esa-sceva/satcom-chunk-collection huggingface dataset:
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 2 Background  
### CoAP  
The Constrained Representational State Transfer (REST)ful Environments (CoRE) working group has designed and developed Constraint Application Protocol (CoAP) [5] which is intended to operate in constrained IP networks and provides RESTful services in constrained devices. The CoAP protocol has been developed as a replacement of HTTP for the IoT. Unlike HTTP, CoAP operates under a simple framework consisting of the following two layers on top of User Datagram Protocol (UDP): the Messaging layer and the Request/Response layer. The messaging layer handles the asynchronous nature of communication and deals with User Datagram Protocol (UDP), while the request/response layer deals with request/response messages using method and response codes. The messaging model supports four types of messages: CON (confirmable), NON (non-confirmable), ACK (Acknowledgement), RST (Reset). The CON message is used to provide a lightweight optional reliability to the CoAP protocol. If a CON request message cannot be answered, the recipient sends RST message instead of ACK. The request/response layer lies on top the messaging layer and deals with request/response message. The CoAP request and response semantics are included in CoAP messages using Method or Response code, respectively. If a CoAP client sends a CON request message to a CoAP server, the response message is generated by the CoAP server with type ACK and sent to the CoAP client immediately. The response data is carried in the ACK, which is called a piggybacked response. If the CoAP sever is not ready to respond immediately, it sends a response message with an empty ACK. If the client receives the empty ACK, it stops retransmitting the same request.  
Various CoAP extensions enable novel applications, departing even further from the traditional client/server model. For instance, CoAP observe is described in [6] as an extension to the CoAP protocol. CoAP observe enables a client to observe a resource through a simple publish/subscribe mechanism. CoAP group communication is another CoAP extension defined in RFC 7390 [7]. The underlying mechanism of CoAP group communication is sending a single CoAP message to a specific group of devices by exploiting UDP/IP multicast for the requests and unicast UDP/IP for the responses. A CoAP group is created by a set of CoAP servers, where each server is configured to receive CoAP group communication requests destined to the group's associated IP multicast address. A CoAP group name to IP multicast address mapping is added to the appropriate DNS servers.
## 5 Unbalanced PSI-CA Protocol Based on Cuckoo Filter
### Definition of Main Participants and Related Symbols  
1. Large medical institution represents the party with a larger dataset and greater computational and storage capabilities.
2. Small health app developer represents the party with a smaller dataset and lesser computational and storage capabilities.
3. \(X\) and \(Y\) represent the dataset of the large medical institution and the small health app developer, respectively.
4. \(\alpha\) represents the private key of the large medical institution in the Diffie-Hellman encryption algorithm.
5. \(\beta_{j}\) represents the random number generated by the small health app developer for the Diffie-Hellman encryption algorithm.
6. \(H\) represents the hash function negotiated by the small health app developer and large medical institution for use.  
Figure 2: Unbalanced PSI-CA protocol based on Cuckoo filter.  
7. \(CF\) represents Cuckoo filter, \(CF.insert\) represents the operation of adding an element to the Cuckoo filter, \(CF.check\) represents the operation of checking whether a specific element exists in the filter.
8. \(X_{i}\) represents the \(i\)-th element of set \(X\). Similarly, \(Y_{i}\), \(C_{i}\), etc., also represent similar meanings.
9. \(C=\{c_{1},c_{2},\ldots,c_{n}\}\) represents the set containing \(n_{2}\) ciphertexts sent by the small health app developer to the large medical institution.
10. \(C^{\prime}\) represents the set containing \(n_{2}\) ciphertexts sent by the large medical institution to the small health app developer.
11. \(item_{j}\) represents the result obtained through a series of exchange and decryption operations, used to retrieve the filter.
12. \(sum\) represents the cardinality of the intersection between the two parties.
## 2 Data and Methods
### Data  
S2 imagery with minimal cloud, ice, and snow cover acquired between April and October 2017-2020 were selected and processed in GEE [25]. Over the entire study area, 36 S2 image dates (representing 233 S2 image tiles) were utilized. These comprised 13-15 images per processing section, of which 3 represented spring conditions, 3-4 represented summer conditions, and 7-8 represented fall conditions. Harnessing seasonal imagery for hydroperiod calculation captures temporary wetlands, typically found in spring, as well as semi-permanent wetlands found well into dry seasons (Figure 2).  
