significant outliers. Statistical analyses were conducted in Python (version 3.12.3) with a significance level set at \(p\) < 0.05. Code is provided in the Supplementary Data for reproducibility. Post hoc multiple comparisons were performed using the Holm-Sidak method to identify significant differences between group means. Results are presented as means \(\pm\) standard deviation (SD).
## 1 Introduction
Today, deep learning approaches have been introduced for breast lesion detection and classification to enhance their accuracy, and have become an important element of CAD systems. The CAD techniques based on image processing are becoming more popular to accelerate the work of doctors for breast cancer diagnosis and reduce diagnosis time [8; 36]. The proposed TTCNN system is effectual for early detection of breast cancer, however, the proposed system is effective for all stages of cancer. Detecting the small tumor in a whole breast is a more difficult task as compared to detecting the large area of tumor at a later stage. To this end, we propose a new approach of early breast cancer detection, which has the following contributions. 1. As a pre-processing step, a modified contrast-limited adaptive histogram equalization (CLAHE) algorithm is utilized to refine the edge details of the source image. 2. We propose a new transferable texture convolutional neural network (TTCNN) for the classification of breast cancer. 3. We use the energy layer (EL) in the proposed TTCNN framework. This allows us to preserve texture information, limit the size of the output vector, and refine the model's overall learning ability. 4. We perform the detailed functions of the most advanced CNN model for breast cancer classification i.e., InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101. 5. A convolutional sparse image decomposition approach is implemented for the feature fusion and entropy controlled firefly approach is employed to optimize the feature selection. 6. Aiming to check the stability of the proposed system, a comprehensive statistical analysis and comparison with the latest algorithms is performed. 7. The proposed approach minimizes the time required for radiologists to diagnose cancer while assuring reliable accuracy of detection. The proposed approach takes less processing time and provides accurate results. The rest of the article is structured as follows: Section 2 briefly expresses the literature review involved in the detection and classification of breast cancer. The detailed methodology of the proposed system is presented in Section 3. Section 4 analyzes the proposed approach performance in comparison with other modern approaches. Finally, we conclude this work with the direction of future research in Section 5.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 3 Alginate Application in Drug Delivery
### Alginate Dialdehyde  
Alginate cannot degrade in mammals due to a lack of alginate-degrading enzymes [2]. Ionically crosslinked alginate hydrogels, on the other hand, can be dissolved by ionic exchange reaction, but the high molecular weights (>50 kDa) of the parent alginate restrain the renal clearance. To address the limitation, alginate dialdehyde (ADA), which [PERSON] first reported, has been widely investigated [47; 48; 49]. This alginate derives from partial oxidation of alginate chains, involving C\({}_{2}\)-C\({}_{3}\) bond cleavage and transforming into an open ring containing two aldehyde groups. This functionalization is selective modification of the hydroxyl moieties. ADA has a lower molecular weight and is more soluble in aqueous media. Its stiffness and persistence length decreases with an increase in the degree of oxidation [50].  
ADA consists of multiple aldehyde groups, a reactive group that can form covalent bonds with free amino groups in gelatin, and chitosan. The development of [PERSON]'s base crosslinking hydrogels between ADA and polymer containing free amino groups has been investigated [51; 52]. A study found that the degree of crosslinking of ADA/PEG(polyethylene glycol)-gelatin hydrogels are higher than ADA/PEG-chitosan hydrogels [53]. This degree of crosslinking is defined as the number of groups that interconnect two materials, generally expressed in mole percent. This can be determined by trinitrobenzene sulfonic acid (TNBS) assay or minhydrin assay [54; 52]. Furthermore, the rheological, degradation, and comprehensive properties of the ADA/PEG-chitosan hydrogel are suggested to be more suitable for the self-crosslinking injectable scaffold.  
The synthesis of hydrogels derived from ADA and gelatin has been reported; the crosslinking between ADA and gelatin was increased by increasing the degree of oxidation of ADA [55]. Such in situ forming hydrogel is used as wound dressing material; it is molded in accordance with the wound shape to enable conformability of the dressing for their application [56]. The advantage of alignates as a wound dressing is the ability to absorb excess wound fluid and maintain physiological moisture and an aseptic environment [57]. Another study successfully performed bone regeneration with RGD-alginate and found that the alginate's system showed an excellent bone formation ability [58]. The application of injectable hydrogel from ADA and gelatin for meniscal injury treatment has also been investigated [59]. Moreover, the same hydrogel could serve as a drug delivery vehicle in the formed nanogel and successfully delivered curcumin as an active ingredient [5].
###### Abstract  
The use of near-Earth space has grown dramatically during the last decades, resulting in thousands of active and inactive satellites and a huge amount of space debris. To observe and monitor the near-Earth space environment, radar systems play a major role as they can be operated at any time and under any weather conditions. The Tracking and Imaging Radar (TIRA) is one of the largest space observation radars in the world. It consists of a 34 m Cassegrain antenna, a precise tracking radar, and a high-resolution imaging radar. Since the 1990s, TIRA contributes to the field of space domain awareness by tracking and imaging space objects and by monitoring the debris population. Due to new technologies, modern satellites become smaller, and satellite extensions become more compact. Thus, sensitive high-resolution space observation systems are needed to detect, track, and image these space objects. To fulfill these requirements, TIRA is undergoing a major upgrade. The current imaging radar in the Ku band will be replaced by a new radar with improved geometrical and radiometric resolution operating in the Ka band. Due to its wideband fully polarimetric capability, the new imaging radar will increase the analysis and characterization of space objects. In addition, the tracking radar in the L band is also being currently refurbished. Through its novel modular structure and open design, highly flexible radar modes and precise tracking concepts can be efficiently implemented for enhanced space domain awareness. The new TIRA system will mark the start of a new era for space observation with radar in Europe.  
Keywords: radar; inverse synthetic aperture radar; space domain awareness; space reconnaissance; space surveillance and tracking; signal processing; resident space objects; space debris; Tracking and Imaging Radar; TIRA +
Footnote †: journal:
## 2 Related Research
### CDN (Contents Delivery Network)  
The content delivery network (CDN) was proposed to enhance the traditional content delivery method, which relied on a single large-scale server and could not guarantee quality while providing content to all users via the internet [14; 15; 16]. CDN provides multiple servers responsible for numerous users to prevent them from receiving content through unreliable internet bandwidth. Figure 3 below illustrates the servers constituting a CDN. Among the components of a CDN, the Staging Server receives content from the Content Provider and stores content to be distributed to other servers. All content distribution begins at the Staging Server and eventually arrives at the Edge Server, which is the server that delivers content to users and serves as the target for content distribution.  
A typical CDN is a distributed network of servers that can efficiently deliver content to users. It minimizes latency by storing cached content on Edge Servers located near the point-of-presence (POP) of the end user. Through the application of edge computing, it enables proactive responses based on the situational awareness of the end users, depending on the type of service. In metaverse virtual concerts, interactions between the performer and user, as well as between users, are crucial for creating a sense of immersion and realism. Consequently, securing a bidirectional data channel is essential. However, there are limitations with the current CDN structure to facilitate this [16].