An Advanced Land-Observing Satellite (ALOS) DEM and topographic derivatives, specifically Topographic Wetness Index (TWI) [12], was applied in the post-processing quality control phase to exclude false flooded areas on slopes due to shadows in times with lower sun angles (i.e., April and October). The 2018 Human Footprint Inventory layer developed by the ABMI [37] was also used to exclude human built-up features, including roads, railways, verges, mines, industrial areas, landfills, wind generation areas, confined feeding operations, and forest harvest areas. Finally, a 30 m forest Canopy Height Model (CHM) based on 2019 Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR transects extrapolated with Landsat data [39] was used to separate the marsh (graminoid) and swamp (shrub and tree) classes. On a global basis, this product has been evaluated as 88% accurate at separating forest (>3 m trees) from non-forest compared to airborne LiDAR validation data [39].  
Figure 1: Study area located in the Grassland Natural Region of southern Alberta, Canada, and its coverage in the Prairie Pothole Region. Background is the Esri Light Gray Canvas baselayer [38]. (Sources: Esri, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, and the GIS User Community).
## 4 Application in Crankshaft Bearing Design
### Comparison between Analytical Method and FEM  
#### 4.4.1 Contact Pressure  
The contact pressure derived from the analytical method and FEM are compared in Table 5. As the actual structure is not a completely regular cylinder nested structure geometrically, there still exist certain errors in the computed results even though the modulus of the cylinders have been reduced. Although some differences are found comparing the two methods, they are basically at the same level, and both of them present that the contact pressure between bearing sleeve and bearing block is larger than that between bearing block and cylinder bearing bore.
## 2 Related Work
### Filter-Based FS Approaches  
Filter-based FS approaches have found applications in various domains. Information gain (IG), also known as mutual information (MI), is a common metric for measuring feature importance in high-dimensional datasets [85]. IG quantifies the expected reduction in entropy value when partitioning data according to a specific feature [86]. The gain ratio (GR) is a modification of IG aimed at addressing its bias towards attributes with many branches [87]. Sympatric uncertainty (SU) mitigates IG's bias issues by assessing the relationship between a feature and the target class [88; 89]. Chi-square measures the dependency between an attribute and the target label, and it is particularly effective with multi-label datasets [90]. Fast Correlation-Based Filter (FCBF) employs SU to compute feature-class correlations, and it eliminates uncorrelated features through a backward sequential search [91; 92]. Inconsistency Rate (IR) provides insight into feature sets by measuring the inconsistency rate across a dataset for a subset of features [93; 94]. Correlation-Based Feature Selection (CFS) evaluates feature subsets using heuristic search functions instead of exhaustive search strategies [95]. Additionally, minimum Redundancy Maximum Relevance (mRMR) ranks the importance of feature subsets for classification problems [96; 97]. CFS assesses the relevance of each feature in a subset to the given class and measures the redundancy between features [98].  
The F-statistic measures how relevant a subset of features is to a specific class in continuous data [99]. The Markov blanket (MB) of a target class represents the best subset of variables for predicting that class, including features statistically linked to the class, while disregarding independent ones [100; 101]. Relief is a common method for weighting features in FS, aiming to accurately select features for classification tasks [102]. However, Relief's stopping criteria are user-defined, which may prioritize lower-weight variables, and it is limited in handling large or noisy data and suitable only for two-class problems [103; 104]. To overcome these challenges, Relief was introduced, using the K-Nearest Neighbor (KNN) classifier to update feature weights, prioritizing higher-weight variables until the stopping criteria are met [105]. The Fisher score identifies the best feature subset for classifying data by maximizing within-class distances and minimizing between-class distances [106; 107]. It selects features with low individual scores but high relevance when grouped, making it widely used and efficient for FS [108].
## 3 Magnetic Negative Stiffness in QZS Isolators
### Mathematical Modeling of Electromagnetic Negative Stiffness Elements  
Owing to its hysteresis and magnetic saturation characteristics, the energized coil is typically wound around ferromagnetic material to constitute an electromagnetic spring. In contrast, permanent magnets are incapable of further magnetization. The magnetic field of the negative stiffness device results from the combination of the magnetic fields generated by the permanent magnet and the coil. The calculation of the magnetic force exerted by the electromagnetic negative stiffness device can be partitioned into two components: the repulsive force between the center permanent magnet and the left and right permanent magnets, and the electromagnetic force induced by the helical coil acting upon the left and right permanent magnets. In accordance with the equivalent magnetic charge theory, the interaction between magnets is determined by their geometric configuration and relative positioning. This assumption entails approximating the permanent magnet structure as a coaxial cylindrical magnet. Given that the material and geometric parameters of the permanent magnets on both sides are identical, we illustrate the repulsive force between the magnets on the right side as an exemplar. Figure 20 elucidates the relative positional dynamics between the magnets. The middle magnet has a radius of \(R_{1}\) and a length of \(l_{1}\), with a magnetic charge surface density of \(\sigma_{1}\) on pole surfaces 1 and 2; the right magnet has a radius of \(R_{2}\) and a length of \(l_{2}\), with a magnetic charge surface density of \(\sigma_{2}\) on pole surfaces 3 and 4. The coordinate origin is established at the center of the permanent magnet. Furthermore, the relative displacement between the magnets is denoted by \(y_{0}\), with the coordinate origin located at the geometric center of the middle permanent magnet. The surface area at a point A on pole surface 1 of the middle permanent magnet can be denoted as \(r_{1}dr_{1}da\), with a magnetic charge of \(q_{A}=r_{1}d\tau_{1}da\). Similarly, the surface area at a point on pole surface 3 of the right permanent magnet can be represented as \(r_{2}dr_{2}da\), with a magnetic charge of \(q_{B}=r_{2}dr_{2}da\). Given that the position of the intermediate permanent magnet remains fixed and magnetic charges repel each other, the micrometric magnetic force exerted by the point charge at point A on pole surface 1 toward the point charge at point B on pole surface 3 is:  
\[\mathrm{d}\overrightarrow{F}_{13}=\frac{\sigma_{2}\sigma_{2}}{4\pi\mu_{0}} \frac{r_{1}r_{2}\mathrm{d}r_{1}\mathrm{d}r_{2}\mathrm{d}\alpha\mathrm{d}\beta }{\left|\overrightarrow{r}_{13}\right|^{3}}\overrightarrow{r}_{13} \tag{36}\]  
where \(\left|\overrightarrow{r}_{13}\right|=\sqrt{y_{0}^{2}+(r_{1}\sin\alpha-r_{2} \sin\beta)^{2}+(r_{1}\cos\alpha-r_{2}\cos\beta)^{2}}\).
## 2 Models and Methods
### Deterministic Optimization Modeling  
Suppose that there are j \(\in\) N = {1,..., n} targets to be fired in a fire operation. Although each target has a different threat level per unit time \(f_{j}\) and a different threat time \(a_{j}\), information about these parameters is certain. Moreover, the time \(p_{j}\) that artillery units have to hit and destroy target j is predefined and these values are also certain. In such a case, the deterministic modeling of the artillery firing scheduling (DFS) problem that minimizes the total threat of an enemy inflicted on friendly forces is as follows:  
\[minimize\sum_{j=1}^{n}\left\{f_{j}\big{(}x_{j}-a_{j}\big{)}+\frac{{f_{j}}^{2}}{2 r_{j}}\right\}\quad\text{(DFS)} \tag{1}\]  
_subject to_  
\[x_{j}+\frac{f_{j}}{r_{j}}\leq x_{j^{\prime}}+M\big{(}1-y_{jj^{\prime}}\big{)} \quad j,j^{\prime}=1,\;2,\ldots,\;n;j^{\prime}\
eq j \tag{2}\]  
\[x_{j^{\prime}}+\frac{f_{j^{\prime}}}{r_{j^{\prime}}}\leq x_{j}+My_{jj^{\prime }}\quad j,j^{\prime}=1,\;2,\ldots,\;n;j^{\prime}\
eq j \tag{3}\]  
\[y_{jj^{\prime}}=0\;or\;1\quad j,j^{\prime}=1,\;2,\ldots,\;n;j^{\prime}\
eq j \tag{4}\]  
\[x_{j}\geq a_{j}\quad\forall\;j \tag{5}\]  
Equation (1) is an objective function that minimizes the total threat an enemy inflicts on friendly forces. \(f_{j}\big{(}x_{j}-a_{j}\big{)}\) is a threat observed from time \(a_{j}\), when a threat from target j is observed, to time \(x_{j}\), when firing starts. Further, \(\frac{{f_{j}}^{2}}{2r_{j}}\) is a threat observed from when firing starts to when target j is destroyed. Based on these assumptions, once the artillery units start firing at target j, the threat level of target j per time \(f_{j}\) decreases linearly and the threat from target j to friendly forces for \(p_{j}\) is \(\frac{f_{j}p_{j}}{2}\), which is equivalent to \(\frac{{f_{j}}^{2}}{2r_{j}}\). Equations (2)-(4) are constraints that determine the target firing sequence, which limits the current target firing that is allowed until the time required to destruct the target has elapsed from the previous target firing time. For example, when target j is hit before target j', the condition \(y_{jj^{\prime}}=1\) is satisfied and time \(x_{j}\), to hit target j' should be set to after the time required to destruct the target \(\frac{f_{j}}{r_{j}}\big{(}=p_{j}\big{)}\) has elapsed from target j firing time \(x_{j}\). Equation (5) limits the firing by artillery units allowed from time \(a_{j}\) when target j's threat occurs.
## 3 Results
### Electrophoretic Profile  
The isolate and hydrolysate of soybean and soybean-maize proteins were analyzed using SDS-PAGE. As shown in Figure 3, the SI presented the typical pattern of electrophoresis: lipoxygenase was observed at 109.88 kDa and 71.74, 68.80 and 53.33 kDa the \(\alpha\), \(\alpha^{\prime}\) and \(\beta\) units of \(\beta\)-conglycinin, respectively. The acidic subunits (A\({}_{3}\), y A\({}_{1a}\), A\({}_{1b}\), A\({}_{2}\) y A\({}_{4}\) indicated by the letter A) and basic (B) glycinin at 47.88 and 21.52 kDa appear in the gel, respectively. This pattern is consistent with the reports by [PERSON] et al. [5] on soybean proteins. The SMI presented a profile similar to soybean but exhibited three additional bands to the typical pattern of 56, 55, and 52 kDa that could be proteins provided by the maize germ. The SH protein did not show the typical band related to lipoxygenase, possibly because it was hydrolyzed into lower molecular weight moieties. The SMH showed a hydrolyzed pattern of the subunits of \(\beta\)-conglycinin. Acidic and basic subunits of glycinin were partially hydrolyzed due to a high number of unidentified fragments in the bottom of the gel.  
The amino acid profile determines the nutritional quality of a protein. Table 3 shows the amino acid composition of isolates of soybean and soybean-maize proteins and includes the aminogram of casein for comparison since it is considered the standard reference protein. Lysine content of soybean and soybean-maize proteins provided 112% and 104% of the requirement for two to five-year-old infants, respectively [27]. This content is comparatively slightly lower compared to casein.  
Figure 3: Electrophoretic profile of isolates and hydrolyses of soybean-maize and soybean proteins (SDS-PAGE). Reference (REF); soybean isolate (SI); soybean-maize isolate (SMI); soybean hydrolysate (SH); soybean-maize hydrolysate (SMH). A typical electrophoretic pattern for soybean protein: L: lipoxygenase; \(\alpha\), \(\alpha^{\prime}\) and \(\beta\) subunits of \(\beta\)-congylcincin; A\({}_{3}\): acidic glycinin subunit; A: acidic glycinin subunits (A\({}_{1a}\), A\({}_{1b}\), A\({}_{2}\), A\({}_{4}\)); B: basic glycinin subunit; G\({}_{1\textbf{-}3}\): globulins from maize germ; Z: zein.3.4. Amino Acid Composition.  
Figure 2: Comparison of ranges among isolates and hydrolyses of soybean and soybean-maize by the Kruskal–Wallis test (\(p<0.05\)).
# A Review of Blast Loading in the Urban Environment
## 2 Current Knowledge
### Facade Failure  
[PERSON] et al. [52] conducted a study where the effects of facade failure were explored. An experimental model was constructed of a long, straight street at a 1:50 scale. Varying percentages of facade gaps were created, termed as the porosity, ranging between 24-77%. It was shown that impulse (measured down-street) decreased in an approximately linear fashion as the porosity increased. This would seem logical, as gaps in the facade would reduce the confining effect of the street. The study was extended using numerical methods (Air3D) to examine the impact of facade failure inside the building. It was found that if a blast could penetrate a building, the resultant loading could cause damage to both the occupants and the building fabric. Ultimately, it was concluded that facade failure is undesirable, so building fabrics should be sufficiently designed to be as robust as reasonably possible. It was argued that it is better to have a larger quantity of buildings damaged externally than fewer buildings damaged internally where more civilians tend to reside.  
[PERSON] et al. [53] explored the shielding capabilities of buildings with varying porosity levels in regard to overpressure measured on a virtual facade situated downstream of the blast. Relatively high-yield blasts (300 tonnes of TNT) were numerically modelled at three different stand-off distances from the porous building: 50 m, 150 m and 450 m. The distance between the porous building and the facade was increased in increments of 5 m between simulations. Four different porosity levels were defined as zero, low, medium and high, with facade porosities of 0%, 38%, 54% and 100%, respectively. The results show that, generally, shielding effects are enhanced with shorter stand-off distances between the explosive and the \"protective\" building; this is in agreement with the findings from [PERSON] et al. [40].  
The zero- and low-porosity buildings provided some form of shielding for all combinations of the stand-off and the gap between the protective building and the facade. Likewise, the medium-porosity building reduced the average overpressure on the facade when the gap was less than 40 m, rarely performing better than the lower porosity levels. However, it should also be acknowledged that the 100% porous building had little effect on the overpressure measured on the virtual facade for the 150 m and 450 m stand-off distances compared to a free-field scenario. Additionally, minor overpressure enhancement for the 50 m stand-off distance was shown to occur when the gap between the building and the virtual facade was 40 m or less. The findings here suggest that care should be taken when predicting blast loads on secondary surfaces behind porous protective elements, as mitigation effects are not always prevalent.
# MRONJ Treatment Strategies: A Systematic Review and Two Case Reports
## 1 Introduction
### Most Associated Drugs with MRONJ  
The level of risk is due to the type of drug, the dose, the frequency of taking, the duration of taking, the mechanism of action, and the date of the last dose. Bisphosphonates (BPs) are analogs of pyrophosphates and strongly bind the mineral component of bone: hydroxyapatite. BPs consist of two phosphorus chains linked to a central ring consisting of a carbon atom linked to two chains R1 and R2, the former responsible for the drug's affinity to hydroxyapatite, and the latter responsible for its potency [10] (Figures 2 and 3).  
Figure 1: Role of the bone remodeling unit (BMU) in bone remodeling process.  
Based on the presence/absence in the R2 chain of an amine group, BPs are distinguished into two pharmacological classes: aminobisphosphonates and non-aminobisphosphonates (NBPs). The former includes zoledronate, pamidronate, alendronate, risedronate, bicarbonate, and neridronate, and the NBPs clodronate, tiludronate, and etidronate. NBPs have a higher affinity for bone and 10- to 1000-fold greater potency than BPs containing no amine groups [11]. NBPs are, to date, the major category among BPs for which an association with the development of BRONJ has been identified [12] (Figure 4).  
In addition to BPs, another drug is widely associated with BPs: denosumab (DB). DB inhibits the RANK ligand (RANKL), which is not only required to stimulate the adult osteoclast to resorb bone but is also required at almost all stages of osteoclast maturation, starting with the mononuclear cell, i.e., the bone marrow precursor of multinucleated osteoclasts. Alendronate and DB are responsible for more than 97% of MRONJ cases in non-oncology patients treated for osteopenia/osteoporosis [13] (Figure 5).  
Figure 4: Chemical structure of the molecule of zoledronic acid (zoledronate).  
Figure 3: Chemical structure of BPs.  
Other drugs have been associated with MRONJ in more recent studies:  
* Tyrosine kinase inhibitors (TKIs) such as sunitinib;
* Additional monoclonal antibodies, angiogenesis inhibitors, such as bevacizumabab;
* Fusion proteins such as affibercept;
* mTOR inhibitors such as everolimus;
* Radiopharmaceuticals such as radium-223;
* Estrogen inhibitors such as raoxifene;
* Immunomodulators (methotrexate and corticosteroids) [14].